From f3ae5af63f6d58a3473b25c6b7ab3f0260e5c373 Mon Sep 17 00:00:00 2001 From: Muhammad Yousuf Date: Thu, 13 Feb 2025 16:35:27 -0500 Subject: [PATCH 1/4] Installation guide updates/fixing link to installations --- README.md | 2 +- .../getting-started/getting-started/index.html | 18 +++++++++--------- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 62d4a0ef..209bd44c 100755 --- a/README.md +++ b/README.md @@ -30,7 +30,7 @@ Among other things, this project also does not provide methods for trajectory op # Installation -See the [documentation](https://cloctools.github.io/docs/getting-started). +See the [documentation](https://cloctools.github.io/lds-ctrl-est/docs/getting-started/getting-started/). # Reporting Issues If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please [open an issue on GitHub](https://github.com/cloctools/lds-ctrl-est/issues) and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes. diff --git a/docs/docs/getting-started/getting-started/index.html b/docs/docs/getting-started/getting-started/index.html index 85282b68..5f3de8bf 100644 --- a/docs/docs/getting-started/getting-started/index.html +++ b/docs/docs/getting-started/getting-started/index.html @@ -216,14 +216,14 @@ 

xcode-select --install

Homebrew is “The Missing Package Manager for macOS” which will make installing lots of things easy. Install like this:

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
-

You can then use it to install CMake and gfortran:

-
brew install cmake gfortran
+

You can then use it to install CMake, gfortran, and pkg-config:

+
brew install cmake gfortran pkg-config

Linux Pre-requisites #

You’ll need Git, CMake, GCC, gfortran, etc.

-
sudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential
+
sudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential ninja-build
 

   Windows Installation
   #
@@ -234,17 +234,17 @@ 

   #
 

 
First, clone the repository along with submodules:

-
git clone https://github.com/cloctools/lds-ctrl-est.git 
-cd lds-ctrl-est
-git submodule update --init
+
git clone https://github.com/cloctools/lds-ctrl-est.git 
+cd lds-ctrl-est
+git submodule update --init
 

   Compilation + Installation
   #
 

 
Now generate the cache and build using your IDE or from the command line as follows.

-
mkdir build && cd build
-cmake ..
-cmake --build .
+
mkdir build && cd build
+cmake ..
+cmake --build .
 
The first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.

 
If you want to use vcpkg set up somewhere besides this repo’s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE’s settings.

 
You can verify the build is working by running ctest from the build folder, which runs all the example scripts.


From a4a9733c05f8e7bf0878e90f268f68c7dd65e8a9 Mon Sep 17 00:00:00 2001
From: Muhammad Yousuf 
Date: Fri, 7 Mar 2025 14:42:48 -0500
Subject: [PATCH 2/4] Installation docs built with hugo

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diff --git a/README.md b/README.md
index 209bd44c..9fb532cf 100755
--- a/README.md
+++ b/README.md
@@ -37,6 +37,9 @@ If you encounter bugs when using this library or have specific feature requests
 # Contributing
 We welcome any community contributions to this project. Please fork the repository and if possible use `clang-format` and `clang-tidy` to conform to the coding format/style of this repository.
 
+When editing any documentation/guides, please use the markdown docs in `misc/docs-hugo` instead of directly editing the HTML docs. 
+Updated docs can be built by running `cd scripts` and `./update-docs.sh`.
+
 # Acknowledgements
 
 Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.
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Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.

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-lds::Controller | LDS C&E
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-  lds::Controller
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Inherited by lds::gaussian::Controller, lds::poisson::Controller, lds::SwitchedController< System >

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-  Public Functions
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Name
Controller() =default
Constructs a new Controller.
Controller(const System & sys, data_t u_lb, data_t u_ub, size_t control_type =0)
Constructs a new Controller.
Controller(System && sys, data_t u_lb, data_t u_ub, size_t control_type =0)
Constructs a new Controller by moving the system object.
const Vector &Control(const Vector & z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)
updates control signal (single-step)
const Vector &ControlOutputReference(const Vector & z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)
updates control signal, given previously-set (single-step)
const System &sys() const
const Matrix &Kc() const
Get state feedback controller gain.
const Matrix &Kc_inty() const
Get integral controller gain.
const Matrix &Kc_u() const
Get input feedback controller gain.
const Vector &g_design() const
Get input gain used in controller design.
const Vector &u_ref() const
Get reference input.
const Vector &x_ref() const
Get reference state.
const Vector &y_ref() const
Get reference output.
size_tcontrol_type() const
Get controller type.
data_ttau_awu() const
Get time constant of anti-integral-windup.
data_tu_lb() const
Get control lower bound.
data_tu_ub() const
Get control upper bound.
voidset_sys(const System & sys)
Set system.
voidset_g_design(const Vector & g_design)
Set input gain used in controller design (g_design)
voidset_u_ref(const Vector & u_ref)
Set reference input (u_ref)
voidset_x_ref(const Vector & x_ref)
Set reference state (x_ref)
virtual voidset_y_ref(const Vector & y_ref)
Set reference output (y_ref)
voidset_Kc(const Matrix & Kc)
Set state controller gain.
voidset_Kc_inty(const Matrix & Kc_inty)
Set integral controller gain.
voidset_Kc_u(const Matrix & Kc_u)
Set input controller gain.
voidset_tau_awu(data_t tau)
Set time constant of anti-integral-windup.
voidset_control_type(size_t control_type)
Sets the control type.
voidset_u_lb(data_t u_lb)
sets control lower bound
voidset_u_ub(data_t u_ub)
Sets control upper bound.
voidReset()
reset system and control variables.
voidPrint()
prints variables to stdout

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-  Protected Attributes
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Name
Systemsys_ 
underlying LDS
Vectoru_ 
control signal
Vectoru_return_ 
control signal that is returned to user
Vectorg_design_ 
input gain of the system used for controller design
Vectoru_ref_ 
reference input
Vectoru_ref_prev_ 
reference input at previous time step
Vectorx_ref_ 
reference state
Vectory_ref_ 
reference output
Vectorcx_ref_
MatrixKc_ 
state controller gain
MatrixKc_u_ 
input controller gain (optional when control updates )
MatrixKc_inty_ 
integral controller gain
Vectordu_ref_
Vectordv_ref_
Vectorv_ref_
Vectordv_
Vectorv_ 
Control after g inversion (e.g., control in physical units)
Vectorint_e_ 
integrated error
Vectorint_e_awu_adjust_ 
anti-windup adjustment to intE
Vectoru_sat_ 
control signal after saturation (for antiWindup)
booldo_control_prev_
booldo_lock_control_prev_
boolu_saturated_ 
whether control signal has reached saturation limits
data_tu_lb_ 
lower bound on control
data_tu_ub_ 
upper bound on control
data_ttau_awu_ 
antiwindup time constant
data_tk_awu_
data_tt_since_control_onset_ 
time since control epoch onset
size_tcontrol_type_ 
controller type

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-  Detailed Description
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template <typename System >
-class lds::Controller;
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-  Public Function Details
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-  Controller
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Controller() =default
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-  Controller
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inline Controller(
-    const System & sys,
-    data_t u_lb,
-    data_t u_ub,
-    size_t control_type =0
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  • sys System (derived from lds::System)
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  • u_lb lower bound on control (u)
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Template Parameters:

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-  Controller
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inline Controller(
-    System && sys,
-    data_t u_lb,
-    data_t u_ub,
-    size_t control_type =0
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  • sys System (derived from lds::System)
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Template Parameters:

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-  Control
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inline const Vector & Control(
-    const Vector & z,
-    bool do_control =true,
-    bool do_lock_control =false,
-    data_t sigma_soft_start =0,
-    data_t sigma_u_noise =0,
-    bool do_reset_at_control_onset =true
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Parameters:

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  • z measurement
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  • do_control [optional] whether to update control (true) or simply feed through u_ref (false)
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  • do_lock_control [optional] whether to lock control at its current value
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  • sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true)
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Return: updated control signal

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Updates the control signal (single-step). This is the most flexible option, but requires user to have set the controller’s y_ref, x_ref, and u_ref variables.

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-  ControlOutputReference
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inline const Vector & ControlOutputReference(
-    const Vector & z,
-    bool do_control =true,
-    bool do_estimation =true,
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Updates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.

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data_t k_awu_ = 0;
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data_t t_since_control_onset_ = 0;
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size_t control_type_ {};
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Updated on 19 May 2022 at 17:16:03 Eastern Daylight Time

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diff --git a/docs/docs/api/classes/classlds_1_1em/index.html b/docs/docs/api/classes/classlds_1_1em/index.html
deleted file mode 100644
index a4f466c9..00000000
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-lds::EM | LDS C&E
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More…

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Inherited by lds::gaussian::FitEM, lds::poisson::FitEM

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-  Public Functions
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Name
EM() =default
Constructs a new EMFit type.
EM(size_t n_x, data_t dt, UniformMatrixList< kMatFreeDim2 > && u_train, UniformMatrixList< kMatFreeDim2 > && z_train)
Constructs a new EMFit type.
EM(const Fit & fit0, UniformMatrixList< kMatFreeDim2 > && u_train, UniformMatrixList< kMatFreeDim2 > && z_train)
Constructs a new EMFit type.
virtual~EM() =default
const Fit &Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)
Runs fitting by Expectation(E)-Maximization(M)
std::tuple< UniformMatrixList< kMatFreeDim2 >, UniformMatrixList< kMatFreeDim2 > >ReturnData()
Returns the input/output data to caller.
const std::vector< Matrix > &x() const
gets estimated state (over time)
const std::vector< Matrix > &y() const
gets estimated output (over time)
const Matrix &sum_E_x_t_x_t() const
gets state-input covariance
const Matrix &sum_E_xu_tm1_xu_tm1() const
gets state-input covariance (t-minus-1)
const Matrix &sum_E_xu_t_xu_tm1() const
gets single lag state-input covariance
size_tn_t_tot()
total number of time samples
const Vector &theta() const
gets parameters updated in M step

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Name
voidExpectation(bool force_common_initial =false)
Expectation step.
voidMaximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)
Maximization step.
voidMaximizeDynamics()
voidMaximizeQ()
voidMaximizeInitial()
virtual voidMaximizeOutput() =0
virtual voidMaximizeMeasurement() =0
voidSmooth(bool force_common_initial)
get smoothed estimates
virtual voidRecurseKe(Matrix & Ke, Cube & P_pre, Cube & P_post, size_t t) =0
recursively update estimator gain Ke
voidReset()
reset to initial conditions
voidInitVars()
Initializes the variables.
VectorUpdateTheta()
updates parameter list, theta

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Name
UniformMatrixList< kMatFreeDim2 >u_ 
input training data
UniformMatrixList< kMatFreeDim2 >z_ 
measurement training data
std::vector< Matrix >x_ 
state estimate
std::vector< Cube >P_ 
state estimate cov
std::vector< Cube >P_t_tm1_ 
single-lag state covariance
std::vector< Matrix >y_ 
output estimate
Matrixdiag_y_
Matrixsum_E_x_t_x_t_ 
state covariance (current time)
Matrixsum_E_xu_tm1_xu_tm1_ 
state-input covariance (t-minus-1)
Matrixsum_E_xu_t_xu_tm1_ 
single lag state-input covariance
Fitfit_
Vectortheta_
data_tdt_ 
sample period
size_tn_u_ 
number of inputs
size_tn_x_ 
number of states
size_tn_y_ 
number of outputs
size_tn_trials_ 
number of input/output data sequences
std::vector< size_t >n_t_ 
number of time steps
size_tn_t_tot_ 
total number of time steps across trials

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-  Detailed Description
-  #
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template <typename Fit >
-class lds::EM;
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-  Public Function Details
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-  EM
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EM() =default
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-  EM
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EM(
-    size_t n_x,
-    data_t dt,
-    UniformMatrixList< kMatFreeDim2 > && u_train,
-    UniformMatrixList< kMatFreeDim2 > && z_train
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Parameters:

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  • n_x number of states
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  • dt sample period
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  • u_train input training data
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-  EM
-  #
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EM(
-    const Fit & fit0,
-    UniformMatrixList< kMatFreeDim2 > && u_train,
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Parameters:

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  • u_train input training data
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virtual ~EM() =default
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-  Run
-  #
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const Fit & Run(
-    bool calc_dynamics =true,
-    bool calc_Q =true,
-    bool calc_init =true,
-    bool calc_output =true,
-    bool calc_measurement =true,
-    size_t max_iter =100,
-    data_t tol =1e-2
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Parameters:

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  • calc_dynamics [optional] whether to calculate dynamics (A, B)
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  • calc_Q [optional] whether to calculate process noise covariance
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  • calc_init [optional] whether to calculate initial conditions
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  • calc_output [optional] whether to calculate output function
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  • calc_measurement [optional] whether to calculate parameters for measurement/observation law
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  • max_iter max number of iterations
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  • tol convergence tolerance (max fractional abs change)
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Return: Fit

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-  ReturnData
-  #
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inline std::tuple< UniformMatrixList< kMatFreeDim2 >, UniformMatrixList< kMatFreeDim2 > > ReturnData()
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Return: tuple(input data, output data)

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-  x
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inline const std::vector< Matrix > & x() const
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-  y
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inline const std::vector< Matrix > & y() const
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inline const Matrix & sum_E_x_t_x_t() const
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inline const Matrix & sum_E_xu_tm1_xu_tm1() const
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inline const Matrix & sum_E_xu_t_xu_tm1() const
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inline size_t n_t_tot()
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inline const Vector & theta() const
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-  Expectation
-  #
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void Expectation(
-    bool force_common_initial =false
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-  Maximization
-  #
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void Maximization(
-    bool calc_dynamics =true,
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Parameters:

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  • calc_dynamics [optional] whether to caclulate dynamics (A, B)
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-  MaximizeDynamics
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void MaximizeDynamics()
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-  MaximizeQ
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void MaximizeQ()
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void MaximizeInitial()
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virtual void MaximizeOutput() =0
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Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput

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virtual void MaximizeMeasurement() =0
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Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement

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void Smooth(
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virtual void RecurseKe(
-    Matrix & Ke,
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Parameters:

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void Reset()
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void InitVars()
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Vector UpdateTheta()
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Return: parameter list

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UniformMatrixList< kMatFreeDim2 > u_;
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UniformMatrixList< kMatFreeDim2 > z_;
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std::vector< Cube > P_;
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std::vector< Cube > P_t_tm1_;
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Matrix sum_E_x_t_x_t_;
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Matrix sum_E_xu_tm1_xu_tm1_;
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Matrix sum_E_xu_t_xu_tm1_;
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Fit fit_;
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Updated on 19 May 2022 at 17:16:03 Eastern Daylight Time

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diff --git a/docs/docs/api/classes/classlds_1_1fit/index.html b/docs/docs/api/classes/classlds_1_1fit/index.html
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LDS Fit Type. 
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 #include <lds_fit.h>

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Inherited by lds::gaussian::Fit, lds::poisson::Fit

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-  Public Functions
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Fit() =default
Constructs a new Fit.
Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)
Constructs a new Fit.
virtual~Fit() =default
size_tn_u() const
gets number of inputs
size_tn_x() const
gets number of states
size_tn_y() const
gets number of outputs
data_tdt() const
gets sample period
const Matrix &A() const
gets state matrix
const Matrix &B() const
gets input matrix
const Vector &g() const
gets input gain
const Vector &m() const
gets process disturbance
const Matrix &Q() const
gets process noise covariance
const Vector &x0() const
gets initial state estimate
const Matrix &P0() const
gets covariance of initial state estimate
const Matrix &C() const
gets output matrix
const Vector &d() const
gets output bias
virtual const Matrix &R() const =0
voidset_A(const Matrix & A)
sets state matrix
voidset_B(const Matrix & B)
sets input matrix
voidset_g(const Vector & g)
sets input gain/conversion factor
voidset_m(const Vector & m)
sets process disturbance
voidset_Q(const Matrix & Q)
sets process noise covariance
virtual voidset_R(const Matrix & R) =0
sets output noise covariance (if any)
voidset_x0(const Vector & x0)
sets initial state estimate
voidset_P0(const Matrix & P0)
sets initial state estimate covariance
voidset_C(const Matrix & C)
sets output matrix
voidset_d(const Vector & d)
sets output bias
Viewf(Matrix & x, const Matrix & u, size_t t)
system dynamics function
Viewf(Matrix & x_pre, const Matrix & x_post, const Matrix & u, size_t t)
system dynamics function
virtual Viewh(Matrix & y, const Matrix & x, size_t t) =0
output function

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Name
data_tdt_ 
sample period
MatrixA_ 
state matrix
MatrixB_ 
input matrix
Vectorg_ 
input gain
Vectorm_ 
process noise mean
MatrixQ_ 
process noise cov
MatrixC_ 
output matrix
Vectord_ 
output bias
MatrixR_ 
measurement noise
Vectorx0_ 
initial state
MatrixP0_ 
initial covar
size_tn_u_ 
number of inputs
size_tn_x_ 
number of states
size_tn_y_ 
number of outputs

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-  Public Function Details
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-  Fit
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Fit() =default
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-  Fit
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Fit(
-    size_t n_u,
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Parameters:

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  • n_u number of inputs
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virtual ~Fit() =default
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inline size_t n_u() const
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-  n_x
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inline size_t n_x() const
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-  n_y
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inline size_t n_y() const
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-  dt
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inline data_t dt() const
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-  A
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inline const Matrix & A() const
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-  B
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inline const Matrix & B() const
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-  g
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inline const Vector & g() const
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-  m
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inline const Vector & m() const
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-  Q
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inline const Matrix & Q() const
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-  x0
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inline const Vector & x0() const
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-  P0
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inline const Matrix & P0() const
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-  C
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inline const Matrix & C() const
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-  d
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inline const Vector & d() const
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-  R
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virtual const Matrix & R() const =0
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Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R

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-  set_A
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inline void set_A(
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inline void set_B(
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-  set_g
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inline void set_g(
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-  set_m
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inline void set_m(
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-  set_Q
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inline void set_Q(
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-  set_R
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virtual void set_R(
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Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R

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inline void set_x0(
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-  set_P0
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inline void set_P0(
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inline void set_C(
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-  set_d
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inline void set_d(
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-  f
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inline View f(
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Return: view of updated state

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-  f
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inline View f(
-    Matrix & x_pre,
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  • x_pre predicted state est.
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Return: view of predicted state

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virtual View h(
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Return: output

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Reimplemented by: lds::poisson::Fit::h, lds::gaussian::Fit::h

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-  Protected Attribute Details
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-  **dt_**
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data_t dt_ {};
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Matrix A_;
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Matrix B_;
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Vector g_;
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Vector m_;
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Matrix Q_;
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Matrix C_;
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Vector d_;
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Matrix R_;
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Vector x0_;
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Matrix P0_;
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size_t n_u_ {};
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size_t n_x_ {};
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size_t n_y_ {};
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diff --git a/docs/docs/api/classes/classlds_1_1gaussian_1_1controller/index.html b/docs/docs/api/classes/classlds_1_1gaussian_1_1controller/index.html
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Gaussian-observation Controller Type. 
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Inherits from lds::Controller< System >

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virtual voidset_y_ref(const Vector & y_ref) override
sets reference output

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Public Functions inherited from lds::Controller< System >

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Controller() =default
Constructs a new Controller.
Controller(const System & sys, data_t u_lb, data_t u_ub, size_t control_type =0)
Constructs a new Controller.
Controller(System && sys, data_t u_lb, data_t u_ub, size_t control_type =0)
Constructs a new Controller by moving the system object.
const Vector &Control(const Vector & z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)
updates control signal (single-step)
const Vector &ControlOutputReference(const Vector & z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)
updates control signal, given previously-set (single-step)
const System &sys() const
const Matrix &Kc() const
Get state feedback controller gain.
const Matrix &Kc_inty() const
Get integral controller gain.
const Matrix &Kc_u() const
Get input feedback controller gain.
const Vector &g_design() const
Get input gain used in controller design.
const Vector &u_ref() const
Get reference input.
const Vector &x_ref() const
Get reference state.
const Vector &y_ref() const
Get reference output.
size_tcontrol_type() const
Get controller type.
data_ttau_awu() const
Get time constant of anti-integral-windup.
data_tu_lb() const
Get control lower bound.
data_tu_ub() const
Get control upper bound.
voidset_sys(const System & sys)
Set system.
voidset_g_design(const Vector & g_design)
Set input gain used in controller design (g_design)
voidset_u_ref(const Vector & u_ref)
Set reference input (u_ref)
voidset_x_ref(const Vector & x_ref)
Set reference state (x_ref)
voidset_Kc(const Matrix & Kc)
Set state controller gain.
voidset_Kc_inty(const Matrix & Kc_inty)
Set integral controller gain.
voidset_Kc_u(const Matrix & Kc_u)
Set input controller gain.
voidset_tau_awu(data_t tau)
Set time constant of anti-integral-windup.
voidset_control_type(size_t control_type)
Sets the control type.
voidset_u_lb(data_t u_lb)
sets control lower bound
voidset_u_ub(data_t u_ub)
Sets control upper bound.
voidReset()
reset system and control variables.
voidPrint()
prints variables to stdout

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Name
Systemsys_ 
underlying LDS
Vectoru_ 
control signal
Vectoru_return_ 
control signal that is returned to user
Vectorg_design_ 
input gain of the system used for controller design
Vectoru_ref_ 
reference input
Vectoru_ref_prev_ 
reference input at previous time step
Vectorx_ref_ 
reference state
Vectory_ref_ 
reference output
Vectorcx_ref_
MatrixKc_ 
state controller gain
MatrixKc_u_ 
input controller gain (optional when control updates )
MatrixKc_inty_ 
integral controller gain
Vectordu_ref_
Vectordv_ref_
Vectorv_ref_
Vectordv_
Vectorv_ 
Control after g inversion (e.g., control in physical units)
Vectorint_e_ 
integrated error
Vectorint_e_awu_adjust_ 
anti-windup adjustment to intE
Vectoru_sat_ 
control signal after saturation (for antiWindup)
booldo_control_prev_
booldo_lock_control_prev_
boolu_saturated_ 
whether control signal has reached saturation limits
data_tu_lb_ 
lower bound on control
data_tu_ub_ 
upper bound on control
data_ttau_awu_ 
antiwindup time constant
data_tk_awu_
data_tt_since_control_onset_ 
time since control epoch onset
size_tcontrol_type_ 
controller type

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-  set_y_ref
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inline virtual void set_y_ref(
-    const Vector & y_ref
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Reimplements: lds::Controller::set_y_ref

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Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time

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diff --git a/docs/docs/api/classes/classlds_1_1gaussian_1_1fit/index.html b/docs/docs/api/classes/classlds_1_1gaussian_1_1fit/index.html
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GLDS Fit Type. 
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 #include <lds_gaussian_fit.h>

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Inherits from lds::Fit

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Fit() =default
Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)
Constructs a new Fit.
virtual const Matrix &R() const override
gets measurement noise covariance
virtual voidset_R(const Matrix & R) override
sets measurement noise covariance
virtual Viewh(Matrix & y, const Matrix & x, size_t t) override
output function

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Public Functions inherited from lds::Fit

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virtual~Fit() =default
size_tn_u() const
gets number of inputs
size_tn_x() const
gets number of states
size_tn_y() const
gets number of outputs
data_tdt() const
gets sample period
const Matrix &A() const
gets state matrix
const Matrix &B() const
gets input matrix
const Vector &g() const
gets input gain
const Vector &m() const
gets process disturbance
const Matrix &Q() const
gets process noise covariance
const Vector &x0() const
gets initial state estimate
const Matrix &P0() const
gets covariance of initial state estimate
const Matrix &C() const
gets output matrix
const Vector &d() const
gets output bias
voidset_A(const Matrix & A)
sets state matrix
voidset_B(const Matrix & B)
sets input matrix
voidset_g(const Vector & g)
sets input gain/conversion factor
voidset_m(const Vector & m)
sets process disturbance
voidset_Q(const Matrix & Q)
sets process noise covariance
voidset_x0(const Vector & x0)
sets initial state estimate
voidset_P0(const Matrix & P0)
sets initial state estimate covariance
voidset_C(const Matrix & C)
sets output matrix
voidset_d(const Vector & d)
sets output bias
Viewf(Matrix & x, const Matrix & u, size_t t)
system dynamics function
Viewf(Matrix & x_pre, const Matrix & x_post, const Matrix & u, size_t t)
system dynamics function

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Protected Attributes inherited from lds::Fit

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Name
data_tdt_ 
sample period
MatrixA_ 
state matrix
MatrixB_ 
input matrix
Vectorg_ 
input gain
Vectorm_ 
process noise mean
MatrixQ_ 
process noise cov
MatrixC_ 
output matrix
Vectord_ 
output bias
MatrixR_ 
measurement noise
Vectorx0_ 
initial state
MatrixP0_ 
initial covar
size_tn_u_ 
number of inputs
size_tn_x_ 
number of states
size_tn_y_ 
number of outputs

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-  Fit
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Fit() =default
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-  Fit
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Fit(
-    size_t n_u,
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GLDS E-M Fit Type.  More…

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EM() =default
Constructs a new EMFit type.
EM(size_t n_x, data_t dt, UniformMatrixList< kMatFreeDim2 > && u_train, UniformMatrixList< kMatFreeDim2 > && z_train)
Constructs a new EMFit type.
EM(const Fit & fit0, UniformMatrixList< kMatFreeDim2 > && u_train, UniformMatrixList< kMatFreeDim2 > && z_train)
Constructs a new EMFit type.
virtual~EM() =default
const Fit &Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)
Runs fitting by Expectation(E)-Maximization(M)
std::tuple< UniformMatrixList< kMatFreeDim2 >, UniformMatrixList< kMatFreeDim2 > >ReturnData()
Returns the input/output data to caller.
const std::vector< Matrix > &x() const
gets estimated state (over time)
const std::vector< Matrix > &y() const
gets estimated output (over time)
const Matrix &sum_E_x_t_x_t() const
gets state-input covariance
const Matrix &sum_E_xu_tm1_xu_tm1() const
gets state-input covariance (t-minus-1)
const Matrix &sum_E_xu_t_xu_tm1() const
gets single lag state-input covariance
size_tn_t_tot()
total number of time samples
const Vector &theta() const
gets parameters updated in M step

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voidExpectation(bool force_common_initial =false)
Expectation step.
voidMaximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)
Maximization step.
voidMaximizeDynamics()
voidMaximizeQ()
voidMaximizeInitial()
voidSmooth(bool force_common_initial)
get smoothed estimates
voidReset()
reset to initial conditions
voidInitVars()
Initializes the variables.
VectorUpdateTheta()
updates parameter list, theta

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UniformMatrixList< kMatFreeDim2 >u_ 
input training data
UniformMatrixList< kMatFreeDim2 >z_ 
measurement training data
std::vector< Matrix >x_ 
state estimate
std::vector< Cube >P_ 
state estimate cov
std::vector< Cube >P_t_tm1_ 
single-lag state covariance
std::vector< Matrix >y_ 
output estimate
Matrixdiag_y_
Matrixsum_E_x_t_x_t_ 
state covariance (current time)
Matrixsum_E_xu_tm1_xu_tm1_ 
state-input covariance (t-minus-1)
Matrixsum_E_xu_t_xu_tm1_ 
single lag state-input covariance
Fitfit_
Vectortheta_
data_tdt_ 
sample period
size_tn_u_ 
number of inputs
size_tn_x_ 
number of states
size_tn_y_ 
number of outputs
size_tn_trials_ 
number of input/output data sequences
std::vector< size_t >n_t_ 
number of time steps
size_tn_t_tot_ 
total number of time steps across trials

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class lds::gaussian::FitEM;
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This type is used in the process of fitting GLDS models by expectation-maximization (EM).

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Subspace Identification (SSID) for GLDS. 
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SSID() =default
Constructs a new SSIDFit type.
SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList< kMatFreeDim2 > && u_train, UniformMatrixList< kMatFreeDim2 > && z_train, const Vector & d =Vector(1).fill(-kInf))
Constructs a new SSIDFit type.
std::tuple< Fit, Vector >Run(SSIDWt ssid_wt)
Runs fitting by subspace identification (SSID)
std::tuple< UniformMatrixList< kMatFreeDim2 >, UniformMatrixList< kMatFreeDim2 > >ReturnData()
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voidCalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)
Using periods of silence in inputs (u), calculates the output \ bias (d)
voidCreateHankelDataMat()
Creates the block-hankel I/O data matrix.
voidCalcSVD(SSIDWt wt)
performs the singular value decomposition (SVD)
voidSolve(data_t wt_dc)
solves for LDS parameters
voidRecomputeExtObs()
recompute extended observability matrix from estimates of A, C

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input training data
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MatrixD_ 
block-Hankel I/O data matrix
Fitfit_ 
fit
Matrixg_dc_ 
I/O gain @ DC.
data_tdt_ 
sample period
size_tn_u_ 
number of inputs
size_tn_x_ 
number of states
size_tn_y_ 
number of outputs
size_tn_h_
size_tn_trials_ 
number of input/output data sequences
std::vector< size_t >n_t_ 
number of time steps
size_tn_t_tot_ 
total number of time steps across trials
MatrixL_ 
lower triangle decomp of covariance matrix
Vectors_ 
singular values
Matrixext_obs_t_ 
extended observability matrix

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Gaussian-observation SwitchedController Type. 
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SwitchedController() =default
Constructs a new SwitchedController.
SwitchedController(const std::vector< System > & systems, data_t u_lb, data_t u_ub, size_t control_type =0)
Constructs a new SwitchedController.
SwitchedController(std::vector< System > && systems, data_t u_lb, data_t u_ub, size_t control_type =0)
Constructs a new SwitchedController (moves systems).
voidSwitch(size_t idx, bool do_force_switch =false)
Switch to a different sub-system/controller.
voidset_Kc(const UniformMatrixList<> & Kc)
sets state feedback gains
voidset_Kc(UniformMatrixList<> && Kc)
sets state feedback gains (moving)
voidset_Kc_inty(const UniformMatrixList<> & Kc_inty)
sets integral feedback gains
voidset_Kc_inty(UniformMatrixList<> && Kc_inty)
sets integral feedback gains (moving)
voidset_Kc_u(const UniformMatrixList<> & Kc_u)
sets input feedback gains
voidset_Kc_u(UniformMatrixList<> && Kc_u)
sets input feedback gains (moving)
voidset_g_design(const UniformVectorList & g)
sets input gain used during controller design
voidset_g_design(UniformVectorList && g)
sets input gain used during controller design (moving)

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std::vector< System >systems_ 
underlying sub-systems which are switched between
size_tn_sys_ 
number of systems
size_tidx_ 
current system/controller index.
UniformMatrixListKc_list_
UniformMatrixListKc_inty_list_
UniformMatrixListKc_u_list_
UniformVectorListg_design_list_

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Constructs a new Controller.
Controller(const System & sys, data_t u_lb, data_t u_ub, size_t control_type =0)
Constructs a new Controller.
Controller(System && sys, data_t u_lb, data_t u_ub, size_t control_type =0)
Constructs a new Controller by moving the system object.
const Vector &Control(const Vector & z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)
updates control signal (single-step)
const Vector &ControlOutputReference(const Vector & z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)
updates control signal, given previously-set (single-step)
const System &sys() const
const Matrix &Kc() const
Get state feedback controller gain.
const Matrix &Kc_inty() const
Get integral controller gain.
const Matrix &Kc_u() const
Get input feedback controller gain.
const Vector &g_design() const
Get input gain used in controller design.
const Vector &u_ref() const
Get reference input.
const Vector &x_ref() const
Get reference state.
const Vector &y_ref() const
Get reference output.
size_tcontrol_type() const
Get controller type.
data_ttau_awu() const
Get time constant of anti-integral-windup.
data_tu_lb() const
Get control lower bound.
data_tu_ub() const
Get control upper bound.
voidset_sys(const System & sys)
Set system.
voidset_g_design(const Vector & g_design)
Set input gain used in controller design (g_design)
voidset_u_ref(const Vector & u_ref)
Set reference input (u_ref)
voidset_x_ref(const Vector & x_ref)
Set reference state (x_ref)
voidset_Kc(const Matrix & Kc)
Set state controller gain.
voidset_Kc_inty(const Matrix & Kc_inty)
Set integral controller gain.
voidset_Kc_u(const Matrix & Kc_u)
Set input controller gain.
voidset_tau_awu(data_t tau)
Set time constant of anti-integral-windup.
voidset_control_type(size_t control_type)
Sets the control type.
voidset_u_lb(data_t u_lb)
sets control lower bound
voidset_u_ub(data_t u_ub)
Sets control upper bound.
voidReset()
reset system and control variables.
voidPrint()
prints variables to stdout

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Systemsys_ 
underlying LDS
Vectoru_ 
control signal
Vectoru_return_ 
control signal that is returned to user
Vectorg_design_ 
input gain of the system used for controller design
Vectoru_ref_ 
reference input
Vectoru_ref_prev_ 
reference input at previous time step
Vectorx_ref_ 
reference state
Vectory_ref_ 
reference output
Vectorcx_ref_
MatrixKc_ 
state controller gain
MatrixKc_u_ 
input controller gain (optional when control updates )
MatrixKc_inty_ 
integral controller gain
Vectordu_ref_
Vectordv_ref_
Vectorv_ref_
Vectordv_
Vectorv_ 
Control after g inversion (e.g., control in physical units)
Vectorint_e_ 
integrated error
Vectorint_e_awu_adjust_ 
anti-windup adjustment to intE
Vectoru_sat_ 
control signal after saturation (for antiWindup)
booldo_control_prev_
booldo_lock_control_prev_
boolu_saturated_ 
whether control signal has reached saturation limits
data_tu_lb_ 
lower bound on control
data_tu_ub_ 
upper bound on control
data_ttau_awu_ 
antiwindup time constant
data_tk_awu_
data_tt_since_control_onset_ 
time since control epoch onset
size_tcontrol_type_ 
controller type

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Gaussian LDS Type. 
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Inherits from lds::System

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System() =default
Constructs a new System.
System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)
Constructs a new Gaussian System.
virtual const Vector &Simulate(const Vector & u_tm1) override
Simulate system measurement.
const Matrix &R() const
Get output noise covariance.
voidset_Q(const Matrix & Q)
voidset_R(const Matrix & R)
Set output noise covariance.
voidset_Ke(const Matrix & Ke)
Set estimator gain.
voidset_Ke_m(const Matrix & Ke_m)
Set disturbance estimator gain.
voidPrint()
Print system variables to stdout.

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virtual voidh() override
System output function.
virtual voidRecurseKe() override
Recursively update estimator gain.

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MatrixR_ 
covariance of output noise
booldo_recurse_Ke_ 
whether to recursively calculate estimator gain

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Public Functions inherited from lds::System

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virtual~System()
voidFilter(const Vector & u_tm1, const Vector & z)
Filter data to produce causal state estimates.
voidf(const Vector & u, bool do_add_noise =false)
system dynamics function
size_tn_u() const
Get number of inputs.
size_tn_x() const
Get number of states.
size_tn_y() const
Get number of outputs.
data_tdt() const
Get sample period.
const Vector &x() const
Get current state.
const Matrix &P() const
Get covariance of state estimate.
const Vector &m() const
Get current process disturbance/bias.
const Matrix &P_m() const
Get covariance of process disturbance estimate.
const Vector &cx() const
Get C*x.
const Vector &y() const
Get output.
const Vector &x0() const
Get initial state.
const Vector &m0() const
Get initial disturbance.
const Matrix &A() const
Get state matrix.
const Matrix &B() const
Get input matrix.
const Vector &g() const
Get input gain/conversion factor.
const Matrix &C() const
Get output matrix.
const Vector &d() const
Get output bias.
const Matrix &Ke() const
Get estimator gain.
const Matrix &Ke_m() const
Get estimator gain for process disturbance (m)
const Matrix &Q()
Get process noise covariance.
const Matrix &Q_m()
Get process noise covariance of disturbance evoluation.
const Matrix &P0()
Get covariance of initial state.
const Matrix &P0_m()
Get covariance of initial process disturbance.
voidset_A(const Matrix & A)
Set state matrix.
voidset_B(const Matrix & B)
Set input matrix.
voidset_m(const Vector & m, bool do_force_assign =false)
Set process disturbance.
voidset_g(const Vector & g)
Set input gain.
voidset_Q_m(const Matrix & Q_m)
Set process noise covariance of disturbance evoluation.
voidset_x0(const Vector & x0)
Set initial state.
voidset_P0(const Matrix & P0)
Set covariance of initial state.
voidset_P0_m(const Matrix & P0_m)
Set covariance of initial process disturbance.
voidset_C(const Matrix & C)
Set output matrix.
voidset_d(const Vector & d)
Set output bias.
voidset_x(const Vector & x)
Set state of system.
voidReset()
Reset system variables.

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booldo_adapt_m 
whether to adaptively estimate disturbance m

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std::size_tn_x_ 
number of states
std::size_tn_u_ 
number of inputs
std::size_tn_y_ 
number of outputs
data_tdt_ 
sample period
Vectorx_ 
state
MatrixP_ 
covariance of state estimate
Vectorm_ 
process disturbance
MatrixP_m_ 
covariance of disturbance estimate
Vectorcx_ 
C*x.
Vectory_ 
output
Vectorz_ 
measurement
Vectorx0_ 
initial state
MatrixP0_ 
covariance of initial state estimate
Vectorm0_ 
initial process disturbance
MatrixP0_m_ 
covariance of initial disturbance est.
MatrixA_ 
state matrix
MatrixB_ 
input matrix
Vectorg_ 
input gain
MatrixQ_ 
covariance of process noise
MatrixQ_m_ 
covariance of disturbance random walk
MatrixC_ 
output matrix
Vectord_ 
output bias
MatrixKe_ 
estimator gain
MatrixKe_m_ 
estimator gain for process disturbance

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-  System
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System() =default
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virtual const Vector & Simulate(
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Simulate system and produce measurement

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inline const Matrix & R() const
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inline void set_Q(
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inline void set_R(
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inline void set_Ke(
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inline void set_Ke_m(
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void Print()
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inline virtual void h() override
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virtual void RecurseKe() override
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bool do_recurse_Ke_ {};
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diff --git a/docs/docs/api/classes/classlds_1_1poisson_1_1controller/index.html b/docs/docs/api/classes/classlds_1_1poisson_1_1controller/index.html
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PLDS Controller Type. 
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Inherits from lds::Controller< System >

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Set reference output.

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Public Functions inherited from lds::Controller< System >

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Controller() =default
Constructs a new Controller.
Controller(const System & sys, data_t u_lb, data_t u_ub, size_t control_type =0)
Constructs a new Controller.
Controller(System && sys, data_t u_lb, data_t u_ub, size_t control_type =0)
Constructs a new Controller by moving the system object.
const Vector &Control(const Vector & z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)
updates control signal (single-step)
const Vector &ControlOutputReference(const Vector & z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)
updates control signal, given previously-set (single-step)
const System &sys() const
const Matrix &Kc() const
Get state feedback controller gain.
const Matrix &Kc_inty() const
Get integral controller gain.
const Matrix &Kc_u() const
Get input feedback controller gain.
const Vector &g_design() const
Get input gain used in controller design.
const Vector &u_ref() const
Get reference input.
const Vector &x_ref() const
Get reference state.
const Vector &y_ref() const
Get reference output.
size_tcontrol_type() const
Get controller type.
data_ttau_awu() const
Get time constant of anti-integral-windup.
data_tu_lb() const
Get control lower bound.
data_tu_ub() const
Get control upper bound.
voidset_sys(const System & sys)
Set system.
voidset_g_design(const Vector & g_design)
Set input gain used in controller design (g_design)
voidset_u_ref(const Vector & u_ref)
Set reference input (u_ref)
voidset_x_ref(const Vector & x_ref)
Set reference state (x_ref)
voidset_Kc(const Matrix & Kc)
Set state controller gain.
voidset_Kc_inty(const Matrix & Kc_inty)
Set integral controller gain.
voidset_Kc_u(const Matrix & Kc_u)
Set input controller gain.
voidset_tau_awu(data_t tau)
Set time constant of anti-integral-windup.
voidset_control_type(size_t control_type)
Sets the control type.
voidset_u_lb(data_t u_lb)
sets control lower bound
voidset_u_ub(data_t u_ub)
Sets control upper bound.
voidReset()
reset system and control variables.
voidPrint()
prints variables to stdout

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Systemsys_ 
underlying LDS
Vectoru_ 
control signal
Vectoru_return_ 
control signal that is returned to user
Vectorg_design_ 
input gain of the system used for controller design
Vectoru_ref_ 
reference input
Vectoru_ref_prev_ 
reference input at previous time step
Vectorx_ref_ 
reference state
Vectory_ref_ 
reference output
Vectorcx_ref_
MatrixKc_ 
state controller gain
MatrixKc_u_ 
input controller gain (optional when control updates )
MatrixKc_inty_ 
integral controller gain
Vectordu_ref_
Vectordv_ref_
Vectorv_ref_
Vectordv_
Vectorv_ 
Control after g inversion (e.g., control in physical units)
Vectorint_e_ 
integrated error
Vectorint_e_awu_adjust_ 
anti-windup adjustment to intE
Vectoru_sat_ 
control signal after saturation (for antiWindup)
booldo_control_prev_
booldo_lock_control_prev_
boolu_saturated_ 
whether control signal has reached saturation limits
data_tu_lb_ 
lower bound on control
data_tu_ub_ 
upper bound on control
data_ttau_awu_ 
antiwindup time constant
data_tk_awu_
data_tt_since_control_onset_ 
time since control epoch onset
size_tcontrol_type_ 
controller type

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PLDS Fit Type. 
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Inherits from lds::Fit

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Fit() =default
Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)
Constructs a new Fit.
virtual Viewh(Matrix & y, const Matrix & x, size_t t) override
output function
virtual voidset_R(const Matrix & R) override
sets output noise covariance (if any)
virtual const Matrix &R() const override

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Public Functions inherited from lds::Fit

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virtual~Fit() =default
size_tn_u() const
gets number of inputs
size_tn_x() const
gets number of states
size_tn_y() const
gets number of outputs
data_tdt() const
gets sample period
const Matrix &A() const
gets state matrix
const Matrix &B() const
gets input matrix
const Vector &g() const
gets input gain
const Vector &m() const
gets process disturbance
const Matrix &Q() const
gets process noise covariance
const Vector &x0() const
gets initial state estimate
const Matrix &P0() const
gets covariance of initial state estimate
const Matrix &C() const
gets output matrix
const Vector &d() const
gets output bias
voidset_A(const Matrix & A)
sets state matrix
voidset_B(const Matrix & B)
sets input matrix
voidset_g(const Vector & g)
sets input gain/conversion factor
voidset_m(const Vector & m)
sets process disturbance
voidset_Q(const Matrix & Q)
sets process noise covariance
voidset_x0(const Vector & x0)
sets initial state estimate
voidset_P0(const Matrix & P0)
sets initial state estimate covariance
voidset_C(const Matrix & C)
sets output matrix
voidset_d(const Vector & d)
sets output bias
Viewf(Matrix & x, const Matrix & u, size_t t)
system dynamics function
Viewf(Matrix & x_pre, const Matrix & x_post, const Matrix & u, size_t t)
system dynamics function

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data_tdt_ 
sample period
MatrixA_ 
state matrix
MatrixB_ 
input matrix
Vectorg_ 
input gain
Vectorm_ 
process noise mean
MatrixQ_ 
process noise cov
MatrixC_ 
output matrix
Vectord_ 
output bias
MatrixR_ 
measurement noise
Vectorx0_ 
initial state
MatrixP0_ 
initial covar
size_tn_u_ 
number of inputs
size_tn_x_ 
number of states
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PLDS E-M Fit Type.  More…

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EM() =default
Constructs a new EMFit type.
EM(size_t n_x, data_t dt, UniformMatrixList< kMatFreeDim2 > && u_train, UniformMatrixList< kMatFreeDim2 > && z_train)
Constructs a new EMFit type.
EM(const Fit & fit0, UniformMatrixList< kMatFreeDim2 > && u_train, UniformMatrixList< kMatFreeDim2 > && z_train)
Constructs a new EMFit type.
virtual~EM() =default
const Fit &Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)
Runs fitting by Expectation(E)-Maximization(M)
std::tuple< UniformMatrixList< kMatFreeDim2 >, UniformMatrixList< kMatFreeDim2 > >ReturnData()
Returns the input/output data to caller.
const std::vector< Matrix > &x() const
gets estimated state (over time)
const std::vector< Matrix > &y() const
gets estimated output (over time)
const Matrix &sum_E_x_t_x_t() const
gets state-input covariance
const Matrix &sum_E_xu_tm1_xu_tm1() const
gets state-input covariance (t-minus-1)
const Matrix &sum_E_xu_t_xu_tm1() const
gets single lag state-input covariance
size_tn_t_tot()
total number of time samples
const Vector &theta() const
gets parameters updated in M step

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voidExpectation(bool force_common_initial =false)
Expectation step.
voidMaximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)
Maximization step.
voidMaximizeDynamics()
voidMaximizeQ()
voidMaximizeInitial()
voidSmooth(bool force_common_initial)
get smoothed estimates
voidReset()
reset to initial conditions
voidInitVars()
Initializes the variables.
VectorUpdateTheta()
updates parameter list, theta

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UniformMatrixList< kMatFreeDim2 >u_ 
input training data
UniformMatrixList< kMatFreeDim2 >z_ 
measurement training data
std::vector< Matrix >x_ 
state estimate
std::vector< Cube >P_ 
state estimate cov
std::vector< Cube >P_t_tm1_ 
single-lag state covariance
std::vector< Matrix >y_ 
output estimate
Matrixdiag_y_
Matrixsum_E_x_t_x_t_ 
state covariance (current time)
Matrixsum_E_xu_tm1_xu_tm1_ 
state-input covariance (t-minus-1)
Matrixsum_E_xu_t_xu_tm1_ 
single lag state-input covariance
Fitfit_
Vectortheta_
data_tdt_ 
sample period
size_tn_u_ 
number of inputs
size_tn_x_ 
number of states
size_tn_y_ 
number of outputs
size_tn_trials_ 
number of input/output data sequences
std::vector< size_t >n_t_ 
number of time steps
size_tn_t_tot_ 
total number of time steps across trials

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class lds::poisson::FitEM;
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This type is used in the process of fitting PLDS models by expectation-maximization (EM).

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Subspace Identification (SSID) for PLDS. 
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SSID() =default
Constructs a new SSIDFit type.
SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList< kMatFreeDim2 > && u_train, UniformMatrixList< kMatFreeDim2 > && z_train, const Vector & d =Vector(1).fill(-kInf))
Constructs a new SSIDFit type.
std::tuple< Fit, Vector >Run(SSIDWt ssid_wt)
Runs fitting by subspace identification (SSID)
std::tuple< UniformMatrixList< kMatFreeDim2 >, UniformMatrixList< kMatFreeDim2 > >ReturnData()
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voidCalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)
Using periods of silence in inputs (u), calculates the output \ bias (d)
voidCreateHankelDataMat()
Creates the block-hankel I/O data matrix.
voidCalcSVD(SSIDWt wt)
performs the singular value decomposition (SVD)
voidSolve(data_t wt_dc)
solves for LDS parameters
voidRecomputeExtObs()
recompute extended observability matrix from estimates of A, C

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UniformMatrixList< kMatFreeDim2 >u_ 
input training data
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measurement training data
MatrixD_ 
block-Hankel I/O data matrix
Fitfit_ 
fit
Matrixg_dc_ 
I/O gain @ DC.
data_tdt_ 
sample period
size_tn_u_ 
number of inputs
size_tn_x_ 
number of states
size_tn_y_ 
number of outputs
size_tn_h_
size_tn_trials_ 
number of input/output data sequences
std::vector< size_t >n_t_ 
number of time steps
size_tn_t_tot_ 
total number of time steps across trials
MatrixL_ 
lower triangle decomp of covariance matrix
Vectors_ 
singular values
Matrixext_obs_t_ 
extended observability matrix

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Poisson-observation SwitchedController Type. 
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Set reference output.

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SwitchedController() =default
Constructs a new SwitchedController.
SwitchedController(const std::vector< System > & systems, data_t u_lb, data_t u_ub, size_t control_type =0)
Constructs a new SwitchedController.
SwitchedController(std::vector< System > && systems, data_t u_lb, data_t u_ub, size_t control_type =0)
Constructs a new SwitchedController (moves systems).
voidSwitch(size_t idx, bool do_force_switch =false)
Switch to a different sub-system/controller.
voidset_Kc(const UniformMatrixList<> & Kc)
sets state feedback gains
voidset_Kc(UniformMatrixList<> && Kc)
sets state feedback gains (moving)
voidset_Kc_inty(const UniformMatrixList<> & Kc_inty)
sets integral feedback gains
voidset_Kc_inty(UniformMatrixList<> && Kc_inty)
sets integral feedback gains (moving)
voidset_Kc_u(const UniformMatrixList<> & Kc_u)
sets input feedback gains
voidset_Kc_u(UniformMatrixList<> && Kc_u)
sets input feedback gains (moving)
voidset_g_design(const UniformVectorList & g)
sets input gain used during controller design
voidset_g_design(UniformVectorList && g)
sets input gain used during controller design (moving)

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std::vector< System >systems_ 
underlying sub-systems which are switched between
size_tn_sys_ 
number of systems
size_tidx_ 
current system/controller index.
UniformMatrixListKc_list_
UniformMatrixListKc_inty_list_
UniformMatrixListKc_u_list_
UniformVectorListg_design_list_

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Constructs a new Controller.
Controller(const System & sys, data_t u_lb, data_t u_ub, size_t control_type =0)
Constructs a new Controller.
Controller(System && sys, data_t u_lb, data_t u_ub, size_t control_type =0)
Constructs a new Controller by moving the system object.
const Vector &Control(const Vector & z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)
updates control signal (single-step)
const Vector &ControlOutputReference(const Vector & z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)
updates control signal, given previously-set (single-step)
const System &sys() const
const Matrix &Kc() const
Get state feedback controller gain.
const Matrix &Kc_inty() const
Get integral controller gain.
const Matrix &Kc_u() const
Get input feedback controller gain.
const Vector &g_design() const
Get input gain used in controller design.
const Vector &u_ref() const
Get reference input.
const Vector &x_ref() const
Get reference state.
const Vector &y_ref() const
Get reference output.
size_tcontrol_type() const
Get controller type.
data_ttau_awu() const
Get time constant of anti-integral-windup.
data_tu_lb() const
Get control lower bound.
data_tu_ub() const
Get control upper bound.
voidset_sys(const System & sys)
Set system.
voidset_g_design(const Vector & g_design)
Set input gain used in controller design (g_design)
voidset_u_ref(const Vector & u_ref)
Set reference input (u_ref)
voidset_x_ref(const Vector & x_ref)
Set reference state (x_ref)
voidset_Kc(const Matrix & Kc)
Set state controller gain.
voidset_Kc_inty(const Matrix & Kc_inty)
Set integral controller gain.
voidset_Kc_u(const Matrix & Kc_u)
Set input controller gain.
voidset_tau_awu(data_t tau)
Set time constant of anti-integral-windup.
voidset_control_type(size_t control_type)
Sets the control type.
voidset_u_lb(data_t u_lb)
sets control lower bound
voidset_u_ub(data_t u_ub)
Sets control upper bound.
voidReset()
reset system and control variables.
voidPrint()
prints variables to stdout

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Systemsys_ 
underlying LDS
Vectoru_ 
control signal
Vectoru_return_ 
control signal that is returned to user
Vectorg_design_ 
input gain of the system used for controller design
Vectoru_ref_ 
reference input
Vectoru_ref_prev_ 
reference input at previous time step
Vectorx_ref_ 
reference state
Vectory_ref_ 
reference output
Vectorcx_ref_
MatrixKc_ 
state controller gain
MatrixKc_u_ 
input controller gain (optional when control updates )
MatrixKc_inty_ 
integral controller gain
Vectordu_ref_
Vectordv_ref_
Vectorv_ref_
Vectordv_
Vectorv_ 
Control after g inversion (e.g., control in physical units)
Vectorint_e_ 
integrated error
Vectorint_e_awu_adjust_ 
anti-windup adjustment to intE
Vectoru_sat_ 
control signal after saturation (for antiWindup)
booldo_control_prev_
booldo_lock_control_prev_
boolu_saturated_ 
whether control signal has reached saturation limits
data_tu_lb_ 
lower bound on control
data_tu_ub_ 
upper bound on control
data_ttau_awu_ 
antiwindup time constant
data_tk_awu_
data_tt_since_control_onset_ 
time since control epoch onset
size_tcontrol_type_ 
controller type

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-  lds::poisson::System
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Poisson System type. 
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 #include <lds_poisson_sys.h>

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Inherits from lds::System

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System() =default
Constructs a new System.
System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)
Constructs a new Poisson System.
virtual const Vector &Simulate(const Vector & u_tm1) override
Simulate system measurement.

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virtual voidh() override
System output function.
virtual voidRecurseKe() override
Recursively recalculate estimator gain (Ke)

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Public Functions inherited from lds::System

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virtual~System()
voidFilter(const Vector & u_tm1, const Vector & z)
Filter data to produce causal state estimates.
voidf(const Vector & u, bool do_add_noise =false)
system dynamics function
size_tn_u() const
Get number of inputs.
size_tn_x() const
Get number of states.
size_tn_y() const
Get number of outputs.
data_tdt() const
Get sample period.
const Vector &x() const
Get current state.
const Matrix &P() const
Get covariance of state estimate.
const Vector &m() const
Get current process disturbance/bias.
const Matrix &P_m() const
Get covariance of process disturbance estimate.
const Vector &cx() const
Get C*x.
const Vector &y() const
Get output.
const Vector &x0() const
Get initial state.
const Vector &m0() const
Get initial disturbance.
const Matrix &A() const
Get state matrix.
const Matrix &B() const
Get input matrix.
const Vector &g() const
Get input gain/conversion factor.
const Matrix &C() const
Get output matrix.
const Vector &d() const
Get output bias.
const Matrix &Ke() const
Get estimator gain.
const Matrix &Ke_m() const
Get estimator gain for process disturbance (m)
const Matrix &Q()
Get process noise covariance.
const Matrix &Q_m()
Get process noise covariance of disturbance evoluation.
const Matrix &P0()
Get covariance of initial state.
const Matrix &P0_m()
Get covariance of initial process disturbance.
voidset_A(const Matrix & A)
Set state matrix.
voidset_B(const Matrix & B)
Set input matrix.
voidset_m(const Vector & m, bool do_force_assign =false)
Set process disturbance.
voidset_g(const Vector & g)
Set input gain.
voidset_Q(const Matrix & Q)
Set process noise covariance.
voidset_Q_m(const Matrix & Q_m)
Set process noise covariance of disturbance evoluation.
voidset_x0(const Vector & x0)
Set initial state.
voidset_P0(const Matrix & P0)
Set covariance of initial state.
voidset_P0_m(const Matrix & P0_m)
Set covariance of initial process disturbance.
voidset_C(const Matrix & C)
Set output matrix.
voidset_d(const Vector & d)
Set output bias.
voidset_x(const Vector & x)
Set state of system.
voidReset()
Reset system variables.
voidPrint()
Print system variables to stdout.

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Protected Functions inherited from lds::System

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voidInitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)

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Public Attributes inherited from lds::System

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booldo_adapt_m 
whether to adaptively estimate disturbance m

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Protected Attributes inherited from lds::System

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std::size_tn_x_ 
number of states
std::size_tn_u_ 
number of inputs
std::size_tn_y_ 
number of outputs
data_tdt_ 
sample period
Vectorx_ 
state
MatrixP_ 
covariance of state estimate
Vectorm_ 
process disturbance
MatrixP_m_ 
covariance of disturbance estimate
Vectorcx_ 
C*x.
Vectory_ 
output
Vectorz_ 
measurement
Vectorx0_ 
initial state
MatrixP0_ 
covariance of initial state estimate
Vectorm0_ 
initial process disturbance
MatrixP0_m_ 
covariance of initial disturbance est.
MatrixA_ 
state matrix
MatrixB_ 
input matrix
Vectorg_ 
input gain
MatrixQ_ 
covariance of process noise
MatrixQ_m_ 
covariance of disturbance random walk
MatrixC_ 
output matrix
Vectord_ 
output bias
MatrixKe_ 
estimator gain
MatrixKe_m_ 
estimator gain for process disturbance

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-  Public Function Details
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-  System
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System() =default
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System(
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virtual const Vector & Simulate(
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Return: z measurement

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Reimplements: lds::System::Simulate

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Simulate system and produce measurement

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inline virtual void h() override
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Reimplements: lds::System::h

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virtual void RecurseKe() override
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Reimplements: lds::System::RecurseKe

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Recursively recalculate estimator gain (Ke).

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References:

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Smith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.

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Eden UT, …, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.

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Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time

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diff --git a/docs/docs/api/classes/classlds_1_1ssid/index.html b/docs/docs/api/classes/classlds_1_1ssid/index.html
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More…

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Inherited by lds::gaussian::FitSSID, lds::poisson::FitSSID

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SSID() =default
Constructs a new SSIDFit type.
SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList< kMatFreeDim2 > && u_train, UniformMatrixList< kMatFreeDim2 > && z_train, const Vector & d =Vector(1).fill(-kInf))
Constructs a new SSIDFit type.
std::tuple< Fit, Vector >Run(SSIDWt ssid_wt)
Runs fitting by subspace identification (SSID)
std::tuple< UniformMatrixList< kMatFreeDim2 >, UniformMatrixList< kMatFreeDim2 > >ReturnData()
Returns the I/O data to caller.

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voidCalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)
Using periods of silence in inputs (u), calculates the output \ bias (d)
voidCreateHankelDataMat()
Creates the block-hankel I/O data matrix.
virtual voidDecomposeData() =0
Decompose data to lower-triangular matrix (used in Solve)
voidCalcSVD(SSIDWt wt)
performs the singular value decomposition (SVD)
voidSolve(data_t wt_dc)
solves for LDS parameters
voidRecomputeExtObs()
recompute extended observability matrix from estimates of A, C

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UniformMatrixList< kMatFreeDim2 >u_ 
input training data
UniformMatrixList< kMatFreeDim2 >z_ 
measurement training data
MatrixD_ 
block-Hankel I/O data matrix
Fitfit_ 
fit
Matrixg_dc_ 
I/O gain @ DC.
data_tdt_ 
sample period
size_tn_u_ 
number of inputs
size_tn_x_ 
number of states
size_tn_y_ 
number of outputs
size_tn_h_
size_tn_trials_ 
number of input/output data sequences
std::vector< size_t >n_t_ 
number of time steps
size_tn_t_tot_ 
total number of time steps across trials
MatrixL_ 
lower triangle decomp of covariance matrix
Vectors_ 
singular values
Matrixext_obs_t_ 
extended observability matrix

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template <typename Fit >
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SSID() =default
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SSID(
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std::tuple< Fit, Vector > Run(
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inline std::tuple< UniformMatrixList< kMatFreeDim2 >, UniformMatrixList< kMatFreeDim2 > > ReturnData()
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Return: tuple(input data, output data)

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void CalcD(
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void CreateHankelDataMat()
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Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.

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virtual void DecomposeData() =0
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void Solve(
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void RecomputeExtObs()
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UniformMatrixList< kMatFreeDim2 > u_;
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UniformMatrixList< kMatFreeDim2 > z_;
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Matrix D_;
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Fit fit_;
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data_t dt_ {};
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size_t n_u_ {};
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Updated on 19 May 2022 at 17:16:03 Eastern Daylight Time

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diff --git a/docs/docs/api/classes/classlds_1_1switchedcontroller/index.html b/docs/docs/api/classes/classlds_1_1switchedcontroller/index.html
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SwitchedController Type.  More…

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Inherits from lds::Controller< System >

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Inherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController

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SwitchedController() =default
Constructs a new SwitchedController.
SwitchedController(const std::vector< System > & systems, data_t u_lb, data_t u_ub, size_t control_type =0)
Constructs a new SwitchedController.
SwitchedController(std::vector< System > && systems, data_t u_lb, data_t u_ub, size_t control_type =0)
Constructs a new SwitchedController (moves systems).
voidSwitch(size_t idx, bool do_force_switch =false)
Switch to a different sub-system/controller.
voidset_Kc(const UniformMatrixList<> & Kc)
sets state feedback gains
voidset_Kc(UniformMatrixList<> && Kc)
sets state feedback gains (moving)
voidset_Kc_inty(const UniformMatrixList<> & Kc_inty)
sets integral feedback gains
voidset_Kc_inty(UniformMatrixList<> && Kc_inty)
sets integral feedback gains (moving)
voidset_Kc_u(const UniformMatrixList<> & Kc_u)
sets input feedback gains
voidset_Kc_u(UniformMatrixList<> && Kc_u)
sets input feedback gains (moving)
voidset_g_design(const UniformVectorList & g)
sets input gain used during controller design
voidset_g_design(UniformVectorList && g)
sets input gain used during controller design (moving)

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std::vector< System >systems_ 
underlying sub-systems which are switched between
size_tn_sys_ 
number of systems
size_tidx_ 
current system/controller index.
UniformMatrixListKc_list_
UniformMatrixListKc_inty_list_
UniformMatrixListKc_u_list_
UniformVectorListg_design_list_

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Public Functions inherited from lds::Controller< System >

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Controller() =default
Constructs a new Controller.
Controller(const System & sys, data_t u_lb, data_t u_ub, size_t control_type =0)
Constructs a new Controller.
Controller(System && sys, data_t u_lb, data_t u_ub, size_t control_type =0)
Constructs a new Controller by moving the system object.
const Vector &Control(const Vector & z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)
updates control signal (single-step)
const Vector &ControlOutputReference(const Vector & z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)
updates control signal, given previously-set (single-step)
const System &sys() const
const Matrix &Kc() const
Get state feedback controller gain.
const Matrix &Kc_inty() const
Get integral controller gain.
const Matrix &Kc_u() const
Get input feedback controller gain.
const Vector &g_design() const
Get input gain used in controller design.
const Vector &u_ref() const
Get reference input.
const Vector &x_ref() const
Get reference state.
const Vector &y_ref() const
Get reference output.
size_tcontrol_type() const
Get controller type.
data_ttau_awu() const
Get time constant of anti-integral-windup.
data_tu_lb() const
Get control lower bound.
data_tu_ub() const
Get control upper bound.
voidset_sys(const System & sys)
Set system.
voidset_u_ref(const Vector & u_ref)
Set reference input (u_ref)
voidset_x_ref(const Vector & x_ref)
Set reference state (x_ref)
virtual voidset_y_ref(const Vector & y_ref)
Set reference output (y_ref)
voidset_tau_awu(data_t tau)
Set time constant of anti-integral-windup.
voidset_control_type(size_t control_type)
Sets the control type.
voidset_u_lb(data_t u_lb)
sets control lower bound
voidset_u_ub(data_t u_ub)
Sets control upper bound.
voidReset()
reset system and control variables.
voidPrint()
prints variables to stdout

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Protected Attributes inherited from lds::Controller< System >

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Name
Systemsys_ 
underlying LDS
Vectoru_ 
control signal
Vectoru_return_ 
control signal that is returned to user
Vectorg_design_ 
input gain of the system used for controller design
Vectoru_ref_ 
reference input
Vectoru_ref_prev_ 
reference input at previous time step
Vectorx_ref_ 
reference state
Vectory_ref_ 
reference output
Vectorcx_ref_
MatrixKc_ 
state controller gain
MatrixKc_u_ 
input controller gain (optional when control updates )
MatrixKc_inty_ 
integral controller gain
Vectordu_ref_
Vectordv_ref_
Vectorv_ref_
Vectordv_
Vectorv_ 
Control after g inversion (e.g., control in physical units)
Vectorint_e_ 
integrated error
Vectorint_e_awu_adjust_ 
anti-windup adjustment to intE
Vectoru_sat_ 
control signal after saturation (for antiWindup)
booldo_control_prev_
booldo_lock_control_prev_
boolu_saturated_ 
whether control signal has reached saturation limits
data_tu_lb_ 
lower bound on control
data_tu_ub_ 
upper bound on control
data_ttau_awu_ 
antiwindup time constant
data_tk_awu_
data_tt_since_control_onset_ 
time since control epoch onset
size_tcontrol_type_ 
controller type

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-  Detailed Description
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template <typename System >
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SwitchedController() =default
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inline SwitchedController(
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inline SwitchedController(
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inline void set_Kc_inty(
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UniformMatrixList Kc_u_list_;
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Updated on 19 May 2022 at 17:16:03 Eastern Daylight Time

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Linear Dynamical System Type. 
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 #include <lds_sys.h>

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Inherited by lds::gaussian::System, lds::poisson::System

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Name
System() =default
Constructs a new System.
System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)
constructs a new System
virtual~System()
voidFilter(const Vector & u_tm1, const Vector & z)
Filter data to produce causal state estimates.
virtual const Vector &Simulate(const Vector & u_tm1) =0
simulates system (single time step)
voidf(const Vector & u, bool do_add_noise =false)
system dynamics function
virtual voidh() =0
system output function
size_tn_u() const
Get number of inputs.
size_tn_x() const
Get number of states.
size_tn_y() const
Get number of outputs.
data_tdt() const
Get sample period.
const Vector &x() const
Get current state.
const Matrix &P() const
Get covariance of state estimate.
const Vector &m() const
Get current process disturbance/bias.
const Matrix &P_m() const
Get covariance of process disturbance estimate.
const Vector &cx() const
Get C*x.
const Vector &y() const
Get output.
const Vector &x0() const
Get initial state.
const Vector &m0() const
Get initial disturbance.
const Matrix &A() const
Get state matrix.
const Matrix &B() const
Get input matrix.
const Vector &g() const
Get input gain/conversion factor.
const Matrix &C() const
Get output matrix.
const Vector &d() const
Get output bias.
const Matrix &Ke() const
Get estimator gain.
const Matrix &Ke_m() const
Get estimator gain for process disturbance (m)
const Matrix &Q()
Get process noise covariance.
const Matrix &Q_m()
Get process noise covariance of disturbance evoluation.
const Matrix &P0()
Get covariance of initial state.
const Matrix &P0_m()
Get covariance of initial process disturbance.
voidset_A(const Matrix & A)
Set state matrix.
voidset_B(const Matrix & B)
Set input matrix.
voidset_m(const Vector & m, bool do_force_assign =false)
Set process disturbance.
voidset_g(const Vector & g)
Set input gain.
voidset_Q(const Matrix & Q)
Set process noise covariance.
voidset_Q_m(const Matrix & Q_m)
Set process noise covariance of disturbance evoluation.
voidset_x0(const Vector & x0)
Set initial state.
voidset_P0(const Matrix & P0)
Set covariance of initial state.
voidset_P0_m(const Matrix & P0_m)
Set covariance of initial process disturbance.
voidset_C(const Matrix & C)
Set output matrix.
voidset_d(const Vector & d)
Set output bias.
voidset_x(const Vector & x)
Set state of system.
voidReset()
Reset system variables.
voidPrint()
Print system variables to stdout.

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virtual voidRecurseKe() =0
Recursively recalculate estimator gain (Ke)
voidInitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)

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booldo_adapt_m 
whether to adaptively estimate disturbance m

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Name
std::size_tn_x_ 
number of states
std::size_tn_u_ 
number of inputs
std::size_tn_y_ 
number of outputs
data_tdt_ 
sample period
Vectorx_ 
state
MatrixP_ 
covariance of state estimate
Vectorm_ 
process disturbance
MatrixP_m_ 
covariance of disturbance estimate
Vectorcx_ 
C*x.
Vectory_ 
output
Vectorz_ 
measurement
Vectorx0_ 
initial state
MatrixP0_ 
covariance of initial state estimate
Vectorm0_ 
initial process disturbance
MatrixP0_m_ 
covariance of initial disturbance est.
MatrixA_ 
state matrix
MatrixB_ 
input matrix
Vectorg_ 
input gain
MatrixQ_ 
covariance of process noise
MatrixQ_m_ 
covariance of disturbance random walk
MatrixC_ 
output matrix
Vectord_ 
output bias
MatrixKe_ 
estimator gain
MatrixKe_m_ 
estimator gain for process disturbance

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System() =default
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-  System
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System(
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inline virtual ~System()
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void Filter(
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Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.

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virtual const Vector & Simulate(
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Reimplemented by: lds::poisson::System::Simulate, lds::gaussian::System::Simulate

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Reimplemented by: lds::poisson::System::h, lds::gaussian::System::h

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diff --git a/docs/docs/api/classes/classlds_1_1uniformmatrixlist/index.html b/docs/docs/api/classes/classlds_1_1uniformmatrixlist/index.html
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UniformMatrixList() =default
Constructs a new UniformMatrixList.
UniformMatrixList(const std::vector< Matrix > & mats, std::array< size_t, 2 > dim ={0, 0})
Constructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent.
UniformMatrixList(std::vector< Matrix > && mats, std::array< size_t, 2 > dim ={0, 0})
Constructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent.
UniformMatrixList(std::initializer_list< Matrix > mats, std::array< size_t, 2 > dim ={0, 0})
Constructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent.
UniformMatrixList(const UniformMatrixList< D > & that)
Constructs a new UniformMatrixList (copy).
UniformMatrixList(UniformMatrixList< D > && that)
Constructs a new UniformMatrixList (move).
~UniformMatrixList() =default
Destroys the object.
const std::array< size_t, 2 > &dim(size_t n =0) const
gets dimensions of uniformly sized matrices
size_tsize()
size of container
const Matrix &at(size_t n)
gets reference to n^th element
voidSwap(Matrix & that, size_t n)
swaps input matrix with n^th matrix of list
UniformMatrixList< D > &operator=(const UniformMatrixList< D > & that)
assigns the contents (copy)
UniformMatrixList< D > &operator=(UniformMatrixList< D > && that)
assigns the contents (move)

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template <MatrixListFreeDim D =kMatFreeDimNone>
-class lds::UniformMatrixList;
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UniformMatrixList() =default
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explicit UniformMatrixList(
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UniformMatrixList(
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UniformMatrixList(
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UniformSystemList() =default
Constructs a new UniformSystemList.
UniformSystemList(const std::vector< System > & systems, std::array< size_t, 3 > dim ={0, 0, 0})
Constructs a new UniformSystemList by copying existing vector of System if dimensions consistent.
UniformSystemList(std::vector< System > && systems, std::array< size_t, 3 > dim ={0, 0, 0})
Constructs a new UniformSystemList by moving existing vector of System if dimensions consistent.
UniformSystemList(std::initializer_list< System > systems, std::array< size_t, 3 > dim ={0, 0, 0})
Constructs a new UniformSystemList from initializer_list of System if dimensions consistent.
UniformSystemList(const UniformSystemList & that)
Constructs a new UniformSystemList (copy).
UniformSystemList(UniformSystemList && that)
Constructs a new UniformSystemList (move).
~UniformSystemList() =default
Destroys the object.
const std::array< size_t, 3 > &dim() const
gets dimensions of the uniformly sized systems
size_tsize()
size of container
const System &at(size_t n)
gets reference to n^th element
voidSwap(System & that, size_t n)
swaps input system with n^th system of list
UniformSystemList &operator=(const UniformSystemList & that)
assigns the contents (copy)
UniformSystemList &operator=(UniformSystemList && that)
assigns the contents (move)

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template <typename System>
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explicit UniformSystemList(
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UniformSystemList(
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UniformSystemList(
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~UniformSystemList() =default
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UniformVectorList() =default
Constructs a new UniformVectorList.
UniformVectorList(const std::vector< Vector > & vecs, size_t dim =0)
Constructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent.
UniformVectorList(std::vector< Vector > && vecs, size_t dim =0)
Constructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent.
UniformVectorList(std::initializer_list< Vector > vecs, size_t dim =0)
Constructs a new UniformVectorList from initializer_list of Vector if dimensions consistent.
UniformVectorList(const UniformVectorList & that)
Constructs a new UniformVectorList (copy)
UniformVectorList(UniformVectorList && that)
Constructs a new UniformVectorList (move)
~UniformVectorList() =default
Destroys the object.
size_tdim() const
gets dimensions of the uniformly sized matrices
size_tsize()
size of container
const Vector &at(size_t n)
gets reference to n^th element
voidSwap(Vector & that, size_t n)
swaps input matrix with n^th vector of list
UniformVectorList &operator=(const UniformVectorList & that)
assigns the contents (copy)
UniformVectorList &operator=(UniformVectorList && that)
assigns the contents (move)

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UniformVectorList() =default
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-    std::vector< Vector > && vecs,
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    • 
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UniformVectorList(
-    std::initializer_list< Vector > vecs,
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-)
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    • 
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    • 
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UniformVectorList(
-    const UniformVectorList & that
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UniformVectorList(
-    UniformVectorList && that
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~UniformVectorList() =default
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    • 
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    • 
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diff --git a/docs/docs/api/classes/index.html b/docs/docs/api/classes/index.html
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-    Classes on LDS C&E
-    https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/
-    Recent content in Classes on LDS C&E
-    Hugo -- gohugo.io
-    
-      lds::Controller
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1controller/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1controller/
-      lds::Controller #  More&hellip;
-Inherited by lds::gaussian::Controller, lds::poisson::Controller, lds::SwitchedController&lt; System &gt;
-Public Functions #      Name      Controller() =default
-Constructs a new Controller.    Controller(const System &amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)
-Constructs a new Controller.    Controller(System &amp;&amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)
-Constructs a new Controller by moving the system object.
-    
-    
-    
-      lds::EM
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1em/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1em/
-      lds::EM #  More&hellip;
-Inherited by lds::gaussian::FitEM, lds::poisson::FitEM
-Public Functions #      Name      EM() =default
-Constructs a new EMFit type.    EM(size_t n_x, data_t dt, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; u_train, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; z_train)
-Constructs a new EMFit type.    EM(const Fit &amp; fit0, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; u_train, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; z_train)
-Constructs a new EMFit type.
-    
-    
-    
-      lds::Fit
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1fit/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1fit/
-      LDS Fit Type.
-    
-    
-    
-      lds::gaussian::Controller
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1controller/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1controller/
-      Gaussian-observation Controller Type.
-    
-    
-    
-      lds::gaussian::Fit
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/
-      GLDS Fit Type.
-    
-    
-    
-      lds::gaussian::FitEM
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitem/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitem/
-      GLDS E-M Fit Type.
-    
-    
-    
-      lds::gaussian::FitSSID
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitssid/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitssid/
-      Subspace Identification (SSID) for GLDS.
-    
-    
-    
-      lds::gaussian::SwitchedController
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1switchedcontroller/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1switchedcontroller/
-      Gaussian-observation SwitchedController Type.
-    
-    
-    
-      lds::gaussian::System
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/
-      Gaussian LDS Type.
-    
-    
-    
-      lds::poisson::Controller
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/
-      PLDS Controller Type.
-    
-    
-    
-      lds::poisson::Fit
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/
-      PLDS Fit Type.
-    
-    
-    
-      lds::poisson::FitEM
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitem/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitem/
-      PLDS E-M Fit Type.
-    
-    
-    
-      lds::poisson::FitSSID
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitssid/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitssid/
-      Subspace Identification (SSID) for PLDS.
-    
-    
-    
-      lds::poisson::SwitchedController
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1switchedcontroller/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1switchedcontroller/
-      Poisson-observation SwitchedController Type.
-    
-    
-    
-      lds::poisson::System
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/
-      Poisson System type.
-    
-    
-    
-      lds::SSID
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/
-      lds::SSID #  More&hellip;
-Inherited by lds::gaussian::FitSSID, lds::poisson::FitSSID
-Public Functions #      Name      SSID() =default
-Constructs a new SSIDFit type.    SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; u_train, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; z_train, const Vector &amp; d =Vector(1).fill(-kInf))
-Constructs a new SSIDFit type.   std::tuple&lt; Fit, Vector &gt; Run(SSIDWt ssid_wt)
-Runs fitting by subspace identification (SSID)   std::tuple&lt; UniformMatrixList&lt; kMatFreeDim2 &gt;, UniformMatrixList&lt; kMatFreeDim2 &gt; &gt; ReturnData()
-    
-    
-    
-      lds::SwitchedController
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/
-      SwitchedController Type.
-    
-    
-    
-      lds::System
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1system/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1system/
-      Linear Dynamical System Type.
-    
-    
-    
-      lds::UniformMatrixList
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/
-      lds::UniformMatrixList #  More&hellip;
-Inherits from std::vector&lt; Matrix &gt;
-Public Functions #      Name      UniformMatrixList() =default
-Constructs a new UniformMatrixList.    UniformMatrixList(const std::vector&lt; Matrix &gt; &amp; mats, std::array&lt; size_t, 2 &gt; dim ={0, 0})
-Constructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent.    UniformMatrixList(std::vector&lt; Matrix &gt; &amp;&amp; mats, std::array&lt; size_t, 2 &gt; dim ={0, 0})
-    
-    
-    
-      lds::UniformSystemList
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/
-      lds::UniformSystemList #  More&hellip;
-Inherits from std::vector&lt; System &gt;
-Public Functions #      Name      UniformSystemList() =default
-Constructs a new UniformSystemList.    UniformSystemList(const std::vector&lt; System &gt; &amp; systems, std::array&lt; size_t, 3 &gt; dim ={0, 0, 0})
-Constructs a new UniformSystemList by copying existing vector of System if dimensions consistent.    UniformSystemList(std::vector&lt; System &gt; &amp;&amp; systems, std::array&lt; size_t, 3 &gt; dim ={0, 0, 0})
-    
-    
-    
-      lds::UniformVectorList
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/
-      lds::UniformVectorList #  Inherits from std::vector&lt; Vector &gt;
-Public Functions #      Name      UniformVectorList() =default
-Constructs a new UniformVectorList.    UniformVectorList(const std::vector&lt; Vector &gt; &amp; vecs, size_t dim =0)
-Constructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent.    UniformVectorList(std::vector&lt; Vector &gt; &amp;&amp; vecs, size_t dim =0)
-Constructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent.
-    
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-  eg_glds_ctrl.cpp
-  #
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Example GLDS Control

-
//===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst>
-
-using lds::Matrix;
-using lds::Vector;
-using lds::data_t;
-using std::cout;
-
-auto main() -> int {
-  cout << " ********** Example Gaussian LDS Control ********** \n\n";
-
-  // Make 1st-order SISO system, sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;
-  size_t n_x = 1;
-  size_t n_y = 1;
-
-  // no time steps for simulation.
-  auto n_t = static_cast<size_t>(5.0 / dt);
-
-  // construct ground truth system to be controlled...
-  // initializes to random walk model with top-most n_y state observed
-  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);
-
-  // Ground-truth parameters for the controlled system
-  // (stand-in for physical system to be controlled)
-  Matrix a_true(n_x, n_x, arma::fill::eye);
-  a_true[0] = exp(-dt / 0.01);
-  Matrix b_true = Matrix(n_x, n_u).fill(2e-4);
-  // control signal to model input unit conversion e.g., V -> mW/mm2:
-  Vector g_true = Vector(n_y).fill(10.0);
-
-  // output noise covariance
-  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;
-
-  size_t which_m = 0;  // whether low or high disturbance (0, 1)
-  data_t m_low = 5 * dt * (1 - a_true[0]);
-  data_t pr_lo2hi = 1e-3;  // probability of going from low to high disturb.
-  data_t m_high = 20 * dt * (1 - a_true[0]);
-  data_t pr_hi2lo = pr_lo2hi;
-
-  // initially let m be low
-  Vector m0_true = Vector(n_x).fill(m_low);
-
-  // Assign params.
-  controlled_system.set_A(a_true);
-  controlled_system.set_B(b_true);
-  controlled_system.set_m(m0_true);
-  controlled_system.set_g(g_true);
-  controlled_system.set_R(r_true);
-
-  cout << ".....................................\n";
-  cout << "controlled_system:\n";
-  cout << ".....................................\n";
-  controlled_system.Print();
-  cout << ".....................................\n";
-
-  // make a controller
-  lds::gaussian::Controller controller;
-  {
-    // Create **incorrect** model used for control.
-    // (e.g., imperfect model fitting)
-    Matrix b_controller = b_true / 2;
-
-    // let's assume zero process disturbance initially
-    // (will be re-estimating)
-    Vector m_controller = Vector(n_x, arma::fill::zeros);
-
-    // for this demo, just use arbitrary default R
-    Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0;
-
-    lds::gaussian::System controller_system(controlled_system);
-    controller_system.set_B(b_controller);
-    controller_system.set_m(m_controller);
-    controller_system.set_R(r_controller);
-    controller_system.Reset();  // reset to new m
-
-    // going to adaptively re-estimate the disturbance
-    controller_system.do_adapt_m = true;
-
-    // set adaptation rate by changing covariance of assumed process noise
-    // acting on random-walk evolution of m
-    Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6;
-    controller_system.set_Q_m(q_m);
-
-    // create controller
-    // lower and upper bounds on control signal (e.g., in Volts)
-    data_t u_lb = 0.0;  // [=] V
-    data_t u_ub = 5.0;  // [=] V
-    controller = std::move(
-        lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub));
-  }
-
-  // Control variables:
-  // if following enabled, adapts set point with re-estimated process
-  // disturbance n.b., should not need integral action if this is enabled as the
-  // adaptive estimator minimizes DC error
-  bool do_adaptive_set_point = false;
-
-  // Reference/target output, controller gains
-  Vector y_ref0 = Vector(n_y).fill(20.0 * dt);
-  Matrix k_x = Matrix(n_u, n_x).fill(100);     // gains on state error
-  Matrix k_inty = Matrix(n_u, n_y).fill(1e3);  // gains on integrated err
-
-  // setting initial state to target to avoid error at onset:
-  Vector x0 = Vector(n_x).fill(y_ref0[0]);
-
-  // set up controller type bit mask so controller knows how to proceed
-  size_t control_type = 0;
-  if (do_adaptive_set_point) {
-    // adapt set point with estimated disturbance
-    control_type = control_type | lds::kControlTypeAdaptM;
-  } else {
-    // use integral action to minimize DC error
-    control_type = control_type | lds::kControlTypeIntY;
-  }
-
-  // set controller type
-  controller.set_control_type(control_type);
-
-  // Let's say these controller gains were designed assuming g was 9 V/(mW/mm2):
-  Vector g_design = Vector(n_u).fill(9);
-
-  // Set params.
-  // **n.b. using arbitrary defaults for Q, R in this example. Really, these
-  // should be set by users, as they tune characteristics of Kalman filter.
-  // Users can also choose not to recursively calculate the estimator gain and
-  // supply it (setKe) instead of covariances.**
-  controller.set_y_ref(y_ref0);
-  controller.set_Kc(k_x);
-  controller.set_Kc_inty(k_inty);
-  controller.set_g_design(g_design);
-
-  cout << ".....................................\n";
-  cout << "control system:\n";
-  cout << ".....................................\n";
-  controller.Print();
-  cout << ".....................................\n";
-
-  // set up variables for simulation
-  // create Matrix to save outputs in...
-  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];
-
-  // Simulated measurements
-  Matrix z(n_y, n_t, arma::fill::zeros);
-
-  // simulated control signal ([=] V)
-  Matrix u(n_u, n_t, arma::fill::zeros);
-
-  // outputs, states and gain/disturbance params
-  // *_hat indicates online estimates
-  Matrix y_hat(n_y, n_t, arma::fill::zeros);
-  Matrix x_hat(n_x, n_t, arma::fill::zeros);
-  Matrix m_hat(n_x, n_t, arma::fill::zeros);
-
-  // *_true indicates ground truth (system being controlled)
-  Matrix y_true(n_y, n_t, arma::fill::zeros);
-  Matrix x_true(n_x, n_t, arma::fill::zeros);
-  Matrix m_true(n_x, n_t, arma::fill::zeros);
-
-  // set initial val
-  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y();
-  y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();
-
-  x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x();
-  x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();
-
-  m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m();
-  m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();
-
-  cout << "Starting " << n_t * dt << " sec simulation ... \n";
-  auto start = std::chrono::high_resolution_clock::now();
-  for (size_t t = 1; t < n_t; t++) {
-    // simulate a stochastically switched disturbance
-    Vector chance = arma::randu<Vector>(1);
-    if (which_m == 0)  // low disturbance
-    {
-      if (chance[0] < pr_lo2hi) {  // switches low -> high disturbance
-        m0_true = std::vector<data_t>(n_x, m_high);
-        which_m = 1;
-      }
-    } else {                       // high disturbance
-      if (chance[0] < pr_hi2lo) {  // swithces high -> low disturbance
-        m0_true = std::vector<data_t>(n_x, m_low);
-        which_m = 0;
-      }
-    }
-    controlled_system.set_m(m0_true);
-
-    // input
-    Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true);
-
-    // Simulate the true system.
-    z.col(t) = controlled_system.Simulate(u_tm1);
-
-    // This method uses a steady-state solution to control problem to calculate
-    // x_ref, u_ref from reference output y_ref. Therefore, it is only
-    // applicable to regulation problems or cases where reference trajectory
-    // changes slowly compared to system dynamics.
-    u.col(t) = controller.ControlOutputReference(z.col(t));
-
-    // save the signals
-    y_true.col(t) = controlled_system.y();
-    x_true.col(t) = controlled_system.x();
-    m_true.col(t) = controlled_system.m();
-
-    y_hat.col(t) = controller.sys().y();
-    x_hat.col(t) = controller.sys().x();
-    m_hat.col(t) = controller.sys().m();
-  }
-
-  auto finish = std::chrono::high_resolution_clock::now();
-  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
-  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
-  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
-
-  cout << "Saving simulation data to disk.\n";
-
-  // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,
-  // xTrue, mTrue saving with hdf5 via armadillo
-  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
-
-  auto dt_vec = Vector(1).fill(dt);
-  dt_vec.save(arma::hdf5_name("eg_glds_ctrl.h5", "dt"));
-  y_ref.save(arma::hdf5_name("eg_glds_ctrl.h5", "y_ref", replace));
-  u.save(arma::hdf5_name("eg_glds_ctrl.h5", "u", replace));
-  z.save(arma::hdf5_name("eg_glds_ctrl.h5", "z", replace));
-  x_true.save(arma::hdf5_name("eg_glds_ctrl.h5", "x_true", replace));
-  m_true.save(arma::hdf5_name("eg_glds_ctrl.h5", "m_true", replace));
-  y_true.save(arma::hdf5_name("eg_glds_ctrl.h5", "y_true", replace));
-  x_hat.save(arma::hdf5_name("eg_glds_ctrl.h5", "x_hat", replace));
-  m_hat.save(arma::hdf5_name("eg_glds_ctrl.h5", "m_hat", replace));
-  y_hat.save(arma::hdf5_name("eg_glds_ctrl.h5", "y_hat", replace));
-
-  cout << "fin.\n";
-  return 0;
-}
-

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Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time

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diff --git a/docs/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/index.html b/docs/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/index.html
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Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).

-
//===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst>
-
-using lds::data_t;
-using lds::Matrix;
-using lds::Vector;
-using std::cout;
-
-auto main() -> int {
-  cout << " ********** Example Gaussian LDS du Control of PLDS ********** \n\n";
-
-  // Make 1st-order SISO system, sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;
-  size_t n_x = 1;
-  size_t n_y = 1;
-
-  // no time steps for simulation.
-  auto n_t = static_cast<size_t>(5.0 / dt);
-
-  // construct ground truth system to be controlled...
-  // initializes to random walk model with top-most n_y state observed
-  lds::poisson::System controlled_system(n_u, n_x, n_y, dt);
-
-  // Ground-truth parameters for the controlled system
-  // (stand-in for physical system to be controlled)
-  Matrix a_true(n_x, n_x, arma::fill::eye);
-  a_true[0] = exp(-dt / 0.01);
-  Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2);
-  // control signal to model input unit conversion e.g., V -> mW/mm2:
-  Vector g_true = Vector(n_y).fill(10.0);
-
-  size_t which_m = 0;  // whether low or high disturbance (0, 1)
-  data_t m_low = log(1 * dt) * (1 - a_true[0]);
-  data_t pr_lo2hi =
-      0;  // 1e-3;  // probability of going from low to high disturb.
-  data_t m_high = log(20 * dt) * (1 - a_true[0]);
-  data_t pr_hi2lo = pr_lo2hi;
-
-  // initially let m be low
-  Vector m0_true = Vector(n_x).fill(m_low);
-  Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);
-
-  // Assign params.
-  controlled_system.set_A(a_true);
-  controlled_system.set_B(b_true);
-  controlled_system.set_m(m0_true);
-  controlled_system.set_g(g_true);
-  controlled_system.set_x0(x0_true);
-  controlled_system.Reset();
-
-  cout << ".....................................\n";
-  cout << "controlled_system:\n";
-  cout << ".....................................\n";
-  controlled_system.Print();
-  cout << ".....................................\n";
-
-  // make a controller
-  lds::gaussian::Controller controller;
-  {
-    // Create **incorrect** model used for control.
-    // (e.g., imperfect model fitting)
-    Matrix b_controller = b_true / 50;
-
-    // let's assume zero process disturbance initially
-    // (will be re-estimating)
-    Vector m_controller = Vector(n_x, arma::fill::zeros);
-
-    // process noise covariance
-    Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8;
-
-    // output noise covariance
-    Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2;
-
-    lds::gaussian::System controller_system(n_u, n_x, n_y, dt);
-    controller_system.set_A(a_true);
-    controller_system.set_B(b_controller);
-    controller_system.set_g(g_true);
-    controller_system.set_m(m_controller);
-    controller_system.set_Q(q_controller);
-    controller_system.set_R(r_controller);
-    controller_system.Reset();  // reset to new m
-
-    // going to adaptively re-estimate the disturbance
-    controller_system.do_adapt_m = true;
-
-    // set adaptation rate by changing covariance of assumed process noise
-    // acting on random-walk evolution of m
-    Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8;
-    controller_system.set_Q_m(q_m);
-
-    // create controller
-    // lower and upper bounds on control signal (e.g., in Volts)
-    data_t u_lb = 0.0;  // [=] V
-    data_t u_ub = 5.0;  // [=] V
-    controller = std::move(
-        lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub));
-  }
-
-  // Control variables:
-  // Reference/target output, controller gains
-  Vector y_ref0 = Vector(n_y).fill(20.0 * dt);
-
-  // to design for this example, augmented state with control and made the input
-  // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du =
-  // 1e-2.
-  Matrix k_x = Matrix(n_u, n_x).fill(2.44);     // gains on state error
-  Matrix k_inty = Matrix(n_u, n_y).fill(97.4);  // gains on integrated err
-  Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2);  // gains on input amp
-
-  // set up controller type bit mask so controller knows how to proceed
-  size_t control_type = 0;
-  // use integral action to minimize DC error
-  control_type = control_type | lds::kControlTypeIntY;
-  // update change in control (LP filters control)
-  control_type = control_type | lds::kControlTypeDeltaU;
-
-  // set controller type
-  controller.set_control_type(control_type);
-
-  // Let's say these controller gains were designed assuming g was 9 V/(mW/mm2):
-  Vector g_design = Vector(n_u).fill(10);
-
-  // Set params.
-  // **n.b. using arbitrary defaults for Q, R in this example. Really, these
-  // should be set by users, as they tune characteristics of Kalman filter.
-  // Users can also choose not to recursively calculate the estimator gain and
-  // supply it (setKe) instead of covariances.**
-  controller.set_y_ref(y_ref0);
-  controller.set_Kc(k_x);
-  controller.set_Kc_inty(k_inty);
-  controller.set_Kc_u(k_u);
-  controller.set_g_design(g_design);
-
-  cout << ".....................................\n";
-  cout << "control system:\n";
-  cout << ".....................................\n";
-  controller.Print();
-  cout << ".....................................\n";
-
-  // set up variables for simulation
-  // create Matrix to save outputs in...
-  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];
-
-  // Simulated measurements
-  Matrix z(n_y, n_t, arma::fill::zeros);
-
-  // simulated control signal ([=] V)
-  Matrix u(n_u, n_t, arma::fill::zeros);
-
-  // outputs, states and gain/disturbance params
-  // *_hat indicates online estimates
-  Matrix y_hat(n_y, n_t, arma::fill::zeros);
-  Matrix x_hat(n_x, n_t, arma::fill::zeros);
-  Matrix m_hat(n_x, n_t, arma::fill::zeros);
-
-  // *_true indicates ground truth (system being controlled)
-  Matrix y_true(n_y, n_t, arma::fill::zeros);
-  Matrix x_true(n_x, n_t, arma::fill::zeros);
-  Matrix m_true(n_x, n_t, arma::fill::zeros);
-
-  // get initial val
-  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y();
-  y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();
-
-  x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x();
-  x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();
-
-  m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m();
-  m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();
-
-  cout << "Starting " << n_t * dt << " sec simulation ... \n";
-  auto start = std::chrono::high_resolution_clock::now();
-  for (size_t t = 1; t < n_t; t++) {
-    // simulate a stochastically switched disturbance
-    Vector chance = arma::randu<Vector>(1);
-    if (which_m == 0)  // low disturbance
-    {
-      if (chance[0] < pr_lo2hi) {  // switches low -> high disturbance
-        m0_true = std::vector<data_t>(n_x, m_high);
-        which_m = 1;
-      }
-    } else {                       // high disturbance
-      if (chance[0] < pr_hi2lo) {  // swithces high -> low disturbance
-        m0_true = std::vector<data_t>(n_x, m_low);
-        which_m = 0;
-      }
-    }
-    controlled_system.set_m(m0_true);
-
-    // input
-    Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true);
-
-    // Simulate the true system.
-    z.col(t) = controlled_system.Simulate(u_tm1);
-
-    // This method uses a steady-state solution to control problem to calculate
-    // x_ref, u_ref from reference output y_ref. Therefore, it is only
-    // applicable to regulation problems or cases where reference trajectory
-    // changes slowly compared to system dynamics.
-    u.col(t) = controller.ControlOutputReference(z.col(t));
-
-    // save the signals
-    y_true.col(t) = controlled_system.y();
-    x_true.col(t) = controlled_system.x();
-    m_true.col(t) = controlled_system.m();
-
-    y_hat.col(t) = controller.sys().y();
-    x_hat.col(t) = controller.sys().x();
-    m_hat.col(t) = controller.sys().m();
-  }
-
-  auto finish = std::chrono::high_resolution_clock::now();
-  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
-  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
-  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
-
-  cout << "Saving simulation data to disk.\n";
-
-  // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,
-  // xTrue, mTrue saving with hdf5 via armadillo
-  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
-
-  auto dt_vec = Vector(1).fill(dt);
-  dt_vec.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "dt"));
-  y_ref.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "y_ref", replace));
-  u.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "u", replace));
-  z.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "z", replace));
-  x_true.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "x_true", replace));
-  m_true.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "m_true", replace));
-  y_true.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "y_true", replace));
-  x_hat.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "x_hat", replace));
-  m_hat.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "m_hat", replace));
-  y_hat.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "y_hat", replace));
-
-  cout << "fin.\n";
-  return 0;
-}
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Example PLDS Control

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//===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst>
-
-using lds::Matrix;
-using lds::Vector;
-using lds::data_t;
-using std::cout;
-
-auto main() -> int {
-  cout << " ********** Example Poisson LDS Control ********** \n\n";
-
-  // Make SISO system sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;
-  size_t n_x = 1;
-  size_t n_y = 1;
-
-  // no time steps for simulation.
-  auto n_t = static_cast<size_t>(10.0 / dt);
-
-  // Control variables: _reference/target output, controller gains
-  // n.b., Can either use Vector (arma::Col) or std::vector
-  Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt;
-  Matrix k_x =
-      Matrix(n_u, n_x, arma::fill::zeros) + 1;  // gains on state error
-  Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) +
-                   10;  // gains on integrated output err
-
-  // Set control type bit mask, so controller knows what to do
-  size_t control_type = lds::kControlTypeIntY;  // integral action
-
-  // Ground-truth parameters for the controlled system
-  // (stand-in for physical system to be controlled)
-  Matrix a_true(n_x, n_x, arma::fill::eye);
-  a_true[0] = 0.986;
-  Matrix b_true(n_x, n_u, arma::fill::zeros);
-  b_true[0] = 0.054;
-  Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);
-
-  size_t which_m = 0;
-  data_t m_low = log(1 * dt) * (1 - a_true[0]);
-  data_t pr_lo2hi = 1e-3;
-  data_t m_high = log(20 * dt) * (1 - a_true[0]);
-  data_t pr_hi2lo = pr_lo2hi;
-
-  Vector m0_true = Vector(n_x, arma::fill::ones) * m_low;
-  // construct ground truth system to be controlled...
-  lds::poisson::System controlled_system(n_u, n_x, n_y, dt);
-
-  // Assign params.
-  controlled_system.set_A(a_true);
-  controlled_system.set_B(b_true);
-  controlled_system.set_m(m0_true);
-  controlled_system.set_x0(x0_true);
-  // reset to initial conditions
-  controlled_system.Reset();
-
-  cout << ".....................................\n";
-  cout << "controlled_system:\n";
-  cout << ".....................................\n";
-  controlled_system.Print();
-  cout << ".....................................\n";
-
-  // Create the controller
-  lds::poisson::Controller controller;
-  {
-    // Create model used for control.
-    lds::poisson::System controller_system(controlled_system);
-
-    // for this example, assume model correct, except disturbance
-    Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low;
-    Vector x0_controller = arma::log(y_ref0);
-    controller_system.set_m(m0_controller);
-    controller_system.set_x0(x0_controller);
-    controller_system.Reset(); //reset to new init condition
-
-    // adaptively re-estimate process disturbance (m)
-    controller_system.do_adapt_m = true;
-
-    // set adaptation rate by changing covariance of assumed process noise
-    // acting on random-walk evolution of m
-    Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5;
-    controller_system.set_Q_m(q_m);
-
-    data_t u_lb = 0.0;
-    data_t u_ub = 5.0;
-    controller = std::move(
-        lds::poisson::Controller(std::move(controller_system), u_lb, u_ub));
-  }
-  // set controller type
-  controller.set_control_type(control_type);
-
-  // set controller gains
-  controller.set_Kc(k_x);
-  controller.set_Kc_inty(k_inty);
-
-  // to protect against integral windup when output is consistently above
-  // target:
-  data_t tau_awu(0.1);
-  controller.set_tau_awu(tau_awu);
-
-  cout << ".....................................\n";
-  cout << "controller:\n";
-  cout << ".....................................\n";
-  controller.Print();
-  cout << ".....................................\n";
-
-  // create Matrix to save outputs in...
-  Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros);
-  y_ref.each_col() += y_ref0;
-
-  // Simulated measurements
-  Matrix z(n_y, n_t, arma::fill::zeros);
-
-  // simulated control signal ([=] V)
-  Matrix u(n_u, n_t, arma::fill::zeros);
-
-  // outputs, states and gain/disturbance params
-  // *_hat indicates online estimates
-  Matrix y_hat(n_y, n_t, arma::fill::zeros);
-  Matrix x_hat(n_x, n_t, arma::fill::zeros);
-  Matrix m_hat(n_y, n_t, arma::fill::zeros);
-
-  // *_true indicates ground truth (system being controlled)
-  Matrix y_true(n_y, n_t, arma::fill::zeros);
-  Matrix x_true(n_x, n_t, arma::fill::zeros);
-  Matrix m_true(n_y, n_t, arma::fill::zeros);
-
-  // set initial val
-  y_hat.col(0) = controller.sys().y();
-  y_true.col(0) = controlled_system.y();
-
-  x_hat.col(0) = controller.sys().x();
-  x_true.col(0) = controlled_system.x();
-
-  m_hat.col(0) = controller.sys().m();
-  m_true.col(0) = controlled_system.m();
-
-  cout << "Starting " << n_t * dt << " sec simulation ... \n";
-  auto start = std::chrono::high_resolution_clock::now();
-  for (size_t t = 1; t < n_t; t++) {
-    // simulate a stochastically switched disturbance
-    Vector chance = arma::randu<Vector>(1);
-    if (which_m == 0)  // low disturbance
-    {
-      if (chance[0] < pr_lo2hi) {  // switches low -> high disturbance
-        m0_true = std::vector<data_t>(n_x, m_high);
-        which_m = 1;
-      }
-    } else {                       // high disturbance
-      if (chance[0] < pr_hi2lo) {  // swithces high -> low disturbance
-        m0_true = std::vector<data_t>(n_x, m_low);
-        which_m = 0;
-      }
-    }
-    controlled_system.set_m(m0_true);
-
-    // e.g., use sinusoidal reference
-    data_t f = 0.5;  // freq [=] Hz
-    Vector t_vec = Vector(n_y, arma::fill::ones) * t;
-    y_ref.col(t) +=
-        y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4);
-
-    // Simulate the true system.
-    z.col(t)=controlled_system.Simulate(u.col(t-1));
-
-    // This method uses a steady-state solution to control problem to calculate
-    // x_ref, u_ref from reference output y_ref. Notably, it does this in the
-    // log-linear space (i.e., log(y)).
-    //
-    // Therefore, it is only applicable to regulation problems or cases where
-    // reference trajectory changes slowly compared to system dynamics.
-    controller.set_y_ref(y_ref.col(t));
-    u.col(t)=controller.ControlOutputReference(z.col(t));
-
-    y_true.col(t) = controlled_system.y();
-    x_true.col(t) = controlled_system.x();
-    m_true.col(t) = controlled_system.m();
-
-    y_hat.col(t) = controller.sys().y();
-    x_hat.col(t) = controller.sys().x();
-    m_hat.col(t) = controller.sys().m();
-  }
-
-  auto finish = std::chrono::high_resolution_clock::now();
-  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
-  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
-  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
-
-  // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,
-  // x_true, m_true saving with hdf5 via armadillo
-  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
-
-  auto dt_vec = Vector(1).fill(dt);
-  dt_vec.save(arma::hdf5_name("eg_plds_ctrl.h5", "dt"));
-  y_ref.save(arma::hdf5_name("eg_plds_ctrl.h5", "y_ref", replace));
-  u.save(arma::hdf5_name("eg_plds_ctrl.h5", "u", replace));
-  z.save(arma::hdf5_name("eg_plds_ctrl.h5", "z", replace));
-  x_true.save(arma::hdf5_name("eg_plds_ctrl.h5", "x_true", replace));
-  m_true.save(arma::hdf5_name("eg_plds_ctrl.h5", "m_true", replace));
-  y_true.save(arma::hdf5_name("eg_plds_ctrl.h5", "y_true", replace));
-  x_hat.save(arma::hdf5_name("eg_plds_ctrl.h5", "x_hat", replace));
-  m_hat.save(arma::hdf5_name("eg_plds_ctrl.h5", "m_hat", replace));
-  y_hat.save(arma::hdf5_name("eg_plds_ctrl.h5", "y_hat", replace));
-
-  return 0;
-}
-

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Example PLDS Estimation

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//===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst>
-
-using lds::Matrix;
-using lds::Vector;
-using lds::data_t;
-using std::cout;
-
-// for generating random input
-Matrix random_walk(size_t n_t, const Matrix& Q, const Vector& x0);
-
-int main() {
-  cout << " ********** Example Poisson LDS Estimation ********** \n\n";
-
-  // Make SISO system sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;                           // no. inputs
-  size_t n_x = 1;                           // no. states
-  size_t n_y = 1;                           // no. outputs
-  auto n_t = static_cast<size_t>(30 / dt);  // no time steps for simulation.
-
-  // construct ground truth system...
-  lds::poisson::System system_true(n_u, n_x, n_y, dt);
-
-  // Model parameters
-  Matrix a_true(n_x, n_x, arma::fill::eye);
-  a_true[0] = exp(-dt / 0.075);
-  Matrix b_true = Matrix(n_x, n_u).fill(1e-2);
-  Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2);  // disturbance
-  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) -
-                                        a_true);  // initial state
-
-  // Assign params.
-  system_true.set_A(a_true);
-  system_true.set_B(b_true);
-  system_true.set_x0(x0_true);
-  system_true.set_m(m0_true);
-  system_true.Reset();
-
-  // Construct system for estimation
-  // e.g., will create a model with incorrect disturbance
-  lds::poisson::System system_estimator(n_u, n_x, n_y, dt);
-
-  // Can copy parameters from another system object
-  system_estimator = system_true;
-
-  // wrong disturbance
-  Vector m0_est = m0_true * 2;
-  system_estimator.set_m(m0_est);
-
-  // set new initial conditions
-  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) -
-                                      a_true);  // initial state
-  system_estimator.set_x0(x0_est);
-  system_estimator.Reset();  // reset to initial condition.
-
-  // turn on adaptive disturbance estimation
-  system_estimator.do_adapt_m = true;
-
-  // set adaptation rate by changing covariance of assumed process noise acting
-  // on random-walk evolution of m
-  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6;
-  system_estimator.set_Q_m(q_m);
-
-  cout << ".....................................\n";
-  cout << "estimator:\n";
-  cout << ".....................................\n";
-  system_estimator.Print();
-  cout << ".....................................\n";
-
-  // Set up simulation :
-  // Simulated measurements
-  Matrix z(n_y, n_t, arma::fill::zeros);
-
-  // Stimulus (generate random stimulus)
-  Matrix q_u =
-      Matrix(n_u, n_u, arma::fill::eye) * 1e-3;  // cov of random walk
-  Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));
-
-  // create matrix to save outputs in...
-  Matrix y_hat(n_y, n_t, arma::fill::zeros);
-  Matrix y_true(n_y, n_t, arma::fill::zeros);
-
-  // states and disturbance params
-  Matrix x_hat(n_x, n_t, arma::fill::zeros);
-  Matrix m_hat(n_x, n_t, arma::fill::zeros);
-
-  Matrix x_true(n_x, n_t, arma::fill::zeros);
-  Matrix m_true(n_x, n_t, arma::fill::zeros);
-
-  // initial conditions
-  y_hat.col(0) = system_estimator.y();
-  y_true.col(0) = system_true.y();
-  x_hat.col(0) = system_estimator.x();
-  x_true.col(0) = system_true.x();
-  m_hat.col(0) = system_estimator.m();
-  m_true.col(0) = system_true.m();
-
-  cout << "Starting " << n_t * dt << " sec simlation ... \n";
-  auto start = std::chrono::high_resolution_clock::now();
-  for (size_t t = 1; t < n_t; t++) {
-    // Simlate the true system.
-    z.col(t) = system_true.Simulate(u.col(t - 1));
-
-    // Filter (predict -> update)
-    system_estimator.Filter(u.col(t - 1), z.col(t));
-
-    // save signals
-    y_hat.col(t) = system_estimator.y();
-    y_true.col(t) = system_true.y();
-
-    x_true.col(t) = system_true.x();
-    m_true.col(t) = system_true.m();
-
-    x_hat.col(t) = system_estimator.x();
-    m_hat.col(t) = system_estimator.m();
-  }
-
-  auto finish = std::chrono::high_resolution_clock::now();
-  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
-  cout << "Finished simlation in " << sim_time_ms.count() << " ms.\n";
-  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
-
-  // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true,
-  // x_true, m_true saving with hdf5 via armadillo
-  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
-
-  auto dt_vec = Vector(1).fill(dt);
-  dt_vec.save(arma::hdf5_name("eg_plds_est.h5", "dt"));
-  u.save(arma::hdf5_name("eg_plds_est.h5", "u", replace));
-  z.save(arma::hdf5_name("eg_plds_est.h5", "z", replace));
-  x_true.save(arma::hdf5_name("eg_plds_est.h5", "x_true", replace));
-  m_true.save(arma::hdf5_name("eg_plds_est.h5", "m_true", replace));
-  y_true.save(arma::hdf5_name("eg_plds_est.h5", "y_true", replace));
-  x_hat.save(arma::hdf5_name("eg_plds_est.h5", "x_hat", replace));
-  m_hat.save(arma::hdf5_name("eg_plds_est.h5", "m_hat", replace));
-  y_hat.save(arma::hdf5_name("eg_plds_est.h5", "y_hat", replace));
-
-  return 0;
-}
-
-// for generating random input
-Matrix random_walk(size_t n_t, const Matrix& Q, const Vector& x0) {
-  size_t n = Q.n_rows;
-
-  if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) {
-    throw std::logic_error("Q must be `n` x `n`.");
-  }
-
-  Matrix x(n, n_t, arma::fill::zeros);
-  x.col(0) = x0;
-  for (size_t t = 1; t < n_t; t++) {
-    x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q);
-  }
-
-  return x;
-}
-

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Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time

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diff --git a/docs/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/index.html b/docs/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/index.html
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-  eg_plds_switched_ctrl.cpp
-  #
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Example Switched PLDS Control

-
//===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst>
-
-using lds::data_t;
-using lds::Matrix;
-using lds::Vector;
-using std::cout;
-
-auto main() -> int {
-  cout << " ********** Example Switched Poisson LDS Control ********** \n\n";
-
-  // whether to do switched control
-  bool do_switch_ctrl = true;
-
-  // Make SISO system sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;
-  size_t n_x = 1;
-  size_t n_y = 1;
-
-  // no time steps for simulation.
-  auto n_t = static_cast<size_t>(30.0 / dt);
-
-  // for simulating switching
-  size_t which_mode = 1;
-  data_t pr_21 = 1e-3;   // prob mode 1 -> 2
-  data_t pr_12 = pr_21;  // prob mode 2 -> 1
-
-  // simulated system being controlled
-  lds::poisson::System controlled_system(n_u, n_x, n_y, dt);
-
-  // **Assume the system is not well characterized by one LDS, but is well
-  // characterized by two LDS models with different input matrices.**
-  data_t scale_sys_b = 2;
-
-  Matrix a(n_x, n_x, arma::fill::eye);
-  a[0] = 0.985;
-  Matrix b1 = Matrix(n_x, n_u).fill(0.05);
-  Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));
-
-  controlled_system.set_A(a);
-  controlled_system.set_B(b1);
-  controlled_system.set_d(d);
-  controlled_system.Reset();  // reset to initial conditions
-
-  // reference
-  Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);
-
-  // Let underlying system 1 be more sensitive than system 2
-  Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);
-
-  // create switched controller
-  lds::poisson::SwitchedController switched_controller;
-  lds::UniformMatrixList<> k_x;  // feedback controller gains
-  {
-    // create switched controller sub-systems
-    // system 1
-    lds::poisson::System sys1(controlled_system);
-
-    // set process noise covariance
-    Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3;
-    sys1.set_Q(q_controller);
-
-    // adaptively estimate process disturbance (m)
-    // n.b. using arbitrary default value for process noise if enabled.
-    sys1.do_adapt_m = true;
-
-    // setting initial mode to target to avoid large error at onset:
-    Vector x0_controller = arma::log(y_ref0) - d;
-    sys1.set_x0(x0_controller);
-    sys1.Reset();  // reset to initial conditions
-
-    cout << ".....................................\n";
-    cout << "sys1:\n";
-    cout << ".....................................\n";
-    sys1.Print();
-    cout << ".....................................\n";
-
-    // system 2
-    lds::poisson::System sys2 = sys1;
-
-    // set parameters
-    sys2.set_B(b2);
-
-    cout << ".....................................\n";
-    cout << "sys2:\n";
-    cout << ".....................................\n";
-    sys2.Print();
-    cout << ".....................................\n";
-
-    lds::UniformSystemList<lds::poisson::System> systems({sys1, sys2});
-
-    // controller gains for underlying system s:
-    Matrix k_x1(n_u, n_x, arma::fill::ones);
-    Matrix k_x2 = scale_sys_b * k_x1;  // system2 is x-times less sensitive.
-    k_x = lds::UniformMatrixList<>({k_x1, k_x2});
-
-    data_t u_lb = 0.0;
-    data_t u_ub = 5.0;
-    switched_controller = std::move(
-        lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub));
-  }
-  // Control variables
-  size_t control_type = 0;  // no integral action, etc
-  switched_controller.set_control_type(control_type);
-  switched_controller.set_Kc(std::move(k_x));
-
-  switched_controller.set_y_ref(y_ref0);
-
-  std::vector<lds::poisson::System> systems_vec(3, lds::poisson::System());
-  lds::UniformSystemList<lds::poisson::System> systems(std::move(systems_vec));
-
-  cout << ".....................................\n";
-  cout << "switched_controller:\n";
-  cout << ".....................................\n";
-  switched_controller.Print();
-  cout << ".....................................\n";
-
-  // Fake measurements
-  Matrix z(n_y, n_t, arma::fill::zeros);
-
-  // Will later contain control.
-  Matrix u(n_u, n_t, arma::fill::zeros);
-
-  // create Matrix to save outputs in...
-  Matrix y_hat(n_y, n_t, arma::fill::zeros);
-  Matrix y_true(n_y, n_t, arma::fill::zeros);
-  Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);
-
-  // modes and gain/disturbance params
-  Matrix x_hat(n_x, n_t, arma::fill::zeros);
-  Matrix x_true(n_x, n_t, arma::fill::zeros);
-  Matrix mode(1, n_t, arma::fill::ones);
-
-  // set initial val
-  y_hat.col(0) = switched_controller.sys().y();
-  y_true.col(0) = controlled_system.y();
-  x_hat.col(0) = switched_controller.sys().x();
-  x_true.col(0) = controlled_system.x();
-
-  cout << "Starting " << n_t * dt << " sec simulation ... \n";
-  auto start = std::chrono::high_resolution_clock::now();
-  for (size_t t = 1; t < n_t; t++) {
-    // Let the controlled system stochastically change gain
-    // Assume another algorithm decodes this mode change and signals the
-    // switched_controller
-    Vector chance(1, arma::fill::randu);
-    if (which_mode == 1)  // mode1
-    {
-      if (chance[0] < pr_21) {
-        which_mode = 2;
-        controlled_system.set_B(b2);
-        if (do_switch_ctrl) {
-          switched_controller.Switch(1);
-        }
-      }
-    } else {  // mode2
-      if (chance[0] < pr_12) {
-        which_mode = 1;
-        controlled_system.set_B(b1);
-        if (do_switch_ctrl) {
-          switched_controller.Switch(0);
-        }
-      }
-    }
-
-    // Simulate the true system.
-    z.col(t) = controlled_system.Simulate(u.col(t - 1));
-
-    // perform control
-    u.col(t) = switched_controller.ControlOutputReference(z.col(t));
-
-    mode.col(t) = which_mode;
-    y_ref.col(t) = y_ref0;
-    y_true.col(t) = controlled_system.y();
-    x_true.col(t) = controlled_system.x();
-    y_hat.col(t) = switched_controller.sys().y();
-    x_hat.col(t) = switched_controller.sys().x();
-  }
-
-  auto finish = std::chrono::high_resolution_clock::now();
-  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
-  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
-  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
-
-  // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,
-  // x_true, m_true saving with hdf5 via armadillo
-  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
-
-  auto dt_vec = Vector(1).fill(dt);
-  dt_vec.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "dt"));
-  y_ref.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "y_ref", replace));
-  u.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "u", replace));
-  z.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "z", replace));
-  x_true.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "x_true", replace));
-  y_true.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "y_true", replace));
-  x_hat.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "x_hat", replace));
-  y_hat.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "y_hat", replace));
-  mode.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "mode", replace));
-
-  return 0;
-}
-

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diff --git a/docs/docs/api/examples/index.html b/docs/docs/api/examples/index.html
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diff --git a/docs/docs/api/examples/index.xml b/docs/docs/api/examples/index.xml
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-  
-    Examples on LDS C&E
-    https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/
-    Recent content in Examples on LDS C&E
-    Hugo -- gohugo.io
-    
-      eg_glds_ctrl.cpp
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/
-      eg_glds_ctrl.cpp #  Example GLDS Control
-//===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the &#34;License&#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an &#34;AS IS&#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-    
-    
-    
-      eg_glds_du_plds_ctrl.cpp
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/
-      eg_glds_du_plds_ctrl.cpp #  Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).
-//===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the &#34;License&#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.
-    
-    
-    
-      eg_plds_ctrl.cpp
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/
-      eg_plds_ctrl.cpp #  Example PLDS Control
-//===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the &#34;License&#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an &#34;AS IS&#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-    
-    
-    
-      eg_plds_est.cpp
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/
-      eg_plds_est.cpp #  Example PLDS Estimation
-//===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the &#34;License&#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an &#34;AS IS&#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-    
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-      eg_plds_switched_ctrl.cpp
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/
-      eg_plds_switched_ctrl.cpp #  Example Switched PLDS Control
-//===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the &#34;License&#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an &#34;AS IS&#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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lds namespace
ldsCtrlEst_h/lds_ctrl.h 
Controller.
ldsCtrlEst_h/lds_fit.h 
LDS base fit type.
ldsCtrlEst_h/lds_fit_em.h 
subspace identification
ldsCtrlEst_h/lds_fit_ssid.h 
subspace identification
ldsCtrlEst_h/lds_gaussian.h 
glds namespace
ldsCtrlEst_h/lds_gaussian_ctrl.h 
GLDS Controller.
ldsCtrlEst_h/lds_gaussian_fit.h 
GLDS fit type.
ldsCtrlEst_h/lds_gaussian_fit_em.h 
GLDS E-M fit type.
ldsCtrlEst_h/lds_gaussian_fit_ssid.h 
GLDS SSID fit type.
ldsCtrlEst_h/lds_gaussian_sctrl.h 
GLDS switched controller type.
ldsCtrlEst_h/lds_gaussian_sys.h 
GLDS base type.
ldsCtrlEst_h/lds_poisson.h 
plds namespace
ldsCtrlEst_h/lds_poisson_ctrl.h 
PLDS controller type.
ldsCtrlEst_h/lds_poisson_fit.h 
PLDS base fit type.
ldsCtrlEst_h/lds_poisson_fit_em.h 
PLDS E-M fit type.
ldsCtrlEst_h/lds_poisson_fit_ssid.h 
PLDS SSID fit type.
ldsCtrlEst_h/lds_poisson_sctrl.h 
PLDS switched controller type.
ldsCtrlEst_h/lds_poisson_sys.h 
PLDS base type.
ldsCtrlEst_h/lds_sctrl.h 
SwitchedController type.
ldsCtrlEst_h/lds_sys.h 
LDS base type.
ldsCtrlEst_h/lds_uniform_mats.h 
List of uniformly sized matrices.
ldsCtrlEst_h/lds_uniform_systems.h 
List of uniformly sized Systems.
ldsCtrlEst_h/lds_uniform_vecs.h 
List of uniformly sized vectors.
ldsCtrlEst_h/mex_c_util.h 
arma <-> mex interoperability utilities (Matlab C API)
ldsCtrlEst_h/mex_cpp_util.h 
arma <-> mex interoperability utilities (Matlab C++ API)

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auto main()
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Going to simulate a switching disturbance (m) acting on system

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//===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst>
-
-using lds::Matrix;
-using lds::Vector;
-using lds::data_t;
-using std::cout;
-
-auto main() -> int {
-  cout << " ********** Example Gaussian LDS Control ********** \n\n";
-
-  // Make 1st-order SISO system, sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;
-  size_t n_x = 1;
-  size_t n_y = 1;
-
-  // no time steps for simulation.
-  auto n_t = static_cast<size_t>(5.0 / dt);
-
-  // construct ground truth system to be controlled...
-  // initializes to random walk model with top-most n_y state observed
-  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);
-
-  // Ground-truth parameters for the controlled system
-  // (stand-in for physical system to be controlled)
-  Matrix a_true(n_x, n_x, arma::fill::eye);
-  a_true[0] = exp(-dt / 0.01);
-  Matrix b_true = Matrix(n_x, n_u).fill(2e-4);
-  // control signal to model input unit conversion e.g., V -> mW/mm2:
-  Vector g_true = Vector(n_y).fill(10.0);
-
-  // output noise covariance
-  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;
-
-  size_t which_m = 0;  // whether low or high disturbance (0, 1)
-  data_t m_low = 5 * dt * (1 - a_true[0]);
-  data_t pr_lo2hi = 1e-3;  // probability of going from low to high disturb.
-  data_t m_high = 20 * dt * (1 - a_true[0]);
-  data_t pr_hi2lo = pr_lo2hi;
-
-  // initially let m be low
-  Vector m0_true = Vector(n_x).fill(m_low);
-
-  // Assign params.
-  controlled_system.set_A(a_true);
-  controlled_system.set_B(b_true);
-  controlled_system.set_m(m0_true);
-  controlled_system.set_g(g_true);
-  controlled_system.set_R(r_true);
-
-  cout << ".....................................\n";
-  cout << "controlled_system:\n";
-  cout << ".....................................\n";
-  controlled_system.Print();
-  cout << ".....................................\n";
-
-  // make a controller
-  lds::gaussian::Controller controller;
-  {
-    // Create **incorrect** model used for control.
-    // (e.g., imperfect model fitting)
-    Matrix b_controller = b_true / 2;
-
-    // let's assume zero process disturbance initially
-    // (will be re-estimating)
-    Vector m_controller = Vector(n_x, arma::fill::zeros);
-
-    // for this demo, just use arbitrary default R
-    Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0;
-
-    lds::gaussian::System controller_system(controlled_system);
-    controller_system.set_B(b_controller);
-    controller_system.set_m(m_controller);
-    controller_system.set_R(r_controller);
-    controller_system.Reset();  // reset to new m
-
-    // going to adaptively re-estimate the disturbance
-    controller_system.do_adapt_m = true;
-
-    // set adaptation rate by changing covariance of assumed process noise
-    // acting on random-walk evolution of m
-    Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6;
-    controller_system.set_Q_m(q_m);
-
-    // create controller
-    // lower and upper bounds on control signal (e.g., in Volts)
-    data_t u_lb = 0.0;  // [=] V
-    data_t u_ub = 5.0;  // [=] V
-    controller = std::move(
-        lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub));
-  }
-
-  // Control variables:
-  // if following enabled, adapts set point with re-estimated process
-  // disturbance n.b., should not need integral action if this is enabled as the
-  // adaptive estimator minimizes DC error
-  bool do_adaptive_set_point = false;
-
-  // Reference/target output, controller gains
-  Vector y_ref0 = Vector(n_y).fill(20.0 * dt);
-  Matrix k_x = Matrix(n_u, n_x).fill(100);     // gains on state error
-  Matrix k_inty = Matrix(n_u, n_y).fill(1e3);  // gains on integrated err
-
-  // setting initial state to target to avoid error at onset:
-  Vector x0 = Vector(n_x).fill(y_ref0[0]);
-
-  // set up controller type bit mask so controller knows how to proceed
-  size_t control_type = 0;
-  if (do_adaptive_set_point) {
-    // adapt set point with estimated disturbance
-    control_type = control_type | lds::kControlTypeAdaptM;
-  } else {
-    // use integral action to minimize DC error
-    control_type = control_type | lds::kControlTypeIntY;
-  }
-
-  // set controller type
-  controller.set_control_type(control_type);
-
-  // Let's say these controller gains were designed assuming g was 9 V/(mW/mm2):
-  Vector g_design = Vector(n_u).fill(9);
-
-  // Set params.
-  // **n.b. using arbitrary defaults for Q, R in this example. Really, these
-  // should be set by users, as they tune characteristics of Kalman filter.
-  // Users can also choose not to recursively calculate the estimator gain and
-  // supply it (setKe) instead of covariances.**
-  controller.set_y_ref(y_ref0);
-  controller.set_Kc(k_x);
-  controller.set_Kc_inty(k_inty);
-  controller.set_g_design(g_design);
-
-  cout << ".....................................\n";
-  cout << "control system:\n";
-  cout << ".....................................\n";
-  controller.Print();
-  cout << ".....................................\n";
-
-  // set up variables for simulation
-  // create Matrix to save outputs in...
-  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];
-
-  // Simulated measurements
-  Matrix z(n_y, n_t, arma::fill::zeros);
-
-  // simulated control signal ([=] V)
-  Matrix u(n_u, n_t, arma::fill::zeros);
-
-  // outputs, states and gain/disturbance params
-  // *_hat indicates online estimates
-  Matrix y_hat(n_y, n_t, arma::fill::zeros);
-  Matrix x_hat(n_x, n_t, arma::fill::zeros);
-  Matrix m_hat(n_x, n_t, arma::fill::zeros);
-
-  // *_true indicates ground truth (system being controlled)
-  Matrix y_true(n_y, n_t, arma::fill::zeros);
-  Matrix x_true(n_x, n_t, arma::fill::zeros);
-  Matrix m_true(n_x, n_t, arma::fill::zeros);
-
-  // set initial val
-  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y();
-  y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();
-
-  x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x();
-  x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();
-
-  m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m();
-  m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();
-
-  cout << "Starting " << n_t * dt << " sec simulation ... \n";
-  auto start = std::chrono::high_resolution_clock::now();
-  for (size_t t = 1; t < n_t; t++) {
-    // simulate a stochastically switched disturbance
-    Vector chance = arma::randu<Vector>(1);
-    if (which_m == 0)  // low disturbance
-    {
-      if (chance[0] < pr_lo2hi) {  // switches low -> high disturbance
-        m0_true = std::vector<data_t>(n_x, m_high);
-        which_m = 1;
-      }
-    } else {                       // high disturbance
-      if (chance[0] < pr_hi2lo) {  // swithces high -> low disturbance
-        m0_true = std::vector<data_t>(n_x, m_low);
-        which_m = 0;
-      }
-    }
-    controlled_system.set_m(m0_true);
-
-    // input
-    Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true);
-
-    // Simulate the true system.
-    z.col(t) = controlled_system.Simulate(u_tm1);
-
-    // This method uses a steady-state solution to control problem to calculate
-    // x_ref, u_ref from reference output y_ref. Therefore, it is only
-    // applicable to regulation problems or cases where reference trajectory
-    // changes slowly compared to system dynamics.
-    u.col(t) = controller.ControlOutputReference(z.col(t));
-
-    // save the signals
-    y_true.col(t) = controlled_system.y();
-    x_true.col(t) = controlled_system.x();
-    m_true.col(t) = controlled_system.m();
-
-    y_hat.col(t) = controller.sys().y();
-    x_hat.col(t) = controller.sys().x();
-    m_hat.col(t) = controller.sys().m();
-  }
-
-  auto finish = std::chrono::high_resolution_clock::now();
-  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
-  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
-  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
-
-  cout << "Saving simulation data to disk.\n";
-
-  // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,
-  // xTrue, mTrue saving with hdf5 via armadillo
-  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
-
-  auto dt_vec = Vector(1).fill(dt);
-  dt_vec.save(arma::hdf5_name("eg_glds_ctrl.h5", "dt"));
-  y_ref.save(arma::hdf5_name("eg_glds_ctrl.h5", "y_ref", replace));
-  u.save(arma::hdf5_name("eg_glds_ctrl.h5", "u", replace));
-  z.save(arma::hdf5_name("eg_glds_ctrl.h5", "z", replace));
-  x_true.save(arma::hdf5_name("eg_glds_ctrl.h5", "x_true", replace));
-  m_true.save(arma::hdf5_name("eg_glds_ctrl.h5", "m_true", replace));
-  y_true.save(arma::hdf5_name("eg_glds_ctrl.h5", "y_true", replace));
-  x_hat.save(arma::hdf5_name("eg_glds_ctrl.h5", "x_hat", replace));
-  m_hat.save(arma::hdf5_name("eg_glds_ctrl.h5", "m_hat", replace));
-  y_hat.save(arma::hdf5_name("eg_glds_ctrl.h5", "y_hat", replace));
-
-  cout << "fin.\n";
-  return 0;
-}
-

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//===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst>
-
-using lds::data_t;
-using lds::Matrix;
-using lds::Vector;
-using std::cout;
-
-auto main() -> int {
-  cout << " ********** Example Gaussian LDS du Control of PLDS ********** \n\n";
-
-  // Make 1st-order SISO system, sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;
-  size_t n_x = 1;
-  size_t n_y = 1;
-
-  // no time steps for simulation.
-  auto n_t = static_cast<size_t>(5.0 / dt);
-
-  // construct ground truth system to be controlled...
-  // initializes to random walk model with top-most n_y state observed
-  lds::poisson::System controlled_system(n_u, n_x, n_y, dt);
-
-  // Ground-truth parameters for the controlled system
-  // (stand-in for physical system to be controlled)
-  Matrix a_true(n_x, n_x, arma::fill::eye);
-  a_true[0] = exp(-dt / 0.01);
-  Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2);
-  // control signal to model input unit conversion e.g., V -> mW/mm2:
-  Vector g_true = Vector(n_y).fill(10.0);
-
-  size_t which_m = 0;  // whether low or high disturbance (0, 1)
-  data_t m_low = log(1 * dt) * (1 - a_true[0]);
-  data_t pr_lo2hi =
-      0;  // 1e-3;  // probability of going from low to high disturb.
-  data_t m_high = log(20 * dt) * (1 - a_true[0]);
-  data_t pr_hi2lo = pr_lo2hi;
-
-  // initially let m be low
-  Vector m0_true = Vector(n_x).fill(m_low);
-  Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);
-
-  // Assign params.
-  controlled_system.set_A(a_true);
-  controlled_system.set_B(b_true);
-  controlled_system.set_m(m0_true);
-  controlled_system.set_g(g_true);
-  controlled_system.set_x0(x0_true);
-  controlled_system.Reset();
-
-  cout << ".....................................\n";
-  cout << "controlled_system:\n";
-  cout << ".....................................\n";
-  controlled_system.Print();
-  cout << ".....................................\n";
-
-  // make a controller
-  lds::gaussian::Controller controller;
-  {
-    // Create **incorrect** model used for control.
-    // (e.g., imperfect model fitting)
-    Matrix b_controller = b_true / 50;
-
-    // let's assume zero process disturbance initially
-    // (will be re-estimating)
-    Vector m_controller = Vector(n_x, arma::fill::zeros);
-
-    // process noise covariance
-    Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8;
-
-    // output noise covariance
-    Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2;
-
-    lds::gaussian::System controller_system(n_u, n_x, n_y, dt);
-    controller_system.set_A(a_true);
-    controller_system.set_B(b_controller);
-    controller_system.set_g(g_true);
-    controller_system.set_m(m_controller);
-    controller_system.set_Q(q_controller);
-    controller_system.set_R(r_controller);
-    controller_system.Reset();  // reset to new m
-
-    // going to adaptively re-estimate the disturbance
-    controller_system.do_adapt_m = true;
-
-    // set adaptation rate by changing covariance of assumed process noise
-    // acting on random-walk evolution of m
-    Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8;
-    controller_system.set_Q_m(q_m);
-
-    // create controller
-    // lower and upper bounds on control signal (e.g., in Volts)
-    data_t u_lb = 0.0;  // [=] V
-    data_t u_ub = 5.0;  // [=] V
-    controller = std::move(
-        lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub));
-  }
-
-  // Control variables:
-  // Reference/target output, controller gains
-  Vector y_ref0 = Vector(n_y).fill(20.0 * dt);
-
-  // to design for this example, augmented state with control and made the input
-  // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du =
-  // 1e-2.
-  Matrix k_x = Matrix(n_u, n_x).fill(2.44);     // gains on state error
-  Matrix k_inty = Matrix(n_u, n_y).fill(97.4);  // gains on integrated err
-  Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2);  // gains on input amp
-
-  // set up controller type bit mask so controller knows how to proceed
-  size_t control_type = 0;
-  // use integral action to minimize DC error
-  control_type = control_type | lds::kControlTypeIntY;
-  // update change in control (LP filters control)
-  control_type = control_type | lds::kControlTypeDeltaU;
-
-  // set controller type
-  controller.set_control_type(control_type);
-
-  // Let's say these controller gains were designed assuming g was 9 V/(mW/mm2):
-  Vector g_design = Vector(n_u).fill(10);
-
-  // Set params.
-  // **n.b. using arbitrary defaults for Q, R in this example. Really, these
-  // should be set by users, as they tune characteristics of Kalman filter.
-  // Users can also choose not to recursively calculate the estimator gain and
-  // supply it (setKe) instead of covariances.**
-  controller.set_y_ref(y_ref0);
-  controller.set_Kc(k_x);
-  controller.set_Kc_inty(k_inty);
-  controller.set_Kc_u(k_u);
-  controller.set_g_design(g_design);
-
-  cout << ".....................................\n";
-  cout << "control system:\n";
-  cout << ".....................................\n";
-  controller.Print();
-  cout << ".....................................\n";
-
-  // set up variables for simulation
-  // create Matrix to save outputs in...
-  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];
-
-  // Simulated measurements
-  Matrix z(n_y, n_t, arma::fill::zeros);
-
-  // simulated control signal ([=] V)
-  Matrix u(n_u, n_t, arma::fill::zeros);
-
-  // outputs, states and gain/disturbance params
-  // *_hat indicates online estimates
-  Matrix y_hat(n_y, n_t, arma::fill::zeros);
-  Matrix x_hat(n_x, n_t, arma::fill::zeros);
-  Matrix m_hat(n_x, n_t, arma::fill::zeros);
-
-  // *_true indicates ground truth (system being controlled)
-  Matrix y_true(n_y, n_t, arma::fill::zeros);
-  Matrix x_true(n_x, n_t, arma::fill::zeros);
-  Matrix m_true(n_x, n_t, arma::fill::zeros);
-
-  // get initial val
-  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y();
-  y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();
-
-  x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x();
-  x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();
-
-  m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m();
-  m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();
-
-  cout << "Starting " << n_t * dt << " sec simulation ... \n";
-  auto start = std::chrono::high_resolution_clock::now();
-  for (size_t t = 1; t < n_t; t++) {
-    // simulate a stochastically switched disturbance
-    Vector chance = arma::randu<Vector>(1);
-    if (which_m == 0)  // low disturbance
-    {
-      if (chance[0] < pr_lo2hi) {  // switches low -> high disturbance
-        m0_true = std::vector<data_t>(n_x, m_high);
-        which_m = 1;
-      }
-    } else {                       // high disturbance
-      if (chance[0] < pr_hi2lo) {  // swithces high -> low disturbance
-        m0_true = std::vector<data_t>(n_x, m_low);
-        which_m = 0;
-      }
-    }
-    controlled_system.set_m(m0_true);
-
-    // input
-    Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true);
-
-    // Simulate the true system.
-    z.col(t) = controlled_system.Simulate(u_tm1);
-
-    // This method uses a steady-state solution to control problem to calculate
-    // x_ref, u_ref from reference output y_ref. Therefore, it is only
-    // applicable to regulation problems or cases where reference trajectory
-    // changes slowly compared to system dynamics.
-    u.col(t) = controller.ControlOutputReference(z.col(t));
-
-    // save the signals
-    y_true.col(t) = controlled_system.y();
-    x_true.col(t) = controlled_system.x();
-    m_true.col(t) = controlled_system.m();
-
-    y_hat.col(t) = controller.sys().y();
-    x_hat.col(t) = controller.sys().x();
-    m_hat.col(t) = controller.sys().m();
-  }
-
-  auto finish = std::chrono::high_resolution_clock::now();
-  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
-  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
-  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
-
-  cout << "Saving simulation data to disk.\n";
-
-  // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,
-  // xTrue, mTrue saving with hdf5 via armadillo
-  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
-
-  auto dt_vec = Vector(1).fill(dt);
-  dt_vec.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "dt"));
-  y_ref.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "y_ref", replace));
-  u.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "u", replace));
-  z.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "z", replace));
-  x_true.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "x_true", replace));
-  m_true.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "m_true", replace));
-  y_true.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "y_true", replace));
-  x_hat.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "x_hat", replace));
-  m_hat.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "m_hat", replace));
-  y_hat.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "y_hat", replace));
-
-  cout << "fin.\n";
-  return 0;
-}
-

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Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time

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auto main()
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Going to simulate a switching disturbance (m) acting on system

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-  Source code
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//===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst>
-
-using lds::Matrix;
-using lds::Vector;
-using lds::data_t;
-using std::cout;
-
-auto main() -> int {
-  cout << " ********** Example Poisson LDS Control ********** \n\n";
-
-  // Make SISO system sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;
-  size_t n_x = 1;
-  size_t n_y = 1;
-
-  // no time steps for simulation.
-  auto n_t = static_cast<size_t>(10.0 / dt);
-
-  // Control variables: _reference/target output, controller gains
-  // n.b., Can either use Vector (arma::Col) or std::vector
-  Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt;
-  Matrix k_x =
-      Matrix(n_u, n_x, arma::fill::zeros) + 1;  // gains on state error
-  Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) +
-                   10;  // gains on integrated output err
-
-  // Set control type bit mask, so controller knows what to do
-  size_t control_type = lds::kControlTypeIntY;  // integral action
-
-  // Ground-truth parameters for the controlled system
-  // (stand-in for physical system to be controlled)
-  Matrix a_true(n_x, n_x, arma::fill::eye);
-  a_true[0] = 0.986;
-  Matrix b_true(n_x, n_u, arma::fill::zeros);
-  b_true[0] = 0.054;
-  Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);
-
-  size_t which_m = 0;
-  data_t m_low = log(1 * dt) * (1 - a_true[0]);
-  data_t pr_lo2hi = 1e-3;
-  data_t m_high = log(20 * dt) * (1 - a_true[0]);
-  data_t pr_hi2lo = pr_lo2hi;
-
-  Vector m0_true = Vector(n_x, arma::fill::ones) * m_low;
-  // construct ground truth system to be controlled...
-  lds::poisson::System controlled_system(n_u, n_x, n_y, dt);
-
-  // Assign params.
-  controlled_system.set_A(a_true);
-  controlled_system.set_B(b_true);
-  controlled_system.set_m(m0_true);
-  controlled_system.set_x0(x0_true);
-  // reset to initial conditions
-  controlled_system.Reset();
-
-  cout << ".....................................\n";
-  cout << "controlled_system:\n";
-  cout << ".....................................\n";
-  controlled_system.Print();
-  cout << ".....................................\n";
-
-  // Create the controller
-  lds::poisson::Controller controller;
-  {
-    // Create model used for control.
-    lds::poisson::System controller_system(controlled_system);
-
-    // for this example, assume model correct, except disturbance
-    Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low;
-    Vector x0_controller = arma::log(y_ref0);
-    controller_system.set_m(m0_controller);
-    controller_system.set_x0(x0_controller);
-    controller_system.Reset(); //reset to new init condition
-
-    // adaptively re-estimate process disturbance (m)
-    controller_system.do_adapt_m = true;
-
-    // set adaptation rate by changing covariance of assumed process noise
-    // acting on random-walk evolution of m
-    Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5;
-    controller_system.set_Q_m(q_m);
-
-    data_t u_lb = 0.0;
-    data_t u_ub = 5.0;
-    controller = std::move(
-        lds::poisson::Controller(std::move(controller_system), u_lb, u_ub));
-  }
-  // set controller type
-  controller.set_control_type(control_type);
-
-  // set controller gains
-  controller.set_Kc(k_x);
-  controller.set_Kc_inty(k_inty);
-
-  // to protect against integral windup when output is consistently above
-  // target:
-  data_t tau_awu(0.1);
-  controller.set_tau_awu(tau_awu);
-
-  cout << ".....................................\n";
-  cout << "controller:\n";
-  cout << ".....................................\n";
-  controller.Print();
-  cout << ".....................................\n";
-
-  // create Matrix to save outputs in...
-  Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros);
-  y_ref.each_col() += y_ref0;
-
-  // Simulated measurements
-  Matrix z(n_y, n_t, arma::fill::zeros);
-
-  // simulated control signal ([=] V)
-  Matrix u(n_u, n_t, arma::fill::zeros);
-
-  // outputs, states and gain/disturbance params
-  // *_hat indicates online estimates
-  Matrix y_hat(n_y, n_t, arma::fill::zeros);
-  Matrix x_hat(n_x, n_t, arma::fill::zeros);
-  Matrix m_hat(n_y, n_t, arma::fill::zeros);
-
-  // *_true indicates ground truth (system being controlled)
-  Matrix y_true(n_y, n_t, arma::fill::zeros);
-  Matrix x_true(n_x, n_t, arma::fill::zeros);
-  Matrix m_true(n_y, n_t, arma::fill::zeros);
-
-  // set initial val
-  y_hat.col(0) = controller.sys().y();
-  y_true.col(0) = controlled_system.y();
-
-  x_hat.col(0) = controller.sys().x();
-  x_true.col(0) = controlled_system.x();
-
-  m_hat.col(0) = controller.sys().m();
-  m_true.col(0) = controlled_system.m();
-
-  cout << "Starting " << n_t * dt << " sec simulation ... \n";
-  auto start = std::chrono::high_resolution_clock::now();
-  for (size_t t = 1; t < n_t; t++) {
-    // simulate a stochastically switched disturbance
-    Vector chance = arma::randu<Vector>(1);
-    if (which_m == 0)  // low disturbance
-    {
-      if (chance[0] < pr_lo2hi) {  // switches low -> high disturbance
-        m0_true = std::vector<data_t>(n_x, m_high);
-        which_m = 1;
-      }
-    } else {                       // high disturbance
-      if (chance[0] < pr_hi2lo) {  // swithces high -> low disturbance
-        m0_true = std::vector<data_t>(n_x, m_low);
-        which_m = 0;
-      }
-    }
-    controlled_system.set_m(m0_true);
-
-    // e.g., use sinusoidal reference
-    data_t f = 0.5;  // freq [=] Hz
-    Vector t_vec = Vector(n_y, arma::fill::ones) * t;
-    y_ref.col(t) +=
-        y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4);
-
-    // Simulate the true system.
-    z.col(t)=controlled_system.Simulate(u.col(t-1));
-
-    // This method uses a steady-state solution to control problem to calculate
-    // x_ref, u_ref from reference output y_ref. Notably, it does this in the
-    // log-linear space (i.e., log(y)).
-    //
-    // Therefore, it is only applicable to regulation problems or cases where
-    // reference trajectory changes slowly compared to system dynamics.
-    controller.set_y_ref(y_ref.col(t));
-    u.col(t)=controller.ControlOutputReference(z.col(t));
-
-    y_true.col(t) = controlled_system.y();
-    x_true.col(t) = controlled_system.x();
-    m_true.col(t) = controlled_system.m();
-
-    y_hat.col(t) = controller.sys().y();
-    x_hat.col(t) = controller.sys().x();
-    m_hat.col(t) = controller.sys().m();
-  }
-
-  auto finish = std::chrono::high_resolution_clock::now();
-  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
-  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
-  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
-
-  // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,
-  // x_true, m_true saving with hdf5 via armadillo
-  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
-
-  auto dt_vec = Vector(1).fill(dt);
-  dt_vec.save(arma::hdf5_name("eg_plds_ctrl.h5", "dt"));
-  y_ref.save(arma::hdf5_name("eg_plds_ctrl.h5", "y_ref", replace));
-  u.save(arma::hdf5_name("eg_plds_ctrl.h5", "u", replace));
-  z.save(arma::hdf5_name("eg_plds_ctrl.h5", "z", replace));
-  x_true.save(arma::hdf5_name("eg_plds_ctrl.h5", "x_true", replace));
-  m_true.save(arma::hdf5_name("eg_plds_ctrl.h5", "m_true", replace));
-  y_true.save(arma::hdf5_name("eg_plds_ctrl.h5", "y_true", replace));
-  x_hat.save(arma::hdf5_name("eg_plds_ctrl.h5", "x_hat", replace));
-  m_hat.save(arma::hdf5_name("eg_plds_ctrl.h5", "m_hat", replace));
-  y_hat.save(arma::hdf5_name("eg_plds_ctrl.h5", "y_hat", replace));
-
-  return 0;
-}
-

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diff --git a/docs/docs/api/files/eg__plds__est_8cpp/index.html b/docs/docs/api/files/eg__plds__est_8cpp/index.html
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intmain()

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-  #
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Matrix random_walk(
-    size_t n_t,
-    const Matrix & Q,
-    const Vector & x0
-)
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-  main
-  #
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-  Source code
-  #
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//===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst>
-
-using lds::Matrix;
-using lds::Vector;
-using lds::data_t;
-using std::cout;
-
-// for generating random input
-Matrix random_walk(size_t n_t, const Matrix& Q, const Vector& x0);
-
-int main() {
-  cout << " ********** Example Poisson LDS Estimation ********** \n\n";
-
-  // Make SISO system sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;                           // no. inputs
-  size_t n_x = 1;                           // no. states
-  size_t n_y = 1;                           // no. outputs
-  auto n_t = static_cast<size_t>(30 / dt);  // no time steps for simulation.
-
-  // construct ground truth system...
-  lds::poisson::System system_true(n_u, n_x, n_y, dt);
-
-  // Model parameters
-  Matrix a_true(n_x, n_x, arma::fill::eye);
-  a_true[0] = exp(-dt / 0.075);
-  Matrix b_true = Matrix(n_x, n_u).fill(1e-2);
-  Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2);  // disturbance
-  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) -
-                                        a_true);  // initial state
-
-  // Assign params.
-  system_true.set_A(a_true);
-  system_true.set_B(b_true);
-  system_true.set_x0(x0_true);
-  system_true.set_m(m0_true);
-  system_true.Reset();
-
-  // Construct system for estimation
-  // e.g., will create a model with incorrect disturbance
-  lds::poisson::System system_estimator(n_u, n_x, n_y, dt);
-
-  // Can copy parameters from another system object
-  system_estimator = system_true;
-
-  // wrong disturbance
-  Vector m0_est = m0_true * 2;
-  system_estimator.set_m(m0_est);
-
-  // set new initial conditions
-  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) -
-                                      a_true);  // initial state
-  system_estimator.set_x0(x0_est);
-  system_estimator.Reset();  // reset to initial condition.
-
-  // turn on adaptive disturbance estimation
-  system_estimator.do_adapt_m = true;
-
-  // set adaptation rate by changing covariance of assumed process noise acting
-  // on random-walk evolution of m
-  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6;
-  system_estimator.set_Q_m(q_m);
-
-  cout << ".....................................\n";
-  cout << "estimator:\n";
-  cout << ".....................................\n";
-  system_estimator.Print();
-  cout << ".....................................\n";
-
-  // Set up simulation :
-  // Simulated measurements
-  Matrix z(n_y, n_t, arma::fill::zeros);
-
-  // Stimulus (generate random stimulus)
-  Matrix q_u =
-      Matrix(n_u, n_u, arma::fill::eye) * 1e-3;  // cov of random walk
-  Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));
-
-  // create matrix to save outputs in...
-  Matrix y_hat(n_y, n_t, arma::fill::zeros);
-  Matrix y_true(n_y, n_t, arma::fill::zeros);
-
-  // states and disturbance params
-  Matrix x_hat(n_x, n_t, arma::fill::zeros);
-  Matrix m_hat(n_x, n_t, arma::fill::zeros);
-
-  Matrix x_true(n_x, n_t, arma::fill::zeros);
-  Matrix m_true(n_x, n_t, arma::fill::zeros);
-
-  // initial conditions
-  y_hat.col(0) = system_estimator.y();
-  y_true.col(0) = system_true.y();
-  x_hat.col(0) = system_estimator.x();
-  x_true.col(0) = system_true.x();
-  m_hat.col(0) = system_estimator.m();
-  m_true.col(0) = system_true.m();
-
-  cout << "Starting " << n_t * dt << " sec simlation ... \n";
-  auto start = std::chrono::high_resolution_clock::now();
-  for (size_t t = 1; t < n_t; t++) {
-    // Simlate the true system.
-    z.col(t) = system_true.Simulate(u.col(t - 1));
-
-    // Filter (predict -> update)
-    system_estimator.Filter(u.col(t - 1), z.col(t));
-
-    // save signals
-    y_hat.col(t) = system_estimator.y();
-    y_true.col(t) = system_true.y();
-
-    x_true.col(t) = system_true.x();
-    m_true.col(t) = system_true.m();
-
-    x_hat.col(t) = system_estimator.x();
-    m_hat.col(t) = system_estimator.m();
-  }
-
-  auto finish = std::chrono::high_resolution_clock::now();
-  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
-  cout << "Finished simlation in " << sim_time_ms.count() << " ms.\n";
-  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
-
-  // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true,
-  // x_true, m_true saving with hdf5 via armadillo
-  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
-
-  auto dt_vec = Vector(1).fill(dt);
-  dt_vec.save(arma::hdf5_name("eg_plds_est.h5", "dt"));
-  u.save(arma::hdf5_name("eg_plds_est.h5", "u", replace));
-  z.save(arma::hdf5_name("eg_plds_est.h5", "z", replace));
-  x_true.save(arma::hdf5_name("eg_plds_est.h5", "x_true", replace));
-  m_true.save(arma::hdf5_name("eg_plds_est.h5", "m_true", replace));
-  y_true.save(arma::hdf5_name("eg_plds_est.h5", "y_true", replace));
-  x_hat.save(arma::hdf5_name("eg_plds_est.h5", "x_hat", replace));
-  m_hat.save(arma::hdf5_name("eg_plds_est.h5", "m_hat", replace));
-  y_hat.save(arma::hdf5_name("eg_plds_est.h5", "y_hat", replace));
-
-  return 0;
-}
-
-// for generating random input
-Matrix random_walk(size_t n_t, const Matrix& Q, const Vector& x0) {
-  size_t n = Q.n_rows;
-
-  if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) {
-    throw std::logic_error("Q must be `n` x `n`.");
-  }
-
-  Matrix x(n, n_t, arma::fill::zeros);
-  x.col(0) = x0;
-  for (size_t t = 1; t < n_t; t++) {
-    x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q);
-  }
-
-  return x;
-}
-

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//===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst>
-
-using lds::data_t;
-using lds::Matrix;
-using lds::Vector;
-using std::cout;
-
-auto main() -> int {
-  cout << " ********** Example Switched Poisson LDS Control ********** \n\n";
-
-  // whether to do switched control
-  bool do_switch_ctrl = true;
-
-  // Make SISO system sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;
-  size_t n_x = 1;
-  size_t n_y = 1;
-
-  // no time steps for simulation.
-  auto n_t = static_cast<size_t>(30.0 / dt);
-
-  // for simulating switching
-  size_t which_mode = 1;
-  data_t pr_21 = 1e-3;   // prob mode 1 -> 2
-  data_t pr_12 = pr_21;  // prob mode 2 -> 1
-
-  // simulated system being controlled
-  lds::poisson::System controlled_system(n_u, n_x, n_y, dt);
-
-  // **Assume the system is not well characterized by one LDS, but is well
-  // characterized by two LDS models with different input matrices.**
-  data_t scale_sys_b = 2;
-
-  Matrix a(n_x, n_x, arma::fill::eye);
-  a[0] = 0.985;
-  Matrix b1 = Matrix(n_x, n_u).fill(0.05);
-  Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));
-
-  controlled_system.set_A(a);
-  controlled_system.set_B(b1);
-  controlled_system.set_d(d);
-  controlled_system.Reset();  // reset to initial conditions
-
-  // reference
-  Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);
-
-  // Let underlying system 1 be more sensitive than system 2
-  Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);
-
-  // create switched controller
-  lds::poisson::SwitchedController switched_controller;
-  lds::UniformMatrixList<> k_x;  // feedback controller gains
-  {
-    // create switched controller sub-systems
-    // system 1
-    lds::poisson::System sys1(controlled_system);
-
-    // set process noise covariance
-    Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3;
-    sys1.set_Q(q_controller);
-
-    // adaptively estimate process disturbance (m)
-    // n.b. using arbitrary default value for process noise if enabled.
-    sys1.do_adapt_m = true;
-
-    // setting initial mode to target to avoid large error at onset:
-    Vector x0_controller = arma::log(y_ref0) - d;
-    sys1.set_x0(x0_controller);
-    sys1.Reset();  // reset to initial conditions
-
-    cout << ".....................................\n";
-    cout << "sys1:\n";
-    cout << ".....................................\n";
-    sys1.Print();
-    cout << ".....................................\n";
-
-    // system 2
-    lds::poisson::System sys2 = sys1;
-
-    // set parameters
-    sys2.set_B(b2);
-
-    cout << ".....................................\n";
-    cout << "sys2:\n";
-    cout << ".....................................\n";
-    sys2.Print();
-    cout << ".....................................\n";
-
-    lds::UniformSystemList<lds::poisson::System> systems({sys1, sys2});
-
-    // controller gains for underlying system s:
-    Matrix k_x1(n_u, n_x, arma::fill::ones);
-    Matrix k_x2 = scale_sys_b * k_x1;  // system2 is x-times less sensitive.
-    k_x = lds::UniformMatrixList<>({k_x1, k_x2});
-
-    data_t u_lb = 0.0;
-    data_t u_ub = 5.0;
-    switched_controller = std::move(
-        lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub));
-  }
-  // Control variables
-  size_t control_type = 0;  // no integral action, etc
-  switched_controller.set_control_type(control_type);
-  switched_controller.set_Kc(std::move(k_x));
-
-  switched_controller.set_y_ref(y_ref0);
-
-  std::vector<lds::poisson::System> systems_vec(3, lds::poisson::System());
-  lds::UniformSystemList<lds::poisson::System> systems(std::move(systems_vec));
-
-  cout << ".....................................\n";
-  cout << "switched_controller:\n";
-  cout << ".....................................\n";
-  switched_controller.Print();
-  cout << ".....................................\n";
-
-  // Fake measurements
-  Matrix z(n_y, n_t, arma::fill::zeros);
-
-  // Will later contain control.
-  Matrix u(n_u, n_t, arma::fill::zeros);
-
-  // create Matrix to save outputs in...
-  Matrix y_hat(n_y, n_t, arma::fill::zeros);
-  Matrix y_true(n_y, n_t, arma::fill::zeros);
-  Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);
-
-  // modes and gain/disturbance params
-  Matrix x_hat(n_x, n_t, arma::fill::zeros);
-  Matrix x_true(n_x, n_t, arma::fill::zeros);
-  Matrix mode(1, n_t, arma::fill::ones);
-
-  // set initial val
-  y_hat.col(0) = switched_controller.sys().y();
-  y_true.col(0) = controlled_system.y();
-  x_hat.col(0) = switched_controller.sys().x();
-  x_true.col(0) = controlled_system.x();
-
-  cout << "Starting " << n_t * dt << " sec simulation ... \n";
-  auto start = std::chrono::high_resolution_clock::now();
-  for (size_t t = 1; t < n_t; t++) {
-    // Let the controlled system stochastically change gain
-    // Assume another algorithm decodes this mode change and signals the
-    // switched_controller
-    Vector chance(1, arma::fill::randu);
-    if (which_mode == 1)  // mode1
-    {
-      if (chance[0] < pr_21) {
-        which_mode = 2;
-        controlled_system.set_B(b2);
-        if (do_switch_ctrl) {
-          switched_controller.Switch(1);
-        }
-      }
-    } else {  // mode2
-      if (chance[0] < pr_12) {
-        which_mode = 1;
-        controlled_system.set_B(b1);
-        if (do_switch_ctrl) {
-          switched_controller.Switch(0);
-        }
-      }
-    }
-
-    // Simulate the true system.
-    z.col(t) = controlled_system.Simulate(u.col(t - 1));
-
-    // perform control
-    u.col(t) = switched_controller.ControlOutputReference(z.col(t));
-
-    mode.col(t) = which_mode;
-    y_ref.col(t) = y_ref0;
-    y_true.col(t) = controlled_system.y();
-    x_true.col(t) = controlled_system.x();
-    y_hat.col(t) = switched_controller.sys().y();
-    x_hat.col(t) = switched_controller.sys().x();
-  }
-
-  auto finish = std::chrono::high_resolution_clock::now();
-  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
-  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
-  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
-
-  // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,
-  // x_true, m_true saving with hdf5 via armadillo
-  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
-
-  auto dt_vec = Vector(1).fill(dt);
-  dt_vec.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "dt"));
-  y_ref.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "y_ref", replace));
-  u.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "u", replace));
-  z.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "z", replace));
-  x_true.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "x_true", replace));
-  y_true.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "y_true", replace));
-  x_hat.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "x_hat", replace));
-  y_hat.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "y_hat", replace));
-  mode.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "mode", replace));
-
-  return 0;
-}
-

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diff --git a/docs/docs/api/files/index.xml b/docs/docs/api/files/index.xml
deleted file mode 100644
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-
-
-  
-    Files on LDS C&E
-    https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/
-    Recent content in Files on LDS C&E
-    Hugo -- gohugo.io
-    
-      examples
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/
-      examples #  Files #     Name     examples/eg_glds_ctrl.cpp   examples/eg_glds_du_plds_ctrl.cpp   examples/eg_plds_ctrl.cpp   examples/eg_plds_est.cpp   examples/eg_plds_switched_ctrl.cpp     Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
-    
-    
-    
-      examples/eg_glds_ctrl.cpp
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/
-      examples/eg_glds_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Going to simulate a switching disturbance (m) acting on system
-Source code #  //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.
-    
-    
-    
-      examples/eg_glds_du_plds_ctrl.cpp
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/
-      examples/eg_glds_du_plds_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Going to simulate a switching disturbance (m) acting on system
-Source code #  //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.
-    
-    
-    
-      examples/eg_plds_ctrl.cpp
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/
-      examples/eg_plds_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Going to simulate a switching disturbance (m) acting on system
-Source code #  //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.
-    
-    
-    
-      examples/eg_plds_est.cpp
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/
-      examples/eg_plds_est.cpp #  Functions #      Name     Matrix random_walk(size_t n_t, const Matrix &amp; Q, const Vector &amp; x0)   int main()    Function Details #  random_walk #  Matrix random_walk( size_t n_t, const Matrix &amp; Q, const Vector &amp; x0 ) main #  int main() Source code #  //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.
-    
-    
-    
-      examples/eg_plds_switched_ctrl.cpp
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/
-      examples/eg_plds_switched_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Source code #  //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the &#34;License&#34;); // you may not use this file except in compliance with the License.
-    
-    
-    
-      include
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/
-      include #  Directories #     Name     ldsCtrlEst_h     Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
-    
-    
-    
-      ldsCtrlEst_h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/
-      ldsCtrlEst_h #  Files #     Name     ldsCtrlEst_h/lds.h lds namespace   ldsCtrlEst_h/lds_ctrl.h Controller.   ldsCtrlEst_h/lds_fit.h LDS base fit type.   ldsCtrlEst_h/lds_fit_em.h subspace identification   ldsCtrlEst_h/lds_fit_ssid.h subspace identification   ldsCtrlEst_h/lds_gaussian.h glds namespace   ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.   ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.   ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.   ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.   ldsCtrlEst_h/lds_gaussian_sctrl.
-    
-    
-    
-      ldsCtrlEst_h/lds.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds_8h/
-      lds namespace
-    
-    
-    
-      ldsCtrlEst_h/lds_ctrl.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__ctrl_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__ctrl_8h/
-      Controller.
-    
-    
-    
-      ldsCtrlEst_h/lds_fit.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit_8h/
-      LDS base fit type.
-    
-    
-    
-      ldsCtrlEst_h/lds_fit_em.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit__em_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit__em_8h/
-      subspace identification
-    
-    
-    
-      ldsCtrlEst_h/lds_fit_ssid.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/
-      subspace identification
-    
-    
-    
-      ldsCtrlEst_h/lds_gaussian.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian_8h/
-      glds namespace
-    
-    
-    
-      ldsCtrlEst_h/lds_gaussian_ctrl.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/
-      GLDS Controller.
-    
-    
-    
-      ldsCtrlEst_h/lds_gaussian_fit.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/
-      GLDS fit type.
-    
-    
-    
-      ldsCtrlEst_h/lds_gaussian_fit_em.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/
-      GLDS E-M fit type.
-    
-    
-    
-      ldsCtrlEst_h/lds_gaussian_fit_ssid.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/
-      GLDS SSID fit type.
-    
-    
-    
-      ldsCtrlEst_h/lds_gaussian_sctrl.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/
-      GLDS switched controller type.
-    
-    
-    
-      ldsCtrlEst_h/lds_gaussian_sys.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/
-      GLDS base type.
-    
-    
-    
-      ldsCtrlEst_h/lds_poisson.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson_8h/
-      plds namespace
-    
-    
-    
-      ldsCtrlEst_h/lds_poisson_ctrl.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/
-      PLDS controller type.
-    
-    
-    
-      ldsCtrlEst_h/lds_poisson_fit.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/
-      PLDS base fit type.
-    
-    
-    
-      ldsCtrlEst_h/lds_poisson_fit_em.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/
-      PLDS E-M fit type.
-    
-    
-    
-      ldsCtrlEst_h/lds_poisson_fit_ssid.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/
-      PLDS SSID fit type.
-    
-    
-    
-      ldsCtrlEst_h/lds_poisson_sctrl.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/
-      PLDS switched controller type.
-    
-    
-    
-      ldsCtrlEst_h/lds_poisson_sys.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/
-      PLDS base type.
-    
-    
-    
-      ldsCtrlEst_h/lds_sctrl.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sctrl_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sctrl_8h/
-      SwitchedController type.
-    
-    
-    
-      ldsCtrlEst_h/lds_sys.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8h/
-      LDS base type.
-    
-    
-    
-      ldsCtrlEst_h/lds_uniform_mats.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/
-      List of uniformly sized matrices.
-    
-    
-    
-      ldsCtrlEst_h/lds_uniform_systems.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/
-      List of uniformly sized Systems.
-    
-    
-    
-      ldsCtrlEst_h/lds_uniform_vecs.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/
-      List of uniformly sized vectors.
-    
-    
-    
-      ldsCtrlEst_h/mex_c_util.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/mex__c__util_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/mex__c__util_8h/
-      arma &lt;-&gt; mex interoperability utilities (Matlab C API)
-    
-    
-    
-      ldsCtrlEst_h/mex_cpp_util.h
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/
-      arma &lt;-&gt; mex interoperability utilities (Matlab C++ API)
-    
-    
-    
-      src
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/
-      src #  Files #     Name     src/lds.cpp misc lds namespace functions   src/lds_gaussian_sys.cpp GLDS base type.   src/lds_poisson_sys.cpp PLDS base type.   src/lds_sys.cpp LDS base type.   src/lds_uniform_vecs.cpp Uniformly sized vectors.     Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
-    
-    
-    
-      src/lds.cpp
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds_8cpp/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds_8cpp/
-      misc lds namespace functions
-    
-    
-    
-      src/lds_gaussian_sys.cpp
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/
-      GLDS base type.
-    
-    
-    
-      src/lds_poisson_sys.cpp
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/
-      PLDS base type.
-    
-    
-    
-      src/lds_sys.cpp
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8cpp/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8cpp/
-      LDS base type.
-    
-    
-    
-      src/lds_uniform_vecs.cpp
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/
-      Uniformly sized vectors.
-    
-    
-  
-
diff --git a/docs/docs/api/files/lds_8cpp/index.html b/docs/docs/api/files/lds_8cpp/index.html
deleted file mode 100644
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-src/lds.cpp | LDS C&E
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-      

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-  src/lds.cpp
-  #
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misc lds namespace functions  More…

-

-  Namespaces
-  #
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Name
lds 
Linear Dynamical Systems (LDS) namespace.

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-  Detailed Description
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This file implements miscellaneous lds namespace functions not bound to a class.

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-  Source code
-  #
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-
//===-- lds.cpp - LDS -----------------------------------------------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst_h/lds.h>
-
-// insert any necessary function definitions here.
-namespace lds {
-
-void ForceSymPD(Matrix& X) {
-  if (X.is_sympd() || !X.is_square()) {
-    return;
-  }
-
-  // make symmetric
-  X = (X + X.t()) / 2;
-
-  // for eigenval decomp
-  bool did_succeed(true);
-  Vector d;
-  Matrix u;
-
-  // see first method (which may not be ideal):
-  // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/
-  size_t k(1);
-  bool is_sympd = X.is_sympd();
-  Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye);
-
-  while (!is_sympd) {
-    if (k > 100) {
-      did_succeed = arma::eig_sym(d, u, X, "std");
-      data_t min_eig = arma::min(d);
-      std::cerr << "After multiple iterations, min eigen val = " << min_eig
-                << ".\n";
-      throw std::runtime_error(
-          "Failed to make matrix symmetric positive definite.");
-      return;
-    }
-
-    // Limit(d, arma::eps(0), kInf);  // force to be positive...
-    // Matrix d_diag = arma::diagmat(d);
-    // X = u * d_diag * u.t();
-
-    did_succeed = arma::eig_sym(d, u, X, "std");
-    if (!did_succeed) {
-      throw std::runtime_error("ForceSymPD failed.");
-    }
-
-    data_t min_eig = arma::min(d);
-    X += id * abs(min_eig) + arma::datum::eps;
-
-    // make sure symm:
-    X = (X + X.t()) / 2;
-    // double check eigenvals positive after symmetrizing:
-    arma::eig_sym(d, u, X, "std");
-    min_eig = arma::min(d);
-    is_sympd = min_eig > 0;
-    k++;
-  }
-}
-
-void ForceSymMinEig(Matrix& X, data_t eig_min) {
-  if (!X.is_square()) {
-    return;
-  }
-
-  // make symmetric
-  X = (X + X.t()) / 2;
-
-  bool did_succeed(true);
-  Vector d;
-  Matrix u;
-  did_succeed = arma::eig_sym(d, u, X, "std");
-  if (!did_succeed) {
-    throw std::runtime_error("ForceSymMinEig failed.");
-  }
-  Limit(d, eig_min + arma::eps(eig_min), kInf);  // enforce lower bound
-  Matrix d_diag = arma::diagmat(d);
-  X = u * d_diag * u.t();
-
-  // double check symmetric
-  X = (X + X.t()) / 2;
-}
-
-void lq(Matrix& L, Matrix& Qt, const Matrix& X) {
-  bool did_succeed(true);
-  did_succeed = arma::qr_econ(Qt, L, X.t());
-  if (!did_succeed) {
-    throw std::runtime_error("LQ decomposition failed.");
-  }
-  arma::inplace_trans(L);
-  arma::inplace_trans(Qt);
-}
-
-Matrix calcCov(const Matrix& A, const Matrix& B) {
-  // subtract out mean
-  auto m_a = arma::mean(A, 1);
-  Matrix a0 = A;
-  a0.each_col() -= m_a;
-
-  auto m_b = arma::mean(B, 1);
-  Matrix b0 = B;
-  b0.each_col() -= m_b;
-
-  Matrix cov = a0 * b0.t() / a0.n_cols;
-  return cov;
-}
-
-}  // namespace lds
-

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lds namespace  More…

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-  Namespaces
-  #
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Name
lds 
Linear Dynamical Systems (LDS) namespace.

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-  Detailed Description
-  #
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This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)) observations.

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_H
-#define LDSCTRLEST_LDS_H
-
-// #ifndef LDSCTRLEST
-// #include <ldsCtrlEst>
-// #endif
-
-#include <armadillo>
-
-namespace lds {
-using data_t = double;  // may change to float (but breaks mex functions)
-using Vector = arma::Col<data_t>;
-using Matrix = arma::Mat<data_t>;
-using Cube = arma::Cube<data_t>;
-using View = arma::subview<data_t>;
-
-namespace fill = arma::fill;
-
-
-static const std::size_t kControlTypeDeltaU = 0x1;
-
-static const std::size_t kControlTypeIntY = kControlTypeDeltaU << 1;
-
-static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU << 2;
-
-static const data_t kInf = std::numeric_limits<data_t>::infinity();
-static const data_t kPi = arma::datum::pi;
-
-static const data_t kDefaultP0 = 1e-6;  
-static const data_t kDefaultQ0 = 1e-6;  
-static const data_t kDefaultR0 = 1e-2;  
-
-enum SSIDWt {
-  kSSIDNone,   
-  kSSIDMOESP,  
-  kSSIDCVA     
-};
-
-enum MatrixListFreeDim {
-  kMatFreeDimNone,  
-  kMatFreeDim1,     
-  kMatFreeDim2      
-};
-
-// TODO(mfbolus): for SwitchedController, may want systems to have differing
-// numbers of states. Use this enum as template parameter?
-// enum SystemListFreeDim {
-//   kSysFreeDimNone,
-//   kSysFreeDimX  ///< allow state dim (x) of systems in list to be hetero
-// };
-
-// place hard limits on contents of vecors/mats
-void Limit(std::vector<data_t>& x, data_t lb, data_t ub);
-void Limit(Vector& x, data_t lb, data_t ub);
-void Limit(Matrix& x, data_t lb, data_t ub);
-
-// in-place assign that errs if there are dimension mismatches:
-void Reassign(Vector& some, const Vector& other,
-              const std::string& parenthetical = "Reassign");
-void Reassign(Matrix& some, const Matrix& other,
-              const std::string& parenthetical = "Reassign");
-
-// TODO(mfbolus): this is a fudge, but for some reason, cov mats often going
-// numerically asymm.
-
-void ForceSymPD(Matrix& X);
-
-void ForceSymMinEig(Matrix& X, data_t eig_min = 0);
-
-void lq(Matrix& L, Matrix& Qt, const Matrix& X);
-
-Matrix calcCov(const Matrix& A, const Matrix& B);
-
-inline void Limit(std::vector<data_t>& x, data_t lb, data_t ub) {
-  for (data_t& el : x) {
-    el = el < lb ? lb : el;
-    el = el > ub ? ub : el;
-  }
-}
-inline void Limit(Vector& x, data_t lb, data_t ub) {
-  for (data_t& el : x) {
-    el = el < lb ? lb : el;
-    el = el > ub ? ub : el;
-  }
-}
-inline void Limit(Matrix& x, data_t lb, data_t ub) {
-  for (data_t& el : x) {
-    el = el < lb ? lb : el;
-    el = el > ub ? ub : el;
-  }
-}
-
-inline void Reassign(Vector& some, const Vector& other,
-                     const std::string& parenthetical) {
-  // check dimensions
-  if (other.n_elem != some.n_elem) {
-    std::ostringstream ss;
-    ss << "cannot reassign vector of size " << some.n_elem
-       << " with vector of size " << other.n_elem << "(" << parenthetical
-       << ")";
-    throw std::runtime_error(ss.str());
-  }
-
-  for (size_t k = 0; k < some.n_elem; k++) {
-    some[k] = other[k];
-  }
-}
-
-inline void Reassign(Matrix& some, const Matrix& other,
-                     const std::string& parenthetical) {
-  // check dimensions
-  if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) {
-    std::ostringstream ss;
-    ss << "cannot reassign matrix of size " << some.n_rows << "x" << some.n_cols
-       << " with matrix of size " << other.n_rows << "x" << other.n_cols << "("
-       << parenthetical << ")";
-    throw std::runtime_error(ss.str());
-  }
-
-  for (size_t k = 0; k < some.n_elem; k++) {
-    some[k] = other[k];
-  }
-}
-
-}  // namespace lds
-
-#endif
-

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diff --git a/docs/docs/api/files/lds__ctrl_8h/index.html b/docs/docs/api/files/lds__ctrl_8h/index.html
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Controller.  More…

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Name
lds 
Linear Dynamical Systems (LDS) namespace.

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classlds::Controller

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-  Detailed Description
-  #
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This file declares the type for control of a linear dynamical system (lds::Controller).

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_CTRL_H
-#define LDSCTRLEST_LDS_CTRL_H
-
-// namespace
-#include "lds.h"
-// system type
-#include "lds_sys.h"
-
-namespace lds {
-
-template <typename System>
-class Controller {
-  static_assert(std::is_base_of<lds::System, System>::value,
-                "System must be derived from lds::System type.");
-
- public:
-  Controller() = default;
-
-  Controller(const System& sys, data_t u_lb, data_t u_ub,
-             size_t control_type = 0);
-
-  Controller(System&& sys, data_t u_lb, data_t u_ub, size_t control_type = 0);
-
-  const Vector& Control(const Vector& z, bool do_control = true,
-                        bool do_lock_control = false,
-                        data_t sigma_soft_start = 0, data_t sigma_u_noise = 0,
-                        bool do_reset_at_control_onset = true);
-
-  const Vector& ControlOutputReference(const Vector& z, bool do_control = true,
-                                       bool do_estimation = true,
-                                       bool do_lock_control = false,
-                                       data_t sigma_soft_start = 0,
-                                       data_t sigma_u_noise = 0,
-                                       bool do_reset_at_control_onset = true);
-
-  // get methods:
-  const System& sys() const { return sys_; };
-  const Matrix& Kc() const { return Kc_; };
-  const Matrix& Kc_inty() const { return Kc_inty_; };
-  const Matrix& Kc_u() const { return Kc_u_; };
-  const Vector& g_design() const { return g_design_; };
-  const Vector& u_ref() const { return u_ref_; };
-  const Vector& x_ref() const { return x_ref_; };
-  const Vector& y_ref() const { return y_ref_; };
-  size_t control_type() const { return control_type_; };
-  data_t tau_awu() const { return tau_awu_; };
-  data_t u_lb() const { return u_lb_; };
-  data_t u_ub() const { return u_ub_; };
-
-  // set methods
-  void set_sys(const System& sys) {
-    bool does_match = sys_.n_u() == sys.n_u();
-    does_match = does_match && (sys_.n_x() == sys.n_x());
-    does_match = does_match && (sys_.n_y() == sys.n_y());
-    if (does_match) {
-      sys_ = sys;
-    } else {
-      throw std::runtime_error(
-          "new system argument to `set_sys` does not match dimensionality of "
-          "existing system");
-    }
-  };
-  void set_g_design(const Vector& g_design) { Reassign(g_design_, g_design); };
-  void set_u_ref(const Vector& u_ref) { Reassign(u_ref_, u_ref); };
-  void set_x_ref(const Vector& x_ref) {
-    Reassign(x_ref_, x_ref);
-    cx_ref_ = sys_.C() * x_ref_;
-  };
-
-  // y_ref needs to be handled differently depending on output fn.
-  // (need to populate cx_ref_ too, which depends on output fn)
-  virtual void set_y_ref(const Vector& y_ref) { Reassign(y_ref_, y_ref); };
-
-  void set_Kc(const Matrix& Kc) { Reassign(Kc_, Kc); };
-  void set_Kc_inty(const Matrix& Kc_inty) { Reassign(Kc_inty_, Kc_inty); };
-  void set_Kc_u(const Matrix& Kc_u) { Reassign(Kc_u_, Kc_u); };
-  void set_tau_awu(data_t tau) {
-    tau_awu_ = tau;
-    k_awu_ = sys_.dt() / tau_awu_;
-  };
-
-  void set_control_type(size_t control_type);
-
-  // There is no reason u_lb/ub should not be public, but making set methods
-  // anyway.
-  void set_u_lb(data_t u_lb) { u_lb_ = u_lb; };
-
-  void set_u_ub(data_t u_ub) { u_ub_ = u_ub; };
-
-  void Reset() {
-    sys_.Reset();
-    u_ref_.zeros();
-    u_ref_prev_.zeros();
-    int_e_.zeros();
-    int_e_awu_adjust_.zeros();
-    u_sat_.zeros();
-    u_saturated_ = false;
-    t_since_control_onset_ = 0.0;
-  };
-
-  void Print() {
-    sys_.Print();
-    std::cout << "g_design : " << g_design_ << "\n";
-    std::cout << "u_lb : " << u_lb_ << "\n";
-    std::cout << "u_ub : " << u_ub_ << "\n";
-  };
-
- protected:
-  System sys_;  
-
-  Vector u_;         
-  Vector u_return_;  
-
-  Vector g_design_;  
-
-  //  reference signals
-  Vector u_ref_;  
-  // create no set method for this:
-  Vector u_ref_prev_;  
-  Vector x_ref_;       
-  Vector y_ref_;       
-  Vector cx_ref_;
-
-  // Controller gains
-  Matrix Kc_;  
-  Matrix
-      Kc_u_;  
-  Matrix Kc_inty_;  
-
-  // control after g inversion
-  // do not need set methods for these.
-  Vector du_ref_;
-  Vector dv_ref_;
-  Vector v_ref_;
-  Vector dv_;
-  Vector v_;  
-
-  // integral error
-  // do not need set method for this
-  Vector int_e_;             
-  Vector int_e_awu_adjust_;  
-  Vector u_sat_;  
-
-  bool do_control_prev_ = false;
-  bool do_lock_control_prev_ = false;
-
-  // whether the g of system has become inverted from what you think it is
-  // (gain_ref)
-  bool u_saturated_ =
-      false;  
-
-  // should be safe to have references here bc nothing needs to be done
-  // (like reset vars) when it changes...
-  data_t u_lb_{};  
-  data_t u_ub_{};  
-
-  data_t tau_awu_{};  
-  data_t k_awu_ = 0;
-
-  data_t t_since_control_onset_ = 0;  
-  size_t control_type_{};             
-
- private:
-  void CalcControl(bool do_control = true, bool do_estimation = true,
-                   bool do_lock_control = false, data_t sigma_soft_start = 0,
-                   data_t sigma_u_noise = 0,
-                   bool do_reset_at_control_onset = true);
-
-  void CalcSteadyStateSetPoint();
-
-  void AntiWindup();
-
-  void InitVars(size_t control_type);
-};
-
-// Implement the above:
-
-template <typename System>
-inline Controller<System>::Controller(const System& sys, data_t u_lb,
-                                      data_t u_ub, size_t control_type)
-    : sys_(sys),
-      u_lb_(u_lb),
-      u_ub_(u_ub),
-      tau_awu_(lds::kInf) {
-  InitVars(control_type);
-}
-
-template <typename System>
-inline Controller<System>::Controller(System&& sys, data_t u_lb, data_t u_ub,
-                                      size_t control_type)
-    : sys_(std::move(sys)),
-      u_lb_(u_lb),
-      u_ub_(u_ub),
-      tau_awu_(lds::kInf) {
-  InitVars(control_type);
-}
-
-template <typename System>
-inline void Controller<System>::set_control_type(size_t control_type) {
-  if (control_type_ == control_type) {
-    return;
-  }
-
-  // creating a blank slate...
-  control_type_ = 0;
-  Kc_inty_.zeros(0, 0);
-  Kc_u_.zeros(0, 0);
-  int_e_.zeros(0, 0);
-  int_e_awu_adjust_.zeros(0, 0);
-
-  // controller was designed to minimize integral error
-  if (control_type & kControlTypeIntY) {
-    Kc_inty_.zeros(sys_.n_u(), sys_.n_y());
-    int_e_.zeros(sys_.n_y());
-    int_e_awu_adjust_.zeros(sys_.n_u());
-    control_type_ = control_type_ | kControlTypeIntY;
-  }
-
-  // controller was designed to minimize deltaU
-  // (i.e. state augmented with u)
-  if (control_type & kControlTypeDeltaU) {
-    Kc_u_.zeros(sys_.n_u(), sys_.n_u());
-    control_type_ = control_type_ | kControlTypeDeltaU;
-  }
-
-  // whether to adapt set point calculate with (re-estimated) process
-  // disturbance (m)
-  if (control_type & kControlTypeAdaptM) {
-    if (sys_.do_adapt_m)  // only if adapting m...
-    {
-      control_type_ = control_type_ | kControlTypeAdaptM;
-    }
-  }
-}  // set_control_type
-
-template <typename System>
-inline const Vector& Controller<System>::Control(
-    const Vector& z, bool do_control, bool do_lock_control,
-    data_t sigma_soft_start, data_t sigma_u_noise,
-    bool do_reset_at_control_onset) {
-  // update state estimates, given latest measurement
-  sys_.Filter(u_, z);
-
-  bool do_estimation = true;  // always have estimator on in this case
-
-  // calculate control signal
-  CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start,
-              sigma_u_noise, do_reset_at_control_onset);
-
-  return u_return_;
-}
-
-template <typename System>
-inline const Vector& Controller<System>::ControlOutputReference(
-    const Vector& z, bool do_control, bool do_estimation, bool do_lock_control,
-    data_t sigma_soft_start, data_t sigma_u_noise,
-    bool do_reset_at_control_onset) {
-  // update state estimates, given latest measurement
-  if (do_estimation) {
-    sys_.Filter(u_, z);
-  } else {
-    sys_.f(u_);
-  }
-
-  // calculate the set point
-  // solves for u_ref and x_ref when output is at y_ref at steady state.
-  if (do_control) {
-    CalcSteadyStateSetPoint();
-  }
-
-  // calculate control signal
-  CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start,
-              sigma_u_noise, do_reset_at_control_onset);
-
-  return u_return_;
-}
-
-template <typename System>
-inline void Controller<System>::CalcControl(bool do_control, bool do_estimation,
-                                            bool do_lock_control,
-                                            data_t sigma_soft_start,
-                                            data_t sigma_u_noise,
-                                            bool do_reset_at_control_onset) {
-  if (do_control && do_estimation) {
-    if (!do_control_prev_) {
-      if (do_reset_at_control_onset) {
-        Reset();
-      }
-      t_since_control_onset_ = 0.0;
-    } else {
-      t_since_control_onset_ += sys_.dt();
-    }
-
-    // enforce softstart on control vars.
-    if (sigma_soft_start > 0) {
-      // half-Gaussian soft-start scaling factor
-      data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) /
-                                     (2 * pow(sigma_soft_start, 2)));
-      u_ref_ *= soft_start_sf;
-      // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too
-      // x_ref_ *= soft_start_sf;
-      // cx_ref_ *= soft_start_sf;
-      // y_ref_ *= soft_start_sf;
-    }
-
-    if (!do_lock_control) {
-      // first do u -> v change of vars. (v = g.*u)
-      // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver
-      // control voltage u[=]V)
-      v_ref_ = g_design_ % u_ref_;
-
-      // Given FB, calc. the change in control
-      if (control_type_ & kControlTypeDeltaU) {
-        // if control designed to minimize not u but deltaU (i.e. state aug with
-        // u):
-
-        // TODO(mfbolus): Commented out for now. See note below.
-        // du_ref_ = u_ref_ - u_ref_prev_;
-        // dv_ref_ = g_design_ % du_ref_;
-
-        // TODO(mfbolus): Assuming users want *smooth* control signals if using
-        // kControlTypeDeltaU, it should be the case that dv_ref_ is --> 0. May
-        // want to revisit, but I am going to force it to be zero unless a
-        // situation arises that argues for keeping the above.
-        dv_ref_.zeros();
-
-        dv_ = dv_ref_;                     // nominally-optimal.
-        dv_ -= Kc_ * (sys_.x() - x_ref_);  // instantaneous state error
-        dv_ -= Kc_u_ * (v_ - v_ref_);      // penalty on amp u (rel to ref)
-
-        if (control_type_ & kControlTypeIntY) {
-          // TODO(mfbolus): one approach to protection against integral windup
-          // would be to not integrate error when control signal saturated:
-
-          // if(!uSaturated)
-          int_e_ += (sys_.cx() - cx_ref_) * sys_.dt();  // integrated error
-          dv_ -= Kc_inty_ * int_e_;  // control for integrated error
-        }
-
-        // update the control
-        v_ += dv_;
-      } else {
-        v_ = v_ref_;                      // nominally-optimal.
-        v_ -= Kc_ * (sys_.x() - x_ref_);  // instantaneous state error
-
-        if (control_type_ & kControlTypeIntY) {
-          // TODO(mfbolus): one approach to protection against integral windup
-          // would be to not integrate error when control signal saturated:
-
-          // if (!uSaturated)
-          int_e_ += (sys_.cx() - cx_ref_) * sys_.dt();  // integrated error
-          v_ -= Kc_inty_ * int_e_;  // control for integrated error
-        }
-      }
-
-      // convert back to control voltage u[=]V
-      u_ = v_ / sys_.g();
-    }       // else do nothing until lock is low
-  } else {  // if not control
-    // feed through u_ref in open loop
-    u_ = u_ref_ % g_design_ / sys_.g();
-    v_ = sys_.g() % u_;
-    u_ref_.zeros();
-    int_e_.zeros();
-    int_e_awu_adjust_.zeros();
-    u_sat_.zeros();
-  }  // ends do_control
-
-  // enforce box constraints (and antiwindup)
-  AntiWindup();
-
-  // add noise to input?
-  // The value for u that is *returned* to user after addition of any noise,
-  // while keeping controller/estimator blind to this addition.
-  u_return_ = u_;
-  if ((sigma_u_noise > 0.0) && (do_control && !do_lock_control)) {
-    u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn);
-    Limit(u_return_, u_lb_, u_ub_);
-  };
-
-  // For next time step:
-  u_ref_prev_ = u_ref_;
-  do_control_prev_ = do_control;
-  do_lock_control_prev_ = do_lock_control;
-}  // CalcControl
-
-template <typename System>
-inline void Controller<System>::CalcSteadyStateSetPoint() {
-  // Linearly-constrained least squares (ls).
-  //
-  // _reference:
-  //  Boyd & Vandenberghe (2018) Introduction to Applied Linear Algebra
-  //
-  Matrix a_ls =
-      join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros));
-  Vector b_ls = cx_ref_;
-  Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye),
-                           sys_.B() * arma::diagmat(sys_.g()));
-  Vector d_ls = -sys_.m0();
-  if (control_type_ & kControlTypeAdaptM) {
-    d_ls = -sys_.m();  // adapt setpoint calc with disturbance?
-  }
-
-  Matrix a_ls_t = a_ls.t();  // TODO(mfbolus): not sure why but causes seg
-                             // fault if I do not do this.
-  Matrix phi_ls =
-      join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()),
-                join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros)));
-  // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse:
-  Matrix inv_phi = pinv(phi_ls);
-  Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls);
-  x_ref_ = xulam.subvec(0, sys_.n_x() - 1);
-  u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1);
-  cx_ref_ = sys_.C() * x_ref_;
-}  // CalcSteadyStateSetPoint
-
-template <typename System>
-void Controller<System>::AntiWindup() {
-  u_saturated_ = false;
-  u_sat_ = u_;
-
-  // limit u and flag whether saturated
-  for (size_t k = 0; k < u_.n_elem; k++) {
-    if (u_[k] < u_lb_) {
-      u_sat_[k] = u_lb_;
-      u_saturated_ = true;
-    }
-
-    if (u_[k] > u_ub_) {
-      u_sat_[k] = u_ub_;
-      u_saturated_ = true;
-    }
-  }
-
-  if ((control_type_ & kControlTypeIntY) && (tau_awu_ < lds::kInf)) {
-    // one-step back-calculation (calculate intE for u=u_sat)
-    // (Astroem, Rundqwist 1989 warn against using this...)
-    // int_e_awu_adjust_ =
-    //     solve(Kc_inty_, (u_ - u_sat_));  // pinv(Kc_inty) * (u-uSat);
-
-    // gradual: see Astroem, Rundqwist 1989
-    // this is a fudge for doing MIMO gradual
-    // n.b., went ahead and multiplied 1/T by dt so don't have to do that here.
-    int_e_awu_adjust_ =
-        k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_);
-    // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_);
-
-    int_e_ += int_e_awu_adjust_;
-  }
-
-  // set u to saturated version
-  u_ = u_sat_;
-}
-
-template <typename System>
-void Controller<System>::InitVars(size_t control_type) {
-  // initialize to default values
-  u_ref_ = Vector(sys_.n_u(), fill::zeros);
-  u_ref_prev_ = Vector(sys_.n_u(), fill::zeros);
-  x_ref_ = Vector(sys_.n_x(), fill::zeros);
-  y_ref_ = Vector(sys_.n_y(), fill::zeros);
-  cx_ref_ = Vector(sys_.n_y(), fill::zeros);
-
-  u_ = Vector(sys_.n_u(), fill::zeros);
-  u_return_ = Vector(sys_.n_u(), fill::zeros);
-  u_sat_ = Vector(sys_.n_u(), fill::zeros);
-
-  // Might not need all these, so zero elements until later.
-  Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros);
-  Kc_u_ = Matrix(0, 0, fill::zeros);
-  Kc_inty_ = Matrix(0, 0, fill::zeros);
-
-  g_design_ = sys_.g();  // by default, same as model
-  dv_ = Vector(sys_.n_u(), fill::zeros);
-  v_ = Vector(sys_.n_u(), fill::zeros);
-  du_ref_ = Vector(sys_.n_u(), fill::zeros);
-  dv_ref_ = Vector(sys_.n_u(), fill::zeros);
-  v_ref_ = Vector(sys_.n_u(), fill::zeros);
-
-  int_e_ = Vector(0, fill::zeros);
-  int_e_awu_adjust_ = Vector(0, fill::zeros);
-
-  set_control_type(control_type);
-}
-
-}  // namespace lds
-
-#endif
-

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Linear Dynamical Systems (LDS) namespace.

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classlds::Fit 
LDS Fit Type.

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-  Detailed Description
-  #
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This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.

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-  Source code
-  #
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//===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDS_FIT_HPP
-#define LDS_FIT_HPP
-
-// namespace
-#include "lds.h"
-#include "lds_uniform_mats.h"
-
-namespace lds {
-class Fit {
- public:
-  Fit() = default;
-  Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt);
-
-  virtual ~Fit() = default;
-
-  // get methods
-  size_t n_u() const { return n_u_; };
-  size_t n_x() const { return n_x_; };
-  size_t n_y() const { return n_y_; };
-  data_t dt() const { return dt_; };
-  const Matrix& A() const { return A_; };
-  const Matrix& B() const { return B_; };
-  const Vector& g() const { return g_; };
-  const Vector& m() const { return m_; };
-  const Matrix& Q() const { return Q_; };
-  const Vector& x0() const { return x0_; };
-  const Matrix& P0() const { return P0_; };
-  const Matrix& C() const { return C_; };
-  const Vector& d() const { return d_; };
-  // gets measurement noise
-  virtual const Matrix& R() const = 0;
-
-  // set methods (e.g., seeding initial fit values)
-  void set_A(const Matrix& A) { Reassign(A_, A); };
-  void set_B(const Matrix& B) { Reassign(B_, B); };
-  void set_g(const Vector& g) { Reassign(g_, g); };
-  void set_m(const Vector& m) { Reassign(m_, m); };
-  void set_Q(const Matrix& Q) {
-    Reassign(Q_, Q);
-    ForceSymPD(Q_);
-  };
-  virtual void set_R(const Matrix& R) = 0;
-  void set_x0(const Vector& x0) { Reassign(x0_, x0); };
-  void set_P0(const Matrix& P0) {
-    Reassign(P0_, P0);
-    ForceSymPD(P0_);
-  };
-  void set_C(const Matrix& C) { Reassign(C_, C); };
-  void set_d(const Vector& d) { Reassign(d_, d); };
-
-  View f(Matrix& x, const Matrix& u, size_t t) {
-    x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_;
-    return x.col(t);
-  };
-
-  View f(Matrix& x_pre, const Matrix& x_post, const Matrix& u, size_t t) {
-    x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_;
-    return x_pre.col(t);
-  };
-
-  virtual View h(Matrix& y, const Matrix& x, size_t t) = 0;
-
- protected:
-  data_t dt_{};  
-
-  // Dynamics
-  Matrix A_;  
-  Matrix B_;  
-  Vector g_;  
-  Vector m_;  
-  Matrix Q_;  
-
-  // Output
-  Matrix C_;  
-  Vector d_;  
-  Matrix R_; 
-
-  // initial conditions
-  Vector x0_;  
-  Matrix P0_;  
-
-  size_t n_u_{};  
-  size_t n_x_{};  
-  size_t n_y_{};  
-};
-
-}  // namespace lds
-#endif
-

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subspace identification  More…

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Linear Dynamical Systems (LDS) namespace.

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This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_EMAX_H
-#define LDSCTRLEST_LDS_EMAX_H
-
-#include "lds_fit.h"
-
-namespace lds {
-
-template <typename Fit>
-class EM {
-  static_assert(std::is_base_of<lds::Fit, Fit>::value,
-                "Fit must be derived from lds::Fit type.");
-
- public:
-  EM() = default;
-
-  EM(size_t n_x, data_t dt, UniformMatrixList<kMatFreeDim2>&& u_train,
-     UniformMatrixList<kMatFreeDim2>&& z_train);
-
-  EM(const Fit& fit0, UniformMatrixList<kMatFreeDim2>&& u_train,
-     UniformMatrixList<kMatFreeDim2>&& z_train);
-
-  virtual ~EM() = default;
-
-  const Fit& Run(bool calc_dynamics = true, bool calc_Q = true,
-                 bool calc_init = true, bool calc_output = true,
-                 bool calc_measurement = true, size_t max_iter = 100,
-                 data_t tol = 1e-2);
-
-  std::tuple<UniformMatrixList<kMatFreeDim2>, UniformMatrixList<kMatFreeDim2>>
-  ReturnData() {
-    auto tuple = std::make_tuple(std::move(u_), std::move(z_));
-    // auto tuple = std::make_tuple(u_, z_);
-    u_ = UniformMatrixList<kMatFreeDim2>();
-    z_ = UniformMatrixList<kMatFreeDim2>();
-    return tuple;
-  }
-
-  const std::vector<Matrix>& x() const { return x_; };
-  const std::vector<Matrix>& y() const { return y_; };
-
-  const Matrix& sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; };
-  const Matrix& sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; };
-  const Matrix& sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; };
-  size_t n_t_tot() { return n_t_tot_; }
-
-  const Vector& theta() const { return theta_; };
-
- protected:
-  void Expectation(bool force_common_initial = false);
-
-  void Maximization(bool calc_dynamics = true, bool calc_Q = true,
-                    bool calc_init = false, bool calc_output = false,
-                    bool calc_measurement = false);
-
-  void MaximizeDynamics();
-  void MaximizeQ();
-  void MaximizeInitial();
-  virtual void MaximizeOutput() = 0;
-  virtual void MaximizeMeasurement() = 0;
-
-  void Smooth(bool force_common_initial);
-
-  virtual void RecurseKe(Matrix& Ke, Cube& P_pre, Cube& P_post, size_t t) = 0;
-
-  void Reset();
-
-  void InitVars();
-
-  Vector UpdateTheta();
-
-  // input/output training data
-  UniformMatrixList<kMatFreeDim2> u_;  
-  UniformMatrixList<kMatFreeDim2> z_;  
-  std::vector<Matrix> x_;              
-  std::vector<Cube> P_;                
-  std::vector<Cube> P_t_tm1_;          
-  std::vector<Matrix> y_;              
-  Matrix diag_y_;
-
-  // expectations calculated in E-step
-  Matrix sum_E_x_t_x_t_;        
-  Matrix sum_E_xu_tm1_xu_tm1_;  
-  Matrix sum_E_xu_t_xu_tm1_;    
-
-  Fit fit_;
-  Vector theta_;
-
-  data_t dt_{};              
-  size_t n_u_{};             
-  size_t n_x_{};             
-  size_t n_y_{};             
-  size_t n_trials_{};        
-  std::vector<size_t> n_t_;  
-  size_t n_t_tot_{};         
-};
-
-template <typename Fit>
-EM<Fit>::EM(size_t n_x, data_t dt, UniformMatrixList<kMatFreeDim2>&& u_train,
-            UniformMatrixList<kMatFreeDim2>&& z_train) {
-  n_u_ = u_train.at(0).n_rows;
-  n_y_ = z_train.at(0).n_rows;
-  fit_ = Fit(n_u_, n_x, n_y_, dt);
-  u_ = std::move(u_train);
-  z_ = std::move(z_train);
-  InitVars();
-}
-
-template <typename Fit>
-EM<Fit>::EM(const Fit& fit0, UniformMatrixList<kMatFreeDim2>&& u_train,
-            UniformMatrixList<kMatFreeDim2>&& z_train) {
-  // make sure fit dims match I/O data
-  if (fit0.n_u() != u_train.at(0).n_rows) {
-    throw std::runtime_error(
-        "Initial fit and input training data have inconsistent dimensions");
-  }
-  if (fit0.n_y() != z_train.at(0).n_rows) {
-    throw std::runtime_error(
-        "Initial fit and output training data have inconsistent dimensions");
-  }
-
-  fit_ = fit0;
-  u_ = std::move(u_train);
-  z_ = std::move(z_train);
-
-  InitVars();
-}
-
-template <typename Fit>
-void EM<Fit>::InitVars() {
-  // check input/output data dimensions are consistent
-  if (z_.size() != u_.size()) {
-    throw std::runtime_error(
-        "I/O training data have different number of trials.");
-  }
-  n_trials_ = u_.size();
-
-  n_t_tot_ = 0;
-  n_t_ = std::vector<size_t>(n_trials_);
-  for (size_t trial = 0; trial < n_trials_; trial++) {
-    if (z_.at(trial).n_cols != u_.at(trial).n_cols) {
-      throw std::runtime_error(
-          "I/O training data have different number of time steps.");
-    }
-    n_t_[trial] = u_.at(trial).n_cols;
-    n_t_tot_ += n_t_[trial];
-  }
-
-  n_u_ = fit_.n_u();
-  n_x_ = fit_.n_x();
-  n_y_ = fit_.n_y();
-  dt_ = fit_.dt();
-
-  x_ = std::vector<Matrix>(n_trials_);
-  P_ = std::vector<Cube>(n_trials_);
-  P_t_tm1_ = std::vector<Cube>(n_trials_);
-  y_ = std::vector<Matrix>(n_trials_);
-  for (size_t trial = 0; trial < n_trials_; trial++) {
-    x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros);
-    P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros);
-    P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros);
-    y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros);
-  }
-
-  diag_y_ = Matrix(n_y_, n_y_, fill::zeros);
-
-  // covariances in expectation step
-  sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros);
-  sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros);
-  sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros);
-}
-
-template <typename Fit>
-const Fit& EM<Fit>::Run(bool calc_dynamics, bool calc_Q, bool calc_init,
-                        bool calc_output, bool calc_measurement,
-                        size_t max_iter, data_t tol) {
-  Reset();  // to initial conditions
-
-  size_t n_params =
-      3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_;
-  Vector theta(n_params);
-  Vector theta_new(n_params);
-  data_t max_dtheta = 1;
-
-  // if solving for initial conditions, allow them be varied.
-  // otherwise, freeze at provided values.
-  bool force_common_initial = !calc_init;
-
-  // go until parameter convergence
-  for (size_t l = 0; l < max_iter; l++) {
-    theta_ = UpdateTheta();
-
-    std::cout << "Iteration " << l + 1 << "/" << max_iter << " ...\n";
-
-    Expectation(force_common_initial);
-    Maximization(calc_dynamics, calc_Q, calc_init, calc_output,
-                 calc_measurement);
-
-    // check convergence
-    theta_new = UpdateTheta();
-
-    Vector dtheta = abs(theta_new - theta_) / abs(theta_);
-    // some parameters could be zero...
-    arma::uvec ubi_finite = find_finite(dtheta);
-
-    max_dtheta = max(dtheta.elem(ubi_finite));
-    std::cout << "max dtheta: " << max_dtheta << "\n";
-    if (max_dtheta < tol) {
-      std::cout << "Converged.\n";
-      break;
-    }
-
-    std::cout << "\n";
-  }
-
-  return fit_;
-}
-
-template <typename Fit>
-void EM<Fit>::Smooth(bool force_common_initial) {
-  Matrix k_e(n_x_, n_y_);  // estimator gain
-  Cube k_backfilt;         // back-filtering gains
-
-  // TODO(mfbolus): this loop could be made parallel
-  for (size_t trial = 0; trial < z_.size(); trial++) {
-    Matrix x_pre(n_x_, n_t_[trial], fill::zeros);
-    Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros);
-    Matrix x_post(n_x_, n_t_[trial], fill::zeros);
-    Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros);
-
-    if (force_common_initial)  // forces all trials to have same initial
-                               // conditions.
-    {
-      x_[trial].col(0) = fit_.x0();
-      P_[trial].slice(0) = fit_.P0();
-    }
-    y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d();
-
-    // This *should not* be necessary but make sure P is symmetric.
-    ForceSymPD(P_[trial].slice(0));
-
-    x_pre.col(0) = x_[trial].col(0);
-    p_pre.slice(0) = P_[trial].slice(0);
-
-    x_post.col(0) = x_[trial].col(0);
-    p_post.slice(0) = P_[trial].slice(0);
-
-    // filter
-    for (size_t t = 1; t < n_t_[trial]; t++) {
-      // predict
-      fit_.f(x_pre, x_post, u_.at(trial), t);
-      fit_.h(y_[trial], x_pre, t);
-      diag_y_.diag() = y_[trial].col(t);  // TODO(mfbolus): change if parallel
-
-      // update --> posterior estimation
-      RecurseKe(k_e, p_pre, p_post, t);
-      x_post.col(t) =
-          x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t));
-      y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d();
-    }
-
-    // backfilter -> Smoothed estimate
-    // Reference:
-    // Shumway et Stoffer (1982)
-    ForceSymPD(p_post.slice(n_t_[trial] - 1));
-    k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros);
-    x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1);
-    P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1);
-    for (size_t t = (n_t_[trial] - 1); t > 0; t--) {
-      // TODO(mfmbolus): should not be necessary to force symm positive def
-      ForceSymPD(p_pre.slice(t));
-      ForceSymPD(p_post.slice(t - 1));
-      ForceSymPD(P_[trial].slice(t));
-      k_backfilt.slice(t - 1) =
-          p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t));
-      x_[trial].col(t - 1) =
-          x_post.col(t - 1) +
-          k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t));
-      P_[trial].slice(t - 1) =
-          p_post.slice(t - 1) + k_backfilt.slice(t - 1) *
-                                    (P_[trial].slice(t) - p_pre.slice(t)) *
-                                    k_backfilt.slice(t - 1).t();
-    }
-
-    // do the same for P_t_tm1
-    Matrix id(n_x_, n_x_, fill::eye);
-    P_t_tm1_[trial].slice(n_t_[trial] - 1) =
-        (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2);
-    for (size_t t = (n_t_[trial] - 1); t > 1; t--) {
-      P_t_tm1_[trial].slice(t - 1) =
-          p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() +
-          k_backfilt.slice(t - 1) *
-              (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) *
-              k_backfilt.slice(t - 2).t();
-    }
-
-    // finally, get smoothed estimate of output
-    for (size_t t = 0; t < n_t_[trial]; t++) {
-      fit_.h(y_[trial], x_[trial], t);
-    }  // samps loop
-  }    // trial loop
-}  // Smooth
-
-// template <typename Fit>
-// void EM<Fit>::RecurseKe(Matrix& Ke, Cube& P_pre, Cube& P_post, size_t t) {
-//   // predict covar
-//   P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q();
-
-//   // update Ke
-//   Ke = P_pre.slice(t) * fit_.C().t() *
-//        inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R());
-
-//   // update cov
-//   // Reference: Ghahramani et Hinton (1996)
-//   P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t);
-
-//   // // n.b. for poisson :
-//   // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() +
-//   fit_.Q();
-//   // // update cov
-//   // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ *
-//   // fit_.C());
-//   // // update Ke
-//   // Ke = P_post.slice(t) * fit_.C();
-// }
-
-template <typename Fit>
-void EM<Fit>::Expectation(bool force_common_initial) {
-  // calculate the mean/cov of state needed for maximizing E[pr(z|theta)]
-  Smooth(force_common_initial);
-
-  // now get the various forms of sum(E[xx']) needed
-  // n.b. Going to start at t=1 rather than 0 bc most max terms need that.
-  // so really "n_t_tot_" is (n_t_tot_-1)
-  n_t_tot_ = 0;
-  sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros);
-  sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros);
-  sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros);
-
-  Vector xu_tm1(n_x_ + n_u_, fill::zeros);
-  Vector xu_t(n_x_ + n_u_, fill::zeros);
-
-  for (size_t trial = 0; trial < z_.size(); trial++) {
-    for (size_t t = 1; t < n_t_[trial]; t++) {
-      // ------------ sum_E_x_t_x_t ------------
-      sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t();
-      sum_E_x_t_x_t_ += P_[trial].slice(t);
-
-      // ------------ sum_E_xu_tm1_xu_tm1 ------------
-      xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1));
-      sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t();
-      sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) +=
-          P_[trial].slice(t - 1);
-
-      // ------------ sum_E_xu_t_xu_tm1 ------------
-      xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t));
-      sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t();
-      sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) +=
-          P_t_tm1_[trial].slice(t);
-
-      n_t_tot_ += 1;
-    }  // time
-  }    // trial
-}  // Expectation
-
-template <typename Fit>
-void EM<Fit>::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init,
-                           bool calc_output, bool calc_measurement) {
-  if (calc_output) {
-    MaximizeOutput();
-  }
-
-  if (calc_measurement) {
-    MaximizeMeasurement();
-  }
-
-  if (calc_dynamics) {
-    MaximizeDynamics();
-  }
-
-  if (calc_Q) {
-    MaximizeQ();
-  }
-
-  if (calc_init) {
-    MaximizeInitial();
-  }
-}  // Maximization
-
-template <typename Fit>
-void EM<Fit>::MaximizeDynamics() {
-  // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996)
-  Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) *
-              inv_sympd(sum_E_xu_tm1_xu_tm1_);
-  fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1));
-  fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1));
-  std::cout << "A_new[0]: " << fit_.A()[0] << "\n";
-  std::cout << "B_new[0]: " << fit_.B()[0] << "\n";
-}
-
-template <typename Fit>
-void EM<Fit>::MaximizeQ() {
-  // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996)
-  // View sum_e_x_t_xu_tm1 =
-  //     sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1);
-  // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 *
-  //                                inv_sympd(sum_E_xu_tm1_xu_tm1_) *
-  //                                sum_e_x_t_xu_tm1.t();
-  // q /= n_t_tot_;
-
-  // this way is same as above iff dynamics were just updated:
-  // View sum_e_x_t_xu_tm1 =
-  //     sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1);
-  // Matrix ab = arma::join_horiz(fit_.A(), fit_.B());
-  // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t();
-  // q /= n_t_tot_;
-
-  // From scratch method:
-  // Q is covariance of the error between state and dynamics-predicted state
-  // (aka process noise)
-  // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})']
-  // t-1 terms:
-  View sum_e_x_tm1_x_tm1 =
-      sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1);
-  View sum_e_u_tm1_u_tm1 =
-      sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1);
-  View sum_e_x_tm1_u_tm1 =
-      sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1);
-
-  // t, t-1 terms:
-  View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1);
-  View sum_e_x_t_u_tm1 =
-      sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1);
-
-  Matrix q = sum_E_x_t_x_t_;
-  q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t();
-  q -= sum_e_x_t_x_tm1 * fit_.A().t();
-  q -= fit_.A() * sum_e_x_t_x_tm1.t();
-  // input-related terms:
-  q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t();
-  q -= sum_e_x_t_u_tm1 * fit_.B().t();
-  q -= fit_.B() * sum_e_x_t_u_tm1.t();
-  q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t();
-  q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t();
-  q /= n_t_tot_;
-
-  fit_.set_Q(q);
-  std::cout << "Q_new[0]: " << fit_.Q()[0] << "\n";
-  // std::cout << "Q_new: \n" << fit_.Q() << "\n";
-}
-
-template <typename Fit>
-void EM<Fit>::MaximizeInitial() {
-  Vector x0 = fit_.x0();
-  x0.zeros();
-  for (size_t trial = 0; trial < z_.size(); trial++) {
-    x0 += x_[trial].col(0);
-  }
-  x0 /= z_.size();
-  std::cout << "x0_new[0]: " << x0[0] << "\n";
-
-  // always recalc P0 even if the initial state is fixed (at zero, for
-  // example)
-  Matrix e_var(n_x_, n_x_, fill::zeros);
-  for (size_t trial = 0; trial < z_.size(); trial++) {
-    e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t();
-  }
-  e_var /= z_.size();
-
-  // go ahead and subtract x0*x0' so don't have to below.
-  e_var -= x0 * x0.t();
-
-  // To get P0, going to get initial P_ per trial and average.
-  // (which might be wrong, but need a single number)
-  Matrix p0 = fit_.P0();
-  p0.zeros();
-  for (size_t trial = 0; trial < z_.size(); trial++) {
-    p0 +=
-        (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var;
-  }
-  p0 /= z_.size();
-
-  fit_.set_P0(p0);
-  std::cout << "P0_new[0]: " << fit_.P0()[0] << "\n";
-}
-
-template <typename Fit>
-void EM<Fit>::MaximizeOutput() {
-  // solve for C+d:
-  Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros);
-  Vector x1(n_x_ + 1, fill::zeros);
-  x1[n_x_] = 1.0;  // augment with one to solve for bias
-  Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros);
-  for (size_t trial = 0; trial < z_.size(); trial++) {
-    for (size_t t = 1; t < n_t_[trial]; t++) {
-      x1.subvec(0, n_x_ - 1) = x_[trial].col(t);
-      sum_zx += z_.at(trial).col(t) * x1.t();
-      sum_e_x1_x1 += x1 * x1.t();
-      sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t);
-    }
-  }
-  Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1);
-  fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1));
-  fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_)));
-  std::cout << "C_new[0]: " << fit_.C()[0] << "\n";
-  std::cout << "d_new[0]: " << fit_.d()[0] << "\n";
-}
-
-template <typename Fit>
-void EM<Fit>::MaximizeMeasurement() {
-  // Solve for measurement noise covar
-  size_t n_t_tot = 0;
-  // Ghahgramani, Hinton 1996:
-  Matrix sum_zz(n_y_, n_y_, fill::zeros);
-  Matrix sum_yz(n_y_, n_y_, fill::zeros);
-  for (size_t trial = 0; trial < z_.size(); trial++) {
-    for (size_t t = 1; t < n_t_[trial]; t++) {
-      sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t();
-      // Use Cnew:
-      sum_yz +=
-          (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t();
-      n_t_tot += 1;
-    }
-  }
-  fit_.set_R((sum_zz - sum_yz) / n_t_tot);
-  std::cout << "R_new[0]: " << fit_.R()[0] << "\n";
-}
-
-template <typename Fit>
-void EM<Fit>::Reset() {
-  // reset to initial conditions
-  for (size_t trial = 0; trial < n_trials_; trial++) {
-    x_[trial].col(0) = fit_.x0();
-    P_[trial].slice(0) = fit_.P0();
-    y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d();
-  }
-}
-
-template <typename Fit>
-Vector EM<Fit>::UpdateTheta() {
-  // TODO(mfbolus): This should include n_y_ more params for d.
-  size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_;
-  if (fit_.R().n_elem > 0) {
-    n_params += n_y_ * n_y_;
-  }
-  Vector theta(n_params);
-
-  size_t idx_start = 0;
-  theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A());
-  idx_start += n_x_ * n_x_;
-  theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B());
-  idx_start += n_x_ * n_u_;
-  theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q());
-  idx_start += n_x_ * n_x_;
-  theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0());
-  idx_start += n_x_;
-  theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0());
-  idx_start += n_x_ * n_x_;
-  theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C());
-  idx_start += n_y_ * n_x_;
-  theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d());
-  idx_start += n_y_;
-  if (fit_.R().n_elem > 0) {
-    theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R());
-  }
-
-  return theta;
-}
-
-}  // namespace lds
-
-#endif
-

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Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time

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subspace identification  More…

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lds 
Linear Dynamical Systems (LDS) namespace.

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classlds::SSID

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-  Detailed Description
-  #
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This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)<Fit>).

-
References: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.

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-  Source code
-  #
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//===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_FIT_SSID_H
-#define LDSCTRLEST_LDS_FIT_SSID_H
-
-#include "lds_fit.h"
-
-namespace lds {
-
-template <typename Fit>
-class SSID {
-  static_assert(std::is_base_of<lds::Fit, Fit>::value,
-                "Fit must be derived from lds::Fit type.");
-
- public:
-  SSID() = default;
-
-  SSID(size_t n_x, size_t n_h, data_t dt,
-       UniformMatrixList<kMatFreeDim2>&& u_train,
-       UniformMatrixList<kMatFreeDim2>&& z_train,
-       const Vector& d = Vector(1).fill(-kInf));
-
-  std::tuple<Fit, Vector> Run(SSIDWt ssid_wt);
-
-  std::tuple<UniformMatrixList<kMatFreeDim2>, UniformMatrixList<kMatFreeDim2>>
-  ReturnData() {
-    auto tuple = std::make_tuple(std::move(u_), std::move(z_));
-    u_ = UniformMatrixList<kMatFreeDim2>();
-    z_ = UniformMatrixList<kMatFreeDim2>();
-    return tuple;
-  }
-
- protected:
-  void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001);
-
-  void CreateHankelDataMat();
-
-  virtual void DecomposeData() = 0;
-
-  void CalcSVD(SSIDWt wt);
-
-  void Solve(data_t wt_dc);
-
-  void RecomputeExtObs();
-
-  // input/output training data
-  UniformMatrixList<kMatFreeDim2> u_;  
-  UniformMatrixList<kMatFreeDim2> z_;  
-  Matrix D_;                           
-
-  Fit fit_;      
-  Matrix g_dc_;  
-
-  data_t dt_{};   
-  size_t n_u_{};  
-  size_t n_x_{};  
-  size_t n_y_{};  
-  size_t n_h_{};
-  size_t n_trials_{};        
-  std::vector<size_t> n_t_;  
-  size_t n_t_tot_{};         
-
-  Matrix L_;          
-  Vector s_;          
-  Matrix ext_obs_t_;  
-};
-
-template <typename Fit>
-SSID<Fit>::SSID(size_t n_x, size_t n_h, data_t dt,
-                UniformMatrixList<kMatFreeDim2>&& u_train,
-                UniformMatrixList<kMatFreeDim2>&& z_train, const Vector& d) {
-  // check input/output data dimensions are consistent
-  if (z_train.size() != u_train.size()) {
-    throw std::runtime_error(
-        "I/O training data have different number of trials.");
-  }
-  n_trials_ = u_train.size();
-
-  n_t_tot_ = 0;
-  n_t_ = std::vector<size_t>(n_trials_);
-  for (size_t trial = 0; trial < n_trials_; trial++) {
-    if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) {
-      throw std::runtime_error(
-          "I/O training data have different number of time steps.");
-    }
-    n_t_[trial] = u_train.at(trial).n_cols;
-    n_t_tot_ += n_t_[trial];
-  }
-
-  dt_ = dt;
-  n_x_ = n_x;
-  n_u_ = u_train.at(0).n_rows;
-  n_y_ = z_train.at(0).n_rows;
-  n_h_ = n_h;
-
-  // dimensionality check for eventual block-hankel data matrix
-  size_t len = n_t_tot_ - 2 * n_h_ + 1;
-  if (len < (2 * n_h_ * (n_u_ + n_y_))) {
-    std::ostringstream ss;
-    ss << "Dataset problem! More rows than columns in block-hankel data "
-          "matrix: 2*(n_u+n_y)*n_h > data-length! Need higher data-length or "
-          "lower n_h.";
-    throw std::runtime_error(ss.str());
-  }
-
-  fit_ = Fit(n_u_, n_x_, n_y_, dt_);
-
-  u_ = std::move(u_train);
-  z_ = std::move(z_train);
-
-  if (!d.is_finite() || (d.n_rows != n_y_)) {
-    // TODO(mfbolus): implement least-square solution for impulse response with
-    // a second input of ones. Data-driven way of accounting for offset *not*
-    // driven by an input.
-    //
-    // For now, calculate output bias (d) as the
-    // output wherever the stimulus has not been on for some amount of time.
-    // convolve u with rectangle and take all samples. This is a reasonable
-    // approach, since often when autonomous systems are fit (i.e., systems with
-    // no input), they will subtract off the mean of the output. This
-    // essentially amounts to setting output bias to the mean of the output when
-    // there is no stimulation.
-    data_t t_silence = 0.1;
-    data_t thresh_silence = 0.001;
-    CalcD(t_silence, thresh_silence);
-  } else {
-    fit_.set_d(d);
-  }
-}
-
-template <typename Fit>
-std::tuple<Fit, Vector> SSID<Fit>::Run(SSIDWt ssid_wt) {
-  // the weight on minimizing dc I/O gain only works for gaussian,
-  // and hopefully not necessary with appropriate dataset.
-  data_t wt_dc = 0;
-  // std::cout << "creating hankel mat\n";
-  CreateHankelDataMat();
-  // std::cout << "decomposing data\n";
-  DecomposeData();
-  // std::cout << "calculating svd\n";
-  CalcSVD(ssid_wt);
-  // std::cout << "solving for params\n";
-  Solve(wt_dc);
-  // std::cout << "fin\n";
-  return std::make_tuple(fit_, s_);
-}
-
-template <typename Fit>
-void SSID<Fit>::CalcD(data_t t_silence, data_t thresh_silence) {
-  Vector d(z_.at(0).n_rows, fill::zeros);
-  Vector win(static_cast<size_t>(t_silence / dt_), fill::ones);
-  Vector sum_z_silence(n_y_, fill::zeros);
-  size_t n_silence(0);
-  for (size_t trial = 0; trial < u_.size(); trial++) {
-    // find silent samples
-    // start by convolving with
-    Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0));
-    Vector u_conv = conv(sum_u, win, "same");
-
-    // get only the samples that are silent...
-    arma::uvec ubi_silence = find(u_conv <= thresh_silence);
-    if (ubi_silence.n_elem > 0) {
-      sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1);
-      n_silence += ubi_silence.n_elem;
-    }
-  }
-  if (n_silence > 0) {
-    d = sum_z_silence / n_silence;
-  }
-  fit_.set_d(d);
-}
-
-template <typename Fit>
-void SSID<Fit>::CreateHankelDataMat() {
-  // temporary copy of data
-  Matrix z(n_y_, n_t_tot_, fill::zeros);
-  Matrix u(n_u_, n_t_tot_, fill::zeros);
-  size_t so_far(0);
-
-  for (size_t trial = 0; trial < z_.size(); trial++) {
-    z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial);
-    u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial);
-    so_far += n_t_.at(trial);
-  }
-
-  // remove output bias
-  z.each_col() -= fit_.d();
-
-  // calculate I/O gain @ DC while data in convenient form
-  g_dc_ = z * pinv(u);
-  // std::cout << "G0_data = " << g_dc_ << "\n";
-
-  // create hankel data matrix
-  size_t len = z.n_cols - 2 * n_h_ + 1;  // data length in hankel mat
-
-  // block-hankel data matrix
-  D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros);
-  // past input
-  auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1);
-  // future input
-  auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1);
-  // past output
-  auto y_p =
-      D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1);
-  // future output
-  auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0,
-                       2 * n_h_ * (n_u_ + n_y_) - 1, len - 1);
-
-  size_t idx = 0;
-  for (size_t k = 0; k < len; k++) {
-    idx = 0;
-    for (size_t kk = k; kk < (n_h_ + k); kk++) {
-      u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk);
-      idx += n_u_;
-    }
-
-    idx = 0;
-    for (size_t kk = (n_h_ + k); kk < (2 * n_h_ + k); kk++) {
-      u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk);
-      idx += n_u_;
-    }
-
-    idx = 0;
-    for (size_t kk = k; kk < (n_h_ + k); kk++) {
-      y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk);
-      idx += n_y_;
-    }
-
-    idx = 0;
-    for (size_t kk = (n_h_ + k); kk < (2 * n_h_ + k); kk++) {
-      y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk);
-      idx += n_y_;
-    }
-  }
-
-  D_ /= sqrt(static_cast<data_t>(len));
-}
-
-// template <typename Fit>
-// void SSID<Fit>::DecomposeData() {
-//   // do LQ decomp instead of calculating covariance expensive way
-//   // Note that "R" in van Overschee is lower-triangular (L), not "R" in QR
-//   // decomp. Very confusing.
-//   Matrix q_t;
-//   lq(L_, q_t, D_);
-//   // van Overschee zeros out the other elements.
-//   L_ = trimatl(L_);
-// }
-
-template <typename Fit>
-void SSID<Fit>::CalcSVD(SSIDWt wt) {
-  // submats that will be needed:
-  auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1,
-                           n_h_ * (2 * n_u_ + n_y_) - 1);
-  auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1);
-  auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1);
-  auto R_23_13 =
-      L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1);
-  auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1,
-                           n_h_ * (2 * n_u_ + n_y_) - 1);
-  auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1,
-                           n_h_ * (2 * n_u_) - 1);
-  auto R_44 =
-      L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1,
-                n_h_ * (2 * n_u_ + n_y_) - 1);
-  auto R_56_14 =
-      L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1,
-                n_h_ * (2 * n_u_ + n_y_) - 1);
-
-  Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14);
-  auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1);
-  auto Luf =
-      Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1);
-  auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1,
-                                n_h_ * (2 * n_u_ + n_y_) - 1);
-
-  // aka: R_f
-  Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0,
-                             2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1);
-  // from van Overschee subid.m:
-  // Rf = R((2*m+l)*i+1:2*(m+l)*i,:);   % Future outputs
-
-  Matrix U;
-  Matrix V;
-  switch (wt) {
-    case kSSIDNone: {
-      // No weighting. (what van Overschee calls "N4SID")
-      Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14;
-      arma::svd(U, s_, V, O_k_sans_Qt, "std");
-    } break;
-    case kSSIDMOESP: {
-      // MOESP weighting
-      // This is what they use in the "robust" algorithm van Overschee, de Moor
-      // 1996
-      Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) -
-                  R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13;
-      Matrix O_k_ortho_Uf_sans_Qt =
-          join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44);
-      svd(U, s_, V, O_k_ortho_Uf_sans_Qt, "std");
-    } break;
-    case kSSIDCVA: {
-      // CVA weighting
-      // See van Overschee's matlab code (subid.m):
-      // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems
-      Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) -
-                  R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13;
-      Matrix O_k_ortho_Uf_sans_Qt =
-          join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44);
-
-      Matrix inv_w1;
-      Matrix qt1;
-      lq(inv_w1, qt1, R_56_16);  // lq decomp of R_f (future output data)
-      inv_w1 = trimatl(inv_w1);
-      inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1);
-      Matrix w_o_w = arma::solve(
-          inv_w1, O_k_ortho_Uf_sans_Qt);  // alternatively
-                                          // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt
-      svd(U, s_, V, w_o_w, "std");
-
-      U = inv_w1 * U;
-      break;
-    }
-  }
-
-  // Truncate to model order (heart of ssid method)
-  auto s_hat = s_.subvec(0, n_x_ - 1);
-  Matrix diag_sqrt_s = diagmat(sqrt(s_hat));
-  auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1);
-
-  // get extended observability and controllability mats
-  ext_obs_t_ = u_hat * diag_sqrt_s;  // extended observability matrix
-}
-
-template <typename Fit>
-void SSID<Fit>::Solve(data_t wt_dc) {
-  // required submats
-  auto R_56_14 =
-      L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1,
-                n_h_ * (2 * n_u_ + n_y_) - 1);
-  auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1,
-                           n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1);
-  auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0,
-                           2 * n_h_ * (n_u_ + n_y_) - 1,
-                           n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1);
-  auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0,
-                           n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1,
-                           n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1);
-  auto R_56_15 =
-      L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1,
-                n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1);
-
-  // Solve for params using appropriate algorithm:
-  // robust deterministic/stochastic algorithm in van Overschee 1996
-  // algorithm that the authors say "works" in practice.
-  auto ext_obs_tm1 = ext_obs_t_.submat(
-      0, 0, ext_obs_t_.n_rows - 1 - n_y_,
-      ext_obs_t_.n_cols - 1);  // extended observability matrix
-
-  // This is what textbook (1996) says:
-  //
-  // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15);
-  //
-  // HOWEVER, do not know why but have to fill the last place with zeros like
-  // authors' matlab implementation (see `subid.m`)
-  // Otherwise, get ridiculous covariances (although A,C estimates are close to
-  // same...)
-  Matrix Tr = join_vert(
-      join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)),
-      R_23_15);
-  Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15);
-  Matrix S = Tl * pinv(Tr);
-
-  // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure
-  // stability.
-  fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1));
-  Matrix ext_obs_t_p1 = join_vert(
-      ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1),
-      Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros));
-  fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1);
-
-  // At this point, van Overschee & de Moor suggest re-calculating ext_obs_t_,
-  // ext_obs_tm1 from (A, C) because it was just an approximation. This is
-  RecomputeExtObs();
-  ext_obs_tm1 = ext_obs_t_.submat(
-      0, 0, ext_obs_t_.n_rows - 1 - n_y_,
-      ext_obs_t_.n_cols - 1);  // extended observability matrix
-  Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15);
-  Tr = join_vert(
-      join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)),
-      R_23_15);
-  S = Tl * pinv(Tr);
-
-  Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_);
-  Matrix Mcurly = pinv(ext_obs_tm1);
-  Matrix Pcurly = Tl - Lcurly * R_56_15;
-  Vector Pvec = vectorise(Pcurly);
-  Matrix Qcurly = R_23_15;
-
-  // Identify [D; B], assuming D=0 and ensuring DC gain is correct
-  Matrix sum_QcurlyT_kron_Ncurly(
-      (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_),
-      fill::zeros);
-
-  Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols,
-                         fill::eye);
-  eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1,
-                         eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1;
-
-  // van Overschee (1996) p. 126
-  Matrix N1_Tl = -Lcurly;
-  N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) +=
-      join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly);
-  N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) +=
-      Matrix(n_y_, n_y_, fill::eye);
-
-  Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros);
-  Matrix N_k;
-  for (size_t k = 0; k < n_h_; k++) {
-    auto Qcurly_k =
-        Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1);
-
-    Nk_Tl.zeros();
-    Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) =
-        N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1);
-    N_k = Nk_Tl * eye_ext_obs_tm1;
-
-    sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k);
-  }
-
-  Matrix err_vec;
-  if (wt_dc > 0) {
-    // Constraints enforced by weighted least squares
-    //
-    // Reference:
-    //
-    // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly
-    // Excited Industrial Systems. Conference in Decision and Control.
-
-    // constraint 1: assume D=0 --> remove the components for Dvec (this is
-    // actually a hard constraint in that it ignores D)
-    Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly;
-    sum_QcurlyT_kron_Ncurly =
-        Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_);
-
-    size_t kkk = 0;
-    for (size_t k = 1; k < (n_u_ + 1); k++) {
-      size_t start_idx = k * (n_y_ + n_x_) - n_x_;
-      for (size_t kk = 0; kk < n_x_; kk++) {
-        sum_QcurlyT_kron_Ncurly.col(kkk) =
-            sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk);
-        kkk++;
-      }
-    }
-
-    // constraint 2: Make sure DC I/O gain is correct
-    Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A());
-    Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_));
-    Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0);
-    Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 =
-        join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0);
-
-    // WEIGHTED LS
-    // Important in practice because I care a lot about at least getting the DC
-    // gain correct. Put x weight on minimizing error at DC, relative to others
-    Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows,
-             sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye);
-    // Make weight on minimizing DC error immense so at least that
-    // should be nailed.
-    size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows;
-    size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows;
-    size_t stop_row = w.n_rows - 1;
-    size_t stop_col = w.n_cols - 1;
-    // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale
-    // weight with data length?
-    w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc;
-    Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w *
-                       sum_QcurlyT_kron_Ncurly_b_to_g0) *
-                   sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec;
-
-    fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_));
-
-    // Calculate residuals and their cov.
-    // Because I've added constraints, I need to re-calculate the right term
-    // with b_vec instead of how van Overschee do in final algorithm.
-    err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec;
-  } else {
-    // default way: *no* constraint on G0 or D=0
-    Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec;
-    // TODO(mfbolus) n.b., this gets thrown away...
-    // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_);
-    fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_));
-    err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec;
-  }
-  // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols);
-
-  // TODO(mfbolus): Something is wrong with the error calculation above.
-  // Use the way van overschee does it in `subid.m`
-  // WARNING: this ignores any above constraints, so Q, R will be approximate...
-  Matrix err = Tl - S * Tr;
-  Matrix cov_err = err * err.t();
-  fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1));
-  fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1));
-}
-
-template <typename Fit>
-void SSID<Fit>::RecomputeExtObs() {
-  ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C();
-  for (size_t k = 2; k < (n_h_ + 1); k++) {
-    ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) =
-        ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1,
-                          ext_obs_t_.n_cols - 1) *
-        fit_.A();
-  }
-}
-
-}  // namespace lds
-
-#endif
-

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lds 
Linear Dynamical Systems (LDS) namespace.
lds::gaussian 
Linear Dynamical Systems with Gaussian observations.

-

-  Detailed Description
-  #
-

-
This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)).

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_GAUSSIAN_H
-#define LDSCTRLEST_LDS_GAUSSIAN_H
-
-// namespace
-#include "lds.h"
-
-namespace lds {
-namespace gaussian {
-// insert any Gaussian-specific things here...
-}  // namespace gaussian
-}  // namespace lds
-
-#endif
-

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Linear Dynamical Systems (LDS) namespace.
lds::gaussian 
Linear Dynamical Systems with Gaussian observations.

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classlds::gaussian::Controller 
Gaussian-observation Controller Type.

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-  #
-

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This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H
-#define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H
-
-// namespace
-#include "lds_gaussian.h"
-// system
-#include "lds_gaussian_sys.h"
-// controller
-#include "lds_ctrl.h"
-
-namespace lds {
-namespace gaussian {
-class Controller : public lds::Controller<System> {
- public:
-  void set_y_ref(const Vector& y_ref) override {
-    Reassign(y_ref_,y_ref);
-    cx_ref_ = y_ref - sys_.d();
-  };
-
-  // make sure base class template methods available
-  using lds::Controller<System>::Controller;
-  using lds::Controller<System>::Control;
-  using lds::Controller<System>::ControlOutputReference;
-
-  using lds::Controller<System>::sys;
-  using lds::Controller<System>::Kc;
-  using lds::Controller<System>::Kc_inty;
-  using lds::Controller<System>::Kc_u;
-  using lds::Controller<System>::g_design;
-  using lds::Controller<System>::u_ref;
-  using lds::Controller<System>::x_ref;
-  using lds::Controller<System>::y_ref;
-  using lds::Controller<System>::control_type;
-
-  using lds::Controller<System>::set_sys;
-  using lds::Controller<System>::set_g_design;
-  using lds::Controller<System>::set_u_ref;
-  using lds::Controller<System>::set_x_ref;
-  using lds::Controller<System>::set_y_ref;
-  using lds::Controller<System>::set_Kc;
-  using lds::Controller<System>::set_Kc_inty;
-  using lds::Controller<System>::set_Kc_u;
-  using lds::Controller<System>::set_tau_awu;
-  using lds::Controller<System>::set_control_type;
-
-  using lds::Controller<System>::Reset;
-  using lds::Controller<System>::Print;
-};
-}  // namespace gaussian
-}  // namespace lds
-
-#endif
-

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Linear Dynamical Systems (LDS) namespace.
lds::gaussian 
Linear Dynamical Systems with Gaussian observations.

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classlds::gaussian::Fit 
GLDS Fit Type.

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-  Detailed Description
-  #
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This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H
-#define LDSCTRLEST_LDS_GAUSSIAN_FIT_H
-
-// namespace
-#include "lds_gaussian.h"
-// fit type
-#include "lds_fit.h"
-
-namespace lds {
-namespace gaussian {
-class Fit : public lds::Fit {
- public:
-  Fit() = default;
-
-  Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt);
-  const Matrix& R() const override { return R_; };
-  void set_R(const Matrix& R) override {
-    Reassign(R_, R);
-    ForceSymPD(R_);
-  };
-
-  View h(Matrix& y, const Matrix& x, size_t t) override {
-    y.col(t) = C_ * x.col(t) + d_;
-    return y.col(t);
-  };
-};
-
-};  // namespace gaussian
-}  // namespace lds
-#endif
-

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Linear Dynamical Systems (LDS) namespace.
lds::gaussian 
Linear Dynamical Systems with Gaussian observations.

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classlds::gaussian::FitEM 
GLDS E-M Fit Type.

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-  #
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-
This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).

-
References: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).

-
[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H
-#define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H
-
-#include "lds_fit_em.h"
-#include "lds_gaussian_fit.h"
-
-namespace lds {
-namespace gaussian {
-class FitEM : public EM<Fit> {
- public:
-  using EM<Fit>::EM;
-
- private:
-  void MaximizeOutput() override;
-
-  void MaximizeMeasurement() override;
-
-  void RecurseKe(Matrix& Ke, Cube& P_pre, Cube& P_post, size_t t) override;
-};
-
-}  // namespace gaussian
-}  // namespace lds
-
-#endif
-

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Linear Dynamical Systems (LDS) namespace.
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Linear Dynamical Systems with Gaussian observations.

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classlds::gaussian::FitSSID 
Subspace Identification (SSID) for GLDS.

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This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).

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-

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-  #
-

-
//===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H
-#define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H
-
-#include "lds_fit_ssid.h"
-#include "lds_gaussian_fit.h"
-
-namespace lds {
-namespace gaussian {
-class FitSSID : public SSID<Fit> {
- public:
-  using SSID<Fit>::SSID;
-  using SSID<Fit>::Run;
-
- private:
-  using SSID<Fit>::CreateHankelDataMat;
-  using SSID<Fit>::CalcSVD;
-  using SSID<Fit>::Solve;
-
-  void DecomposeData() override;
-
-  void SolveVanOverschee();
-};
-}  // namespace gaussian
-}  // namespace lds
-#endif
-

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Linear Dynamical Systems (LDS) namespace.
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Linear Dynamical Systems with Gaussian observations.

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classlds::gaussian::SwitchedController 
Gaussian-observation SwitchedController Type.

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-  Detailed Description
-  #
-

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This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H
-#define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H
-
-// controller type
-#include "lds_gaussian_ctrl.h"
-// switched controller
-#include "lds_sctrl.h"
-
-namespace lds {
-namespace gaussian {
-class SwitchedController : public lds::SwitchedController<System> {
- public:
-  void set_y_ref(const Vector& y_ref) override {
-    Reassign(y_ref_, y_ref);
-    cx_ref_ = y_ref - sys_.d();
-  }
-
-  // make sure base class template methods available
-  using lds::SwitchedController<System>::SwitchedController;
-  using lds::SwitchedController<System>::Switch;
-  using lds::SwitchedController<System>::Control;
-  using lds::SwitchedController<System>::ControlOutputReference;
-
-  using lds::SwitchedController<System>::sys;
-  using lds::SwitchedController<System>::Kc;
-  using lds::SwitchedController<System>::Kc_inty;
-  using lds::SwitchedController<System>::Kc_u;
-  using lds::SwitchedController<System>::g_design;
-  using lds::SwitchedController<System>::u_ref;
-  using lds::SwitchedController<System>::x_ref;
-  using lds::SwitchedController<System>::y_ref;
-  using lds::SwitchedController<System>::control_type;
-
-  using lds::SwitchedController<System>::set_g_design;
-  using lds::SwitchedController<System>::set_u_ref;
-  using lds::SwitchedController<System>::set_x_ref;
-  using lds::SwitchedController<System>::set_y_ref;
-  using lds::SwitchedController<System>::set_Kc;
-  using lds::SwitchedController<System>::set_Kc_inty;
-  using lds::SwitchedController<System>::set_Kc_u;
-  using lds::SwitchedController<System>::set_tau_awu;
-  using lds::SwitchedController<System>::set_control_type;
-
-  using lds::SwitchedController<System>::Reset;
-  using lds::SwitchedController<System>::Print;
-};  // SwitchedController
-}  // namespace gaussian
-}  // namespace lds
-
-#endif
-

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This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).

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//===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst_h/lds_gaussian_sys.h>
-
-lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0,
-                   data_t q0, data_t r0)
-    : lds::System(n_u, n_x, n_y, dt, p0, q0) {
-
-  R_.zeros(n_y, n_y);
-  R_.diag().fill(r0);
-
-  do_recurse_Ke_=true;
-};
-
-// recursively estimate Ke
-void lds::gaussian::System::RecurseKe() {
-  if (!do_recurse_Ke_) {
-    return;
-  }
-
-  // predict covariance
-  P_ = A_ * P_ * A_.t() + Q_;
-
-  // calc Kalman gain
-  Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_);
-
-  // update covariance
-  // Reference: Ghahramani et Hinton (1996)
-  P_ = P_ - Ke_ * C_ * P_;
-
-  if (do_adapt_m) {
-    P_m_ += Q_m_;  // A_m = I (i.e., random walk)
-    Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_);
-    P_m_ = P_m_ - Ke_m_ * C_ * P_m_;
-  }
-}
-
-// Simulate
-const lds::Vector& lds::gaussian::System::Simulate(const Vector& u_tm1){
-  f(u_tm1, true);//simulate dynamics with noise added
-  h();//output
-  z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure
-  return z_;
-}
-
-void lds::gaussian::System::Print() {
-  lds::System::Print();
-  std::cout << "R: \n" << R_ << "\n";
-}
-

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lds 
Linear Dynamical Systems (LDS) namespace.
lds::gaussian 
Linear Dynamical Systems with Gaussian observations.

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classlds::gaussian::System 
Gaussian LDS Type.

-

-  Detailed Description
-  #
-

-
This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)).

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H
-#define LDSCTRLEST_LDS_GAUSSIAN_SYS_H
-
-// namespace
-#include "lds_gaussian.h"
-// system
-#include "lds_sys.h"
-
-namespace lds {
-namespace gaussian {
-class System : public lds::System {
- public:
-  System() = default;
-
-  System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt,
-         data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0,
-         data_t r0 = kDefaultR0);
-
-  const Vector& Simulate(const Vector& u_tm1) override;
-
-  // get methods
-  const Matrix& R() const { return R_; };
-
-  // set methods
-  void set_Q(const Matrix& Q) {
-    lds::System::set_Q(Q);
-    do_recurse_Ke_ = true;
-  }
-  void set_R(const Matrix& R) {
-    Reassign(R_,R);
-    do_recurse_Ke_ = true;
-  };
-
-  void set_Ke(const Matrix& Ke) {
-    Reassign(Ke_,Ke);
-    // if users have set Ke, they must not want to calculate it online.
-    do_recurse_Ke_ = false;
-  };
-  void set_Ke_m(const Matrix& Ke_m) {
-    Reassign(Ke_m_,Ke_m);
-    // if users have set Ke, they must not want to calculate it online.
-    do_recurse_Ke_ = false;
-  };
-
-  void Print();
-
- protected:
-  void h() override {
-    cx_ = C_ * x_;
-    y_ = cx_ + d_;
-  };
-
-  void RecurseKe() override;
-
-  // Gaussian-output-specific
-  Matrix R_;           
-  bool do_recurse_Ke_{};  
-};                      // System
-}  // namespace gaussian
-}  // namespace lds
-
-#endif
-

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Name
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Linear Dynamical Systems (LDS) namespace.
lds::poisson 
Linear Dynamical Systems with Poisson observations.

-

-  Detailed Description
-  #
-

-
This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)).

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_POISSON_H
-#define LDSCTRLEST_LDS_POISSON_H
-
-#include "lds.h"
-
-namespace lds {
-namespace poisson {
-// TODO(mfbolus): Not sure if defining these as static here makes the most
-// sense. Is there a downside to letting multiple poisson System objects share a
-// common random number generator?
-static std::random_device rd;  
-static std::mt19937 rng = std::mt19937(
-    rd());  
-}  // namespace poisson
-}  // namespace lds
-
-#endif
-

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PLDS controller type.  More…

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lds 
Linear Dynamical Systems (LDS) namespace.
lds::poisson 
Linear Dynamical Systems with Poisson observations.

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classlds::poisson::Controller 
PLDS Controller Type.

-

-  Detailed Description
-  #
-

-
This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/)), including state estimation.

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_POISSON_CTRL_H
-#define LDSCTRLEST_LDS_POISSON_CTRL_H
-
-// namespace
-#include "lds_poisson.h"
-// system type
-#include "lds_poisson_sys.h"
-// control type
-#include "lds_ctrl.h"
-
-namespace lds {
-namespace poisson {
-class Controller : public lds::Controller<System> {
- public:
-  void set_y_ref(const Vector& y_ref) override {
-    Reassign(y_ref_, y_ref);
-    lds::Limit(y_ref_, kYRefLb, lds::kInf);
-    cx_ref_ = log(y_ref_) - sys_.d();
-  };
-
-  // make sure base class template methods available
-  using lds::Controller<System>::Controller;
-  using lds::Controller<System>::Control;
-  using lds::Controller<System>::ControlOutputReference;
-
-  using lds::Controller<System>::sys;
-  using lds::Controller<System>::Kc;
-  using lds::Controller<System>::Kc_inty;
-  using lds::Controller<System>::Kc_u;
-  using lds::Controller<System>::g_design;
-  using lds::Controller<System>::u_ref;
-  using lds::Controller<System>::x_ref;
-  using lds::Controller<System>::y_ref;
-  using lds::Controller<System>::control_type;
-
-  using lds::Controller<System>::set_sys;
-  using lds::Controller<System>::set_g_design;
-  using lds::Controller<System>::set_u_ref;
-  using lds::Controller<System>::set_x_ref;
-  using lds::Controller<System>::set_y_ref;
-  using lds::Controller<System>::set_Kc;
-  using lds::Controller<System>::set_Kc_inty;
-  using lds::Controller<System>::set_Kc_u;
-  using lds::Controller<System>::set_tau_awu;
-  using lds::Controller<System>::set_control_type;
-
-  using lds::Controller<System>::Reset;
-  using lds::Controller<System>::Print;
-
- private:
-  constexpr static const data_t kYRefLb =
-      1e-4;  
-};
-}  // namespace poisson
-}  // namespace lds
-
-#endif
-

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Linear Dynamical Systems (LDS) namespace.
lds::poisson 
Linear Dynamical Systems with Poisson observations.

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classlds::poisson::Fit 
PLDS Fit Type.

-

-  Detailed Description
-  #
-

-
This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_POISSON_FIT_H
-#define LDSCTRLEST_LDS_POISSON_FIT_H
-
-// namespace
-#include "lds_poisson.h"
-// fit
-#include "lds_fit.h"
-
-namespace lds {
-namespace poisson {
-class Fit : public lds::Fit {
- public:
-  Fit() = default;
-
-  Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)
-      : lds::Fit(n_u, n_x, n_y, dt){};
-
-  View h(Matrix& y, const Matrix& x, size_t t) override {
-    y.col(t) = exp(C_ * x.col(t) + d_);
-    return y.col(t);
-  };
-
-  void set_R(const Matrix& R) override {
-    std::cerr
-        << "WARNING: Cannot set R (R[0] = " << R.at(0)
-        << "). No Gaussian measurement noise in Poisson observation model.\n";
-  };
-
-  const Matrix& R() const override {
-    return R_;
-  };
-
-};
-
-};  // namespace poisson
-}  // namespace lds
-
-#endif
-

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lds 
Linear Dynamical Systems (LDS) namespace.
lds::poisson 
Linear Dynamical Systems with Poisson observations.

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classlds::poisson::FitEM 
PLDS E-M Fit Type.

-

-  Detailed Description
-  #
-

-
This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).

-
References: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).

-
[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.

-
[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H
-#define LDSCTRLEST_LDS_POISSON_FIT_EM_H
-
-#include "lds_fit_em.h"
-#include "lds_poisson_fit.h"
-
-namespace lds {
-namespace poisson {
-class FitEM : public EM<Fit> {
- public:
-  using EM<Fit>::EM;
-
- private:
-  void MaximizeOutput() override;
-
-  void MaximizeMeasurement() override{};
-
-  void RecurseKe(Matrix& Ke, Cube& P_pre, Cube& P_post, size_t t) override;
-
-  data_t NewtonSolveC();
-
-  void AnalyticalSolveD();
-};
-
-}  // namespace poisson
-}  // namespace lds
-
-#endif
-

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Linear Dynamical Systems (LDS) namespace.
lds::poisson 
Linear Dynamical Systems with Poisson observations.

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classlds::poisson::FitSSID 
Subspace Identification (SSID) for PLDS.

-

-  Detailed Description
-  #
-

-
This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitssid/)).

-
References: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H
-#define LDSCTRLEST_LDS_POISSON_FIT_SSID_H
-
-#include "lds_fit_ssid.h"
-#include "lds_poisson_fit.h"
-
-namespace lds {
-namespace poisson {
-class FitSSID : public SSID<Fit> {
- public:
-  using SSID<Fit>::SSID;
-
- private:
-  void DecomposeData() override;
-
-  void CalcCov();
-
-  void PoissonToGaussianMoments();
-  Matrix cov_;
-};
-
-}  // namespace poisson
-}  // namespace lds
-#endif
-

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lds 
Linear Dynamical Systems (LDS) namespace.
lds::poisson 
Linear Dynamical Systems with Poisson observations.

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classlds::poisson::SwitchedController 
Poisson-observation SwitchedController Type.

-

-  Detailed Description
-  #
-

-
This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H
-#define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H
-
-#include "lds_poisson_ctrl.h"
-#include "lds_sctrl.h"
-
-namespace lds {
-namespace poisson {
-class SwitchedController : public lds::SwitchedController<System> {
- public:
-  void set_y_ref(const Vector& y_ref) override {
-    Reassign(y_ref_,y_ref);
-    lds::Limit(y_ref_, kYRefLB, lds::kInf);
-    cx_ref_ = log(y_ref_) - sys_.d();
-  };
-
-  // make sure base class template methods available
-  using lds::SwitchedController<System>::SwitchedController;
-  using lds::SwitchedController<System>::Switch;
-  using lds::SwitchedController<System>::Control;
-  using lds::SwitchedController<System>::ControlOutputReference;
-
-  using lds::SwitchedController<System>::sys;
-  using lds::SwitchedController<System>::Kc;
-  using lds::SwitchedController<System>::Kc_inty;
-  using lds::SwitchedController<System>::Kc_u;
-  using lds::SwitchedController<System>::g_design;
-  using lds::SwitchedController<System>::u_ref;
-  using lds::SwitchedController<System>::x_ref;
-  using lds::SwitchedController<System>::y_ref;
-  using lds::SwitchedController<System>::control_type;
-
-  using lds::SwitchedController<System>::set_g_design;
-  using lds::SwitchedController<System>::set_u_ref;
-  using lds::SwitchedController<System>::set_x_ref;
-  using lds::SwitchedController<System>::set_y_ref;
-  using lds::SwitchedController<System>::set_Kc;
-  using lds::SwitchedController<System>::set_Kc_inty;
-  using lds::SwitchedController<System>::set_Kc_u;
-  using lds::SwitchedController<System>::set_tau_awu;
-  using lds::SwitchedController<System>::set_control_type;
-
-  using lds::SwitchedController<System>::Reset;
-  using lds::SwitchedController<System>::Print;
-
- private:
-  constexpr static data_t kYRefLB =
-      1e-4;  
-};
-}  // namespace poisson
-}  // namespace lds
-
-#endif
-

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-  Detailed Description
-  #
-

-
This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).

-

-  Source code
-  #
-

-
//===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst_h/lds_poisson_sys.h>
-
-lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt,
-                             data_t p0, data_t q0)
-    : lds::System(n_u, n_x, n_y, dt, p0, q0) {
-  diag_y_ = diagmat(y_);
-  pd_ = std::poisson_distribution<size_t>(0);
-};
-
-// Correct: Given measurement (z) and current input (u), update estimate of the
-// state, covar, output.
-//
-// see Eden et al. 2004
-void lds::poisson::System::RecurseKe() {
-  // predict covariance
-  P_ = A_ * P_ * A_.t() + Q_;
-
-  // update cov
-  P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_);
-  Ke_ = P_ * C_.t();
-  if (do_adapt_m) {
-    P_m_ += Q_m_;                                     // predict (A_m = I)
-    P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_);  // update
-    Ke_m_ = P_m_ * C_.t();
-  }
-}
-
-// Simulate Measurement: z ~ Poisson(y)
-const lds::Vector& lds::poisson::System::Simulate(const Vector& u_tm1) {
-  f(u_tm1, true);  // simulate dynamics with noise added
-  h();             // output
-
-  z_.zeros();
-  for (std::size_t k = 0; k < n_y_; k++) {
-    // construct a Poisson distribution object with mean y[k]
-    pd_ = std::poisson_distribution<size_t>(y_[k]);
-    // pull random sample from this distribution
-    z_[k] = pd_(rng);
-  }
-
-  return z_;
-}
-// ******************* SYS_T *******************
-

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Name
lds 
Linear Dynamical Systems (LDS) namespace.
lds::poisson 
Linear Dynamical Systems with Poisson observations.

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-  Classes
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-

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Name
classlds::poisson::System 
Poisson System type.

-

-  Detailed Description
-  #
-

-
This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)).

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_POISSON_SYS_H
-#define LDSCTRLEST_LDS_POISSON_SYS_H
-
-// namespace
-#include "lds_poisson.h"
-// system
-#include "lds_sys.h"
-
-// needed for Poisson random number generation
-#include <random>
-
-namespace lds {
-namespace poisson {
-
-class System : public lds::System {
- public:
-  System() = default;
-
-  System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt,
-         data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0);
-
-  const Vector& Simulate(const Vector& u_tm1) override;
-
- protected:
-  void h() override {
-    cx_ = C_ * x_;
-    y_ = exp(cx_ + d_);
-    diag_y_.diag() = y_;
-  };
-
-  void RecurseKe() override;
-
- private:
-  // Poisson-output-specific
-  Matrix diag_y_;                 
-  std::poisson_distribution<size_t>
-      pd_;  
-};          // System
-}  // namespace poisson
-}  // namespace lds
-
-#endif
-

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-  ldsCtrlEst_h/lds_sctrl.h
-  #
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SwitchedController type.  More…

-

-  Namespaces
-  #
-

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Name
lds 
Linear Dynamical Systems (LDS) namespace.

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-  Classes
-  #
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classlds::SwitchedController 
SwitchedController Type.

-

-  Detailed Description
-  #
-

-
This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_SCTRL_H
-#define LDSCTRLEST_LDS_SCTRL_H
-
-#include "lds_ctrl.h"
-#include "lds_uniform_mats.h"
-#include "lds_uniform_vecs.h"
-
-namespace lds {
-template <typename System>
-class SwitchedController : public Controller<System> {
- public:
-  SwitchedController() = default;
-
-  SwitchedController(const std::vector<System>& systems, data_t u_lb,
-                     data_t u_ub, size_t control_type = 0);
-
-  SwitchedController(std::vector<System>&& systems, data_t u_lb, data_t u_ub,
-                     size_t control_type = 0);
-
-  void Switch(size_t idx, bool do_force_switch = false);
-
-  void set_Kc(const UniformMatrixList<>& Kc) {
-    Kc_list_ = Kc;
-    Kc_ = Kc_list_.at(0);  // set to first
-    if (idx_ != 0) {
-      Switch(idx_, true);
-    }
-  };
-  void set_Kc(UniformMatrixList<>&& Kc) {
-    Kc_list_ = std::move(Kc);
-    Kc_ = Kc_list_.at(0);  // set to first
-    if (idx_ != 0) {
-      Switch(idx_, true);
-    }
-  };
-
-  void set_Kc_inty(const UniformMatrixList<>& Kc_inty) {
-    Kc_inty_list_ = Kc_inty;
-    Kc_inty_ = Kc_inty_list_.at(0);  // set to first
-    if (idx_ != 0) {
-      Switch(idx_, true);
-    }
-  };
-  void set_Kc_inty(UniformMatrixList<>&& Kc_inty) {
-    Kc_inty_list_ = std::move(Kc_inty);
-    Kc_inty_ = Kc_inty_list_.at(0);  // set to first
-    if (idx_ != 0) {
-      Switch(idx_, true);
-    }
-  };
-
-  void set_Kc_u(const UniformMatrixList<>& Kc_u) {
-    Kc_u_list_ = Kc_u;
-    Kc_u_ = Kc_u_list_.at(0);  // set to first
-    if (idx_ != 0) {
-      Switch(idx_, true);
-    }
-  };
-  void set_Kc_u(UniformMatrixList<>&& Kc_u) {
-    Kc_u_list_ = std::move(Kc_u);
-    Kc_u_ = Kc_u_list_.at(0);  // set to first
-    if (idx_ != 0) {
-      Switch(idx_, true);
-    }
-  };
-
-  void set_g_design(const UniformVectorList& g) {
-    g_design_list_ = g;
-    g_design_ = g_design_list_.at(0);  // set to first
-    if (idx_ != 0) {
-      Switch(idx_, true);
-    }
-  };
-  void set_g_design(UniformVectorList&& g) {
-    g_design_list_ = std::move(g);
-    g_design_ = g_design_list_.at(0);  // set to first
-    if (idx_ != 0) {
-      Switch(idx_, true);
-    }
-  };
-
-  // make sure base class template methods available
-  using lds::Controller<System>::Controller;
-  using lds::Controller<System>::Control;
-  using lds::Controller<System>::ControlOutputReference;
-
-  using lds::Controller<System>::sys;
-  using lds::Controller<System>::Kc;
-  using lds::Controller<System>::Kc_inty;
-  using lds::Controller<System>::Kc_u;
-  using lds::Controller<System>::g_design;
-  using lds::Controller<System>::u_ref;
-  using lds::Controller<System>::x_ref;
-  using lds::Controller<System>::y_ref;
-  using lds::Controller<System>::control_type;
-
-  using lds::Controller<System>::set_u_ref;
-  using lds::Controller<System>::set_x_ref;
-  using lds::Controller<System>::set_y_ref;
-  using lds::Controller<System>::set_tau_awu;
-  using lds::Controller<System>::set_control_type;
-
-  using lds::Controller<System>::Reset;
-  using lds::Controller<System>::Print;
-
- protected:
-  std::vector<System>
-      systems_;     
-  size_t n_sys_{};  
-  size_t idx_{};    
-
-  // controller gains could be different for each
-  UniformMatrixList<> Kc_list_;
-  UniformMatrixList<> Kc_inty_list_;
-  UniformMatrixList<> Kc_u_list_;
-
-  // design-phase input gain could also be different
-  UniformVectorList g_design_list_;
-
-  // TODO(mfbolus): not sure why I need to do this.
-  using Controller<System>::Kc_;
-  using Controller<System>::Kc_inty_;
-  using Controller<System>::Kc_u_;
-  using Controller<System>::g_design_;
-  using Controller<System>::sys_;
-  // using Controller<System>::u_ref_;
-  // using Controller<System>::x_ref_;
-  // using Controller<System>::y_ref_;
-  //
-  using Controller<System>::control_type_;
-
- private:
-  void InitVars();
-
-  using lds::Controller<System>::set_sys;
-  // using Controller<System>::set_Kc;
-  // using Controller<System>::set_Kc_inty;
-  // using Controller<System>::set_Kc_u;
-  // using Controller<System>::set_g_design;
-};
-
-template <typename System>
-inline SwitchedController<System>::SwitchedController(
-    const std::vector<System>& systems, data_t u_lb, data_t u_ub,
-    size_t control_type)
-    : Controller<System>(systems.at(0), u_lb, u_ub, control_type),
-      systems_(systems) {
-  InitVars();
-}
-
-template <typename System>
-inline SwitchedController<System>::SwitchedController(
-    std::vector<System>&& systems, data_t u_lb, data_t u_ub,
-    size_t control_type)
-    : Controller<System>(System(systems.at(0).n_u(), systems.at(0).n_x(),
-                                systems.at(0).n_y(), systems.at(0).dt()),
-                         u_lb, u_ub, control_type),
-      systems_(std::move(systems)) {
-  InitVars();
-}
-
-template <typename System>
-inline void SwitchedController<System>::InitVars() {
-  n_sys_ = systems_.size();
-  sys_ = systems_.at(0);
-
-  Kc_list_ = UniformMatrixList<>(std::vector<Matrix>(n_sys_, Kc_));
-  Kc_inty_list_ = UniformMatrixList<>(std::vector<Matrix>(n_sys_, Kc_inty_));
-  Kc_u_list_ = UniformMatrixList<>(std::vector<Matrix>(n_sys_, Kc_inty_));
-  g_design_list_ = UniformVectorList(std::vector<Vector>(n_sys_, g_design_));
-}
-
-template <typename System>
-inline void SwitchedController<System>::Switch(size_t idx,
-                                               bool do_force_switch) {
-  if ((idx == idx_) && !do_force_switch) {
-    return;  // already there.
-  }
-
-  // put old up and get new one out
-  systems_.at(idx_) = std::move(sys_);
-  sys_ = std::move(systems_.at(idx));
-
-  // set the state of this system to that of the previous system
-  // TODO(mfbolus): This will only work as intended if state matrix is the same.
-  // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp.
-  sys_.set_m(systems_.at(idx_).m(), true);
-  sys_.set_x(systems_.at(idx_).x());
-
-  // swap controller gains
-  Kc_list_.Swap(Kc_, idx_);
-  Kc_list_.Swap(Kc_, idx);
-
-  if (control_type_ & kControlTypeIntY) {
-    Kc_inty_list_.Swap(Kc_inty_, idx_);
-    Kc_inty_list_.Swap(Kc_inty_, idx);
-  }
-
-  if (control_type_ & kControlTypeDeltaU) {
-    Kc_u_list_.Swap(Kc_u_, idx_);
-    Kc_u_list_.Swap(Kc_u_, idx);
-  }
-
-  g_design_list_.Swap(g_design_, idx_);
-  g_design_list_.Swap(g_design_, idx);
-
-  idx_ = idx;
-}  // Switch
-
-}  // namespace lds
-
-#endif
-

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Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time

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-  #
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-  Detailed Description
-  #
-

-
This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.

-

-  Source code
-  #
-

-
//===-- lds_sys.cpp - LDS -------------------------------------------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst_h/lds_sys.h>
-
-lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0,
-                    data_t q0)
-    : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) {
-  InitVars(p0, q0);
-}
-
-void lds::System::InitVars(data_t p0, data_t q0) {
-  // initial conditions.
-  x0_ = Vector(n_x_, fill::zeros);  // includes bias (nY) and g (nU)
-  P0_ = p0 * Matrix(n_x_, n_x_, fill::eye);
-
-  m0_ = x0_;
-  P0_m_ = P0_;
-
-  // signals
-  x_ = x0_;
-  P_ = P0_;
-  m_ = m0_;
-  P_m_ = P0_m_;
-  y_ = Vector(n_y_, fill::zeros);
-  cx_ = Vector(n_y_, fill::zeros);
-  z_ = Vector(n_y_, fill::zeros);
-
-  // By default, random walk where each state is independent
-  // In this way, provides independent estimates of rate per channel of output.
-  A_ = Matrix(n_x_, n_x_, fill::eye);
-  B_ = Matrix(n_x_, n_u_, fill::zeros);
-  g_ = Vector(n_u_, fill::ones);
-  Q_ = q0 * Matrix(n_x_, n_x_, fill::eye);
-  Q_m_ = Q_;
-
-  C_ = Matrix(n_y_, n_x_, fill::eye);  // each state will map to an output by
-  d_ = Vector(n_y_, fill::zeros);
-
-  Ke_ = Matrix(n_x_, n_y_, fill::zeros);    // estimator gain.
-  Ke_m_ = Matrix(n_x_, n_y_, fill::zeros);  // estimator gain for m adaptation.
-
-  do_adapt_m = false;
-}
-
-// Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state
-// and update estimate of the state, covar, output using Kalman filter
-void lds::System::Filter(const Vector& u_tm1, const Vector& z_t) {
-  // predict mean
-  f(u_tm1);  // dynamics
-
-  h();  // output
-
-  // recursively calculate esimator gains (or just keep existing values)
-  // (also predicts+updates estimate covariance)
-  RecurseKe();
-
-  // update
-  x_ += Ke_ * (z_t - y_);
-  if (do_adapt_m) {
-    m_ += Ke_m_ * (z_t - y_);  // adaptively estimating disturbance
-  }
-
-  // With new state, estimate output.
-  h();  // --> posterior
-}
-
-void lds::System::Reset() {
-  // reset to initial conditions
-  x_ = x0_;      // mean
-  P_ = P0_;      // cov of state estimate
-  m_ = m0_;      // process disturbance
-  P_m_ = P0_m_;  // cov of disturbance estimate
-  h();
-}
-
-void lds::System::Print() {
-  std::cout << "\n ********** SYSTEM ********** \n";
-  std::cout << "x: \n" << x_ << "\n";
-  std::cout << "P: \n" << P_ << "\n";
-  std::cout << "A: \n" << A_ << "\n";
-  std::cout << "B: \n" << B_ << "\n";
-  std::cout << "g: \n" << g_ << "\n";
-  std::cout << "m: \n" << m_ << "\n";
-  std::cout << "Q: \n" << Q_ << "\n";
-  std::cout << "Q_m: \n" << Q_m_ << "\n";
-  std::cout << "d: \n" << d_ << "\n";
-  std::cout << "C: \n" << C_ << "\n";
-  std::cout << "y: \n" << y_ << "\n";
-}
-
-//******************* SYS_T *******************
-

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lds 
Linear Dynamical Systems (LDS) namespace.

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classlds::System 
Linear Dynamical System Type.

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-  Detailed Description
-  #
-

-
This file declares and partially defines the base type for linear dynamical systems ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// Limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_SYS_H
-#define LDSCTRLEST_LDS_SYS_H
-
-#include "lds.h"
-
-namespace lds {
-class System {
- public:
-  System() = default;
-
-  System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0,
-         data_t q0 = kDefaultQ0);
-
-  virtual ~System() {}
-
-  void Filter(const Vector& u_tm1, const Vector& z);
-
-  virtual const Vector& Simulate(const Vector& u_tm1) = 0;
-
-  void f(const Vector& u, bool do_add_noise = false) {
-    x_ = A_ * x_ + B_ * (g_ % u) + m_;
-    if (do_add_noise) {
-      x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_);
-    }
-  };
-
-  virtual void h() = 0;
-
-  size_t n_u() const { return n_u_; };
-  size_t n_x() const { return n_x_; };
-  size_t n_y() const { return n_y_; };
-  data_t dt() const { return dt_; };
-
-  const Vector& x() const { return x_; };
-  const Matrix& P() const { return P_; };
-  const Vector& m() const { return m_; };
-  const Matrix& P_m() const { return P_m_; };
-  const Vector& cx() const { return cx_; };
-  const Vector& y() const { return y_; };
-
-  const Vector& x0() const { return x0_; };
-  const Vector& m0() const { return m0_; };
-
-  const Matrix& A() const { return A_; };
-  const Matrix& B() const { return B_; };
-  const Vector& g() const { return g_; };
-  const Matrix& C() const { return C_; };
-  const Vector& d() const { return d_; };
-  const Matrix& Ke() const { return Ke_; };
-  const Matrix& Ke_m() const { return Ke_m_; };
-  const Matrix& Q() { return Q_; };
-  const Matrix& Q_m() { return Q_m_; };
-  const Matrix& P0() { return P0_; };
-  const Matrix& P0_m() { return P0_m_; };
-
-  void set_A(const Matrix& A) { Reassign(A_, A); };
-  void set_B(const Matrix& B) { Reassign(B_, B); };
-  void set_m(const Vector& m, bool do_force_assign=false) {
-    Reassign(m0_, m);
-    if ((!do_adapt_m) || do_force_assign) {
-      Reassign(m_, m);
-    }
-  };
-  void set_g(const Vector& g) { Reassign(g_, g); };
-  void set_Q(const Matrix& Q) { Reassign(Q_, Q); };
-  void set_Q_m(const Matrix& Q_m) { Reassign(Q_m_, Q_m); };
-  void set_x0(const Vector& x0) { Reassign(x0_, x0); };
-  void set_P0(const Matrix& P0) { Reassign(P0_, P0); };
-  void set_P0_m(const Matrix& P0_m) { Reassign(P0_m_, P0_m); };
-  void set_C(const Matrix& C) { Reassign(C_, C); };
-  void set_d(const Vector& d) { Reassign(d_, d); };
-  void set_x(const Vector& x) {
-    Reassign(x_, x);
-    h();
-  };
-
-  void Reset();
-
-  void Print();
-
-  // safe to leave this public and non-const
-  bool do_adapt_m{};  
-
- protected:
-  virtual void RecurseKe() = 0;
-  void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0);
-
-  std::size_t n_x_{};  
-  std::size_t n_u_{};  
-  std::size_t n_y_{};  
-  data_t dt_{};        
-
-  // Signals:
-  Vector x_;    
-  Matrix P_;    
-  Vector m_;    
-  Matrix P_m_;  
-  Vector cx_;   
-  Vector y_;    
-  Vector z_;    
-
-  // Parameters:
-  Vector x0_;    
-  Matrix P0_;    
-  Vector m0_;    
-  Matrix P0_m_;  
-  Matrix A_;     
-  Matrix B_;     
-  Vector g_;     
-  Matrix Q_;     
-  Matrix Q_m_;   
-  Matrix C_;     
-  Vector d_;     
-
-  Matrix Ke_;    
-  Matrix Ke_m_;  
-};               // System
-
-}  // namespace lds
-
-#endif
-

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diff --git a/docs/docs/api/files/lds__uniform__mats_8h/index.html b/docs/docs/api/files/lds__uniform__mats_8h/index.html
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List of uniformly sized matrices.  More…

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-  Namespaces
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Linear Dynamical Systems (LDS) namespace.

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This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// Limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H
-#define LDSCTRLEST_LDS_UNIFORM_MATS_H
-
-#include <array>   // std::array
-#include <vector>  // std::vector
-
-#include "lds.h"
-
-namespace lds {
-template <MatrixListFreeDim D = kMatFreeDimNone>
-class UniformMatrixList : public std::vector<Matrix> {
- private:
-  // TODO(mfbolus): would rather *uncomment* the below for sake of conversion
-  // using std::vector<Matrix>::vector;
-  using std::vector<Matrix>::operator=;
-  using std::vector<Matrix>::operator[];
-  using std::vector<Matrix>::begin;
-  using std::vector<Matrix>::end;
-  using std::vector<Matrix>::size;
-
- public:
-  using std::vector<Matrix>::at;
-  UniformMatrixList() = default;
-
-  explicit UniformMatrixList(const std::vector<Matrix>& mats,
-                             std::array<size_t, 2> dim = {0, 0});
-
-  explicit UniformMatrixList(std::vector<Matrix>&& mats,
-                             std::array<size_t, 2> dim = {0, 0});
-
-  UniformMatrixList(std::initializer_list<Matrix> mats,
-                    std::array<size_t, 2> dim = {0, 0});
-
-  UniformMatrixList(const UniformMatrixList<D>& that);
-
-  UniformMatrixList(UniformMatrixList<D>&& that) noexcept;
-
-  ~UniformMatrixList() = default;
-
-  const std::array<size_t, 2>& dim(size_t n = 0) const { return dim_.at(n); }
-
-  size_t size() { return std::vector<Matrix>::size(); };
-
-  const Matrix& at(size_t n) { return std::vector<Matrix>::at(n); };
-
-  void Swap(Matrix& that, size_t n);
-
-  UniformMatrixList<D>& operator=(const UniformMatrixList<D>& that);
-  UniformMatrixList<D>& operator=(UniformMatrixList<D>&& that) noexcept;
-
- private:
-  void CheckDimensions(std::array<size_t, 2> dim);
-  std::vector<std::array<size_t, 2>> dim_;
-};
-
-template <MatrixListFreeDim D>
-inline void UniformMatrixList<D>::Swap(Matrix& that, size_t n) {
-  // make sure request in range
-  if (n >= this->size()) {
-    std::cerr
-        << "Requested UniformMatrixList element out of bounds. Skipping.\n";
-    return;
-  }
-  // check dim
-  bool does_match = true;
-  if (!(D == kMatFreeDim1)) {
-    does_match = does_match && (dim_[0][0] == that.n_rows);
-  }
-  if (!(D == kMatFreeDim2)) {
-    does_match = does_match && (dim_[0][1] == that.n_cols);
-  }
-  if (!does_match) {
-    std::cerr << "Cannot swap a UniformMatrixList element for an element of "
-                 "different size. Skipping.\n";
-    return;
-  }
-  // if checks pass, perform swap
-  Matrix tmp = std::move((*this)[n]);
-  (*this)[n] = std::move(that);
-  that = std::move(tmp);
-
-  if (D == kMatFreeDim1) {
-    this->dim_[n][0] = (*this)[n].n_rows;
-  }
-  if (D == kMatFreeDim2) {
-    this->dim_[n][1] = (*this)[n].n_cols;
-  }
-}
-
-template <MatrixListFreeDim D>
-inline UniformMatrixList<D>& UniformMatrixList<D>::operator=(
-    const UniformMatrixList<D>& that) {
-  // make sure dim_ vector is initialized
-  if (dim_.empty()) {
-    dim_ = std::vector<std::array<size_t, 2>>(that.size(), {0, 0});
-  }
-  // check dimensions
-  if (!this->empty()) {
-    if (this->size() != that.size()) {
-      std::ostringstream ss;
-      ss << "cannot reassign " << this->size() << " matrices with "
-         << that.size() << " matrices";
-      throw std::runtime_error(ss.str());
-    }
-
-    // if dimensions a not zero and do not match, skip move with error message.
-    bool dims_nonzero = true;
-    for (auto d : dim_) {
-      if (!(D == kMatFreeDim1) && d[0] < 1) {
-        dims_nonzero = false;
-        break;
-      }
-      if (!(D == kMatFreeDim2) && d[1] < 1) {
-        dims_nonzero = false;
-        break;
-      }
-    }
-    if (dims_nonzero) {
-      bool does_match = true;
-      if (!(D == kMatFreeDim1)) {
-        does_match = does_match && (dim_[0][0] == that.at(0).n_rows);
-      }
-      if (!(D == kMatFreeDim2)) {
-        does_match = does_match && (dim_[0][1] == that.at(0).n_cols);
-      }
-      if (!does_match) {
-        std::ostringstream ss;
-        ss << "cannot reassign matrices of size " << dim_[0][0] << "x"
-           << dim_[0][1] << " with matrices of size " << that.at(0).n_rows
-           << "x" << that.at(0).n_cols;
-        throw std::runtime_error(ss.str());
-      }
-    }
-  }
-
-  for (size_t k = 0; k < this->size(); k++) {
-    (*this)[k] = that[k];
-    dim_[k] = that.dim(k);
-  }
-
-  return (*this);
-}
-
-template <MatrixListFreeDim D>
-inline UniformMatrixList<D>& UniformMatrixList<D>::operator=(
-    UniformMatrixList<D>&& that) noexcept {
-  // // check dimensions
-  // // if empty, assume a default constructed object and safe to move
-  // if (!this->empty()) {
-  //   if (this->size() != that.size()) {
-  //     std::cerr << "Cannot reassign " << this->size() << " matrices with "
-  //               << that.size() << " matrices. Skipping.\n";
-  //     return (*this);
-  //   }
-  //
-  //   // if dimensions a not zero and do not match, skip move with error
-  //   message. bool dims_nonzero = true; for (auto d : dim_) {
-  //     if (!(D == kMatFreeDim1) && (d[0] < 1)) {
-  //       dims_nonzero = false;
-  //       break;
-  //     }
-  //     if (!(D == kMatFreeDim2) && (d[1] < 1)) {
-  //       dims_nonzero = false;
-  //       break;
-  //     }
-  //   }
-  //
-  //   if (dims_nonzero) {
-  //     bool does_match = true;
-  //     if (!(D == kMatFreeDim1)) {
-  //       does_match = does_match && (dim_[0][0] == that.at(0).n_rows);
-  //     }
-  //
-  //     if (!(D == kMatFreeDim2)) {
-  //       does_match = does_match && (dim_[0][1] == that.at(0).n_cols);
-  //     }
-  //
-  //     if (!does_match) {
-  //       this->at(0).print("this[0] = ");
-  //       that.at(0).print("that[0] = ");
-  //       std::cerr
-  //           << "Cannot move a UniformMatrixList element of size (" <<
-  //           that.at(0).n_rows << "," << that.at(0).n_cols << ") for an
-  //           element of size (" << dim_[0][0] << "," << dim_[0][1] << ").
-  //           Skipping.\n";
-  //       return (*this);
-  //     }
-  //   }
-  // }
-
-  dim_ = that.dim_;
-  std::vector<Matrix>::operator=(std::move(that));
-
-  return (*this);
-}
-
-template <MatrixListFreeDim D>
-UniformMatrixList<D>::UniformMatrixList(const std::vector<Matrix>& mats,
-                                        std::array<size_t, 2> dim)
-    : vector(mats) {
-  CheckDimensions(dim);
-}
-
-template <MatrixListFreeDim D>
-UniformMatrixList<D>::UniformMatrixList(std::vector<Matrix>&& mats,
-                                        std::array<size_t, 2> dim)
-    : vector(std::move(mats)) {
-  CheckDimensions(dim);
-};
-
-template <MatrixListFreeDim D>
-UniformMatrixList<D>::UniformMatrixList(std::initializer_list<Matrix> mats,
-                                        std::array<size_t, 2> dim)
-    : vector(mats) {
-  CheckDimensions(dim);
-};
-
-template <MatrixListFreeDim D>
-UniformMatrixList<D>::UniformMatrixList(const UniformMatrixList<D>& that)
-    : vector(that) {
-  (*this) = that;
-}
-
-template <MatrixListFreeDim D>
-UniformMatrixList<D>::UniformMatrixList(UniformMatrixList<D>&& that) noexcept
-    : vector(std::move(that)) {
-  for (size_t k = 0; k < this->size(); k++) {
-    std::array<size_t, 2> dim_k({this->at(k).n_rows, this->at(k).n_cols});
-    dim_.push_back(dim_k);
-  }
-}
-
-template <MatrixListFreeDim D>
-void UniformMatrixList<D>::CheckDimensions(std::array<size_t, 2> dim) {
-  // change behavior based on free dim D
-  if ((dim[0] == 0) && !(D == kMatFreeDim1)) {
-    dim[0] = this->at(0).n_rows;
-  }
-  if ((dim[1] == 0) && !(D == kMatFreeDim2)) {
-    dim[1] = this->at(0).n_cols;
-  }
-
-  // make sure dimensiolaties are all uniform
-  bool does_match(true);
-  for (const Matrix& mat : *this) {
-    if (!(D == kMatFreeDim1)) {
-      does_match = does_match && (mat.n_rows == dim[0]);
-    }
-    if (!(D == kMatFreeDim2)) {
-      does_match = does_match && (mat.n_cols == dim[1]);
-    }
-    if (!does_match) {
-      throw std::runtime_error(
-          "Dimensionality of one or more input matrices are not uniform.");
-    }
-  }
-
-  dim_ = std::vector<std::array<size_t, 2>>(this->size(), dim);
-  for (size_t k = 0; k < this->size(); k++) {
-    dim_[k][0] = (*this)[k].n_rows;
-    dim_[k][1] = (*this)[k].n_cols;
-  }
-}
-
-}  // namespace lds
-
-#endif
-

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-  ldsCtrlEst_h/lds_uniform_systems.h
-  #
-

-
List of uniformly sized Systems.  More…

-

-  Namespaces
-  #
-

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Name
lds 
Linear Dynamical Systems (LDS) namespace.

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Name
classlds::UniformSystemList

-

-  Detailed Description
-  #
-

-
This file provides a container for uniformly sized Systems.

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// Limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H
-#define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H
-
-#include <array>   // std::array
-#include <vector>  // std::vector
-
-// namespace
-#include "lds.h"
-// System type
-#include "lds_sys.h"
-
-namespace lds {
-template <typename System>
-class UniformSystemList : public std::vector<System> {
-  static_assert(std::is_base_of<lds::System, System>::value,
-                "System must be derived from lds::System type.");
-
- private:
-  // TODO(mfbolus): would rather *uncomment* the below for sake of conversion
-  // using std::vector<System>::vector;
-  using std::vector<System>::operator=;
-  using std::vector<System>::operator[];
-  using std::vector<System>::at;
-  using std::vector<System>::begin;
-  using std::vector<System>::end;
-  using std::vector<System>::size;
-
- public:
-  UniformSystemList() = default;
-
-  explicit UniformSystemList(const std::vector<System>& systems,
-                             std::array<size_t, 3> dim = {0, 0, 0});
-
-  explicit UniformSystemList(std::vector<System>&& systems,
-                             std::array<size_t, 3> dim = {0, 0, 0});
-
-  UniformSystemList(std::initializer_list<System> systems,
-                    std::array<size_t, 3> dim = {0, 0, 0});
-
-  UniformSystemList(const UniformSystemList& that);
-  UniformSystemList(UniformSystemList&& that) noexcept;
-
-  ~UniformSystemList() = default;
-
-  const std::array<size_t, 3>& dim() const { return dim_; }
-
-  size_t size() { return std::vector<System>::size(); };
-
-  const System& at(size_t n) { return std::vector<System>::at(n); };
-
-  void Swap(System& that, size_t n);
-
-  UniformSystemList& operator=(const UniformSystemList& that);
-  UniformSystemList& operator=(UniformSystemList&& that) noexcept;
-
- private:
-  void CheckDimensions(std::array<size_t, 3> dim);
-  std::array<size_t, 3> dim_{};
-};
-
-template <typename System>
-inline void UniformSystemList<System>::Swap(System& that, size_t n) {
-  // make sure request in range
-  if (n >= this->size()) {
-    std::cerr
-        << "Requested UniformSystemList element out of bounds. Skipping.\n";
-    return;
-  }
-  // check dim
-  bool does_match = (dim_[0] == that.n_u()) && (dim_[1] == that.n_x()) &&
-                    (dim_[2] == that.n_y());
-  if (!does_match) {
-    std::cerr << "Cannot swap a UniformSystemList element for an element of "
-                 "different size. Skipping.\n";
-    return;
-  }
-  // if checks pass, perform swap
-  System tmp = std::move((*this)[n]);
-  (*this)[n] = std::move(that);
-  that = std::move(tmp);
-}
-
-template <typename System>
-inline UniformSystemList<System>& UniformSystemList<System>::operator=(
-    const UniformSystemList& that) {
-  // check dimensions
-  if (!this->empty()) {
-    if (this->size() != that.size()) {
-      std::ostringstream ss;
-      ss << "cannot reassign " << this->size() << " systems with "
-         << that.size() << " systems";
-      throw std::runtime_error(ss.str());
-    }
-
-    if (dim_[0] + dim_[1] + dim_[2]) {
-      std::array<size_t, 3> other_dim(that.dim());
-      if (dim_ != other_dim) {
-        std::ostringstream ss;
-        ss << "cannot reassign systems of size " << dim_[0] << "x" << dim_[1]
-           << "x" << dim_[2] << " with systems of size " << other_dim[0] << "x"
-           << other_dim[1] << "x" << dim_[2];
-        throw std::runtime_error(ss.str());
-      }
-    }
-  }
-
-  for (size_t k = 0; k < this->size(); k++) {
-    (*this)[k] = that[k];
-  }
-  return (*this);
-}
-
-template <typename System>
-inline UniformSystemList<System>& UniformSystemList<System>::operator=(
-    UniformSystemList&& that) noexcept {
-  // // check dimensions
-  // // if empty, assume a default constructed object and safe to move
-  // if (!this->empty()) {
-  //   if (this->size() != that.size()) {
-  //     std::cerr << "Cannot reassign " << this->size() << " systems with "
-  //               << that.size() << " systems. Skipping.\n";
-  //     return (*this);
-  //   }
-  //
-  //   // if dimensions a not zero and do not match, skip move with error
-  //   message. if (dim_[0] + dim_[1] + dim_[2]) {
-  //     bool does_match = (dim_[0] == that.at(0).n_u()) &&
-  //                       (dim_[1] == that.at(0).n_x()) &&
-  //                       (dim_[2] == that.at(0).n_y());
-  //     if (!does_match) {
-  //       std::cerr
-  //           << "Cannot move a UniformSystemList element for an element of "
-  //              "different size. Skipping.\n";
-  //       return (*this);
-  //     }
-  //   }
-  // }
-
-  dim_ = that.dim_;
-  std::vector<System>::operator=(std::move(that));
-
-  return (*this);
-}
-
-template <typename System>
-UniformSystemList<System>::UniformSystemList(const std::vector<System>& systems,
-                                             std::array<size_t, 3> dim)
-    : std::vector<System>(systems) {
-  CheckDimensions(dim);
-}
-
-template <typename System>
-UniformSystemList<System>::UniformSystemList(std::vector<System>&& systems,
-                                             std::array<size_t, 3> dim)
-    : std::vector<System>(std::move(systems)) {
-  CheckDimensions(dim);
-};
-
-template <typename System>
-UniformSystemList<System>::UniformSystemList(
-    std::initializer_list<System> systems, std::array<size_t, 3> dim)
-    : std::vector<System>(systems) {
-  CheckDimensions(dim);
-};
-
-template <typename System>
-UniformSystemList<System>::UniformSystemList(const UniformSystemList& that)
-    : std::vector<System>(that) {
-  (*this) = that;
-}
-
-template <typename System>
-UniformSystemList<System>::UniformSystemList(UniformSystemList&& that) noexcept
-    : std::vector<System>(std::move(that)) {
-  this->dim_[0] = this->at(0).n_u();
-  this->dim_[1] = this->at(0).n_x();
-  this->dim_[2] = this->at(0).n_y();
-}
-
-template <typename System>
-void UniformSystemList<System>::CheckDimensions(std::array<size_t, 3> dim) {
-  if (dim[0] + dim[1] + dim[2]) {
-    dim_ = dim;
-  } else {
-    dim_[0] = this->at(0).n_u();
-    dim_[1] = this->at(0).n_x();
-    dim_[2] = this->at(0).n_y();
-  }
-
-  // make sure dimensiolaties are all uniform
-  bool does_match(true);
-  for (const System& sys : *this) {
-    does_match = does_match && (sys.n_u() == dim_[0]);
-    does_match = does_match && (sys.n_x() == dim_[1]);
-    does_match = does_match && (sys.n_y() == dim_[2]);
-    if (!does_match) {
-      throw std::runtime_error(
-          "Dimensionality of one or more input systems are not uniform.");
-    }
-  }
-}
-
-}  // namespace lds
-
-#endif
-

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Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time

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Uniformly sized vectors.  More…

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-  Namespaces
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Linear Dynamical Systems (LDS) namespace.

-

-  Detailed Description
-  #
-

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This file provides a container for uniformly sized vectors.

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// Limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst_h/lds_uniform_vecs.h>
-
-namespace lds {
-
-UniformVectorList::UniformVectorList(const std::vector<Vector>& vecs,
-                                     size_t dim)
-    : vector(vecs) {
-  CheckDimensions(dim);
-}
-
-UniformVectorList::UniformVectorList(std::vector<Vector>&& vecs, size_t dim)
-    : vector(std::move(vecs)) {
-  CheckDimensions(dim);
-};
-
-UniformVectorList::UniformVectorList(std::initializer_list<Vector> vecs,
-                                     size_t dim)
-    : vector(vecs) {
-  CheckDimensions(dim);
-};
-
-UniformVectorList::UniformVectorList(const UniformVectorList& that)
-    : vector(that) {
-  (*this) = that;
-}
-
-UniformVectorList::UniformVectorList(UniformVectorList&& that) noexcept
-    : vector(std::move(that)) {
-  this->dim_ = this->at(0).n_elem;
-}
-
-void UniformVectorList::CheckDimensions(size_t dim) {
-  if (dim) {
-    dim_ = dim;
-  } else {
-    dim_ = this->at(0).n_elem;
-  }
-
-  // make sure dimensiolaties are all uniform
-  bool does_match(true);
-  for (const Vector& vec : *this) {
-    does_match = does_match && (vec.n_elem == dim_);
-    if (!does_match) {
-      throw std::runtime_error(
-          "Dimensionality of one or more input matrices are not uniform.");
-    }
-  }
-}
-
-}  // namespace lds
-

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Linear Dynamical Systems (LDS) namespace.

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-

-
//===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// Limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H
-#define LDSCTRLEST_LDS_UNIFORM_VECS_H
-
-#include <array>   // std::array
-#include <vector>  // std::vector
-
-#include "lds.h"
-
-namespace lds {
-
-class UniformVectorList : public std::vector<Vector> {
- private:
-  // TODO(mfbolus): would rather *uncomment* the below for sake of conversion
-  // using std::vector<Vector>::vector;
-  using std::vector<Vector>::operator=;
-  using std::vector<Vector>::operator[];
-  using std::vector<Vector>::at;
-  using std::vector<Vector>::begin;
-  using std::vector<Vector>::end;
-  using std::vector<Vector>::size;
-
- public:
-  UniformVectorList() = default;
-
-  explicit UniformVectorList(const std::vector<Vector>& vecs, size_t dim = 0);
-
-  explicit UniformVectorList(std::vector<Vector>&& vecs, size_t dim = 0);
-
-  UniformVectorList(std::initializer_list<Vector> vecs, size_t dim = 0);
-
-  UniformVectorList(const UniformVectorList& that);
-  UniformVectorList(UniformVectorList&& that) noexcept;
-  ~UniformVectorList() = default;
-
-  size_t dim() const { return dim_; }
-
-  size_t size() { return std::vector<Vector>::size(); };
-
-  const Vector& at(size_t n) { return std::vector<Vector>::at(n); };
-
-  void Swap(Vector& that, size_t n);
-
-  UniformVectorList& operator=(const UniformVectorList& that);
-  UniformVectorList& operator=(UniformVectorList&& that) noexcept;
-
- private:
-  void CheckDimensions(size_t dim);
-  size_t dim_{};
-};
-
-inline void UniformVectorList::Swap(Vector& that, size_t n) {
-  // make sure request in range
-  if (n >= this->size()) {
-    std::cerr
-        << "Requested UniformMatrixList element out of bounds. Skipping.\n";
-    return;
-  }
-  // check dim
-  bool does_match = dim_ == that.n_elem;
-  if (!does_match) {
-    std::cerr << "Cannot swap a UniformMatrixList element for an element of "
-                 "different size. Skipping.\n";
-    return;
-  }
-  // if checks pass, perform swap
-  Vector tmp = std::move((*this)[n]);
-  (*this)[n] = std::move(that);
-  that = std::move(tmp);
-}
-
-inline UniformVectorList& UniformVectorList::operator=(
-    const UniformVectorList& that) {
-  // check dimensions
-  if (!this->empty()) {
-    if (this->size() != that.size()) {
-      std::ostringstream ss;
-      ss << "cannot reassign " << this->size() << " vectors with "
-         << that.size() << " vectors";
-      throw std::runtime_error(ss.str());
-    }
-
-    if (dim_) {
-      size_t other_dim(that.dim());
-      if (dim_ != other_dim) {
-        std::ostringstream ss;
-        ss << "cannot reassign vectors of size " << dim_
-           << " with vectors of size " << other_dim;
-        throw std::runtime_error(ss.str());
-      }
-    }
-  }
-
-  for (size_t k = 0; k < this->size(); k++) {
-    (*this)[k] = that[k];
-  }
-
-  return (*this);
-}
-
-inline UniformVectorList& UniformVectorList::operator=(
-    UniformVectorList&& that) noexcept {
-  // // check dimensions
-  // if (!this->empty()) {
-  //   if (this->size() != that.size()) {
-  //     std::cerr << "Cannot reassign " << this->size() << " vectors with "
-  //               << that.size() << " vectors. Skipping.\n";
-  //     return (*this);
-  //   }
-  //
-  //   if (dim_) {
-  //     size_t other_dim(that.dim());
-  //     if (dim_ != other_dim) {
-  //       std::cerr << "Cannot reassign vectors of size " << dim_
-  //                 << " with matrices of size " << other_dim << ".
-  //                 Skipping.\n";
-  //       return (*this);
-  //     }
-  //   }
-  // }
-
-  dim_ = that.dim_;
-  std::vector<Vector>::operator=(std::move(that));
-
-  return (*this);
-}
-
-}  // namespace lds
-
-#endif
-

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-  Namespaces
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Name
armamexc 
arma/mex interface using Matlab C API

-

-  Detailed Description
-  #
-

-
This file defines utility functions for interoperability between armadillo and Matlab/Octave’s C mex API.

-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_MEXC_UTIL_H
-#define LDSCTRLEST_MEXC_UTIL_H
-
-#include <ldsCtrlEst>
-
-#include "mex.h"
-
-// // If Matlab_FOUND, include matrix.h.
-// // (Octave does not need/have it.)
-// #ifdef Matlab_FOUND
-// #include "matrix.h"
-// #endif
-
-namespace armamexc {
-template <class T>
-inline auto m2T_scalar(const mxArray *matlab_scalar) -> T {
-  if (mxGetData(matlab_scalar)) {
-    return static_cast<T>(mxGetScalar(matlab_scalar));
-  }
-  mexErrMsgTxt("No data available.");
-  return 0;
-}
-
-template <class T>
-inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false,
-                    bool strict = true) -> arma::Mat<T> {
-  if (mxGetData(matlab_mat)) {
-    const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat);
-    if (n_dim == 2) {
-      return arma::Mat<T>(static_cast<T *>(mxGetData(matlab_mat)),
-                          mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem,
-                          strict);
-    }
-    mexErrMsgTxt("Number of dimensions must be 2.");
-    return arma::Mat<T>();
-  }
-  mexErrMsgTxt("No data available.");
-  return arma::Mat<T>();
-}
-
-// TODO(mfbolus): make these templated.
-
-template <typename T>
-inline auto a2m_mat(arma::Mat<T> const &arma_mat) -> mxArray * {
-  mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols,
-                                              mxDOUBLE_CLASS, mxREAL);
-  if (matlab_mat) {
-    auto *dst_pointer = static_cast<T *>(mxGetData(matlab_mat));
-    const auto *src_pointer = const_cast<T *>(arma_mat.memptr());
-    // TODO(mfbolus): I just want to MOVE the data, not copy.
-    std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem);
-    return matlab_mat;
-  }
-  mexErrMsgTxt("Failed to create matlab mat from arma::Mat.");
-  return nullptr;
-}
-
-template <typename T>
-inline auto a2m_vec(arma::Col<T> const &arma_vec) -> mxArray * {
-  mxArray *matlab_mat =
-      mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL);
-  if (matlab_mat) {
-    auto *dst_pointer = static_cast<T *>(mxGetData(matlab_mat));
-    const auto *src_pointer = const_cast<T *>(arma_vec.memptr());
-    // TODO(mfbolus): I just want to MOVE the data, not copy.
-    std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem);
-    return matlab_mat;
-  }
-  mexErrMsgTxt("Failed to create matlab mat from arma::Col.");
-  return nullptr;
-}
-
-}  // namespace armamexc
-
-#endif
-

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arma/mex interface using Matlab C++ API

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-  Detailed Description
-  #
-

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-

-  Source code
-  #
-

-
//===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_MEXCPP_UTIL_H
-#define LDSCTRLEST_MEXCPP_UTIL_H
-
-#include <ldsCtrlEst>
-
-#include "mex.hpp"
-#include "mexAdapter.hpp"
-
-namespace armamexcpp {
-template <class T>
-std::vector<arma::Mat<T>> m2a_cellmat(matlab::data::CellArray& matlab_cell) {
-  size_t n_cells = matlab_cell.getNumberOfElements();
-  std::vector<arma::Mat<T>> arma_mat(n_cells,
-                                     arma::Mat<T>(1, 1, arma::fill::zeros));
-  for (size_t k = 0; k < n_cells; k++) {
-    matlab::data::TypedArray<T> matlab_mat = matlab_cell[k];
-    auto dims = matlab_mat.getDimensions();
-    arma_mat[k] = arma::Mat<T>(matlab_mat.release().get(), dims[0], dims[1]);
-  }
-  return arma_mat;
-};
-
-template <class T>
-std::vector<T> m2s_vec(matlab::data::TypedArray<T>& matlab_array) {
-  size_t n_elem = matlab_array.getNumberOfElements();
-  T* ptr = matlab_array.release().get();
-  std::vector<T> vec(ptr, ptr + n_elem);
-  return vec;
-};
-
-template <class T>
-arma::Col<T> m2a_vec(matlab::data::TypedArray<T> matlab_array) {
-  size_t n_elem = matlab_array.getNumberOfElements();
-  // T* ptr = matlab_array.release().get();
-  // arma::Col<T> vec(ptr, n_elem);  //, false);
-  // TODO(mfbolus): for some reason, using the above pointer at times leads to
-  // getting garbage values. matlab array values may be stored in non-contiguous
-  // memory?
-  arma::Col<T> vec(n_elem, arma::fill::zeros);
-  for (size_t k = 0; k < n_elem; k++) {
-    vec[k] = matlab_array[k];
-  }
-  return vec;
-};
-
-template <class T>
-arma::Mat<T> m2a_mat(matlab::data::TypedArray<T> matlab_array) {
-  // ArrayDimensions == std::vector<size_t>
-  auto dims = matlab_array.getDimensions();
-  // T* ptr = matlab_array.release().get();
-  // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false)
-  // arma::Mat<T> mat(ptr, dims[0], dims[1]);  //, false);
-
-  // TODO(mfbolus): for some reason, using the above pointer at times leads to
-  // getting garbage values. matlab array values may be stored in non-contiguous
-  // memory?
-  //
-  // armadillo and matlab both use column-major ordering, so this should work:
-  size_t n_elem = dims[0] * dims[1];
-  arma::Mat<T> mat(dims[0], dims[1], arma::fill::zeros);
-  size_t k(0);
-  for (auto m: matlab_array) {
-    mat[k] = m;
-    k++;
-  }
-  return mat;
-};
-
-template <class T>
-matlab::data::TypedArray<T> a2m_mat(const arma::Mat<T>& arma_mat,
-                                    matlab::data::ArrayFactory& factory) {
-  const matlab::data::TypedArray<T> matlab_mat = factory.createArray<T>(
-      {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(),
-      arma_mat.memptr() + arma_mat.n_elem);
-  return matlab_mat;
-};
-
-template <class T>
-matlab::data::TypedArray<T> a2m_vec(const arma::Col<T>& arma_vec,
-                                    matlab::data::ArrayFactory& factory) {
-  const matlab::data::TypedArray<T> matlab_mat =
-      factory.createArray<T>({arma_vec.n_elem, 1}, arma_vec.memptr(),
-                             arma_vec.memptr() + arma_vec.n_elem);
-  return matlab_mat;
-};
-
-template <class T>
-matlab::data::TypedArray<T> s2m_vec(const std::vector<T>& std_vec,
-                                    matlab::data::ArrayFactory& factory) {
-  const matlab::data::TypedArray<T> matlab_mat = factory.createArray<T>(
-      {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size());
-  return matlab_mat;
-};
-}  // namespace armamexcpp
-
-#endif
-

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const std::size_tkControlTypeDeltaU 
control designed to penalize change in input
const std::size_tkControlTypeIntY 
control using integral action
const std::size_tkControlTypeAdaptM 
adapt control setpoint with re-estimated disturbance m

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Control mode bit masks. These can be bit-wise OR’d to use in combination.

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-  Attribute Details
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-

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-  #
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static const std::size_t kControlTypeDeltaU = 0x1;
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Control was designed to penalize change in input (i.e., the state was augmented with input u)

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static const std::size_t kControlTypeIntY = kControlTypeDeltaU << 1;
-
Control using integral action (i.e., the state was augmented with output y during design)

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-  #
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static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU << 2;
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Adapt control setpoint adapted with re-estimated process disturbance m.

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const data_tkDefaultP0 
default state estimate covar
const data_tkDefaultQ0 
default process noise covar
const data_tkDefaultR0 
default output noise covar

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Default values for common variables (e.g., default diagonal elements of covariances)

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static const data_t kDefaultP0 = 1e-6;
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static const data_t kDefaultQ0 = 1e-6;
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static const data_t kDefaultR0 = 1e-2;
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-
-  
-    Modules on LDS C&E
-    https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/
-    Recent content in Modules on LDS C&E
-    Hugo -- gohugo.io
-    
-      Control Mode Bit Masks
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__control__masks/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__control__masks/
-      provides fill types for constructing new armadillo vectors, matrices
-    
-    
-    
-      Defaults
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__defaults/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__defaults/
-      Defaults #  
-More&hellip; Attributes #      Name     const data_t kDefaultP0 default state estimate covar   const data_t kDefaultQ0 default process noise covar   const data_t kDefaultR0 default output noise covar    Detailed Description #  Default values for common variables (e.g., default diagonal elements of covariances)
-Attribute Details #  kDefaultP0 #  static const data_t kDefaultP0 = 1e-6; kDefaultQ0 #  static const data_t kDefaultQ0 = 1e-6; kDefaultR0 #  static const data_t kDefaultR0 = 1e-2;  Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
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diff --git a/docs/docs/api/namespaces/index.html b/docs/docs/api/namespaces/index.html
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    armamexc 
    arma/mex interface using Matlab C API
    
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    armamexcpp 
    arma/mex interface using Matlab C++ API
    
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    Linear Dynamical Systems with Gaussian observations.
    
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    Linear Dynamical Systems with Poisson observations.
    
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-    Namespaces on LDS C&E
-    https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/
-    Recent content in Namespaces on LDS C&E
-    Hugo -- gohugo.io
-    
-      armamexc
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/
-      arma/mex interface using Matlab C API
-    
-    
-    
-      armamexcpp
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/
-      arma/mex interface using Matlab C++ API
-    
-    
-    
-      lds
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/
-      Linear Dynamical Systems (LDS) namespace.
-    
-    
-    
-      lds::gaussian
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/
-      Linear Dynamical Systems with Gaussian observations.
-    
-    
-    
-      lds::poisson
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/
-      Linear Dynamical Systems with Poisson observations.
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diff --git a/docs/docs/api/namespaces/namespacearmamexc/index.html b/docs/docs/api/namespaces/namespacearmamexc/index.html
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arma/mex interface using Matlab C API 
 
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template <class T > 
auto		m2T_scalar(const mxArray * matlab_scalar)
Convert Matlab mxArray to scalar of type T.
template <class T > 
auto		m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)
Convert matlab matrix to armadillo.
template <typename T > 
auto		a2m_mat(arma::Mat< T > const & arma_mat)
Convert armadillo to matlab matrix.
template <typename T > 
auto		a2m_vec(arma::Col< T > const & arma_vec)
Convert armadillo to matlab vector.

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-  Detailed Description
-  #
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-
Utilities for arma/mex interface using Matlab C API

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-  Function Details
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-  m2T_scalar
-  #
-

-
template <class T >
-inline auto m2T_scalar(
-    const mxArray * matlab_scalar
-)
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Parameters:

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    • 
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-  #
-

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template <class T >
-inline auto m2a_mat(
-    const mxArray * matlab_mat,
-    bool copy_aux_mem =false,
-    bool strict =true
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    • 
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Return: armadillo matrix of type T

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-  a2m_mat
-  #
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template <typename T >
-inline auto a2m_mat(
-    arma::Mat< T > const & arma_mat
-)
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Parameters:

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-
  • arma_mat armadillo matrix
    • 
-

-
Return: matlab matrix

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-  a2m_vec
-  #
-

-
template <typename T >
-inline auto a2m_vec(
-    arma::Col< T > const & arma_vec
-)
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Parameters:

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  • arma_vec armadillo vector
    • 
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Return: matlab vector

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template <class T > 
std::vector< arma::Mat< T > >		m2a_cellmat(matlab::data::CellArray & matlab_cell)
Convert matlab cell array to vector of armadillo matrices.
template <class T > 
std::vector< T >		m2s_vec(matlab::data::TypedArray< T > & matlab_array)
Convert matlab matrix to a vector of scalars.
template <class T > 
arma::Col< T >		m2a_vec(matlab::data::TypedArray< T > matlab_array)
Convert matlab to armadillo vector.
template <class T > 
arma::Mat< T >		m2a_mat(matlab::data::TypedArray< T > matlab_array)
Convert matlab to armadillo matrix.
template <class T > 
matlab::data::TypedArray< T >		a2m_mat(const arma::Mat< T > & arma_mat, matlab::data::ArrayFactory & factory)
Convert armadillo to matlab matrix.
template <class T > 
matlab::data::TypedArray< T >		a2m_vec(const arma::Col< T > & arma_vec, matlab::data::ArrayFactory & factory)
Convert armadillo to matlab vector.
template <class T > 
matlab::data::TypedArray< T >		s2m_vec(const std::vector< T > & std_vec, matlab::data::ArrayFactory & factory)
Convert vector of scalar T to matlab matrix.

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-  Detailed Description
-  #
-

-
utilities for arma/mex interface using Matlab C++ API

-

-  Function Details
-  #
-

-

-  m2a_cellmat
-  #
-

-
template <class T >
-std::vector< arma::Mat< T > > m2a_cellmat(
-    matlab::data::CellArray & matlab_cell
-)
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Parameters:

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    • 
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    • 
-

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Return: vector of armadillo matrices of type T

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-  m2s_vec
-  #
-

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template <class T >
-std::vector< T > m2s_vec(
-    matlab::data::TypedArray< T > & matlab_array
-)
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Parameters:

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  • matlab_array matlab array
    • 
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    • 
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-  #
-

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template <class T >
-arma::Col< T > m2a_vec(
-    matlab::data::TypedArray< T > matlab_array
-)
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Parameters:

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  • matlab_array matlab array
    • 
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    • 
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-  #
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template <class T >
-arma::Mat< T > m2a_mat(
-    matlab::data::TypedArray< T > matlab_array
-)
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Parameters:

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  • matlab_array matlab matrix
    • 
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    • 
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Return: armadillo matrix of type T

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-  a2m_mat
-  #
-

-
template <class T >
-matlab::data::TypedArray< T > a2m_mat(
-    const arma::Mat< T > & arma_mat,
-    matlab::data::ArrayFactory & factory
-)
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Parameters:

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  • arma_mat arma matrix
    • 
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  • factory matlab “array factory”
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  • T type
    • 
-

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Return: matlab matrix

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-  a2m_vec
-  #
-

-
template <class T >
-matlab::data::TypedArray< T > a2m_vec(
-    const arma::Col< T > & arma_vec,
-    matlab::data::ArrayFactory & factory
-)
-
Parameters:

-
    
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    • 
-
  • factory matlab “array factory”
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    • 
-

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Return: matlab matrix

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-  s2m_vec
-  #
-

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template <class T >
-matlab::data::TypedArray< T > s2m_vec(
-    const std::vector< T > & std_vec,
-    matlab::data::ArrayFactory & factory
-)
-
Parameters:

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  • std_vec standard vector
    • 
-
  • factory matlab “array factory”
    • 
-

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Template Parameters:

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  • T type
    • 
-

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Return: matlab matrix

-

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Linear Dynamical Systems (LDS) namespace. 

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lds::gaussian 
Linear Dynamical Systems with Gaussian observations.
lds::poisson 
Linear Dynamical Systems with Poisson observations.

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classlds::Controller
classlds::EM
classlds::Fit 
LDS Fit Type.
classlds::SSID
classlds::SwitchedController 
SwitchedController Type.
classlds::System 
Linear Dynamical System Type.
classlds::UniformMatrixList
classlds::UniformSystemList
classlds::UniformVectorList

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enumSSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}
weighting options for SSID
enumMatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2}
using doubledata_t
using arma::Col< data_t >Vector
using arma::Mat< data_t >Matrix
using arma::Cube< data_t >Cube
using arma::subview< data_t >View

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voidLimit(std::vector< data_t > & x, data_t lb, data_t ub)
voidLimit(Vector & x, data_t lb, data_t ub)
voidLimit(Matrix & x, data_t lb, data_t ub)
voidReassign(Vector & some, const Vector & other, const std::string & parenthetical =“Reassign”)
reassigns contents of some Vector in place
voidReassign(Matrix & some, const Matrix & other, const std::string & parenthetical =“Reassign”)
reassigns contents of some Matrix in place
voidForceSymPD(Matrix & X)
forces matrix to be symmetric positive-definite
voidForceSymMinEig(Matrix & X, data_t eig_min =0)
forces matrix to be symmetric and have a minimum eigenvalue
voidlq(Matrix & L, Matrix & Qt, const Matrix & X)
LQ decomposition.
MatrixcalcCov(const Matrix & A, const Matrix & B)
Calculate covariance matrix.

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const std::size_tkControlTypeDeltaU 
control designed to penalize change in input
const std::size_tkControlTypeIntY 
control using integral action
const std::size_tkControlTypeAdaptM 
adapt control setpoint with re-estimated disturbance m
const data_tkInf 
Some useful numbers.
const data_tkPi
const data_tkDefaultP0 
default state estimate covar
const data_tkDefaultQ0 
default process noise covar
const data_tkDefaultR0 
default output noise covar

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-  Type Details
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EnumeratorValueDescription
kSSIDNoneNone.
kSSIDMOESPMOESP (AKA “robust method” in van Overschee 1996)
kSSIDCVACVA “Canonical Variate Analysis”.

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Weighting options for singular value decomposition performed during subspace identification (SSID)

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van Overschee, de Moor. 1996. Subspace Identification for Linear Systems.

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kMatFreeDimNoneneither dim free to be hetero in mat list
kMatFreeDim1allow 1st dim of mats in list to be hetero
kMatFreeDim2allow 2nd dim of mats in list to be hetero

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-  data_t
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using lds::data_t = typedef double;
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Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.

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-  Vector
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using lds::Vector = typedef arma::Col<data_t>;
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using lds::Matrix = typedef arma::Mat<data_t>;
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using lds::Cube = typedef arma::Cube<data_t>;
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using lds::View = typedef arma::subview<data_t>;
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-  Function Details
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-  Limit
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inline void Limit(
-    std::vector< data_t > & x,
-    data_t lb,
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inline void Limit(
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inline void Limit(
-    Matrix & x,
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inline void Reassign(
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inline void Reassign(
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void ForceSymPD(
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void lq(
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Matrix calcCov(
-    const Matrix & A,
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Return: covariance

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-  Attribute Details
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static const std::size_t kControlTypeDeltaU = 0x1;
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Control was designed to penalize change in input (i.e., the state was augmented with input u)

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static const std::size_t kControlTypeIntY = kControlTypeDeltaU << 1;
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Control using integral action (i.e., the state was augmented with output y during design)

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static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU << 2;
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Adapt control setpoint adapted with re-estimated process disturbance m.

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-  kInf
-  #
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static const data_t kInf = std::numeric_limits<data_t>::infinity();
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-  kPi
-  #
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static const data_t kPi = arma::datum::pi;
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-  #
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static const data_t kDefaultP0 = 1e-6;
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static const data_t kDefaultQ0 = 1e-6;
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static const data_t kDefaultR0 = 1e-2;
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Updated on 19 May 2022 at 17:16:03 Eastern Daylight Time

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diff --git a/docs/docs/api/namespaces/namespacelds_1_1gaussian/index.html b/docs/docs/api/namespaces/namespacelds_1_1gaussian/index.html
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Linear Dynamical Systems with Gaussian observations. 

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classlds::gaussian::Controller 
Gaussian-observation Controller Type.
classlds::gaussian::Fit 
GLDS Fit Type.
classlds::gaussian::FitEM 
GLDS E-M Fit Type.
classlds::gaussian::FitSSID 
Subspace Identification (SSID) for GLDS.
classlds::gaussian::SwitchedController 
Gaussian-observation SwitchedController Type.
classlds::gaussian::System 
Gaussian LDS Type.

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Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time

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diff --git a/docs/docs/api/namespaces/namespacelds_1_1poisson/index.html b/docs/docs/api/namespaces/namespacelds_1_1poisson/index.html
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Linear Dynamical Systems with Poisson observations. 

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classlds::poisson::Controller 
PLDS Controller Type.
classlds::poisson::Fit 
PLDS Fit Type.
classlds::poisson::FitEM 
PLDS E-M Fit Type.
classlds::poisson::FitSSID 
Subspace Identification (SSID) for PLDS.
classlds::poisson::SwitchedController 
Poisson-observation SwitchedController Type.
classlds::poisson::System 
Poisson System type.

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std::random_devicerd 
random device for simulating poisson data
std::mt19937rng 
random number generator for simulating poisson data

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-  Attribute Details
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-  rd
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static std::random_device rd;
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static std::mt19937 rng = std::mt19937(
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Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time

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Updated on 19 May 2022 at 17:16:06 Eastern Daylight Time

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-    Pages on LDS C&E
-    https://stanley-rozell.github.io/lds-ctrl-est/docs/api/pages/
-    Recent content in Pages on LDS C&E
-    Hugo -- gohugo.io
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diff --git a/docs/docs/getting-started/getting-started/index.html b/docs/docs/getting-started/getting-started/index.html
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-  #
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This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.

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ldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.

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-  Tested Configurations
-  #
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Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the RelWithDebInfo build type in the CMake configure command (-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo):

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    • 
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    • 
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    • 
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That being said, if you want to debug a build for a single platform, here are some things you can try:

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    • 
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    • 
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-  Mac Pre-requisities
-  #
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Xcode Command Line Tools will get you clang, gcc, make, and git:

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xcode-select --install
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Homebrew is “The Missing Package Manager for macOS” which will make installing lots of things easy. Install like this:

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/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
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brew install cmake gfortran pkg-config
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-  Linux Pre-requisites
-  #
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You’ll need Git, CMake, GCC, gfortran, etc.

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sudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential ninja-build
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-  Windows Installation
-  #
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Look here for Windows-specific instructions.

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-  Downloading the Library
-  #
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First, clone the repository along with submodules:

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git clone https://github.com/cloctools/lds-ctrl-est.git 
-cd lds-ctrl-est
-git submodule update --init
-

-  Compilation + Installation
-  #
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Now generate the cache and build using your IDE or from the command line as follows.

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mkdir build && cd build
-cmake ..
-cmake --build .
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The first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.

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If you want to use vcpkg set up somewhere besides this repo’s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE’s settings.

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You can verify the build is working by running ctest from the build folder, which runs all the example scripts.

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-  Options
-  #
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This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.

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  1. LDSCTRLEST_BUILD_EXAMPLES  : [default=ON] whether to build example programs located under examples/ in the source tree
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    3. 
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  3. LDSCTRLEST_BUILD_STATIC    : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use
    4. 
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n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.

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    For basic project build & install
    
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    cd /path/to/repository
-mkdir build && cd build
-cmake .. #configure build
-cmake --build #build the project
-sudo make install #[optional] installs to default location (OS-specific)
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    • 
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  • 
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    To set the install prefix
    
-
    cd /path/to/repository
-mkdir build && cd build
-cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location
-cmake --build #build the project
-make install #install to /your/install/prefix
-
    • 
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-mkdir build && cd build
-cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library
-make #build the project
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    n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.
    
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      2. 
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      3. 
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On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.

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-  Python bindings package ldsctrlest
-  #
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With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython3_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn’t use an undesired version. That environment needs to have NumPy installed.

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cmake --build . --target python_modules
-
The bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory (pip install pytest matplotlib first if you need to).

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See python/ldsctrlest/README.md for usage details.

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Also, beware that a single build will probably not work for both the standalone library and the Python package, since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with  -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.

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-  Common issues
-  #
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  1. “I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.”
    2. 
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If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.

-
export CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix
-export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix
-
    
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  1. vcpkg fails on configuration
    2. 
-

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Try running ./bootstrap-vcpkg from the vcpkg folder and try again. If that doesn’t work, try updating vcpkg to a newer version (in the source control tab, click on the commit hash by the vcpkg repo then select from the dropdown) and running boostsrap-vcpkg again. You can also try upgrading your system (e.g., apt update, apt upgrade).

-
    
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  1. 
-
    Could not find Python3 (missing: Python3_NumPy_INCLUDE_DIRS NumPy)
    
-
    Make sure NumPy is installed in the Python environment you specified. If CMake still can’t find it, you may need to tell CMake exactly where to find it by adding an argument to the configure command: -DPython3_NumPy_INCLUDE_DIR=.... You can find that location like this: python -c 'import numpy; print(numpy.get_include())'
    
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-  Downloading the Library
-  #
-

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The source code for this library can be downloaded from stanley-rozell/ldsCtrlEst either by downloading a snapshot or cloning the repository via git.

-
git clone https://github.com/stanley-rozell/ldsCtrlEst.git
-
By default, this would check out the master branch. In most cases, we suggest downloading or checking out the latest release instead.

-
cd /path/to/ldsCtrlEst
-git checkout 0.6
-

-  Dependencies
-  #
-

-
Note that the primary dependencies of this project listed below must be installed along with their header files and with CMake config files or pkg-config files. The latter files are used to configure this project’s build. It is strongly encouraged to install the dependencies below using a package manager (e.g., apt, pacman, macports).

-
    
-
  • For project configuration, install cmake as well as pkg-config. The latter is optional.
    • 
-
  • The linear algebra library armadillo is used throughout this repository.
    • 
-
  • The HDF5 library is used to save output from example test programs.
    • 
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  • For use of this library in Matlab executables (mex) on Linux operating systems, you will need OpenBlas, ensuring the static library libopenblas.a is installed. You will also need to install gfortran.
    • 
-

-

-  Compilation + Installation
-  #
-

-
This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.

-
    
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  1. LDSCTRLEST_BUILD_EXAMPLES  : [default= ON] whether to build example programs located under examples/ in the source tree
    2. 
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  2. LDSCTRLEST_BUILD_FIT       : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation
    3. 
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  3. LDSCTRLEST_BUILD_STATIC    : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use
    4. 
-

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n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.

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Below are example usages of cmake/make to configure/build the library.

-
    
-
  • 
-
    For basic project build & install
    
-
    cd /path/to/repository
-mkdir build && cd build
-cmake .. #configure build
-make #build the project
-sudo make install #[optional] installs to default location (OS-specific)
-
    • 
-
  • 
-
    To set the install prefix
    
-
    cd /path/to/repository
-mkdir build && cd build
-cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location
-make #build the project
-make install #install to /your/install/prefix
-
    • 
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  • 
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    To build the bare bones project, excluding fit code and Matlab mex code.
    
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    cd /path/to/repository
-mkdir build && cd build
-cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library
-make #build the project
-
    n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.
    
-
      
-
    1. LD_LIBRARY_PATH: search path for dynamically loaded libraries
      2. 
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    2. PKG_CONFIG_PATH: search path for pkg-config tool
      3. 
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    3. CMAKE_PREFIX_PATH: search path of prefix where CMake will look for package config files
      4. 
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    e.g., Assuming you set -DCMAKE_INSTALL_PREFIX=/your/install/prefix during project configuration and your login shell uses the ~/.profile startup file, open ~/.profile in a text editor and add …
    
-
    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/your/install/prefix/lib
-export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix/lib/pkgconfig
-export CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix
-
    • 
-

-

-  Common issues
-  #
-

-
    
-
  1. “I have installed all the dependencies including gfortran with a package manager as suggested; however, cmake complains it cannot find the gfortran library.”
    2. 
-

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When gfortran is installed, its library is usually not installed in a standard location like /usr/lib. gfortran is part of the gcc suite, so their libraries are organized together. e.g., When you install gfortran on Ubuntu using apt, its location is /usr/lib/gcc/x86_64-linux-gnu/9, in the case that gcc version 9 is installed. The build configuration script in ldsCtrlEst is written to add LD_LIBRARY_PATH (Unix) or PATH (Windows) to the CMake library search path on Unix or Windows systems, respectively. Therefore, to fix this issue, simply add the directory in which libgfortran was installed to the OS-appropriate environment variable. Continuing with the Ubuntu example above and assuming a Unix login shell whose startup file is ~/.profile, add the following to the file.

-
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/gcc/x86_64-linux-gnu/9
-
    
-
  1. “I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.”
    2. 
-

-
If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.

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export CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix
-export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix
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diff --git a/docs/docs/getting-started/windows/index.html b/docs/docs/getting-started/windows/index.html
deleted file mode 100644
index 8449a330..00000000
--- a/docs/docs/getting-started/windows/index.html
+++ /dev/null
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-  Windows Installation
-  #
-

-

-  Windows Pre-requisites
-  #
-

-
Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:

-
Set-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time
-iwr get.scoop.sh | Invoke-Expression
-
Install Git and CMake if you don’t already have them:

-
scoop install git cmake
-
If that didn’t work, follow more detailed instructions here.

-
The easiest way to compile C++ project on Windows is with Visual Studio’s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on “Desktop development with C++.” If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the “Installation details” sidebar under optional features.

-
And the easiest way to use Visual Studio’s build tools is with VS Code, along with the CMake Tools extension. Install them and you should be ready to go.

-

-  Downloading the Library
-  #
-

-
First, clone the repository, either from VS Code or the command line:

-
git clone https://github.com/cloctools/lds-ctrl-est.git 
-cd lds-ctrl-est
-
You’ll need to initialize the submodules from the command line after the repo is cloned:

-
git submodule update --init
-

-  Installation
-  #
-

-
When you open the folder in VS Code, you will like be prompted by the CMake Tools extension to configure the project. Make sure you select the kit (you’ll be prompted when you configure–else there’s an icon in the bar on the bottom of the window or type Ctrl+Shift+P, then “cmake select kit”). Choose Clang [latest version] with GNU CLI ... amd64 assuming you are running a 64-bit OS. (MSVC may work okay too if you don’t need to build Python bindings.)

-
Follow along with the “Getting Started” instructions, but where you see config options specified as -DLDSCTREST_BUILD_STATIC=OFF or -DPython3_ROOT_DIR=..., you will enter those in settings: open with Ctrl+,, click “workspace”, then search for “CMake: Configure Args” and enter each of your desired arguments as a separate item.

-
To configure, use Ctrl+Shift+P and search for the “CMake: Configure” command. To build, click the “Build” button on the bottom bar. Then click the “CTest” button to run the example scripts.

-

-  Considerations
-  #
-

-
Development on Windows has been more prone to bugs than on Unix systems, so if you encounter many problems, consider switching—WSL (Windows Subsystem for Linux) is a good option for Windows users who don’t want to work on a different machine.

-
Compilation has been successfully tested in VS Code using the following kit, using the “RelWithDebInfo” config:

-
Clang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64)
-

-  Troubleshooting
-  #
-

-
    
-
  1. The build appears to work, but tests fail with code 0xc0000135 OR “I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.”
    2. 
-

-
Have you installed the library? In VS Code, use Shift+F7 to build a specific target, in this case INSTALL. If that doesn’t solve your problem, you will likely need to add the build or install folder to your PATH environment variable, which you can do using the settings GUI (search for “Edit the system environment variables”).

-
    
-
  1. On Windows, “Generate CMake Cache” step errs because creating symbolic links is not permitted.
    2. 
-

-
Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -> Administrative Tools -> Local Policies -> User Rights Assignment -> Create Symbolic Links.

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-LDS C+E Documentation | LDS C&E
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-  LDS Control & Estimation Documentation
-  #
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-    LDS C+E Documentation on LDS C&E
-    https://stanley-rozell.github.io/lds-ctrl-est/docs/
-    Recent content in LDS C+E Documentation on LDS C&E
-    Hugo -- gohugo.io
-    
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/getting-started/getting-started/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/getting-started/getting-started/
-      Getting Started #  This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.
-ldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.
-Tested Configurations #  Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another.
-    
-    
-    
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/getting-started/windows/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/getting-started/windows/
-      Windows Installation #  Windows Pre-requisites #  Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:
-Set-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time iwr get.scoop.sh | Invoke-Expression Install Git and CMake if you don&rsquo;t already have them:
-scoop install git cmake If that didn&rsquo;t work, follow more detailed instructions here.
-The easiest way to compile C++ project on Windows is with Visual Studio&rsquo;s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.
-    
-    
-    
-      C&E
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/terminology/control-estimation/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/terminology/control-estimation/
-      Control &amp; Estimation #  The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.
-    
-    
-    
-      Models
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/terminology/model/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/terminology/model/
-      Model Definitions #  This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form:   \[
\mathbf{x}_{t&#43;1} = f\left( \mathbf{x}_{t}, \mathbf{v}_{t} \right) = \mathbf{A} \mathbf{x}_{t} &#43; \mathbf{B} \mathbf{v}_{t} &#43; \mathbf{m}_{t} &#43; \mathbf{w}_{t}
\]   \[
\mathbf{y}_{t} = h\left( \mathbf{x}_{t} \right)
\]  t : time index
x : system state
v = g%u : input (e.g., in physical units used for model fit)
u : control signal sent to actuator (e.
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diff --git a/docs/docs/terminology/control-estimation/index.html b/docs/docs/terminology/control-estimation/index.html
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-  Control & Estimation
-  #
-

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The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.

-

-  State estimation
-  #
-

-
In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by
-
-
-
-
-  \[
-\widehat{\mathbf{x}}_{t|t-1} = \mathbf{A}\widehat{\mathbf{x}}_{t-1|t-1} + \mathbf{B} u_{t-1} + \mathbf{m}_{t-1} \;,
\]
-
-

-
-  \[
-\widehat{\mathbf{x}}_{t|t} = \widehat{\mathbf{x}}_{t|t-1} + \mathbf{K}^{\rm e}_t \left(\mathbf{z}_t - \widehat{\mathbf{y}}_{t|t-1}\right)\;,
\]
-
-
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where 
-  \( \hat{\left(\cdot\right)}_{t|j} \)
-
- indicates an estimate at time 
-  \( t \)
-
- given data up to time 
-  \( j \)
-
- inclusive, 
-  \( \mathbf{K}^{\rm e} \)
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- is the estimator gain, and

-
-  \[ \widehat{\mathbf{y}}_{t|t-1} = h\left( \widehat{\mathbf{x}}_{t|t-1} \right) \; .\]
-
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In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.

-
In the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.

-

-  Adaptive estimation of process disturbance
-  #
-

-
Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.

-
When parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk
-
-  \[
-\mathbf{m}_{t} = \mathbf{m}_{t-1} + \mathbf{w}^m_{t-1} \;,
\]
-
-
-where 
-  \( \mathbf{w}^m \sim \mathcal{N}\left(0, \mathbf{Q}_m\right)\)
-
-. Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.

-

-  Control
-  #
-

-
Given the estimated state, the controller updates the inputs to the system according to the following law:
-
-  \[
-\mathbf{u}_{t} = \mathbf{u}^{\rm ref}_t - \mathbf{K}^c_x \left( \widehat{\mathbf{x}}_t - \mathbf{x}^{\rm ref}_t\right)\;,
\]
-
-

-
where 
-  \( \left( \cdot \right)^{\rm ref} \)
-
- correspond to reference/target signals and 
-  \( \mathbf{K}^c_x \)
-
- is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.

-
If users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as

-
-  \[
-\mathbf{u}_{t} = \mathbf{u}^{\rm ref}_t - \mathbf{K}^c_x \left( \widehat{\mathbf{x}}_t - \mathbf{x}^{\rm ref}_t\right) - \mathbf{K}^c_{\rm inty} \sum_{j=1}^{t}\left( \widehat{\mathbf{y}}_j - \mathbf{y}^{\rm ref}_j \right) \;.
\]
-
-
-
An additional option available to users is a control law that updates the change in u,

-
-  \[
-\Delta\mathbf{u}_{t} = -\mathbf{K}^c_u \left(\mathbf{u}_{t-1} - \mathbf{u}^{\rm ref}_{t-1} \right) - \mathbf{K}^c_x \left( \widehat{\mathbf{x}}_t - \mathbf{x}^{\rm ref}_t\right)\;,
\]
-
-
-
-  \[
-\mathbf{u}_{t} = \mathbf{u}_{t-1} + \Delta\mathbf{u}_{t} \; .
\]
-
-
-
Notice that this takes the form of a first-order difference equation for updating control (i.e., 
-  \( \Delta\mathbf{u}_{t} = -\mathbf{K}^c_u \mathbf{u}_{t-1} + \epsilon_{t-1} \)
-
-), effectively low-pass filtering the input depending on the characteristics of 
-  \( \mathbf{K}^c_u \)
-
-. This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.

-
Integral action and the 
-  \( \Delta \mathbf{u} \)
-
- control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR’d to use in combination.

-

-  Calculating reference state-control from output
-  #
-

-
In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce 
-  \( \mathbf{x}^{\rm ref} \)
-
- and 
-  \( \mathbf{u}^{\rm ref} \)
-
-. Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller<System>::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.

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diff --git a/docs/docs/terminology/model/index.html b/docs/docs/terminology/model/index.html
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-Models | LDS C&E
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-  Model Definitions
-  #
-

-
This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form:
-
-
-
-
-  \[
-\mathbf{x}_{t+1} = f\left( \mathbf{x}_{t}, \mathbf{v}_{t} \right) = \mathbf{A} \mathbf{x}_{t} + \mathbf{B} \mathbf{v}_{t} + \mathbf{m}_{t} + \mathbf{w}_{t}
\]
-
-

-
-  \[
-\mathbf{y}_{t} = h\left( \mathbf{x}_{t} \right)
\]
-
-
-
t           : time index
-x           : system state
-v = g%u     : input (e.g., in physical units used for model fit)
-u           : control signal sent to actuator (e.g., in Volts)
-y           : system output
-m           : process disturbance
-w ~ N(0, Q) : process noise/disturbance
-
-A           : state matrix
-B           : input coupling matrix
-g           : input gain (e.g., for converting to control signal actuator voltage)
-              n.b., assumes this conversion is linear
-Q           : process noise covariance
-
-%           : element-wise multiplication
-

-

-  LDS with Gaussian Observations
-  #
-

-
For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.

-
-  \[
-\mathbf{y}_{t} = \mathbf{C} \mathbf{x}_{t} + \mathbf{d}
\]
-
-
-
-  \[
-\mathbf{z}_{t} \sim \mathcal{N}\left(\mathbf{y}_{t} , \mathbf{R} \right)
\]
-
-
-
z           : measurement
-
-C           : output matrix
-d           : output bias
-R           : measurement noise covariance
-

-

-  LDS with Poisson Observations
-  #
-

-
For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.

-
-  \[
-y_{t}^{i} = \exp \left(\mathbf{c}^i \mathbf{x}_{t} + d^i\right)
\]
-
-
-
-  \[
-z_{t}^i \sim \rm{Poisson} \left(y_{t}^i \right)
\]
-
-
-
i           : output index
-
-z           : measurement (count data)
-
-c           : i^th row of output matrix (C)
-d           : output bias
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-GLDS Control | LDS C&E
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-  GLDS Control Tutorial
-  #
-

-
This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.

-
The full code for this can be found here.

-

-  Preamble
-  #
-

-
In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.

-
#include <ldsCtrlEst>
-
-using lds::Matrix;
-using lds::Vector;
-using lds::data_t;
-using std::cout;
-
Note that lds::Matrix and lds::Vector are typedefs for arma::Mat<data_t> and arma::Col<data_t>, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).

-

-  Creating a simulated system
-  #
-

-
A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.

-
  // Make 1st-order SISO system, sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;
-  size_t n_x = 1;
-  size_t n_y = 1;
-
-  // no time steps for simulation.
-  auto n_t = static_cast<size_t>(5.0 / dt);
-
When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.

-
  // construct ground truth system to be controlled...
-  // initializes to random walk model with top-most n_y state observed
-  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);
-
This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example,
-
-
-
-
-  \[
-x_{t+1} = x_t + w_t
\]
-
-

-
-  \[
-y_{t} = x_t
\]
-
-
-
where 
-  \( w_{t} \sim \mathcal{N}\left( 0, Q \right) \)
-
-.

-
Now, create non-default parameters for this model.

-
  // Ground-truth parameters for the controlled system
-  // (stand-in for physical system to be controlled)
-  Matrix a_true(n_x, n_x, arma::fill::eye);
-  a_true[0] = exp(-dt / 0.01);
-  Matrix b_true = Matrix(n_x, n_u).fill(2e-4);
-  // control signal to model input unit conversion e.g., V -> mW/mm2:
-  Vector g_true = Vector(n_y).fill(10.0);
-
-  // output noise covariance
-  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;
-
As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.

-
  /// Going to simulate a switching disturbance (m) acting on system
-  size_t which_m = 0;  // whether low or high disturbance (0, 1)
-  data_t m_low = 5 * dt * (1 - a_true[0]);
-  data_t pr_lo2hi = 1e-3;  // probability of going from low to high disturb.
-  data_t m_high = 20 * dt * (1 - a_true[0]);
-  data_t pr_hi2lo = pr_lo2hi;
-
-  // initially let m be low
-  Vector m0_true = Vector(n_y).fill(m_low);
-
Finally, assign the parameters using corresponding set-methods.

-
 // Assign params.
-  controlled_system.set_A(a_true);
-  controlled_system.set_B(b_true);
-  controlled_system.set_m(m0_true);
-  controlled_system.set_g(g_true);
-  controlled_system.set_R(r_true);
-

-  Creating the controller
-  #
-

-
Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.

-
For the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.

-
  // make a controller
-  lds::gaussian::Controller controller;
-  {
-    // Create **incorrect** model used for control.
-    // (e.g., imperfect model fitting)
-    Matrix b_controller = b_true / 2;
-
-    // let's assume zero process disturbance initially
-    // (will be re-estimating)
-    Vector m_controller = Vector(n_x, arma::fill::zeros);
-
-    // for this demo, just use arbitrary default R
-    Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0;
-
-    lds::gaussian::System controller_system(controlled_system);
-    controller_system.set_B(b_controller);
-    controller_system.set_m(m_controller);
-    controller_system.set_R(r_controller);
-    controller_system.Reset();  // reset to new m
-
-    // going to adaptively re-estimate the disturbance
-    controller_system.do_adapt_m = true;
-
-    // set adaptation rate by changing covariance of assumed process noise
-    // acting on random-walk evolution of m
-    Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6;
-    controller_system.set_Q_m(q_m);
-
-    // create controller
-    // lower and upper bounds on control signal (e.g., in Volts)
-    data_t u_lb = 0.0;  // [=] V
-    data_t u_ub = 5.0;  // [=] V
-    controller = std::move(
-        lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub));
-  }
-
Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.

-
With the controller constructed, control variables may be set.

-
  // Control variables:
-  // if following enabled, adapts set point with re-estimated process
-  // disturbance n.b., should not need integral action if this is enabled as the
-  // adaptive estimator minimizes DC error
-  bool do_adaptive_set_point = false;
-
-  // Reference/target output, controller gains
-  Vector y_ref0 = Vector(n_y).fill(20.0 * dt);
-  Matrix k_x = Matrix(n_u, n_x).fill(100);     // gains on state error
-  Matrix k_inty = Matrix(n_u, n_y).fill(1e3);  // gains on integrated err
-
-  // setting initial state to target to avoid error at onset:
-  Vector x0 = Vector(n_x).fill(y_ref0[0]);
-
-  // set up controller type bit mask so controller knows how to proceed
-  size_t control_type = 0;
-  if (do_adaptive_set_point) {
-    // adapt set point with estimated disturbance
-    control_type = control_type | lds::kControlTypeAdaptM;
-  } else {
-    // use integral action to minimize DC error
-    control_type = control_type | lds::kControlTypeIntY;
-  }
-
-  // set controller type
-  controller.set_control_type(control_type);
-
-  // Let's say these controller gains were designed assuming g was 9 V/(mW/mm2):
-  Vector g_design = Vector(n_u).fill(9);
-
-  // Set params.
-  // **n.b. using arbitrary defaults for Q, R in this example. Really, these
-  // should be set by users, as they tune characteristics of Kalman filter.
-  // Users can also choose not to recursively calculate the estimator gain and
-  // supply it (setKe) instead of covariances.**
-  controller.set_y_ref(y_ref0);
-  controller.set_Kc(k_x);
-  controller.set_Kc_inty(k_inty);
-  controller.set_g_design(g_design);
-

-  Simulating control
-  #
-

-
In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.

-
The control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.

-
  // Simulate the true system.
-  z.col(t) = controlled_system.Simulate(u_tm1);
-
-  // This method uses a steady-state solution to control problem to calculate
-  // x_ref, u_ref from reference output y_ref. Therefore, it is only
-  // applicable to regulation problems or cases where reference trajectory
-  // changes slowly compared to system dynamics.
-  u.col(t) = controller.ControlOutputReference(z.col(t));
-

-  Example simulation result
-  #
-

-
Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller’s online estimates of the output, state, and disturbance are given in purple.

-
example control output

-

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diff --git a/docs/docs/tutorials/eg_plds_state_estimation/index.html b/docs/docs/tutorials/eg_plds_state_estimation/index.html
deleted file mode 100644
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-PLDS State Estimation | LDS C&E
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-  PLDS State Estimation Tutorial
-  #
-

-
This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.

-
The full code for this can be found here.

-

-  Preamble
-  #
-

-
In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.

-
#include <ldsCtrlEst>
-
-using lds::Matrix;
-using lds::Vector;
-using lds::data_t;
-using std::cout;
-
Note that lds::Matrix and lds::Vector are typedefs for arma::Mat<data_t> and arma::Col<data_t>, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).

-

-  Creating a simulated system
-  #
-

-
A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.

-
  // Make SISO system sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;                           // no. inputs
-  size_t n_x = 1;                           // no. states
-  size_t n_y = 1;                           // no. outputs
-  auto n_t = static_cast<size_t>(30 / dt);  // no time steps for simulation.
-
When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.

-
  // construct ground truth system...
-  lds::poisson::System system_true(n_u, n_x, n_y, dt);
-
This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example,
-
-
-
-
-  \[
-x_{t+1} = x_t + w_t
\]
-
-

-
-  \[
-y_{t} = \exp\left(x_t\right)
\]
-
-
-
where 
-  \( w_{t} \sim \mathcal{N}\left( 0, Q \right) \)
-
-.

-
Now, create non-default parameters for this model.

-
  // Model parameters
-  Matrix a_true(n_x, n_x, arma::fill::eye);
-  a_true[0] = exp(-dt / 0.075);
-  Matrix b_true = Matrix(n_x, n_u).fill(1e-2);
-  Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2);  // disturbance
-  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) -
-                                        a_true);  // initial state
-
Finally, assign the parameters using corresponding set-methods.

-
  // Assign params.
-  system_true.set_A(a_true);
-  system_true.set_B(b_true);
-  system_true.set_x0(x0_true);
-  system_true.set_m(m0_true);
-  system_true.Reset();
-

-  Creating the estimator
-  #
-

-
Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.

-
  // Construct system for estimation
-  // e.g., will create a model with incorrect disturbance
-  lds::poisson::System system_estimator(n_u, n_x, n_y, dt);
-
-  // Can copy parameters from another system object
-  system_estimator = system_true;
-
-  // wrong disturbance
-  Vector m0_est = m0_true * 2;
-  system_estimator.set_m(m0_est);
-
-  // set new initial conditions
-  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) -
-                                      a_true);  // initial state
-  system_estimator.set_x0(x0_est);
-  system_estimator.Reset();  // reset to initial condition.
-
To ensure robust estimates, adaptively re-estimate the process disturbance.

-
  // turn on adaptive disturbance estimation
-  system_estimator.do_adapt_m = true;
-
-  // set adaptation rate by changing covariance of assumed process noise acting
-  // on random-walk evolution of m
-  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6;
-  system_estimator.set_Q_m(q_m);
-

-  Simulating estimation
-  #
-

-
In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.

-
    // Simlate the true system.
-    z.col(t) = system_true.Simulate(u.col(t - 1));
-
-    // Filter (predict -> update)
-    system_estimator.Filter(u.col(t - 1), z.col(t));
-

-  Example simulation result
-  #
-

-
Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.

-
example estimator output

-
With this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.

-
example estimator output histogram

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diff --git a/docs/docs/tutorials/eg_switched_plds_control/index.html b/docs/docs/tutorials/eg_switched_plds_control/index.html
deleted file mode 100644
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-PLDS Switched Control | LDS C&E
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-  PLDS Switched Control Tutorial
-  #
-

-
This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.

-
In the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.

-
The full code for this can be found here.

-

-  Preamble
-  #
-

-
In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.

-
#include <ldsCtrlEst>
-
-using lds::Matrix;
-using lds::Vector;
-using lds::data_t;
-using std::cout;
-
Note that lds::Matrix and lds::Vector are typedefs for arma::Mat<data_t> and arma::Col<data_t>, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).

-

-  Creating the simulated system
-  #
-

-
A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.

-
  // whether to do switched control
-  bool do_switch_ctrl = true;
-
-  // Make SISO system sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;
-  size_t n_x = 1;
-  size_t n_y = 1;
-
-  // no time steps for simulation.
-  auto n_t = static_cast<size_t>(30.0 / dt);
-
The system’s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.

-
  // for simulating switching
-  size_t which_mode = 1;
-  data_t pr_21 = 1e-3;   // prob mode 1 -> 2
-  data_t pr_12 = pr_21;  // prob mode 2 -> 1
-
Initially, the system will be in “mode” 1, where B = b1.

-
  // simulated system being controlled
-  lds::poisson::System controlled_system(n_u, n_x, n_y, dt);
-
-  // **Assume the system is not well characterized by one LDS, but is well
-  // characterized by two LDS models with different input matrices.**
-  data_t scale_sys_b = 2;
-
-  Matrix a(n_x, n_x, arma::fill::eye);
-  a[0] = 0.985;
-  Matrix b1 = Matrix(n_x, n_u).fill(0.05);
-  Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));
-
-  controlled_system.set_A(a);
-  controlled_system.set_B(b1);
-  controlled_system.set_d(d);
-  controlled_system.Reset();  // reset to initial conditions
-
See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.

-

-  Creating the controller
-  #
-

-
Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.

-
Similar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.

-
Moreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.

-
Putting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.

-
  // create switched controller
-  lds::poisson::SwitchedController switched_controller;
-  lds::UniformMatrixList<> k_x; // feedback controller gains
-  {
-    // create switched controller sub-systems
-    // system 1
-    lds::poisson::System sys1(controlled_system);
-
-    // set process noise covariance
-    Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3;
-    sys1.set_Q(q_controller);
-
-    // adaptively estimate process disturbance (m)
-    // n.b. using arbitrary default value for process noise if enabled.
-    sys1.do_adapt_m = true;
-
-    // setting initial mode to target to avoid large error at onset:
-    Vector x0_controller = arma::log(y_ref0) - d;
-    sys1.set_x0(x0_controller);
-    sys1.Reset();  // reset to initial conditions
-
-    // system 2
-    lds::poisson::System sys2 = sys1;
-
-    // set parameters
-    sys2.set_B(b2);
-
-    lds::UniformSystemList<lds::poisson::System> systems({sys1, sys2});
-
-    // controller gains for underlying systems:
-    Matrix k_x1(n_u, n_x, arma::fill::ones);
-    Matrix k_x2 = scale_sys_b * k_x1;  // system2 is x-times less sensitive.
-    k_x = lds::UniformMatrixList<>({k_x1, k_x2});
-
-    data_t u_lb = 0.0;
-    data_t u_ub = 5.0;
-    switched_controller = std::move(
-        lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub));
-  }
-
Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.

-
Now that the SwitchedController is instantiated, assign its parameters.

-
    // Control variables
-  size_t control_type = 0;  // no integral action, etc
-  switched_controller.set_control_type(control_type);
-  switched_controller.set_Kc(std::move(k_x));
-  switched_controller.set_y_ref(y_ref0);
-

-  Simulating control
-  #
-

-
In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).

-
The control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.

-
  // Let the controlled system stochastically change gain
-  // Assume another algorithm decodes this mode change and signals the
-  // switched_controller
-  Vector chance(1, arma::fill::randu);
-  if (which_mode == 1)  // mode1
-  {
-    if (chance[0] < pr_21) {
-      which_mode = 2;
-      controlled_system.set_B(b2);
-      if (do_switch_ctrl) {
-        switched_controller.Switch(1);
-      }
-    }
-  } else {  // mode2
-    if (chance[0] < pr_12) {
-      which_mode = 1;
-      controlled_system.set_B(b1);
-      if (do_switch_ctrl) {
-        switched_controller.Switch(0);
-      }
-    }
-  }
-
-  // Simulate the true system.
-  z.col(t) = controlled_system.Simulate(u.col(t - 1));
-
-  // perform control
-  u.col(t) = switched_controller.ControlOutputReference(z.col(t));
-
Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system’s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.

-

-  Example simulation result
-  #
-

-
Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller’s online estimates of the output and state are shown in purple.

-
example control output

-
Note that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.

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diff --git a/docs/docs/tutorials/index.xml b/docs/docs/tutorials/index.xml
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-
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-    LDS C+E Examples on LDS C&E
-    https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/
-    Recent content in LDS C+E Examples on LDS C&E
-    Hugo -- gohugo.io
-    
-      GLDS Control
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_glds_control/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_glds_control/
-      GLDS Control Tutorial #  This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system.
-    
-    
-    
-      PLDS State Estimation
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/
-      PLDS State Estimation Tutorial #  This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.
-The full code for this can be found here.
-    
-    
-    
-      PLDS Switched Control
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/
-      Mon, 01 Jan 0001 00:00:00 +0000
-      
-      https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/
-      PLDS Switched Control Tutorial #  This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.
-In the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system.
-    
-    
-  
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diff --git a/docs/eg_glds_ctrl_output.png b/docs/eg_glds_ctrl_output.png
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-[{"id":0,"href":"/lds-ctrl-est/docs/","title":"LDS C+E Documentation","section":"LDS Control \u0026 Estimation","content":"LDS Control \u0026amp; Estimation Documentation #  "},{"id":1,"href":"/lds-ctrl-est/docs/tutorials/","title":"LDS C+E Examples","section":"LDS C+E Documentation","content":"Examples #  "},{"id":2,"href":"/lds-ctrl-est/acknowledgements/","title":"Acknowledgements","section":"LDS Control \u0026 Estimation","content":"Acknowledgements #  Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.\n"},{"id":3,"href":"/lds-ctrl-est/docs/getting-started/getting-started/","title":"Getting Started","section":"LDS C+E Documentation","content":"Getting Started #  This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.\nldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.\nDownloading the Library #  First, clone the repository along with submodules:\ngit clone --recurse-submodules https://github.com/stanley-rozell/lds-ctrl-est.git cd lds-ctrl-est\r# use git submodule update --init if you clone the repo without --recurse-submodules\rCompilation + Installation #  Now generate the cache and build using your IDE or from the command line as follows.\nmkdir build \u0026amp;\u0026amp; cd build\rcmake ..\rcmake --build .\rThe first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.\nIf you want to use vcpkg set up somewhere besides this repo\u0026rsquo;s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE\u0026rsquo;s settings.\nYou can verify the build is working by running ctest from the build folder, which runs all the example scripts.\nOptions #  This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\n LDSCTRLEST_BUILD_EXAMPLES : [default=ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use  n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake to configure/build the library.\n  For basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build cmake --build #build the project sudo make install #[optional] installs to default location (OS-specific)   To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location cmake --build #build the project make install #install to /your/install/prefix   To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\n LD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool    On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.\nPython bindings package ldsctrlest #  With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn\u0026rsquo;t use an undesired version. That environment needs to have NumPy installed.\nThe bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory.\nSee python/ldsctrlest/README.md for usage details.\nAlso, beware that a single build might not work for both the standalone library and the Python package (especially on Windows), since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.\nConsiderations for Windows #  First of all, development on Windows has been more prone to bugs, so if you encounter many problems, consider using a Unix-based system—WSL (Windows Subsystem for Linux) is a good option for Windows users who don\u0026rsquo;t want to work on a different machine.\nYou will likely need to get compiler tools through the Visual Studio installer. Compilation was tested in VS Code using the following kit:\nClang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64)\r"},{"id":4,"href":"/lds-ctrl-est/docs/getting-started/linux-macos/","title":"Linux Macos","section":"LDS C+E Documentation","content":"Downloading the Library #  The source code for this library can be downloaded from stanley-rozell/ldsCtrlEst either by downloading a snapshot or cloning the repository via git.\ngit clone https://github.com/stanley-rozell/ldsCtrlEst.git By default, this would check out the master branch. In most cases, we suggest downloading or checking out the latest release instead.\ncd /path/to/ldsCtrlEst git checkout 0.6 Dependencies #  Note that the primary dependencies of this project listed below must be installed along with their header files and with CMake config files or pkg-config files. The latter files are used to configure this project\u0026rsquo;s build. It is strongly encouraged to install the dependencies below using a package manager (e.g., apt, pacman, macports).\n For project configuration, install cmake as well as pkg-config. The latter is optional. The linear algebra library armadillo is used throughout this repository. The HDF5 library is used to save output from example test programs. For use of this library in Matlab executables (mex) on Linux operating systems, you will need OpenBlas, ensuring the static library libopenblas.a is installed. You will also need to install gfortran.  Compilation + Installation #  This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\n LDSCTRLEST_BUILD_EXAMPLES : [default= ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use  n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake/make to configure/build the library.\n  For basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build make #build the project sudo make install #[optional] installs to default location (OS-specific)   To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location make #build the project make install #install to /your/install/prefix   To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\n LD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool CMAKE_PREFIX_PATH: search path of prefix where CMake will look for package config files  e.g., Assuming you set -DCMAKE_INSTALL_PREFIX=/your/install/prefix during project configuration and your login shell uses the ~/.profile startup file, open ~/.profile in a text editor and add \u0026hellip;\nexport LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/your/install/prefix/lib export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix/lib/pkgconfig export CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix   Common issues #   \u0026ldquo;I have installed all the dependencies including gfortran with a package manager as suggested; however, cmake complains it cannot find the gfortran library.\u0026rdquo;  When gfortran is installed, its library is usually not installed in a standard location like /usr/lib. gfortran is part of the gcc suite, so their libraries are organized together. e.g., When you install gfortran on Ubuntu using apt, its location is /usr/lib/gcc/x86_64-linux-gnu/9, in the case that gcc version 9 is installed. The build configuration script in ldsCtrlEst is written to add LD_LIBRARY_PATH (Unix) or PATH (Windows) to the CMake library search path on Unix or Windows systems, respectively. Therefore, to fix this issue, simply add the directory in which libgfortran was installed to the OS-appropriate environment variable. Continuing with the Ubuntu example above and assuming a Unix login shell whose startup file is ~/.profile, add the following to the file.\nexport LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/gcc/x86_64-linux-gnu/9 \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo;  If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.\nexport CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix "},{"id":5,"href":"/lds-ctrl-est/docs/getting-started/windows/","title":"Windows","section":"LDS C+E Documentation","content":"Dependencies #  To configure and compile the library, you will need Microsoft Visual Studio. After Visual Studio, install the \u0026ldquo;Desktop Development with C++\u0026rdquo; extension under Tools-\u0026gt;Get Tools and Features, ensuring that the \u0026ldquo;C++ Clang Tools for Windows\u0026rdquo; and \u0026ldquo;CMake C++ Tools for Windows\u0026rdquo; options are checked.\nIn addition to the dependencies mentioned in Linux/MacOS, on Windows, this library opts to use Intel\u0026rsquo;s Math Kernel Library (MKL) for linear algebra, rather than OpenBLAS/gfotran. MKL is part of the OneAPI suite. When using the installation wizard, users may either install the entire base toolkit or just the MKL portion. After installing MKL as well as HDF5, add these libraries to the user\u0026rsquo;s PATH environment variable (System Properties-\u0026gt;Advanced-\u0026gt;Environment Variables). Your installation location may vary, but for example, add the following lines to PATH: C:\\Program Files (x86)\\Intel\\oneAPI\\mkl\\latest\\redist\\intel64, C:\\Program Files\\HDF_Group\\HDF5\\1.12.0\\lib .\nOnce HDF5, MKL, and Visual Studio have been installed, download armadillo as detailed previously for Linux/MacOS. Open the source code in Visual Studio, configure the build (Project -\u0026gt; Generate CMake Cache), and install (Build -\u0026gt; Install). Take note of the install location and add this to an environment variable called CMAKE_PREFIX_PATH; create this variable if it does not already exist.\nCompilation #  After installing the dependencies above and downloading the ldsCtrlEst library source code (see Linux/MacOS), open this directory in Visual Studio. Configure the build (Project -\u0026gt; Generate CMake Cache) and build/install the targets (Build -\u0026gt; Install). Finally, take note of the install location and add this to the environment variable PATH.\nCommon issues #   \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted.  Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":6,"href":"/lds-ctrl-est/issues-contributing/","title":"Issues Contributing","section":"LDS Control \u0026 Estimation","content":"Reporting Issues #  If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.\nContributing #  We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.\n"},{"id":7,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/","title":"armamexc","section":"Namespaces","content":"armamexc #  arma/mex interface using Matlab C API More\u0026hellip; Functions #      Name     template \u0026lt;class T \u0026gt; auto m2T_scalar(const mxArray * matlab_scalar)\nConvert Matlab mxArray to scalar of type T.   template \u0026lt;class T \u0026gt; auto m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)\nConvert matlab matrix to armadillo.   template \u0026lt;typename T \u0026gt; auto a2m_mat(arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat)\nConvert armadillo to matlab matrix.   template \u0026lt;typename T \u0026gt; auto a2m_vec(arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec)\nConvert armadillo to matlab vector.    Detailed Description #  Utilities for arma/mex interface using Matlab C API\nFunction Details #  m2T_scalar #  template \u0026lt;class T \u0026gt; inline auto m2T_scalar( const mxArray * matlab_scalar ) Parameters:\n matlab_scalar matlab scalar  Template Parameters:\n T type  Return: scalar of type T\nm2a_mat #  template \u0026lt;class T \u0026gt; inline auto m2a_mat( const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true ) Parameters:\n matlab_mat matlab matrix copy_aux_mem [optional] whether to copy auxiliary memory strict [optional] strictly enforce the above  Template Parameters:\n T type  Return: armadillo matrix of type T\na2m_mat #  template \u0026lt;typename T \u0026gt; inline auto a2m_mat( arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat ) Parameters:\n arma_mat armadillo matrix  Return: matlab matrix\na2m_vec #  template \u0026lt;typename T \u0026gt; inline auto a2m_vec( arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec ) Parameters:\n arma_vec armadillo vector  Return: matlab vector\n Updated on 4 May 2022 at 16:34:51 Eastern Daylight Time\n"},{"id":8,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/","title":"armamexcpp","section":"Namespaces","content":"armamexcpp #  arma/mex interface using Matlab C++ API More\u0026hellip; Functions #      Name     template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat(matlab::data::CellArray \u0026amp; matlab_cell)\nConvert matlab cell array to vector of armadillo matrices.   template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array)\nConvert matlab matrix to a vector of scalars.   template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo vector.   template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo matrix.   template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat(const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab matrix.   template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec(const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab vector.   template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec(const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert vector of scalar T to matlab matrix.    Detailed Description #  utilities for arma/mex interface using Matlab C++ API\nFunction Details #  m2a_cellmat #  template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat( matlab::data::CellArray \u0026amp; matlab_cell ) Parameters:\n matlab_cell matlab cell  Template Parameters:\n T type  Return: vector of armadillo matrices of type T\nm2s_vec #  template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array ) Parameters:\n matlab_array matlab array  Template Parameters:\n T type  Return: vector of type T\nm2a_vec #  template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\n matlab_array matlab array  Template Parameters:\n T type  Return: armadillo vector of type T\nm2a_mat #  template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\n matlab_array matlab matrix  Template Parameters:\n T type  Return: armadillo matrix of type T\na2m_mat #  template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat( const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\n arma_mat arma matrix factory matlab \u0026ldquo;array factory\u0026rdquo;  Template Parameters:\n T type  Return: matlab matrix\na2m_vec #  template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec( const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\n arma_vec armadillo vector factory matlab \u0026ldquo;array factory\u0026rdquo;  Template Parameters:\n T type  Return: matlab matrix\ns2m_vec #  template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec( const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\n std_vec standard vector factory matlab \u0026ldquo;array factory\u0026rdquo;  Template Parameters:\n T type  Return: matlab matrix\n Updated on 4 May 2022 at 16:34:51 Eastern Daylight Time\n"},{"id":9,"href":"/lds-ctrl-est/docs/terminology/control-estimation/","title":"C\u0026E","section":"LDS C+E Documentation","content":"Control \u0026amp; Estimation #  The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.\nState estimation #  In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by   \\[\r\\widehat{\\mathbf{x}}_{t|t-1} = \\mathbf{A}\\widehat{\\mathbf{x}}_{t-1|t-1} \u0026#43; \\mathbf{B} u_{t-1} \u0026#43; \\mathbf{m}_{t-1} \\;,\r\\]   \\[\r\\widehat{\\mathbf{x}}_{t|t} = \\widehat{\\mathbf{x}}_{t|t-1} \u0026#43; \\mathbf{K}^{\\rm e}_t \\left(\\mathbf{z}_t - \\widehat{\\mathbf{y}}_{t|t-1}\\right)\\;,\r\\]  where  \\( \\hat{\\left(\\cdot\\right)}_{t|j} \\)  indicates an estimate at time  \\( t \\)  given data up to time  \\( j \\)  inclusive,  \\( \\mathbf{K}^{\\rm e} \\)  is the estimator gain, and\n \\[ \\widehat{\\mathbf{y}}_{t|t-1} = h\\left( \\widehat{\\mathbf{x}}_{t|t-1} \\right) \\; .\\]  In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.\nIn the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.\nAdaptive estimation of process disturbance #  Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.\nWhen parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk  \\[\r\\mathbf{m}_{t} = \\mathbf{m}_{t-1} \u0026#43; \\mathbf{w}^m_{t-1} \\;,\r\\]  where  \\( \\mathbf{w}^m \\sim \\mathcal{N}\\left(0, \\mathbf{Q}_m\\right)\\)  . Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.\nControl #  Given the estimated state, the controller updates the inputs to the system according to the following law:  \\[\r\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\r\\]  where  \\( \\left( \\cdot \\right)^{\\rm ref} \\)  correspond to reference/target signals and  \\( \\mathbf{K}^c_x \\)  is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.\nIf users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as\n \\[\r\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right) - \\mathbf{K}^c_{\\rm inty} \\sum_{j=1}^{t}\\left( \\widehat{\\mathbf{y}}_j - \\mathbf{y}^{\\rm ref}_j \\right) \\;.\r\\]  An additional option available to users is a control law that updates the change in u,\n \\[\r\\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\left(\\mathbf{u}_{t-1} - \\mathbf{u}^{\\rm ref}_{t-1} \\right) - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\r\\]   \\[\r\\mathbf{u}_{t} = \\mathbf{u}_{t-1} \u0026#43; \\Delta\\mathbf{u}_{t} \\; .\r\\]  Notice that this takes the form of a first-order difference equation for updating control (i.e.,  \\( \\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\mathbf{u}_{t-1} \u0026#43; \\epsilon_{t-1} \\)  ), effectively low-pass filtering the input depending on the characteristics of  \\( \\mathbf{K}^c_u \\)  . This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.\nIntegral action and the  \\( \\Delta \\mathbf{u} \\)  control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR\u0026rsquo;d to use in combination.\nCalculating reference state-control from output #  In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce  \\( \\mathbf{x}^{\\rm ref} \\)  and  \\( \\mathbf{u}^{\\rm ref} \\)  . Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.\n"},{"id":10,"href":"/lds-ctrl-est/docs/api/classes/","title":"Classes","section":"LDS C+E Documentation","content":"Classes #    lds::Controller\n  lds::EM\n  lds::Fit LDS Fit Type.\n  lds::SSID\n  lds::SwitchedController SwitchedController Type.\n  lds::System Linear Dynamical System Type.\n  lds::UniformMatrixList\n  lds::UniformSystemList\n  lds::UniformVectorList\n  lds::gaussian::Controller Gaussian-observation Controller Type.\n  lds::gaussian::Fit GLDS Fit Type.\n  lds::gaussian::FitEM GLDS E-M Fit Type.\n  lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.\n  lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.\n  lds::gaussian::System Gaussian LDS Type.\n  lds::poisson::Controller PLDS Controller Type.\n  lds::poisson::Fit PLDS Fit Type.\n  lds::poisson::FitEM PLDS E-M Fit Type.\n  lds::poisson::FitSSID Subspace Identification (SSID) for PLDS.\n  lds::poisson::SwitchedController Poisson-observation SwitchedController Type.\n  lds::poisson::System Poisson System type.\n   Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":11,"href":"/lds-ctrl-est/docs/api/modules/group__control__masks/","title":"Control Mode Bit Masks","section":"Modules","content":"Control Mode Bit Masks #  provides fill types for constructing new armadillo vectors, matrices More\u0026hellip; Attributes #      Name     const std::size_t kControlTypeDeltaU control designed to penalize change in input   const std::size_t kControlTypeIntY control using integral action   const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m    Detailed Description #  Control mode bit masks. These can be bit-wise OR\u0026rsquo;d to use in combination.\nAttribute Details #  kControlTypeDeltaU #  static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY #  static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM #  static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\n Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":12,"href":"/lds-ctrl-est/docs/api/modules/group__defaults/","title":"Defaults","section":"Modules","content":"Defaults #  \nMore\u0026hellip; Attributes #      Name     const data_t kDefaultP0 default state estimate covar   const data_t kDefaultQ0 default process noise covar   const data_t kDefaultR0 default output noise covar    Detailed Description #  Default values for common variables (e.g., default diagonal elements of covariances)\nAttribute Details #  kDefaultP0 #  static const data_t kDefaultP0 = 1e-6; kDefaultQ0 #  static const data_t kDefaultQ0 = 1e-6; kDefaultR0 #  static const data_t kDefaultR0 = 1e-2;  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":13,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/","title":"eg_glds_ctrl.cpp","section":"Examples","content":"eg_glds_ctrl.cpp #  Example GLDS Control\n//===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); // output noise covariance  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb.  data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_x).fill(m_low); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables:  // if following enabled, adapts set point with re-estimated process  // disturbance n.b., should not need integral action if this is enabled as the  // adaptive estimator minimizes DC error  bool do_adaptive_set_point = false; // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err  // setting initial state to target to avoid error at onset:  Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance  control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; } // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(9); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation  // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input  Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals  y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,  // xTrue, mTrue saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; }  Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":14,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/","title":"eg_glds_du_plds_ctrl.cpp","section":"Examples","content":"eg_glds_du_plds_ctrl.cpp #  Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).\n//===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb.  data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance  Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables:  // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input  // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du =  // 1e-2.  Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err  Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp  // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control)  control_type = control_type | lds::kControlTypeDeltaU; // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(10); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation  // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input  Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals  y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,  // xTrue, mTrue saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; }  Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":15,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/","title":"eg_plds_ctrl.cpp","section":"Examples","content":"eg_plds_ctrl.cpp #  Example PLDS Control\n//===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains  // n.b., Can either use Vector (arma::Col) or std::vector  Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error  Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err  // Set control type bit mask, so controller knows what to do  size_t control_type = lds::kControlTypeIntY; // integral action  // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled...  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions  controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller  lds::poisson::Controller controller; { // Create model used for control.  lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance  Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition  // adaptively re-estimate process disturbance (m)  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type  controller.set_control_type(control_type); // set controller gains  controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above  // target:  data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val  y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference  data_t f = 0.5; // freq [=] Hz  Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system.  z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Notably, it does this in the  // log-linear space (i.e., log(y)).  //  // Therefore, it is only applicable to regulation problems or cases where  // reference trajectory changes slowly compared to system dynamics.  controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; }  Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":16,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/","title":"eg_plds_est.cpp","section":"Examples","content":"eg_plds_est.cpp #  Example PLDS Estimation\n//===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; // no. inputs  size_t n_x = 1; // no. states  size_t n_y = 1; // no. outputs  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.  // construct ground truth system...  lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  // Assign params.  system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation  // e.g., will create a model with incorrect disturbance  lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object  system_estimator = system_true; // wrong disturbance  Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.  // turn on adaptive disturbance estimation  system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting  // on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation :  // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus)  Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk  Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in...  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params  Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions  y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system.  z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update)  system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals  y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; }  Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":17,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/","title":"eg_plds_switched_ctrl.cpp","section":"Examples","content":"eg_plds_switched_ctrl.cpp #  Example Switched PLDS Control\n//===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control  bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching  size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2  data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1  // simulated system being controlled  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well  // characterized by two LDS models with different input matrices.**  data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions  // reference  Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2  Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller  lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains  { // create switched controller sub-systems  // system 1  lds::poisson::System sys1(controlled_system); // set process noise covariance  Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m)  // n.b. using arbitrary default value for process noise if enabled.  sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset:  Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions  cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2  lds::poisson::System sys2 = sys1; // set parameters  sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s:  Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive.  k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables  size_t control_type = 0; // no integral action, etc  switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements  Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control.  Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in...  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params  Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val  y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain  // Assume another algorithm decodes this mode change and signals the  // switched_controller  Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1  { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2  if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system.  z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control  u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; }  Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":18,"href":"/lds-ctrl-est/docs/api/examples/","title":"Examples","section":"LDS C+E Documentation","content":"Examples #    eg_glds_ctrl.cpp\n  eg_glds_du_plds_ctrl.cpp\n  eg_plds_ctrl.cpp\n  eg_plds_est.cpp\n  eg_plds_switched_ctrl.cpp\n   Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":19,"href":"/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/","title":"examples","section":"Files","content":"examples #  Files #     Name     examples/eg_glds_ctrl.cpp   examples/eg_glds_du_plds_ctrl.cpp   examples/eg_plds_ctrl.cpp   examples/eg_plds_est.cpp   examples/eg_plds_switched_ctrl.cpp     Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":20,"href":"/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/","title":"examples/eg_glds_ctrl.cpp","section":"Files","content":"examples/eg_glds_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Going to simulate a switching disturbance (m) acting on system\nSource code #  //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); // output noise covariance  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb.  data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_x).fill(m_low); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables:  // if following enabled, adapts set point with re-estimated process  // disturbance n.b., should not need integral action if this is enabled as the  // adaptive estimator minimizes DC error  bool do_adaptive_set_point = false; // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err  // setting initial state to target to avoid error at onset:  Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance  control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; } // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(9); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation  // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input  Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals  y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,  // xTrue, mTrue saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; }  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":21,"href":"/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/","title":"examples/eg_glds_du_plds_ctrl.cpp","section":"Files","content":"examples/eg_glds_du_plds_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Going to simulate a switching disturbance (m) acting on system\nSource code #  //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb.  data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance  Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables:  // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input  // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du =  // 1e-2.  Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err  Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp  // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control)  control_type = control_type | lds::kControlTypeDeltaU; // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(10); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation  // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input  Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals  y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,  // xTrue, mTrue saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; }  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":22,"href":"/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/","title":"examples/eg_plds_ctrl.cpp","section":"Files","content":"examples/eg_plds_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Going to simulate a switching disturbance (m) acting on system\nSource code #  //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains  // n.b., Can either use Vector (arma::Col) or std::vector  Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error  Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err  // Set control type bit mask, so controller knows what to do  size_t control_type = lds::kControlTypeIntY; // integral action  // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled...  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions  controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller  lds::poisson::Controller controller; { // Create model used for control.  lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance  Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition  // adaptively re-estimate process disturbance (m)  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type  controller.set_control_type(control_type); // set controller gains  controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above  // target:  data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val  y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference  data_t f = 0.5; // freq [=] Hz  Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system.  z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Notably, it does this in the  // log-linear space (i.e., log(y)).  //  // Therefore, it is only applicable to regulation problems or cases where  // reference trajectory changes slowly compared to system dynamics.  controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; }  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":23,"href":"/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/","title":"examples/eg_plds_est.cpp","section":"Files","content":"examples/eg_plds_est.cpp #  Functions #      Name     Matrix random_walk(size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0)   int main()    Function Details #  random_walk #  Matrix random_walk( size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0 ) main #  int main() Source code #  //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; // no. inputs  size_t n_x = 1; // no. states  size_t n_y = 1; // no. outputs  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.  // construct ground truth system...  lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  // Assign params.  system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation  // e.g., will create a model with incorrect disturbance  lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object  system_estimator = system_true; // wrong disturbance  Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.  // turn on adaptive disturbance estimation  system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting  // on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation :  // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus)  Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk  Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in...  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params  Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions  y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system.  z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update)  system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals  y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; }  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":24,"href":"/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/","title":"examples/eg_plds_switched_ctrl.cpp","section":"Files","content":"examples/eg_plds_switched_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Source code #  //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control  bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching  size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2  data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1  // simulated system being controlled  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well  // characterized by two LDS models with different input matrices.**  data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions  // reference  Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2  Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller  lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains  { // create switched controller sub-systems  // system 1  lds::poisson::System sys1(controlled_system); // set process noise covariance  Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m)  // n.b. using arbitrary default value for process noise if enabled.  sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset:  Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions  cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2  lds::poisson::System sys2 = sys1; // set parameters  sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s:  Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive.  k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables  size_t control_type = 0; // no integral action, etc  switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements  Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control.  Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in...  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params  Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val  y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain  // Assume another algorithm decodes this mode change and signals the  // switched_controller  Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1  { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2  if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system.  z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control  u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; }  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":25,"href":"/lds-ctrl-est/docs/api/files/","title":"Files","section":"LDS C+E Documentation","content":"Files #    examples/eg_glds_ctrl.cpp\n  examples/eg_glds_du_plds_ctrl.cpp\n  examples/eg_plds_ctrl.cpp\n  examples/eg_plds_est.cpp\n  examples/eg_plds_switched_ctrl.cpp\n  ldsCtrlEst_h/lds.h lds namespace\n  ldsCtrlEst_h/lds_ctrl.h Controller.\n  ldsCtrlEst_h/lds_fit.h LDS base fit type.\n  ldsCtrlEst_h/lds_fit_em.h subspace identification\n  ldsCtrlEst_h/lds_fit_ssid.h subspace identification\n  ldsCtrlEst_h/lds_gaussian.h glds namespace\n  ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.\n  ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.\n  ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.\n  ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.\n  ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.\n  ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.\n  ldsCtrlEst_h/lds_poisson.h plds namespace\n  ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.\n  ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.\n  ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.\n  ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.\n  ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.\n  ldsCtrlEst_h/lds_poisson_sys.h PLDS base type.\n  ldsCtrlEst_h/lds_sctrl.h SwitchedController type.\n  ldsCtrlEst_h/lds_sys.h LDS base type.\n  ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.\n  ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.\n  ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.\n  ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)\n  ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)\n  src/lds.cpp misc lds namespace functions\n  src/lds_gaussian_sys.cpp GLDS base type.\n  src/lds_poisson_sys.cpp PLDS base type.\n  src/lds_sys.cpp LDS base type.\n  src/lds_uniform_vecs.cpp Uniformly sized vectors.\n   Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":26,"href":"/lds-ctrl-est/docs/tutorials/eg_glds_control/","title":"GLDS Control","section":"LDS C+E Examples","content":"GLDS Control Tutorial #  This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.\nThe full code for this can be found here.\nPreamble #  In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system #  A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.\n// Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example,   \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\]   \\[\ry_{t} = x_t\r\\]  where  \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\)  .\nNow, create non-default parameters for this model.\n// Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); // output noise covariance  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.\n/// Going to simulate a switching disturbance (m) acting on system  size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb.  data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_y).fill(m_low); Finally, assign the parameters using corresponding set-methods.\n// Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); Creating the controller #  Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.\nFor the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.\n// make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nWith the controller constructed, control variables may be set.\n// Control variables:  // if following enabled, adapts set point with re-estimated process  // disturbance n.b., should not need integral action if this is enabled as the  // adaptive estimator minimizes DC error  bool do_adaptive_set_point = false; // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err  // setting initial state to target to avoid error at onset:  Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance  control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; } // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(9); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); Simulating control #  In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.\nThe control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.\n// Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); Example simulation result #  Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller\u0026rsquo;s online estimates of the output, state, and disturbance are given in purple.\n"},{"id":27,"href":"/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/","title":"include","section":"Files","content":"include #  Directories #     Name     ldsCtrlEst_h     Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":28,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds/","title":"lds","section":"Namespaces","content":"lds #  Linear Dynamical Systems (LDS) namespace. Namespaces #     Name     lds::gaussian Linear Dynamical Systems with Gaussian observations.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::Controller   class lds::EM   class lds::Fit LDS Fit Type.   class lds::SSID   class lds::SwitchedController SwitchedController Type.   class lds::System Linear Dynamical System Type.   class lds::UniformMatrixList   class lds::UniformSystemList   class lds::UniformVectorList    Types #      Name     enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}\nweighting options for SSID   enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2}   using double data_t   using arma::Col\u0026lt; data_t \u0026gt; Vector   using arma::Mat\u0026lt; data_t \u0026gt; Matrix   using arma::Cube\u0026lt; data_t \u0026gt; Cube   using arma::subview\u0026lt; data_t \u0026gt; View    Functions #      Name     void Limit(std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub)   void Limit(Vector \u0026amp; x, data_t lb, data_t ub)   void Limit(Matrix \u0026amp; x, data_t lb, data_t ub)   void Reassign(Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Vector in place   void Reassign(Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Matrix in place   void ForceSymPD(Matrix \u0026amp; X)\nforces matrix to be symmetric positive-definite   void ForceSymMinEig(Matrix \u0026amp; X, data_t eig_min =0)\nforces matrix to be symmetric and have a minimum eigenvalue   void lq(Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X)\nLQ decomposition.   Matrix calcCov(const Matrix \u0026amp; A, const Matrix \u0026amp; B)\nCalculate covariance matrix.    Attributes #      Name     const std::size_t kControlTypeDeltaU control designed to penalize change in input   const std::size_t kControlTypeIntY control using integral action   const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m   const data_t kInf Some useful numbers.   const data_t kPi   const data_t kDefaultP0 default state estimate covar   const data_t kDefaultQ0 default process noise covar   const data_t kDefaultR0 default output noise covar    Type Details #  SSIDWt #     Enumerator Value Description     kSSIDNone  None.   kSSIDMOESP  MOESP (AKA \u0026ldquo;robust method\u0026rdquo; in van Overschee 1996)   kSSIDCVA  CVA \u0026ldquo;Canonical Variate Analysis\u0026rdquo;.    Weighting options for singular value decomposition performed during subspace identification (SSID)\nReference:\nvan Overschee, de Moor. 1996. Subspace Identification for Linear Systems.\nMatrixListFreeDim #     Enumerator Value Description     kMatFreeDimNone  neither dim free to be hetero in mat list   kMatFreeDim1  allow 1st dim of mats in list to be hetero   kMatFreeDim2  allow 2nd dim of mats in list to be hetero    data_t #  using lds::data_t = typedef double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nVector #  using lds::Vector = typedef arma::Col\u0026lt;data_t\u0026gt;; Matrix #  using lds::Matrix = typedef arma::Mat\u0026lt;data_t\u0026gt;; Cube #  using lds::Cube = typedef arma::Cube\u0026lt;data_t\u0026gt;; View #  using lds::View = typedef arma::subview\u0026lt;data_t\u0026gt;; Function Details #  Limit #  inline void Limit( std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub ) Limit #  inline void Limit( Vector \u0026amp; x, data_t lb, data_t ub ) Limit #  inline void Limit( Matrix \u0026amp; x, data_t lb, data_t ub ) Reassign #  inline void Reassign( Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\n some some Vector other other Vector parenthetical optional description provided by caller to ease debugging  Reassign #  inline void Reassign( Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\n some some Matrix other other Matrix parenthetical optional description provided by caller to ease debugging  ForceSymPD #  void ForceSymPD( Matrix \u0026amp; X ) Parameters:\n X mutated matrix  ForceSymMinEig #  void ForceSymMinEig( Matrix \u0026amp; X, data_t eig_min =0 ) Parameters:\n X mutated matrix eig_min [optional] minimum eigen value  lq #  void lq( Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X ) Parameters:\n L lower triangle matrix Qt orthonormal matrix (transposed cf QR decomp) X matrix being decomposed  calcCov #  Matrix calcCov( const Matrix \u0026amp; A, const Matrix \u0026amp; B ) Parameters:\n A some matrix B some other matrix  Return: covariance\nAttribute Details #  kControlTypeDeltaU #  static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY #  static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM #  static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\nkInf #  static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); kPi #  static const data_t kPi = arma::datum::pi; kDefaultP0 #  static const data_t kDefaultP0 = 1e-6; kDefaultQ0 #  static const data_t kDefaultQ0 = 1e-6; kDefaultR0 #  static const data_t kDefaultR0 = 1e-2;  Updated on 4 May 2022 at 16:34:51 Eastern Daylight Time\n"},{"id":29,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1controller/","title":"lds::Controller","section":"Classes","content":"lds::Controller #  More\u0026hellip;\nInherited by lds::gaussian::Controller, lds::poisson::Controller, lds::SwitchedController\u0026lt; System \u0026gt;\nPublic Functions #      Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes #      Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type    Detailed Description #  template \u0026lt;typename System \u0026gt; class lds::Controller;   Public Function Details #  Controller #  Controller() =default  Controller #  inline Controller( const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\n sys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask  Template Parameters:\n System type derived from lds::System   Controller #  inline Controller( System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\n sys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask  Template Parameters:\n System type derived from lds::System   Control #  inline const Vector \u0026amp; Control( const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\n z measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true)  Return: updated control signal\nUpdates the control signal (single-step). This is the most flexible option, but requires user to have set the controller\u0026rsquo;s y_ref, x_ref, and u_ref variables.\n ControlOutputReference #  inline const Vector \u0026amp; ControlOutputReference( const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\n z measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_estimation [optional] whether to update state estimate (if false, effectively open-loop control) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true)  Return: updated control signal\nUpdates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.\n sys #  inline const System \u0026amp; sys() const  Kc #  inline const Matrix \u0026amp; Kc() const  Kc_inty #  inline const Matrix \u0026amp; Kc_inty() const  Kc_u #  inline const Matrix \u0026amp; Kc_u() const  g_design #  inline const Vector \u0026amp; g_design() const  u_ref #  inline const Vector \u0026amp; u_ref() const  x_ref #  inline const Vector \u0026amp; x_ref() const  y_ref #  inline const Vector \u0026amp; y_ref() const  control_type #  inline size_t control_type() const  tau_awu #  inline data_t tau_awu() const  u_lb #  inline data_t u_lb() const  u_ub #  inline data_t u_ub() const  set_sys #  inline void set_sys( const System \u0026amp; sys )  set_g_design #  inline void set_g_design( const Vector \u0026amp; g_design )  set_u_ref #  inline void set_u_ref( const Vector \u0026amp; u_ref )  set_x_ref #  inline void set_x_ref( const Vector \u0026amp; x_ref )  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) Reimplemented by: lds::poisson::SwitchedController::set_y_ref, lds::gaussian::SwitchedController::set_y_ref, lds::poisson::Controller::set_y_ref, lds::gaussian::Controller::set_y_ref\n set_Kc #  inline void set_Kc( const Matrix \u0026amp; Kc )  set_Kc_inty #  inline void set_Kc_inty( const Matrix \u0026amp; Kc_inty )  set_Kc_u #  inline void set_Kc_u( const Matrix \u0026amp; Kc_u )  set_tau_awu #  inline void set_tau_awu( data_t tau )  set_control_type #  inline void set_control_type( size_t control_type ) Parameters:\n control_type control type bit mask  Template Parameters:\n System type derived from lds::System   set_u_lb #  inline void set_u_lb( data_t u_lb ) Parameters:\n u_lb control lower bound   set_u_ub #  inline void set_u_ub( data_t u_ub ) Parameters:\n u_ub control upper bound   Reset #  inline void Reset()  Print #  inline void Print()  Protected Attribute Details #  **sys_** #  System sys_;  **u_** #  Vector u_;  **u_return_** #  Vector u_return_;  **g_design_** #  Vector g_design_;  **u_ref_** #  Vector u_ref_;  **u_ref_prev_** #  Vector u_ref_prev_;  **x_ref_** #  Vector x_ref_;  **y_ref_** #  Vector y_ref_;  **cx_ref_** #  Vector cx_ref_;  **Kc_** #  Matrix Kc_;  **Kc_u_** #  Matrix Kc_u_;  **Kc_inty_** #  Matrix Kc_inty_;  **du_ref_** #  Vector du_ref_;  **dv_ref_** #  Vector dv_ref_;  **v_ref_** #  Vector v_ref_;  **dv_** #  Vector dv_;  **v_** #  Vector v_;  **int_e_** #  Vector int_e_;  **int_e_awu_adjust_** #  Vector int_e_awu_adjust_;  **u_sat_** #  Vector u_sat_;  **do_control_prev_** #  bool do_control_prev_ = false;  **do_lock_control_prev_** #  bool do_lock_control_prev_ = false;  **u_saturated_** #  bool u_saturated_ = false;  **u_lb_** #  data_t u_lb_ {};  **u_ub_** #  data_t u_ub_ {};  **tau_awu_** #  data_t tau_awu_ {};  **k_awu_** #  data_t k_awu_ = 0;  **t_since_control_onset_** #  data_t t_since_control_onset_ = 0;  **control_type_** #  size_t control_type_ {};   Updated on 4 May 2022 at 16:34:51 Eastern Daylight Time\n"},{"id":30,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1em/","title":"lds::EM","section":"Classes","content":"lds::EM #  More\u0026hellip;\nInherited by lds::gaussian::FitEM, lds::poisson::FitEM\nPublic Functions #      Name      EM() =default\nConstructs a new EMFit type.    EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.    EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.   virtual ~EM() =default   const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller.   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time)   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time)   const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance   const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1)   const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance   size_t n_t_tot()\ntotal number of time samples   const Vector \u0026amp; theta() const\ngets parameters updated in M step    Protected Functions #      Name     void Expectation(bool force_common_initial =false)\nExpectation step.   void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step.   void MaximizeDynamics()   void MaximizeQ()   void MaximizeInitial()   virtual void MaximizeOutput() =0   virtual void MaximizeMeasurement() =0   void Smooth(bool force_common_initial)\nget smoothed estimates   virtual void RecurseKe(Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t) =0\nrecursively update estimator gain Ke   void Reset()\nreset to initial conditions   void InitVars()\nInitializes the variables.   Vector UpdateTheta()\nupdates parameter list, theta    Protected Attributes #      Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   std::vector\u0026lt; Matrix \u0026gt; x_ state estimate   std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov   std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance   std::vector\u0026lt; Matrix \u0026gt; y_ output estimate   Matrix diag_y_   Matrix sum_E_x_t_x_t_ state covariance (current time)   Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1)   Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance   Fit fit_   Vector theta_   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials    Detailed Description #  template \u0026lt;typename Fit \u0026gt; class lds::EM;   Public Function Details #  EM #  EM() =default  EM #  EM( size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\n n_x number of states dt sample period u_train input training data z_train measurement training data   EM #  EM( const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\n fit0 initial fit u_train input training data z_train measurement training data   ~EM #  virtual ~EM() =default  Run #  const Fit \u0026amp; Run( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2 ) Parameters:\n calc_dynamics [optional] whether to calculate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law max_iter max number of iterations tol convergence tolerance (max fractional abs change)  Return: Fit\n ReturnData #  inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\n x #  inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const  y #  inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const  sum_E_x_t_x_t #  inline const Matrix \u0026amp; sum_E_x_t_x_t() const  sum_E_xu_tm1_xu_tm1 #  inline const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const  sum_E_xu_t_xu_tm1 #  inline const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const  n_t_tot #  inline size_t n_t_tot()  theta #  inline const Vector \u0026amp; theta() const  Protected Function Details #  Expectation #  void Expectation( bool force_common_initial =false ) Parameters:\n force_common_initial whether to force common initial condition for all trials   Maximization #  void Maximization( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false ) Parameters:\n calc_dynamics [optional] whether to caclulate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law   MaximizeDynamics #  void MaximizeDynamics()  MaximizeQ #  void MaximizeQ()  MaximizeInitial #  void MaximizeInitial()  MaximizeOutput #  virtual void MaximizeOutput() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput\n MaximizeMeasurement #  virtual void MaximizeMeasurement() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement\n Smooth #  void Smooth( bool force_common_initial ) Parameters:\n force_common_initial whether to force common initial conditions   RecurseKe #  virtual void RecurseKe( Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t ) =0 Parameters:\n Ke estimator gain P_pre cov of predicted state est. P_post cov of postior sate est. t time  Reimplemented by: lds::gaussian::FitEM::RecurseKe, lds::poisson::FitEM::RecurseKe\n Reset #  void Reset()  InitVars #  void InitVars()  UpdateTheta #  Vector UpdateTheta() Return: parameter list\n Protected Attribute Details #  **u_** #  UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_;  **z_** #  UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_;  **x_** #  std::vector\u0026lt; Matrix \u0026gt; x_;  **P_** #  std::vector\u0026lt; Cube \u0026gt; P_;  **P_t_tm1_** #  std::vector\u0026lt; Cube \u0026gt; P_t_tm1_;  **y_** #  std::vector\u0026lt; Matrix \u0026gt; y_;  **diag_y_** #  Matrix diag_y_;  **sum_E_x_t_x_t_** #  Matrix sum_E_x_t_x_t_;  **sum_E_xu_tm1_xu_tm1_** #  Matrix sum_E_xu_tm1_xu_tm1_;  **sum_E_xu_t_xu_tm1_** #  Matrix sum_E_xu_t_xu_tm1_;  **fit_** #  Fit fit_;  **theta_** #  Vector theta_;  **dt_** #  data_t dt_ {};  **n_u_** #  size_t n_u_ {};  **n_x_** #  size_t n_x_ {};  **n_y_** #  size_t n_y_ {};  **n_trials_** #  size_t n_trials_ {};  **n_t_** #  std::vector\u0026lt; size_t \u0026gt; n_t_;  **n_t_tot_** #  size_t n_t_tot_ {};   Updated on 4 May 2022 at 16:34:51 Eastern Daylight Time\n"},{"id":31,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1fit/","title":"lds::Fit","section":"Classes","content":"lds::Fit #  LDS Fit Type. #include \u0026lt;lds_fit.h\u0026gt;\nInherited by lds::gaussian::Fit, lds::poisson::Fit\nPublic Functions #      Name      Fit() =default\nConstructs a new Fit.    Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit.   virtual ~Fit() =default   size_t n_u() const\ngets number of inputs   size_t n_x() const\ngets number of states   size_t n_y() const\ngets number of outputs   data_t dt() const\ngets sample period   const Matrix \u0026amp; A() const\ngets state matrix   const Matrix \u0026amp; B() const\ngets input matrix   const Vector \u0026amp; g() const\ngets input gain   const Vector \u0026amp; m() const\ngets process disturbance   const Matrix \u0026amp; Q() const\ngets process noise covariance   const Vector \u0026amp; x0() const\ngets initial state estimate   const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate   const Matrix \u0026amp; C() const\ngets output matrix   const Vector \u0026amp; d() const\ngets output bias   virtual const Matrix \u0026amp; R() const =0   void set_A(const Matrix \u0026amp; A)\nsets state matrix   void set_B(const Matrix \u0026amp; B)\nsets input matrix   void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor   void set_m(const Vector \u0026amp; m)\nsets process disturbance   void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance   virtual void set_R(const Matrix \u0026amp; R) =0\nsets output noise covariance (if any)   void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate   void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance   void set_C(const Matrix \u0026amp; C)\nsets output matrix   void set_d(const Vector \u0026amp; d)\nsets output bias   View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function   View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function   virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) =0\noutput function    Protected Attributes #      Name     data_t dt_ sample period   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Vector m_ process noise mean   Matrix Q_ process noise cov   Matrix C_ output matrix   Vector d_ output bias   Matrix R_ measurement noise   Vector x0_ initial state   Matrix P0_ initial covar   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs      Public Function Details #  Fit #  Fit() =default  Fit #  Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period   ~Fit #  virtual ~Fit() =default  n_u #  inline size_t n_u() const  n_x #  inline size_t n_x() const  n_y #  inline size_t n_y() const  dt #  inline data_t dt() const  A #  inline const Matrix \u0026amp; A() const  B #  inline const Matrix \u0026amp; B() const  g #  inline const Vector \u0026amp; g() const  m #  inline const Vector \u0026amp; m() const  Q #  inline const Matrix \u0026amp; Q() const  x0 #  inline const Vector \u0026amp; x0() const  P0 #  inline const Matrix \u0026amp; P0() const  C #  inline const Matrix \u0026amp; C() const  d #  inline const Vector \u0026amp; d() const  R #  virtual const Matrix \u0026amp; R() const =0 Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R\n set_A #  inline void set_A( const Matrix \u0026amp; A )  set_B #  inline void set_B( const Matrix \u0026amp; B )  set_g #  inline void set_g( const Vector \u0026amp; g )  set_m #  inline void set_m( const Vector \u0026amp; m )  set_Q #  inline void set_Q( const Matrix \u0026amp; Q )  set_R #  virtual void set_R( const Matrix \u0026amp; R ) =0 Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R\n set_x0 #  inline void set_x0( const Vector \u0026amp; x0 )  set_P0 #  inline void set_P0( const Matrix \u0026amp; P0 )  set_C #  inline void set_C( const Matrix \u0026amp; C )  set_d #  inline void set_d( const Vector \u0026amp; d )  f #  inline View f( Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t ) Parameters:\n x state estimate (over time) u input (over time) t time index  Return: view of updated state\n f #  inline View f( Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t ) Parameters:\n x_pre predicted state est. x_post posterior state est. u input (over time) t time index  Return: view of predicted state\n h #  virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) =0 Parameters:\n y output estimate (over time) x state estimate (over time) t time index  Return: output\nReimplemented by: lds::poisson::Fit::h, lds::gaussian::Fit::h\n Protected Attribute Details #  **dt_** #  data_t dt_ {};  **A_** #  Matrix A_;  **B_** #  Matrix B_;  **g_** #  Vector g_;  **m_** #  Vector m_;  **Q_** #  Matrix Q_;  **C_** #  Matrix C_;  **d_** #  Vector d_;  **R_** #  Matrix R_;  **x0_** #  Vector x0_;  **P0_** #  Matrix P0_;  **n_u_** #  size_t n_u_ {};  **n_x_** #  size_t n_x_ {};  **n_y_** #  size_t n_y_ {};   Updated on 4 May 2022 at 16:34:51 Eastern Daylight Time\n"},{"id":32,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/","title":"lds::gaussian","section":"Namespaces","content":"lds::gaussian #  Linear Dynamical Systems with Gaussian observations. Classes #      Name     class lds::gaussian::Controller Gaussian-observation Controller Type.   class lds::gaussian::Fit GLDS Fit Type.   class lds::gaussian::FitEM GLDS E-M Fit Type.   class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.   class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.   class lds::gaussian::System Gaussian LDS Type.     Updated on 4 May 2022 at 16:34:51 Eastern Daylight Time\n"},{"id":33,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1controller/","title":"lds::gaussian::Controller","section":"Classes","content":"lds::gaussian::Controller #  Gaussian-observation Controller Type. #include \u0026lt;lds_gaussian_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions #      Name     virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output    Additional inherited members #  Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type      Public Function Details #  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\n  Updated on 4 May 2022 at 16:34:51 Eastern Daylight Time\n"},{"id":34,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/","title":"lds::gaussian::Fit","section":"Classes","content":"lds::gaussian::Fit #  GLDS Fit Type. #include \u0026lt;lds_gaussian_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions #      Name      Fit() =default    Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit.   virtual const Matrix \u0026amp; R() const override\ngets measurement noise covariance   virtual void set_R(const Matrix \u0026amp; R) override\nsets measurement noise covariance   virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function    Additional inherited members #  Public Functions inherited from lds::Fit\n    Name     virtual ~Fit() =default   size_t n_u() const\ngets number of inputs   size_t n_x() const\ngets number of states   size_t n_y() const\ngets number of outputs   data_t dt() const\ngets sample period   const Matrix \u0026amp; A() const\ngets state matrix   const Matrix \u0026amp; B() const\ngets input matrix   const Vector \u0026amp; g() const\ngets input gain   const Vector \u0026amp; m() const\ngets process disturbance   const Matrix \u0026amp; Q() const\ngets process noise covariance   const Vector \u0026amp; x0() const\ngets initial state estimate   const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate   const Matrix \u0026amp; C() const\ngets output matrix   const Vector \u0026amp; d() const\ngets output bias   void set_A(const Matrix \u0026amp; A)\nsets state matrix   void set_B(const Matrix \u0026amp; B)\nsets input matrix   void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor   void set_m(const Vector \u0026amp; m)\nsets process disturbance   void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance   void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate   void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance   void set_C(const Matrix \u0026amp; C)\nsets output matrix   void set_d(const Vector \u0026amp; d)\nsets output bias   View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function   View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function    Protected Attributes inherited from lds::Fit\n    Name     data_t dt_ sample period   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Vector m_ process noise mean   Matrix Q_ process noise cov   Matrix C_ output matrix   Vector d_ output bias   Matrix R_ measurement noise   Vector x0_ initial state   Matrix P0_ initial covar   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs      Public Function Details #  Fit #  Fit() =default  Fit #  Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period   R #  inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\n set_R #  inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\n h #  inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\n y output estimate (over time) x state estimate (over time) t time index  Return: output\nReimplements: lds::Fit::h\n  Updated on 4 May 2022 at 16:34:51 Eastern Daylight Time\n"},{"id":35,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitem/","title":"lds::gaussian::FitEM","section":"Classes","content":"lds::gaussian::FitEM #  GLDS E-M Fit Type. More\u0026hellip;\n\n#include \u0026lt;lds_gaussian_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members #  Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name      EM() =default\nConstructs a new EMFit type.    EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.    EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.   virtual ~EM() =default   const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller.   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time)   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time)   const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance   const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1)   const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance   size_t n_t_tot()\ntotal number of time samples   const Vector \u0026amp; theta() const\ngets parameters updated in M step    Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name     void Expectation(bool force_common_initial =false)\nExpectation step.   void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step.   void MaximizeDynamics()   void MaximizeQ()   void MaximizeInitial()   void Smooth(bool force_common_initial)\nget smoothed estimates   void Reset()\nreset to initial conditions   void InitVars()\nInitializes the variables.   Vector UpdateTheta()\nupdates parameter list, theta    Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   std::vector\u0026lt; Matrix \u0026gt; x_ state estimate   std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov   std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance   std::vector\u0026lt; Matrix \u0026gt; y_ output estimate   Matrix diag_y_   Matrix sum_E_x_t_x_t_ state covariance (current time)   Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1)   Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance   Fit fit_   Vector theta_   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials    Detailed Description #  class lds::gaussian::FitEM; This type is used in the process of fitting GLDS models by expectation-maximization (EM).\n   Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":36,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitssid/","title":"lds::gaussian::FitSSID","section":"Classes","content":"lds::gaussian::FitSSID #  Subspace Identification (SSID) for GLDS. #include \u0026lt;lds_gaussian_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members #  Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name      SSID() =default\nConstructs a new SSIDFit type.    SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type.   std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller.    Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name     void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d)   void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix.   void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD)   void Solve(data_t wt_dc)\nsolves for LDS parameters   void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C    Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   Matrix D_ block-Hankel I/O data matrix   Fit fit_ fit   Matrix g_dc_ I/O gain @ DC.   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_h_   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials   Matrix L_ lower triangle decomp of covariance matrix   Vector s_ singular values   Matrix ext_obs_t_ extended observability matrix       Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":37,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1switchedcontroller/","title":"lds::gaussian::SwitchedController","section":"Classes","content":"lds::gaussian::SwitchedController #  Gaussian-observation SwitchedController Type. #include \u0026lt;lds_gaussian_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions #      Name     virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output    Additional inherited members #  Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\n    Name      SwitchedController() =default\nConstructs a new SwitchedController.    SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController.    SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems).   void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller.   void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains   void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving)   void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains   void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving)   void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains   void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving)   void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design   void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving)    Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\n    Name     std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between   size_t n_sys_ number of systems   size_t idx_ current system/controller index.   UniformMatrixList Kc_list_   UniformMatrixList Kc_inty_list_   UniformMatrixList Kc_u_list_   UniformVectorList g_design_list_    Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type      Public Function Details #  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\n  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":38,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/","title":"lds::gaussian::System","section":"Classes","content":"lds::gaussian::System #  Gaussian LDS Type. #include \u0026lt;lds_gaussian_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions #      Name      System() =default\nConstructs a new System.    System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)\nConstructs a new Gaussian System.   virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement.   const Matrix \u0026amp; R() const\nGet output noise covariance.   void set_Q(const Matrix \u0026amp; Q)   void set_R(const Matrix \u0026amp; R)\nSet output noise covariance.   void set_Ke(const Matrix \u0026amp; Ke)\nSet estimator gain.   void set_Ke_m(const Matrix \u0026amp; Ke_m)\nSet disturbance estimator gain.   void Print()\nPrint system variables to stdout.    Protected Functions #      Name     virtual void h() override\nSystem output function.   virtual void RecurseKe() override\nRecursively update estimator gain.    Protected Attributes #      Name     Matrix R_ covariance of output noise   bool do_recurse_Ke_ whether to recursively calculate estimator gain    Additional inherited members #  Public Functions inherited from lds::System\n    Name     virtual ~System()   void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates.   void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function   size_t n_u() const\nGet number of inputs.   size_t n_x() const\nGet number of states.   size_t n_y() const\nGet number of outputs.   data_t dt() const\nGet sample period.   const Vector \u0026amp; x() const\nGet current state.   const Matrix \u0026amp; P() const\nGet covariance of state estimate.   const Vector \u0026amp; m() const\nGet current process disturbance/bias.   const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate.   const Vector \u0026amp; cx() const\nGet C*x.   const Vector \u0026amp; y() const\nGet output.   const Vector \u0026amp; x0() const\nGet initial state.   const Vector \u0026amp; m0() const\nGet initial disturbance.   const Matrix \u0026amp; A() const\nGet state matrix.   const Matrix \u0026amp; B() const\nGet input matrix.   const Vector \u0026amp; g() const\nGet input gain/conversion factor.   const Matrix \u0026amp; C() const\nGet output matrix.   const Vector \u0026amp; d() const\nGet output bias.   const Matrix \u0026amp; Ke() const\nGet estimator gain.   const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m)   const Matrix \u0026amp; Q()\nGet process noise covariance.   const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation.   const Matrix \u0026amp; P0()\nGet covariance of initial state.   const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance.   void set_A(const Matrix \u0026amp; A)\nSet state matrix.   void set_B(const Matrix \u0026amp; B)\nSet input matrix.   void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance.   void set_g(const Vector \u0026amp; g)\nSet input gain.   void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation.   void set_x0(const Vector \u0026amp; x0)\nSet initial state.   void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state.   void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance.   void set_C(const Matrix \u0026amp; C)\nSet output matrix.   void set_d(const Vector \u0026amp; d)\nSet output bias.   void set_x(const Vector \u0026amp; x)\nSet state of system.   void Reset()\nReset system variables.    Protected Functions inherited from lds::System\n    Name     void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)    Public Attributes inherited from lds::System\n    Name     bool do_adapt_m whether to adaptively estimate disturbance m    Protected Attributes inherited from lds::System\n    Name     std::size_t n_x_ number of states   std::size_t n_u_ number of inputs   std::size_t n_y_ number of outputs   data_t dt_ sample period   Vector x_ state   Matrix P_ covariance of state estimate   Vector m_ process disturbance   Matrix P_m_ covariance of disturbance estimate   Vector cx_ C*x.   Vector y_ output   Vector z_ measurement   Vector x0_ initial state   Matrix P0_ covariance of initial state estimate   Vector m0_ initial process disturbance   Matrix P0_m_ covariance of initial disturbance est.   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Matrix Q_ covariance of process noise   Matrix Q_m_ covariance of disturbance random walk   Matrix C_ output matrix   Vector d_ output bias   Matrix Ke_ estimator gain   Matrix Ke_m_ estimator gain for process disturbance      Public Function Details #  System #  System() =default  System #  System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0 ) Parameters:\n n_u number of inputs (u) n_x number of states (x) n_y number of outputs (y) dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) r0 [optional] initial diagonal elements of output noise covariance (R)   Simulate #  virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\n u_tm1 input at t-1  Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\n R #  inline const Matrix \u0026amp; R() const  set_Q #  inline void set_Q( const Matrix \u0026amp; Q )  set_R #  inline void set_R( const Matrix \u0026amp; R )  set_Ke #  inline void set_Ke( const Matrix \u0026amp; Ke )  set_Ke_m #  inline void set_Ke_m( const Matrix \u0026amp; Ke_m )  Print #  void Print()  Protected Function Details #  h #  inline virtual void h() override Reimplements: lds::System::h\n RecurseKe #  virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\n Protected Attribute Details #  **R_** #  Matrix R_;  **do_recurse_Ke_** #  bool do_recurse_Ke_ {};   Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":39,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/","title":"lds::poisson","section":"Namespaces","content":"lds::poisson #  Linear Dynamical Systems with Poisson observations. Classes #      Name     class lds::poisson::Controller PLDS Controller Type.   class lds::poisson::Fit PLDS Fit Type.   class lds::poisson::FitEM PLDS E-M Fit Type.   class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS.   class lds::poisson::SwitchedController Poisson-observation SwitchedController Type.   class lds::poisson::System Poisson System type.    Attributes #      Name     std::random_device rd random device for simulating poisson data   std::mt19937 rng random number generator for simulating poisson data    Attribute Details #  rd #  static std::random_device rd; rng #  static std::mt19937 rng = std::mt19937( rd());  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":40,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/","title":"lds::poisson::Controller","section":"Classes","content":"lds::poisson::Controller #  PLDS Controller Type. #include \u0026lt;lds_poisson_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions #      Name     virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output.    Additional inherited members #  Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type      Public Function Details #  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\n  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":41,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/","title":"lds::poisson::Fit","section":"Classes","content":"lds::poisson::Fit #  PLDS Fit Type. #include \u0026lt;lds_poisson_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions #      Name      Fit() =default    Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit.   virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function   virtual void set_R(const Matrix \u0026amp; R) override\nsets output noise covariance (if any)   virtual const Matrix \u0026amp; R() const override    Additional inherited members #  Public Functions inherited from lds::Fit\n    Name     virtual ~Fit() =default   size_t n_u() const\ngets number of inputs   size_t n_x() const\ngets number of states   size_t n_y() const\ngets number of outputs   data_t dt() const\ngets sample period   const Matrix \u0026amp; A() const\ngets state matrix   const Matrix \u0026amp; B() const\ngets input matrix   const Vector \u0026amp; g() const\ngets input gain   const Vector \u0026amp; m() const\ngets process disturbance   const Matrix \u0026amp; Q() const\ngets process noise covariance   const Vector \u0026amp; x0() const\ngets initial state estimate   const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate   const Matrix \u0026amp; C() const\ngets output matrix   const Vector \u0026amp; d() const\ngets output bias   void set_A(const Matrix \u0026amp; A)\nsets state matrix   void set_B(const Matrix \u0026amp; B)\nsets input matrix   void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor   void set_m(const Vector \u0026amp; m)\nsets process disturbance   void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance   void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate   void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance   void set_C(const Matrix \u0026amp; C)\nsets output matrix   void set_d(const Vector \u0026amp; d)\nsets output bias   View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function   View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function    Protected Attributes inherited from lds::Fit\n    Name     data_t dt_ sample period   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Vector m_ process noise mean   Matrix Q_ process noise cov   Matrix C_ output matrix   Vector d_ output bias   Matrix R_ measurement noise   Vector x0_ initial state   Matrix P0_ initial covar   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs      Public Function Details #  Fit #  Fit() =default  Fit #  inline Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period   h #  inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\n y output estimate (over time) x state estimate (over time) t time index  Return: output\nReimplements: lds::Fit::h\n set_R #  inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\n R #  inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\n  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":42,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitem/","title":"lds::poisson::FitEM","section":"Classes","content":"lds::poisson::FitEM #  PLDS E-M Fit Type. More\u0026hellip;\n\n#include \u0026lt;lds_poisson_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members #  Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name      EM() =default\nConstructs a new EMFit type.    EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.    EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.   virtual ~EM() =default   const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller.   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time)   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time)   const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance   const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1)   const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance   size_t n_t_tot()\ntotal number of time samples   const Vector \u0026amp; theta() const\ngets parameters updated in M step    Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name     void Expectation(bool force_common_initial =false)\nExpectation step.   void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step.   void MaximizeDynamics()   void MaximizeQ()   void MaximizeInitial()   void Smooth(bool force_common_initial)\nget smoothed estimates   void Reset()\nreset to initial conditions   void InitVars()\nInitializes the variables.   Vector UpdateTheta()\nupdates parameter list, theta    Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   std::vector\u0026lt; Matrix \u0026gt; x_ state estimate   std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov   std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance   std::vector\u0026lt; Matrix \u0026gt; y_ output estimate   Matrix diag_y_   Matrix sum_E_x_t_x_t_ state covariance (current time)   Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1)   Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance   Fit fit_   Vector theta_   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials    Detailed Description #  class lds::poisson::FitEM; This type is used in the process of fitting PLDS models by expectation-maximization (EM).\n   Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":43,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitssid/","title":"lds::poisson::FitSSID","section":"Classes","content":"lds::poisson::FitSSID #  Subspace Identification (SSID) for PLDS. #include \u0026lt;lds_poisson_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members #  Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name      SSID() =default\nConstructs a new SSIDFit type.    SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type.   std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller.    Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name     void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d)   void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix.   void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD)   void Solve(data_t wt_dc)\nsolves for LDS parameters   void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C    Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   Matrix D_ block-Hankel I/O data matrix   Fit fit_ fit   Matrix g_dc_ I/O gain @ DC.   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_h_   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials   Matrix L_ lower triangle decomp of covariance matrix   Vector s_ singular values   Matrix ext_obs_t_ extended observability matrix       Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":44,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1switchedcontroller/","title":"lds::poisson::SwitchedController","section":"Classes","content":"lds::poisson::SwitchedController #  Poisson-observation SwitchedController Type. #include \u0026lt;lds_poisson_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions #      Name     virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output.    Additional inherited members #  Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\n    Name      SwitchedController() =default\nConstructs a new SwitchedController.    SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController.    SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems).   void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller.   void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains   void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving)   void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains   void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving)   void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains   void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving)   void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design   void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving)    Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\n    Name     std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between   size_t n_sys_ number of systems   size_t idx_ current system/controller index.   UniformMatrixList Kc_list_   UniformMatrixList Kc_inty_list_   UniformMatrixList Kc_u_list_   UniformVectorList g_design_list_    Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type      Public Function Details #  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\n  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":45,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/","title":"lds::poisson::System","section":"Classes","content":"lds::poisson::System #  Poisson System type. #include \u0026lt;lds_poisson_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions #      Name      System() =default\nConstructs a new System.    System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nConstructs a new Poisson System.   virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement.    Protected Functions #      Name     virtual void h() override\nSystem output function.   virtual void RecurseKe() override\nRecursively recalculate estimator gain (Ke)    Additional inherited members #  Public Functions inherited from lds::System\n    Name     virtual ~System()   void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates.   void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function   size_t n_u() const\nGet number of inputs.   size_t n_x() const\nGet number of states.   size_t n_y() const\nGet number of outputs.   data_t dt() const\nGet sample period.   const Vector \u0026amp; x() const\nGet current state.   const Matrix \u0026amp; P() const\nGet covariance of state estimate.   const Vector \u0026amp; m() const\nGet current process disturbance/bias.   const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate.   const Vector \u0026amp; cx() const\nGet C*x.   const Vector \u0026amp; y() const\nGet output.   const Vector \u0026amp; x0() const\nGet initial state.   const Vector \u0026amp; m0() const\nGet initial disturbance.   const Matrix \u0026amp; A() const\nGet state matrix.   const Matrix \u0026amp; B() const\nGet input matrix.   const Vector \u0026amp; g() const\nGet input gain/conversion factor.   const Matrix \u0026amp; C() const\nGet output matrix.   const Vector \u0026amp; d() const\nGet output bias.   const Matrix \u0026amp; Ke() const\nGet estimator gain.   const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m)   const Matrix \u0026amp; Q()\nGet process noise covariance.   const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation.   const Matrix \u0026amp; P0()\nGet covariance of initial state.   const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance.   void set_A(const Matrix \u0026amp; A)\nSet state matrix.   void set_B(const Matrix \u0026amp; B)\nSet input matrix.   void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance.   void set_g(const Vector \u0026amp; g)\nSet input gain.   void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance.   void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation.   void set_x0(const Vector \u0026amp; x0)\nSet initial state.   void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state.   void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance.   void set_C(const Matrix \u0026amp; C)\nSet output matrix.   void set_d(const Vector \u0026amp; d)\nSet output bias.   void set_x(const Vector \u0026amp; x)\nSet state of system.   void Reset()\nReset system variables.   void Print()\nPrint system variables to stdout.    Protected Functions inherited from lds::System\n    Name     void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)    Public Attributes inherited from lds::System\n    Name     bool do_adapt_m whether to adaptively estimate disturbance m    Protected Attributes inherited from lds::System\n    Name     std::size_t n_x_ number of states   std::size_t n_u_ number of inputs   std::size_t n_y_ number of outputs   data_t dt_ sample period   Vector x_ state   Matrix P_ covariance of state estimate   Vector m_ process disturbance   Matrix P_m_ covariance of disturbance estimate   Vector cx_ C*x.   Vector y_ output   Vector z_ measurement   Vector x0_ initial state   Matrix P0_ covariance of initial state estimate   Vector m0_ initial process disturbance   Matrix P0_m_ covariance of initial disturbance est.   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Matrix Q_ covariance of process noise   Matrix Q_m_ covariance of disturbance random walk   Matrix C_ output matrix   Vector d_ output bias   Matrix Ke_ estimator gain   Matrix Ke_m_ estimator gain for process disturbance      Public Function Details #  System #  System() =default  System #  System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q)   Simulate #  virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\n u_tm1 input at t-1  Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\n Protected Function Details #  h #  inline virtual void h() override Reimplements: lds::System::h\n RecurseKe #  virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nRecursively recalculate estimator gain (Ke).\nReferences:\nSmith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.\nEden UT, \u0026hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.\n  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":46,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/","title":"lds::SSID","section":"Classes","content":"lds::SSID #  More\u0026hellip;\nInherited by lds::gaussian::FitSSID, lds::poisson::FitSSID\nPublic Functions #      Name      SSID() =default\nConstructs a new SSIDFit type.    SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type.   std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller.    Protected Functions #      Name     void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d)   void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix.   virtual void DecomposeData() =0\nDecompose data to lower-triangular matrix (used in Solve)   void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD)   void Solve(data_t wt_dc)\nsolves for LDS parameters   void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C    Protected Attributes #      Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   Matrix D_ block-Hankel I/O data matrix   Fit fit_ fit   Matrix g_dc_ I/O gain @ DC.   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_h_   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials   Matrix L_ lower triangle decomp of covariance matrix   Vector s_ singular values   Matrix ext_obs_t_ extended observability matrix    Detailed Description #  template \u0026lt;typename Fit \u0026gt; class lds::SSID;   Public Function Details #  SSID #  SSID() =default  SSID #  SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf) ) Parameters:\n n_x number of states n_h size of block-hankel data matrix dt sample period u_train input training data z_train measurement training data d output bias   Run #  std::tuple\u0026lt; Fit, Vector \u0026gt; Run( SSIDWt ssid_wt ) Parameters:\n ssid_wt weight for singular value decomp  Return: tuple (Fit, singular values)\n ReturnData #  inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\n Protected Function Details #  CalcD #  void CalcD( data_t t_silence =0.1, data_t thresh_silence =0.001 ) Parameters:\n t_silence threshold on period of time that qualifies as \u0026ldquo;silence\u0026rdquo; thresh_silence threshold on input amplitude u that qualifies as \u0026ldquo;silence\u0026rdquo;   CreateHankelDataMat #  void CreateHankelDataMat() Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.\n DecomposeData #  virtual void DecomposeData() =0 Reimplemented by: lds::poisson::FitSSID::DecomposeData, lds::gaussian::FitSSID::DecomposeData\n CalcSVD #  void CalcSVD( SSIDWt wt ) Parameters:\n ssid_wt weight for SVD   Solve #  void Solve( data_t wt_dc ) Parameters:\n wt_dc weight placed on getting correct DC I/O gain   RecomputeExtObs #  void RecomputeExtObs()  Protected Attribute Details #  **u_** #  UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_;  **z_** #  UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_;  **D_** #  Matrix D_;  **fit_** #  Fit fit_;  **g_dc_** #  Matrix g_dc_;  **dt_** #  data_t dt_ {};  **n_u_** #  size_t n_u_ {};  **n_x_** #  size_t n_x_ {};  **n_y_** #  size_t n_y_ {};  **n_h_** #  size_t n_h_ {};  **n_trials_** #  size_t n_trials_ {};  **n_t_** #  std::vector\u0026lt; size_t \u0026gt; n_t_;  **n_t_tot_** #  size_t n_t_tot_ {};  **L_** #  Matrix L_;  **s_** #  Vector s_;  **ext_obs_t_** #  Matrix ext_obs_t_;   Updated on 4 May 2022 at 16:34:51 Eastern Daylight Time\n"},{"id":47,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/","title":"lds::SwitchedController","section":"Classes","content":"lds::SwitchedController #  SwitchedController Type. More\u0026hellip;\n\n#include \u0026lt;lds_sctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nInherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController\nPublic Functions #      Name      SwitchedController() =default\nConstructs a new SwitchedController.    SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController.    SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems).   void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller.   void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains   void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving)   void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains   void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving)   void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains   void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving)   void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design   void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving)    Protected Attributes #      Name     std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between   size_t n_sys_ number of systems   size_t idx_ current system/controller index.   UniformMatrixList Kc_list_   UniformMatrixList Kc_inty_list_   UniformMatrixList Kc_u_list_   UniformVectorList g_design_list_    Additional inherited members #  Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref)   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type    Detailed Description #  template \u0026lt;typename System \u0026gt; class lds::SwitchedController;   Public Function Details #  SwitchedController #  SwitchedController() =default  SwitchedController #  inline SwitchedController( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\n systems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask   SwitchedController #  inline SwitchedController( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\n systems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask   Switch #  inline void Switch( size_t idx, bool do_force_switch =false ) Parameters:\n idx index do_force_switch whether to force a system switch even if already there.   set_Kc #  inline void set_Kc( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc )  set_Kc #  inline void set_Kc( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc )  set_Kc_inty #  inline void set_Kc_inty( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty )  set_Kc_inty #  inline void set_Kc_inty( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty )  set_Kc_u #  inline void set_Kc_u( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u )  set_Kc_u #  inline void set_Kc_u( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u )  set_g_design #  inline void set_g_design( const UniformVectorList \u0026amp; g )  set_g_design #  inline void set_g_design( UniformVectorList \u0026amp;\u0026amp; g )  Protected Attribute Details #  **systems_** #  std::vector\u0026lt; System \u0026gt; systems_;  **n_sys_** #  size_t n_sys_ {};  **idx_** #  size_t idx_ {};  **Kc_list_** #  UniformMatrixList Kc_list_;  **Kc_inty_list_** #  UniformMatrixList Kc_inty_list_;  **Kc_u_list_** #  UniformMatrixList Kc_u_list_;  **g_design_list_** #  UniformVectorList g_design_list_;   Updated on 4 May 2022 at 16:34:51 Eastern Daylight Time\n"},{"id":48,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1system/","title":"lds::System","section":"Classes","content":"lds::System #  Linear Dynamical System Type. #include \u0026lt;lds_sys.h\u0026gt;\nInherited by lds::gaussian::System, lds::poisson::System\nPublic Functions #      Name      System() =default\nConstructs a new System.    System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nconstructs a new System   virtual ~System()   void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates.   virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) =0\nsimulates system (single time step)   void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function   virtual void h() =0\nsystem output function   size_t n_u() const\nGet number of inputs.   size_t n_x() const\nGet number of states.   size_t n_y() const\nGet number of outputs.   data_t dt() const\nGet sample period.   const Vector \u0026amp; x() const\nGet current state.   const Matrix \u0026amp; P() const\nGet covariance of state estimate.   const Vector \u0026amp; m() const\nGet current process disturbance/bias.   const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate.   const Vector \u0026amp; cx() const\nGet C*x.   const Vector \u0026amp; y() const\nGet output.   const Vector \u0026amp; x0() const\nGet initial state.   const Vector \u0026amp; m0() const\nGet initial disturbance.   const Matrix \u0026amp; A() const\nGet state matrix.   const Matrix \u0026amp; B() const\nGet input matrix.   const Vector \u0026amp; g() const\nGet input gain/conversion factor.   const Matrix \u0026amp; C() const\nGet output matrix.   const Vector \u0026amp; d() const\nGet output bias.   const Matrix \u0026amp; Ke() const\nGet estimator gain.   const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m)   const Matrix \u0026amp; Q()\nGet process noise covariance.   const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation.   const Matrix \u0026amp; P0()\nGet covariance of initial state.   const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance.   void set_A(const Matrix \u0026amp; A)\nSet state matrix.   void set_B(const Matrix \u0026amp; B)\nSet input matrix.   void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance.   void set_g(const Vector \u0026amp; g)\nSet input gain.   void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance.   void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation.   void set_x0(const Vector \u0026amp; x0)\nSet initial state.   void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state.   void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance.   void set_C(const Matrix \u0026amp; C)\nSet output matrix.   void set_d(const Vector \u0026amp; d)\nSet output bias.   void set_x(const Vector \u0026amp; x)\nSet state of system.   void Reset()\nReset system variables.   void Print()\nPrint system variables to stdout.    Protected Functions #      Name     virtual void RecurseKe() =0\nRecursively recalculate estimator gain (Ke)   void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)    Public Attributes #      Name     bool do_adapt_m whether to adaptively estimate disturbance m    Protected Attributes #      Name     std::size_t n_x_ number of states   std::size_t n_u_ number of inputs   std::size_t n_y_ number of outputs   data_t dt_ sample period   Vector x_ state   Matrix P_ covariance of state estimate   Vector m_ process disturbance   Matrix P_m_ covariance of disturbance estimate   Vector cx_ C*x.   Vector y_ output   Vector z_ measurement   Vector x0_ initial state   Matrix P0_ covariance of initial state estimate   Vector m0_ initial process disturbance   Matrix P0_m_ covariance of initial disturbance est.   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Matrix Q_ covariance of process noise   Matrix Q_m_ covariance of disturbance random walk   Matrix C_ output matrix   Vector d_ output bias   Matrix Ke_ estimator gain   Matrix Ke_m_ estimator gain for process disturbance      Public Function Details #  System #  System() =default  System #  System( size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period p0 diagonal elements for state estimate covariance q0 diagonal elements for process noise covariance   ~System #  inline virtual ~System()  Filter #  void Filter( const Vector \u0026amp; u_tm1, const Vector \u0026amp; z ) Parameters:\n u_tm1 input at t-minus-1 z_t current measurement  Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.\n Simulate #  virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) =0 Parameters:\n u_tm1 input at time t-1  Return: simulated measurement at time t\nReimplemented by: lds::poisson::System::Simulate, lds::gaussian::System::Simulate\n f #  inline void f( const Vector \u0026amp; u, bool do_add_noise =false ) Parameters:\n u input do_add_noise whether to add simulated process noise   h #  virtual void h() =0 Reimplemented by: lds::poisson::System::h, lds::gaussian::System::h\n n_u #  inline size_t n_u() const  n_x #  inline size_t n_x() const  n_y #  inline size_t n_y() const  dt #  inline data_t dt() const  x #  inline const Vector \u0026amp; x() const  P #  inline const Matrix \u0026amp; P() const  m #  inline const Vector \u0026amp; m() const  P_m #  inline const Matrix \u0026amp; P_m() const  cx #  inline const Vector \u0026amp; cx() const  y #  inline const Vector \u0026amp; y() const  x0 #  inline const Vector \u0026amp; x0() const  m0 #  inline const Vector \u0026amp; m0() const  A #  inline const Matrix \u0026amp; A() const  B #  inline const Matrix \u0026amp; B() const  g #  inline const Vector \u0026amp; g() const  C #  inline const Matrix \u0026amp; C() const  d #  inline const Vector \u0026amp; d() const  Ke #  inline const Matrix \u0026amp; Ke() const  Ke_m #  inline const Matrix \u0026amp; Ke_m() const  Q #  inline const Matrix \u0026amp; Q()  Q_m #  inline const Matrix \u0026amp; Q_m()  P0 #  inline const Matrix \u0026amp; P0()  P0_m #  inline const Matrix \u0026amp; P0_m()  set_A #  inline void set_A( const Matrix \u0026amp; A )  set_B #  inline void set_B( const Matrix \u0026amp; B )  set_m #  inline void set_m( const Vector \u0026amp; m, bool do_force_assign =false )  set_g #  inline void set_g( const Vector \u0026amp; g )  set_Q #  inline void set_Q( const Matrix \u0026amp; Q )  set_Q_m #  inline void set_Q_m( const Matrix \u0026amp; Q_m )  set_x0 #  inline void set_x0( const Vector \u0026amp; x0 )  set_P0 #  inline void set_P0( const Matrix \u0026amp; P0 )  set_P0_m #  inline void set_P0_m( const Matrix \u0026amp; P0_m )  set_C #  inline void set_C( const Matrix \u0026amp; C )  set_d #  inline void set_d( const Vector \u0026amp; d )  set_x #  inline void set_x( const Vector \u0026amp; x )  Reset #  void Reset()  Print #  void Print()  Protected Function Details #  RecurseKe #  virtual void RecurseKe() =0 Reimplemented by: lds::poisson::System::RecurseKe, lds::gaussian::System::RecurseKe\n InitVars #  void InitVars( data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 )  Public Attribute Details #  do_adapt_m #  bool do_adapt_m {};  Protected Attribute Details #  **n_x_** #  std::size_t n_x_ {};  **n_u_** #  std::size_t n_u_ {};  **n_y_** #  std::size_t n_y_ {};  **dt_** #  data_t dt_ {};  **x_** #  Vector x_;  **P_** #  Matrix P_;  **m_** #  Vector m_;  **P_m_** #  Matrix P_m_;  **cx_** #  Vector cx_;  **y_** #  Vector y_;  **z_** #  Vector z_;  **x0_** #  Vector x0_;  **P0_** #  Matrix P0_;  **m0_** #  Vector m0_;  **P0_m_** #  Matrix P0_m_;  **A_** #  Matrix A_;  **B_** #  Matrix B_;  **g_** #  Vector g_;  **Q_** #  Matrix Q_;  **Q_m_** #  Matrix Q_m_;  **C_** #  Matrix C_;  **d_** #  Vector d_;  **Ke_** #  Matrix Ke_;  **Ke_m_** #  Matrix Ke_m_;   Updated on 4 May 2022 at 16:34:51 Eastern Daylight Time\n"},{"id":49,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/","title":"lds::UniformMatrixList","section":"Classes","content":"lds::UniformMatrixList #  More\u0026hellip;\nInherits from std::vector\u0026lt; Matrix \u0026gt;\nPublic Functions #      Name      UniformMatrixList() =default\nConstructs a new UniformMatrixList.    UniformMatrixList(const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent.    UniformMatrixList(std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent.    UniformMatrixList(std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent.    UniformMatrixList(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nConstructs a new UniformMatrixList (copy).    UniformMatrixList(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nConstructs a new UniformMatrixList (move).    ~UniformMatrixList() =default\nDestroys the object.   const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim(size_t n =0) const\ngets dimensions of uniformly sized matrices   size_t size()\nsize of container   const Matrix \u0026amp; at(size_t n)\ngets reference to n^th element   void Swap(Matrix \u0026amp; that, size_t n)\nswaps input matrix with n^th matrix of list   UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nassigns the contents (copy)   UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nassigns the contents (move)    Detailed Description #  template \u0026lt;MatrixListFreeDim D =kMatFreeDimNone\u0026gt; class lds::UniformMatrixList;   Public Function Details #  UniformMatrixList #  UniformMatrixList() =default  UniformMatrixList #  explicit UniformMatrixList( const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\n mats input matrices dim dimensions   UniformMatrixList #  explicit UniformMatrixList( std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\n mats input matrices dim dimensions   UniformMatrixList #  UniformMatrixList( std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\n mats input matrices dim dimensions   UniformMatrixList #  UniformMatrixList( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\n that another UniformMatrixList   UniformMatrixList #  UniformMatrixList( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\n that another UniformMatrixList   ~UniformMatrixList #  ~UniformMatrixList() =default  dim #  inline const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim( size_t n =0 ) const Parameters:\n n [optional] index in list of matrices  Return: dimensions\n size #  inline size_t size()  at #  inline const Matrix \u0026amp; at( size_t n )  Swap #  inline void Swap( Matrix \u0026amp; that, size_t n ) Parameters:\n that input matrix n index where the matrix is moved   operator= #  inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\n that another UniformMatrixList  Return: reference to object\n operator= #  inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\n that another UniformMatrixList  Return: reference to object\n  Updated on 4 May 2022 at 16:34:51 Eastern Daylight Time\n"},{"id":50,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/","title":"lds::UniformSystemList","section":"Classes","content":"lds::UniformSystemList #  More\u0026hellip;\nInherits from std::vector\u0026lt; System \u0026gt;\nPublic Functions #      Name      UniformSystemList() =default\nConstructs a new UniformSystemList.    UniformSystemList(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by copying existing vector of System if dimensions consistent.    UniformSystemList(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by moving existing vector of System if dimensions consistent.    UniformSystemList(std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList from initializer_list of System if dimensions consistent.    UniformSystemList(const UniformSystemList \u0026amp; that)\nConstructs a new UniformSystemList (copy).    UniformSystemList(UniformSystemList \u0026amp;\u0026amp; that)\nConstructs a new UniformSystemList (move).    ~UniformSystemList() =default\nDestroys the object.   const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const\ngets dimensions of the uniformly sized systems   size_t size()\nsize of container   const System \u0026amp; at(size_t n)\ngets reference to n^th element   void Swap(System \u0026amp; that, size_t n)\nswaps input system with n^th system of list   UniformSystemList \u0026amp; operator=(const UniformSystemList \u0026amp; that)\nassigns the contents (copy)   UniformSystemList \u0026amp; operator=(UniformSystemList \u0026amp;\u0026amp; that)\nassigns the contents (move)    Detailed Description #  template \u0026lt;typename System\u0026gt; class lds::UniformSystemList;   Public Function Details #  UniformSystemList #  UniformSystemList() =default  UniformSystemList #  explicit UniformSystemList( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\n systems input systems dim dimensions (n_u, n_x, n_y)   UniformSystemList #  explicit UniformSystemList( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\n systems input systems dim dimensions (n_u, n_x, n_y)   UniformSystemList #  UniformSystemList( std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\n systems input systems dim dimensions (n_u, n_x, n_y)   UniformSystemList #  UniformSystemList( const UniformSystemList \u0026amp; that ) Parameters:\n that another UniformSystemList   UniformSystemList #  UniformSystemList( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\n that another UniformSystemList   ~UniformSystemList #  ~UniformSystemList() =default  dim #  inline const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const  size #  inline size_t size()  at #  inline const System \u0026amp; at( size_t n )  Swap #  inline void Swap( System \u0026amp; that, size_t n ) Parameters:\n that input system n index where the system is moved   operator= #  inline UniformSystemList \u0026amp; operator=( const UniformSystemList \u0026amp; that ) Parameters:\n that another UniformSystemList  Return: reference to object\n operator= #  inline UniformSystemList \u0026amp; operator=( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\n that another UniformSystemList  Return: reference to object\n  Updated on 4 May 2022 at 16:34:51 Eastern Daylight Time\n"},{"id":51,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/","title":"lds::UniformVectorList","section":"Classes","content":"lds::UniformVectorList #  Inherits from std::vector\u0026lt; Vector \u0026gt;\nPublic Functions #      Name      UniformVectorList() =default\nConstructs a new UniformVectorList.    UniformVectorList(const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent.    UniformVectorList(std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent.    UniformVectorList(std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0)\nConstructs a new UniformVectorList from initializer_list of Vector if dimensions consistent.    UniformVectorList(const UniformVectorList \u0026amp; that)\nConstructs a new UniformVectorList (copy)    UniformVectorList(UniformVectorList \u0026amp;\u0026amp; that)\nConstructs a new UniformVectorList (move)    ~UniformVectorList() =default\nDestroys the object.   size_t dim() const\ngets dimensions of the uniformly sized matrices   size_t size()\nsize of container   const Vector \u0026amp; at(size_t n)\ngets reference to n^th element   void Swap(Vector \u0026amp; that, size_t n)\nswaps input matrix with n^th vector of list   UniformVectorList \u0026amp; operator=(const UniformVectorList \u0026amp; that)\nassigns the contents (copy)   UniformVectorList \u0026amp; operator=(UniformVectorList \u0026amp;\u0026amp; that)\nassigns the contents (move)      Public Function Details #  UniformVectorList #  UniformVectorList() =default  UniformVectorList #  explicit UniformVectorList( const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0 ) Parameters:\n vecs input vectors dims dimension   UniformVectorList #  explicit UniformVectorList( std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0 ) Parameters:\n vecs input vectors dim dimension   UniformVectorList #  UniformVectorList( std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0 ) Parameters:\n vecs input vectors dim dimension   UniformVectorList #  UniformVectorList( const UniformVectorList \u0026amp; that ) Parameters:\n that another UniformVectorList   UniformVectorList #  UniformVectorList( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\n that another UniformVectorList   ~UniformVectorList #  ~UniformVectorList() =default  dim #  inline size_t dim() const  size #  inline size_t size()  at #  inline const Vector \u0026amp; at( size_t n )  Swap #  inline void Swap( Vector \u0026amp; that, size_t n ) Parameters:\n that input vector n index where the vector is moved   operator= #  inline UniformVectorList \u0026amp; operator=( const UniformVectorList \u0026amp; that ) Parameters:\n that another UniformVectorList  Return: reference to object\n operator= #  inline UniformVectorList \u0026amp; operator=( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\n that another UniformVectorList  Return: reference to object\n  Updated on 4 May 2022 at 16:34:51 Eastern Daylight Time\n"},{"id":52,"href":"/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/","title":"ldsCtrlEst_h","section":"Files","content":"ldsCtrlEst_h #  Files #     Name     ldsCtrlEst_h/lds.h lds namespace   ldsCtrlEst_h/lds_ctrl.h Controller.   ldsCtrlEst_h/lds_fit.h LDS base fit type.   ldsCtrlEst_h/lds_fit_em.h subspace identification   ldsCtrlEst_h/lds_fit_ssid.h subspace identification   ldsCtrlEst_h/lds_gaussian.h glds namespace   ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.   ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.   ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.   ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.   ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.   ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.   ldsCtrlEst_h/lds_poisson.h plds namespace   ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.   ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.   ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.   ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.   ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.   ldsCtrlEst_h/lds_poisson_sys.h PLDS base type.   ldsCtrlEst_h/lds_sctrl.h SwitchedController type.   ldsCtrlEst_h/lds_sys.h LDS base type.   ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.   ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.   ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.   ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)   ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)     Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":53,"href":"/lds-ctrl-est/docs/api/files/lds_8h/","title":"ldsCtrlEst_h/lds.h","section":"Files","content":"ldsCtrlEst_h/lds.h #  lds namespace More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Detailed Description #  This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)) observations.\nSource code #  //===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_H #define LDSCTRLEST_LDS_H  // #ifndef LDSCTRLEST // #include \u0026lt;ldsCtrlEst\u0026gt; // #endif  #include \u0026lt;armadillo\u0026gt; namespace lds { using data_t = double; // may change to float (but breaks mex functions) using Vector = arma::Col\u0026lt;data_t\u0026gt;; using Matrix = arma::Mat\u0026lt;data_t\u0026gt;; using Cube = arma::Cube\u0026lt;data_t\u0026gt;; using View = arma::subview\u0026lt;data_t\u0026gt;; namespace fill = arma::fill; static const std::size_t kControlTypeDeltaU = 0x1; static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); static const data_t kPi = arma::datum::pi; static const data_t kDefaultP0 = 1e-6; static const data_t kDefaultQ0 = 1e-6; static const data_t kDefaultR0 = 1e-2; enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA }; enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2 }; // TODO(mfbolus): for SwitchedController, may want systems to have differing // numbers of states. Use this enum as template parameter? // enum SystemListFreeDim { // kSysFreeDimNone, // kSysFreeDimX ///\u0026lt; allow state dim (x) of systems in list to be hetero // };  // place hard limits on contents of vecors/mats void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub); void Limit(Vector\u0026amp; x, data_t lb, data_t ub); void Limit(Matrix\u0026amp; x, data_t lb, data_t ub); // in-place assign that errs if there are dimension mismatches: void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); // TODO(mfbolus): this is a fudge, but for some reason, cov mats often going // numerically asymm.  void ForceSymPD(Matrix\u0026amp; X); void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min = 0); void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X); Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B); inline void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Vector\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Matrix\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions  if (other.n_elem != some.n_elem) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vector of size \u0026#34; \u0026lt;\u0026lt; some.n_elem \u0026lt;\u0026lt; \u0026#34; with vector of size \u0026#34; \u0026lt;\u0026lt; other.n_elem \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } inline void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions  if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrix of size \u0026#34; \u0026lt;\u0026lt; some.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; some.n_cols \u0026lt;\u0026lt; \u0026#34; with matrix of size \u0026#34; \u0026lt;\u0026lt; other.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other.n_cols \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } } // namespace lds  #endif  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":54,"href":"/lds-ctrl-est/docs/api/files/lds__ctrl_8h/","title":"ldsCtrlEst_h/lds_ctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_ctrl.h #  Controller. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::Controller    Detailed Description #  This file declares the type for control of a linear dynamical system (lds::Controller).\nSource code #  //===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_CTRL_H #define LDSCTRLEST_LDS_CTRL_H  // namespace #include \u0026#34;lds.h\u0026#34;// system type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class Controller { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); public: Controller() = default; Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); const Vector\u0026amp; Control(const Vector\u0026amp; z, bool do_control = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); const Vector\u0026amp; ControlOutputReference(const Vector\u0026amp; z, bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); // get methods:  const System\u0026amp; sys() const { return sys_; }; const Matrix\u0026amp; Kc() const { return Kc_; }; const Matrix\u0026amp; Kc_inty() const { return Kc_inty_; }; const Matrix\u0026amp; Kc_u() const { return Kc_u_; }; const Vector\u0026amp; g_design() const { return g_design_; }; const Vector\u0026amp; u_ref() const { return u_ref_; }; const Vector\u0026amp; x_ref() const { return x_ref_; }; const Vector\u0026amp; y_ref() const { return y_ref_; }; size_t control_type() const { return control_type_; }; data_t tau_awu() const { return tau_awu_; }; data_t u_lb() const { return u_lb_; }; data_t u_ub() const { return u_ub_; }; // set methods  void set_sys(const System\u0026amp; sys) { bool does_match = sys_.n_u() == sys.n_u(); does_match = does_match \u0026amp;\u0026amp; (sys_.n_x() == sys.n_x()); does_match = does_match \u0026amp;\u0026amp; (sys_.n_y() == sys.n_y()); if (does_match) { sys_ = sys; } else { throw std::runtime_error( \u0026#34;new system argument to `set_sys` does not match dimensionality of \u0026#34; \u0026#34;existing system\u0026#34;); } }; void set_g_design(const Vector\u0026amp; g_design) { Reassign(g_design_, g_design); }; void set_u_ref(const Vector\u0026amp; u_ref) { Reassign(u_ref_, u_ref); }; void set_x_ref(const Vector\u0026amp; x_ref) { Reassign(x_ref_, x_ref); cx_ref_ = sys_.C() * x_ref_; }; // y_ref needs to be handled differently depending on output fn.  // (need to populate cx_ref_ too, which depends on output fn)  virtual void set_y_ref(const Vector\u0026amp; y_ref) { Reassign(y_ref_, y_ref); }; void set_Kc(const Matrix\u0026amp; Kc) { Reassign(Kc_, Kc); }; void set_Kc_inty(const Matrix\u0026amp; Kc_inty) { Reassign(Kc_inty_, Kc_inty); }; void set_Kc_u(const Matrix\u0026amp; Kc_u) { Reassign(Kc_u_, Kc_u); }; void set_tau_awu(data_t tau) { tau_awu_ = tau; k_awu_ = sys_.dt() / tau_awu_; }; void set_control_type(size_t control_type); // There is no reason u_lb/ub should not be public, but making set methods  // anyway.  void set_u_lb(data_t u_lb) { u_lb_ = u_lb; }; void set_u_ub(data_t u_ub) { u_ub_ = u_ub; }; void Reset() { sys_.Reset(); u_ref_.zeros(); u_ref_prev_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); u_saturated_ = false; t_since_control_onset_ = 0.0; }; void Print() { sys_.Print(); std::cout \u0026lt;\u0026lt; \u0026#34;g_design : \u0026#34; \u0026lt;\u0026lt; g_design_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_lb : \u0026#34; \u0026lt;\u0026lt; u_lb_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_ub : \u0026#34; \u0026lt;\u0026lt; u_ub_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }; protected: System sys_; Vector u_; Vector u_return_; Vector g_design_; // reference signals  Vector u_ref_; // create no set method for this:  Vector u_ref_prev_; Vector x_ref_; Vector y_ref_; Vector cx_ref_; // Controller gains  Matrix Kc_; Matrix Kc_u_; Matrix Kc_inty_; // control after g inversion  // do not need set methods for these.  Vector du_ref_; Vector dv_ref_; Vector v_ref_; Vector dv_; Vector v_; // integral error  // do not need set method for this  Vector int_e_; Vector int_e_awu_adjust_; Vector u_sat_; bool do_control_prev_ = false; bool do_lock_control_prev_ = false; // whether the g of system has become inverted from what you think it is  // (gain_ref)  bool u_saturated_ = false; // should be safe to have references here bc nothing needs to be done  // (like reset vars) when it changes...  data_t u_lb_{}; data_t u_ub_{}; data_t tau_awu_{}; data_t k_awu_ = 0; data_t t_since_control_onset_ = 0; size_t control_type_{}; private: void CalcControl(bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); void CalcSteadyStateSetPoint(); void AntiWindup(); void InitVars(size_t control_type); }; // Implement the above:  template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(sys), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(std::move(sys)), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::set_control_type(size_t control_type) { if (control_type_ == control_type) { return; } // creating a blank slate...  control_type_ = 0; Kc_inty_.zeros(0, 0); Kc_u_.zeros(0, 0); int_e_.zeros(0, 0); int_e_awu_adjust_.zeros(0, 0); // controller was designed to minimize integral error  if (control_type \u0026amp; kControlTypeIntY) { Kc_inty_.zeros(sys_.n_u(), sys_.n_y()); int_e_.zeros(sys_.n_y()); int_e_awu_adjust_.zeros(sys_.n_u()); control_type_ = control_type_ | kControlTypeIntY; } // controller was designed to minimize deltaU  // (i.e. state augmented with u)  if (control_type \u0026amp; kControlTypeDeltaU) { Kc_u_.zeros(sys_.n_u(), sys_.n_u()); control_type_ = control_type_ | kControlTypeDeltaU; } // whether to adapt set point calculate with (re-estimated) process  // disturbance (m)  if (control_type \u0026amp; kControlTypeAdaptM) { if (sys_.do_adapt_m) // only if adapting m...  { control_type_ = control_type_ | kControlTypeAdaptM; } } } // set_control_type  template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::Control( const Vector\u0026amp; z, bool do_control, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement  sys_.Filter(u_, z); bool do_estimation = true; // always have estimator on in this case  // calculate control signal  CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::ControlOutputReference( const Vector\u0026amp; z, bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement  if (do_estimation) { sys_.Filter(u_, z); } else { sys_.f(u_); } // calculate the set point  // solves for u_ref and x_ref when output is at y_ref at steady state.  if (do_control) { CalcSteadyStateSetPoint(); } // calculate control signal  CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcControl(bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { if (do_control \u0026amp;\u0026amp; do_estimation) { if (!do_control_prev_) { if (do_reset_at_control_onset) { Reset(); } t_since_control_onset_ = 0.0; } else { t_since_control_onset_ += sys_.dt(); } // enforce softstart on control vars.  if (sigma_soft_start \u0026gt; 0) { // half-Gaussian soft-start scaling factor  data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) / (2 * pow(sigma_soft_start, 2))); u_ref_ *= soft_start_sf; // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too  // x_ref_ *= soft_start_sf;  // cx_ref_ *= soft_start_sf;  // y_ref_ *= soft_start_sf;  } if (!do_lock_control) { // first do u -\u0026gt; v change of vars. (v = g.*u)  // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver  // control voltage u[=]V)  v_ref_ = g_design_ % u_ref_; // Given FB, calc. the change in control  if (control_type_ \u0026amp; kControlTypeDeltaU) { // if control designed to minimize not u but deltaU (i.e. state aug with  // u):  // TODO(mfbolus): Commented out for now. See note below.  // du_ref_ = u_ref_ - u_ref_prev_;  // dv_ref_ = g_design_ % du_ref_;  // TODO(mfbolus): Assuming users want *smooth* control signals if using  // kControlTypeDeltaU, it should be the case that dv_ref_ is --\u0026gt; 0. May  // want to revisit, but I am going to force it to be zero unless a  // situation arises that argues for keeping the above.  dv_ref_.zeros(); dv_ = dv_ref_; // nominally-optimal.  dv_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error  dv_ -= Kc_u_ * (v_ - v_ref_); // penalty on amp u (rel to ref)  if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup  // would be to not integrate error when control signal saturated:  // if(!uSaturated)  int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error  dv_ -= Kc_inty_ * int_e_; // control for integrated error  } // update the control  v_ += dv_; } else { v_ = v_ref_; // nominally-optimal.  v_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error  if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup  // would be to not integrate error when control signal saturated:  // if (!uSaturated)  int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error  v_ -= Kc_inty_ * int_e_; // control for integrated error  } } // convert back to control voltage u[=]V  u_ = v_ / sys_.g(); } // else do nothing until lock is low  } else { // if not control  // feed through u_ref in open loop  u_ = u_ref_ % g_design_ / sys_.g(); v_ = sys_.g() % u_; u_ref_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); } // ends do_control  // enforce box constraints (and antiwindup)  AntiWindup(); // add noise to input?  // The value for u that is *returned* to user after addition of any noise,  // while keeping controller/estimator blind to this addition.  u_return_ = u_; if ((sigma_u_noise \u0026gt; 0.0) \u0026amp;\u0026amp; (do_control \u0026amp;\u0026amp; !do_lock_control)) { u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn); Limit(u_return_, u_lb_, u_ub_); }; // For next time step:  u_ref_prev_ = u_ref_; do_control_prev_ = do_control; do_lock_control_prev_ = do_lock_control; } // CalcControl  template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcSteadyStateSetPoint() { // Linearly-constrained least squares (ls).  //  // _reference:  // Boyd \u0026amp; Vandenberghe (2018) Introduction to Applied Linear Algebra  //  Matrix a_ls = join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros)); Vector b_ls = cx_ref_; Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye), sys_.B() * arma::diagmat(sys_.g())); Vector d_ls = -sys_.m0(); if (control_type_ \u0026amp; kControlTypeAdaptM) { d_ls = -sys_.m(); // adapt setpoint calc with disturbance?  } Matrix a_ls_t = a_ls.t(); // TODO(mfbolus): not sure why but causes seg  // fault if I do not do this.  Matrix phi_ls = join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()), join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros))); // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse:  Matrix inv_phi = pinv(phi_ls); Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls); x_ref_ = xulam.subvec(0, sys_.n_x() - 1); u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1); cx_ref_ = sys_.C() * x_ref_; } // CalcSteadyStateSetPoint  template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::AntiWindup() { u_saturated_ = false; u_sat_ = u_; // limit u and flag whether saturated  for (size_t k = 0; k \u0026lt; u_.n_elem; k++) { if (u_[k] \u0026lt; u_lb_) { u_sat_[k] = u_lb_; u_saturated_ = true; } if (u_[k] \u0026gt; u_ub_) { u_sat_[k] = u_ub_; u_saturated_ = true; } } if ((control_type_ \u0026amp; kControlTypeIntY) \u0026amp;\u0026amp; (tau_awu_ \u0026lt; lds::kInf)) { // one-step back-calculation (calculate intE for u=u_sat)  // (Astroem, Rundqwist 1989 warn against using this...)  // int_e_awu_adjust_ =  // solve(Kc_inty_, (u_ - u_sat_)); // pinv(Kc_inty) * (u-uSat);  // gradual: see Astroem, Rundqwist 1989  // this is a fudge for doing MIMO gradual  // n.b., went ahead and multiplied 1/T by dt so don\u0026#39;t have to do that here.  int_e_awu_adjust_ = k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_); // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_);  int_e_ += int_e_awu_adjust_; } // set u to saturated version  u_ = u_sat_; } template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::InitVars(size_t control_type) { // initialize to default values  u_ref_ = Vector(sys_.n_u(), fill::zeros); u_ref_prev_ = Vector(sys_.n_u(), fill::zeros); x_ref_ = Vector(sys_.n_x(), fill::zeros); y_ref_ = Vector(sys_.n_y(), fill::zeros); cx_ref_ = Vector(sys_.n_y(), fill::zeros); u_ = Vector(sys_.n_u(), fill::zeros); u_return_ = Vector(sys_.n_u(), fill::zeros); u_sat_ = Vector(sys_.n_u(), fill::zeros); // Might not need all these, so zero elements until later.  Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros); Kc_u_ = Matrix(0, 0, fill::zeros); Kc_inty_ = Matrix(0, 0, fill::zeros); g_design_ = sys_.g(); // by default, same as model  dv_ = Vector(sys_.n_u(), fill::zeros); v_ = Vector(sys_.n_u(), fill::zeros); du_ref_ = Vector(sys_.n_u(), fill::zeros); dv_ref_ = Vector(sys_.n_u(), fill::zeros); v_ref_ = Vector(sys_.n_u(), fill::zeros); int_e_ = Vector(0, fill::zeros); int_e_awu_adjust_ = Vector(0, fill::zeros); set_control_type(control_type); } } // namespace lds  #endif  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":55,"href":"/lds-ctrl-est/docs/api/files/lds__fit_8h/","title":"ldsCtrlEst_h/lds_fit.h","section":"Files","content":"ldsCtrlEst_h/lds_fit.h #  LDS base fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::Fit LDS Fit Type.    Detailed Description #  This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.\nSource code #  //===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDS_FIT_HPP #define LDS_FIT_HPP  // namespace #include \u0026#34;lds.h\u0026#34;#include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); virtual ~Fit() = default; // get methods  size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; Q() const { return Q_; }; const Vector\u0026amp; x0() const { return x0_; }; const Matrix\u0026amp; P0() const { return P0_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; // gets measurement noise  virtual const Matrix\u0026amp; R() const = 0; // set methods (e.g., seeding initial fit values)  void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_m(const Vector\u0026amp; m) { Reassign(m_, m); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); ForceSymPD(Q_); }; virtual void set_R(const Matrix\u0026amp; R) = 0; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); ForceSymPD(P0_); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; View f(Matrix\u0026amp; x, const Matrix\u0026amp; u, size_t t) { x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x.col(t); }; View f(Matrix\u0026amp; x_pre, const Matrix\u0026amp; x_post, const Matrix\u0026amp; u, size_t t) { x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x_pre.col(t); }; virtual View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) = 0; protected: data_t dt_{}; // Dynamics  Matrix A_; Matrix B_; Vector g_; Vector m_; Matrix Q_; // Output  Matrix C_; Vector d_; Matrix R_; // initial conditions  Vector x0_; Matrix P0_; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; }; } // namespace lds #endif  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":56,"href":"/lds-ctrl-est/docs/api/files/lds__fit__em_8h/","title":"ldsCtrlEst_h/lds_fit_em.h","section":"Files","content":"ldsCtrlEst_h/lds_fit_em.h #  subspace identification More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::EM    Detailed Description #  This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).\nSource code #  //===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_EMAX_H #define LDSCTRLEST_LDS_EMAX_H  #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class EM { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: EM() = default; EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); virtual ~EM() = default; const Fit\u0026amp; Run(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = true, bool calc_output = true, bool calc_measurement = true, size_t max_iter = 100, data_t tol = 1e-2); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); // auto tuple = std::make_tuple(u_, z_);  u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; x() const { return x_; }; const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; y() const { return y_; }; const Matrix\u0026amp; sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; }; const Matrix\u0026amp; sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; }; const Matrix\u0026amp; sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; }; size_t n_t_tot() { return n_t_tot_; } const Vector\u0026amp; theta() const { return theta_; }; protected: void Expectation(bool force_common_initial = false); void Maximization(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = false, bool calc_output = false, bool calc_measurement = false); void MaximizeDynamics(); void MaximizeQ(); void MaximizeInitial(); virtual void MaximizeOutput() = 0; virtual void MaximizeMeasurement() = 0; void Smooth(bool force_common_initial); virtual void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) = 0; void Reset(); void InitVars(); Vector UpdateTheta(); // input/output training data  UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; std::vector\u0026lt;Matrix\u0026gt; x_; std::vector\u0026lt;Cube\u0026gt; P_; std::vector\u0026lt;Cube\u0026gt; P_t_tm1_; std::vector\u0026lt;Matrix\u0026gt; y_; Matrix diag_y_; // expectations calculated in E-step  Matrix sum_E_x_t_x_t_; Matrix sum_E_xu_tm1_xu_tm1_; Matrix sum_E_xu_t_xu_tm1_; Fit fit_; Vector theta_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; }; template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; fit_ = Fit(n_u_, n_x, n_y_, dt); u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { // make sure fit dims match I/O data  if (fit0.n_u() != u_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and input training data have inconsistent dimensions\u0026#34;); } if (fit0.n_y() != z_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and output training data have inconsistent dimensions\u0026#34;); } fit_ = fit0; u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::InitVars() { // check input/output data dimensions are consistent  if (z_.size() != u_.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_.at(trial).n_cols != u_.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } n_u_ = fit_.n_u(); n_x_ = fit_.n_x(); n_y_ = fit_.n_y(); dt_ = fit_.dt(); x_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); P_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); P_t_tm1_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); y_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros); P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros); } diag_y_ = Matrix(n_y_, n_y_, fill::zeros); // covariances in expectation step  sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); } template \u0026lt;typename Fit\u0026gt; const Fit\u0026amp; EM\u0026lt;Fit\u0026gt;::Run(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement, size_t max_iter, data_t tol) { Reset(); // to initial conditions  size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_; Vector theta(n_params); Vector theta_new(n_params); data_t max_dtheta = 1; // if solving for initial conditions, allow them be varied.  // otherwise, freeze at provided values.  bool force_common_initial = !calc_init; // go until parameter convergence  for (size_t l = 0; l \u0026lt; max_iter; l++) { theta_ = UpdateTheta(); std::cout \u0026lt;\u0026lt; \u0026#34;Iteration \u0026#34; \u0026lt;\u0026lt; l + 1 \u0026lt;\u0026lt; \u0026#34;/\u0026#34; \u0026lt;\u0026lt; max_iter \u0026lt;\u0026lt; \u0026#34; ...\\n\u0026#34;; Expectation(force_common_initial); Maximization(calc_dynamics, calc_Q, calc_init, calc_output, calc_measurement); // check convergence  theta_new = UpdateTheta(); Vector dtheta = abs(theta_new - theta_) / abs(theta_); // some parameters could be zero...  arma::uvec ubi_finite = find_finite(dtheta); max_dtheta = max(dtheta.elem(ubi_finite)); std::cout \u0026lt;\u0026lt; \u0026#34;max dtheta: \u0026#34; \u0026lt;\u0026lt; max_dtheta \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; if (max_dtheta \u0026lt; tol) { std::cout \u0026lt;\u0026lt; \u0026#34;Converged.\\n\u0026#34;; break; } std::cout \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } return fit_; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Smooth(bool force_common_initial) { Matrix k_e(n_x_, n_y_); // estimator gain  Cube k_backfilt; // back-filtering gains  // TODO(mfbolus): this loop could be made parallel  for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { Matrix x_pre(n_x_, n_t_[trial], fill::zeros); Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros); Matrix x_post(n_x_, n_t_[trial], fill::zeros); Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros); if (force_common_initial) // forces all trials to have same initial  // conditions.  { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); } y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); // This *should not* be necessary but make sure P is symmetric.  ForceSymPD(P_[trial].slice(0)); x_pre.col(0) = x_[trial].col(0); p_pre.slice(0) = P_[trial].slice(0); x_post.col(0) = x_[trial].col(0); p_post.slice(0) = P_[trial].slice(0); // filter  for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // predict  fit_.f(x_pre, x_post, u_.at(trial), t); fit_.h(y_[trial], x_pre, t); diag_y_.diag() = y_[trial].col(t); // TODO(mfbolus): change if parallel  // update --\u0026gt; posterior estimation  RecurseKe(k_e, p_pre, p_post, t); x_post.col(t) = x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t)); y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d(); } // backfilter -\u0026gt; Smoothed estimate  // Reference:  // Shumway et Stoffer (1982)  ForceSymPD(p_post.slice(n_t_[trial] - 1)); k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1); P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 0; t--) { // TODO(mfmbolus): should not be necessary to force symm positive def  ForceSymPD(p_pre.slice(t)); ForceSymPD(p_post.slice(t - 1)); ForceSymPD(P_[trial].slice(t)); k_backfilt.slice(t - 1) = p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t)); x_[trial].col(t - 1) = x_post.col(t - 1) + k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t)); P_[trial].slice(t - 1) = p_post.slice(t - 1) + k_backfilt.slice(t - 1) * (P_[trial].slice(t) - p_pre.slice(t)) * k_backfilt.slice(t - 1).t(); } // do the same for P_t_tm1  Matrix id(n_x_, n_x_, fill::eye); P_t_tm1_[trial].slice(n_t_[trial] - 1) = (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 1; t--) { P_t_tm1_[trial].slice(t - 1) = p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() + k_backfilt.slice(t - 1) * (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) * k_backfilt.slice(t - 2).t(); } // finally, get smoothed estimate of output  for (size_t t = 0; t \u0026lt; n_t_[trial]; t++) { fit_.h(y_[trial], x_[trial], t); } // samps loop  } // trial loop } // Smooth  // template \u0026lt;typename Fit\u0026gt; // void EM\u0026lt;Fit\u0026gt;::RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) { // // predict covar // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q();  // // update Ke // Ke = P_pre.slice(t) * fit_.C().t() * // inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R());  // // update cov // // Reference: Ghahramani et Hinton (1996) // P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t);  // // // n.b. for poisson : // // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + // fit_.Q(); // // // update cov // // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ * // // fit_.C()); // // // update Ke // // Ke = P_post.slice(t) * fit_.C(); // }  template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Expectation(bool force_common_initial) { // calculate the mean/cov of state needed for maximizing E[pr(z|theta)]  Smooth(force_common_initial); // now get the various forms of sum(E[xx\u0026#39;]) needed  // n.b. Going to start at t=1 rather than 0 bc most max terms need that.  // so really \u0026#34;n_t_tot_\u0026#34; is (n_t_tot_-1)  n_t_tot_ = 0; sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); Vector xu_tm1(n_x_ + n_u_, fill::zeros); Vector xu_t(n_x_ + n_u_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // ------------ sum_E_x_t_x_t ------------  sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t(); sum_E_x_t_x_t_ += P_[trial].slice(t); // ------------ sum_E_xu_tm1_xu_tm1 ------------  xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1)); sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t(); sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t - 1); // ------------ sum_E_xu_t_xu_tm1 ------------  xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t)); sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t(); sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_t_tm1_[trial].slice(t); n_t_tot_ += 1; } // time  } // trial } // Expectation  template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement) { if (calc_output) { MaximizeOutput(); } if (calc_measurement) { MaximizeMeasurement(); } if (calc_dynamics) { MaximizeDynamics(); } if (calc_Q) { MaximizeQ(); } if (calc_init) { MaximizeInitial(); } } // Maximization  template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeDynamics() { // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996)  Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) * inv_sympd(sum_E_xu_tm1_xu_tm1_); fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1)); std::cout \u0026lt;\u0026lt; \u0026#34;A_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.A()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.B()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeQ() { // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996)  // View sum_e_x_t_xu_tm1 =  // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1);  // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 *  // inv_sympd(sum_E_xu_tm1_xu_tm1_) *  // sum_e_x_t_xu_tm1.t();  // q /= n_t_tot_;  // this way is same as above iff dynamics were just updated:  // View sum_e_x_t_xu_tm1 =  // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1);  // Matrix ab = arma::join_horiz(fit_.A(), fit_.B());  // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t();  // q /= n_t_tot_;  // From scratch method:  // Q is covariance of the error between state and dynamics-predicted state  // (aka process noise)  // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})\u0026#39;]  // t-1 terms:  View sum_e_x_tm1_x_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_u_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1); View sum_e_x_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); // t, t-1 terms:  View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_x_t_u_tm1 = sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); Matrix q = sum_E_x_t_x_t_; q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t(); q -= sum_e_x_t_x_tm1 * fit_.A().t(); q -= fit_.A() * sum_e_x_t_x_tm1.t(); // input-related terms:  q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t(); q -= sum_e_x_t_u_tm1 * fit_.B().t(); q -= fit_.B() * sum_e_x_t_u_tm1.t(); q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t(); q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t(); q /= n_t_tot_; fit_.set_Q(q); std::cout \u0026lt;\u0026lt; \u0026#34;Q_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.Q()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // std::cout \u0026lt;\u0026lt; \u0026#34;Q_new: \\n\u0026#34; \u0026lt;\u0026lt; fit_.Q() \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeInitial() { Vector x0 = fit_.x0(); x0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { x0 += x_[trial].col(0); } x0 /= z_.size(); std::cout \u0026lt;\u0026lt; \u0026#34;x0_new[0]: \u0026#34; \u0026lt;\u0026lt; x0[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // always recalc P0 even if the initial state is fixed (at zero, for  // example)  Matrix e_var(n_x_, n_x_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t(); } e_var /= z_.size(); // go ahead and subtract x0*x0\u0026#39; so don\u0026#39;t have to below.  e_var -= x0 * x0.t(); // To get P0, going to get initial P_ per trial and average.  // (which might be wrong, but need a single number)  Matrix p0 = fit_.P0(); p0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { p0 += (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var; } p0 /= z_.size(); fit_.set_P0(p0); std::cout \u0026lt;\u0026lt; \u0026#34;P0_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.P0()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeOutput() { // solve for C+d:  Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros); Vector x1(n_x_ + 1, fill::zeros); x1[n_x_] = 1.0; // augment with one to solve for bias  Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { x1.subvec(0, n_x_ - 1) = x_[trial].col(t); sum_zx += z_.at(trial).col(t) * x1.t(); sum_e_x1_x1 += x1 * x1.t(); sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t); } } Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1); fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1)); fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_))); std::cout \u0026lt;\u0026lt; \u0026#34;C_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.C()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.d()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeMeasurement() { // Solve for measurement noise covar  size_t n_t_tot = 0; // Ghahgramani, Hinton 1996:  Matrix sum_zz(n_y_, n_y_, fill::zeros); Matrix sum_yz(n_y_, n_y_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t(); // Use Cnew:  sum_yz += (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t(); n_t_tot += 1; } } fit_.set_R((sum_zz - sum_yz) / n_t_tot); std::cout \u0026lt;\u0026lt; \u0026#34;R_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.R()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Reset() { // reset to initial conditions  for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); } } template \u0026lt;typename Fit\u0026gt; Vector EM\u0026lt;Fit\u0026gt;::UpdateTheta() { // TODO(mfbolus): This should include n_y_ more params for d.  size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_; if (fit_.R().n_elem \u0026gt; 0) { n_params += n_y_ * n_y_; } Vector theta(n_params); size_t idx_start = 0; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B()); idx_start += n_x_ * n_u_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0()); idx_start += n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C()); idx_start += n_y_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d()); idx_start += n_y_; if (fit_.R().n_elem \u0026gt; 0) { theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R()); } return theta; } } // namespace lds  #endif  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":57,"href":"/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_fit_ssid.h","section":"Files","content":"ldsCtrlEst_h/lds_fit_ssid.h #  subspace identification More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::SSID    Detailed Description #  This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)\u0026lt;Fit\u0026gt;).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code #  //===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_FIT_SSID_H #define LDSCTRLEST_LDS_FIT_SSID_H  #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class SSID { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: SSID() = default; SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d = Vector(1).fill(-kInf)); std::tuple\u0026lt;Fit, Vector\u0026gt; Run(SSIDWt ssid_wt); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } protected: void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001); void CreateHankelDataMat(); virtual void DecomposeData() = 0; void CalcSVD(SSIDWt wt); void Solve(data_t wt_dc); void RecomputeExtObs(); // input/output training data  UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; Matrix D_; Fit fit_; Matrix g_dc_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_h_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; Matrix L_; Vector s_; Matrix ext_obs_t_; }; template \u0026lt;typename Fit\u0026gt; SSID\u0026lt;Fit\u0026gt;::SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d) { // check input/output data dimensions are consistent  if (z_train.size() != u_train.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_train.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_train.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } dt_ = dt; n_x_ = n_x; n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; n_h_ = n_h; // dimensionality check for eventual block-hankel data matrix  size_t len = n_t_tot_ - 2 * n_h_ + 1; if (len \u0026lt; (2 * n_h_ * (n_u_ + n_y_))) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;Dataset problem! More rows than columns in block-hankel data \u0026#34; \u0026#34;matrix: 2*(n_u+n_y)*n_h \u0026gt; data-length! Need higher data-length or \u0026#34; \u0026#34;lower n_h.\u0026#34;; throw std::runtime_error(ss.str()); } fit_ = Fit(n_u_, n_x_, n_y_, dt_); u_ = std::move(u_train); z_ = std::move(z_train); if (!d.is_finite() || (d.n_rows != n_y_)) { // TODO(mfbolus): implement least-square solution for impulse response with  // a second input of ones. Data-driven way of accounting for offset *not*  // driven by an input.  //  // For now, calculate output bias (d) as the  // output wherever the stimulus has not been on for some amount of time.  // convolve u with rectangle and take all samples. This is a reasonable  // approach, since often when autonomous systems are fit (i.e., systems with  // no input), they will subtract off the mean of the output. This  // essentially amounts to setting output bias to the mean of the output when  // there is no stimulation.  data_t t_silence = 0.1; data_t thresh_silence = 0.001; CalcD(t_silence, thresh_silence); } else { fit_.set_d(d); } } template \u0026lt;typename Fit\u0026gt; std::tuple\u0026lt;Fit, Vector\u0026gt; SSID\u0026lt;Fit\u0026gt;::Run(SSIDWt ssid_wt) { // the weight on minimizing dc I/O gain only works for gaussian,  // and hopefully not necessary with appropriate dataset.  data_t wt_dc = 0; // std::cout \u0026lt;\u0026lt; \u0026#34;creating hankel mat\\n\u0026#34;;  CreateHankelDataMat(); // std::cout \u0026lt;\u0026lt; \u0026#34;decomposing data\\n\u0026#34;;  DecomposeData(); // std::cout \u0026lt;\u0026lt; \u0026#34;calculating svd\\n\u0026#34;;  CalcSVD(ssid_wt); // std::cout \u0026lt;\u0026lt; \u0026#34;solving for params\\n\u0026#34;;  Solve(wt_dc); // std::cout \u0026lt;\u0026lt; \u0026#34;fin\\n\u0026#34;;  return std::make_tuple(fit_, s_); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcD(data_t t_silence, data_t thresh_silence) { Vector d(z_.at(0).n_rows, fill::zeros); Vector win(static_cast\u0026lt;size_t\u0026gt;(t_silence / dt_), fill::ones); Vector sum_z_silence(n_y_, fill::zeros); size_t n_silence(0); for (size_t trial = 0; trial \u0026lt; u_.size(); trial++) { // find silent samples  // start by convolving with  Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0)); Vector u_conv = conv(sum_u, win, \u0026#34;same\u0026#34;); // get only the samples that are silent...  arma::uvec ubi_silence = find(u_conv \u0026lt;= thresh_silence); if (ubi_silence.n_elem \u0026gt; 0) { sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1); n_silence += ubi_silence.n_elem; } } if (n_silence \u0026gt; 0) { d = sum_z_silence / n_silence; } fit_.set_d(d); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat() { // temporary copy of data  Matrix z(n_y_, n_t_tot_, fill::zeros); Matrix u(n_u_, n_t_tot_, fill::zeros); size_t so_far(0); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial); u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial); so_far += n_t_.at(trial); } // remove output bias  z.each_col() -= fit_.d(); // calculate I/O gain @ DC while data in convenient form  g_dc_ = z * pinv(u); // std::cout \u0026lt;\u0026lt; \u0026#34;G0_data = \u0026#34; \u0026lt;\u0026lt; g_dc_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;;  // create hankel data matrix  size_t len = z.n_cols - 2 * n_h_ + 1; // data length in hankel mat  // block-hankel data matrix  D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros); // past input  auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1); // future input  auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1); // past output  auto y_p = D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1); // future output  auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, len - 1); size_t idx = 0; for (size_t k = 0; k \u0026lt; len; k++) { idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } } D_ /= sqrt(static_cast\u0026lt;data_t\u0026gt;(len)); } // template \u0026lt;typename Fit\u0026gt; // void SSID\u0026lt;Fit\u0026gt;::DecomposeData() { // // do LQ decomp instead of calculating covariance expensive way // // Note that \u0026#34;R\u0026#34; in van Overschee is lower-triangular (L), not \u0026#34;R\u0026#34; in QR // // decomp. Very confusing. // Matrix q_t; // lq(L_, q_t, D_); // // van Overschee zeros out the other elements. // L_ = trimatl(L_); // }  template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcSVD(SSIDWt wt) { // submats that will be needed:  auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1); auto R_23_13 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1); auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_) - 1); auto R_44 = L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14); auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1); auto Luf = Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1); auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); // aka: R_f  Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1); // from van Overschee subid.m:  // Rf = R((2*m+l)*i+1:2*(m+l)*i,:); % Future outputs  Matrix U; Matrix V; switch (wt) { case kSSIDNone: { // No weighting. (what van Overschee calls \u0026#34;N4SID\u0026#34;)  Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14; arma::svd(U, s_, V, O_k_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDMOESP: { // MOESP weighting  // This is what they use in the \u0026#34;robust\u0026#34; algorithm van Overschee, de Moor  // 1996  Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); svd(U, s_, V, O_k_ortho_Uf_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDCVA: { // CVA weighting  // See van Overschee\u0026#39;s matlab code (subid.m):  // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems  Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); Matrix inv_w1; Matrix qt1; lq(inv_w1, qt1, R_56_16); // lq decomp of R_f (future output data)  inv_w1 = trimatl(inv_w1); inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1); Matrix w_o_w = arma::solve( inv_w1, O_k_ortho_Uf_sans_Qt); // alternatively  // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt  svd(U, s_, V, w_o_w, \u0026#34;std\u0026#34;); U = inv_w1 * U; break; } } // Truncate to model order (heart of ssid method)  auto s_hat = s_.subvec(0, n_x_ - 1); Matrix diag_sqrt_s = diagmat(sqrt(s_hat)); auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1); // get extended observability and controllability mats  ext_obs_t_ = u_hat * diag_sqrt_s; // extended observability matrix } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::Solve(data_t wt_dc) { // required submats  auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_56_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); // Solve for params using appropriate algorithm:  // robust deterministic/stochastic algorithm in van Overschee 1996  // algorithm that the authors say \u0026#34;works\u0026#34; in practice.  auto ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix  // This is what textbook (1996) says:  //  // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15);  //  // HOWEVER, do not know why but have to fill the last place with zeros like  // authors\u0026#39; matlab implementation (see `subid.m`)  // Otherwise, get ridiculous covariances (although A,C estimates are close to  // same...)  Matrix Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Matrix S = Tl * pinv(Tr); // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure  // stability.  fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1)); Matrix ext_obs_t_p1 = join_vert( ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1), Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros)); fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1); // At this point, van Overschee \u0026amp; de Moor suggest re-calculating ext_obs_t_,  // ext_obs_tm1 from (A, C) because it was just an approximation. This is  RecomputeExtObs(); ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix  Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); S = Tl * pinv(Tr); Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_); Matrix Mcurly = pinv(ext_obs_tm1); Matrix Pcurly = Tl - Lcurly * R_56_15; Vector Pvec = vectorise(Pcurly); Matrix Qcurly = R_23_15; // Identify [D; B], assuming D=0 and ensuring DC gain is correct  Matrix sum_QcurlyT_kron_Ncurly( (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_), fill::zeros); Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols, fill::eye); eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1, eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1; // van Overschee (1996) p. 126  Matrix N1_Tl = -Lcurly; N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) += join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly); N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) += Matrix(n_y_, n_y_, fill::eye); Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros); Matrix N_k; for (size_t k = 0; k \u0026lt; n_h_; k++) { auto Qcurly_k = Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1); Nk_Tl.zeros(); Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) = N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1); N_k = Nk_Tl * eye_ext_obs_tm1; sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k); } Matrix err_vec; if (wt_dc \u0026gt; 0) { // Constraints enforced by weighted least squares  //  // Reference:  //  // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly  // Excited Industrial Systems. Conference in Decision and Control.  // constraint 1: assume D=0 --\u0026gt; remove the components for Dvec (this is  // actually a hard constraint in that it ignores D)  Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly; sum_QcurlyT_kron_Ncurly = Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_); size_t kkk = 0; for (size_t k = 1; k \u0026lt; (n_u_ + 1); k++) { size_t start_idx = k * (n_y_ + n_x_) - n_x_; for (size_t kk = 0; kk \u0026lt; n_x_; kk++) { sum_QcurlyT_kron_Ncurly.col(kkk) = sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk); kkk++; } } // constraint 2: Make sure DC I/O gain is correct  Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A()); Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_)); Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0); Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 = join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0); // WEIGHTED LS  // Important in practice because I care a lot about at least getting the DC  // gain correct. Put x weight on minimizing error at DC, relative to others  Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye); // Make weight on minimizing DC error immense so at least that  // should be nailed.  size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows; size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows; size_t stop_row = w.n_rows - 1; size_t stop_col = w.n_cols - 1; // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale  // weight with data length?  w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc; Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * sum_QcurlyT_kron_Ncurly_b_to_g0) * sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec; fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_)); // Calculate residuals and their cov.  // Because I\u0026#39;ve added constraints, I need to re-calculate the right term  // with b_vec instead of how van Overschee do in final algorithm.  err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec; } else { // default way: *no* constraint on G0 or D=0  Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec; // TODO(mfbolus) n.b., this gets thrown away...  // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_);  fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_)); err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec; } // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols);  // TODO(mfbolus): Something is wrong with the error calculation above.  // Use the way van overschee does it in `subid.m`  // WARNING: this ignores any above constraints, so Q, R will be approximate...  Matrix err = Tl - S * Tr; Matrix cov_err = err * err.t(); fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1)); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::RecomputeExtObs() { ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C(); for (size_t k = 2; k \u0026lt; (n_h_ + 1); k++) { ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) = ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1, ext_obs_t_.n_cols - 1) * fit_.A(); } } } // namespace lds  #endif  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":58,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian_8h/","title":"ldsCtrlEst_h/lds_gaussian.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian.h #  glds namespace More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Detailed Description #  This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)).\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_H #define LDSCTRLEST_LDS_GAUSSIAN_H  // namespace #include \u0026#34;lds.h\u0026#34; namespace lds { namespace gaussian { // insert any Gaussian-specific things here... } // namespace gaussian } // namespace lds  #endif  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":59,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_ctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_ctrl.h #  GLDS Controller. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::Controller Gaussian-observation Controller Type.    Detailed Description #  This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H  // namespace #include \u0026#34;lds_gaussian.h\u0026#34;// system #include \u0026#34;lds_gaussian_sys.h\u0026#34;// controller #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace gaussian { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); cx_ref_ = y_ref - sys_.d(); }; // make sure base class template methods available  using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; }; } // namespace gaussian } // namespace lds  #endif  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":60,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_fit.h #  GLDS fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::Fit GLDS Fit Type.    Detailed Description #  This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_H  // namespace #include \u0026#34;lds_gaussian.h\u0026#34;// fit type #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace gaussian { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); const Matrix\u0026amp; R() const override { return R_; }; void set_R(const Matrix\u0026amp; R) override { Reassign(R_, R); ForceSymPD(R_); }; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = C_ * x.col(t) + d_; return y.col(t); }; }; }; // namespace gaussian } // namespace lds #endif  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":61,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_em.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_fit_em.h #  GLDS E-M fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::FitEM GLDS E-M Fit Type.    Detailed Description #  This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H  #include \u0026#34;lds_fit_em.h\u0026#34;#include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; }; } // namespace gaussian } // namespace lds  #endif  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":62,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h #  GLDS SSID fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.    Detailed Description #  This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H  #include \u0026#34;lds_fit_ssid.h\u0026#34;#include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; using SSID\u0026lt;Fit\u0026gt;::Run; private: using SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat; using SSID\u0026lt;Fit\u0026gt;::CalcSVD; using SSID\u0026lt;Fit\u0026gt;::Solve; void DecomposeData() override; void SolveVanOverschee(); }; } // namespace gaussian } // namespace lds #endif  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":63,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_sctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_sctrl.h #  GLDS switched controller type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.    Detailed Description #  This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H  // controller type #include \u0026#34;lds_gaussian_ctrl.h\u0026#34;// switched controller #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace gaussian { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); cx_ref_ = y_ref - sys_.d(); } // make sure base class template methods available  using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; }; // SwitchedController } // namespace gaussian } // namespace lds  #endif  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":64,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/","title":"ldsCtrlEst_h/lds_gaussian_sys.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_sys.h #  GLDS base type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::System Gaussian LDS Type.    Detailed Description #  This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)).\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H #define LDSCTRLEST_LDS_GAUSSIAN_SYS_H  // namespace #include \u0026#34;lds_gaussian.h\u0026#34;// system #include \u0026#34;lds_sys.h\u0026#34; namespace lds { namespace gaussian { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0, data_t r0 = kDefaultR0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; // get methods  const Matrix\u0026amp; R() const { return R_; }; // set methods  void set_Q(const Matrix\u0026amp; Q) { lds::System::set_Q(Q); do_recurse_Ke_ = true; } void set_R(const Matrix\u0026amp; R) { Reassign(R_,R); do_recurse_Ke_ = true; }; void set_Ke(const Matrix\u0026amp; Ke) { Reassign(Ke_,Ke); // if users have set Ke, they must not want to calculate it online.  do_recurse_Ke_ = false; }; void set_Ke_m(const Matrix\u0026amp; Ke_m) { Reassign(Ke_m_,Ke_m); // if users have set Ke, they must not want to calculate it online.  do_recurse_Ke_ = false; }; void Print(); protected: void h() override { cx_ = C_ * x_; y_ = cx_ + d_; }; void RecurseKe() override; // Gaussian-output-specific  Matrix R_; bool do_recurse_Ke_{}; }; // System } // namespace gaussian } // namespace lds  #endif  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":65,"href":"/lds-ctrl-est/docs/api/files/lds__poisson_8h/","title":"ldsCtrlEst_h/lds_poisson.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson.h #  plds namespace More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Detailed Description #  This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)).\nSource code #  //===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_H #define LDSCTRLEST_LDS_POISSON_H  #include \u0026#34;lds.h\u0026#34; namespace lds { namespace poisson { // TODO(mfbolus): Not sure if defining these as static here makes the most // sense. Is there a downside to letting multiple poisson System objects share a // common random number generator? static std::random_device rd; static std::mt19937 rng = std::mt19937( rd()); } // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":66,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_ctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_ctrl.h #  PLDS controller type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::Controller PLDS Controller Type.    Detailed Description #  This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/)), including state estimation.\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_CTRL_H #define LDSCTRLEST_LDS_POISSON_CTRL_H  // namespace #include \u0026#34;lds_poisson.h\u0026#34;// system type #include \u0026#34;lds_poisson_sys.h\u0026#34;// control type #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace poisson { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); lds::Limit(y_ref_, kYRefLb, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available  using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; private: constexpr static const data_t kYRefLb = 1e-4; }; } // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":67,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/","title":"ldsCtrlEst_h/lds_poisson_fit.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_fit.h #  PLDS base fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::Fit PLDS Fit Type.    Detailed Description #  This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_FIT_H #define LDSCTRLEST_LDS_POISSON_FIT_H  // namespace #include \u0026#34;lds_poisson.h\u0026#34;// fit #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace poisson { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) : lds::Fit(n_u, n_x, n_y, dt){}; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = exp(C_ * x.col(t) + d_); return y.col(t); }; void set_R(const Matrix\u0026amp; R) override { std::cerr \u0026lt;\u0026lt; \u0026#34;WARNING: Cannot set R (R[0] = \u0026#34; \u0026lt;\u0026lt; R.at(0) \u0026lt;\u0026lt; \u0026#34;). No Gaussian measurement noise in Poisson observation model.\\n\u0026#34;; }; const Matrix\u0026amp; R() const override { return R_; }; }; }; // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":68,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_em.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_fit_em.h #  PLDS E-M fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::FitEM PLDS E-M Fit Type.    Detailed Description #  This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\n[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H #define LDSCTRLEST_LDS_POISSON_FIT_EM_H  #include \u0026#34;lds_fit_em.h\u0026#34;#include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override{}; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; data_t NewtonSolveC(); void AnalyticalSolveD(); }; } // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":69,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_ssid.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_fit_ssid.h #  PLDS SSID fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS.    Detailed Description #  This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitssid/)).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H #define LDSCTRLEST_LDS_POISSON_FIT_SSID_H  #include \u0026#34;lds_fit_ssid.h\u0026#34;#include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; private: void DecomposeData() override; void CalcCov(); void PoissonToGaussianMoments(); Matrix cov_; }; } // namespace poisson } // namespace lds #endif  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":70,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_sctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_sctrl.h #  PLDS switched controller type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::SwitchedController Poisson-observation SwitchedController Type.    Detailed Description #  This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H  #include \u0026#34;lds_poisson_ctrl.h\u0026#34;#include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace poisson { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); lds::Limit(y_ref_, kYRefLB, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available  using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; private: constexpr static data_t kYRefLB = 1e-4; }; } // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":71,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/","title":"ldsCtrlEst_h/lds_poisson_sys.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_sys.h #  PLDS base type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::System Poisson System type.    Detailed Description #  This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)).\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_SYS_H #define LDSCTRLEST_LDS_POISSON_SYS_H  // namespace #include \u0026#34;lds_poisson.h\u0026#34;// system #include \u0026#34;lds_sys.h\u0026#34; // needed for Poisson random number generation #include \u0026lt;random\u0026gt; namespace lds { namespace poisson { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; protected: void h() override { cx_ = C_ * x_; y_ = exp(cx_ + d_); diag_y_.diag() = y_; }; void RecurseKe() override; private: // Poisson-output-specific  Matrix diag_y_; std::poisson_distribution\u0026lt;size_t\u0026gt; pd_; }; // System } // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":72,"href":"/lds-ctrl-est/docs/api/files/lds__sctrl_8h/","title":"ldsCtrlEst_h/lds_sctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_sctrl.h #  SwitchedController type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::SwitchedController SwitchedController Type.    Detailed Description #  This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code #  //===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_SCTRL_H #define LDSCTRLEST_LDS_SCTRL_H  #include \u0026#34;lds_ctrl.h\u0026#34;#include \u0026#34;lds_uniform_mats.h\u0026#34;#include \u0026#34;lds_uniform_vecs.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class SwitchedController : public Controller\u0026lt;System\u0026gt; { public: SwitchedController() = default; SwitchedController(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); SwitchedController(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); void Switch(size_t idx, bool do_force_switch = false); void set_Kc(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc) { Kc_list_ = Kc; Kc_ = Kc_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc) { Kc_list_ = std::move(Kc); Kc_ = Kc_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_inty) { Kc_inty_list_ = Kc_inty; Kc_inty_ = Kc_inty_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_inty) { Kc_inty_list_ = std::move(Kc_inty); Kc_inty_ = Kc_inty_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_u) { Kc_u_list_ = Kc_u; Kc_u_ = Kc_u_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_u) { Kc_u_list_ = std::move(Kc_u); Kc_u_ = Kc_u_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(const UniformVectorList\u0026amp; g) { g_design_list_ = g; g_design_ = g_design_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(UniformVectorList\u0026amp;\u0026amp; g) { g_design_list_ = std::move(g); g_design_ = g_design_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; // make sure base class template methods available  using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; protected: std::vector\u0026lt;System\u0026gt; systems_; size_t n_sys_{}; size_t idx_{}; // controller gains could be different for each  UniformMatrixList\u0026lt;\u0026gt; Kc_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_inty_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_u_list_; // design-phase input gain could also be different  UniformVectorList g_design_list_; // TODO(mfbolus): not sure why I need to do this.  using Controller\u0026lt;System\u0026gt;::Kc_; using Controller\u0026lt;System\u0026gt;::Kc_inty_; using Controller\u0026lt;System\u0026gt;::Kc_u_; using Controller\u0026lt;System\u0026gt;::g_design_; using Controller\u0026lt;System\u0026gt;::sys_; // using Controller\u0026lt;System\u0026gt;::u_ref_;  // using Controller\u0026lt;System\u0026gt;::x_ref_;  // using Controller\u0026lt;System\u0026gt;::y_ref_;  //  using Controller\u0026lt;System\u0026gt;::control_type_; private: void InitVars(); using lds::Controller\u0026lt;System\u0026gt;::set_sys; // using Controller\u0026lt;System\u0026gt;::set_Kc;  // using Controller\u0026lt;System\u0026gt;::set_Kc_inty;  // using Controller\u0026lt;System\u0026gt;::set_Kc_u;  // using Controller\u0026lt;System\u0026gt;::set_g_design; }; template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(systems.at(0), u_lb, u_ub, control_type), systems_(systems) { InitVars(); } template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(System(systems.at(0).n_u(), systems.at(0).n_x(), systems.at(0).n_y(), systems.at(0).dt()), u_lb, u_ub, control_type), systems_(std::move(systems)) { InitVars(); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::InitVars() { n_sys_ = systems_.size(); sys_ = systems_.at(0); Kc_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_)); Kc_inty_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); Kc_u_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); g_design_list_ = UniformVectorList(std::vector\u0026lt;Vector\u0026gt;(n_sys_, g_design_)); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::Switch(size_t idx, bool do_force_switch) { if ((idx == idx_) \u0026amp;\u0026amp; !do_force_switch) { return; // already there.  } // put old up and get new one out  systems_.at(idx_) = std::move(sys_); sys_ = std::move(systems_.at(idx)); // set the state of this system to that of the previous system  // TODO(mfbolus): This will only work as intended if state matrix is the same.  // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp.  sys_.set_m(systems_.at(idx_).m(), true); sys_.set_x(systems_.at(idx_).x()); // swap controller gains  Kc_list_.Swap(Kc_, idx_); Kc_list_.Swap(Kc_, idx); if (control_type_ \u0026amp; kControlTypeIntY) { Kc_inty_list_.Swap(Kc_inty_, idx_); Kc_inty_list_.Swap(Kc_inty_, idx); } if (control_type_ \u0026amp; kControlTypeDeltaU) { Kc_u_list_.Swap(Kc_u_, idx_); Kc_u_list_.Swap(Kc_u_, idx); } g_design_list_.Swap(g_design_, idx_); g_design_list_.Swap(g_design_, idx); idx_ = idx; } // Switch  } // namespace lds  #endif  Updated on 4 May 2022 at 16:34:52 Eastern Daylight Time\n"},{"id":73,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8h/","title":"ldsCtrlEst_h/lds_sys.h","section":"Files","content":"ldsCtrlEst_h/lds_sys.h #  LDS base type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::System Linear Dynamical System Type.    Detailed Description #  This file declares and partially defines the base type for linear dynamical systems ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code #  //===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_SYS_H #define LDSCTRLEST_LDS_SYS_H  #include \u0026#34;lds.h\u0026#34; namespace lds { class System { public: System() = default; System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); virtual ~System() {} void Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z); virtual const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) = 0; void f(const Vector\u0026amp; u, bool do_add_noise = false) { x_ = A_ * x_ + B_ * (g_ % u) + m_; if (do_add_noise) { x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_); } }; virtual void h() = 0; size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Vector\u0026amp; x() const { return x_; }; const Matrix\u0026amp; P() const { return P_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; P_m() const { return P_m_; }; const Vector\u0026amp; cx() const { return cx_; }; const Vector\u0026amp; y() const { return y_; }; const Vector\u0026amp; x0() const { return x0_; }; const Vector\u0026amp; m0() const { return m0_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; const Matrix\u0026amp; Ke() const { return Ke_; }; const Matrix\u0026amp; Ke_m() const { return Ke_m_; }; const Matrix\u0026amp; Q() { return Q_; }; const Matrix\u0026amp; Q_m() { return Q_m_; }; const Matrix\u0026amp; P0() { return P0_; }; const Matrix\u0026amp; P0_m() { return P0_m_; }; void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_m(const Vector\u0026amp; m, bool do_force_assign=false) { Reassign(m0_, m); if ((!do_adapt_m) || do_force_assign) { Reassign(m_, m); } }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); }; void set_Q_m(const Matrix\u0026amp; Q_m) { Reassign(Q_m_, Q_m); }; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); }; void set_P0_m(const Matrix\u0026amp; P0_m) { Reassign(P0_m_, P0_m); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; void set_x(const Vector\u0026amp; x) { Reassign(x_, x); h(); }; void Reset(); void Print(); // safe to leave this public and non-const  bool do_adapt_m{}; protected: virtual void RecurseKe() = 0; void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); std::size_t n_x_{}; std::size_t n_u_{}; std::size_t n_y_{}; data_t dt_{}; // Signals:  Vector x_; Matrix P_; Vector m_; Matrix P_m_; Vector cx_; Vector y_; Vector z_; // Parameters:  Vector x0_; Matrix P0_; Vector m0_; Matrix P0_m_; Matrix A_; Matrix B_; Vector g_; Matrix Q_; Matrix Q_m_; Matrix C_; Vector d_; Matrix Ke_; Matrix Ke_m_; }; // System  } // namespace lds  #endif  Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":74,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/","title":"ldsCtrlEst_h/lds_uniform_mats.h","section":"Files","content":"ldsCtrlEst_h/lds_uniform_mats.h #  List of uniformly sized matrices. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::UniformMatrixList    Detailed Description #  This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.\nSource code #  //===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H #define LDSCTRLEST_LDS_UNIFORM_MATS_H  #include \u0026lt;array\u0026gt; // std::array#include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { template \u0026lt;MatrixListFreeDim D = kMatFreeDimNone\u0026gt; class UniformMatrixList : public std::vector\u0026lt;Matrix\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion  // using std::vector\u0026lt;Matrix\u0026gt;::vector;  using std::vector\u0026lt;Matrix\u0026gt;::operator=; using std::vector\u0026lt;Matrix\u0026gt;::operator[]; using std::vector\u0026lt;Matrix\u0026gt;::begin; using std::vector\u0026lt;Matrix\u0026gt;::end; using std::vector\u0026lt;Matrix\u0026gt;::size; public: using std::vector\u0026lt;Matrix\u0026gt;::at; UniformMatrixList() = default; explicit UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); explicit UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; ~UniformMatrixList() = default; const std::array\u0026lt;size_t, 2\u0026gt;\u0026amp; dim(size_t n = 0) const { return dim_.at(n); } size_t size() { return std::vector\u0026lt;Matrix\u0026gt;::size(); }; const Matrix\u0026amp; at(size_t n) { return std::vector\u0026lt;Matrix\u0026gt;::at(n); }; void Swap(Matrix\u0026amp; that, size_t n); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim); std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt; dim_; }; template \u0026lt;MatrixListFreeDim D\u0026gt; inline void UniformMatrixList\u0026lt;D\u0026gt;::Swap(Matrix\u0026amp; that, size_t n) { // make sure request in range  if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim  bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.n_cols); } if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap  Matrix tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); if (D == kMatFreeDim1) { this-\u0026gt;dim_[n][0] = (*this)[n].n_rows; } if (D == kMatFreeDim2) { this-\u0026gt;dim_[n][1] = (*this)[n].n_cols; } } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) { // make sure dim_ vector is initialized  if (dim_.empty()) { dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(that.size(), {0, 0}); } // check dimensions  if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices\u0026#34;; throw std::runtime_error(ss.str()); } // if dimensions a not zero and do not match, skip move with error message.  bool dims_nonzero = true; for (auto d : dim_) { if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; d[0] \u0026lt; 1) { dims_nonzero = false; break; } if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; d[1] \u0026lt; 1) { dims_nonzero = false; break; } } if (dims_nonzero) { bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); } if (!does_match) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrices of size \u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; dim_[k] = that.dim(k); } return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept { // // check dimensions  // // if empty, assume a default constructed object and safe to move  // if (!this-\u0026gt;empty()) {  // if (this-\u0026gt;size() != that.size()) {  // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34;  // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices. Skipping.\\n\u0026#34;;  // return (*this);  // }  //  // // if dimensions a not zero and do not match, skip move with error  // message. bool dims_nonzero = true; for (auto d : dim_) {  // if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; (d[0] \u0026lt; 1)) {  // dims_nonzero = false;  // break;  // }  // if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; (d[1] \u0026lt; 1)) {  // dims_nonzero = false;  // break;  // }  // }  //  // if (dims_nonzero) {  // bool does_match = true;  // if (!(D == kMatFreeDim1)) {  // does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows);  // }  //  // if (!(D == kMatFreeDim2)) {  // does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols);  // }  //  // if (!does_match) {  // this-\u0026gt;at(0).print(\u0026#34;this[0] = \u0026#34;);  // that.at(0).print(\u0026#34;that[0] = \u0026#34;);  // std::cerr  // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformMatrixList element of size (\u0026#34; \u0026lt;\u0026lt;  // that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols \u0026lt;\u0026lt; \u0026#34;) for an  // element of size (\u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34;).  // Skipping.\\n\u0026#34;;  // return (*this);  // }  // }  // }  dim_ = that.dim_; std::vector\u0026lt;Matrix\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(std::move(mats)) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) : vector(that) { (*this) = that; } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { std::array\u0026lt;size_t, 2\u0026gt; dim_k({this-\u0026gt;at(k).n_rows, this-\u0026gt;at(k).n_cols}); dim_.push_back(dim_k); } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim) { // change behavior based on free dim D  if ((dim[0] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim1)) { dim[0] = this-\u0026gt;at(0).n_rows; } if ((dim[1] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim2)) { dim[1] = this-\u0026gt;at(0).n_cols; } // make sure dimensiolaties are all uniform  bool does_match(true); for (const Matrix\u0026amp; mat : *this) { if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_rows == dim[0]); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_cols == dim[1]); } if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(this-\u0026gt;size(), dim); for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { dim_[k][0] = (*this)[k].n_rows; dim_[k][1] = (*this)[k].n_cols; } } } // namespace lds  #endif  Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":75,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/","title":"ldsCtrlEst_h/lds_uniform_systems.h","section":"Files","content":"ldsCtrlEst_h/lds_uniform_systems.h #  List of uniformly sized Systems. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::UniformSystemList    Detailed Description #  This file provides a container for uniformly sized Systems.\nSource code #  //===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H  #include \u0026lt;array\u0026gt; // std::array#include \u0026lt;vector\u0026gt; // std::vector // namespace #include \u0026#34;lds.h\u0026#34;// System type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class UniformSystemList : public std::vector\u0026lt;System\u0026gt; { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion  // using std::vector\u0026lt;System\u0026gt;::vector;  using std::vector\u0026lt;System\u0026gt;::operator=; using std::vector\u0026lt;System\u0026gt;::operator[]; using std::vector\u0026lt;System\u0026gt;::at; using std::vector\u0026lt;System\u0026gt;::begin; using std::vector\u0026lt;System\u0026gt;::end; using std::vector\u0026lt;System\u0026gt;::size; public: UniformSystemList() = default; explicit UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); explicit UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(const UniformSystemList\u0026amp; that); UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept; ~UniformSystemList() = default; const std::array\u0026lt;size_t, 3\u0026gt;\u0026amp; dim() const { return dim_; } size_t size() { return std::vector\u0026lt;System\u0026gt;::size(); }; const System\u0026amp; at(size_t n) { return std::vector\u0026lt;System\u0026gt;::at(n); }; void Swap(System\u0026amp; that, size_t n); UniformSystemList\u0026amp; operator=(const UniformSystemList\u0026amp; that); UniformSystemList\u0026amp; operator=(UniformSystemList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim); std::array\u0026lt;size_t, 3\u0026gt; dim_{}; }; template \u0026lt;typename System\u0026gt; inline void UniformSystemList\u0026lt;System\u0026gt;::Swap(System\u0026amp; that, size_t n) { // make sure request in range  if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformSystemList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim  bool does_match = (dim_[0] == that.n_u()) \u0026amp;\u0026amp; (dim_[1] == that.n_x()) \u0026amp;\u0026amp; (dim_[2] == that.n_y()); if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformSystemList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap  System tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( const UniformSystemList\u0026amp; that) { // check dimensions  if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_[0] + dim_[1] + dim_[2]) { std::array\u0026lt;size_t, 3\u0026gt; other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign systems of size \u0026#34; \u0026lt;\u0026lt; dim_[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2] \u0026lt;\u0026lt; \u0026#34; with systems of size \u0026#34; \u0026lt;\u0026lt; other_dim[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other_dim[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2]; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( UniformSystemList\u0026amp;\u0026amp; that) noexcept { // // check dimensions  // // if empty, assume a default constructed object and safe to move  // if (!this-\u0026gt;empty()) {  // if (this-\u0026gt;size() != that.size()) {  // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34;  // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems. Skipping.\\n\u0026#34;;  // return (*this);  // }  //  // // if dimensions a not zero and do not match, skip move with error  // message. if (dim_[0] + dim_[1] + dim_[2]) {  // bool does_match = (dim_[0] == that.at(0).n_u()) \u0026amp;\u0026amp;  // (dim_[1] == that.at(0).n_x()) \u0026amp;\u0026amp;  // (dim_[2] == that.at(0).n_y());  // if (!does_match) {  // std::cerr  // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformSystemList element for an element of \u0026#34;  // \u0026#34;different size. Skipping.\\n\u0026#34;;  // return (*this);  // }  // }  // }  dim_ = that.dim_; std::vector\u0026lt;System\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(std::move(systems)) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList( std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const UniformSystemList\u0026amp; that) : std::vector\u0026lt;System\u0026gt;(that) { (*this) = that; } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept : std::vector\u0026lt;System\u0026gt;(std::move(that)) { this-\u0026gt;dim_[0] = this-\u0026gt;at(0).n_u(); this-\u0026gt;dim_[1] = this-\u0026gt;at(0).n_x(); this-\u0026gt;dim_[2] = this-\u0026gt;at(0).n_y(); } template \u0026lt;typename System\u0026gt; void UniformSystemList\u0026lt;System\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim) { if (dim[0] + dim[1] + dim[2]) { dim_ = dim; } else { dim_[0] = this-\u0026gt;at(0).n_u(); dim_[1] = this-\u0026gt;at(0).n_x(); dim_[2] = this-\u0026gt;at(0).n_y(); } // make sure dimensiolaties are all uniform  bool does_match(true); for (const System\u0026amp; sys : *this) { does_match = does_match \u0026amp;\u0026amp; (sys.n_u() == dim_[0]); does_match = does_match \u0026amp;\u0026amp; (sys.n_x() == dim_[1]); does_match = does_match \u0026amp;\u0026amp; (sys.n_y() == dim_[2]); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input systems are not uniform.\u0026#34;); } } } } // namespace lds  #endif  Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":76,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/","title":"ldsCtrlEst_h/lds_uniform_vecs.h","section":"Files","content":"ldsCtrlEst_h/lds_uniform_vecs.h #  List of uniformly sized vectors. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::UniformVectorList    Detailed Description #  This file provides a container for uniformly sized vectors.\nSource code #  //===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H #define LDSCTRLEST_LDS_UNIFORM_VECS_H  #include \u0026lt;array\u0026gt; // std::array#include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { class UniformVectorList : public std::vector\u0026lt;Vector\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion  // using std::vector\u0026lt;Vector\u0026gt;::vector;  using std::vector\u0026lt;Vector\u0026gt;::operator=; using std::vector\u0026lt;Vector\u0026gt;::operator[]; using std::vector\u0026lt;Vector\u0026gt;::at; using std::vector\u0026lt;Vector\u0026gt;::begin; using std::vector\u0026lt;Vector\u0026gt;::end; using std::vector\u0026lt;Vector\u0026gt;::size; public: UniformVectorList() = default; explicit UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim = 0); explicit UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim = 0); UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim = 0); UniformVectorList(const UniformVectorList\u0026amp; that); UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept; ~UniformVectorList() = default; size_t dim() const { return dim_; } size_t size() { return std::vector\u0026lt;Vector\u0026gt;::size(); }; const Vector\u0026amp; at(size_t n) { return std::vector\u0026lt;Vector\u0026gt;::at(n); }; void Swap(Vector\u0026amp; that, size_t n); UniformVectorList\u0026amp; operator=(const UniformVectorList\u0026amp; that); UniformVectorList\u0026amp; operator=(UniformVectorList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(size_t dim); size_t dim_{}; }; inline void UniformVectorList::Swap(Vector\u0026amp; that, size_t n) { // make sure request in range  if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim  bool does_match = dim_ == that.n_elem; if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap  Vector tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( const UniformVectorList\u0026amp; that) { // check dimensions  if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_) { size_t other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ \u0026lt;\u0026lt; \u0026#34; with vectors of size \u0026#34; \u0026lt;\u0026lt; other_dim; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( UniformVectorList\u0026amp;\u0026amp; that) noexcept { // // check dimensions  // if (!this-\u0026gt;empty()) {  // if (this-\u0026gt;size() != that.size()) {  // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34;  // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors. Skipping.\\n\u0026#34;;  // return (*this);  // }  //  // if (dim_) {  // size_t other_dim(that.dim());  // if (dim_ != other_dim) {  // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_  // \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; other_dim \u0026lt;\u0026lt; \u0026#34;.  // Skipping.\\n\u0026#34;;  // return (*this);  // }  // }  // }  dim_ = that.dim_; std::vector\u0026lt;Vector\u0026gt;::operator=(std::move(that)); return (*this); } } // namespace lds  #endif  Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":77,"href":"/lds-ctrl-est/docs/api/files/mex__c__util_8h/","title":"ldsCtrlEst_h/mex_c_util.h","section":"Files","content":"ldsCtrlEst_h/mex_c_util.h #  arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) More\u0026hellip;\nNamespaces #     Name     armamexc arma/mex interface using Matlab C API    Detailed Description #  This file defines utility functions for interoperability between armadillo and Matlab/Octave\u0026rsquo;s C mex API.\nSource code #  //===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_MEXC_UTIL_H #define LDSCTRLEST_MEXC_UTIL_H  #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.h\u0026#34; // // If Matlab_FOUND, include matrix.h. // // (Octave does not need/have it.) // #ifdef Matlab_FOUND // #include \u0026#34;matrix.h\u0026#34; // #endif  namespace armamexc { template \u0026lt;class T\u0026gt; inline auto m2T_scalar(const mxArray *matlab_scalar) -\u0026gt; T { if (mxGetData(matlab_scalar)) { return static_cast\u0026lt;T\u0026gt;(mxGetScalar(matlab_scalar)); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return 0; } template \u0026lt;class T\u0026gt; inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false, bool strict = true) -\u0026gt; arma::Mat\u0026lt;T\u0026gt; { if (mxGetData(matlab_mat)) { const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat); if (n_dim == 2) { return arma::Mat\u0026lt;T\u0026gt;(static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)), mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem, strict); } mexErrMsgTxt(\u0026#34;Number of dimensions must be 2.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } // TODO(mfbolus): make these templated.  template \u0026lt;typename T\u0026gt; inline auto a2m_mat(arma::Mat\u0026lt;T\u0026gt; const \u0026amp;arma_mat) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_mat.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy.  std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Mat.\u0026#34;); return nullptr; } template \u0026lt;typename T\u0026gt; inline auto a2m_vec(arma::Col\u0026lt;T\u0026gt; const \u0026amp;arma_vec) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_vec.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy.  std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Col.\u0026#34;); return nullptr; } } // namespace armamexc  #endif  Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":78,"href":"/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/","title":"ldsCtrlEst_h/mex_cpp_util.h","section":"Files","content":"ldsCtrlEst_h/mex_cpp_util.h #  arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) More\u0026hellip;\nNamespaces #     Name     armamexcpp arma/mex interface using Matlab C++ API    Detailed Description #  This file defines utility functions for interoperability between armadillo and Matlab\u0026rsquo;s C++ mex API.\nSource code #  //===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_MEXCPP_UTIL_H #define LDSCTRLEST_MEXCPP_UTIL_H  #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.hpp\u0026#34;#include \u0026#34;mexAdapter.hpp\u0026#34; namespace armamexcpp { template \u0026lt;class T\u0026gt; std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; m2a_cellmat(matlab::data::CellArray\u0026amp; matlab_cell) { size_t n_cells = matlab_cell.getNumberOfElements(); std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; arma_mat(n_cells, arma::Mat\u0026lt;T\u0026gt;(1, 1, arma::fill::zeros)); for (size_t k = 0; k \u0026lt; n_cells; k++) { matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = matlab_cell[k]; auto dims = matlab_mat.getDimensions(); arma_mat[k] = arma::Mat\u0026lt;T\u0026gt;(matlab_mat.release().get(), dims[0], dims[1]); } return arma_mat; }; template \u0026lt;class T\u0026gt; std::vector\u0026lt;T\u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt;T\u0026gt;\u0026amp; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); T* ptr = matlab_array.release().get(); std::vector\u0026lt;T\u0026gt; vec(ptr, ptr + n_elem); return vec; }; template \u0026lt;class T\u0026gt; arma::Col\u0026lt;T\u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); // T* ptr = matlab_array.release().get();  // arma::Col\u0026lt;T\u0026gt; vec(ptr, n_elem); //, false);  // TODO(mfbolus): for some reason, using the above pointer at times leads to  // getting garbage values. matlab array values may be stored in non-contiguous  // memory?  arma::Col\u0026lt;T\u0026gt; vec(n_elem, arma::fill::zeros); for (size_t k = 0; k \u0026lt; n_elem; k++) { vec[k] = matlab_array[k]; } return vec; }; template \u0026lt;class T\u0026gt; arma::Mat\u0026lt;T\u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { // ArrayDimensions == std::vector\u0026lt;size_t\u0026gt;  auto dims = matlab_array.getDimensions(); // T* ptr = matlab_array.release().get();  // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false)  // arma::Mat\u0026lt;T\u0026gt; mat(ptr, dims[0], dims[1]); //, false);  // TODO(mfbolus): for some reason, using the above pointer at times leads to  // getting garbage values. matlab array values may be stored in non-contiguous  // memory?  //  // armadillo and matlab both use column-major ordering, so this should work:  size_t n_elem = dims[0] * dims[1]; arma::Mat\u0026lt;T\u0026gt; mat(dims[0], dims[1], arma::fill::zeros); size_t k(0); for (auto m: matlab_array) { mat[k] = m; k++; } return mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_mat(const arma::Mat\u0026lt;T\u0026gt;\u0026amp; arma_mat, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(), arma_mat.memptr() + arma_mat.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_vec(const arma::Col\u0026lt;T\u0026gt;\u0026amp; arma_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;({arma_vec.n_elem, 1}, arma_vec.memptr(), arma_vec.memptr() + arma_vec.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; s2m_vec(const std::vector\u0026lt;T\u0026gt;\u0026amp; std_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size()); return matlab_mat; }; } // namespace armamexcpp  #endif  Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":79,"href":"/lds-ctrl-est/docs/terminology/model/","title":"Models","section":"LDS C+E Documentation","content":"Model Definitions #  This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form:   \\[\r\\mathbf{x}_{t\u0026#43;1} = f\\left( \\mathbf{x}_{t}, \\mathbf{v}_{t} \\right) = \\mathbf{A} \\mathbf{x}_{t} \u0026#43; \\mathbf{B} \\mathbf{v}_{t} \u0026#43; \\mathbf{m}_{t} \u0026#43; \\mathbf{w}_{t}\r\\]   \\[\r\\mathbf{y}_{t} = h\\left( \\mathbf{x}_{t} \\right)\r\\]  t : time index\rx : system state\rv = g%u : input (e.g., in physical units used for model fit)\ru : control signal sent to actuator (e.g., in Volts)\ry : system output\rm : process disturbance\rw ~ N(0, Q) : process noise/disturbance\rA : state matrix\rB : input coupling matrix\rg : input gain (e.g., for converting to control signal actuator voltage)\rn.b., assumes this conversion is linear\rQ : process noise covariance\r% : element-wise multiplication\r LDS with Gaussian Observations #  For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.\n \\[\r\\mathbf{y}_{t} = \\mathbf{C} \\mathbf{x}_{t} \u0026#43; \\mathbf{d}\r\\]   \\[\r\\mathbf{z}_{t} \\sim \\mathcal{N}\\left(\\mathbf{y}_{t} , \\mathbf{R} \\right)\r\\]  z : measurement\rC : output matrix\rd : output bias\rR : measurement noise covariance\r LDS with Poisson Observations #  For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.\n \\[\ry_{t}^{i} = \\exp \\left(\\mathbf{c}^i \\mathbf{x}_{t} \u0026#43; d^i\\right)\r\\]   \\[\rz_{t}^i \\sim \\rm{Poisson} \\left(y_{t}^i \\right)\r\\]  i : output index\rz : measurement (count data)\rc : i^th row of output matrix (C)\rd : output bias\r "},{"id":80,"href":"/lds-ctrl-est/docs/api/modules/","title":"Modules","section":"LDS C+E Documentation","content":"Modules #    Control Mode Bit Masks provides fill types for constructing new armadillo vectors, matrices\n  Defaults\n   Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":81,"href":"/lds-ctrl-est/docs/api/namespaces/","title":"Namespaces","section":"LDS C+E Documentation","content":"Namespaces #    armamexc arma/mex interface using Matlab C API\n  armamexcpp arma/mex interface using Matlab C++ API\n  lds::gaussian Linear Dynamical Systems with Gaussian observations.\n  lds::poisson Linear Dynamical Systems with Poisson observations.\n   Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":82,"href":"/lds-ctrl-est/docs/api/pages/","title":"Pages","section":"LDS C+E Documentation","content":"Pages #   Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":83,"href":"/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/","title":"PLDS State Estimation","section":"LDS C+E Examples","content":"PLDS State Estimation Tutorial #  This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.\nThe full code for this can be found here.\nPreamble #  In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system #  A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; // no. inputs  size_t n_x = 1; // no. states  size_t n_y = 1; // no. outputs  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system...  lds::poisson::System system_true(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example,   \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\]   \\[\ry_{t} = \\exp\\left(x_t\\right)\r\\]  where  \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\)  .\nNow, create non-default parameters for this model.\n// Model parameters  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state Finally, assign the parameters using corresponding set-methods.\n// Assign params.  system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); Creating the estimator #  Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.\n// Construct system for estimation  // e.g., will create a model with incorrect disturbance  lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object  system_estimator = system_true; // wrong disturbance  Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. To ensure robust estimates, adaptively re-estimate the process disturbance.\n// turn on adaptive disturbance estimation  system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting  // on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); Simulating estimation #  In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.\n// Simlate the true system.  z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update)  system_estimator.Filter(u.col(t - 1), z.col(t)); Example simulation result #  Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.\nWith this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.\n"},{"id":84,"href":"/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/","title":"PLDS Switched Control","section":"LDS C+E Examples","content":"PLDS Switched Control Tutorial #  This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.\nIn the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.\nThe full code for this can be found here.\nPreamble #  In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating the simulated system #  A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// whether to do switched control  bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); The system\u0026rsquo;s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.\n// for simulating switching  size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2  data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 Initially, the system will be in \u0026ldquo;mode\u0026rdquo; 1, where B = b1.\n// simulated system being controlled  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well  // characterized by two LDS models with different input matrices.**  data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.\nCreating the controller #  Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.\nSimilar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.\nMoreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.\nPutting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.\n// create switched controller  lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains  { // create switched controller sub-systems  // system 1  lds::poisson::System sys1(controlled_system); // set process noise covariance  Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m)  // n.b. using arbitrary default value for process noise if enabled.  sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset:  Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions  // system 2  lds::poisson::System sys2 = sys1; // set parameters  sys2.set_B(b2); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying systems:  Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive.  k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nNow that the SwitchedController is instantiated, assign its parameters.\n// Control variables  size_t control_type = 0; // no integral action, etc  switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); Simulating control #  In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).\nThe control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.\n// Let the controlled system stochastically change gain  // Assume another algorithm decodes this mode change and signals the  // switched_controller  Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1  { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2  if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system.  z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control  u.col(t) = switched_controller.ControlOutputReference(z.col(t)); Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system\u0026rsquo;s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.\nExample simulation result #  Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller\u0026rsquo;s online estimates of the output and state are shown in purple.\nNote that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.\n"},{"id":85,"href":"/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/","title":"src","section":"Files","content":"src #  Files #     Name     src/lds.cpp misc lds namespace functions   src/lds_gaussian_sys.cpp GLDS base type.   src/lds_poisson_sys.cpp PLDS base type.   src/lds_sys.cpp LDS base type.   src/lds_uniform_vecs.cpp Uniformly sized vectors.     Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":86,"href":"/lds-ctrl-est/docs/api/files/lds_8cpp/","title":"src/lds.cpp","section":"Files","content":"src/lds.cpp #  misc lds namespace functions More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Detailed Description #  This file implements miscellaneous lds namespace functions not bound to a class.\nSource code #  //===-- lds.cpp - LDS -----------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds.h\u0026gt; // insert any necessary function definitions here. namespace lds { void ForceSymPD(Matrix\u0026amp; X) { if (X.is_sympd() || !X.is_square()) { return; } // make symmetric  X = (X + X.t()) / 2; // for eigenval decomp  bool did_succeed(true); Vector d; Matrix u; // see first method (which may not be ideal):  // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/  size_t k(1); bool is_sympd = X.is_sympd(); Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye); while (!is_sympd) { if (k \u0026gt; 100) { did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); data_t min_eig = arma::min(d); std::cerr \u0026lt;\u0026lt; \u0026#34;After multiple iterations, min eigen val = \u0026#34; \u0026lt;\u0026lt; min_eig \u0026lt;\u0026lt; \u0026#34;.\\n\u0026#34;; throw std::runtime_error( \u0026#34;Failed to make matrix symmetric positive definite.\u0026#34;); return; } // Limit(d, arma::eps(0), kInf); // force to be positive...  // Matrix d_diag = arma::diagmat(d);  // X = u * d_diag * u.t();  did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymPD failed.\u0026#34;); } data_t min_eig = arma::min(d); X += id * abs(min_eig) + arma::datum::eps; // make sure symm:  X = (X + X.t()) / 2; // double check eigenvals positive after symmetrizing:  arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); min_eig = arma::min(d); is_sympd = min_eig \u0026gt; 0; k++; } } void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min) { if (!X.is_square()) { return; } // make symmetric  X = (X + X.t()) / 2; bool did_succeed(true); Vector d; Matrix u; did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymMinEig failed.\u0026#34;); } Limit(d, eig_min + arma::eps(eig_min), kInf); // enforce lower bound  Matrix d_diag = arma::diagmat(d); X = u * d_diag * u.t(); // double check symmetric  X = (X + X.t()) / 2; } void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X) { bool did_succeed(true); did_succeed = arma::qr_econ(Qt, L, X.t()); if (!did_succeed) { throw std::runtime_error(\u0026#34;LQ decomposition failed.\u0026#34;); } arma::inplace_trans(L); arma::inplace_trans(Qt); } Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B) { // subtract out mean  auto m_a = arma::mean(A, 1); Matrix a0 = A; a0.each_col() -= m_a; auto m_b = arma::mean(B, 1); Matrix b0 = B; b0.each_col() -= m_b; Matrix cov = a0 * b0.t() / a0.n_cols; return cov; } } // namespace lds  Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":87,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/","title":"src/lds_gaussian_sys.cpp","section":"Files","content":"src/lds_gaussian_sys.cpp #  GLDS base type. More\u0026hellip;\nDetailed Description #  This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code #  //===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds_gaussian_sys.h\u0026gt; lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0, data_t r0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { R_.zeros(n_y, n_y); R_.diag().fill(r0); do_recurse_Ke_=true; }; // recursively estimate Ke void lds::gaussian::System::RecurseKe() { if (!do_recurse_Ke_) { return; } // predict covariance  P_ = A_ * P_ * A_.t() + Q_; // calc Kalman gain  Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_); // update covariance  // Reference: Ghahramani et Hinton (1996)  P_ = P_ - Ke_ * C_ * P_; if (do_adapt_m) { P_m_ += Q_m_; // A_m = I (i.e., random walk)  Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_); P_m_ = P_m_ - Ke_m_ * C_ * P_m_; } } // Simulate const lds::Vector\u0026amp; lds::gaussian::System::Simulate(const Vector\u0026amp; u_tm1){ f(u_tm1, true);//simulate dynamics with noise added  h();//output  z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure  return z_; } void lds::gaussian::System::Print() { lds::System::Print(); std::cout \u0026lt;\u0026lt; \u0026#34;R: \\n\u0026#34; \u0026lt;\u0026lt; R_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }  Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":88,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/","title":"src/lds_poisson_sys.cpp","section":"Files","content":"src/lds_poisson_sys.cpp #  PLDS base type. More\u0026hellip;\nDetailed Description #  This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code #  //===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds_poisson_sys.h\u0026gt; lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { diag_y_ = diagmat(y_); pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(0); }; // Correct: Given measurement (z) and current input (u), update estimate of the // state, covar, output. // // see Eden et al. 2004 void lds::poisson::System::RecurseKe() { // predict covariance  P_ = A_ * P_ * A_.t() + Q_; // update cov  P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_); Ke_ = P_ * C_.t(); if (do_adapt_m) { P_m_ += Q_m_; // predict (A_m = I)  P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_); // update  Ke_m_ = P_m_ * C_.t(); } } // Simulate Measurement: z ~ Poisson(y) const lds::Vector\u0026amp; lds::poisson::System::Simulate(const Vector\u0026amp; u_tm1) { f(u_tm1, true); // simulate dynamics with noise added  h(); // output  z_.zeros(); for (std::size_t k = 0; k \u0026lt; n_y_; k++) { // construct a Poisson distribution object with mean y[k]  pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(y_[k]); // pull random sample from this distribution  z_[k] = pd_(rng); } return z_; } // ******************* SYS_T *******************  Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":89,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8cpp/","title":"src/lds_sys.cpp","section":"Files","content":"src/lds_sys.cpp #  LDS base type. More\u0026hellip;\nDetailed Description #  This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code #  //===-- lds_sys.cpp - LDS -------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds_sys.h\u0026gt; lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) { InitVars(p0, q0); } void lds::System::InitVars(data_t p0, data_t q0) { // initial conditions.  x0_ = Vector(n_x_, fill::zeros); // includes bias (nY) and g (nU)  P0_ = p0 * Matrix(n_x_, n_x_, fill::eye); m0_ = x0_; P0_m_ = P0_; // signals  x_ = x0_; P_ = P0_; m_ = m0_; P_m_ = P0_m_; y_ = Vector(n_y_, fill::zeros); cx_ = Vector(n_y_, fill::zeros); z_ = Vector(n_y_, fill::zeros); // By default, random walk where each state is independent  // In this way, provides independent estimates of rate per channel of output.  A_ = Matrix(n_x_, n_x_, fill::eye); B_ = Matrix(n_x_, n_u_, fill::zeros); g_ = Vector(n_u_, fill::ones); Q_ = q0 * Matrix(n_x_, n_x_, fill::eye); Q_m_ = Q_; C_ = Matrix(n_y_, n_x_, fill::eye); // each state will map to an output by  d_ = Vector(n_y_, fill::zeros); Ke_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain.  Ke_m_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain for m adaptation.  do_adapt_m = false; } // Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state // and update estimate of the state, covar, output using Kalman filter void lds::System::Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z_t) { // predict mean  f(u_tm1); // dynamics  h(); // output  // recursively calculate esimator gains (or just keep existing values)  // (also predicts+updates estimate covariance)  RecurseKe(); // update  x_ += Ke_ * (z_t - y_); if (do_adapt_m) { m_ += Ke_m_ * (z_t - y_); // adaptively estimating disturbance  } // With new state, estimate output.  h(); // --\u0026gt; posterior } void lds::System::Reset() { // reset to initial conditions  x_ = x0_; // mean  P_ = P0_; // cov of state estimate  m_ = m0_; // process disturbance  P_m_ = P0_m_; // cov of disturbance estimate  h(); } void lds::System::Print() { std::cout \u0026lt;\u0026lt; \u0026#34;\\n********** SYSTEM ********** \\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;x: \\n\u0026#34; \u0026lt;\u0026lt; x_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;P: \\n\u0026#34; \u0026lt;\u0026lt; P_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;A: \\n\u0026#34; \u0026lt;\u0026lt; A_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B: \\n\u0026#34; \u0026lt;\u0026lt; B_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;g: \\n\u0026#34; \u0026lt;\u0026lt; g_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;m: \\n\u0026#34; \u0026lt;\u0026lt; m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q: \\n\u0026#34; \u0026lt;\u0026lt; Q_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q_m: \\n\u0026#34; \u0026lt;\u0026lt; Q_m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d: \\n\u0026#34; \u0026lt;\u0026lt; d_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;C: \\n\u0026#34; \u0026lt;\u0026lt; C_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;y: \\n\u0026#34; \u0026lt;\u0026lt; y_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } //******************* SYS_T *******************  Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"},{"id":90,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/","title":"src/lds_uniform_vecs.cpp","section":"Files","content":"src/lds_uniform_vecs.cpp #  Uniformly sized vectors. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Detailed Description #  This file provides a container for uniformly sized vectors.\nSource code #  //===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds_uniform_vecs.h\u0026gt; namespace lds { UniformVectorList::UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); } UniformVectorList::UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim) : vector(std::move(vecs)) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(const UniformVectorList\u0026amp; that) : vector(that) { (*this) = that; } UniformVectorList::UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { this-\u0026gt;dim_ = this-\u0026gt;at(0).n_elem; } void UniformVectorList::CheckDimensions(size_t dim) { if (dim) { dim_ = dim; } else { dim_ = this-\u0026gt;at(0).n_elem; } // make sure dimensiolaties are all uniform  bool does_match(true); for (const Vector\u0026amp; vec : *this) { does_match = does_match \u0026amp;\u0026amp; (vec.n_elem == dim_); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } } } // namespace lds  Updated on 4 May 2022 at 16:34:53 Eastern Daylight Time\n"}]
\ No newline at end of file
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deleted file mode 100644
index e32f676e..00000000
--- a/docs/en.search-data.min.799bfc19f929cf1e231669cf06cae550679543dfef2bae61c8d816ffca12f5c7.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"id":0,"href":"/lds-ctrl-est/docs/","title":"LDS C+E Documentation","section":"LDS Control \u0026 Estimation","content":"LDS Control \u0026amp; Estimation Documentation #  "},{"id":1,"href":"/lds-ctrl-est/docs/tutorials/","title":"LDS C+E Examples","section":"LDS C+E Documentation","content":"Examples #  "},{"id":2,"href":"/lds-ctrl-est/acknowledgements/","title":"Acknowledgements","section":"LDS Control \u0026 Estimation","content":"Acknowledgements #  Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.\n"},{"id":3,"href":"/lds-ctrl-est/docs/getting-started/getting-started/","title":"Getting Started","section":"LDS C+E Documentation","content":"Getting Started #  This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.\nldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.\nDownloading the Library #  First, clone the repository along with submodules:\ngit clone --recurse-submodules https://github.com/stanley-rozell/lds-ctrl-est.git cd lds-ctrl-est\r# use git submodule update --init if you clone the repo without --recurse-submodules\rCompilation + Installation #  Now generate the cache and build using your IDE or from the command line as follows.\nmkdir build \u0026amp;\u0026amp; cd build\rcmake ..\rcmake --build .\rThe first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.\nIf you want to use vcpkg set up somewhere besides this repo\u0026rsquo;s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE\u0026rsquo;s settings.\nYou can verify the build is working by running ctest from the build folder, which runs all the example scripts.\nOptions #  This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\n LDSCTRLEST_BUILD_EXAMPLES : [default=ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use  n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake to configure/build the library.\n  For basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build cmake --build #build the project sudo make install #[optional] installs to default location (OS-specific)   To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location cmake --build #build the project make install #install to /your/install/prefix   To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\n LD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool    On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.\nPython bindings package ldsctrlest #  With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn\u0026rsquo;t use an undesired version. That environment needs to have NumPy installed.\nThe bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory.\nSee python/ldsctrlest/README.md for usage details.\nAlso, beware that a single build might not work for both the standalone library and the Python package (especially on Windows), since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.\nConsiderations for Windows #  First of all, development on Windows has been more prone to bugs, so if you encounter many problems, consider using a Unix-based system—WSL (Windows Subsystem for Linux) is a good option for Windows users who don\u0026rsquo;t want to work on a different machine.\nYou will likely need to get compiler tools through the Visual Studio installer. Compilation was tested in VS Code using the following kit:\nClang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64)\rCommon issues #   \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo;  If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.\nexport CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix On Windows, you will likely need to add the build or install folder to your PATH environment variable, which you can do using the settings GUI (search for \u0026ldquo;Edit the system environment variables\u0026rdquo;).\nOn Windows, \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted.  Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":4,"href":"/lds-ctrl-est/docs/getting-started/windows/","title":"Windows","section":"LDS C+E Documentation","content":"Dependencies #  To configure and compile the library, you will need Microsoft Visual Studio. After Visual Studio, install the \u0026ldquo;Desktop Development with C++\u0026rdquo; extension under Tools-\u0026gt;Get Tools and Features, ensuring that the \u0026ldquo;C++ Clang Tools for Windows\u0026rdquo; and \u0026ldquo;CMake C++ Tools for Windows\u0026rdquo; options are checked.\nIn addition to the dependencies mentioned in Linux/MacOS, on Windows, this library opts to use Intel\u0026rsquo;s Math Kernel Library (MKL) for linear algebra, rather than OpenBLAS/gfotran. MKL is part of the OneAPI suite. When using the installation wizard, users may either install the entire base toolkit or just the MKL portion. After installing MKL as well as HDF5, add these libraries to the user\u0026rsquo;s PATH environment variable (System Properties-\u0026gt;Advanced-\u0026gt;Environment Variables). Your installation location may vary, but for example, add the following lines to PATH: C:\\Program Files (x86)\\Intel\\oneAPI\\mkl\\latest\\redist\\intel64, C:\\Program Files\\HDF_Group\\HDF5\\1.12.0\\lib .\nOnce HDF5, MKL, and Visual Studio have been installed, download armadillo as detailed previously for Linux/MacOS. Open the source code in Visual Studio, configure the build (Project -\u0026gt; Generate CMake Cache), and install (Build -\u0026gt; Install). Take note of the install location and add this to an environment variable called CMAKE_PREFIX_PATH; create this variable if it does not already exist.\nCompilation #  After installing the dependencies above and downloading the ldsCtrlEst library source code (see Linux/MacOS), open this directory in Visual Studio. Configure the build (Project -\u0026gt; Generate CMake Cache) and build/install the targets (Build -\u0026gt; Install). Finally, take note of the install location and add this to the environment variable PATH.\nCommon issues #   \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted.  Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":5,"href":"/lds-ctrl-est/issues-contributing/","title":"Issues Contributing","section":"LDS Control \u0026 Estimation","content":"Reporting Issues #  If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.\nContributing #  We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.\n"},{"id":6,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/","title":"armamexc","section":"Namespaces","content":"armamexc #  arma/mex interface using Matlab C API More\u0026hellip; Functions #      Name     template \u0026lt;class T \u0026gt; auto m2T_scalar(const mxArray * matlab_scalar)\nConvert Matlab mxArray to scalar of type T.   template \u0026lt;class T \u0026gt; auto m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)\nConvert matlab matrix to armadillo.   template \u0026lt;typename T \u0026gt; auto a2m_mat(arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat)\nConvert armadillo to matlab matrix.   template \u0026lt;typename T \u0026gt; auto a2m_vec(arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec)\nConvert armadillo to matlab vector.    Detailed Description #  Utilities for arma/mex interface using Matlab C API\nFunction Details #  m2T_scalar #  template \u0026lt;class T \u0026gt; inline auto m2T_scalar( const mxArray * matlab_scalar ) Parameters:\n matlab_scalar matlab scalar  Template Parameters:\n T type  Return: scalar of type T\nm2a_mat #  template \u0026lt;class T \u0026gt; inline auto m2a_mat( const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true ) Parameters:\n matlab_mat matlab matrix copy_aux_mem [optional] whether to copy auxiliary memory strict [optional] strictly enforce the above  Template Parameters:\n T type  Return: armadillo matrix of type T\na2m_mat #  template \u0026lt;typename T \u0026gt; inline auto a2m_mat( arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat ) Parameters:\n arma_mat armadillo matrix  Return: matlab matrix\na2m_vec #  template \u0026lt;typename T \u0026gt; inline auto a2m_vec( arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec ) Parameters:\n arma_vec armadillo vector  Return: matlab vector\n Updated on 4 May 2022 at 16:57:50 Eastern Daylight Time\n"},{"id":7,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/","title":"armamexcpp","section":"Namespaces","content":"armamexcpp #  arma/mex interface using Matlab C++ API More\u0026hellip; Functions #      Name     template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat(matlab::data::CellArray \u0026amp; matlab_cell)\nConvert matlab cell array to vector of armadillo matrices.   template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array)\nConvert matlab matrix to a vector of scalars.   template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo vector.   template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo matrix.   template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat(const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab matrix.   template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec(const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab vector.   template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec(const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert vector of scalar T to matlab matrix.    Detailed Description #  utilities for arma/mex interface using Matlab C++ API\nFunction Details #  m2a_cellmat #  template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat( matlab::data::CellArray \u0026amp; matlab_cell ) Parameters:\n matlab_cell matlab cell  Template Parameters:\n T type  Return: vector of armadillo matrices of type T\nm2s_vec #  template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array ) Parameters:\n matlab_array matlab array  Template Parameters:\n T type  Return: vector of type T\nm2a_vec #  template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\n matlab_array matlab array  Template Parameters:\n T type  Return: armadillo vector of type T\nm2a_mat #  template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\n matlab_array matlab matrix  Template Parameters:\n T type  Return: armadillo matrix of type T\na2m_mat #  template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat( const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\n arma_mat arma matrix factory matlab \u0026ldquo;array factory\u0026rdquo;  Template Parameters:\n T type  Return: matlab matrix\na2m_vec #  template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec( const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\n arma_vec armadillo vector factory matlab \u0026ldquo;array factory\u0026rdquo;  Template Parameters:\n T type  Return: matlab matrix\ns2m_vec #  template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec( const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\n std_vec standard vector factory matlab \u0026ldquo;array factory\u0026rdquo;  Template Parameters:\n T type  Return: matlab matrix\n Updated on 4 May 2022 at 16:57:50 Eastern Daylight Time\n"},{"id":8,"href":"/lds-ctrl-est/docs/terminology/control-estimation/","title":"C\u0026E","section":"LDS C+E Documentation","content":"Control \u0026amp; Estimation #  The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.\nState estimation #  In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by   \\[\r\\widehat{\\mathbf{x}}_{t|t-1} = \\mathbf{A}\\widehat{\\mathbf{x}}_{t-1|t-1} \u0026#43; \\mathbf{B} u_{t-1} \u0026#43; \\mathbf{m}_{t-1} \\;,\r\\]   \\[\r\\widehat{\\mathbf{x}}_{t|t} = \\widehat{\\mathbf{x}}_{t|t-1} \u0026#43; \\mathbf{K}^{\\rm e}_t \\left(\\mathbf{z}_t - \\widehat{\\mathbf{y}}_{t|t-1}\\right)\\;,\r\\]  where  \\( \\hat{\\left(\\cdot\\right)}_{t|j} \\)  indicates an estimate at time  \\( t \\)  given data up to time  \\( j \\)  inclusive,  \\( \\mathbf{K}^{\\rm e} \\)  is the estimator gain, and\n \\[ \\widehat{\\mathbf{y}}_{t|t-1} = h\\left( \\widehat{\\mathbf{x}}_{t|t-1} \\right) \\; .\\]  In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.\nIn the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.\nAdaptive estimation of process disturbance #  Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.\nWhen parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk  \\[\r\\mathbf{m}_{t} = \\mathbf{m}_{t-1} \u0026#43; \\mathbf{w}^m_{t-1} \\;,\r\\]  where  \\( \\mathbf{w}^m \\sim \\mathcal{N}\\left(0, \\mathbf{Q}_m\\right)\\)  . Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.\nControl #  Given the estimated state, the controller updates the inputs to the system according to the following law:  \\[\r\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\r\\]  where  \\( \\left( \\cdot \\right)^{\\rm ref} \\)  correspond to reference/target signals and  \\( \\mathbf{K}^c_x \\)  is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.\nIf users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as\n \\[\r\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right) - \\mathbf{K}^c_{\\rm inty} \\sum_{j=1}^{t}\\left( \\widehat{\\mathbf{y}}_j - \\mathbf{y}^{\\rm ref}_j \\right) \\;.\r\\]  An additional option available to users is a control law that updates the change in u,\n \\[\r\\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\left(\\mathbf{u}_{t-1} - \\mathbf{u}^{\\rm ref}_{t-1} \\right) - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\r\\]   \\[\r\\mathbf{u}_{t} = \\mathbf{u}_{t-1} \u0026#43; \\Delta\\mathbf{u}_{t} \\; .\r\\]  Notice that this takes the form of a first-order difference equation for updating control (i.e.,  \\( \\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\mathbf{u}_{t-1} \u0026#43; \\epsilon_{t-1} \\)  ), effectively low-pass filtering the input depending on the characteristics of  \\( \\mathbf{K}^c_u \\)  . This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.\nIntegral action and the  \\( \\Delta \\mathbf{u} \\)  control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR\u0026rsquo;d to use in combination.\nCalculating reference state-control from output #  In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce  \\( \\mathbf{x}^{\\rm ref} \\)  and  \\( \\mathbf{u}^{\\rm ref} \\)  . Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.\n"},{"id":9,"href":"/lds-ctrl-est/docs/api/classes/","title":"Classes","section":"LDS C+E Documentation","content":"Classes #    lds::Controller\n  lds::EM\n  lds::Fit LDS Fit Type.\n  lds::SSID\n  lds::SwitchedController SwitchedController Type.\n  lds::System Linear Dynamical System Type.\n  lds::UniformMatrixList\n  lds::UniformSystemList\n  lds::UniformVectorList\n  lds::gaussian::Controller Gaussian-observation Controller Type.\n  lds::gaussian::Fit GLDS Fit Type.\n  lds::gaussian::FitEM GLDS E-M Fit Type.\n  lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.\n  lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.\n  lds::gaussian::System Gaussian LDS Type.\n  lds::poisson::Controller PLDS Controller Type.\n  lds::poisson::Fit PLDS Fit Type.\n  lds::poisson::FitEM PLDS E-M Fit Type.\n  lds::poisson::FitSSID Subspace Identification (SSID) for PLDS.\n  lds::poisson::SwitchedController Poisson-observation SwitchedController Type.\n  lds::poisson::System Poisson System type.\n   Updated on 4 May 2022 at 16:57:52 Eastern Daylight Time\n"},{"id":10,"href":"/lds-ctrl-est/docs/api/modules/group__control__masks/","title":"Control Mode Bit Masks","section":"Modules","content":"Control Mode Bit Masks #  provides fill types for constructing new armadillo vectors, matrices More\u0026hellip; Attributes #      Name     const std::size_t kControlTypeDeltaU control designed to penalize change in input   const std::size_t kControlTypeIntY control using integral action   const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m    Detailed Description #  Control mode bit masks. These can be bit-wise OR\u0026rsquo;d to use in combination.\nAttribute Details #  kControlTypeDeltaU #  static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY #  static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM #  static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\n Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":11,"href":"/lds-ctrl-est/docs/api/modules/group__defaults/","title":"Defaults","section":"Modules","content":"Defaults #  \nMore\u0026hellip; Attributes #      Name     const data_t kDefaultP0 default state estimate covar   const data_t kDefaultQ0 default process noise covar   const data_t kDefaultR0 default output noise covar    Detailed Description #  Default values for common variables (e.g., default diagonal elements of covariances)\nAttribute Details #  kDefaultP0 #  static const data_t kDefaultP0 = 1e-6; kDefaultQ0 #  static const data_t kDefaultQ0 = 1e-6; kDefaultR0 #  static const data_t kDefaultR0 = 1e-2;  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":12,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/","title":"eg_glds_ctrl.cpp","section":"Examples","content":"eg_glds_ctrl.cpp #  Example GLDS Control\n//===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); // output noise covariance  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb.  data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_x).fill(m_low); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables:  // if following enabled, adapts set point with re-estimated process  // disturbance n.b., should not need integral action if this is enabled as the  // adaptive estimator minimizes DC error  bool do_adaptive_set_point = false; // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err  // setting initial state to target to avoid error at onset:  Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance  control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; } // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(9); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation  // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input  Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals  y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,  // xTrue, mTrue saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; }  Updated on 4 May 2022 at 16:57:52 Eastern Daylight Time\n"},{"id":13,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/","title":"eg_glds_du_plds_ctrl.cpp","section":"Examples","content":"eg_glds_du_plds_ctrl.cpp #  Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).\n//===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb.  data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance  Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables:  // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input  // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du =  // 1e-2.  Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err  Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp  // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control)  control_type = control_type | lds::kControlTypeDeltaU; // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(10); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation  // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input  Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals  y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,  // xTrue, mTrue saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; }  Updated on 4 May 2022 at 16:57:52 Eastern Daylight Time\n"},{"id":14,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/","title":"eg_plds_ctrl.cpp","section":"Examples","content":"eg_plds_ctrl.cpp #  Example PLDS Control\n//===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains  // n.b., Can either use Vector (arma::Col) or std::vector  Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error  Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err  // Set control type bit mask, so controller knows what to do  size_t control_type = lds::kControlTypeIntY; // integral action  // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled...  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions  controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller  lds::poisson::Controller controller; { // Create model used for control.  lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance  Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition  // adaptively re-estimate process disturbance (m)  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type  controller.set_control_type(control_type); // set controller gains  controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above  // target:  data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val  y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference  data_t f = 0.5; // freq [=] Hz  Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system.  z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Notably, it does this in the  // log-linear space (i.e., log(y)).  //  // Therefore, it is only applicable to regulation problems or cases where  // reference trajectory changes slowly compared to system dynamics.  controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; }  Updated on 4 May 2022 at 16:57:52 Eastern Daylight Time\n"},{"id":15,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/","title":"eg_plds_est.cpp","section":"Examples","content":"eg_plds_est.cpp #  Example PLDS Estimation\n//===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; // no. inputs  size_t n_x = 1; // no. states  size_t n_y = 1; // no. outputs  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.  // construct ground truth system...  lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  // Assign params.  system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation  // e.g., will create a model with incorrect disturbance  lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object  system_estimator = system_true; // wrong disturbance  Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.  // turn on adaptive disturbance estimation  system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting  // on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation :  // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus)  Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk  Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in...  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params  Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions  y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system.  z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update)  system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals  y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; }  Updated on 4 May 2022 at 16:57:52 Eastern Daylight Time\n"},{"id":16,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/","title":"eg_plds_switched_ctrl.cpp","section":"Examples","content":"eg_plds_switched_ctrl.cpp #  Example Switched PLDS Control\n//===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control  bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching  size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2  data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1  // simulated system being controlled  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well  // characterized by two LDS models with different input matrices.**  data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions  // reference  Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2  Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller  lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains  { // create switched controller sub-systems  // system 1  lds::poisson::System sys1(controlled_system); // set process noise covariance  Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m)  // n.b. using arbitrary default value for process noise if enabled.  sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset:  Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions  cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2  lds::poisson::System sys2 = sys1; // set parameters  sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s:  Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive.  k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables  size_t control_type = 0; // no integral action, etc  switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements  Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control.  Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in...  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params  Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val  y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain  // Assume another algorithm decodes this mode change and signals the  // switched_controller  Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1  { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2  if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system.  z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control  u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; }  Updated on 4 May 2022 at 16:57:52 Eastern Daylight Time\n"},{"id":17,"href":"/lds-ctrl-est/docs/api/examples/","title":"Examples","section":"LDS C+E Documentation","content":"Examples #    eg_glds_ctrl.cpp\n  eg_glds_du_plds_ctrl.cpp\n  eg_plds_ctrl.cpp\n  eg_plds_est.cpp\n  eg_plds_switched_ctrl.cpp\n   Updated on 4 May 2022 at 16:57:52 Eastern Daylight Time\n"},{"id":18,"href":"/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/","title":"examples","section":"Files","content":"examples #  Files #     Name     examples/eg_glds_ctrl.cpp   examples/eg_glds_du_plds_ctrl.cpp   examples/eg_plds_ctrl.cpp   examples/eg_plds_est.cpp   examples/eg_plds_switched_ctrl.cpp     Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":19,"href":"/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/","title":"examples/eg_glds_ctrl.cpp","section":"Files","content":"examples/eg_glds_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Going to simulate a switching disturbance (m) acting on system\nSource code #  //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); // output noise covariance  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb.  data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_x).fill(m_low); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables:  // if following enabled, adapts set point with re-estimated process  // disturbance n.b., should not need integral action if this is enabled as the  // adaptive estimator minimizes DC error  bool do_adaptive_set_point = false; // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err  // setting initial state to target to avoid error at onset:  Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance  control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; } // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(9); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation  // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input  Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals  y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,  // xTrue, mTrue saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; }  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":20,"href":"/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/","title":"examples/eg_glds_du_plds_ctrl.cpp","section":"Files","content":"examples/eg_glds_du_plds_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Going to simulate a switching disturbance (m) acting on system\nSource code #  //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb.  data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance  Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables:  // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input  // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du =  // 1e-2.  Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err  Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp  // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control)  control_type = control_type | lds::kControlTypeDeltaU; // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(10); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation  // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input  Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals  y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,  // xTrue, mTrue saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; }  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":21,"href":"/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/","title":"examples/eg_plds_ctrl.cpp","section":"Files","content":"examples/eg_plds_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Going to simulate a switching disturbance (m) acting on system\nSource code #  //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains  // n.b., Can either use Vector (arma::Col) or std::vector  Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error  Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err  // Set control type bit mask, so controller knows what to do  size_t control_type = lds::kControlTypeIntY; // integral action  // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled...  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions  controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller  lds::poisson::Controller controller; { // Create model used for control.  lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance  Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition  // adaptively re-estimate process disturbance (m)  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type  controller.set_control_type(control_type); // set controller gains  controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above  // target:  data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val  y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference  data_t f = 0.5; // freq [=] Hz  Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system.  z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Notably, it does this in the  // log-linear space (i.e., log(y)).  //  // Therefore, it is only applicable to regulation problems or cases where  // reference trajectory changes slowly compared to system dynamics.  controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; }  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":22,"href":"/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/","title":"examples/eg_plds_est.cpp","section":"Files","content":"examples/eg_plds_est.cpp #  Functions #      Name     Matrix random_walk(size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0)   int main()    Function Details #  random_walk #  Matrix random_walk( size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0 ) main #  int main() Source code #  //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; // no. inputs  size_t n_x = 1; // no. states  size_t n_y = 1; // no. outputs  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.  // construct ground truth system...  lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  // Assign params.  system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation  // e.g., will create a model with incorrect disturbance  lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object  system_estimator = system_true; // wrong disturbance  Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.  // turn on adaptive disturbance estimation  system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting  // on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation :  // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus)  Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk  Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in...  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params  Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions  y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system.  z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update)  system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals  y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; }  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":23,"href":"/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/","title":"examples/eg_plds_switched_ctrl.cpp","section":"Files","content":"examples/eg_plds_switched_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Source code #  //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control  bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching  size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2  data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1  // simulated system being controlled  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well  // characterized by two LDS models with different input matrices.**  data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions  // reference  Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2  Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller  lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains  { // create switched controller sub-systems  // system 1  lds::poisson::System sys1(controlled_system); // set process noise covariance  Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m)  // n.b. using arbitrary default value for process noise if enabled.  sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset:  Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions  cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2  lds::poisson::System sys2 = sys1; // set parameters  sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s:  Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive.  k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables  size_t control_type = 0; // no integral action, etc  switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements  Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control.  Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in...  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params  Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val  y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain  // Assume another algorithm decodes this mode change and signals the  // switched_controller  Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1  { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2  if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system.  z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control  u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; }  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":24,"href":"/lds-ctrl-est/docs/api/files/","title":"Files","section":"LDS C+E Documentation","content":"Files #    examples/eg_glds_ctrl.cpp\n  examples/eg_glds_du_plds_ctrl.cpp\n  examples/eg_plds_ctrl.cpp\n  examples/eg_plds_est.cpp\n  examples/eg_plds_switched_ctrl.cpp\n  ldsCtrlEst_h/lds.h lds namespace\n  ldsCtrlEst_h/lds_ctrl.h Controller.\n  ldsCtrlEst_h/lds_fit.h LDS base fit type.\n  ldsCtrlEst_h/lds_fit_em.h subspace identification\n  ldsCtrlEst_h/lds_fit_ssid.h subspace identification\n  ldsCtrlEst_h/lds_gaussian.h glds namespace\n  ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.\n  ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.\n  ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.\n  ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.\n  ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.\n  ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.\n  ldsCtrlEst_h/lds_poisson.h plds namespace\n  ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.\n  ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.\n  ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.\n  ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.\n  ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.\n  ldsCtrlEst_h/lds_poisson_sys.h PLDS base type.\n  ldsCtrlEst_h/lds_sctrl.h SwitchedController type.\n  ldsCtrlEst_h/lds_sys.h LDS base type.\n  ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.\n  ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.\n  ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.\n  ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)\n  ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)\n  src/lds.cpp misc lds namespace functions\n  src/lds_gaussian_sys.cpp GLDS base type.\n  src/lds_poisson_sys.cpp PLDS base type.\n  src/lds_sys.cpp LDS base type.\n  src/lds_uniform_vecs.cpp Uniformly sized vectors.\n   Updated on 4 May 2022 at 16:57:52 Eastern Daylight Time\n"},{"id":25,"href":"/lds-ctrl-est/docs/tutorials/eg_glds_control/","title":"GLDS Control","section":"LDS C+E Examples","content":"GLDS Control Tutorial #  This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.\nThe full code for this can be found here.\nPreamble #  In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system #  A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.\n// Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example,   \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\]   \\[\ry_{t} = x_t\r\\]  where  \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\)  .\nNow, create non-default parameters for this model.\n// Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); // output noise covariance  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.\n/// Going to simulate a switching disturbance (m) acting on system  size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb.  data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_y).fill(m_low); Finally, assign the parameters using corresponding set-methods.\n// Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); Creating the controller #  Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.\nFor the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.\n// make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nWith the controller constructed, control variables may be set.\n// Control variables:  // if following enabled, adapts set point with re-estimated process  // disturbance n.b., should not need integral action if this is enabled as the  // adaptive estimator minimizes DC error  bool do_adaptive_set_point = false; // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err  // setting initial state to target to avoid error at onset:  Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance  control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; } // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(9); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); Simulating control #  In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.\nThe control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.\n// Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); Example simulation result #  Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller\u0026rsquo;s online estimates of the output, state, and disturbance are given in purple.\n"},{"id":26,"href":"/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/","title":"include","section":"Files","content":"include #  Directories #     Name     ldsCtrlEst_h     Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":27,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds/","title":"lds","section":"Namespaces","content":"lds #  Linear Dynamical Systems (LDS) namespace. Namespaces #     Name     lds::gaussian Linear Dynamical Systems with Gaussian observations.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::Controller   class lds::EM   class lds::Fit LDS Fit Type.   class lds::SSID   class lds::SwitchedController SwitchedController Type.   class lds::System Linear Dynamical System Type.   class lds::UniformMatrixList   class lds::UniformSystemList   class lds::UniformVectorList    Types #      Name     enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}\nweighting options for SSID   enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2}   using double data_t   using arma::Col\u0026lt; data_t \u0026gt; Vector   using arma::Mat\u0026lt; data_t \u0026gt; Matrix   using arma::Cube\u0026lt; data_t \u0026gt; Cube   using arma::subview\u0026lt; data_t \u0026gt; View    Functions #      Name     void Limit(std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub)   void Limit(Vector \u0026amp; x, data_t lb, data_t ub)   void Limit(Matrix \u0026amp; x, data_t lb, data_t ub)   void Reassign(Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Vector in place   void Reassign(Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Matrix in place   void ForceSymPD(Matrix \u0026amp; X)\nforces matrix to be symmetric positive-definite   void ForceSymMinEig(Matrix \u0026amp; X, data_t eig_min =0)\nforces matrix to be symmetric and have a minimum eigenvalue   void lq(Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X)\nLQ decomposition.   Matrix calcCov(const Matrix \u0026amp; A, const Matrix \u0026amp; B)\nCalculate covariance matrix.    Attributes #      Name     const std::size_t kControlTypeDeltaU control designed to penalize change in input   const std::size_t kControlTypeIntY control using integral action   const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m   const data_t kInf Some useful numbers.   const data_t kPi   const data_t kDefaultP0 default state estimate covar   const data_t kDefaultQ0 default process noise covar   const data_t kDefaultR0 default output noise covar    Type Details #  SSIDWt #     Enumerator Value Description     kSSIDNone  None.   kSSIDMOESP  MOESP (AKA \u0026ldquo;robust method\u0026rdquo; in van Overschee 1996)   kSSIDCVA  CVA \u0026ldquo;Canonical Variate Analysis\u0026rdquo;.    Weighting options for singular value decomposition performed during subspace identification (SSID)\nReference:\nvan Overschee, de Moor. 1996. Subspace Identification for Linear Systems.\nMatrixListFreeDim #     Enumerator Value Description     kMatFreeDimNone  neither dim free to be hetero in mat list   kMatFreeDim1  allow 1st dim of mats in list to be hetero   kMatFreeDim2  allow 2nd dim of mats in list to be hetero    data_t #  using lds::data_t = typedef double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nVector #  using lds::Vector = typedef arma::Col\u0026lt;data_t\u0026gt;; Matrix #  using lds::Matrix = typedef arma::Mat\u0026lt;data_t\u0026gt;; Cube #  using lds::Cube = typedef arma::Cube\u0026lt;data_t\u0026gt;; View #  using lds::View = typedef arma::subview\u0026lt;data_t\u0026gt;; Function Details #  Limit #  inline void Limit( std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub ) Limit #  inline void Limit( Vector \u0026amp; x, data_t lb, data_t ub ) Limit #  inline void Limit( Matrix \u0026amp; x, data_t lb, data_t ub ) Reassign #  inline void Reassign( Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\n some some Vector other other Vector parenthetical optional description provided by caller to ease debugging  Reassign #  inline void Reassign( Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\n some some Matrix other other Matrix parenthetical optional description provided by caller to ease debugging  ForceSymPD #  void ForceSymPD( Matrix \u0026amp; X ) Parameters:\n X mutated matrix  ForceSymMinEig #  void ForceSymMinEig( Matrix \u0026amp; X, data_t eig_min =0 ) Parameters:\n X mutated matrix eig_min [optional] minimum eigen value  lq #  void lq( Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X ) Parameters:\n L lower triangle matrix Qt orthonormal matrix (transposed cf QR decomp) X matrix being decomposed  calcCov #  Matrix calcCov( const Matrix \u0026amp; A, const Matrix \u0026amp; B ) Parameters:\n A some matrix B some other matrix  Return: covariance\nAttribute Details #  kControlTypeDeltaU #  static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY #  static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM #  static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\nkInf #  static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); kPi #  static const data_t kPi = arma::datum::pi; kDefaultP0 #  static const data_t kDefaultP0 = 1e-6; kDefaultQ0 #  static const data_t kDefaultQ0 = 1e-6; kDefaultR0 #  static const data_t kDefaultR0 = 1e-2;  Updated on 4 May 2022 at 16:57:50 Eastern Daylight Time\n"},{"id":28,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1controller/","title":"lds::Controller","section":"Classes","content":"lds::Controller #  More\u0026hellip;\nInherited by lds::gaussian::Controller, lds::poisson::Controller, lds::SwitchedController\u0026lt; System \u0026gt;\nPublic Functions #      Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes #      Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type    Detailed Description #  template \u0026lt;typename System \u0026gt; class lds::Controller;   Public Function Details #  Controller #  Controller() =default  Controller #  inline Controller( const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\n sys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask  Template Parameters:\n System type derived from lds::System   Controller #  inline Controller( System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\n sys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask  Template Parameters:\n System type derived from lds::System   Control #  inline const Vector \u0026amp; Control( const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\n z measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true)  Return: updated control signal\nUpdates the control signal (single-step). This is the most flexible option, but requires user to have set the controller\u0026rsquo;s y_ref, x_ref, and u_ref variables.\n ControlOutputReference #  inline const Vector \u0026amp; ControlOutputReference( const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\n z measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_estimation [optional] whether to update state estimate (if false, effectively open-loop control) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true)  Return: updated control signal\nUpdates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.\n sys #  inline const System \u0026amp; sys() const  Kc #  inline const Matrix \u0026amp; Kc() const  Kc_inty #  inline const Matrix \u0026amp; Kc_inty() const  Kc_u #  inline const Matrix \u0026amp; Kc_u() const  g_design #  inline const Vector \u0026amp; g_design() const  u_ref #  inline const Vector \u0026amp; u_ref() const  x_ref #  inline const Vector \u0026amp; x_ref() const  y_ref #  inline const Vector \u0026amp; y_ref() const  control_type #  inline size_t control_type() const  tau_awu #  inline data_t tau_awu() const  u_lb #  inline data_t u_lb() const  u_ub #  inline data_t u_ub() const  set_sys #  inline void set_sys( const System \u0026amp; sys )  set_g_design #  inline void set_g_design( const Vector \u0026amp; g_design )  set_u_ref #  inline void set_u_ref( const Vector \u0026amp; u_ref )  set_x_ref #  inline void set_x_ref( const Vector \u0026amp; x_ref )  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) Reimplemented by: lds::poisson::SwitchedController::set_y_ref, lds::gaussian::SwitchedController::set_y_ref, lds::poisson::Controller::set_y_ref, lds::gaussian::Controller::set_y_ref\n set_Kc #  inline void set_Kc( const Matrix \u0026amp; Kc )  set_Kc_inty #  inline void set_Kc_inty( const Matrix \u0026amp; Kc_inty )  set_Kc_u #  inline void set_Kc_u( const Matrix \u0026amp; Kc_u )  set_tau_awu #  inline void set_tau_awu( data_t tau )  set_control_type #  inline void set_control_type( size_t control_type ) Parameters:\n control_type control type bit mask  Template Parameters:\n System type derived from lds::System   set_u_lb #  inline void set_u_lb( data_t u_lb ) Parameters:\n u_lb control lower bound   set_u_ub #  inline void set_u_ub( data_t u_ub ) Parameters:\n u_ub control upper bound   Reset #  inline void Reset()  Print #  inline void Print()  Protected Attribute Details #  **sys_** #  System sys_;  **u_** #  Vector u_;  **u_return_** #  Vector u_return_;  **g_design_** #  Vector g_design_;  **u_ref_** #  Vector u_ref_;  **u_ref_prev_** #  Vector u_ref_prev_;  **x_ref_** #  Vector x_ref_;  **y_ref_** #  Vector y_ref_;  **cx_ref_** #  Vector cx_ref_;  **Kc_** #  Matrix Kc_;  **Kc_u_** #  Matrix Kc_u_;  **Kc_inty_** #  Matrix Kc_inty_;  **du_ref_** #  Vector du_ref_;  **dv_ref_** #  Vector dv_ref_;  **v_ref_** #  Vector v_ref_;  **dv_** #  Vector dv_;  **v_** #  Vector v_;  **int_e_** #  Vector int_e_;  **int_e_awu_adjust_** #  Vector int_e_awu_adjust_;  **u_sat_** #  Vector u_sat_;  **do_control_prev_** #  bool do_control_prev_ = false;  **do_lock_control_prev_** #  bool do_lock_control_prev_ = false;  **u_saturated_** #  bool u_saturated_ = false;  **u_lb_** #  data_t u_lb_ {};  **u_ub_** #  data_t u_ub_ {};  **tau_awu_** #  data_t tau_awu_ {};  **k_awu_** #  data_t k_awu_ = 0;  **t_since_control_onset_** #  data_t t_since_control_onset_ = 0;  **control_type_** #  size_t control_type_ {};   Updated on 4 May 2022 at 16:57:50 Eastern Daylight Time\n"},{"id":29,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1em/","title":"lds::EM","section":"Classes","content":"lds::EM #  More\u0026hellip;\nInherited by lds::gaussian::FitEM, lds::poisson::FitEM\nPublic Functions #      Name      EM() =default\nConstructs a new EMFit type.    EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.    EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.   virtual ~EM() =default   const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller.   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time)   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time)   const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance   const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1)   const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance   size_t n_t_tot()\ntotal number of time samples   const Vector \u0026amp; theta() const\ngets parameters updated in M step    Protected Functions #      Name     void Expectation(bool force_common_initial =false)\nExpectation step.   void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step.   void MaximizeDynamics()   void MaximizeQ()   void MaximizeInitial()   virtual void MaximizeOutput() =0   virtual void MaximizeMeasurement() =0   void Smooth(bool force_common_initial)\nget smoothed estimates   virtual void RecurseKe(Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t) =0\nrecursively update estimator gain Ke   void Reset()\nreset to initial conditions   void InitVars()\nInitializes the variables.   Vector UpdateTheta()\nupdates parameter list, theta    Protected Attributes #      Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   std::vector\u0026lt; Matrix \u0026gt; x_ state estimate   std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov   std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance   std::vector\u0026lt; Matrix \u0026gt; y_ output estimate   Matrix diag_y_   Matrix sum_E_x_t_x_t_ state covariance (current time)   Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1)   Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance   Fit fit_   Vector theta_   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials    Detailed Description #  template \u0026lt;typename Fit \u0026gt; class lds::EM;   Public Function Details #  EM #  EM() =default  EM #  EM( size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\n n_x number of states dt sample period u_train input training data z_train measurement training data   EM #  EM( const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\n fit0 initial fit u_train input training data z_train measurement training data   ~EM #  virtual ~EM() =default  Run #  const Fit \u0026amp; Run( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2 ) Parameters:\n calc_dynamics [optional] whether to calculate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law max_iter max number of iterations tol convergence tolerance (max fractional abs change)  Return: Fit\n ReturnData #  inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\n x #  inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const  y #  inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const  sum_E_x_t_x_t #  inline const Matrix \u0026amp; sum_E_x_t_x_t() const  sum_E_xu_tm1_xu_tm1 #  inline const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const  sum_E_xu_t_xu_tm1 #  inline const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const  n_t_tot #  inline size_t n_t_tot()  theta #  inline const Vector \u0026amp; theta() const  Protected Function Details #  Expectation #  void Expectation( bool force_common_initial =false ) Parameters:\n force_common_initial whether to force common initial condition for all trials   Maximization #  void Maximization( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false ) Parameters:\n calc_dynamics [optional] whether to caclulate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law   MaximizeDynamics #  void MaximizeDynamics()  MaximizeQ #  void MaximizeQ()  MaximizeInitial #  void MaximizeInitial()  MaximizeOutput #  virtual void MaximizeOutput() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput\n MaximizeMeasurement #  virtual void MaximizeMeasurement() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement\n Smooth #  void Smooth( bool force_common_initial ) Parameters:\n force_common_initial whether to force common initial conditions   RecurseKe #  virtual void RecurseKe( Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t ) =0 Parameters:\n Ke estimator gain P_pre cov of predicted state est. P_post cov of postior sate est. t time  Reimplemented by: lds::gaussian::FitEM::RecurseKe, lds::poisson::FitEM::RecurseKe\n Reset #  void Reset()  InitVars #  void InitVars()  UpdateTheta #  Vector UpdateTheta() Return: parameter list\n Protected Attribute Details #  **u_** #  UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_;  **z_** #  UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_;  **x_** #  std::vector\u0026lt; Matrix \u0026gt; x_;  **P_** #  std::vector\u0026lt; Cube \u0026gt; P_;  **P_t_tm1_** #  std::vector\u0026lt; Cube \u0026gt; P_t_tm1_;  **y_** #  std::vector\u0026lt; Matrix \u0026gt; y_;  **diag_y_** #  Matrix diag_y_;  **sum_E_x_t_x_t_** #  Matrix sum_E_x_t_x_t_;  **sum_E_xu_tm1_xu_tm1_** #  Matrix sum_E_xu_tm1_xu_tm1_;  **sum_E_xu_t_xu_tm1_** #  Matrix sum_E_xu_t_xu_tm1_;  **fit_** #  Fit fit_;  **theta_** #  Vector theta_;  **dt_** #  data_t dt_ {};  **n_u_** #  size_t n_u_ {};  **n_x_** #  size_t n_x_ {};  **n_y_** #  size_t n_y_ {};  **n_trials_** #  size_t n_trials_ {};  **n_t_** #  std::vector\u0026lt; size_t \u0026gt; n_t_;  **n_t_tot_** #  size_t n_t_tot_ {};   Updated on 4 May 2022 at 16:57:50 Eastern Daylight Time\n"},{"id":30,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1fit/","title":"lds::Fit","section":"Classes","content":"lds::Fit #  LDS Fit Type. #include \u0026lt;lds_fit.h\u0026gt;\nInherited by lds::gaussian::Fit, lds::poisson::Fit\nPublic Functions #      Name      Fit() =default\nConstructs a new Fit.    Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit.   virtual ~Fit() =default   size_t n_u() const\ngets number of inputs   size_t n_x() const\ngets number of states   size_t n_y() const\ngets number of outputs   data_t dt() const\ngets sample period   const Matrix \u0026amp; A() const\ngets state matrix   const Matrix \u0026amp; B() const\ngets input matrix   const Vector \u0026amp; g() const\ngets input gain   const Vector \u0026amp; m() const\ngets process disturbance   const Matrix \u0026amp; Q() const\ngets process noise covariance   const Vector \u0026amp; x0() const\ngets initial state estimate   const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate   const Matrix \u0026amp; C() const\ngets output matrix   const Vector \u0026amp; d() const\ngets output bias   virtual const Matrix \u0026amp; R() const =0   void set_A(const Matrix \u0026amp; A)\nsets state matrix   void set_B(const Matrix \u0026amp; B)\nsets input matrix   void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor   void set_m(const Vector \u0026amp; m)\nsets process disturbance   void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance   virtual void set_R(const Matrix \u0026amp; R) =0\nsets output noise covariance (if any)   void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate   void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance   void set_C(const Matrix \u0026amp; C)\nsets output matrix   void set_d(const Vector \u0026amp; d)\nsets output bias   View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function   View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function   virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) =0\noutput function    Protected Attributes #      Name     data_t dt_ sample period   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Vector m_ process noise mean   Matrix Q_ process noise cov   Matrix C_ output matrix   Vector d_ output bias   Matrix R_ measurement noise   Vector x0_ initial state   Matrix P0_ initial covar   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs      Public Function Details #  Fit #  Fit() =default  Fit #  Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period   ~Fit #  virtual ~Fit() =default  n_u #  inline size_t n_u() const  n_x #  inline size_t n_x() const  n_y #  inline size_t n_y() const  dt #  inline data_t dt() const  A #  inline const Matrix \u0026amp; A() const  B #  inline const Matrix \u0026amp; B() const  g #  inline const Vector \u0026amp; g() const  m #  inline const Vector \u0026amp; m() const  Q #  inline const Matrix \u0026amp; Q() const  x0 #  inline const Vector \u0026amp; x0() const  P0 #  inline const Matrix \u0026amp; P0() const  C #  inline const Matrix \u0026amp; C() const  d #  inline const Vector \u0026amp; d() const  R #  virtual const Matrix \u0026amp; R() const =0 Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R\n set_A #  inline void set_A( const Matrix \u0026amp; A )  set_B #  inline void set_B( const Matrix \u0026amp; B )  set_g #  inline void set_g( const Vector \u0026amp; g )  set_m #  inline void set_m( const Vector \u0026amp; m )  set_Q #  inline void set_Q( const Matrix \u0026amp; Q )  set_R #  virtual void set_R( const Matrix \u0026amp; R ) =0 Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R\n set_x0 #  inline void set_x0( const Vector \u0026amp; x0 )  set_P0 #  inline void set_P0( const Matrix \u0026amp; P0 )  set_C #  inline void set_C( const Matrix \u0026amp; C )  set_d #  inline void set_d( const Vector \u0026amp; d )  f #  inline View f( Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t ) Parameters:\n x state estimate (over time) u input (over time) t time index  Return: view of updated state\n f #  inline View f( Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t ) Parameters:\n x_pre predicted state est. x_post posterior state est. u input (over time) t time index  Return: view of predicted state\n h #  virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) =0 Parameters:\n y output estimate (over time) x state estimate (over time) t time index  Return: output\nReimplemented by: lds::poisson::Fit::h, lds::gaussian::Fit::h\n Protected Attribute Details #  **dt_** #  data_t dt_ {};  **A_** #  Matrix A_;  **B_** #  Matrix B_;  **g_** #  Vector g_;  **m_** #  Vector m_;  **Q_** #  Matrix Q_;  **C_** #  Matrix C_;  **d_** #  Vector d_;  **R_** #  Matrix R_;  **x0_** #  Vector x0_;  **P0_** #  Matrix P0_;  **n_u_** #  size_t n_u_ {};  **n_x_** #  size_t n_x_ {};  **n_y_** #  size_t n_y_ {};   Updated on 4 May 2022 at 16:57:50 Eastern Daylight Time\n"},{"id":31,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/","title":"lds::gaussian","section":"Namespaces","content":"lds::gaussian #  Linear Dynamical Systems with Gaussian observations. Classes #      Name     class lds::gaussian::Controller Gaussian-observation Controller Type.   class lds::gaussian::Fit GLDS Fit Type.   class lds::gaussian::FitEM GLDS E-M Fit Type.   class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.   class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.   class lds::gaussian::System Gaussian LDS Type.     Updated on 4 May 2022 at 16:57:50 Eastern Daylight Time\n"},{"id":32,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1controller/","title":"lds::gaussian::Controller","section":"Classes","content":"lds::gaussian::Controller #  Gaussian-observation Controller Type. #include \u0026lt;lds_gaussian_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions #      Name     virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output    Additional inherited members #  Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type      Public Function Details #  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\n  Updated on 4 May 2022 at 16:57:50 Eastern Daylight Time\n"},{"id":33,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/","title":"lds::gaussian::Fit","section":"Classes","content":"lds::gaussian::Fit #  GLDS Fit Type. #include \u0026lt;lds_gaussian_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions #      Name      Fit() =default    Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit.   virtual const Matrix \u0026amp; R() const override\ngets measurement noise covariance   virtual void set_R(const Matrix \u0026amp; R) override\nsets measurement noise covariance   virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function    Additional inherited members #  Public Functions inherited from lds::Fit\n    Name     virtual ~Fit() =default   size_t n_u() const\ngets number of inputs   size_t n_x() const\ngets number of states   size_t n_y() const\ngets number of outputs   data_t dt() const\ngets sample period   const Matrix \u0026amp; A() const\ngets state matrix   const Matrix \u0026amp; B() const\ngets input matrix   const Vector \u0026amp; g() const\ngets input gain   const Vector \u0026amp; m() const\ngets process disturbance   const Matrix \u0026amp; Q() const\ngets process noise covariance   const Vector \u0026amp; x0() const\ngets initial state estimate   const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate   const Matrix \u0026amp; C() const\ngets output matrix   const Vector \u0026amp; d() const\ngets output bias   void set_A(const Matrix \u0026amp; A)\nsets state matrix   void set_B(const Matrix \u0026amp; B)\nsets input matrix   void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor   void set_m(const Vector \u0026amp; m)\nsets process disturbance   void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance   void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate   void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance   void set_C(const Matrix \u0026amp; C)\nsets output matrix   void set_d(const Vector \u0026amp; d)\nsets output bias   View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function   View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function    Protected Attributes inherited from lds::Fit\n    Name     data_t dt_ sample period   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Vector m_ process noise mean   Matrix Q_ process noise cov   Matrix C_ output matrix   Vector d_ output bias   Matrix R_ measurement noise   Vector x0_ initial state   Matrix P0_ initial covar   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs      Public Function Details #  Fit #  Fit() =default  Fit #  Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period   R #  inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\n set_R #  inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\n h #  inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\n y output estimate (over time) x state estimate (over time) t time index  Return: output\nReimplements: lds::Fit::h\n  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":34,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitem/","title":"lds::gaussian::FitEM","section":"Classes","content":"lds::gaussian::FitEM #  GLDS E-M Fit Type. More\u0026hellip;\n\n#include \u0026lt;lds_gaussian_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members #  Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name      EM() =default\nConstructs a new EMFit type.    EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.    EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.   virtual ~EM() =default   const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller.   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time)   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time)   const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance   const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1)   const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance   size_t n_t_tot()\ntotal number of time samples   const Vector \u0026amp; theta() const\ngets parameters updated in M step    Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name     void Expectation(bool force_common_initial =false)\nExpectation step.   void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step.   void MaximizeDynamics()   void MaximizeQ()   void MaximizeInitial()   void Smooth(bool force_common_initial)\nget smoothed estimates   void Reset()\nreset to initial conditions   void InitVars()\nInitializes the variables.   Vector UpdateTheta()\nupdates parameter list, theta    Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   std::vector\u0026lt; Matrix \u0026gt; x_ state estimate   std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov   std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance   std::vector\u0026lt; Matrix \u0026gt; y_ output estimate   Matrix diag_y_   Matrix sum_E_x_t_x_t_ state covariance (current time)   Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1)   Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance   Fit fit_   Vector theta_   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials    Detailed Description #  class lds::gaussian::FitEM; This type is used in the process of fitting GLDS models by expectation-maximization (EM).\n   Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":35,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitssid/","title":"lds::gaussian::FitSSID","section":"Classes","content":"lds::gaussian::FitSSID #  Subspace Identification (SSID) for GLDS. #include \u0026lt;lds_gaussian_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members #  Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name      SSID() =default\nConstructs a new SSIDFit type.    SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type.   std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller.    Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name     void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d)   void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix.   void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD)   void Solve(data_t wt_dc)\nsolves for LDS parameters   void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C    Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   Matrix D_ block-Hankel I/O data matrix   Fit fit_ fit   Matrix g_dc_ I/O gain @ DC.   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_h_   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials   Matrix L_ lower triangle decomp of covariance matrix   Vector s_ singular values   Matrix ext_obs_t_ extended observability matrix       Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":36,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1switchedcontroller/","title":"lds::gaussian::SwitchedController","section":"Classes","content":"lds::gaussian::SwitchedController #  Gaussian-observation SwitchedController Type. #include \u0026lt;lds_gaussian_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions #      Name     virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output    Additional inherited members #  Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\n    Name      SwitchedController() =default\nConstructs a new SwitchedController.    SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController.    SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems).   void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller.   void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains   void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving)   void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains   void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving)   void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains   void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving)   void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design   void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving)    Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\n    Name     std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between   size_t n_sys_ number of systems   size_t idx_ current system/controller index.   UniformMatrixList Kc_list_   UniformMatrixList Kc_inty_list_   UniformMatrixList Kc_u_list_   UniformVectorList g_design_list_    Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type      Public Function Details #  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\n  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":37,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/","title":"lds::gaussian::System","section":"Classes","content":"lds::gaussian::System #  Gaussian LDS Type. #include \u0026lt;lds_gaussian_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions #      Name      System() =default\nConstructs a new System.    System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)\nConstructs a new Gaussian System.   virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement.   const Matrix \u0026amp; R() const\nGet output noise covariance.   void set_Q(const Matrix \u0026amp; Q)   void set_R(const Matrix \u0026amp; R)\nSet output noise covariance.   void set_Ke(const Matrix \u0026amp; Ke)\nSet estimator gain.   void set_Ke_m(const Matrix \u0026amp; Ke_m)\nSet disturbance estimator gain.   void Print()\nPrint system variables to stdout.    Protected Functions #      Name     virtual void h() override\nSystem output function.   virtual void RecurseKe() override\nRecursively update estimator gain.    Protected Attributes #      Name     Matrix R_ covariance of output noise   bool do_recurse_Ke_ whether to recursively calculate estimator gain    Additional inherited members #  Public Functions inherited from lds::System\n    Name     virtual ~System()   void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates.   void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function   size_t n_u() const\nGet number of inputs.   size_t n_x() const\nGet number of states.   size_t n_y() const\nGet number of outputs.   data_t dt() const\nGet sample period.   const Vector \u0026amp; x() const\nGet current state.   const Matrix \u0026amp; P() const\nGet covariance of state estimate.   const Vector \u0026amp; m() const\nGet current process disturbance/bias.   const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate.   const Vector \u0026amp; cx() const\nGet C*x.   const Vector \u0026amp; y() const\nGet output.   const Vector \u0026amp; x0() const\nGet initial state.   const Vector \u0026amp; m0() const\nGet initial disturbance.   const Matrix \u0026amp; A() const\nGet state matrix.   const Matrix \u0026amp; B() const\nGet input matrix.   const Vector \u0026amp; g() const\nGet input gain/conversion factor.   const Matrix \u0026amp; C() const\nGet output matrix.   const Vector \u0026amp; d() const\nGet output bias.   const Matrix \u0026amp; Ke() const\nGet estimator gain.   const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m)   const Matrix \u0026amp; Q()\nGet process noise covariance.   const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation.   const Matrix \u0026amp; P0()\nGet covariance of initial state.   const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance.   void set_A(const Matrix \u0026amp; A)\nSet state matrix.   void set_B(const Matrix \u0026amp; B)\nSet input matrix.   void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance.   void set_g(const Vector \u0026amp; g)\nSet input gain.   void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation.   void set_x0(const Vector \u0026amp; x0)\nSet initial state.   void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state.   void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance.   void set_C(const Matrix \u0026amp; C)\nSet output matrix.   void set_d(const Vector \u0026amp; d)\nSet output bias.   void set_x(const Vector \u0026amp; x)\nSet state of system.   void Reset()\nReset system variables.    Protected Functions inherited from lds::System\n    Name     void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)    Public Attributes inherited from lds::System\n    Name     bool do_adapt_m whether to adaptively estimate disturbance m    Protected Attributes inherited from lds::System\n    Name     std::size_t n_x_ number of states   std::size_t n_u_ number of inputs   std::size_t n_y_ number of outputs   data_t dt_ sample period   Vector x_ state   Matrix P_ covariance of state estimate   Vector m_ process disturbance   Matrix P_m_ covariance of disturbance estimate   Vector cx_ C*x.   Vector y_ output   Vector z_ measurement   Vector x0_ initial state   Matrix P0_ covariance of initial state estimate   Vector m0_ initial process disturbance   Matrix P0_m_ covariance of initial disturbance est.   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Matrix Q_ covariance of process noise   Matrix Q_m_ covariance of disturbance random walk   Matrix C_ output matrix   Vector d_ output bias   Matrix Ke_ estimator gain   Matrix Ke_m_ estimator gain for process disturbance      Public Function Details #  System #  System() =default  System #  System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0 ) Parameters:\n n_u number of inputs (u) n_x number of states (x) n_y number of outputs (y) dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) r0 [optional] initial diagonal elements of output noise covariance (R)   Simulate #  virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\n u_tm1 input at t-1  Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\n R #  inline const Matrix \u0026amp; R() const  set_Q #  inline void set_Q( const Matrix \u0026amp; Q )  set_R #  inline void set_R( const Matrix \u0026amp; R )  set_Ke #  inline void set_Ke( const Matrix \u0026amp; Ke )  set_Ke_m #  inline void set_Ke_m( const Matrix \u0026amp; Ke_m )  Print #  void Print()  Protected Function Details #  h #  inline virtual void h() override Reimplements: lds::System::h\n RecurseKe #  virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\n Protected Attribute Details #  **R_** #  Matrix R_;  **do_recurse_Ke_** #  bool do_recurse_Ke_ {};   Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":38,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/","title":"lds::poisson","section":"Namespaces","content":"lds::poisson #  Linear Dynamical Systems with Poisson observations. Classes #      Name     class lds::poisson::Controller PLDS Controller Type.   class lds::poisson::Fit PLDS Fit Type.   class lds::poisson::FitEM PLDS E-M Fit Type.   class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS.   class lds::poisson::SwitchedController Poisson-observation SwitchedController Type.   class lds::poisson::System Poisson System type.    Attributes #      Name     std::random_device rd random device for simulating poisson data   std::mt19937 rng random number generator for simulating poisson data    Attribute Details #  rd #  static std::random_device rd; rng #  static std::mt19937 rng = std::mt19937( rd());  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":39,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/","title":"lds::poisson::Controller","section":"Classes","content":"lds::poisson::Controller #  PLDS Controller Type. #include \u0026lt;lds_poisson_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions #      Name     virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output.    Additional inherited members #  Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type      Public Function Details #  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\n  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":40,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/","title":"lds::poisson::Fit","section":"Classes","content":"lds::poisson::Fit #  PLDS Fit Type. #include \u0026lt;lds_poisson_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions #      Name      Fit() =default    Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit.   virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function   virtual void set_R(const Matrix \u0026amp; R) override\nsets output noise covariance (if any)   virtual const Matrix \u0026amp; R() const override    Additional inherited members #  Public Functions inherited from lds::Fit\n    Name     virtual ~Fit() =default   size_t n_u() const\ngets number of inputs   size_t n_x() const\ngets number of states   size_t n_y() const\ngets number of outputs   data_t dt() const\ngets sample period   const Matrix \u0026amp; A() const\ngets state matrix   const Matrix \u0026amp; B() const\ngets input matrix   const Vector \u0026amp; g() const\ngets input gain   const Vector \u0026amp; m() const\ngets process disturbance   const Matrix \u0026amp; Q() const\ngets process noise covariance   const Vector \u0026amp; x0() const\ngets initial state estimate   const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate   const Matrix \u0026amp; C() const\ngets output matrix   const Vector \u0026amp; d() const\ngets output bias   void set_A(const Matrix \u0026amp; A)\nsets state matrix   void set_B(const Matrix \u0026amp; B)\nsets input matrix   void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor   void set_m(const Vector \u0026amp; m)\nsets process disturbance   void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance   void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate   void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance   void set_C(const Matrix \u0026amp; C)\nsets output matrix   void set_d(const Vector \u0026amp; d)\nsets output bias   View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function   View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function    Protected Attributes inherited from lds::Fit\n    Name     data_t dt_ sample period   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Vector m_ process noise mean   Matrix Q_ process noise cov   Matrix C_ output matrix   Vector d_ output bias   Matrix R_ measurement noise   Vector x0_ initial state   Matrix P0_ initial covar   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs      Public Function Details #  Fit #  Fit() =default  Fit #  inline Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period   h #  inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\n y output estimate (over time) x state estimate (over time) t time index  Return: output\nReimplements: lds::Fit::h\n set_R #  inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\n R #  inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\n  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":41,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitem/","title":"lds::poisson::FitEM","section":"Classes","content":"lds::poisson::FitEM #  PLDS E-M Fit Type. More\u0026hellip;\n\n#include \u0026lt;lds_poisson_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members #  Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name      EM() =default\nConstructs a new EMFit type.    EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.    EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.   virtual ~EM() =default   const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller.   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time)   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time)   const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance   const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1)   const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance   size_t n_t_tot()\ntotal number of time samples   const Vector \u0026amp; theta() const\ngets parameters updated in M step    Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name     void Expectation(bool force_common_initial =false)\nExpectation step.   void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step.   void MaximizeDynamics()   void MaximizeQ()   void MaximizeInitial()   void Smooth(bool force_common_initial)\nget smoothed estimates   void Reset()\nreset to initial conditions   void InitVars()\nInitializes the variables.   Vector UpdateTheta()\nupdates parameter list, theta    Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   std::vector\u0026lt; Matrix \u0026gt; x_ state estimate   std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov   std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance   std::vector\u0026lt; Matrix \u0026gt; y_ output estimate   Matrix diag_y_   Matrix sum_E_x_t_x_t_ state covariance (current time)   Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1)   Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance   Fit fit_   Vector theta_   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials    Detailed Description #  class lds::poisson::FitEM; This type is used in the process of fitting PLDS models by expectation-maximization (EM).\n   Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":42,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitssid/","title":"lds::poisson::FitSSID","section":"Classes","content":"lds::poisson::FitSSID #  Subspace Identification (SSID) for PLDS. #include \u0026lt;lds_poisson_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members #  Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name      SSID() =default\nConstructs a new SSIDFit type.    SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type.   std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller.    Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name     void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d)   void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix.   void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD)   void Solve(data_t wt_dc)\nsolves for LDS parameters   void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C    Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   Matrix D_ block-Hankel I/O data matrix   Fit fit_ fit   Matrix g_dc_ I/O gain @ DC.   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_h_   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials   Matrix L_ lower triangle decomp of covariance matrix   Vector s_ singular values   Matrix ext_obs_t_ extended observability matrix       Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":43,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1switchedcontroller/","title":"lds::poisson::SwitchedController","section":"Classes","content":"lds::poisson::SwitchedController #  Poisson-observation SwitchedController Type. #include \u0026lt;lds_poisson_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions #      Name     virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output.    Additional inherited members #  Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\n    Name      SwitchedController() =default\nConstructs a new SwitchedController.    SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController.    SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems).   void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller.   void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains   void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving)   void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains   void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving)   void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains   void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving)   void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design   void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving)    Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\n    Name     std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between   size_t n_sys_ number of systems   size_t idx_ current system/controller index.   UniformMatrixList Kc_list_   UniformMatrixList Kc_inty_list_   UniformMatrixList Kc_u_list_   UniformVectorList g_design_list_    Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type      Public Function Details #  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\n  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":44,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/","title":"lds::poisson::System","section":"Classes","content":"lds::poisson::System #  Poisson System type. #include \u0026lt;lds_poisson_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions #      Name      System() =default\nConstructs a new System.    System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nConstructs a new Poisson System.   virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement.    Protected Functions #      Name     virtual void h() override\nSystem output function.   virtual void RecurseKe() override\nRecursively recalculate estimator gain (Ke)    Additional inherited members #  Public Functions inherited from lds::System\n    Name     virtual ~System()   void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates.   void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function   size_t n_u() const\nGet number of inputs.   size_t n_x() const\nGet number of states.   size_t n_y() const\nGet number of outputs.   data_t dt() const\nGet sample period.   const Vector \u0026amp; x() const\nGet current state.   const Matrix \u0026amp; P() const\nGet covariance of state estimate.   const Vector \u0026amp; m() const\nGet current process disturbance/bias.   const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate.   const Vector \u0026amp; cx() const\nGet C*x.   const Vector \u0026amp; y() const\nGet output.   const Vector \u0026amp; x0() const\nGet initial state.   const Vector \u0026amp; m0() const\nGet initial disturbance.   const Matrix \u0026amp; A() const\nGet state matrix.   const Matrix \u0026amp; B() const\nGet input matrix.   const Vector \u0026amp; g() const\nGet input gain/conversion factor.   const Matrix \u0026amp; C() const\nGet output matrix.   const Vector \u0026amp; d() const\nGet output bias.   const Matrix \u0026amp; Ke() const\nGet estimator gain.   const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m)   const Matrix \u0026amp; Q()\nGet process noise covariance.   const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation.   const Matrix \u0026amp; P0()\nGet covariance of initial state.   const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance.   void set_A(const Matrix \u0026amp; A)\nSet state matrix.   void set_B(const Matrix \u0026amp; B)\nSet input matrix.   void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance.   void set_g(const Vector \u0026amp; g)\nSet input gain.   void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance.   void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation.   void set_x0(const Vector \u0026amp; x0)\nSet initial state.   void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state.   void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance.   void set_C(const Matrix \u0026amp; C)\nSet output matrix.   void set_d(const Vector \u0026amp; d)\nSet output bias.   void set_x(const Vector \u0026amp; x)\nSet state of system.   void Reset()\nReset system variables.   void Print()\nPrint system variables to stdout.    Protected Functions inherited from lds::System\n    Name     void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)    Public Attributes inherited from lds::System\n    Name     bool do_adapt_m whether to adaptively estimate disturbance m    Protected Attributes inherited from lds::System\n    Name     std::size_t n_x_ number of states   std::size_t n_u_ number of inputs   std::size_t n_y_ number of outputs   data_t dt_ sample period   Vector x_ state   Matrix P_ covariance of state estimate   Vector m_ process disturbance   Matrix P_m_ covariance of disturbance estimate   Vector cx_ C*x.   Vector y_ output   Vector z_ measurement   Vector x0_ initial state   Matrix P0_ covariance of initial state estimate   Vector m0_ initial process disturbance   Matrix P0_m_ covariance of initial disturbance est.   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Matrix Q_ covariance of process noise   Matrix Q_m_ covariance of disturbance random walk   Matrix C_ output matrix   Vector d_ output bias   Matrix Ke_ estimator gain   Matrix Ke_m_ estimator gain for process disturbance      Public Function Details #  System #  System() =default  System #  System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q)   Simulate #  virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\n u_tm1 input at t-1  Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\n Protected Function Details #  h #  inline virtual void h() override Reimplements: lds::System::h\n RecurseKe #  virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nRecursively recalculate estimator gain (Ke).\nReferences:\nSmith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.\nEden UT, \u0026hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.\n  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":45,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/","title":"lds::SSID","section":"Classes","content":"lds::SSID #  More\u0026hellip;\nInherited by lds::gaussian::FitSSID, lds::poisson::FitSSID\nPublic Functions #      Name      SSID() =default\nConstructs a new SSIDFit type.    SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type.   std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller.    Protected Functions #      Name     void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d)   void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix.   virtual void DecomposeData() =0\nDecompose data to lower-triangular matrix (used in Solve)   void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD)   void Solve(data_t wt_dc)\nsolves for LDS parameters   void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C    Protected Attributes #      Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   Matrix D_ block-Hankel I/O data matrix   Fit fit_ fit   Matrix g_dc_ I/O gain @ DC.   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_h_   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials   Matrix L_ lower triangle decomp of covariance matrix   Vector s_ singular values   Matrix ext_obs_t_ extended observability matrix    Detailed Description #  template \u0026lt;typename Fit \u0026gt; class lds::SSID;   Public Function Details #  SSID #  SSID() =default  SSID #  SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf) ) Parameters:\n n_x number of states n_h size of block-hankel data matrix dt sample period u_train input training data z_train measurement training data d output bias   Run #  std::tuple\u0026lt; Fit, Vector \u0026gt; Run( SSIDWt ssid_wt ) Parameters:\n ssid_wt weight for singular value decomp  Return: tuple (Fit, singular values)\n ReturnData #  inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\n Protected Function Details #  CalcD #  void CalcD( data_t t_silence =0.1, data_t thresh_silence =0.001 ) Parameters:\n t_silence threshold on period of time that qualifies as \u0026ldquo;silence\u0026rdquo; thresh_silence threshold on input amplitude u that qualifies as \u0026ldquo;silence\u0026rdquo;   CreateHankelDataMat #  void CreateHankelDataMat() Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.\n DecomposeData #  virtual void DecomposeData() =0 Reimplemented by: lds::poisson::FitSSID::DecomposeData, lds::gaussian::FitSSID::DecomposeData\n CalcSVD #  void CalcSVD( SSIDWt wt ) Parameters:\n ssid_wt weight for SVD   Solve #  void Solve( data_t wt_dc ) Parameters:\n wt_dc weight placed on getting correct DC I/O gain   RecomputeExtObs #  void RecomputeExtObs()  Protected Attribute Details #  **u_** #  UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_;  **z_** #  UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_;  **D_** #  Matrix D_;  **fit_** #  Fit fit_;  **g_dc_** #  Matrix g_dc_;  **dt_** #  data_t dt_ {};  **n_u_** #  size_t n_u_ {};  **n_x_** #  size_t n_x_ {};  **n_y_** #  size_t n_y_ {};  **n_h_** #  size_t n_h_ {};  **n_trials_** #  size_t n_trials_ {};  **n_t_** #  std::vector\u0026lt; size_t \u0026gt; n_t_;  **n_t_tot_** #  size_t n_t_tot_ {};  **L_** #  Matrix L_;  **s_** #  Vector s_;  **ext_obs_t_** #  Matrix ext_obs_t_;   Updated on 4 May 2022 at 16:57:50 Eastern Daylight Time\n"},{"id":46,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/","title":"lds::SwitchedController","section":"Classes","content":"lds::SwitchedController #  SwitchedController Type. More\u0026hellip;\n\n#include \u0026lt;lds_sctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nInherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController\nPublic Functions #      Name      SwitchedController() =default\nConstructs a new SwitchedController.    SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController.    SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems).   void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller.   void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains   void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving)   void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains   void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving)   void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains   void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving)   void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design   void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving)    Protected Attributes #      Name     std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between   size_t n_sys_ number of systems   size_t idx_ current system/controller index.   UniformMatrixList Kc_list_   UniformMatrixList Kc_inty_list_   UniformMatrixList Kc_u_list_   UniformVectorList g_design_list_    Additional inherited members #  Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref)   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type    Detailed Description #  template \u0026lt;typename System \u0026gt; class lds::SwitchedController;   Public Function Details #  SwitchedController #  SwitchedController() =default  SwitchedController #  inline SwitchedController( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\n systems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask   SwitchedController #  inline SwitchedController( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\n systems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask   Switch #  inline void Switch( size_t idx, bool do_force_switch =false ) Parameters:\n idx index do_force_switch whether to force a system switch even if already there.   set_Kc #  inline void set_Kc( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc )  set_Kc #  inline void set_Kc( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc )  set_Kc_inty #  inline void set_Kc_inty( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty )  set_Kc_inty #  inline void set_Kc_inty( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty )  set_Kc_u #  inline void set_Kc_u( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u )  set_Kc_u #  inline void set_Kc_u( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u )  set_g_design #  inline void set_g_design( const UniformVectorList \u0026amp; g )  set_g_design #  inline void set_g_design( UniformVectorList \u0026amp;\u0026amp; g )  Protected Attribute Details #  **systems_** #  std::vector\u0026lt; System \u0026gt; systems_;  **n_sys_** #  size_t n_sys_ {};  **idx_** #  size_t idx_ {};  **Kc_list_** #  UniformMatrixList Kc_list_;  **Kc_inty_list_** #  UniformMatrixList Kc_inty_list_;  **Kc_u_list_** #  UniformMatrixList Kc_u_list_;  **g_design_list_** #  UniformVectorList g_design_list_;   Updated on 4 May 2022 at 16:57:50 Eastern Daylight Time\n"},{"id":47,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1system/","title":"lds::System","section":"Classes","content":"lds::System #  Linear Dynamical System Type. #include \u0026lt;lds_sys.h\u0026gt;\nInherited by lds::gaussian::System, lds::poisson::System\nPublic Functions #      Name      System() =default\nConstructs a new System.    System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nconstructs a new System   virtual ~System()   void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates.   virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) =0\nsimulates system (single time step)   void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function   virtual void h() =0\nsystem output function   size_t n_u() const\nGet number of inputs.   size_t n_x() const\nGet number of states.   size_t n_y() const\nGet number of outputs.   data_t dt() const\nGet sample period.   const Vector \u0026amp; x() const\nGet current state.   const Matrix \u0026amp; P() const\nGet covariance of state estimate.   const Vector \u0026amp; m() const\nGet current process disturbance/bias.   const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate.   const Vector \u0026amp; cx() const\nGet C*x.   const Vector \u0026amp; y() const\nGet output.   const Vector \u0026amp; x0() const\nGet initial state.   const Vector \u0026amp; m0() const\nGet initial disturbance.   const Matrix \u0026amp; A() const\nGet state matrix.   const Matrix \u0026amp; B() const\nGet input matrix.   const Vector \u0026amp; g() const\nGet input gain/conversion factor.   const Matrix \u0026amp; C() const\nGet output matrix.   const Vector \u0026amp; d() const\nGet output bias.   const Matrix \u0026amp; Ke() const\nGet estimator gain.   const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m)   const Matrix \u0026amp; Q()\nGet process noise covariance.   const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation.   const Matrix \u0026amp; P0()\nGet covariance of initial state.   const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance.   void set_A(const Matrix \u0026amp; A)\nSet state matrix.   void set_B(const Matrix \u0026amp; B)\nSet input matrix.   void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance.   void set_g(const Vector \u0026amp; g)\nSet input gain.   void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance.   void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation.   void set_x0(const Vector \u0026amp; x0)\nSet initial state.   void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state.   void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance.   void set_C(const Matrix \u0026amp; C)\nSet output matrix.   void set_d(const Vector \u0026amp; d)\nSet output bias.   void set_x(const Vector \u0026amp; x)\nSet state of system.   void Reset()\nReset system variables.   void Print()\nPrint system variables to stdout.    Protected Functions #      Name     virtual void RecurseKe() =0\nRecursively recalculate estimator gain (Ke)   void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)    Public Attributes #      Name     bool do_adapt_m whether to adaptively estimate disturbance m    Protected Attributes #      Name     std::size_t n_x_ number of states   std::size_t n_u_ number of inputs   std::size_t n_y_ number of outputs   data_t dt_ sample period   Vector x_ state   Matrix P_ covariance of state estimate   Vector m_ process disturbance   Matrix P_m_ covariance of disturbance estimate   Vector cx_ C*x.   Vector y_ output   Vector z_ measurement   Vector x0_ initial state   Matrix P0_ covariance of initial state estimate   Vector m0_ initial process disturbance   Matrix P0_m_ covariance of initial disturbance est.   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Matrix Q_ covariance of process noise   Matrix Q_m_ covariance of disturbance random walk   Matrix C_ output matrix   Vector d_ output bias   Matrix Ke_ estimator gain   Matrix Ke_m_ estimator gain for process disturbance      Public Function Details #  System #  System() =default  System #  System( size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period p0 diagonal elements for state estimate covariance q0 diagonal elements for process noise covariance   ~System #  inline virtual ~System()  Filter #  void Filter( const Vector \u0026amp; u_tm1, const Vector \u0026amp; z ) Parameters:\n u_tm1 input at t-minus-1 z_t current measurement  Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.\n Simulate #  virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) =0 Parameters:\n u_tm1 input at time t-1  Return: simulated measurement at time t\nReimplemented by: lds::poisson::System::Simulate, lds::gaussian::System::Simulate\n f #  inline void f( const Vector \u0026amp; u, bool do_add_noise =false ) Parameters:\n u input do_add_noise whether to add simulated process noise   h #  virtual void h() =0 Reimplemented by: lds::poisson::System::h, lds::gaussian::System::h\n n_u #  inline size_t n_u() const  n_x #  inline size_t n_x() const  n_y #  inline size_t n_y() const  dt #  inline data_t dt() const  x #  inline const Vector \u0026amp; x() const  P #  inline const Matrix \u0026amp; P() const  m #  inline const Vector \u0026amp; m() const  P_m #  inline const Matrix \u0026amp; P_m() const  cx #  inline const Vector \u0026amp; cx() const  y #  inline const Vector \u0026amp; y() const  x0 #  inline const Vector \u0026amp; x0() const  m0 #  inline const Vector \u0026amp; m0() const  A #  inline const Matrix \u0026amp; A() const  B #  inline const Matrix \u0026amp; B() const  g #  inline const Vector \u0026amp; g() const  C #  inline const Matrix \u0026amp; C() const  d #  inline const Vector \u0026amp; d() const  Ke #  inline const Matrix \u0026amp; Ke() const  Ke_m #  inline const Matrix \u0026amp; Ke_m() const  Q #  inline const Matrix \u0026amp; Q()  Q_m #  inline const Matrix \u0026amp; Q_m()  P0 #  inline const Matrix \u0026amp; P0()  P0_m #  inline const Matrix \u0026amp; P0_m()  set_A #  inline void set_A( const Matrix \u0026amp; A )  set_B #  inline void set_B( const Matrix \u0026amp; B )  set_m #  inline void set_m( const Vector \u0026amp; m, bool do_force_assign =false )  set_g #  inline void set_g( const Vector \u0026amp; g )  set_Q #  inline void set_Q( const Matrix \u0026amp; Q )  set_Q_m #  inline void set_Q_m( const Matrix \u0026amp; Q_m )  set_x0 #  inline void set_x0( const Vector \u0026amp; x0 )  set_P0 #  inline void set_P0( const Matrix \u0026amp; P0 )  set_P0_m #  inline void set_P0_m( const Matrix \u0026amp; P0_m )  set_C #  inline void set_C( const Matrix \u0026amp; C )  set_d #  inline void set_d( const Vector \u0026amp; d )  set_x #  inline void set_x( const Vector \u0026amp; x )  Reset #  void Reset()  Print #  void Print()  Protected Function Details #  RecurseKe #  virtual void RecurseKe() =0 Reimplemented by: lds::poisson::System::RecurseKe, lds::gaussian::System::RecurseKe\n InitVars #  void InitVars( data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 )  Public Attribute Details #  do_adapt_m #  bool do_adapt_m {};  Protected Attribute Details #  **n_x_** #  std::size_t n_x_ {};  **n_u_** #  std::size_t n_u_ {};  **n_y_** #  std::size_t n_y_ {};  **dt_** #  data_t dt_ {};  **x_** #  Vector x_;  **P_** #  Matrix P_;  **m_** #  Vector m_;  **P_m_** #  Matrix P_m_;  **cx_** #  Vector cx_;  **y_** #  Vector y_;  **z_** #  Vector z_;  **x0_** #  Vector x0_;  **P0_** #  Matrix P0_;  **m0_** #  Vector m0_;  **P0_m_** #  Matrix P0_m_;  **A_** #  Matrix A_;  **B_** #  Matrix B_;  **g_** #  Vector g_;  **Q_** #  Matrix Q_;  **Q_m_** #  Matrix Q_m_;  **C_** #  Matrix C_;  **d_** #  Vector d_;  **Ke_** #  Matrix Ke_;  **Ke_m_** #  Matrix Ke_m_;   Updated on 4 May 2022 at 16:57:50 Eastern Daylight Time\n"},{"id":48,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/","title":"lds::UniformMatrixList","section":"Classes","content":"lds::UniformMatrixList #  More\u0026hellip;\nInherits from std::vector\u0026lt; Matrix \u0026gt;\nPublic Functions #      Name      UniformMatrixList() =default\nConstructs a new UniformMatrixList.    UniformMatrixList(const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent.    UniformMatrixList(std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent.    UniformMatrixList(std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent.    UniformMatrixList(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nConstructs a new UniformMatrixList (copy).    UniformMatrixList(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nConstructs a new UniformMatrixList (move).    ~UniformMatrixList() =default\nDestroys the object.   const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim(size_t n =0) const\ngets dimensions of uniformly sized matrices   size_t size()\nsize of container   const Matrix \u0026amp; at(size_t n)\ngets reference to n^th element   void Swap(Matrix \u0026amp; that, size_t n)\nswaps input matrix with n^th matrix of list   UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nassigns the contents (copy)   UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nassigns the contents (move)    Detailed Description #  template \u0026lt;MatrixListFreeDim D =kMatFreeDimNone\u0026gt; class lds::UniformMatrixList;   Public Function Details #  UniformMatrixList #  UniformMatrixList() =default  UniformMatrixList #  explicit UniformMatrixList( const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\n mats input matrices dim dimensions   UniformMatrixList #  explicit UniformMatrixList( std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\n mats input matrices dim dimensions   UniformMatrixList #  UniformMatrixList( std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\n mats input matrices dim dimensions   UniformMatrixList #  UniformMatrixList( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\n that another UniformMatrixList   UniformMatrixList #  UniformMatrixList( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\n that another UniformMatrixList   ~UniformMatrixList #  ~UniformMatrixList() =default  dim #  inline const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim( size_t n =0 ) const Parameters:\n n [optional] index in list of matrices  Return: dimensions\n size #  inline size_t size()  at #  inline const Matrix \u0026amp; at( size_t n )  Swap #  inline void Swap( Matrix \u0026amp; that, size_t n ) Parameters:\n that input matrix n index where the matrix is moved   operator= #  inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\n that another UniformMatrixList  Return: reference to object\n operator= #  inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\n that another UniformMatrixList  Return: reference to object\n  Updated on 4 May 2022 at 16:57:50 Eastern Daylight Time\n"},{"id":49,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/","title":"lds::UniformSystemList","section":"Classes","content":"lds::UniformSystemList #  More\u0026hellip;\nInherits from std::vector\u0026lt; System \u0026gt;\nPublic Functions #      Name      UniformSystemList() =default\nConstructs a new UniformSystemList.    UniformSystemList(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by copying existing vector of System if dimensions consistent.    UniformSystemList(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by moving existing vector of System if dimensions consistent.    UniformSystemList(std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList from initializer_list of System if dimensions consistent.    UniformSystemList(const UniformSystemList \u0026amp; that)\nConstructs a new UniformSystemList (copy).    UniformSystemList(UniformSystemList \u0026amp;\u0026amp; that)\nConstructs a new UniformSystemList (move).    ~UniformSystemList() =default\nDestroys the object.   const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const\ngets dimensions of the uniformly sized systems   size_t size()\nsize of container   const System \u0026amp; at(size_t n)\ngets reference to n^th element   void Swap(System \u0026amp; that, size_t n)\nswaps input system with n^th system of list   UniformSystemList \u0026amp; operator=(const UniformSystemList \u0026amp; that)\nassigns the contents (copy)   UniformSystemList \u0026amp; operator=(UniformSystemList \u0026amp;\u0026amp; that)\nassigns the contents (move)    Detailed Description #  template \u0026lt;typename System\u0026gt; class lds::UniformSystemList;   Public Function Details #  UniformSystemList #  UniformSystemList() =default  UniformSystemList #  explicit UniformSystemList( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\n systems input systems dim dimensions (n_u, n_x, n_y)   UniformSystemList #  explicit UniformSystemList( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\n systems input systems dim dimensions (n_u, n_x, n_y)   UniformSystemList #  UniformSystemList( std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\n systems input systems dim dimensions (n_u, n_x, n_y)   UniformSystemList #  UniformSystemList( const UniformSystemList \u0026amp; that ) Parameters:\n that another UniformSystemList   UniformSystemList #  UniformSystemList( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\n that another UniformSystemList   ~UniformSystemList #  ~UniformSystemList() =default  dim #  inline const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const  size #  inline size_t size()  at #  inline const System \u0026amp; at( size_t n )  Swap #  inline void Swap( System \u0026amp; that, size_t n ) Parameters:\n that input system n index where the system is moved   operator= #  inline UniformSystemList \u0026amp; operator=( const UniformSystemList \u0026amp; that ) Parameters:\n that another UniformSystemList  Return: reference to object\n operator= #  inline UniformSystemList \u0026amp; operator=( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\n that another UniformSystemList  Return: reference to object\n  Updated on 4 May 2022 at 16:57:50 Eastern Daylight Time\n"},{"id":50,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/","title":"lds::UniformVectorList","section":"Classes","content":"lds::UniformVectorList #  Inherits from std::vector\u0026lt; Vector \u0026gt;\nPublic Functions #      Name      UniformVectorList() =default\nConstructs a new UniformVectorList.    UniformVectorList(const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent.    UniformVectorList(std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent.    UniformVectorList(std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0)\nConstructs a new UniformVectorList from initializer_list of Vector if dimensions consistent.    UniformVectorList(const UniformVectorList \u0026amp; that)\nConstructs a new UniformVectorList (copy)    UniformVectorList(UniformVectorList \u0026amp;\u0026amp; that)\nConstructs a new UniformVectorList (move)    ~UniformVectorList() =default\nDestroys the object.   size_t dim() const\ngets dimensions of the uniformly sized matrices   size_t size()\nsize of container   const Vector \u0026amp; at(size_t n)\ngets reference to n^th element   void Swap(Vector \u0026amp; that, size_t n)\nswaps input matrix with n^th vector of list   UniformVectorList \u0026amp; operator=(const UniformVectorList \u0026amp; that)\nassigns the contents (copy)   UniformVectorList \u0026amp; operator=(UniformVectorList \u0026amp;\u0026amp; that)\nassigns the contents (move)      Public Function Details #  UniformVectorList #  UniformVectorList() =default  UniformVectorList #  explicit UniformVectorList( const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0 ) Parameters:\n vecs input vectors dims dimension   UniformVectorList #  explicit UniformVectorList( std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0 ) Parameters:\n vecs input vectors dim dimension   UniformVectorList #  UniformVectorList( std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0 ) Parameters:\n vecs input vectors dim dimension   UniformVectorList #  UniformVectorList( const UniformVectorList \u0026amp; that ) Parameters:\n that another UniformVectorList   UniformVectorList #  UniformVectorList( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\n that another UniformVectorList   ~UniformVectorList #  ~UniformVectorList() =default  dim #  inline size_t dim() const  size #  inline size_t size()  at #  inline const Vector \u0026amp; at( size_t n )  Swap #  inline void Swap( Vector \u0026amp; that, size_t n ) Parameters:\n that input vector n index where the vector is moved   operator= #  inline UniformVectorList \u0026amp; operator=( const UniformVectorList \u0026amp; that ) Parameters:\n that another UniformVectorList  Return: reference to object\n operator= #  inline UniformVectorList \u0026amp; operator=( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\n that another UniformVectorList  Return: reference to object\n  Updated on 4 May 2022 at 16:57:50 Eastern Daylight Time\n"},{"id":51,"href":"/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/","title":"ldsCtrlEst_h","section":"Files","content":"ldsCtrlEst_h #  Files #     Name     ldsCtrlEst_h/lds.h lds namespace   ldsCtrlEst_h/lds_ctrl.h Controller.   ldsCtrlEst_h/lds_fit.h LDS base fit type.   ldsCtrlEst_h/lds_fit_em.h subspace identification   ldsCtrlEst_h/lds_fit_ssid.h subspace identification   ldsCtrlEst_h/lds_gaussian.h glds namespace   ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.   ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.   ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.   ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.   ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.   ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.   ldsCtrlEst_h/lds_poisson.h plds namespace   ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.   ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.   ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.   ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.   ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.   ldsCtrlEst_h/lds_poisson_sys.h PLDS base type.   ldsCtrlEst_h/lds_sctrl.h SwitchedController type.   ldsCtrlEst_h/lds_sys.h LDS base type.   ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.   ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.   ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.   ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)   ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)     Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":52,"href":"/lds-ctrl-est/docs/api/files/lds_8h/","title":"ldsCtrlEst_h/lds.h","section":"Files","content":"ldsCtrlEst_h/lds.h #  lds namespace More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Detailed Description #  This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)) observations.\nSource code #  //===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_H #define LDSCTRLEST_LDS_H  // #ifndef LDSCTRLEST // #include \u0026lt;ldsCtrlEst\u0026gt; // #endif  #include \u0026lt;armadillo\u0026gt; namespace lds { using data_t = double; // may change to float (but breaks mex functions) using Vector = arma::Col\u0026lt;data_t\u0026gt;; using Matrix = arma::Mat\u0026lt;data_t\u0026gt;; using Cube = arma::Cube\u0026lt;data_t\u0026gt;; using View = arma::subview\u0026lt;data_t\u0026gt;; namespace fill = arma::fill; static const std::size_t kControlTypeDeltaU = 0x1; static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); static const data_t kPi = arma::datum::pi; static const data_t kDefaultP0 = 1e-6; static const data_t kDefaultQ0 = 1e-6; static const data_t kDefaultR0 = 1e-2; enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA }; enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2 }; // TODO(mfbolus): for SwitchedController, may want systems to have differing // numbers of states. Use this enum as template parameter? // enum SystemListFreeDim { // kSysFreeDimNone, // kSysFreeDimX ///\u0026lt; allow state dim (x) of systems in list to be hetero // };  // place hard limits on contents of vecors/mats void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub); void Limit(Vector\u0026amp; x, data_t lb, data_t ub); void Limit(Matrix\u0026amp; x, data_t lb, data_t ub); // in-place assign that errs if there are dimension mismatches: void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); // TODO(mfbolus): this is a fudge, but for some reason, cov mats often going // numerically asymm.  void ForceSymPD(Matrix\u0026amp; X); void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min = 0); void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X); Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B); inline void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Vector\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Matrix\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions  if (other.n_elem != some.n_elem) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vector of size \u0026#34; \u0026lt;\u0026lt; some.n_elem \u0026lt;\u0026lt; \u0026#34; with vector of size \u0026#34; \u0026lt;\u0026lt; other.n_elem \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } inline void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions  if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrix of size \u0026#34; \u0026lt;\u0026lt; some.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; some.n_cols \u0026lt;\u0026lt; \u0026#34; with matrix of size \u0026#34; \u0026lt;\u0026lt; other.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other.n_cols \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } } // namespace lds  #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":53,"href":"/lds-ctrl-est/docs/api/files/lds__ctrl_8h/","title":"ldsCtrlEst_h/lds_ctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_ctrl.h #  Controller. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::Controller    Detailed Description #  This file declares the type for control of a linear dynamical system (lds::Controller).\nSource code #  //===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_CTRL_H #define LDSCTRLEST_LDS_CTRL_H  // namespace #include \u0026#34;lds.h\u0026#34;// system type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class Controller { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); public: Controller() = default; Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); const Vector\u0026amp; Control(const Vector\u0026amp; z, bool do_control = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); const Vector\u0026amp; ControlOutputReference(const Vector\u0026amp; z, bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); // get methods:  const System\u0026amp; sys() const { return sys_; }; const Matrix\u0026amp; Kc() const { return Kc_; }; const Matrix\u0026amp; Kc_inty() const { return Kc_inty_; }; const Matrix\u0026amp; Kc_u() const { return Kc_u_; }; const Vector\u0026amp; g_design() const { return g_design_; }; const Vector\u0026amp; u_ref() const { return u_ref_; }; const Vector\u0026amp; x_ref() const { return x_ref_; }; const Vector\u0026amp; y_ref() const { return y_ref_; }; size_t control_type() const { return control_type_; }; data_t tau_awu() const { return tau_awu_; }; data_t u_lb() const { return u_lb_; }; data_t u_ub() const { return u_ub_; }; // set methods  void set_sys(const System\u0026amp; sys) { bool does_match = sys_.n_u() == sys.n_u(); does_match = does_match \u0026amp;\u0026amp; (sys_.n_x() == sys.n_x()); does_match = does_match \u0026amp;\u0026amp; (sys_.n_y() == sys.n_y()); if (does_match) { sys_ = sys; } else { throw std::runtime_error( \u0026#34;new system argument to `set_sys` does not match dimensionality of \u0026#34; \u0026#34;existing system\u0026#34;); } }; void set_g_design(const Vector\u0026amp; g_design) { Reassign(g_design_, g_design); }; void set_u_ref(const Vector\u0026amp; u_ref) { Reassign(u_ref_, u_ref); }; void set_x_ref(const Vector\u0026amp; x_ref) { Reassign(x_ref_, x_ref); cx_ref_ = sys_.C() * x_ref_; }; // y_ref needs to be handled differently depending on output fn.  // (need to populate cx_ref_ too, which depends on output fn)  virtual void set_y_ref(const Vector\u0026amp; y_ref) { Reassign(y_ref_, y_ref); }; void set_Kc(const Matrix\u0026amp; Kc) { Reassign(Kc_, Kc); }; void set_Kc_inty(const Matrix\u0026amp; Kc_inty) { Reassign(Kc_inty_, Kc_inty); }; void set_Kc_u(const Matrix\u0026amp; Kc_u) { Reassign(Kc_u_, Kc_u); }; void set_tau_awu(data_t tau) { tau_awu_ = tau; k_awu_ = sys_.dt() / tau_awu_; }; void set_control_type(size_t control_type); // There is no reason u_lb/ub should not be public, but making set methods  // anyway.  void set_u_lb(data_t u_lb) { u_lb_ = u_lb; }; void set_u_ub(data_t u_ub) { u_ub_ = u_ub; }; void Reset() { sys_.Reset(); u_ref_.zeros(); u_ref_prev_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); u_saturated_ = false; t_since_control_onset_ = 0.0; }; void Print() { sys_.Print(); std::cout \u0026lt;\u0026lt; \u0026#34;g_design : \u0026#34; \u0026lt;\u0026lt; g_design_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_lb : \u0026#34; \u0026lt;\u0026lt; u_lb_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_ub : \u0026#34; \u0026lt;\u0026lt; u_ub_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }; protected: System sys_; Vector u_; Vector u_return_; Vector g_design_; // reference signals  Vector u_ref_; // create no set method for this:  Vector u_ref_prev_; Vector x_ref_; Vector y_ref_; Vector cx_ref_; // Controller gains  Matrix Kc_; Matrix Kc_u_; Matrix Kc_inty_; // control after g inversion  // do not need set methods for these.  Vector du_ref_; Vector dv_ref_; Vector v_ref_; Vector dv_; Vector v_; // integral error  // do not need set method for this  Vector int_e_; Vector int_e_awu_adjust_; Vector u_sat_; bool do_control_prev_ = false; bool do_lock_control_prev_ = false; // whether the g of system has become inverted from what you think it is  // (gain_ref)  bool u_saturated_ = false; // should be safe to have references here bc nothing needs to be done  // (like reset vars) when it changes...  data_t u_lb_{}; data_t u_ub_{}; data_t tau_awu_{}; data_t k_awu_ = 0; data_t t_since_control_onset_ = 0; size_t control_type_{}; private: void CalcControl(bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); void CalcSteadyStateSetPoint(); void AntiWindup(); void InitVars(size_t control_type); }; // Implement the above:  template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(sys), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(std::move(sys)), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::set_control_type(size_t control_type) { if (control_type_ == control_type) { return; } // creating a blank slate...  control_type_ = 0; Kc_inty_.zeros(0, 0); Kc_u_.zeros(0, 0); int_e_.zeros(0, 0); int_e_awu_adjust_.zeros(0, 0); // controller was designed to minimize integral error  if (control_type \u0026amp; kControlTypeIntY) { Kc_inty_.zeros(sys_.n_u(), sys_.n_y()); int_e_.zeros(sys_.n_y()); int_e_awu_adjust_.zeros(sys_.n_u()); control_type_ = control_type_ | kControlTypeIntY; } // controller was designed to minimize deltaU  // (i.e. state augmented with u)  if (control_type \u0026amp; kControlTypeDeltaU) { Kc_u_.zeros(sys_.n_u(), sys_.n_u()); control_type_ = control_type_ | kControlTypeDeltaU; } // whether to adapt set point calculate with (re-estimated) process  // disturbance (m)  if (control_type \u0026amp; kControlTypeAdaptM) { if (sys_.do_adapt_m) // only if adapting m...  { control_type_ = control_type_ | kControlTypeAdaptM; } } } // set_control_type  template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::Control( const Vector\u0026amp; z, bool do_control, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement  sys_.Filter(u_, z); bool do_estimation = true; // always have estimator on in this case  // calculate control signal  CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::ControlOutputReference( const Vector\u0026amp; z, bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement  if (do_estimation) { sys_.Filter(u_, z); } else { sys_.f(u_); } // calculate the set point  // solves for u_ref and x_ref when output is at y_ref at steady state.  if (do_control) { CalcSteadyStateSetPoint(); } // calculate control signal  CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcControl(bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { if (do_control \u0026amp;\u0026amp; do_estimation) { if (!do_control_prev_) { if (do_reset_at_control_onset) { Reset(); } t_since_control_onset_ = 0.0; } else { t_since_control_onset_ += sys_.dt(); } // enforce softstart on control vars.  if (sigma_soft_start \u0026gt; 0) { // half-Gaussian soft-start scaling factor  data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) / (2 * pow(sigma_soft_start, 2))); u_ref_ *= soft_start_sf; // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too  // x_ref_ *= soft_start_sf;  // cx_ref_ *= soft_start_sf;  // y_ref_ *= soft_start_sf;  } if (!do_lock_control) { // first do u -\u0026gt; v change of vars. (v = g.*u)  // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver  // control voltage u[=]V)  v_ref_ = g_design_ % u_ref_; // Given FB, calc. the change in control  if (control_type_ \u0026amp; kControlTypeDeltaU) { // if control designed to minimize not u but deltaU (i.e. state aug with  // u):  // TODO(mfbolus): Commented out for now. See note below.  // du_ref_ = u_ref_ - u_ref_prev_;  // dv_ref_ = g_design_ % du_ref_;  // TODO(mfbolus): Assuming users want *smooth* control signals if using  // kControlTypeDeltaU, it should be the case that dv_ref_ is --\u0026gt; 0. May  // want to revisit, but I am going to force it to be zero unless a  // situation arises that argues for keeping the above.  dv_ref_.zeros(); dv_ = dv_ref_; // nominally-optimal.  dv_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error  dv_ -= Kc_u_ * (v_ - v_ref_); // penalty on amp u (rel to ref)  if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup  // would be to not integrate error when control signal saturated:  // if(!uSaturated)  int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error  dv_ -= Kc_inty_ * int_e_; // control for integrated error  } // update the control  v_ += dv_; } else { v_ = v_ref_; // nominally-optimal.  v_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error  if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup  // would be to not integrate error when control signal saturated:  // if (!uSaturated)  int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error  v_ -= Kc_inty_ * int_e_; // control for integrated error  } } // convert back to control voltage u[=]V  u_ = v_ / sys_.g(); } // else do nothing until lock is low  } else { // if not control  // feed through u_ref in open loop  u_ = u_ref_ % g_design_ / sys_.g(); v_ = sys_.g() % u_; u_ref_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); } // ends do_control  // enforce box constraints (and antiwindup)  AntiWindup(); // add noise to input?  // The value for u that is *returned* to user after addition of any noise,  // while keeping controller/estimator blind to this addition.  u_return_ = u_; if ((sigma_u_noise \u0026gt; 0.0) \u0026amp;\u0026amp; (do_control \u0026amp;\u0026amp; !do_lock_control)) { u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn); Limit(u_return_, u_lb_, u_ub_); }; // For next time step:  u_ref_prev_ = u_ref_; do_control_prev_ = do_control; do_lock_control_prev_ = do_lock_control; } // CalcControl  template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcSteadyStateSetPoint() { // Linearly-constrained least squares (ls).  //  // _reference:  // Boyd \u0026amp; Vandenberghe (2018) Introduction to Applied Linear Algebra  //  Matrix a_ls = join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros)); Vector b_ls = cx_ref_; Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye), sys_.B() * arma::diagmat(sys_.g())); Vector d_ls = -sys_.m0(); if (control_type_ \u0026amp; kControlTypeAdaptM) { d_ls = -sys_.m(); // adapt setpoint calc with disturbance?  } Matrix a_ls_t = a_ls.t(); // TODO(mfbolus): not sure why but causes seg  // fault if I do not do this.  Matrix phi_ls = join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()), join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros))); // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse:  Matrix inv_phi = pinv(phi_ls); Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls); x_ref_ = xulam.subvec(0, sys_.n_x() - 1); u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1); cx_ref_ = sys_.C() * x_ref_; } // CalcSteadyStateSetPoint  template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::AntiWindup() { u_saturated_ = false; u_sat_ = u_; // limit u and flag whether saturated  for (size_t k = 0; k \u0026lt; u_.n_elem; k++) { if (u_[k] \u0026lt; u_lb_) { u_sat_[k] = u_lb_; u_saturated_ = true; } if (u_[k] \u0026gt; u_ub_) { u_sat_[k] = u_ub_; u_saturated_ = true; } } if ((control_type_ \u0026amp; kControlTypeIntY) \u0026amp;\u0026amp; (tau_awu_ \u0026lt; lds::kInf)) { // one-step back-calculation (calculate intE for u=u_sat)  // (Astroem, Rundqwist 1989 warn against using this...)  // int_e_awu_adjust_ =  // solve(Kc_inty_, (u_ - u_sat_)); // pinv(Kc_inty) * (u-uSat);  // gradual: see Astroem, Rundqwist 1989  // this is a fudge for doing MIMO gradual  // n.b., went ahead and multiplied 1/T by dt so don\u0026#39;t have to do that here.  int_e_awu_adjust_ = k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_); // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_);  int_e_ += int_e_awu_adjust_; } // set u to saturated version  u_ = u_sat_; } template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::InitVars(size_t control_type) { // initialize to default values  u_ref_ = Vector(sys_.n_u(), fill::zeros); u_ref_prev_ = Vector(sys_.n_u(), fill::zeros); x_ref_ = Vector(sys_.n_x(), fill::zeros); y_ref_ = Vector(sys_.n_y(), fill::zeros); cx_ref_ = Vector(sys_.n_y(), fill::zeros); u_ = Vector(sys_.n_u(), fill::zeros); u_return_ = Vector(sys_.n_u(), fill::zeros); u_sat_ = Vector(sys_.n_u(), fill::zeros); // Might not need all these, so zero elements until later.  Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros); Kc_u_ = Matrix(0, 0, fill::zeros); Kc_inty_ = Matrix(0, 0, fill::zeros); g_design_ = sys_.g(); // by default, same as model  dv_ = Vector(sys_.n_u(), fill::zeros); v_ = Vector(sys_.n_u(), fill::zeros); du_ref_ = Vector(sys_.n_u(), fill::zeros); dv_ref_ = Vector(sys_.n_u(), fill::zeros); v_ref_ = Vector(sys_.n_u(), fill::zeros); int_e_ = Vector(0, fill::zeros); int_e_awu_adjust_ = Vector(0, fill::zeros); set_control_type(control_type); } } // namespace lds  #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":54,"href":"/lds-ctrl-est/docs/api/files/lds__fit_8h/","title":"ldsCtrlEst_h/lds_fit.h","section":"Files","content":"ldsCtrlEst_h/lds_fit.h #  LDS base fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::Fit LDS Fit Type.    Detailed Description #  This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.\nSource code #  //===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDS_FIT_HPP #define LDS_FIT_HPP  // namespace #include \u0026#34;lds.h\u0026#34;#include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); virtual ~Fit() = default; // get methods  size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; Q() const { return Q_; }; const Vector\u0026amp; x0() const { return x0_; }; const Matrix\u0026amp; P0() const { return P0_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; // gets measurement noise  virtual const Matrix\u0026amp; R() const = 0; // set methods (e.g., seeding initial fit values)  void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_m(const Vector\u0026amp; m) { Reassign(m_, m); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); ForceSymPD(Q_); }; virtual void set_R(const Matrix\u0026amp; R) = 0; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); ForceSymPD(P0_); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; View f(Matrix\u0026amp; x, const Matrix\u0026amp; u, size_t t) { x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x.col(t); }; View f(Matrix\u0026amp; x_pre, const Matrix\u0026amp; x_post, const Matrix\u0026amp; u, size_t t) { x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x_pre.col(t); }; virtual View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) = 0; protected: data_t dt_{}; // Dynamics  Matrix A_; Matrix B_; Vector g_; Vector m_; Matrix Q_; // Output  Matrix C_; Vector d_; Matrix R_; // initial conditions  Vector x0_; Matrix P0_; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; }; } // namespace lds #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":55,"href":"/lds-ctrl-est/docs/api/files/lds__fit__em_8h/","title":"ldsCtrlEst_h/lds_fit_em.h","section":"Files","content":"ldsCtrlEst_h/lds_fit_em.h #  subspace identification More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::EM    Detailed Description #  This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).\nSource code #  //===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_EMAX_H #define LDSCTRLEST_LDS_EMAX_H  #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class EM { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: EM() = default; EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); virtual ~EM() = default; const Fit\u0026amp; Run(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = true, bool calc_output = true, bool calc_measurement = true, size_t max_iter = 100, data_t tol = 1e-2); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); // auto tuple = std::make_tuple(u_, z_);  u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; x() const { return x_; }; const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; y() const { return y_; }; const Matrix\u0026amp; sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; }; const Matrix\u0026amp; sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; }; const Matrix\u0026amp; sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; }; size_t n_t_tot() { return n_t_tot_; } const Vector\u0026amp; theta() const { return theta_; }; protected: void Expectation(bool force_common_initial = false); void Maximization(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = false, bool calc_output = false, bool calc_measurement = false); void MaximizeDynamics(); void MaximizeQ(); void MaximizeInitial(); virtual void MaximizeOutput() = 0; virtual void MaximizeMeasurement() = 0; void Smooth(bool force_common_initial); virtual void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) = 0; void Reset(); void InitVars(); Vector UpdateTheta(); // input/output training data  UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; std::vector\u0026lt;Matrix\u0026gt; x_; std::vector\u0026lt;Cube\u0026gt; P_; std::vector\u0026lt;Cube\u0026gt; P_t_tm1_; std::vector\u0026lt;Matrix\u0026gt; y_; Matrix diag_y_; // expectations calculated in E-step  Matrix sum_E_x_t_x_t_; Matrix sum_E_xu_tm1_xu_tm1_; Matrix sum_E_xu_t_xu_tm1_; Fit fit_; Vector theta_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; }; template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; fit_ = Fit(n_u_, n_x, n_y_, dt); u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { // make sure fit dims match I/O data  if (fit0.n_u() != u_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and input training data have inconsistent dimensions\u0026#34;); } if (fit0.n_y() != z_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and output training data have inconsistent dimensions\u0026#34;); } fit_ = fit0; u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::InitVars() { // check input/output data dimensions are consistent  if (z_.size() != u_.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_.at(trial).n_cols != u_.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } n_u_ = fit_.n_u(); n_x_ = fit_.n_x(); n_y_ = fit_.n_y(); dt_ = fit_.dt(); x_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); P_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); P_t_tm1_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); y_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros); P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros); } diag_y_ = Matrix(n_y_, n_y_, fill::zeros); // covariances in expectation step  sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); } template \u0026lt;typename Fit\u0026gt; const Fit\u0026amp; EM\u0026lt;Fit\u0026gt;::Run(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement, size_t max_iter, data_t tol) { Reset(); // to initial conditions  size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_; Vector theta(n_params); Vector theta_new(n_params); data_t max_dtheta = 1; // if solving for initial conditions, allow them be varied.  // otherwise, freeze at provided values.  bool force_common_initial = !calc_init; // go until parameter convergence  for (size_t l = 0; l \u0026lt; max_iter; l++) { theta_ = UpdateTheta(); std::cout \u0026lt;\u0026lt; \u0026#34;Iteration \u0026#34; \u0026lt;\u0026lt; l + 1 \u0026lt;\u0026lt; \u0026#34;/\u0026#34; \u0026lt;\u0026lt; max_iter \u0026lt;\u0026lt; \u0026#34; ...\\n\u0026#34;; Expectation(force_common_initial); Maximization(calc_dynamics, calc_Q, calc_init, calc_output, calc_measurement); // check convergence  theta_new = UpdateTheta(); Vector dtheta = abs(theta_new - theta_) / abs(theta_); // some parameters could be zero...  arma::uvec ubi_finite = find_finite(dtheta); max_dtheta = max(dtheta.elem(ubi_finite)); std::cout \u0026lt;\u0026lt; \u0026#34;max dtheta: \u0026#34; \u0026lt;\u0026lt; max_dtheta \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; if (max_dtheta \u0026lt; tol) { std::cout \u0026lt;\u0026lt; \u0026#34;Converged.\\n\u0026#34;; break; } std::cout \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } return fit_; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Smooth(bool force_common_initial) { Matrix k_e(n_x_, n_y_); // estimator gain  Cube k_backfilt; // back-filtering gains  // TODO(mfbolus): this loop could be made parallel  for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { Matrix x_pre(n_x_, n_t_[trial], fill::zeros); Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros); Matrix x_post(n_x_, n_t_[trial], fill::zeros); Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros); if (force_common_initial) // forces all trials to have same initial  // conditions.  { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); } y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); // This *should not* be necessary but make sure P is symmetric.  ForceSymPD(P_[trial].slice(0)); x_pre.col(0) = x_[trial].col(0); p_pre.slice(0) = P_[trial].slice(0); x_post.col(0) = x_[trial].col(0); p_post.slice(0) = P_[trial].slice(0); // filter  for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // predict  fit_.f(x_pre, x_post, u_.at(trial), t); fit_.h(y_[trial], x_pre, t); diag_y_.diag() = y_[trial].col(t); // TODO(mfbolus): change if parallel  // update --\u0026gt; posterior estimation  RecurseKe(k_e, p_pre, p_post, t); x_post.col(t) = x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t)); y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d(); } // backfilter -\u0026gt; Smoothed estimate  // Reference:  // Shumway et Stoffer (1982)  ForceSymPD(p_post.slice(n_t_[trial] - 1)); k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1); P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 0; t--) { // TODO(mfmbolus): should not be necessary to force symm positive def  ForceSymPD(p_pre.slice(t)); ForceSymPD(p_post.slice(t - 1)); ForceSymPD(P_[trial].slice(t)); k_backfilt.slice(t - 1) = p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t)); x_[trial].col(t - 1) = x_post.col(t - 1) + k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t)); P_[trial].slice(t - 1) = p_post.slice(t - 1) + k_backfilt.slice(t - 1) * (P_[trial].slice(t) - p_pre.slice(t)) * k_backfilt.slice(t - 1).t(); } // do the same for P_t_tm1  Matrix id(n_x_, n_x_, fill::eye); P_t_tm1_[trial].slice(n_t_[trial] - 1) = (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 1; t--) { P_t_tm1_[trial].slice(t - 1) = p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() + k_backfilt.slice(t - 1) * (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) * k_backfilt.slice(t - 2).t(); } // finally, get smoothed estimate of output  for (size_t t = 0; t \u0026lt; n_t_[trial]; t++) { fit_.h(y_[trial], x_[trial], t); } // samps loop  } // trial loop } // Smooth  // template \u0026lt;typename Fit\u0026gt; // void EM\u0026lt;Fit\u0026gt;::RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) { // // predict covar // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q();  // // update Ke // Ke = P_pre.slice(t) * fit_.C().t() * // inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R());  // // update cov // // Reference: Ghahramani et Hinton (1996) // P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t);  // // // n.b. for poisson : // // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + // fit_.Q(); // // // update cov // // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ * // // fit_.C()); // // // update Ke // // Ke = P_post.slice(t) * fit_.C(); // }  template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Expectation(bool force_common_initial) { // calculate the mean/cov of state needed for maximizing E[pr(z|theta)]  Smooth(force_common_initial); // now get the various forms of sum(E[xx\u0026#39;]) needed  // n.b. Going to start at t=1 rather than 0 bc most max terms need that.  // so really \u0026#34;n_t_tot_\u0026#34; is (n_t_tot_-1)  n_t_tot_ = 0; sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); Vector xu_tm1(n_x_ + n_u_, fill::zeros); Vector xu_t(n_x_ + n_u_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // ------------ sum_E_x_t_x_t ------------  sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t(); sum_E_x_t_x_t_ += P_[trial].slice(t); // ------------ sum_E_xu_tm1_xu_tm1 ------------  xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1)); sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t(); sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t - 1); // ------------ sum_E_xu_t_xu_tm1 ------------  xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t)); sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t(); sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_t_tm1_[trial].slice(t); n_t_tot_ += 1; } // time  } // trial } // Expectation  template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement) { if (calc_output) { MaximizeOutput(); } if (calc_measurement) { MaximizeMeasurement(); } if (calc_dynamics) { MaximizeDynamics(); } if (calc_Q) { MaximizeQ(); } if (calc_init) { MaximizeInitial(); } } // Maximization  template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeDynamics() { // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996)  Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) * inv_sympd(sum_E_xu_tm1_xu_tm1_); fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1)); std::cout \u0026lt;\u0026lt; \u0026#34;A_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.A()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.B()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeQ() { // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996)  // View sum_e_x_t_xu_tm1 =  // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1);  // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 *  // inv_sympd(sum_E_xu_tm1_xu_tm1_) *  // sum_e_x_t_xu_tm1.t();  // q /= n_t_tot_;  // this way is same as above iff dynamics were just updated:  // View sum_e_x_t_xu_tm1 =  // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1);  // Matrix ab = arma::join_horiz(fit_.A(), fit_.B());  // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t();  // q /= n_t_tot_;  // From scratch method:  // Q is covariance of the error between state and dynamics-predicted state  // (aka process noise)  // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})\u0026#39;]  // t-1 terms:  View sum_e_x_tm1_x_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_u_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1); View sum_e_x_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); // t, t-1 terms:  View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_x_t_u_tm1 = sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); Matrix q = sum_E_x_t_x_t_; q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t(); q -= sum_e_x_t_x_tm1 * fit_.A().t(); q -= fit_.A() * sum_e_x_t_x_tm1.t(); // input-related terms:  q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t(); q -= sum_e_x_t_u_tm1 * fit_.B().t(); q -= fit_.B() * sum_e_x_t_u_tm1.t(); q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t(); q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t(); q /= n_t_tot_; fit_.set_Q(q); std::cout \u0026lt;\u0026lt; \u0026#34;Q_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.Q()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // std::cout \u0026lt;\u0026lt; \u0026#34;Q_new: \\n\u0026#34; \u0026lt;\u0026lt; fit_.Q() \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeInitial() { Vector x0 = fit_.x0(); x0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { x0 += x_[trial].col(0); } x0 /= z_.size(); std::cout \u0026lt;\u0026lt; \u0026#34;x0_new[0]: \u0026#34; \u0026lt;\u0026lt; x0[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // always recalc P0 even if the initial state is fixed (at zero, for  // example)  Matrix e_var(n_x_, n_x_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t(); } e_var /= z_.size(); // go ahead and subtract x0*x0\u0026#39; so don\u0026#39;t have to below.  e_var -= x0 * x0.t(); // To get P0, going to get initial P_ per trial and average.  // (which might be wrong, but need a single number)  Matrix p0 = fit_.P0(); p0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { p0 += (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var; } p0 /= z_.size(); fit_.set_P0(p0); std::cout \u0026lt;\u0026lt; \u0026#34;P0_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.P0()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeOutput() { // solve for C+d:  Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros); Vector x1(n_x_ + 1, fill::zeros); x1[n_x_] = 1.0; // augment with one to solve for bias  Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { x1.subvec(0, n_x_ - 1) = x_[trial].col(t); sum_zx += z_.at(trial).col(t) * x1.t(); sum_e_x1_x1 += x1 * x1.t(); sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t); } } Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1); fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1)); fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_))); std::cout \u0026lt;\u0026lt; \u0026#34;C_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.C()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.d()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeMeasurement() { // Solve for measurement noise covar  size_t n_t_tot = 0; // Ghahgramani, Hinton 1996:  Matrix sum_zz(n_y_, n_y_, fill::zeros); Matrix sum_yz(n_y_, n_y_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t(); // Use Cnew:  sum_yz += (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t(); n_t_tot += 1; } } fit_.set_R((sum_zz - sum_yz) / n_t_tot); std::cout \u0026lt;\u0026lt; \u0026#34;R_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.R()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Reset() { // reset to initial conditions  for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); } } template \u0026lt;typename Fit\u0026gt; Vector EM\u0026lt;Fit\u0026gt;::UpdateTheta() { // TODO(mfbolus): This should include n_y_ more params for d.  size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_; if (fit_.R().n_elem \u0026gt; 0) { n_params += n_y_ * n_y_; } Vector theta(n_params); size_t idx_start = 0; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B()); idx_start += n_x_ * n_u_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0()); idx_start += n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C()); idx_start += n_y_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d()); idx_start += n_y_; if (fit_.R().n_elem \u0026gt; 0) { theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R()); } return theta; } } // namespace lds  #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":56,"href":"/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_fit_ssid.h","section":"Files","content":"ldsCtrlEst_h/lds_fit_ssid.h #  subspace identification More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::SSID    Detailed Description #  This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)\u0026lt;Fit\u0026gt;).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code #  //===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_FIT_SSID_H #define LDSCTRLEST_LDS_FIT_SSID_H  #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class SSID { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: SSID() = default; SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d = Vector(1).fill(-kInf)); std::tuple\u0026lt;Fit, Vector\u0026gt; Run(SSIDWt ssid_wt); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } protected: void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001); void CreateHankelDataMat(); virtual void DecomposeData() = 0; void CalcSVD(SSIDWt wt); void Solve(data_t wt_dc); void RecomputeExtObs(); // input/output training data  UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; Matrix D_; Fit fit_; Matrix g_dc_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_h_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; Matrix L_; Vector s_; Matrix ext_obs_t_; }; template \u0026lt;typename Fit\u0026gt; SSID\u0026lt;Fit\u0026gt;::SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d) { // check input/output data dimensions are consistent  if (z_train.size() != u_train.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_train.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_train.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } dt_ = dt; n_x_ = n_x; n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; n_h_ = n_h; // dimensionality check for eventual block-hankel data matrix  size_t len = n_t_tot_ - 2 * n_h_ + 1; if (len \u0026lt; (2 * n_h_ * (n_u_ + n_y_))) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;Dataset problem! More rows than columns in block-hankel data \u0026#34; \u0026#34;matrix: 2*(n_u+n_y)*n_h \u0026gt; data-length! Need higher data-length or \u0026#34; \u0026#34;lower n_h.\u0026#34;; throw std::runtime_error(ss.str()); } fit_ = Fit(n_u_, n_x_, n_y_, dt_); u_ = std::move(u_train); z_ = std::move(z_train); if (!d.is_finite() || (d.n_rows != n_y_)) { // TODO(mfbolus): implement least-square solution for impulse response with  // a second input of ones. Data-driven way of accounting for offset *not*  // driven by an input.  //  // For now, calculate output bias (d) as the  // output wherever the stimulus has not been on for some amount of time.  // convolve u with rectangle and take all samples. This is a reasonable  // approach, since often when autonomous systems are fit (i.e., systems with  // no input), they will subtract off the mean of the output. This  // essentially amounts to setting output bias to the mean of the output when  // there is no stimulation.  data_t t_silence = 0.1; data_t thresh_silence = 0.001; CalcD(t_silence, thresh_silence); } else { fit_.set_d(d); } } template \u0026lt;typename Fit\u0026gt; std::tuple\u0026lt;Fit, Vector\u0026gt; SSID\u0026lt;Fit\u0026gt;::Run(SSIDWt ssid_wt) { // the weight on minimizing dc I/O gain only works for gaussian,  // and hopefully not necessary with appropriate dataset.  data_t wt_dc = 0; // std::cout \u0026lt;\u0026lt; \u0026#34;creating hankel mat\\n\u0026#34;;  CreateHankelDataMat(); // std::cout \u0026lt;\u0026lt; \u0026#34;decomposing data\\n\u0026#34;;  DecomposeData(); // std::cout \u0026lt;\u0026lt; \u0026#34;calculating svd\\n\u0026#34;;  CalcSVD(ssid_wt); // std::cout \u0026lt;\u0026lt; \u0026#34;solving for params\\n\u0026#34;;  Solve(wt_dc); // std::cout \u0026lt;\u0026lt; \u0026#34;fin\\n\u0026#34;;  return std::make_tuple(fit_, s_); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcD(data_t t_silence, data_t thresh_silence) { Vector d(z_.at(0).n_rows, fill::zeros); Vector win(static_cast\u0026lt;size_t\u0026gt;(t_silence / dt_), fill::ones); Vector sum_z_silence(n_y_, fill::zeros); size_t n_silence(0); for (size_t trial = 0; trial \u0026lt; u_.size(); trial++) { // find silent samples  // start by convolving with  Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0)); Vector u_conv = conv(sum_u, win, \u0026#34;same\u0026#34;); // get only the samples that are silent...  arma::uvec ubi_silence = find(u_conv \u0026lt;= thresh_silence); if (ubi_silence.n_elem \u0026gt; 0) { sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1); n_silence += ubi_silence.n_elem; } } if (n_silence \u0026gt; 0) { d = sum_z_silence / n_silence; } fit_.set_d(d); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat() { // temporary copy of data  Matrix z(n_y_, n_t_tot_, fill::zeros); Matrix u(n_u_, n_t_tot_, fill::zeros); size_t so_far(0); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial); u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial); so_far += n_t_.at(trial); } // remove output bias  z.each_col() -= fit_.d(); // calculate I/O gain @ DC while data in convenient form  g_dc_ = z * pinv(u); // std::cout \u0026lt;\u0026lt; \u0026#34;G0_data = \u0026#34; \u0026lt;\u0026lt; g_dc_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;;  // create hankel data matrix  size_t len = z.n_cols - 2 * n_h_ + 1; // data length in hankel mat  // block-hankel data matrix  D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros); // past input  auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1); // future input  auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1); // past output  auto y_p = D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1); // future output  auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, len - 1); size_t idx = 0; for (size_t k = 0; k \u0026lt; len; k++) { idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } } D_ /= sqrt(static_cast\u0026lt;data_t\u0026gt;(len)); } // template \u0026lt;typename Fit\u0026gt; // void SSID\u0026lt;Fit\u0026gt;::DecomposeData() { // // do LQ decomp instead of calculating covariance expensive way // // Note that \u0026#34;R\u0026#34; in van Overschee is lower-triangular (L), not \u0026#34;R\u0026#34; in QR // // decomp. Very confusing. // Matrix q_t; // lq(L_, q_t, D_); // // van Overschee zeros out the other elements. // L_ = trimatl(L_); // }  template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcSVD(SSIDWt wt) { // submats that will be needed:  auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1); auto R_23_13 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1); auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_) - 1); auto R_44 = L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14); auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1); auto Luf = Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1); auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); // aka: R_f  Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1); // from van Overschee subid.m:  // Rf = R((2*m+l)*i+1:2*(m+l)*i,:); % Future outputs  Matrix U; Matrix V; switch (wt) { case kSSIDNone: { // No weighting. (what van Overschee calls \u0026#34;N4SID\u0026#34;)  Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14; arma::svd(U, s_, V, O_k_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDMOESP: { // MOESP weighting  // This is what they use in the \u0026#34;robust\u0026#34; algorithm van Overschee, de Moor  // 1996  Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); svd(U, s_, V, O_k_ortho_Uf_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDCVA: { // CVA weighting  // See van Overschee\u0026#39;s matlab code (subid.m):  // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems  Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); Matrix inv_w1; Matrix qt1; lq(inv_w1, qt1, R_56_16); // lq decomp of R_f (future output data)  inv_w1 = trimatl(inv_w1); inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1); Matrix w_o_w = arma::solve( inv_w1, O_k_ortho_Uf_sans_Qt); // alternatively  // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt  svd(U, s_, V, w_o_w, \u0026#34;std\u0026#34;); U = inv_w1 * U; break; } } // Truncate to model order (heart of ssid method)  auto s_hat = s_.subvec(0, n_x_ - 1); Matrix diag_sqrt_s = diagmat(sqrt(s_hat)); auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1); // get extended observability and controllability mats  ext_obs_t_ = u_hat * diag_sqrt_s; // extended observability matrix } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::Solve(data_t wt_dc) { // required submats  auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_56_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); // Solve for params using appropriate algorithm:  // robust deterministic/stochastic algorithm in van Overschee 1996  // algorithm that the authors say \u0026#34;works\u0026#34; in practice.  auto ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix  // This is what textbook (1996) says:  //  // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15);  //  // HOWEVER, do not know why but have to fill the last place with zeros like  // authors\u0026#39; matlab implementation (see `subid.m`)  // Otherwise, get ridiculous covariances (although A,C estimates are close to  // same...)  Matrix Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Matrix S = Tl * pinv(Tr); // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure  // stability.  fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1)); Matrix ext_obs_t_p1 = join_vert( ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1), Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros)); fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1); // At this point, van Overschee \u0026amp; de Moor suggest re-calculating ext_obs_t_,  // ext_obs_tm1 from (A, C) because it was just an approximation. This is  RecomputeExtObs(); ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix  Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); S = Tl * pinv(Tr); Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_); Matrix Mcurly = pinv(ext_obs_tm1); Matrix Pcurly = Tl - Lcurly * R_56_15; Vector Pvec = vectorise(Pcurly); Matrix Qcurly = R_23_15; // Identify [D; B], assuming D=0 and ensuring DC gain is correct  Matrix sum_QcurlyT_kron_Ncurly( (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_), fill::zeros); Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols, fill::eye); eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1, eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1; // van Overschee (1996) p. 126  Matrix N1_Tl = -Lcurly; N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) += join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly); N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) += Matrix(n_y_, n_y_, fill::eye); Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros); Matrix N_k; for (size_t k = 0; k \u0026lt; n_h_; k++) { auto Qcurly_k = Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1); Nk_Tl.zeros(); Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) = N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1); N_k = Nk_Tl * eye_ext_obs_tm1; sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k); } Matrix err_vec; if (wt_dc \u0026gt; 0) { // Constraints enforced by weighted least squares  //  // Reference:  //  // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly  // Excited Industrial Systems. Conference in Decision and Control.  // constraint 1: assume D=0 --\u0026gt; remove the components for Dvec (this is  // actually a hard constraint in that it ignores D)  Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly; sum_QcurlyT_kron_Ncurly = Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_); size_t kkk = 0; for (size_t k = 1; k \u0026lt; (n_u_ + 1); k++) { size_t start_idx = k * (n_y_ + n_x_) - n_x_; for (size_t kk = 0; kk \u0026lt; n_x_; kk++) { sum_QcurlyT_kron_Ncurly.col(kkk) = sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk); kkk++; } } // constraint 2: Make sure DC I/O gain is correct  Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A()); Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_)); Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0); Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 = join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0); // WEIGHTED LS  // Important in practice because I care a lot about at least getting the DC  // gain correct. Put x weight on minimizing error at DC, relative to others  Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye); // Make weight on minimizing DC error immense so at least that  // should be nailed.  size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows; size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows; size_t stop_row = w.n_rows - 1; size_t stop_col = w.n_cols - 1; // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale  // weight with data length?  w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc; Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * sum_QcurlyT_kron_Ncurly_b_to_g0) * sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec; fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_)); // Calculate residuals and their cov.  // Because I\u0026#39;ve added constraints, I need to re-calculate the right term  // with b_vec instead of how van Overschee do in final algorithm.  err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec; } else { // default way: *no* constraint on G0 or D=0  Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec; // TODO(mfbolus) n.b., this gets thrown away...  // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_);  fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_)); err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec; } // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols);  // TODO(mfbolus): Something is wrong with the error calculation above.  // Use the way van overschee does it in `subid.m`  // WARNING: this ignores any above constraints, so Q, R will be approximate...  Matrix err = Tl - S * Tr; Matrix cov_err = err * err.t(); fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1)); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::RecomputeExtObs() { ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C(); for (size_t k = 2; k \u0026lt; (n_h_ + 1); k++) { ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) = ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1, ext_obs_t_.n_cols - 1) * fit_.A(); } } } // namespace lds  #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":57,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian_8h/","title":"ldsCtrlEst_h/lds_gaussian.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian.h #  glds namespace More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Detailed Description #  This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)).\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_H #define LDSCTRLEST_LDS_GAUSSIAN_H  // namespace #include \u0026#34;lds.h\u0026#34; namespace lds { namespace gaussian { // insert any Gaussian-specific things here... } // namespace gaussian } // namespace lds  #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":58,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_ctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_ctrl.h #  GLDS Controller. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::Controller Gaussian-observation Controller Type.    Detailed Description #  This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H  // namespace #include \u0026#34;lds_gaussian.h\u0026#34;// system #include \u0026#34;lds_gaussian_sys.h\u0026#34;// controller #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace gaussian { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); cx_ref_ = y_ref - sys_.d(); }; // make sure base class template methods available  using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; }; } // namespace gaussian } // namespace lds  #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":59,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_fit.h #  GLDS fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::Fit GLDS Fit Type.    Detailed Description #  This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_H  // namespace #include \u0026#34;lds_gaussian.h\u0026#34;// fit type #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace gaussian { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); const Matrix\u0026amp; R() const override { return R_; }; void set_R(const Matrix\u0026amp; R) override { Reassign(R_, R); ForceSymPD(R_); }; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = C_ * x.col(t) + d_; return y.col(t); }; }; }; // namespace gaussian } // namespace lds #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":60,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_em.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_fit_em.h #  GLDS E-M fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::FitEM GLDS E-M Fit Type.    Detailed Description #  This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H  #include \u0026#34;lds_fit_em.h\u0026#34;#include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; }; } // namespace gaussian } // namespace lds  #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":61,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h #  GLDS SSID fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.    Detailed Description #  This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H  #include \u0026#34;lds_fit_ssid.h\u0026#34;#include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; using SSID\u0026lt;Fit\u0026gt;::Run; private: using SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat; using SSID\u0026lt;Fit\u0026gt;::CalcSVD; using SSID\u0026lt;Fit\u0026gt;::Solve; void DecomposeData() override; void SolveVanOverschee(); }; } // namespace gaussian } // namespace lds #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":62,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_sctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_sctrl.h #  GLDS switched controller type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.    Detailed Description #  This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H  // controller type #include \u0026#34;lds_gaussian_ctrl.h\u0026#34;// switched controller #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace gaussian { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); cx_ref_ = y_ref - sys_.d(); } // make sure base class template methods available  using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; }; // SwitchedController } // namespace gaussian } // namespace lds  #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":63,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/","title":"ldsCtrlEst_h/lds_gaussian_sys.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_sys.h #  GLDS base type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::System Gaussian LDS Type.    Detailed Description #  This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)).\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H #define LDSCTRLEST_LDS_GAUSSIAN_SYS_H  // namespace #include \u0026#34;lds_gaussian.h\u0026#34;// system #include \u0026#34;lds_sys.h\u0026#34; namespace lds { namespace gaussian { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0, data_t r0 = kDefaultR0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; // get methods  const Matrix\u0026amp; R() const { return R_; }; // set methods  void set_Q(const Matrix\u0026amp; Q) { lds::System::set_Q(Q); do_recurse_Ke_ = true; } void set_R(const Matrix\u0026amp; R) { Reassign(R_,R); do_recurse_Ke_ = true; }; void set_Ke(const Matrix\u0026amp; Ke) { Reassign(Ke_,Ke); // if users have set Ke, they must not want to calculate it online.  do_recurse_Ke_ = false; }; void set_Ke_m(const Matrix\u0026amp; Ke_m) { Reassign(Ke_m_,Ke_m); // if users have set Ke, they must not want to calculate it online.  do_recurse_Ke_ = false; }; void Print(); protected: void h() override { cx_ = C_ * x_; y_ = cx_ + d_; }; void RecurseKe() override; // Gaussian-output-specific  Matrix R_; bool do_recurse_Ke_{}; }; // System } // namespace gaussian } // namespace lds  #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":64,"href":"/lds-ctrl-est/docs/api/files/lds__poisson_8h/","title":"ldsCtrlEst_h/lds_poisson.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson.h #  plds namespace More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Detailed Description #  This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)).\nSource code #  //===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_H #define LDSCTRLEST_LDS_POISSON_H  #include \u0026#34;lds.h\u0026#34; namespace lds { namespace poisson { // TODO(mfbolus): Not sure if defining these as static here makes the most // sense. Is there a downside to letting multiple poisson System objects share a // common random number generator? static std::random_device rd; static std::mt19937 rng = std::mt19937( rd()); } // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":65,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_ctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_ctrl.h #  PLDS controller type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::Controller PLDS Controller Type.    Detailed Description #  This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/)), including state estimation.\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_CTRL_H #define LDSCTRLEST_LDS_POISSON_CTRL_H  // namespace #include \u0026#34;lds_poisson.h\u0026#34;// system type #include \u0026#34;lds_poisson_sys.h\u0026#34;// control type #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace poisson { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); lds::Limit(y_ref_, kYRefLb, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available  using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; private: constexpr static const data_t kYRefLb = 1e-4; }; } // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":66,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/","title":"ldsCtrlEst_h/lds_poisson_fit.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_fit.h #  PLDS base fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::Fit PLDS Fit Type.    Detailed Description #  This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_FIT_H #define LDSCTRLEST_LDS_POISSON_FIT_H  // namespace #include \u0026#34;lds_poisson.h\u0026#34;// fit #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace poisson { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) : lds::Fit(n_u, n_x, n_y, dt){}; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = exp(C_ * x.col(t) + d_); return y.col(t); }; void set_R(const Matrix\u0026amp; R) override { std::cerr \u0026lt;\u0026lt; \u0026#34;WARNING: Cannot set R (R[0] = \u0026#34; \u0026lt;\u0026lt; R.at(0) \u0026lt;\u0026lt; \u0026#34;). No Gaussian measurement noise in Poisson observation model.\\n\u0026#34;; }; const Matrix\u0026amp; R() const override { return R_; }; }; }; // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":67,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_em.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_fit_em.h #  PLDS E-M fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::FitEM PLDS E-M Fit Type.    Detailed Description #  This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\n[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H #define LDSCTRLEST_LDS_POISSON_FIT_EM_H  #include \u0026#34;lds_fit_em.h\u0026#34;#include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override{}; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; data_t NewtonSolveC(); void AnalyticalSolveD(); }; } // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":68,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_ssid.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_fit_ssid.h #  PLDS SSID fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS.    Detailed Description #  This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitssid/)).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H #define LDSCTRLEST_LDS_POISSON_FIT_SSID_H  #include \u0026#34;lds_fit_ssid.h\u0026#34;#include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; private: void DecomposeData() override; void CalcCov(); void PoissonToGaussianMoments(); Matrix cov_; }; } // namespace poisson } // namespace lds #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":69,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_sctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_sctrl.h #  PLDS switched controller type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::SwitchedController Poisson-observation SwitchedController Type.    Detailed Description #  This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H  #include \u0026#34;lds_poisson_ctrl.h\u0026#34;#include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace poisson { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); lds::Limit(y_ref_, kYRefLB, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available  using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; private: constexpr static data_t kYRefLB = 1e-4; }; } // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":70,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/","title":"ldsCtrlEst_h/lds_poisson_sys.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_sys.h #  PLDS base type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::System Poisson System type.    Detailed Description #  This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)).\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_SYS_H #define LDSCTRLEST_LDS_POISSON_SYS_H  // namespace #include \u0026#34;lds_poisson.h\u0026#34;// system #include \u0026#34;lds_sys.h\u0026#34; // needed for Poisson random number generation #include \u0026lt;random\u0026gt; namespace lds { namespace poisson { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; protected: void h() override { cx_ = C_ * x_; y_ = exp(cx_ + d_); diag_y_.diag() = y_; }; void RecurseKe() override; private: // Poisson-output-specific  Matrix diag_y_; std::poisson_distribution\u0026lt;size_t\u0026gt; pd_; }; // System } // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":71,"href":"/lds-ctrl-est/docs/api/files/lds__sctrl_8h/","title":"ldsCtrlEst_h/lds_sctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_sctrl.h #  SwitchedController type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::SwitchedController SwitchedController Type.    Detailed Description #  This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code #  //===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_SCTRL_H #define LDSCTRLEST_LDS_SCTRL_H  #include \u0026#34;lds_ctrl.h\u0026#34;#include \u0026#34;lds_uniform_mats.h\u0026#34;#include \u0026#34;lds_uniform_vecs.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class SwitchedController : public Controller\u0026lt;System\u0026gt; { public: SwitchedController() = default; SwitchedController(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); SwitchedController(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); void Switch(size_t idx, bool do_force_switch = false); void set_Kc(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc) { Kc_list_ = Kc; Kc_ = Kc_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc) { Kc_list_ = std::move(Kc); Kc_ = Kc_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_inty) { Kc_inty_list_ = Kc_inty; Kc_inty_ = Kc_inty_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_inty) { Kc_inty_list_ = std::move(Kc_inty); Kc_inty_ = Kc_inty_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_u) { Kc_u_list_ = Kc_u; Kc_u_ = Kc_u_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_u) { Kc_u_list_ = std::move(Kc_u); Kc_u_ = Kc_u_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(const UniformVectorList\u0026amp; g) { g_design_list_ = g; g_design_ = g_design_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(UniformVectorList\u0026amp;\u0026amp; g) { g_design_list_ = std::move(g); g_design_ = g_design_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; // make sure base class template methods available  using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; protected: std::vector\u0026lt;System\u0026gt; systems_; size_t n_sys_{}; size_t idx_{}; // controller gains could be different for each  UniformMatrixList\u0026lt;\u0026gt; Kc_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_inty_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_u_list_; // design-phase input gain could also be different  UniformVectorList g_design_list_; // TODO(mfbolus): not sure why I need to do this.  using Controller\u0026lt;System\u0026gt;::Kc_; using Controller\u0026lt;System\u0026gt;::Kc_inty_; using Controller\u0026lt;System\u0026gt;::Kc_u_; using Controller\u0026lt;System\u0026gt;::g_design_; using Controller\u0026lt;System\u0026gt;::sys_; // using Controller\u0026lt;System\u0026gt;::u_ref_;  // using Controller\u0026lt;System\u0026gt;::x_ref_;  // using Controller\u0026lt;System\u0026gt;::y_ref_;  //  using Controller\u0026lt;System\u0026gt;::control_type_; private: void InitVars(); using lds::Controller\u0026lt;System\u0026gt;::set_sys; // using Controller\u0026lt;System\u0026gt;::set_Kc;  // using Controller\u0026lt;System\u0026gt;::set_Kc_inty;  // using Controller\u0026lt;System\u0026gt;::set_Kc_u;  // using Controller\u0026lt;System\u0026gt;::set_g_design; }; template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(systems.at(0), u_lb, u_ub, control_type), systems_(systems) { InitVars(); } template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(System(systems.at(0).n_u(), systems.at(0).n_x(), systems.at(0).n_y(), systems.at(0).dt()), u_lb, u_ub, control_type), systems_(std::move(systems)) { InitVars(); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::InitVars() { n_sys_ = systems_.size(); sys_ = systems_.at(0); Kc_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_)); Kc_inty_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); Kc_u_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); g_design_list_ = UniformVectorList(std::vector\u0026lt;Vector\u0026gt;(n_sys_, g_design_)); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::Switch(size_t idx, bool do_force_switch) { if ((idx == idx_) \u0026amp;\u0026amp; !do_force_switch) { return; // already there.  } // put old up and get new one out  systems_.at(idx_) = std::move(sys_); sys_ = std::move(systems_.at(idx)); // set the state of this system to that of the previous system  // TODO(mfbolus): This will only work as intended if state matrix is the same.  // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp.  sys_.set_m(systems_.at(idx_).m(), true); sys_.set_x(systems_.at(idx_).x()); // swap controller gains  Kc_list_.Swap(Kc_, idx_); Kc_list_.Swap(Kc_, idx); if (control_type_ \u0026amp; kControlTypeIntY) { Kc_inty_list_.Swap(Kc_inty_, idx_); Kc_inty_list_.Swap(Kc_inty_, idx); } if (control_type_ \u0026amp; kControlTypeDeltaU) { Kc_u_list_.Swap(Kc_u_, idx_); Kc_u_list_.Swap(Kc_u_, idx); } g_design_list_.Swap(g_design_, idx_); g_design_list_.Swap(g_design_, idx); idx_ = idx; } // Switch  } // namespace lds  #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":72,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8h/","title":"ldsCtrlEst_h/lds_sys.h","section":"Files","content":"ldsCtrlEst_h/lds_sys.h #  LDS base type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::System Linear Dynamical System Type.    Detailed Description #  This file declares and partially defines the base type for linear dynamical systems ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code #  //===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_SYS_H #define LDSCTRLEST_LDS_SYS_H  #include \u0026#34;lds.h\u0026#34; namespace lds { class System { public: System() = default; System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); virtual ~System() {} void Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z); virtual const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) = 0; void f(const Vector\u0026amp; u, bool do_add_noise = false) { x_ = A_ * x_ + B_ * (g_ % u) + m_; if (do_add_noise) { x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_); } }; virtual void h() = 0; size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Vector\u0026amp; x() const { return x_; }; const Matrix\u0026amp; P() const { return P_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; P_m() const { return P_m_; }; const Vector\u0026amp; cx() const { return cx_; }; const Vector\u0026amp; y() const { return y_; }; const Vector\u0026amp; x0() const { return x0_; }; const Vector\u0026amp; m0() const { return m0_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; const Matrix\u0026amp; Ke() const { return Ke_; }; const Matrix\u0026amp; Ke_m() const { return Ke_m_; }; const Matrix\u0026amp; Q() { return Q_; }; const Matrix\u0026amp; Q_m() { return Q_m_; }; const Matrix\u0026amp; P0() { return P0_; }; const Matrix\u0026amp; P0_m() { return P0_m_; }; void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_m(const Vector\u0026amp; m, bool do_force_assign=false) { Reassign(m0_, m); if ((!do_adapt_m) || do_force_assign) { Reassign(m_, m); } }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); }; void set_Q_m(const Matrix\u0026amp; Q_m) { Reassign(Q_m_, Q_m); }; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); }; void set_P0_m(const Matrix\u0026amp; P0_m) { Reassign(P0_m_, P0_m); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; void set_x(const Vector\u0026amp; x) { Reassign(x_, x); h(); }; void Reset(); void Print(); // safe to leave this public and non-const  bool do_adapt_m{}; protected: virtual void RecurseKe() = 0; void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); std::size_t n_x_{}; std::size_t n_u_{}; std::size_t n_y_{}; data_t dt_{}; // Signals:  Vector x_; Matrix P_; Vector m_; Matrix P_m_; Vector cx_; Vector y_; Vector z_; // Parameters:  Vector x0_; Matrix P0_; Vector m0_; Matrix P0_m_; Matrix A_; Matrix B_; Vector g_; Matrix Q_; Matrix Q_m_; Matrix C_; Vector d_; Matrix Ke_; Matrix Ke_m_; }; // System  } // namespace lds  #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":73,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/","title":"ldsCtrlEst_h/lds_uniform_mats.h","section":"Files","content":"ldsCtrlEst_h/lds_uniform_mats.h #  List of uniformly sized matrices. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::UniformMatrixList    Detailed Description #  This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.\nSource code #  //===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H #define LDSCTRLEST_LDS_UNIFORM_MATS_H  #include \u0026lt;array\u0026gt; // std::array#include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { template \u0026lt;MatrixListFreeDim D = kMatFreeDimNone\u0026gt; class UniformMatrixList : public std::vector\u0026lt;Matrix\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion  // using std::vector\u0026lt;Matrix\u0026gt;::vector;  using std::vector\u0026lt;Matrix\u0026gt;::operator=; using std::vector\u0026lt;Matrix\u0026gt;::operator[]; using std::vector\u0026lt;Matrix\u0026gt;::begin; using std::vector\u0026lt;Matrix\u0026gt;::end; using std::vector\u0026lt;Matrix\u0026gt;::size; public: using std::vector\u0026lt;Matrix\u0026gt;::at; UniformMatrixList() = default; explicit UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); explicit UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; ~UniformMatrixList() = default; const std::array\u0026lt;size_t, 2\u0026gt;\u0026amp; dim(size_t n = 0) const { return dim_.at(n); } size_t size() { return std::vector\u0026lt;Matrix\u0026gt;::size(); }; const Matrix\u0026amp; at(size_t n) { return std::vector\u0026lt;Matrix\u0026gt;::at(n); }; void Swap(Matrix\u0026amp; that, size_t n); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim); std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt; dim_; }; template \u0026lt;MatrixListFreeDim D\u0026gt; inline void UniformMatrixList\u0026lt;D\u0026gt;::Swap(Matrix\u0026amp; that, size_t n) { // make sure request in range  if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim  bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.n_cols); } if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap  Matrix tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); if (D == kMatFreeDim1) { this-\u0026gt;dim_[n][0] = (*this)[n].n_rows; } if (D == kMatFreeDim2) { this-\u0026gt;dim_[n][1] = (*this)[n].n_cols; } } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) { // make sure dim_ vector is initialized  if (dim_.empty()) { dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(that.size(), {0, 0}); } // check dimensions  if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices\u0026#34;; throw std::runtime_error(ss.str()); } // if dimensions a not zero and do not match, skip move with error message.  bool dims_nonzero = true; for (auto d : dim_) { if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; d[0] \u0026lt; 1) { dims_nonzero = false; break; } if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; d[1] \u0026lt; 1) { dims_nonzero = false; break; } } if (dims_nonzero) { bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); } if (!does_match) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrices of size \u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; dim_[k] = that.dim(k); } return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept { // // check dimensions  // // if empty, assume a default constructed object and safe to move  // if (!this-\u0026gt;empty()) {  // if (this-\u0026gt;size() != that.size()) {  // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34;  // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices. Skipping.\\n\u0026#34;;  // return (*this);  // }  //  // // if dimensions a not zero and do not match, skip move with error  // message. bool dims_nonzero = true; for (auto d : dim_) {  // if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; (d[0] \u0026lt; 1)) {  // dims_nonzero = false;  // break;  // }  // if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; (d[1] \u0026lt; 1)) {  // dims_nonzero = false;  // break;  // }  // }  //  // if (dims_nonzero) {  // bool does_match = true;  // if (!(D == kMatFreeDim1)) {  // does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows);  // }  //  // if (!(D == kMatFreeDim2)) {  // does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols);  // }  //  // if (!does_match) {  // this-\u0026gt;at(0).print(\u0026#34;this[0] = \u0026#34;);  // that.at(0).print(\u0026#34;that[0] = \u0026#34;);  // std::cerr  // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformMatrixList element of size (\u0026#34; \u0026lt;\u0026lt;  // that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols \u0026lt;\u0026lt; \u0026#34;) for an  // element of size (\u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34;).  // Skipping.\\n\u0026#34;;  // return (*this);  // }  // }  // }  dim_ = that.dim_; std::vector\u0026lt;Matrix\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(std::move(mats)) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) : vector(that) { (*this) = that; } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { std::array\u0026lt;size_t, 2\u0026gt; dim_k({this-\u0026gt;at(k).n_rows, this-\u0026gt;at(k).n_cols}); dim_.push_back(dim_k); } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim) { // change behavior based on free dim D  if ((dim[0] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim1)) { dim[0] = this-\u0026gt;at(0).n_rows; } if ((dim[1] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim2)) { dim[1] = this-\u0026gt;at(0).n_cols; } // make sure dimensiolaties are all uniform  bool does_match(true); for (const Matrix\u0026amp; mat : *this) { if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_rows == dim[0]); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_cols == dim[1]); } if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(this-\u0026gt;size(), dim); for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { dim_[k][0] = (*this)[k].n_rows; dim_[k][1] = (*this)[k].n_cols; } } } // namespace lds  #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":74,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/","title":"ldsCtrlEst_h/lds_uniform_systems.h","section":"Files","content":"ldsCtrlEst_h/lds_uniform_systems.h #  List of uniformly sized Systems. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::UniformSystemList    Detailed Description #  This file provides a container for uniformly sized Systems.\nSource code #  //===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H  #include \u0026lt;array\u0026gt; // std::array#include \u0026lt;vector\u0026gt; // std::vector // namespace #include \u0026#34;lds.h\u0026#34;// System type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class UniformSystemList : public std::vector\u0026lt;System\u0026gt; { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion  // using std::vector\u0026lt;System\u0026gt;::vector;  using std::vector\u0026lt;System\u0026gt;::operator=; using std::vector\u0026lt;System\u0026gt;::operator[]; using std::vector\u0026lt;System\u0026gt;::at; using std::vector\u0026lt;System\u0026gt;::begin; using std::vector\u0026lt;System\u0026gt;::end; using std::vector\u0026lt;System\u0026gt;::size; public: UniformSystemList() = default; explicit UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); explicit UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(const UniformSystemList\u0026amp; that); UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept; ~UniformSystemList() = default; const std::array\u0026lt;size_t, 3\u0026gt;\u0026amp; dim() const { return dim_; } size_t size() { return std::vector\u0026lt;System\u0026gt;::size(); }; const System\u0026amp; at(size_t n) { return std::vector\u0026lt;System\u0026gt;::at(n); }; void Swap(System\u0026amp; that, size_t n); UniformSystemList\u0026amp; operator=(const UniformSystemList\u0026amp; that); UniformSystemList\u0026amp; operator=(UniformSystemList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim); std::array\u0026lt;size_t, 3\u0026gt; dim_{}; }; template \u0026lt;typename System\u0026gt; inline void UniformSystemList\u0026lt;System\u0026gt;::Swap(System\u0026amp; that, size_t n) { // make sure request in range  if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformSystemList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim  bool does_match = (dim_[0] == that.n_u()) \u0026amp;\u0026amp; (dim_[1] == that.n_x()) \u0026amp;\u0026amp; (dim_[2] == that.n_y()); if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformSystemList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap  System tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( const UniformSystemList\u0026amp; that) { // check dimensions  if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_[0] + dim_[1] + dim_[2]) { std::array\u0026lt;size_t, 3\u0026gt; other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign systems of size \u0026#34; \u0026lt;\u0026lt; dim_[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2] \u0026lt;\u0026lt; \u0026#34; with systems of size \u0026#34; \u0026lt;\u0026lt; other_dim[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other_dim[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2]; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( UniformSystemList\u0026amp;\u0026amp; that) noexcept { // // check dimensions  // // if empty, assume a default constructed object and safe to move  // if (!this-\u0026gt;empty()) {  // if (this-\u0026gt;size() != that.size()) {  // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34;  // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems. Skipping.\\n\u0026#34;;  // return (*this);  // }  //  // // if dimensions a not zero and do not match, skip move with error  // message. if (dim_[0] + dim_[1] + dim_[2]) {  // bool does_match = (dim_[0] == that.at(0).n_u()) \u0026amp;\u0026amp;  // (dim_[1] == that.at(0).n_x()) \u0026amp;\u0026amp;  // (dim_[2] == that.at(0).n_y());  // if (!does_match) {  // std::cerr  // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformSystemList element for an element of \u0026#34;  // \u0026#34;different size. Skipping.\\n\u0026#34;;  // return (*this);  // }  // }  // }  dim_ = that.dim_; std::vector\u0026lt;System\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(std::move(systems)) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList( std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const UniformSystemList\u0026amp; that) : std::vector\u0026lt;System\u0026gt;(that) { (*this) = that; } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept : std::vector\u0026lt;System\u0026gt;(std::move(that)) { this-\u0026gt;dim_[0] = this-\u0026gt;at(0).n_u(); this-\u0026gt;dim_[1] = this-\u0026gt;at(0).n_x(); this-\u0026gt;dim_[2] = this-\u0026gt;at(0).n_y(); } template \u0026lt;typename System\u0026gt; void UniformSystemList\u0026lt;System\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim) { if (dim[0] + dim[1] + dim[2]) { dim_ = dim; } else { dim_[0] = this-\u0026gt;at(0).n_u(); dim_[1] = this-\u0026gt;at(0).n_x(); dim_[2] = this-\u0026gt;at(0).n_y(); } // make sure dimensiolaties are all uniform  bool does_match(true); for (const System\u0026amp; sys : *this) { does_match = does_match \u0026amp;\u0026amp; (sys.n_u() == dim_[0]); does_match = does_match \u0026amp;\u0026amp; (sys.n_x() == dim_[1]); does_match = does_match \u0026amp;\u0026amp; (sys.n_y() == dim_[2]); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input systems are not uniform.\u0026#34;); } } } } // namespace lds  #endif  Updated on 4 May 2022 at 16:57:51 Eastern Daylight Time\n"},{"id":75,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/","title":"ldsCtrlEst_h/lds_uniform_vecs.h","section":"Files","content":"ldsCtrlEst_h/lds_uniform_vecs.h #  List of uniformly sized vectors. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::UniformVectorList    Detailed Description #  This file provides a container for uniformly sized vectors.\nSource code #  //===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H #define LDSCTRLEST_LDS_UNIFORM_VECS_H  #include \u0026lt;array\u0026gt; // std::array#include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { class UniformVectorList : public std::vector\u0026lt;Vector\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion  // using std::vector\u0026lt;Vector\u0026gt;::vector;  using std::vector\u0026lt;Vector\u0026gt;::operator=; using std::vector\u0026lt;Vector\u0026gt;::operator[]; using std::vector\u0026lt;Vector\u0026gt;::at; using std::vector\u0026lt;Vector\u0026gt;::begin; using std::vector\u0026lt;Vector\u0026gt;::end; using std::vector\u0026lt;Vector\u0026gt;::size; public: UniformVectorList() = default; explicit UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim = 0); explicit UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim = 0); UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim = 0); UniformVectorList(const UniformVectorList\u0026amp; that); UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept; ~UniformVectorList() = default; size_t dim() const { return dim_; } size_t size() { return std::vector\u0026lt;Vector\u0026gt;::size(); }; const Vector\u0026amp; at(size_t n) { return std::vector\u0026lt;Vector\u0026gt;::at(n); }; void Swap(Vector\u0026amp; that, size_t n); UniformVectorList\u0026amp; operator=(const UniformVectorList\u0026amp; that); UniformVectorList\u0026amp; operator=(UniformVectorList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(size_t dim); size_t dim_{}; }; inline void UniformVectorList::Swap(Vector\u0026amp; that, size_t n) { // make sure request in range  if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim  bool does_match = dim_ == that.n_elem; if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap  Vector tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( const UniformVectorList\u0026amp; that) { // check dimensions  if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_) { size_t other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ \u0026lt;\u0026lt; \u0026#34; with vectors of size \u0026#34; \u0026lt;\u0026lt; other_dim; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( UniformVectorList\u0026amp;\u0026amp; that) noexcept { // // check dimensions  // if (!this-\u0026gt;empty()) {  // if (this-\u0026gt;size() != that.size()) {  // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34;  // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors. Skipping.\\n\u0026#34;;  // return (*this);  // }  //  // if (dim_) {  // size_t other_dim(that.dim());  // if (dim_ != other_dim) {  // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_  // \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; other_dim \u0026lt;\u0026lt; \u0026#34;.  // Skipping.\\n\u0026#34;;  // return (*this);  // }  // }  // }  dim_ = that.dim_; std::vector\u0026lt;Vector\u0026gt;::operator=(std::move(that)); return (*this); } } // namespace lds  #endif  Updated on 4 May 2022 at 16:57:52 Eastern Daylight Time\n"},{"id":76,"href":"/lds-ctrl-est/docs/api/files/mex__c__util_8h/","title":"ldsCtrlEst_h/mex_c_util.h","section":"Files","content":"ldsCtrlEst_h/mex_c_util.h #  arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) More\u0026hellip;\nNamespaces #     Name     armamexc arma/mex interface using Matlab C API    Detailed Description #  This file defines utility functions for interoperability between armadillo and Matlab/Octave\u0026rsquo;s C mex API.\nSource code #  //===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_MEXC_UTIL_H #define LDSCTRLEST_MEXC_UTIL_H  #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.h\u0026#34; // // If Matlab_FOUND, include matrix.h. // // (Octave does not need/have it.) // #ifdef Matlab_FOUND // #include \u0026#34;matrix.h\u0026#34; // #endif  namespace armamexc { template \u0026lt;class T\u0026gt; inline auto m2T_scalar(const mxArray *matlab_scalar) -\u0026gt; T { if (mxGetData(matlab_scalar)) { return static_cast\u0026lt;T\u0026gt;(mxGetScalar(matlab_scalar)); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return 0; } template \u0026lt;class T\u0026gt; inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false, bool strict = true) -\u0026gt; arma::Mat\u0026lt;T\u0026gt; { if (mxGetData(matlab_mat)) { const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat); if (n_dim == 2) { return arma::Mat\u0026lt;T\u0026gt;(static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)), mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem, strict); } mexErrMsgTxt(\u0026#34;Number of dimensions must be 2.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } // TODO(mfbolus): make these templated.  template \u0026lt;typename T\u0026gt; inline auto a2m_mat(arma::Mat\u0026lt;T\u0026gt; const \u0026amp;arma_mat) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_mat.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy.  std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Mat.\u0026#34;); return nullptr; } template \u0026lt;typename T\u0026gt; inline auto a2m_vec(arma::Col\u0026lt;T\u0026gt; const \u0026amp;arma_vec) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_vec.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy.  std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Col.\u0026#34;); return nullptr; } } // namespace armamexc  #endif  Updated on 4 May 2022 at 16:57:52 Eastern Daylight Time\n"},{"id":77,"href":"/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/","title":"ldsCtrlEst_h/mex_cpp_util.h","section":"Files","content":"ldsCtrlEst_h/mex_cpp_util.h #  arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) More\u0026hellip;\nNamespaces #     Name     armamexcpp arma/mex interface using Matlab C++ API    Detailed Description #  This file defines utility functions for interoperability between armadillo and Matlab\u0026rsquo;s C++ mex API.\nSource code #  //===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_MEXCPP_UTIL_H #define LDSCTRLEST_MEXCPP_UTIL_H  #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.hpp\u0026#34;#include \u0026#34;mexAdapter.hpp\u0026#34; namespace armamexcpp { template \u0026lt;class T\u0026gt; std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; m2a_cellmat(matlab::data::CellArray\u0026amp; matlab_cell) { size_t n_cells = matlab_cell.getNumberOfElements(); std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; arma_mat(n_cells, arma::Mat\u0026lt;T\u0026gt;(1, 1, arma::fill::zeros)); for (size_t k = 0; k \u0026lt; n_cells; k++) { matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = matlab_cell[k]; auto dims = matlab_mat.getDimensions(); arma_mat[k] = arma::Mat\u0026lt;T\u0026gt;(matlab_mat.release().get(), dims[0], dims[1]); } return arma_mat; }; template \u0026lt;class T\u0026gt; std::vector\u0026lt;T\u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt;T\u0026gt;\u0026amp; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); T* ptr = matlab_array.release().get(); std::vector\u0026lt;T\u0026gt; vec(ptr, ptr + n_elem); return vec; }; template \u0026lt;class T\u0026gt; arma::Col\u0026lt;T\u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); // T* ptr = matlab_array.release().get();  // arma::Col\u0026lt;T\u0026gt; vec(ptr, n_elem); //, false);  // TODO(mfbolus): for some reason, using the above pointer at times leads to  // getting garbage values. matlab array values may be stored in non-contiguous  // memory?  arma::Col\u0026lt;T\u0026gt; vec(n_elem, arma::fill::zeros); for (size_t k = 0; k \u0026lt; n_elem; k++) { vec[k] = matlab_array[k]; } return vec; }; template \u0026lt;class T\u0026gt; arma::Mat\u0026lt;T\u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { // ArrayDimensions == std::vector\u0026lt;size_t\u0026gt;  auto dims = matlab_array.getDimensions(); // T* ptr = matlab_array.release().get();  // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false)  // arma::Mat\u0026lt;T\u0026gt; mat(ptr, dims[0], dims[1]); //, false);  // TODO(mfbolus): for some reason, using the above pointer at times leads to  // getting garbage values. matlab array values may be stored in non-contiguous  // memory?  //  // armadillo and matlab both use column-major ordering, so this should work:  size_t n_elem = dims[0] * dims[1]; arma::Mat\u0026lt;T\u0026gt; mat(dims[0], dims[1], arma::fill::zeros); size_t k(0); for (auto m: matlab_array) { mat[k] = m; k++; } return mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_mat(const arma::Mat\u0026lt;T\u0026gt;\u0026amp; arma_mat, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(), arma_mat.memptr() + arma_mat.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_vec(const arma::Col\u0026lt;T\u0026gt;\u0026amp; arma_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;({arma_vec.n_elem, 1}, arma_vec.memptr(), arma_vec.memptr() + arma_vec.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; s2m_vec(const std::vector\u0026lt;T\u0026gt;\u0026amp; std_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size()); return matlab_mat; }; } // namespace armamexcpp  #endif  Updated on 4 May 2022 at 16:57:52 Eastern Daylight Time\n"},{"id":78,"href":"/lds-ctrl-est/docs/terminology/model/","title":"Models","section":"LDS C+E Documentation","content":"Model Definitions #  This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form:   \\[\r\\mathbf{x}_{t\u0026#43;1} = f\\left( \\mathbf{x}_{t}, \\mathbf{v}_{t} \\right) = \\mathbf{A} \\mathbf{x}_{t} \u0026#43; \\mathbf{B} \\mathbf{v}_{t} \u0026#43; \\mathbf{m}_{t} \u0026#43; \\mathbf{w}_{t}\r\\]   \\[\r\\mathbf{y}_{t} = h\\left( \\mathbf{x}_{t} \\right)\r\\]  t : time index\rx : system state\rv = g%u : input (e.g., in physical units used for model fit)\ru : control signal sent to actuator (e.g., in Volts)\ry : system output\rm : process disturbance\rw ~ N(0, Q) : process noise/disturbance\rA : state matrix\rB : input coupling matrix\rg : input gain (e.g., for converting to control signal actuator voltage)\rn.b., assumes this conversion is linear\rQ : process noise covariance\r% : element-wise multiplication\r LDS with Gaussian Observations #  For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.\n \\[\r\\mathbf{y}_{t} = \\mathbf{C} \\mathbf{x}_{t} \u0026#43; \\mathbf{d}\r\\]   \\[\r\\mathbf{z}_{t} \\sim \\mathcal{N}\\left(\\mathbf{y}_{t} , \\mathbf{R} \\right)\r\\]  z : measurement\rC : output matrix\rd : output bias\rR : measurement noise covariance\r LDS with Poisson Observations #  For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.\n \\[\ry_{t}^{i} = \\exp \\left(\\mathbf{c}^i \\mathbf{x}_{t} \u0026#43; d^i\\right)\r\\]   \\[\rz_{t}^i \\sim \\rm{Poisson} \\left(y_{t}^i \\right)\r\\]  i : output index\rz : measurement (count data)\rc : i^th row of output matrix (C)\rd : output bias\r "},{"id":79,"href":"/lds-ctrl-est/docs/api/modules/","title":"Modules","section":"LDS C+E Documentation","content":"Modules #    Control Mode Bit Masks provides fill types for constructing new armadillo vectors, matrices\n  Defaults\n   Updated on 4 May 2022 at 16:57:52 Eastern Daylight Time\n"},{"id":80,"href":"/lds-ctrl-est/docs/api/namespaces/","title":"Namespaces","section":"LDS C+E Documentation","content":"Namespaces #    armamexc arma/mex interface using Matlab C API\n  armamexcpp arma/mex interface using Matlab C++ API\n  lds::gaussian Linear Dynamical Systems with Gaussian observations.\n  lds::poisson Linear Dynamical Systems with Poisson observations.\n   Updated on 4 May 2022 at 16:57:52 Eastern Daylight Time\n"},{"id":81,"href":"/lds-ctrl-est/docs/api/pages/","title":"Pages","section":"LDS C+E Documentation","content":"Pages #   Updated on 4 May 2022 at 16:57:52 Eastern Daylight Time\n"},{"id":82,"href":"/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/","title":"PLDS State Estimation","section":"LDS C+E Examples","content":"PLDS State Estimation Tutorial #  This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.\nThe full code for this can be found here.\nPreamble #  In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system #  A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; // no. inputs  size_t n_x = 1; // no. states  size_t n_y = 1; // no. outputs  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system...  lds::poisson::System system_true(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example,   \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\]   \\[\ry_{t} = \\exp\\left(x_t\\right)\r\\]  where  \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\)  .\nNow, create non-default parameters for this model.\n// Model parameters  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state Finally, assign the parameters using corresponding set-methods.\n// Assign params.  system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); Creating the estimator #  Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.\n// Construct system for estimation  // e.g., will create a model with incorrect disturbance  lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object  system_estimator = system_true; // wrong disturbance  Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. To ensure robust estimates, adaptively re-estimate the process disturbance.\n// turn on adaptive disturbance estimation  system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting  // on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); Simulating estimation #  In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.\n// Simlate the true system.  z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update)  system_estimator.Filter(u.col(t - 1), z.col(t)); Example simulation result #  Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.\nWith this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.\n"},{"id":83,"href":"/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/","title":"PLDS Switched Control","section":"LDS C+E Examples","content":"PLDS Switched Control Tutorial #  This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.\nIn the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.\nThe full code for this can be found here.\nPreamble #  In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating the simulated system #  A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// whether to do switched control  bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); The system\u0026rsquo;s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.\n// for simulating switching  size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2  data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 Initially, the system will be in \u0026ldquo;mode\u0026rdquo; 1, where B = b1.\n// simulated system being controlled  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well  // characterized by two LDS models with different input matrices.**  data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.\nCreating the controller #  Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.\nSimilar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.\nMoreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.\nPutting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.\n// create switched controller  lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains  { // create switched controller sub-systems  // system 1  lds::poisson::System sys1(controlled_system); // set process noise covariance  Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m)  // n.b. using arbitrary default value for process noise if enabled.  sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset:  Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions  // system 2  lds::poisson::System sys2 = sys1; // set parameters  sys2.set_B(b2); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying systems:  Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive.  k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nNow that the SwitchedController is instantiated, assign its parameters.\n// Control variables  size_t control_type = 0; // no integral action, etc  switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); Simulating control #  In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).\nThe control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.\n// Let the controlled system stochastically change gain  // Assume another algorithm decodes this mode change and signals the  // switched_controller  Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1  { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2  if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system.  z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control  u.col(t) = switched_controller.ControlOutputReference(z.col(t)); Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system\u0026rsquo;s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.\nExample simulation result #  Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller\u0026rsquo;s online estimates of the output and state are shown in purple.\nNote that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.\n"},{"id":84,"href":"/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/","title":"src","section":"Files","content":"src #  Files #     Name     src/lds.cpp misc lds namespace functions   src/lds_gaussian_sys.cpp GLDS base type.   src/lds_poisson_sys.cpp PLDS base type.   src/lds_sys.cpp LDS base type.   src/lds_uniform_vecs.cpp Uniformly sized vectors.     Updated on 4 May 2022 at 16:57:52 Eastern Daylight Time\n"},{"id":85,"href":"/lds-ctrl-est/docs/api/files/lds_8cpp/","title":"src/lds.cpp","section":"Files","content":"src/lds.cpp #  misc lds namespace functions More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Detailed Description #  This file implements miscellaneous lds namespace functions not bound to a class.\nSource code #  //===-- lds.cpp - LDS -----------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds.h\u0026gt; // insert any necessary function definitions here. namespace lds { void ForceSymPD(Matrix\u0026amp; X) { if (X.is_sympd() || !X.is_square()) { return; } // make symmetric  X = (X + X.t()) / 2; // for eigenval decomp  bool did_succeed(true); Vector d; Matrix u; // see first method (which may not be ideal):  // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/  size_t k(1); bool is_sympd = X.is_sympd(); Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye); while (!is_sympd) { if (k \u0026gt; 100) { did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); data_t min_eig = arma::min(d); std::cerr \u0026lt;\u0026lt; \u0026#34;After multiple iterations, min eigen val = \u0026#34; \u0026lt;\u0026lt; min_eig \u0026lt;\u0026lt; \u0026#34;.\\n\u0026#34;; throw std::runtime_error( \u0026#34;Failed to make matrix symmetric positive definite.\u0026#34;); return; } // Limit(d, arma::eps(0), kInf); // force to be positive...  // Matrix d_diag = arma::diagmat(d);  // X = u * d_diag * u.t();  did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymPD failed.\u0026#34;); } data_t min_eig = arma::min(d); X += id * abs(min_eig) + arma::datum::eps; // make sure symm:  X = (X + X.t()) / 2; // double check eigenvals positive after symmetrizing:  arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); min_eig = arma::min(d); is_sympd = min_eig \u0026gt; 0; k++; } } void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min) { if (!X.is_square()) { return; } // make symmetric  X = (X + X.t()) / 2; bool did_succeed(true); Vector d; Matrix u; did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymMinEig failed.\u0026#34;); } Limit(d, eig_min + arma::eps(eig_min), kInf); // enforce lower bound  Matrix d_diag = arma::diagmat(d); X = u * d_diag * u.t(); // double check symmetric  X = (X + X.t()) / 2; } void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X) { bool did_succeed(true); did_succeed = arma::qr_econ(Qt, L, X.t()); if (!did_succeed) { throw std::runtime_error(\u0026#34;LQ decomposition failed.\u0026#34;); } arma::inplace_trans(L); arma::inplace_trans(Qt); } Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B) { // subtract out mean  auto m_a = arma::mean(A, 1); Matrix a0 = A; a0.each_col() -= m_a; auto m_b = arma::mean(B, 1); Matrix b0 = B; b0.each_col() -= m_b; Matrix cov = a0 * b0.t() / a0.n_cols; return cov; } } // namespace lds  Updated on 4 May 2022 at 16:57:52 Eastern Daylight Time\n"},{"id":86,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/","title":"src/lds_gaussian_sys.cpp","section":"Files","content":"src/lds_gaussian_sys.cpp #  GLDS base type. More\u0026hellip;\nDetailed Description #  This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code #  //===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds_gaussian_sys.h\u0026gt; lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0, data_t r0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { R_.zeros(n_y, n_y); R_.diag().fill(r0); do_recurse_Ke_=true; }; // recursively estimate Ke void lds::gaussian::System::RecurseKe() { if (!do_recurse_Ke_) { return; } // predict covariance  P_ = A_ * P_ * A_.t() + Q_; // calc Kalman gain  Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_); // update covariance  // Reference: Ghahramani et Hinton (1996)  P_ = P_ - Ke_ * C_ * P_; if (do_adapt_m) { P_m_ += Q_m_; // A_m = I (i.e., random walk)  Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_); P_m_ = P_m_ - Ke_m_ * C_ * P_m_; } } // Simulate const lds::Vector\u0026amp; lds::gaussian::System::Simulate(const Vector\u0026amp; u_tm1){ f(u_tm1, true);//simulate dynamics with noise added  h();//output  z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure  return z_; } void lds::gaussian::System::Print() { lds::System::Print(); std::cout \u0026lt;\u0026lt; \u0026#34;R: \\n\u0026#34; \u0026lt;\u0026lt; R_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }  Updated on 4 May 2022 at 16:57:52 Eastern Daylight Time\n"},{"id":87,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/","title":"src/lds_poisson_sys.cpp","section":"Files","content":"src/lds_poisson_sys.cpp #  PLDS base type. More\u0026hellip;\nDetailed Description #  This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code #  //===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds_poisson_sys.h\u0026gt; lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { diag_y_ = diagmat(y_); pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(0); }; // Correct: Given measurement (z) and current input (u), update estimate of the // state, covar, output. // // see Eden et al. 2004 void lds::poisson::System::RecurseKe() { // predict covariance  P_ = A_ * P_ * A_.t() + Q_; // update cov  P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_); Ke_ = P_ * C_.t(); if (do_adapt_m) { P_m_ += Q_m_; // predict (A_m = I)  P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_); // update  Ke_m_ = P_m_ * C_.t(); } } // Simulate Measurement: z ~ Poisson(y) const lds::Vector\u0026amp; lds::poisson::System::Simulate(const Vector\u0026amp; u_tm1) { f(u_tm1, true); // simulate dynamics with noise added  h(); // output  z_.zeros(); for (std::size_t k = 0; k \u0026lt; n_y_; k++) { // construct a Poisson distribution object with mean y[k]  pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(y_[k]); // pull random sample from this distribution  z_[k] = pd_(rng); } return z_; } // ******************* SYS_T *******************  Updated on 4 May 2022 at 16:57:52 Eastern Daylight Time\n"},{"id":88,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8cpp/","title":"src/lds_sys.cpp","section":"Files","content":"src/lds_sys.cpp #  LDS base type. More\u0026hellip;\nDetailed Description #  This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code #  //===-- lds_sys.cpp - LDS -------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds_sys.h\u0026gt; lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) { InitVars(p0, q0); } void lds::System::InitVars(data_t p0, data_t q0) { // initial conditions.  x0_ = Vector(n_x_, fill::zeros); // includes bias (nY) and g (nU)  P0_ = p0 * Matrix(n_x_, n_x_, fill::eye); m0_ = x0_; P0_m_ = P0_; // signals  x_ = x0_; P_ = P0_; m_ = m0_; P_m_ = P0_m_; y_ = Vector(n_y_, fill::zeros); cx_ = Vector(n_y_, fill::zeros); z_ = Vector(n_y_, fill::zeros); // By default, random walk where each state is independent  // In this way, provides independent estimates of rate per channel of output.  A_ = Matrix(n_x_, n_x_, fill::eye); B_ = Matrix(n_x_, n_u_, fill::zeros); g_ = Vector(n_u_, fill::ones); Q_ = q0 * Matrix(n_x_, n_x_, fill::eye); Q_m_ = Q_; C_ = Matrix(n_y_, n_x_, fill::eye); // each state will map to an output by  d_ = Vector(n_y_, fill::zeros); Ke_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain.  Ke_m_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain for m adaptation.  do_adapt_m = false; } // Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state // and update estimate of the state, covar, output using Kalman filter void lds::System::Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z_t) { // predict mean  f(u_tm1); // dynamics  h(); // output  // recursively calculate esimator gains (or just keep existing values)  // (also predicts+updates estimate covariance)  RecurseKe(); // update  x_ += Ke_ * (z_t - y_); if (do_adapt_m) { m_ += Ke_m_ * (z_t - y_); // adaptively estimating disturbance  } // With new state, estimate output.  h(); // --\u0026gt; posterior } void lds::System::Reset() { // reset to initial conditions  x_ = x0_; // mean  P_ = P0_; // cov of state estimate  m_ = m0_; // process disturbance  P_m_ = P0_m_; // cov of disturbance estimate  h(); } void lds::System::Print() { std::cout \u0026lt;\u0026lt; \u0026#34;\\n********** SYSTEM ********** \\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;x: \\n\u0026#34; \u0026lt;\u0026lt; x_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;P: \\n\u0026#34; \u0026lt;\u0026lt; P_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;A: \\n\u0026#34; \u0026lt;\u0026lt; A_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B: \\n\u0026#34; \u0026lt;\u0026lt; B_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;g: \\n\u0026#34; \u0026lt;\u0026lt; g_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;m: \\n\u0026#34; \u0026lt;\u0026lt; m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q: \\n\u0026#34; \u0026lt;\u0026lt; Q_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q_m: \\n\u0026#34; \u0026lt;\u0026lt; Q_m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d: \\n\u0026#34; \u0026lt;\u0026lt; d_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;C: \\n\u0026#34; \u0026lt;\u0026lt; C_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;y: \\n\u0026#34; \u0026lt;\u0026lt; y_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } //******************* SYS_T *******************  Updated on 4 May 2022 at 16:57:52 Eastern Daylight Time\n"},{"id":89,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/","title":"src/lds_uniform_vecs.cpp","section":"Files","content":"src/lds_uniform_vecs.cpp #  Uniformly sized vectors. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Detailed Description #  This file provides a container for uniformly sized vectors.\nSource code #  //===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds_uniform_vecs.h\u0026gt; namespace lds { UniformVectorList::UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); } UniformVectorList::UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim) : vector(std::move(vecs)) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(const UniformVectorList\u0026amp; that) : vector(that) { (*this) = that; } UniformVectorList::UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { this-\u0026gt;dim_ = this-\u0026gt;at(0).n_elem; } void UniformVectorList::CheckDimensions(size_t dim) { if (dim) { dim_ = dim; } else { dim_ = this-\u0026gt;at(0).n_elem; } // make sure dimensiolaties are all uniform  bool does_match(true); for (const Vector\u0026amp; vec : *this) { does_match = does_match \u0026amp;\u0026amp; (vec.n_elem == dim_); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } } } // namespace lds  Updated on 4 May 2022 at 16:57:52 Eastern Daylight Time\n"}]
\ No newline at end of file
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--- a/docs/en.search-data.min.9ed82488f1147302e58d3c3e65dd55018a6e72b6e0fd48f0583a54196c1ecb52.json
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@@ -1 +0,0 @@
-[{"id":0,"href":"/lds-ctrl-est/docs/","title":"LDS C+E Documentation","section":"LDS Control \u0026 Estimation","content":"LDS Control \u0026amp; Estimation Documentation #  "},{"id":1,"href":"/lds-ctrl-est/docs/tutorials/","title":"LDS C+E Examples","section":"LDS C+E Documentation","content":"Examples #  "},{"id":2,"href":"/lds-ctrl-est/acknowledgements/","title":"Acknowledgements","section":"LDS Control \u0026 Estimation","content":"Acknowledgements #  Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.\n"},{"id":3,"href":"/lds-ctrl-est/docs/getting-started/getting-started/","title":"Getting Started","section":"LDS C+E Documentation","content":"Getting Started #  This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.\nldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.\nDownloading the Library #  First, clone the repository along with submodules:\ngit clone --recurse-submodules https://github.com/stanley-rozell/lds-ctrl-est.git cd lds-ctrl-est\r# use git submodule update --init if you clone the repo without --recurse-submodules\rCompilation + Installation #  Now generate the cache and build using your IDE or from the command line as follows.\nmkdir build \u0026amp;\u0026amp; cd build\rcmake ..\rcmake --build .\rThe first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.\nIf you want to use vcpkg set up somewhere besides this repo\u0026rsquo;s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE\u0026rsquo;s settings.\nYou can verify the build is working by running ctest from the build folder, which runs all the example scripts.\nOptions #  This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\n LDSCTRLEST_BUILD_EXAMPLES : [default=ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use  n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake to configure/build the library.\n  For basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build cmake --build #build the project sudo make install #[optional] installs to default location (OS-specific)   To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location cmake --build #build the project make install #install to /your/install/prefix   To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\n LD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool    On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.\nPython bindings package ldsctrlest #  With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn\u0026rsquo;t use an undesired version. That environment needs to have NumPy installed.\nThe bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory.\nSee python/ldsctrlest/README.md for usage details.\nAlso, beware that a single build might not work for both the standalone library and the Python package (especially on Windows), since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.\nConsiderations for Windows #  First of all, development on Windows has been more prone to bugs, so if you encounter many problems, consider using a Unix-based system—WSL (Windows Subsystem for Linux) is a good option for Windows users who don\u0026rsquo;t want to work on a different machine.\nYou will likely need to get compiler tools through the Visual Studio installer. Compilation was tested in VS Code using the following kit:\nClang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64)\rCommon issues #   \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo;  If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.\nexport CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix On Windows, you will likely need to add the build or install folder to your PATH environment variable, which you can do using the settings GUI (search for \u0026ldquo;Edit the system environment variables\u0026rdquo;).\nOn Windows, \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted.  Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":4,"href":"/lds-ctrl-est/issues-contributing/","title":"Issues Contributing","section":"LDS Control \u0026 Estimation","content":"Reporting Issues #  If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.\nContributing #  We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.\n"},{"id":5,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/","title":"armamexc","section":"Namespaces","content":"armamexc #  arma/mex interface using Matlab C API More\u0026hellip; Functions #      Name     template \u0026lt;class T \u0026gt; auto m2T_scalar(const mxArray * matlab_scalar)\nConvert Matlab mxArray to scalar of type T.   template \u0026lt;class T \u0026gt; auto m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)\nConvert matlab matrix to armadillo.   template \u0026lt;typename T \u0026gt; auto a2m_mat(arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat)\nConvert armadillo to matlab matrix.   template \u0026lt;typename T \u0026gt; auto a2m_vec(arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec)\nConvert armadillo to matlab vector.    Detailed Description #  Utilities for arma/mex interface using Matlab C API\nFunction Details #  m2T_scalar #  template \u0026lt;class T \u0026gt; inline auto m2T_scalar( const mxArray * matlab_scalar ) Parameters:\n matlab_scalar matlab scalar  Template Parameters:\n T type  Return: scalar of type T\nm2a_mat #  template \u0026lt;class T \u0026gt; inline auto m2a_mat( const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true ) Parameters:\n matlab_mat matlab matrix copy_aux_mem [optional] whether to copy auxiliary memory strict [optional] strictly enforce the above  Template Parameters:\n T type  Return: armadillo matrix of type T\na2m_mat #  template \u0026lt;typename T \u0026gt; inline auto a2m_mat( arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat ) Parameters:\n arma_mat armadillo matrix  Return: matlab matrix\na2m_vec #  template \u0026lt;typename T \u0026gt; inline auto a2m_vec( arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec ) Parameters:\n arma_vec armadillo vector  Return: matlab vector\n Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":6,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/","title":"armamexcpp","section":"Namespaces","content":"armamexcpp #  arma/mex interface using Matlab C++ API More\u0026hellip; Functions #      Name     template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat(matlab::data::CellArray \u0026amp; matlab_cell)\nConvert matlab cell array to vector of armadillo matrices.   template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array)\nConvert matlab matrix to a vector of scalars.   template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo vector.   template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo matrix.   template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat(const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab matrix.   template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec(const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab vector.   template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec(const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert vector of scalar T to matlab matrix.    Detailed Description #  utilities for arma/mex interface using Matlab C++ API\nFunction Details #  m2a_cellmat #  template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat( matlab::data::CellArray \u0026amp; matlab_cell ) Parameters:\n matlab_cell matlab cell  Template Parameters:\n T type  Return: vector of armadillo matrices of type T\nm2s_vec #  template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array ) Parameters:\n matlab_array matlab array  Template Parameters:\n T type  Return: vector of type T\nm2a_vec #  template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\n matlab_array matlab array  Template Parameters:\n T type  Return: armadillo vector of type T\nm2a_mat #  template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\n matlab_array matlab matrix  Template Parameters:\n T type  Return: armadillo matrix of type T\na2m_mat #  template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat( const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\n arma_mat arma matrix factory matlab \u0026ldquo;array factory\u0026rdquo;  Template Parameters:\n T type  Return: matlab matrix\na2m_vec #  template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec( const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\n arma_vec armadillo vector factory matlab \u0026ldquo;array factory\u0026rdquo;  Template Parameters:\n T type  Return: matlab matrix\ns2m_vec #  template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec( const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\n std_vec standard vector factory matlab \u0026ldquo;array factory\u0026rdquo;  Template Parameters:\n T type  Return: matlab matrix\n Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":7,"href":"/lds-ctrl-est/docs/terminology/control-estimation/","title":"C\u0026E","section":"LDS C+E Documentation","content":"Control \u0026amp; Estimation #  The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.\nState estimation #  In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by   \\[\r\\widehat{\\mathbf{x}}_{t|t-1} = \\mathbf{A}\\widehat{\\mathbf{x}}_{t-1|t-1} \u0026#43; \\mathbf{B} u_{t-1} \u0026#43; \\mathbf{m}_{t-1} \\;,\r\\]   \\[\r\\widehat{\\mathbf{x}}_{t|t} = \\widehat{\\mathbf{x}}_{t|t-1} \u0026#43; \\mathbf{K}^{\\rm e}_t \\left(\\mathbf{z}_t - \\widehat{\\mathbf{y}}_{t|t-1}\\right)\\;,\r\\]  where  \\( \\hat{\\left(\\cdot\\right)}_{t|j} \\)  indicates an estimate at time  \\( t \\)  given data up to time  \\( j \\)  inclusive,  \\( \\mathbf{K}^{\\rm e} \\)  is the estimator gain, and\n \\[ \\widehat{\\mathbf{y}}_{t|t-1} = h\\left( \\widehat{\\mathbf{x}}_{t|t-1} \\right) \\; .\\]  In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.\nIn the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.\nAdaptive estimation of process disturbance #  Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.\nWhen parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk  \\[\r\\mathbf{m}_{t} = \\mathbf{m}_{t-1} \u0026#43; \\mathbf{w}^m_{t-1} \\;,\r\\]  where  \\( \\mathbf{w}^m \\sim \\mathcal{N}\\left(0, \\mathbf{Q}_m\\right)\\)  . Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.\nControl #  Given the estimated state, the controller updates the inputs to the system according to the following law:  \\[\r\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\r\\]  where  \\( \\left( \\cdot \\right)^{\\rm ref} \\)  correspond to reference/target signals and  \\( \\mathbf{K}^c_x \\)  is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.\nIf users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as\n \\[\r\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right) - \\mathbf{K}^c_{\\rm inty} \\sum_{j=1}^{t}\\left( \\widehat{\\mathbf{y}}_j - \\mathbf{y}^{\\rm ref}_j \\right) \\;.\r\\]  An additional option available to users is a control law that updates the change in u,\n \\[\r\\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\left(\\mathbf{u}_{t-1} - \\mathbf{u}^{\\rm ref}_{t-1} \\right) - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\r\\]   \\[\r\\mathbf{u}_{t} = \\mathbf{u}_{t-1} \u0026#43; \\Delta\\mathbf{u}_{t} \\; .\r\\]  Notice that this takes the form of a first-order difference equation for updating control (i.e.,  \\( \\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\mathbf{u}_{t-1} \u0026#43; \\epsilon_{t-1} \\)  ), effectively low-pass filtering the input depending on the characteristics of  \\( \\mathbf{K}^c_u \\)  . This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.\nIntegral action and the  \\( \\Delta \\mathbf{u} \\)  control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR\u0026rsquo;d to use in combination.\nCalculating reference state-control from output #  In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce  \\( \\mathbf{x}^{\\rm ref} \\)  and  \\( \\mathbf{u}^{\\rm ref} \\)  . Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.\n"},{"id":8,"href":"/lds-ctrl-est/docs/api/classes/","title":"Classes","section":"LDS C+E Documentation","content":"Classes #    lds::Controller\n  lds::EM\n  lds::Fit LDS Fit Type.\n  lds::SSID\n  lds::SwitchedController SwitchedController Type.\n  lds::System Linear Dynamical System Type.\n  lds::UniformMatrixList\n  lds::UniformSystemList\n  lds::UniformVectorList\n  lds::gaussian::Controller Gaussian-observation Controller Type.\n  lds::gaussian::Fit GLDS Fit Type.\n  lds::gaussian::FitEM GLDS E-M Fit Type.\n  lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.\n  lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.\n  lds::gaussian::System Gaussian LDS Type.\n  lds::poisson::Controller PLDS Controller Type.\n  lds::poisson::Fit PLDS Fit Type.\n  lds::poisson::FitEM PLDS E-M Fit Type.\n  lds::poisson::FitSSID Subspace Identification (SSID) for PLDS.\n  lds::poisson::SwitchedController Poisson-observation SwitchedController Type.\n  lds::poisson::System Poisson System type.\n   Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":9,"href":"/lds-ctrl-est/docs/api/modules/group__control__masks/","title":"Control Mode Bit Masks","section":"Modules","content":"Control Mode Bit Masks #  provides fill types for constructing new armadillo vectors, matrices More\u0026hellip; Attributes #      Name     const std::size_t kControlTypeDeltaU control designed to penalize change in input   const std::size_t kControlTypeIntY control using integral action   const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m    Detailed Description #  Control mode bit masks. These can be bit-wise OR\u0026rsquo;d to use in combination.\nAttribute Details #  kControlTypeDeltaU #  static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY #  static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM #  static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\n Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":10,"href":"/lds-ctrl-est/docs/api/modules/group__defaults/","title":"Defaults","section":"Modules","content":"Defaults #  \nMore\u0026hellip; Attributes #      Name     const data_t kDefaultP0 default state estimate covar   const data_t kDefaultQ0 default process noise covar   const data_t kDefaultR0 default output noise covar    Detailed Description #  Default values for common variables (e.g., default diagonal elements of covariances)\nAttribute Details #  kDefaultP0 #  static const data_t kDefaultP0 = 1e-6; kDefaultQ0 #  static const data_t kDefaultQ0 = 1e-6; kDefaultR0 #  static const data_t kDefaultR0 = 1e-2;  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":11,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/","title":"eg_glds_ctrl.cpp","section":"Examples","content":"eg_glds_ctrl.cpp #  Example GLDS Control\n//===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); // output noise covariance  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb.  data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_x).fill(m_low); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables:  // if following enabled, adapts set point with re-estimated process  // disturbance n.b., should not need integral action if this is enabled as the  // adaptive estimator minimizes DC error  bool do_adaptive_set_point = false; // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err  // setting initial state to target to avoid error at onset:  Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance  control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; } // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(9); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation  // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input  Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals  y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,  // xTrue, mTrue saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":12,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/","title":"eg_glds_du_plds_ctrl.cpp","section":"Examples","content":"eg_glds_du_plds_ctrl.cpp #  Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).\n//===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb.  data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance  Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables:  // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input  // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du =  // 1e-2.  Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err  Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp  // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control)  control_type = control_type | lds::kControlTypeDeltaU; // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(10); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation  // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input  Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals  y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,  // xTrue, mTrue saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":13,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/","title":"eg_plds_ctrl.cpp","section":"Examples","content":"eg_plds_ctrl.cpp #  Example PLDS Control\n//===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains  // n.b., Can either use Vector (arma::Col) or std::vector  Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error  Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err  // Set control type bit mask, so controller knows what to do  size_t control_type = lds::kControlTypeIntY; // integral action  // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled...  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions  controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller  lds::poisson::Controller controller; { // Create model used for control.  lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance  Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition  // adaptively re-estimate process disturbance (m)  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type  controller.set_control_type(control_type); // set controller gains  controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above  // target:  data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val  y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference  data_t f = 0.5; // freq [=] Hz  Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system.  z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Notably, it does this in the  // log-linear space (i.e., log(y)).  //  // Therefore, it is only applicable to regulation problems or cases where  // reference trajectory changes slowly compared to system dynamics.  controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":14,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/","title":"eg_plds_est.cpp","section":"Examples","content":"eg_plds_est.cpp #  Example PLDS Estimation\n//===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; // no. inputs  size_t n_x = 1; // no. states  size_t n_y = 1; // no. outputs  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.  // construct ground truth system...  lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  // Assign params.  system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation  // e.g., will create a model with incorrect disturbance  lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object  system_estimator = system_true; // wrong disturbance  Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.  // turn on adaptive disturbance estimation  system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting  // on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation :  // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus)  Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk  Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in...  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params  Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions  y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system.  z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update)  system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals  y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":15,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/","title":"eg_plds_switched_ctrl.cpp","section":"Examples","content":"eg_plds_switched_ctrl.cpp #  Example Switched PLDS Control\n//===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control  bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching  size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2  data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1  // simulated system being controlled  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well  // characterized by two LDS models with different input matrices.**  data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions  // reference  Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2  Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller  lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains  { // create switched controller sub-systems  // system 1  lds::poisson::System sys1(controlled_system); // set process noise covariance  Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m)  // n.b. using arbitrary default value for process noise if enabled.  sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset:  Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions  cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2  lds::poisson::System sys2 = sys1; // set parameters  sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s:  Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive.  k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables  size_t control_type = 0; // no integral action, etc  switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements  Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control.  Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in...  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params  Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val  y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain  // Assume another algorithm decodes this mode change and signals the  // switched_controller  Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1  { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2  if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system.  z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control  u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":16,"href":"/lds-ctrl-est/docs/api/examples/","title":"Examples","section":"LDS C+E Documentation","content":"Examples #    eg_glds_ctrl.cpp\n  eg_glds_du_plds_ctrl.cpp\n  eg_plds_ctrl.cpp\n  eg_plds_est.cpp\n  eg_plds_switched_ctrl.cpp\n   Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":17,"href":"/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/","title":"examples","section":"Files","content":"examples #  Files #     Name     examples/eg_glds_ctrl.cpp   examples/eg_glds_du_plds_ctrl.cpp   examples/eg_plds_ctrl.cpp   examples/eg_plds_est.cpp   examples/eg_plds_switched_ctrl.cpp     Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":18,"href":"/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/","title":"examples/eg_glds_ctrl.cpp","section":"Files","content":"examples/eg_glds_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Going to simulate a switching disturbance (m) acting on system\nSource code #  //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); // output noise covariance  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb.  data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_x).fill(m_low); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables:  // if following enabled, adapts set point with re-estimated process  // disturbance n.b., should not need integral action if this is enabled as the  // adaptive estimator minimizes DC error  bool do_adaptive_set_point = false; // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err  // setting initial state to target to avoid error at onset:  Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance  control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; } // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(9); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation  // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input  Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals  y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,  // xTrue, mTrue saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":19,"href":"/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/","title":"examples/eg_glds_du_plds_ctrl.cpp","section":"Files","content":"examples/eg_glds_du_plds_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Going to simulate a switching disturbance (m) acting on system\nSource code #  //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb.  data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance  Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables:  // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input  // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du =  // 1e-2.  Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err  Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp  // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control)  control_type = control_type | lds::kControlTypeDeltaU; // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(10); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation  // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input  Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals  y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,  // xTrue, mTrue saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":20,"href":"/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/","title":"examples/eg_plds_ctrl.cpp","section":"Files","content":"examples/eg_plds_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Going to simulate a switching disturbance (m) acting on system\nSource code #  //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains  // n.b., Can either use Vector (arma::Col) or std::vector  Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error  Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err  // Set control type bit mask, so controller knows what to do  size_t control_type = lds::kControlTypeIntY; // integral action  // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled...  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions  controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller  lds::poisson::Controller controller; { // Create model used for control.  lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance  Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition  // adaptively re-estimate process disturbance (m)  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type  controller.set_control_type(control_type); // set controller gains  controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above  // target:  data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val  y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference  data_t f = 0.5; // freq [=] Hz  Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system.  z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Notably, it does this in the  // log-linear space (i.e., log(y)).  //  // Therefore, it is only applicable to regulation problems or cases where  // reference trajectory changes slowly compared to system dynamics.  controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":21,"href":"/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/","title":"examples/eg_plds_est.cpp","section":"Files","content":"examples/eg_plds_est.cpp #  Functions #      Name     Matrix random_walk(size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0)   int main()    Function Details #  random_walk #  Matrix random_walk( size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0 ) main #  int main() Source code #  //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; // no. inputs  size_t n_x = 1; // no. states  size_t n_y = 1; // no. outputs  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.  // construct ground truth system...  lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  // Assign params.  system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation  // e.g., will create a model with incorrect disturbance  lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object  system_estimator = system_true; // wrong disturbance  Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.  // turn on adaptive disturbance estimation  system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting  // on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation :  // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus)  Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk  Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in...  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params  Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions  y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system.  z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update)  system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals  y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":22,"href":"/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/","title":"examples/eg_plds_switched_ctrl.cpp","section":"Files","content":"examples/eg_plds_switched_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Source code #  //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control  bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching  size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2  data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1  // simulated system being controlled  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well  // characterized by two LDS models with different input matrices.**  data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions  // reference  Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2  Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller  lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains  { // create switched controller sub-systems  // system 1  lds::poisson::System sys1(controlled_system); // set process noise covariance  Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m)  // n.b. using arbitrary default value for process noise if enabled.  sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset:  Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions  cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2  lds::poisson::System sys2 = sys1; // set parameters  sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s:  Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive.  k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables  size_t control_type = 0; // no integral action, etc  switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements  Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control.  Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in...  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params  Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val  y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain  // Assume another algorithm decodes this mode change and signals the  // switched_controller  Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1  { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2  if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system.  z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control  u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":23,"href":"/lds-ctrl-est/docs/api/files/","title":"Files","section":"LDS C+E Documentation","content":"Files #    examples/eg_glds_ctrl.cpp\n  examples/eg_glds_du_plds_ctrl.cpp\n  examples/eg_plds_ctrl.cpp\n  examples/eg_plds_est.cpp\n  examples/eg_plds_switched_ctrl.cpp\n  ldsCtrlEst_h/lds.h lds namespace\n  ldsCtrlEst_h/lds_ctrl.h Controller.\n  ldsCtrlEst_h/lds_fit.h LDS base fit type.\n  ldsCtrlEst_h/lds_fit_em.h subspace identification\n  ldsCtrlEst_h/lds_fit_ssid.h subspace identification\n  ldsCtrlEst_h/lds_gaussian.h glds namespace\n  ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.\n  ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.\n  ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.\n  ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.\n  ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.\n  ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.\n  ldsCtrlEst_h/lds_poisson.h plds namespace\n  ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.\n  ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.\n  ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.\n  ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.\n  ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.\n  ldsCtrlEst_h/lds_poisson_sys.h PLDS base type.\n  ldsCtrlEst_h/lds_sctrl.h SwitchedController type.\n  ldsCtrlEst_h/lds_sys.h LDS base type.\n  ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.\n  ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.\n  ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.\n  ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)\n  ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)\n  src/lds.cpp misc lds namespace functions\n  src/lds_gaussian_sys.cpp GLDS base type.\n  src/lds_poisson_sys.cpp PLDS base type.\n  src/lds_sys.cpp LDS base type.\n  src/lds_uniform_vecs.cpp Uniformly sized vectors.\n   Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":24,"href":"/lds-ctrl-est/docs/tutorials/eg_glds_control/","title":"GLDS Control","section":"LDS C+E Examples","content":"GLDS Control Tutorial #  This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.\nThe full code for this can be found here.\nPreamble #  In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system #  A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.\n// Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example,   \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\]   \\[\ry_{t} = x_t\r\\]  where  \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\)  .\nNow, create non-default parameters for this model.\n// Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); // output noise covariance  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.\n/// Going to simulate a switching disturbance (m) acting on system  size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb.  data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_y).fill(m_low); Finally, assign the parameters using corresponding set-methods.\n// Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); Creating the controller #  Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.\nFor the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.\n// make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nWith the controller constructed, control variables may be set.\n// Control variables:  // if following enabled, adapts set point with re-estimated process  // disturbance n.b., should not need integral action if this is enabled as the  // adaptive estimator minimizes DC error  bool do_adaptive_set_point = false; // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err  // setting initial state to target to avoid error at onset:  Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance  control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; } // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(9); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); Simulating control #  In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.\nThe control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.\n// Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); Example simulation result #  Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller\u0026rsquo;s online estimates of the output, state, and disturbance are given in purple.\n"},{"id":25,"href":"/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/","title":"include","section":"Files","content":"include #  Directories #     Name     ldsCtrlEst_h     Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":26,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds/","title":"lds","section":"Namespaces","content":"lds #  Linear Dynamical Systems (LDS) namespace. Namespaces #     Name     lds::gaussian Linear Dynamical Systems with Gaussian observations.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::Controller   class lds::EM   class lds::Fit LDS Fit Type.   class lds::SSID   class lds::SwitchedController SwitchedController Type.   class lds::System Linear Dynamical System Type.   class lds::UniformMatrixList   class lds::UniformSystemList   class lds::UniformVectorList    Types #      Name     enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}\nweighting options for SSID   enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2}   using double data_t   using arma::Col\u0026lt; data_t \u0026gt; Vector   using arma::Mat\u0026lt; data_t \u0026gt; Matrix   using arma::Cube\u0026lt; data_t \u0026gt; Cube   using arma::subview\u0026lt; data_t \u0026gt; View    Functions #      Name     void Limit(std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub)   void Limit(Vector \u0026amp; x, data_t lb, data_t ub)   void Limit(Matrix \u0026amp; x, data_t lb, data_t ub)   void Reassign(Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Vector in place   void Reassign(Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Matrix in place   void ForceSymPD(Matrix \u0026amp; X)\nforces matrix to be symmetric positive-definite   void ForceSymMinEig(Matrix \u0026amp; X, data_t eig_min =0)\nforces matrix to be symmetric and have a minimum eigenvalue   void lq(Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X)\nLQ decomposition.   Matrix calcCov(const Matrix \u0026amp; A, const Matrix \u0026amp; B)\nCalculate covariance matrix.    Attributes #      Name     const std::size_t kControlTypeDeltaU control designed to penalize change in input   const std::size_t kControlTypeIntY control using integral action   const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m   const data_t kInf Some useful numbers.   const data_t kPi   const data_t kDefaultP0 default state estimate covar   const data_t kDefaultQ0 default process noise covar   const data_t kDefaultR0 default output noise covar    Type Details #  SSIDWt #     Enumerator Value Description     kSSIDNone  None.   kSSIDMOESP  MOESP (AKA \u0026ldquo;robust method\u0026rdquo; in van Overschee 1996)   kSSIDCVA  CVA \u0026ldquo;Canonical Variate Analysis\u0026rdquo;.    Weighting options for singular value decomposition performed during subspace identification (SSID)\nReference:\nvan Overschee, de Moor. 1996. Subspace Identification for Linear Systems.\nMatrixListFreeDim #     Enumerator Value Description     kMatFreeDimNone  neither dim free to be hetero in mat list   kMatFreeDim1  allow 1st dim of mats in list to be hetero   kMatFreeDim2  allow 2nd dim of mats in list to be hetero    data_t #  using lds::data_t = typedef double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nVector #  using lds::Vector = typedef arma::Col\u0026lt;data_t\u0026gt;; Matrix #  using lds::Matrix = typedef arma::Mat\u0026lt;data_t\u0026gt;; Cube #  using lds::Cube = typedef arma::Cube\u0026lt;data_t\u0026gt;; View #  using lds::View = typedef arma::subview\u0026lt;data_t\u0026gt;; Function Details #  Limit #  inline void Limit( std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub ) Limit #  inline void Limit( Vector \u0026amp; x, data_t lb, data_t ub ) Limit #  inline void Limit( Matrix \u0026amp; x, data_t lb, data_t ub ) Reassign #  inline void Reassign( Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\n some some Vector other other Vector parenthetical optional description provided by caller to ease debugging  Reassign #  inline void Reassign( Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\n some some Matrix other other Matrix parenthetical optional description provided by caller to ease debugging  ForceSymPD #  void ForceSymPD( Matrix \u0026amp; X ) Parameters:\n X mutated matrix  ForceSymMinEig #  void ForceSymMinEig( Matrix \u0026amp; X, data_t eig_min =0 ) Parameters:\n X mutated matrix eig_min [optional] minimum eigen value  lq #  void lq( Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X ) Parameters:\n L lower triangle matrix Qt orthonormal matrix (transposed cf QR decomp) X matrix being decomposed  calcCov #  Matrix calcCov( const Matrix \u0026amp; A, const Matrix \u0026amp; B ) Parameters:\n A some matrix B some other matrix  Return: covariance\nAttribute Details #  kControlTypeDeltaU #  static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY #  static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM #  static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\nkInf #  static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); kPi #  static const data_t kPi = arma::datum::pi; kDefaultP0 #  static const data_t kDefaultP0 = 1e-6; kDefaultQ0 #  static const data_t kDefaultQ0 = 1e-6; kDefaultR0 #  static const data_t kDefaultR0 = 1e-2;  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":27,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1controller/","title":"lds::Controller","section":"Classes","content":"lds::Controller #  More\u0026hellip;\nInherited by lds::gaussian::Controller, lds::poisson::Controller, lds::SwitchedController\u0026lt; System \u0026gt;\nPublic Functions #      Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes #      Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type    Detailed Description #  template \u0026lt;typename System \u0026gt; class lds::Controller;   Public Function Details #  Controller #  Controller() =default  Controller #  inline Controller( const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\n sys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask  Template Parameters:\n System type derived from lds::System   Controller #  inline Controller( System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\n sys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask  Template Parameters:\n System type derived from lds::System   Control #  inline const Vector \u0026amp; Control( const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\n z measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true)  Return: updated control signal\nUpdates the control signal (single-step). This is the most flexible option, but requires user to have set the controller\u0026rsquo;s y_ref, x_ref, and u_ref variables.\n ControlOutputReference #  inline const Vector \u0026amp; ControlOutputReference( const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\n z measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_estimation [optional] whether to update state estimate (if false, effectively open-loop control) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true)  Return: updated control signal\nUpdates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.\n sys #  inline const System \u0026amp; sys() const  Kc #  inline const Matrix \u0026amp; Kc() const  Kc_inty #  inline const Matrix \u0026amp; Kc_inty() const  Kc_u #  inline const Matrix \u0026amp; Kc_u() const  g_design #  inline const Vector \u0026amp; g_design() const  u_ref #  inline const Vector \u0026amp; u_ref() const  x_ref #  inline const Vector \u0026amp; x_ref() const  y_ref #  inline const Vector \u0026amp; y_ref() const  control_type #  inline size_t control_type() const  tau_awu #  inline data_t tau_awu() const  u_lb #  inline data_t u_lb() const  u_ub #  inline data_t u_ub() const  set_sys #  inline void set_sys( const System \u0026amp; sys )  set_g_design #  inline void set_g_design( const Vector \u0026amp; g_design )  set_u_ref #  inline void set_u_ref( const Vector \u0026amp; u_ref )  set_x_ref #  inline void set_x_ref( const Vector \u0026amp; x_ref )  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) Reimplemented by: lds::poisson::SwitchedController::set_y_ref, lds::gaussian::SwitchedController::set_y_ref, lds::poisson::Controller::set_y_ref, lds::gaussian::Controller::set_y_ref\n set_Kc #  inline void set_Kc( const Matrix \u0026amp; Kc )  set_Kc_inty #  inline void set_Kc_inty( const Matrix \u0026amp; Kc_inty )  set_Kc_u #  inline void set_Kc_u( const Matrix \u0026amp; Kc_u )  set_tau_awu #  inline void set_tau_awu( data_t tau )  set_control_type #  inline void set_control_type( size_t control_type ) Parameters:\n control_type control type bit mask  Template Parameters:\n System type derived from lds::System   set_u_lb #  inline void set_u_lb( data_t u_lb ) Parameters:\n u_lb control lower bound   set_u_ub #  inline void set_u_ub( data_t u_ub ) Parameters:\n u_ub control upper bound   Reset #  inline void Reset()  Print #  inline void Print()  Protected Attribute Details #  **sys_** #  System sys_;  **u_** #  Vector u_;  **u_return_** #  Vector u_return_;  **g_design_** #  Vector g_design_;  **u_ref_** #  Vector u_ref_;  **u_ref_prev_** #  Vector u_ref_prev_;  **x_ref_** #  Vector x_ref_;  **y_ref_** #  Vector y_ref_;  **cx_ref_** #  Vector cx_ref_;  **Kc_** #  Matrix Kc_;  **Kc_u_** #  Matrix Kc_u_;  **Kc_inty_** #  Matrix Kc_inty_;  **du_ref_** #  Vector du_ref_;  **dv_ref_** #  Vector dv_ref_;  **v_ref_** #  Vector v_ref_;  **dv_** #  Vector dv_;  **v_** #  Vector v_;  **int_e_** #  Vector int_e_;  **int_e_awu_adjust_** #  Vector int_e_awu_adjust_;  **u_sat_** #  Vector u_sat_;  **do_control_prev_** #  bool do_control_prev_ = false;  **do_lock_control_prev_** #  bool do_lock_control_prev_ = false;  **u_saturated_** #  bool u_saturated_ = false;  **u_lb_** #  data_t u_lb_ {};  **u_ub_** #  data_t u_ub_ {};  **tau_awu_** #  data_t tau_awu_ {};  **k_awu_** #  data_t k_awu_ = 0;  **t_since_control_onset_** #  data_t t_since_control_onset_ = 0;  **control_type_** #  size_t control_type_ {};   Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":28,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1em/","title":"lds::EM","section":"Classes","content":"lds::EM #  More\u0026hellip;\nInherited by lds::gaussian::FitEM, lds::poisson::FitEM\nPublic Functions #      Name      EM() =default\nConstructs a new EMFit type.    EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.    EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.   virtual ~EM() =default   const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller.   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time)   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time)   const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance   const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1)   const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance   size_t n_t_tot()\ntotal number of time samples   const Vector \u0026amp; theta() const\ngets parameters updated in M step    Protected Functions #      Name     void Expectation(bool force_common_initial =false)\nExpectation step.   void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step.   void MaximizeDynamics()   void MaximizeQ()   void MaximizeInitial()   virtual void MaximizeOutput() =0   virtual void MaximizeMeasurement() =0   void Smooth(bool force_common_initial)\nget smoothed estimates   virtual void RecurseKe(Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t) =0\nrecursively update estimator gain Ke   void Reset()\nreset to initial conditions   void InitVars()\nInitializes the variables.   Vector UpdateTheta()\nupdates parameter list, theta    Protected Attributes #      Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   std::vector\u0026lt; Matrix \u0026gt; x_ state estimate   std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov   std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance   std::vector\u0026lt; Matrix \u0026gt; y_ output estimate   Matrix diag_y_   Matrix sum_E_x_t_x_t_ state covariance (current time)   Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1)   Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance   Fit fit_   Vector theta_   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials    Detailed Description #  template \u0026lt;typename Fit \u0026gt; class lds::EM;   Public Function Details #  EM #  EM() =default  EM #  EM( size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\n n_x number of states dt sample period u_train input training data z_train measurement training data   EM #  EM( const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\n fit0 initial fit u_train input training data z_train measurement training data   ~EM #  virtual ~EM() =default  Run #  const Fit \u0026amp; Run( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2 ) Parameters:\n calc_dynamics [optional] whether to calculate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law max_iter max number of iterations tol convergence tolerance (max fractional abs change)  Return: Fit\n ReturnData #  inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\n x #  inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const  y #  inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const  sum_E_x_t_x_t #  inline const Matrix \u0026amp; sum_E_x_t_x_t() const  sum_E_xu_tm1_xu_tm1 #  inline const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const  sum_E_xu_t_xu_tm1 #  inline const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const  n_t_tot #  inline size_t n_t_tot()  theta #  inline const Vector \u0026amp; theta() const  Protected Function Details #  Expectation #  void Expectation( bool force_common_initial =false ) Parameters:\n force_common_initial whether to force common initial condition for all trials   Maximization #  void Maximization( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false ) Parameters:\n calc_dynamics [optional] whether to caclulate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law   MaximizeDynamics #  void MaximizeDynamics()  MaximizeQ #  void MaximizeQ()  MaximizeInitial #  void MaximizeInitial()  MaximizeOutput #  virtual void MaximizeOutput() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput\n MaximizeMeasurement #  virtual void MaximizeMeasurement() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement\n Smooth #  void Smooth( bool force_common_initial ) Parameters:\n force_common_initial whether to force common initial conditions   RecurseKe #  virtual void RecurseKe( Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t ) =0 Parameters:\n Ke estimator gain P_pre cov of predicted state est. P_post cov of postior sate est. t time  Reimplemented by: lds::gaussian::FitEM::RecurseKe, lds::poisson::FitEM::RecurseKe\n Reset #  void Reset()  InitVars #  void InitVars()  UpdateTheta #  Vector UpdateTheta() Return: parameter list\n Protected Attribute Details #  **u_** #  UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_;  **z_** #  UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_;  **x_** #  std::vector\u0026lt; Matrix \u0026gt; x_;  **P_** #  std::vector\u0026lt; Cube \u0026gt; P_;  **P_t_tm1_** #  std::vector\u0026lt; Cube \u0026gt; P_t_tm1_;  **y_** #  std::vector\u0026lt; Matrix \u0026gt; y_;  **diag_y_** #  Matrix diag_y_;  **sum_E_x_t_x_t_** #  Matrix sum_E_x_t_x_t_;  **sum_E_xu_tm1_xu_tm1_** #  Matrix sum_E_xu_tm1_xu_tm1_;  **sum_E_xu_t_xu_tm1_** #  Matrix sum_E_xu_t_xu_tm1_;  **fit_** #  Fit fit_;  **theta_** #  Vector theta_;  **dt_** #  data_t dt_ {};  **n_u_** #  size_t n_u_ {};  **n_x_** #  size_t n_x_ {};  **n_y_** #  size_t n_y_ {};  **n_trials_** #  size_t n_trials_ {};  **n_t_** #  std::vector\u0026lt; size_t \u0026gt; n_t_;  **n_t_tot_** #  size_t n_t_tot_ {};   Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":29,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1fit/","title":"lds::Fit","section":"Classes","content":"lds::Fit #  LDS Fit Type. #include \u0026lt;lds_fit.h\u0026gt;\nInherited by lds::gaussian::Fit, lds::poisson::Fit\nPublic Functions #      Name      Fit() =default\nConstructs a new Fit.    Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit.   virtual ~Fit() =default   size_t n_u() const\ngets number of inputs   size_t n_x() const\ngets number of states   size_t n_y() const\ngets number of outputs   data_t dt() const\ngets sample period   const Matrix \u0026amp; A() const\ngets state matrix   const Matrix \u0026amp; B() const\ngets input matrix   const Vector \u0026amp; g() const\ngets input gain   const Vector \u0026amp; m() const\ngets process disturbance   const Matrix \u0026amp; Q() const\ngets process noise covariance   const Vector \u0026amp; x0() const\ngets initial state estimate   const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate   const Matrix \u0026amp; C() const\ngets output matrix   const Vector \u0026amp; d() const\ngets output bias   virtual const Matrix \u0026amp; R() const =0   void set_A(const Matrix \u0026amp; A)\nsets state matrix   void set_B(const Matrix \u0026amp; B)\nsets input matrix   void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor   void set_m(const Vector \u0026amp; m)\nsets process disturbance   void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance   virtual void set_R(const Matrix \u0026amp; R) =0\nsets output noise covariance (if any)   void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate   void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance   void set_C(const Matrix \u0026amp; C)\nsets output matrix   void set_d(const Vector \u0026amp; d)\nsets output bias   View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function   View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function   virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) =0\noutput function    Protected Attributes #      Name     data_t dt_ sample period   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Vector m_ process noise mean   Matrix Q_ process noise cov   Matrix C_ output matrix   Vector d_ output bias   Matrix R_ measurement noise   Vector x0_ initial state   Matrix P0_ initial covar   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs      Public Function Details #  Fit #  Fit() =default  Fit #  Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period   ~Fit #  virtual ~Fit() =default  n_u #  inline size_t n_u() const  n_x #  inline size_t n_x() const  n_y #  inline size_t n_y() const  dt #  inline data_t dt() const  A #  inline const Matrix \u0026amp; A() const  B #  inline const Matrix \u0026amp; B() const  g #  inline const Vector \u0026amp; g() const  m #  inline const Vector \u0026amp; m() const  Q #  inline const Matrix \u0026amp; Q() const  x0 #  inline const Vector \u0026amp; x0() const  P0 #  inline const Matrix \u0026amp; P0() const  C #  inline const Matrix \u0026amp; C() const  d #  inline const Vector \u0026amp; d() const  R #  virtual const Matrix \u0026amp; R() const =0 Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R\n set_A #  inline void set_A( const Matrix \u0026amp; A )  set_B #  inline void set_B( const Matrix \u0026amp; B )  set_g #  inline void set_g( const Vector \u0026amp; g )  set_m #  inline void set_m( const Vector \u0026amp; m )  set_Q #  inline void set_Q( const Matrix \u0026amp; Q )  set_R #  virtual void set_R( const Matrix \u0026amp; R ) =0 Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R\n set_x0 #  inline void set_x0( const Vector \u0026amp; x0 )  set_P0 #  inline void set_P0( const Matrix \u0026amp; P0 )  set_C #  inline void set_C( const Matrix \u0026amp; C )  set_d #  inline void set_d( const Vector \u0026amp; d )  f #  inline View f( Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t ) Parameters:\n x state estimate (over time) u input (over time) t time index  Return: view of updated state\n f #  inline View f( Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t ) Parameters:\n x_pre predicted state est. x_post posterior state est. u input (over time) t time index  Return: view of predicted state\n h #  virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) =0 Parameters:\n y output estimate (over time) x state estimate (over time) t time index  Return: output\nReimplemented by: lds::poisson::Fit::h, lds::gaussian::Fit::h\n Protected Attribute Details #  **dt_** #  data_t dt_ {};  **A_** #  Matrix A_;  **B_** #  Matrix B_;  **g_** #  Vector g_;  **m_** #  Vector m_;  **Q_** #  Matrix Q_;  **C_** #  Matrix C_;  **d_** #  Vector d_;  **R_** #  Matrix R_;  **x0_** #  Vector x0_;  **P0_** #  Matrix P0_;  **n_u_** #  size_t n_u_ {};  **n_x_** #  size_t n_x_ {};  **n_y_** #  size_t n_y_ {};   Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":30,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/","title":"lds::gaussian","section":"Namespaces","content":"lds::gaussian #  Linear Dynamical Systems with Gaussian observations. Classes #      Name     class lds::gaussian::Controller Gaussian-observation Controller Type.   class lds::gaussian::Fit GLDS Fit Type.   class lds::gaussian::FitEM GLDS E-M Fit Type.   class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.   class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.   class lds::gaussian::System Gaussian LDS Type.     Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":31,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1controller/","title":"lds::gaussian::Controller","section":"Classes","content":"lds::gaussian::Controller #  Gaussian-observation Controller Type. #include \u0026lt;lds_gaussian_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions #      Name     virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output    Additional inherited members #  Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type      Public Function Details #  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\n  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":32,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/","title":"lds::gaussian::Fit","section":"Classes","content":"lds::gaussian::Fit #  GLDS Fit Type. #include \u0026lt;lds_gaussian_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions #      Name      Fit() =default    Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit.   virtual const Matrix \u0026amp; R() const override\ngets measurement noise covariance   virtual void set_R(const Matrix \u0026amp; R) override\nsets measurement noise covariance   virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function    Additional inherited members #  Public Functions inherited from lds::Fit\n    Name     virtual ~Fit() =default   size_t n_u() const\ngets number of inputs   size_t n_x() const\ngets number of states   size_t n_y() const\ngets number of outputs   data_t dt() const\ngets sample period   const Matrix \u0026amp; A() const\ngets state matrix   const Matrix \u0026amp; B() const\ngets input matrix   const Vector \u0026amp; g() const\ngets input gain   const Vector \u0026amp; m() const\ngets process disturbance   const Matrix \u0026amp; Q() const\ngets process noise covariance   const Vector \u0026amp; x0() const\ngets initial state estimate   const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate   const Matrix \u0026amp; C() const\ngets output matrix   const Vector \u0026amp; d() const\ngets output bias   void set_A(const Matrix \u0026amp; A)\nsets state matrix   void set_B(const Matrix \u0026amp; B)\nsets input matrix   void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor   void set_m(const Vector \u0026amp; m)\nsets process disturbance   void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance   void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate   void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance   void set_C(const Matrix \u0026amp; C)\nsets output matrix   void set_d(const Vector \u0026amp; d)\nsets output bias   View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function   View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function    Protected Attributes inherited from lds::Fit\n    Name     data_t dt_ sample period   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Vector m_ process noise mean   Matrix Q_ process noise cov   Matrix C_ output matrix   Vector d_ output bias   Matrix R_ measurement noise   Vector x0_ initial state   Matrix P0_ initial covar   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs      Public Function Details #  Fit #  Fit() =default  Fit #  Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period   R #  inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\n set_R #  inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\n h #  inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\n y output estimate (over time) x state estimate (over time) t time index  Return: output\nReimplements: lds::Fit::h\n  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":33,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitem/","title":"lds::gaussian::FitEM","section":"Classes","content":"lds::gaussian::FitEM #  GLDS E-M Fit Type. More\u0026hellip;\n\n#include \u0026lt;lds_gaussian_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members #  Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name      EM() =default\nConstructs a new EMFit type.    EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.    EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.   virtual ~EM() =default   const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller.   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time)   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time)   const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance   const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1)   const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance   size_t n_t_tot()\ntotal number of time samples   const Vector \u0026amp; theta() const\ngets parameters updated in M step    Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name     void Expectation(bool force_common_initial =false)\nExpectation step.   void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step.   void MaximizeDynamics()   void MaximizeQ()   void MaximizeInitial()   void Smooth(bool force_common_initial)\nget smoothed estimates   void Reset()\nreset to initial conditions   void InitVars()\nInitializes the variables.   Vector UpdateTheta()\nupdates parameter list, theta    Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   std::vector\u0026lt; Matrix \u0026gt; x_ state estimate   std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov   std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance   std::vector\u0026lt; Matrix \u0026gt; y_ output estimate   Matrix diag_y_   Matrix sum_E_x_t_x_t_ state covariance (current time)   Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1)   Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance   Fit fit_   Vector theta_   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials    Detailed Description #  class lds::gaussian::FitEM; This type is used in the process of fitting GLDS models by expectation-maximization (EM).\n   Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":34,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitssid/","title":"lds::gaussian::FitSSID","section":"Classes","content":"lds::gaussian::FitSSID #  Subspace Identification (SSID) for GLDS. #include \u0026lt;lds_gaussian_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members #  Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name      SSID() =default\nConstructs a new SSIDFit type.    SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type.   std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller.    Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name     void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d)   void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix.   void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD)   void Solve(data_t wt_dc)\nsolves for LDS parameters   void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C    Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   Matrix D_ block-Hankel I/O data matrix   Fit fit_ fit   Matrix g_dc_ I/O gain @ DC.   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_h_   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials   Matrix L_ lower triangle decomp of covariance matrix   Vector s_ singular values   Matrix ext_obs_t_ extended observability matrix       Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":35,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1switchedcontroller/","title":"lds::gaussian::SwitchedController","section":"Classes","content":"lds::gaussian::SwitchedController #  Gaussian-observation SwitchedController Type. #include \u0026lt;lds_gaussian_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions #      Name     virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output    Additional inherited members #  Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\n    Name      SwitchedController() =default\nConstructs a new SwitchedController.    SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController.    SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems).   void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller.   void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains   void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving)   void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains   void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving)   void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains   void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving)   void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design   void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving)    Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\n    Name     std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between   size_t n_sys_ number of systems   size_t idx_ current system/controller index.   UniformMatrixList Kc_list_   UniformMatrixList Kc_inty_list_   UniformMatrixList Kc_u_list_   UniformVectorList g_design_list_    Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type      Public Function Details #  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\n  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":36,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/","title":"lds::gaussian::System","section":"Classes","content":"lds::gaussian::System #  Gaussian LDS Type. #include \u0026lt;lds_gaussian_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions #      Name      System() =default\nConstructs a new System.    System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)\nConstructs a new Gaussian System.   virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement.   const Matrix \u0026amp; R() const\nGet output noise covariance.   void set_Q(const Matrix \u0026amp; Q)   void set_R(const Matrix \u0026amp; R)\nSet output noise covariance.   void set_Ke(const Matrix \u0026amp; Ke)\nSet estimator gain.   void set_Ke_m(const Matrix \u0026amp; Ke_m)\nSet disturbance estimator gain.   void Print()\nPrint system variables to stdout.    Protected Functions #      Name     virtual void h() override\nSystem output function.   virtual void RecurseKe() override\nRecursively update estimator gain.    Protected Attributes #      Name     Matrix R_ covariance of output noise   bool do_recurse_Ke_ whether to recursively calculate estimator gain    Additional inherited members #  Public Functions inherited from lds::System\n    Name     virtual ~System()   void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates.   void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function   size_t n_u() const\nGet number of inputs.   size_t n_x() const\nGet number of states.   size_t n_y() const\nGet number of outputs.   data_t dt() const\nGet sample period.   const Vector \u0026amp; x() const\nGet current state.   const Matrix \u0026amp; P() const\nGet covariance of state estimate.   const Vector \u0026amp; m() const\nGet current process disturbance/bias.   const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate.   const Vector \u0026amp; cx() const\nGet C*x.   const Vector \u0026amp; y() const\nGet output.   const Vector \u0026amp; x0() const\nGet initial state.   const Vector \u0026amp; m0() const\nGet initial disturbance.   const Matrix \u0026amp; A() const\nGet state matrix.   const Matrix \u0026amp; B() const\nGet input matrix.   const Vector \u0026amp; g() const\nGet input gain/conversion factor.   const Matrix \u0026amp; C() const\nGet output matrix.   const Vector \u0026amp; d() const\nGet output bias.   const Matrix \u0026amp; Ke() const\nGet estimator gain.   const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m)   const Matrix \u0026amp; Q()\nGet process noise covariance.   const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation.   const Matrix \u0026amp; P0()\nGet covariance of initial state.   const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance.   void set_A(const Matrix \u0026amp; A)\nSet state matrix.   void set_B(const Matrix \u0026amp; B)\nSet input matrix.   void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance.   void set_g(const Vector \u0026amp; g)\nSet input gain.   void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation.   void set_x0(const Vector \u0026amp; x0)\nSet initial state.   void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state.   void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance.   void set_C(const Matrix \u0026amp; C)\nSet output matrix.   void set_d(const Vector \u0026amp; d)\nSet output bias.   void set_x(const Vector \u0026amp; x)\nSet state of system.   void Reset()\nReset system variables.    Protected Functions inherited from lds::System\n    Name     void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)    Public Attributes inherited from lds::System\n    Name     bool do_adapt_m whether to adaptively estimate disturbance m    Protected Attributes inherited from lds::System\n    Name     std::size_t n_x_ number of states   std::size_t n_u_ number of inputs   std::size_t n_y_ number of outputs   data_t dt_ sample period   Vector x_ state   Matrix P_ covariance of state estimate   Vector m_ process disturbance   Matrix P_m_ covariance of disturbance estimate   Vector cx_ C*x.   Vector y_ output   Vector z_ measurement   Vector x0_ initial state   Matrix P0_ covariance of initial state estimate   Vector m0_ initial process disturbance   Matrix P0_m_ covariance of initial disturbance est.   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Matrix Q_ covariance of process noise   Matrix Q_m_ covariance of disturbance random walk   Matrix C_ output matrix   Vector d_ output bias   Matrix Ke_ estimator gain   Matrix Ke_m_ estimator gain for process disturbance      Public Function Details #  System #  System() =default  System #  System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0 ) Parameters:\n n_u number of inputs (u) n_x number of states (x) n_y number of outputs (y) dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) r0 [optional] initial diagonal elements of output noise covariance (R)   Simulate #  virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\n u_tm1 input at t-1  Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\n R #  inline const Matrix \u0026amp; R() const  set_Q #  inline void set_Q( const Matrix \u0026amp; Q )  set_R #  inline void set_R( const Matrix \u0026amp; R )  set_Ke #  inline void set_Ke( const Matrix \u0026amp; Ke )  set_Ke_m #  inline void set_Ke_m( const Matrix \u0026amp; Ke_m )  Print #  void Print()  Protected Function Details #  h #  inline virtual void h() override Reimplements: lds::System::h\n RecurseKe #  virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\n Protected Attribute Details #  **R_** #  Matrix R_;  **do_recurse_Ke_** #  bool do_recurse_Ke_ {};   Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":37,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/","title":"lds::poisson","section":"Namespaces","content":"lds::poisson #  Linear Dynamical Systems with Poisson observations. Classes #      Name     class lds::poisson::Controller PLDS Controller Type.   class lds::poisson::Fit PLDS Fit Type.   class lds::poisson::FitEM PLDS E-M Fit Type.   class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS.   class lds::poisson::SwitchedController Poisson-observation SwitchedController Type.   class lds::poisson::System Poisson System type.    Attributes #      Name     std::random_device rd random device for simulating poisson data   std::mt19937 rng random number generator for simulating poisson data    Attribute Details #  rd #  static std::random_device rd; rng #  static std::mt19937 rng = std::mt19937( rd());  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":38,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/","title":"lds::poisson::Controller","section":"Classes","content":"lds::poisson::Controller #  PLDS Controller Type. #include \u0026lt;lds_poisson_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions #      Name     virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output.    Additional inherited members #  Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type      Public Function Details #  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\n  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":39,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/","title":"lds::poisson::Fit","section":"Classes","content":"lds::poisson::Fit #  PLDS Fit Type. #include \u0026lt;lds_poisson_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions #      Name      Fit() =default    Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit.   virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function   virtual void set_R(const Matrix \u0026amp; R) override\nsets output noise covariance (if any)   virtual const Matrix \u0026amp; R() const override    Additional inherited members #  Public Functions inherited from lds::Fit\n    Name     virtual ~Fit() =default   size_t n_u() const\ngets number of inputs   size_t n_x() const\ngets number of states   size_t n_y() const\ngets number of outputs   data_t dt() const\ngets sample period   const Matrix \u0026amp; A() const\ngets state matrix   const Matrix \u0026amp; B() const\ngets input matrix   const Vector \u0026amp; g() const\ngets input gain   const Vector \u0026amp; m() const\ngets process disturbance   const Matrix \u0026amp; Q() const\ngets process noise covariance   const Vector \u0026amp; x0() const\ngets initial state estimate   const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate   const Matrix \u0026amp; C() const\ngets output matrix   const Vector \u0026amp; d() const\ngets output bias   void set_A(const Matrix \u0026amp; A)\nsets state matrix   void set_B(const Matrix \u0026amp; B)\nsets input matrix   void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor   void set_m(const Vector \u0026amp; m)\nsets process disturbance   void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance   void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate   void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance   void set_C(const Matrix \u0026amp; C)\nsets output matrix   void set_d(const Vector \u0026amp; d)\nsets output bias   View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function   View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function    Protected Attributes inherited from lds::Fit\n    Name     data_t dt_ sample period   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Vector m_ process noise mean   Matrix Q_ process noise cov   Matrix C_ output matrix   Vector d_ output bias   Matrix R_ measurement noise   Vector x0_ initial state   Matrix P0_ initial covar   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs      Public Function Details #  Fit #  Fit() =default  Fit #  inline Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period   h #  inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\n y output estimate (over time) x state estimate (over time) t time index  Return: output\nReimplements: lds::Fit::h\n set_R #  inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\n R #  inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\n  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":40,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitem/","title":"lds::poisson::FitEM","section":"Classes","content":"lds::poisson::FitEM #  PLDS E-M Fit Type. More\u0026hellip;\n\n#include \u0026lt;lds_poisson_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members #  Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name      EM() =default\nConstructs a new EMFit type.    EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.    EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.   virtual ~EM() =default   const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller.   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time)   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time)   const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance   const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1)   const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance   size_t n_t_tot()\ntotal number of time samples   const Vector \u0026amp; theta() const\ngets parameters updated in M step    Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name     void Expectation(bool force_common_initial =false)\nExpectation step.   void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step.   void MaximizeDynamics()   void MaximizeQ()   void MaximizeInitial()   void Smooth(bool force_common_initial)\nget smoothed estimates   void Reset()\nreset to initial conditions   void InitVars()\nInitializes the variables.   Vector UpdateTheta()\nupdates parameter list, theta    Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   std::vector\u0026lt; Matrix \u0026gt; x_ state estimate   std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov   std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance   std::vector\u0026lt; Matrix \u0026gt; y_ output estimate   Matrix diag_y_   Matrix sum_E_x_t_x_t_ state covariance (current time)   Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1)   Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance   Fit fit_   Vector theta_   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials    Detailed Description #  class lds::poisson::FitEM; This type is used in the process of fitting PLDS models by expectation-maximization (EM).\n   Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":41,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitssid/","title":"lds::poisson::FitSSID","section":"Classes","content":"lds::poisson::FitSSID #  Subspace Identification (SSID) for PLDS. #include \u0026lt;lds_poisson_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members #  Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name      SSID() =default\nConstructs a new SSIDFit type.    SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type.   std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller.    Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name     void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d)   void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix.   void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD)   void Solve(data_t wt_dc)\nsolves for LDS parameters   void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C    Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   Matrix D_ block-Hankel I/O data matrix   Fit fit_ fit   Matrix g_dc_ I/O gain @ DC.   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_h_   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials   Matrix L_ lower triangle decomp of covariance matrix   Vector s_ singular values   Matrix ext_obs_t_ extended observability matrix       Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":42,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1switchedcontroller/","title":"lds::poisson::SwitchedController","section":"Classes","content":"lds::poisson::SwitchedController #  Poisson-observation SwitchedController Type. #include \u0026lt;lds_poisson_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions #      Name     virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output.    Additional inherited members #  Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\n    Name      SwitchedController() =default\nConstructs a new SwitchedController.    SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController.    SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems).   void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller.   void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains   void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving)   void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains   void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving)   void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains   void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving)   void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design   void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving)    Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\n    Name     std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between   size_t n_sys_ number of systems   size_t idx_ current system/controller index.   UniformMatrixList Kc_list_   UniformMatrixList Kc_inty_list_   UniformMatrixList Kc_u_list_   UniformVectorList g_design_list_    Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type      Public Function Details #  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\n  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":43,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/","title":"lds::poisson::System","section":"Classes","content":"lds::poisson::System #  Poisson System type. #include \u0026lt;lds_poisson_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions #      Name      System() =default\nConstructs a new System.    System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nConstructs a new Poisson System.   virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement.    Protected Functions #      Name     virtual void h() override\nSystem output function.   virtual void RecurseKe() override\nRecursively recalculate estimator gain (Ke)    Additional inherited members #  Public Functions inherited from lds::System\n    Name     virtual ~System()   void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates.   void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function   size_t n_u() const\nGet number of inputs.   size_t n_x() const\nGet number of states.   size_t n_y() const\nGet number of outputs.   data_t dt() const\nGet sample period.   const Vector \u0026amp; x() const\nGet current state.   const Matrix \u0026amp; P() const\nGet covariance of state estimate.   const Vector \u0026amp; m() const\nGet current process disturbance/bias.   const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate.   const Vector \u0026amp; cx() const\nGet C*x.   const Vector \u0026amp; y() const\nGet output.   const Vector \u0026amp; x0() const\nGet initial state.   const Vector \u0026amp; m0() const\nGet initial disturbance.   const Matrix \u0026amp; A() const\nGet state matrix.   const Matrix \u0026amp; B() const\nGet input matrix.   const Vector \u0026amp; g() const\nGet input gain/conversion factor.   const Matrix \u0026amp; C() const\nGet output matrix.   const Vector \u0026amp; d() const\nGet output bias.   const Matrix \u0026amp; Ke() const\nGet estimator gain.   const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m)   const Matrix \u0026amp; Q()\nGet process noise covariance.   const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation.   const Matrix \u0026amp; P0()\nGet covariance of initial state.   const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance.   void set_A(const Matrix \u0026amp; A)\nSet state matrix.   void set_B(const Matrix \u0026amp; B)\nSet input matrix.   void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance.   void set_g(const Vector \u0026amp; g)\nSet input gain.   void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance.   void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation.   void set_x0(const Vector \u0026amp; x0)\nSet initial state.   void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state.   void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance.   void set_C(const Matrix \u0026amp; C)\nSet output matrix.   void set_d(const Vector \u0026amp; d)\nSet output bias.   void set_x(const Vector \u0026amp; x)\nSet state of system.   void Reset()\nReset system variables.   void Print()\nPrint system variables to stdout.    Protected Functions inherited from lds::System\n    Name     void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)    Public Attributes inherited from lds::System\n    Name     bool do_adapt_m whether to adaptively estimate disturbance m    Protected Attributes inherited from lds::System\n    Name     std::size_t n_x_ number of states   std::size_t n_u_ number of inputs   std::size_t n_y_ number of outputs   data_t dt_ sample period   Vector x_ state   Matrix P_ covariance of state estimate   Vector m_ process disturbance   Matrix P_m_ covariance of disturbance estimate   Vector cx_ C*x.   Vector y_ output   Vector z_ measurement   Vector x0_ initial state   Matrix P0_ covariance of initial state estimate   Vector m0_ initial process disturbance   Matrix P0_m_ covariance of initial disturbance est.   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Matrix Q_ covariance of process noise   Matrix Q_m_ covariance of disturbance random walk   Matrix C_ output matrix   Vector d_ output bias   Matrix Ke_ estimator gain   Matrix Ke_m_ estimator gain for process disturbance      Public Function Details #  System #  System() =default  System #  System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q)   Simulate #  virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\n u_tm1 input at t-1  Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\n Protected Function Details #  h #  inline virtual void h() override Reimplements: lds::System::h\n RecurseKe #  virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nRecursively recalculate estimator gain (Ke).\nReferences:\nSmith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.\nEden UT, \u0026hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.\n  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":44,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/","title":"lds::SSID","section":"Classes","content":"lds::SSID #  More\u0026hellip;\nInherited by lds::gaussian::FitSSID, lds::poisson::FitSSID\nPublic Functions #      Name      SSID() =default\nConstructs a new SSIDFit type.    SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type.   std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller.    Protected Functions #      Name     void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d)   void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix.   virtual void DecomposeData() =0\nDecompose data to lower-triangular matrix (used in Solve)   void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD)   void Solve(data_t wt_dc)\nsolves for LDS parameters   void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C    Protected Attributes #      Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   Matrix D_ block-Hankel I/O data matrix   Fit fit_ fit   Matrix g_dc_ I/O gain @ DC.   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_h_   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials   Matrix L_ lower triangle decomp of covariance matrix   Vector s_ singular values   Matrix ext_obs_t_ extended observability matrix    Detailed Description #  template \u0026lt;typename Fit \u0026gt; class lds::SSID;   Public Function Details #  SSID #  SSID() =default  SSID #  SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf) ) Parameters:\n n_x number of states n_h size of block-hankel data matrix dt sample period u_train input training data z_train measurement training data d output bias   Run #  std::tuple\u0026lt; Fit, Vector \u0026gt; Run( SSIDWt ssid_wt ) Parameters:\n ssid_wt weight for singular value decomp  Return: tuple (Fit, singular values)\n ReturnData #  inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\n Protected Function Details #  CalcD #  void CalcD( data_t t_silence =0.1, data_t thresh_silence =0.001 ) Parameters:\n t_silence threshold on period of time that qualifies as \u0026ldquo;silence\u0026rdquo; thresh_silence threshold on input amplitude u that qualifies as \u0026ldquo;silence\u0026rdquo;   CreateHankelDataMat #  void CreateHankelDataMat() Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.\n DecomposeData #  virtual void DecomposeData() =0 Reimplemented by: lds::poisson::FitSSID::DecomposeData, lds::gaussian::FitSSID::DecomposeData\n CalcSVD #  void CalcSVD( SSIDWt wt ) Parameters:\n ssid_wt weight for SVD   Solve #  void Solve( data_t wt_dc ) Parameters:\n wt_dc weight placed on getting correct DC I/O gain   RecomputeExtObs #  void RecomputeExtObs()  Protected Attribute Details #  **u_** #  UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_;  **z_** #  UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_;  **D_** #  Matrix D_;  **fit_** #  Fit fit_;  **g_dc_** #  Matrix g_dc_;  **dt_** #  data_t dt_ {};  **n_u_** #  size_t n_u_ {};  **n_x_** #  size_t n_x_ {};  **n_y_** #  size_t n_y_ {};  **n_h_** #  size_t n_h_ {};  **n_trials_** #  size_t n_trials_ {};  **n_t_** #  std::vector\u0026lt; size_t \u0026gt; n_t_;  **n_t_tot_** #  size_t n_t_tot_ {};  **L_** #  Matrix L_;  **s_** #  Vector s_;  **ext_obs_t_** #  Matrix ext_obs_t_;   Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":45,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/","title":"lds::SwitchedController","section":"Classes","content":"lds::SwitchedController #  SwitchedController Type. More\u0026hellip;\n\n#include \u0026lt;lds_sctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nInherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController\nPublic Functions #      Name      SwitchedController() =default\nConstructs a new SwitchedController.    SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController.    SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems).   void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller.   void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains   void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving)   void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains   void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving)   void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains   void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving)   void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design   void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving)    Protected Attributes #      Name     std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between   size_t n_sys_ number of systems   size_t idx_ current system/controller index.   UniformMatrixList Kc_list_   UniformMatrixList Kc_inty_list_   UniformMatrixList Kc_u_list_   UniformVectorList g_design_list_    Additional inherited members #  Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref)   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type    Detailed Description #  template \u0026lt;typename System \u0026gt; class lds::SwitchedController;   Public Function Details #  SwitchedController #  SwitchedController() =default  SwitchedController #  inline SwitchedController( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\n systems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask   SwitchedController #  inline SwitchedController( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\n systems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask   Switch #  inline void Switch( size_t idx, bool do_force_switch =false ) Parameters:\n idx index do_force_switch whether to force a system switch even if already there.   set_Kc #  inline void set_Kc( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc )  set_Kc #  inline void set_Kc( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc )  set_Kc_inty #  inline void set_Kc_inty( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty )  set_Kc_inty #  inline void set_Kc_inty( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty )  set_Kc_u #  inline void set_Kc_u( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u )  set_Kc_u #  inline void set_Kc_u( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u )  set_g_design #  inline void set_g_design( const UniformVectorList \u0026amp; g )  set_g_design #  inline void set_g_design( UniformVectorList \u0026amp;\u0026amp; g )  Protected Attribute Details #  **systems_** #  std::vector\u0026lt; System \u0026gt; systems_;  **n_sys_** #  size_t n_sys_ {};  **idx_** #  size_t idx_ {};  **Kc_list_** #  UniformMatrixList Kc_list_;  **Kc_inty_list_** #  UniformMatrixList Kc_inty_list_;  **Kc_u_list_** #  UniformMatrixList Kc_u_list_;  **g_design_list_** #  UniformVectorList g_design_list_;   Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":46,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1system/","title":"lds::System","section":"Classes","content":"lds::System #  Linear Dynamical System Type. #include \u0026lt;lds_sys.h\u0026gt;\nInherited by lds::gaussian::System, lds::poisson::System\nPublic Functions #      Name      System() =default\nConstructs a new System.    System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nconstructs a new System   virtual ~System()   void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates.   virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) =0\nsimulates system (single time step)   void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function   virtual void h() =0\nsystem output function   size_t n_u() const\nGet number of inputs.   size_t n_x() const\nGet number of states.   size_t n_y() const\nGet number of outputs.   data_t dt() const\nGet sample period.   const Vector \u0026amp; x() const\nGet current state.   const Matrix \u0026amp; P() const\nGet covariance of state estimate.   const Vector \u0026amp; m() const\nGet current process disturbance/bias.   const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate.   const Vector \u0026amp; cx() const\nGet C*x.   const Vector \u0026amp; y() const\nGet output.   const Vector \u0026amp; x0() const\nGet initial state.   const Vector \u0026amp; m0() const\nGet initial disturbance.   const Matrix \u0026amp; A() const\nGet state matrix.   const Matrix \u0026amp; B() const\nGet input matrix.   const Vector \u0026amp; g() const\nGet input gain/conversion factor.   const Matrix \u0026amp; C() const\nGet output matrix.   const Vector \u0026amp; d() const\nGet output bias.   const Matrix \u0026amp; Ke() const\nGet estimator gain.   const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m)   const Matrix \u0026amp; Q()\nGet process noise covariance.   const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation.   const Matrix \u0026amp; P0()\nGet covariance of initial state.   const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance.   void set_A(const Matrix \u0026amp; A)\nSet state matrix.   void set_B(const Matrix \u0026amp; B)\nSet input matrix.   void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance.   void set_g(const Vector \u0026amp; g)\nSet input gain.   void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance.   void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation.   void set_x0(const Vector \u0026amp; x0)\nSet initial state.   void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state.   void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance.   void set_C(const Matrix \u0026amp; C)\nSet output matrix.   void set_d(const Vector \u0026amp; d)\nSet output bias.   void set_x(const Vector \u0026amp; x)\nSet state of system.   void Reset()\nReset system variables.   void Print()\nPrint system variables to stdout.    Protected Functions #      Name     virtual void RecurseKe() =0\nRecursively recalculate estimator gain (Ke)   void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)    Public Attributes #      Name     bool do_adapt_m whether to adaptively estimate disturbance m    Protected Attributes #      Name     std::size_t n_x_ number of states   std::size_t n_u_ number of inputs   std::size_t n_y_ number of outputs   data_t dt_ sample period   Vector x_ state   Matrix P_ covariance of state estimate   Vector m_ process disturbance   Matrix P_m_ covariance of disturbance estimate   Vector cx_ C*x.   Vector y_ output   Vector z_ measurement   Vector x0_ initial state   Matrix P0_ covariance of initial state estimate   Vector m0_ initial process disturbance   Matrix P0_m_ covariance of initial disturbance est.   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Matrix Q_ covariance of process noise   Matrix Q_m_ covariance of disturbance random walk   Matrix C_ output matrix   Vector d_ output bias   Matrix Ke_ estimator gain   Matrix Ke_m_ estimator gain for process disturbance      Public Function Details #  System #  System() =default  System #  System( size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period p0 diagonal elements for state estimate covariance q0 diagonal elements for process noise covariance   ~System #  inline virtual ~System()  Filter #  void Filter( const Vector \u0026amp; u_tm1, const Vector \u0026amp; z ) Parameters:\n u_tm1 input at t-minus-1 z_t current measurement  Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.\n Simulate #  virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) =0 Parameters:\n u_tm1 input at time t-1  Return: simulated measurement at time t\nReimplemented by: lds::poisson::System::Simulate, lds::gaussian::System::Simulate\n f #  inline void f( const Vector \u0026amp; u, bool do_add_noise =false ) Parameters:\n u input do_add_noise whether to add simulated process noise   h #  virtual void h() =0 Reimplemented by: lds::poisson::System::h, lds::gaussian::System::h\n n_u #  inline size_t n_u() const  n_x #  inline size_t n_x() const  n_y #  inline size_t n_y() const  dt #  inline data_t dt() const  x #  inline const Vector \u0026amp; x() const  P #  inline const Matrix \u0026amp; P() const  m #  inline const Vector \u0026amp; m() const  P_m #  inline const Matrix \u0026amp; P_m() const  cx #  inline const Vector \u0026amp; cx() const  y #  inline const Vector \u0026amp; y() const  x0 #  inline const Vector \u0026amp; x0() const  m0 #  inline const Vector \u0026amp; m0() const  A #  inline const Matrix \u0026amp; A() const  B #  inline const Matrix \u0026amp; B() const  g #  inline const Vector \u0026amp; g() const  C #  inline const Matrix \u0026amp; C() const  d #  inline const Vector \u0026amp; d() const  Ke #  inline const Matrix \u0026amp; Ke() const  Ke_m #  inline const Matrix \u0026amp; Ke_m() const  Q #  inline const Matrix \u0026amp; Q()  Q_m #  inline const Matrix \u0026amp; Q_m()  P0 #  inline const Matrix \u0026amp; P0()  P0_m #  inline const Matrix \u0026amp; P0_m()  set_A #  inline void set_A( const Matrix \u0026amp; A )  set_B #  inline void set_B( const Matrix \u0026amp; B )  set_m #  inline void set_m( const Vector \u0026amp; m, bool do_force_assign =false )  set_g #  inline void set_g( const Vector \u0026amp; g )  set_Q #  inline void set_Q( const Matrix \u0026amp; Q )  set_Q_m #  inline void set_Q_m( const Matrix \u0026amp; Q_m )  set_x0 #  inline void set_x0( const Vector \u0026amp; x0 )  set_P0 #  inline void set_P0( const Matrix \u0026amp; P0 )  set_P0_m #  inline void set_P0_m( const Matrix \u0026amp; P0_m )  set_C #  inline void set_C( const Matrix \u0026amp; C )  set_d #  inline void set_d( const Vector \u0026amp; d )  set_x #  inline void set_x( const Vector \u0026amp; x )  Reset #  void Reset()  Print #  void Print()  Protected Function Details #  RecurseKe #  virtual void RecurseKe() =0 Reimplemented by: lds::poisson::System::RecurseKe, lds::gaussian::System::RecurseKe\n InitVars #  void InitVars( data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 )  Public Attribute Details #  do_adapt_m #  bool do_adapt_m {};  Protected Attribute Details #  **n_x_** #  std::size_t n_x_ {};  **n_u_** #  std::size_t n_u_ {};  **n_y_** #  std::size_t n_y_ {};  **dt_** #  data_t dt_ {};  **x_** #  Vector x_;  **P_** #  Matrix P_;  **m_** #  Vector m_;  **P_m_** #  Matrix P_m_;  **cx_** #  Vector cx_;  **y_** #  Vector y_;  **z_** #  Vector z_;  **x0_** #  Vector x0_;  **P0_** #  Matrix P0_;  **m0_** #  Vector m0_;  **P0_m_** #  Matrix P0_m_;  **A_** #  Matrix A_;  **B_** #  Matrix B_;  **g_** #  Vector g_;  **Q_** #  Matrix Q_;  **Q_m_** #  Matrix Q_m_;  **C_** #  Matrix C_;  **d_** #  Vector d_;  **Ke_** #  Matrix Ke_;  **Ke_m_** #  Matrix Ke_m_;   Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":47,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/","title":"lds::UniformMatrixList","section":"Classes","content":"lds::UniformMatrixList #  More\u0026hellip;\nInherits from std::vector\u0026lt; Matrix \u0026gt;\nPublic Functions #      Name      UniformMatrixList() =default\nConstructs a new UniformMatrixList.    UniformMatrixList(const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent.    UniformMatrixList(std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent.    UniformMatrixList(std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent.    UniformMatrixList(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nConstructs a new UniformMatrixList (copy).    UniformMatrixList(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nConstructs a new UniformMatrixList (move).    ~UniformMatrixList() =default\nDestroys the object.   const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim(size_t n =0) const\ngets dimensions of uniformly sized matrices   size_t size()\nsize of container   const Matrix \u0026amp; at(size_t n)\ngets reference to n^th element   void Swap(Matrix \u0026amp; that, size_t n)\nswaps input matrix with n^th matrix of list   UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nassigns the contents (copy)   UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nassigns the contents (move)    Detailed Description #  template \u0026lt;MatrixListFreeDim D =kMatFreeDimNone\u0026gt; class lds::UniformMatrixList;   Public Function Details #  UniformMatrixList #  UniformMatrixList() =default  UniformMatrixList #  explicit UniformMatrixList( const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\n mats input matrices dim dimensions   UniformMatrixList #  explicit UniformMatrixList( std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\n mats input matrices dim dimensions   UniformMatrixList #  UniformMatrixList( std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\n mats input matrices dim dimensions   UniformMatrixList #  UniformMatrixList( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\n that another UniformMatrixList   UniformMatrixList #  UniformMatrixList( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\n that another UniformMatrixList   ~UniformMatrixList #  ~UniformMatrixList() =default  dim #  inline const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim( size_t n =0 ) const Parameters:\n n [optional] index in list of matrices  Return: dimensions\n size #  inline size_t size()  at #  inline const Matrix \u0026amp; at( size_t n )  Swap #  inline void Swap( Matrix \u0026amp; that, size_t n ) Parameters:\n that input matrix n index where the matrix is moved   operator= #  inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\n that another UniformMatrixList  Return: reference to object\n operator= #  inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\n that another UniformMatrixList  Return: reference to object\n  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":48,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/","title":"lds::UniformSystemList","section":"Classes","content":"lds::UniformSystemList #  More\u0026hellip;\nInherits from std::vector\u0026lt; System \u0026gt;\nPublic Functions #      Name      UniformSystemList() =default\nConstructs a new UniformSystemList.    UniformSystemList(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by copying existing vector of System if dimensions consistent.    UniformSystemList(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by moving existing vector of System if dimensions consistent.    UniformSystemList(std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList from initializer_list of System if dimensions consistent.    UniformSystemList(const UniformSystemList \u0026amp; that)\nConstructs a new UniformSystemList (copy).    UniformSystemList(UniformSystemList \u0026amp;\u0026amp; that)\nConstructs a new UniformSystemList (move).    ~UniformSystemList() =default\nDestroys the object.   const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const\ngets dimensions of the uniformly sized systems   size_t size()\nsize of container   const System \u0026amp; at(size_t n)\ngets reference to n^th element   void Swap(System \u0026amp; that, size_t n)\nswaps input system with n^th system of list   UniformSystemList \u0026amp; operator=(const UniformSystemList \u0026amp; that)\nassigns the contents (copy)   UniformSystemList \u0026amp; operator=(UniformSystemList \u0026amp;\u0026amp; that)\nassigns the contents (move)    Detailed Description #  template \u0026lt;typename System\u0026gt; class lds::UniformSystemList;   Public Function Details #  UniformSystemList #  UniformSystemList() =default  UniformSystemList #  explicit UniformSystemList( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\n systems input systems dim dimensions (n_u, n_x, n_y)   UniformSystemList #  explicit UniformSystemList( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\n systems input systems dim dimensions (n_u, n_x, n_y)   UniformSystemList #  UniformSystemList( std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\n systems input systems dim dimensions (n_u, n_x, n_y)   UniformSystemList #  UniformSystemList( const UniformSystemList \u0026amp; that ) Parameters:\n that another UniformSystemList   UniformSystemList #  UniformSystemList( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\n that another UniformSystemList   ~UniformSystemList #  ~UniformSystemList() =default  dim #  inline const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const  size #  inline size_t size()  at #  inline const System \u0026amp; at( size_t n )  Swap #  inline void Swap( System \u0026amp; that, size_t n ) Parameters:\n that input system n index where the system is moved   operator= #  inline UniformSystemList \u0026amp; operator=( const UniformSystemList \u0026amp; that ) Parameters:\n that another UniformSystemList  Return: reference to object\n operator= #  inline UniformSystemList \u0026amp; operator=( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\n that another UniformSystemList  Return: reference to object\n  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":49,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/","title":"lds::UniformVectorList","section":"Classes","content":"lds::UniformVectorList #  Inherits from std::vector\u0026lt; Vector \u0026gt;\nPublic Functions #      Name      UniformVectorList() =default\nConstructs a new UniformVectorList.    UniformVectorList(const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent.    UniformVectorList(std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent.    UniformVectorList(std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0)\nConstructs a new UniformVectorList from initializer_list of Vector if dimensions consistent.    UniformVectorList(const UniformVectorList \u0026amp; that)\nConstructs a new UniformVectorList (copy)    UniformVectorList(UniformVectorList \u0026amp;\u0026amp; that)\nConstructs a new UniformVectorList (move)    ~UniformVectorList() =default\nDestroys the object.   size_t dim() const\ngets dimensions of the uniformly sized matrices   size_t size()\nsize of container   const Vector \u0026amp; at(size_t n)\ngets reference to n^th element   void Swap(Vector \u0026amp; that, size_t n)\nswaps input matrix with n^th vector of list   UniformVectorList \u0026amp; operator=(const UniformVectorList \u0026amp; that)\nassigns the contents (copy)   UniformVectorList \u0026amp; operator=(UniformVectorList \u0026amp;\u0026amp; that)\nassigns the contents (move)      Public Function Details #  UniformVectorList #  UniformVectorList() =default  UniformVectorList #  explicit UniformVectorList( const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0 ) Parameters:\n vecs input vectors dims dimension   UniformVectorList #  explicit UniformVectorList( std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0 ) Parameters:\n vecs input vectors dim dimension   UniformVectorList #  UniformVectorList( std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0 ) Parameters:\n vecs input vectors dim dimension   UniformVectorList #  UniformVectorList( const UniformVectorList \u0026amp; that ) Parameters:\n that another UniformVectorList   UniformVectorList #  UniformVectorList( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\n that another UniformVectorList   ~UniformVectorList #  ~UniformVectorList() =default  dim #  inline size_t dim() const  size #  inline size_t size()  at #  inline const Vector \u0026amp; at( size_t n )  Swap #  inline void Swap( Vector \u0026amp; that, size_t n ) Parameters:\n that input vector n index where the vector is moved   operator= #  inline UniformVectorList \u0026amp; operator=( const UniformVectorList \u0026amp; that ) Parameters:\n that another UniformVectorList  Return: reference to object\n operator= #  inline UniformVectorList \u0026amp; operator=( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\n that another UniformVectorList  Return: reference to object\n  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":50,"href":"/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/","title":"ldsCtrlEst_h","section":"Files","content":"ldsCtrlEst_h #  Files #     Name     ldsCtrlEst_h/lds.h lds namespace   ldsCtrlEst_h/lds_ctrl.h Controller.   ldsCtrlEst_h/lds_fit.h LDS base fit type.   ldsCtrlEst_h/lds_fit_em.h subspace identification   ldsCtrlEst_h/lds_fit_ssid.h subspace identification   ldsCtrlEst_h/lds_gaussian.h glds namespace   ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.   ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.   ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.   ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.   ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.   ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.   ldsCtrlEst_h/lds_poisson.h plds namespace   ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.   ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.   ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.   ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.   ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.   ldsCtrlEst_h/lds_poisson_sys.h PLDS base type.   ldsCtrlEst_h/lds_sctrl.h SwitchedController type.   ldsCtrlEst_h/lds_sys.h LDS base type.   ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.   ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.   ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.   ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)   ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)     Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":51,"href":"/lds-ctrl-est/docs/api/files/lds_8h/","title":"ldsCtrlEst_h/lds.h","section":"Files","content":"ldsCtrlEst_h/lds.h #  lds namespace More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Detailed Description #  This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)) observations.\nSource code #  //===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_H #define LDSCTRLEST_LDS_H  // #ifndef LDSCTRLEST // #include \u0026lt;ldsCtrlEst\u0026gt; // #endif  #include \u0026lt;armadillo\u0026gt; namespace lds { using data_t = double; // may change to float (but breaks mex functions) using Vector = arma::Col\u0026lt;data_t\u0026gt;; using Matrix = arma::Mat\u0026lt;data_t\u0026gt;; using Cube = arma::Cube\u0026lt;data_t\u0026gt;; using View = arma::subview\u0026lt;data_t\u0026gt;; namespace fill = arma::fill; static const std::size_t kControlTypeDeltaU = 0x1; static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); static const data_t kPi = arma::datum::pi; static const data_t kDefaultP0 = 1e-6; static const data_t kDefaultQ0 = 1e-6; static const data_t kDefaultR0 = 1e-2; enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA }; enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2 }; // TODO(mfbolus): for SwitchedController, may want systems to have differing // numbers of states. Use this enum as template parameter? // enum SystemListFreeDim { // kSysFreeDimNone, // kSysFreeDimX ///\u0026lt; allow state dim (x) of systems in list to be hetero // };  // place hard limits on contents of vecors/mats void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub); void Limit(Vector\u0026amp; x, data_t lb, data_t ub); void Limit(Matrix\u0026amp; x, data_t lb, data_t ub); // in-place assign that errs if there are dimension mismatches: void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); // TODO(mfbolus): this is a fudge, but for some reason, cov mats often going // numerically asymm.  void ForceSymPD(Matrix\u0026amp; X); void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min = 0); void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X); Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B); inline void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Vector\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Matrix\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions  if (other.n_elem != some.n_elem) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vector of size \u0026#34; \u0026lt;\u0026lt; some.n_elem \u0026lt;\u0026lt; \u0026#34; with vector of size \u0026#34; \u0026lt;\u0026lt; other.n_elem \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } inline void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions  if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrix of size \u0026#34; \u0026lt;\u0026lt; some.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; some.n_cols \u0026lt;\u0026lt; \u0026#34; with matrix of size \u0026#34; \u0026lt;\u0026lt; other.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other.n_cols \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":52,"href":"/lds-ctrl-est/docs/api/files/lds__ctrl_8h/","title":"ldsCtrlEst_h/lds_ctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_ctrl.h #  Controller. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::Controller    Detailed Description #  This file declares the type for control of a linear dynamical system (lds::Controller).\nSource code #  //===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_CTRL_H #define LDSCTRLEST_LDS_CTRL_H  // namespace #include \u0026#34;lds.h\u0026#34;// system type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class Controller { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); public: Controller() = default; Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); const Vector\u0026amp; Control(const Vector\u0026amp; z, bool do_control = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); const Vector\u0026amp; ControlOutputReference(const Vector\u0026amp; z, bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); // get methods:  const System\u0026amp; sys() const { return sys_; }; const Matrix\u0026amp; Kc() const { return Kc_; }; const Matrix\u0026amp; Kc_inty() const { return Kc_inty_; }; const Matrix\u0026amp; Kc_u() const { return Kc_u_; }; const Vector\u0026amp; g_design() const { return g_design_; }; const Vector\u0026amp; u_ref() const { return u_ref_; }; const Vector\u0026amp; x_ref() const { return x_ref_; }; const Vector\u0026amp; y_ref() const { return y_ref_; }; size_t control_type() const { return control_type_; }; data_t tau_awu() const { return tau_awu_; }; data_t u_lb() const { return u_lb_; }; data_t u_ub() const { return u_ub_; }; // set methods  void set_sys(const System\u0026amp; sys) { bool does_match = sys_.n_u() == sys.n_u(); does_match = does_match \u0026amp;\u0026amp; (sys_.n_x() == sys.n_x()); does_match = does_match \u0026amp;\u0026amp; (sys_.n_y() == sys.n_y()); if (does_match) { sys_ = sys; } else { throw std::runtime_error( \u0026#34;new system argument to `set_sys` does not match dimensionality of \u0026#34; \u0026#34;existing system\u0026#34;); } }; void set_g_design(const Vector\u0026amp; g_design) { Reassign(g_design_, g_design); }; void set_u_ref(const Vector\u0026amp; u_ref) { Reassign(u_ref_, u_ref); }; void set_x_ref(const Vector\u0026amp; x_ref) { Reassign(x_ref_, x_ref); cx_ref_ = sys_.C() * x_ref_; }; // y_ref needs to be handled differently depending on output fn.  // (need to populate cx_ref_ too, which depends on output fn)  virtual void set_y_ref(const Vector\u0026amp; y_ref) { Reassign(y_ref_, y_ref); }; void set_Kc(const Matrix\u0026amp; Kc) { Reassign(Kc_, Kc); }; void set_Kc_inty(const Matrix\u0026amp; Kc_inty) { Reassign(Kc_inty_, Kc_inty); }; void set_Kc_u(const Matrix\u0026amp; Kc_u) { Reassign(Kc_u_, Kc_u); }; void set_tau_awu(data_t tau) { tau_awu_ = tau; k_awu_ = sys_.dt() / tau_awu_; }; void set_control_type(size_t control_type); // There is no reason u_lb/ub should not be public, but making set methods  // anyway.  void set_u_lb(data_t u_lb) { u_lb_ = u_lb; }; void set_u_ub(data_t u_ub) { u_ub_ = u_ub; }; void Reset() { sys_.Reset(); u_ref_.zeros(); u_ref_prev_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); u_saturated_ = false; t_since_control_onset_ = 0.0; }; void Print() { sys_.Print(); std::cout \u0026lt;\u0026lt; \u0026#34;g_design : \u0026#34; \u0026lt;\u0026lt; g_design_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_lb : \u0026#34; \u0026lt;\u0026lt; u_lb_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_ub : \u0026#34; \u0026lt;\u0026lt; u_ub_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }; protected: System sys_; Vector u_; Vector u_return_; Vector g_design_; // reference signals  Vector u_ref_; // create no set method for this:  Vector u_ref_prev_; Vector x_ref_; Vector y_ref_; Vector cx_ref_; // Controller gains  Matrix Kc_; Matrix Kc_u_; Matrix Kc_inty_; // control after g inversion  // do not need set methods for these.  Vector du_ref_; Vector dv_ref_; Vector v_ref_; Vector dv_; Vector v_; // integral error  // do not need set method for this  Vector int_e_; Vector int_e_awu_adjust_; Vector u_sat_; bool do_control_prev_ = false; bool do_lock_control_prev_ = false; // whether the g of system has become inverted from what you think it is  // (gain_ref)  bool u_saturated_ = false; // should be safe to have references here bc nothing needs to be done  // (like reset vars) when it changes...  data_t u_lb_{}; data_t u_ub_{}; data_t tau_awu_{}; data_t k_awu_ = 0; data_t t_since_control_onset_ = 0; size_t control_type_{}; private: void CalcControl(bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); void CalcSteadyStateSetPoint(); void AntiWindup(); void InitVars(size_t control_type); }; // Implement the above:  template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(sys), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(std::move(sys)), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::set_control_type(size_t control_type) { if (control_type_ == control_type) { return; } // creating a blank slate...  control_type_ = 0; Kc_inty_.zeros(0, 0); Kc_u_.zeros(0, 0); int_e_.zeros(0, 0); int_e_awu_adjust_.zeros(0, 0); // controller was designed to minimize integral error  if (control_type \u0026amp; kControlTypeIntY) { Kc_inty_.zeros(sys_.n_u(), sys_.n_y()); int_e_.zeros(sys_.n_y()); int_e_awu_adjust_.zeros(sys_.n_u()); control_type_ = control_type_ | kControlTypeIntY; } // controller was designed to minimize deltaU  // (i.e. state augmented with u)  if (control_type \u0026amp; kControlTypeDeltaU) { Kc_u_.zeros(sys_.n_u(), sys_.n_u()); control_type_ = control_type_ | kControlTypeDeltaU; } // whether to adapt set point calculate with (re-estimated) process  // disturbance (m)  if (control_type \u0026amp; kControlTypeAdaptM) { if (sys_.do_adapt_m) // only if adapting m...  { control_type_ = control_type_ | kControlTypeAdaptM; } } } // set_control_type  template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::Control( const Vector\u0026amp; z, bool do_control, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement  sys_.Filter(u_, z); bool do_estimation = true; // always have estimator on in this case  // calculate control signal  CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::ControlOutputReference( const Vector\u0026amp; z, bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement  if (do_estimation) { sys_.Filter(u_, z); } else { sys_.f(u_); } // calculate the set point  // solves for u_ref and x_ref when output is at y_ref at steady state.  if (do_control) { CalcSteadyStateSetPoint(); } // calculate control signal  CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcControl(bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { if (do_control \u0026amp;\u0026amp; do_estimation) { if (!do_control_prev_) { if (do_reset_at_control_onset) { Reset(); } t_since_control_onset_ = 0.0; } else { t_since_control_onset_ += sys_.dt(); } // enforce softstart on control vars.  if (sigma_soft_start \u0026gt; 0) { // half-Gaussian soft-start scaling factor  data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) / (2 * pow(sigma_soft_start, 2))); u_ref_ *= soft_start_sf; // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too  // x_ref_ *= soft_start_sf;  // cx_ref_ *= soft_start_sf;  // y_ref_ *= soft_start_sf;  } if (!do_lock_control) { // first do u -\u0026gt; v change of vars. (v = g.*u)  // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver  // control voltage u[=]V)  v_ref_ = g_design_ % u_ref_; // Given FB, calc. the change in control  if (control_type_ \u0026amp; kControlTypeDeltaU) { // if control designed to minimize not u but deltaU (i.e. state aug with  // u):  // TODO(mfbolus): Commented out for now. See note below.  // du_ref_ = u_ref_ - u_ref_prev_;  // dv_ref_ = g_design_ % du_ref_;  // TODO(mfbolus): Assuming users want *smooth* control signals if using  // kControlTypeDeltaU, it should be the case that dv_ref_ is --\u0026gt; 0. May  // want to revisit, but I am going to force it to be zero unless a  // situation arises that argues for keeping the above.  dv_ref_.zeros(); dv_ = dv_ref_; // nominally-optimal.  dv_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error  dv_ -= Kc_u_ * (v_ - v_ref_); // penalty on amp u (rel to ref)  if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup  // would be to not integrate error when control signal saturated:  // if(!uSaturated)  int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error  dv_ -= Kc_inty_ * int_e_; // control for integrated error  } // update the control  v_ += dv_; } else { v_ = v_ref_; // nominally-optimal.  v_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error  if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup  // would be to not integrate error when control signal saturated:  // if (!uSaturated)  int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error  v_ -= Kc_inty_ * int_e_; // control for integrated error  } } // convert back to control voltage u[=]V  u_ = v_ / sys_.g(); } // else do nothing until lock is low  } else { // if not control  // feed through u_ref in open loop  u_ = u_ref_ % g_design_ / sys_.g(); v_ = sys_.g() % u_; u_ref_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); } // ends do_control  // enforce box constraints (and antiwindup)  AntiWindup(); // add noise to input?  // The value for u that is *returned* to user after addition of any noise,  // while keeping controller/estimator blind to this addition.  u_return_ = u_; if ((sigma_u_noise \u0026gt; 0.0) \u0026amp;\u0026amp; (do_control \u0026amp;\u0026amp; !do_lock_control)) { u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn); Limit(u_return_, u_lb_, u_ub_); }; // For next time step:  u_ref_prev_ = u_ref_; do_control_prev_ = do_control; do_lock_control_prev_ = do_lock_control; } // CalcControl  template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcSteadyStateSetPoint() { // Linearly-constrained least squares (ls).  //  // _reference:  // Boyd \u0026amp; Vandenberghe (2018) Introduction to Applied Linear Algebra  //  Matrix a_ls = join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros)); Vector b_ls = cx_ref_; Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye), sys_.B() * arma::diagmat(sys_.g())); Vector d_ls = -sys_.m0(); if (control_type_ \u0026amp; kControlTypeAdaptM) { d_ls = -sys_.m(); // adapt setpoint calc with disturbance?  } Matrix a_ls_t = a_ls.t(); // TODO(mfbolus): not sure why but causes seg  // fault if I do not do this.  Matrix phi_ls = join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()), join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros))); // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse:  Matrix inv_phi = pinv(phi_ls); Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls); x_ref_ = xulam.subvec(0, sys_.n_x() - 1); u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1); cx_ref_ = sys_.C() * x_ref_; } // CalcSteadyStateSetPoint  template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::AntiWindup() { u_saturated_ = false; u_sat_ = u_; // limit u and flag whether saturated  for (size_t k = 0; k \u0026lt; u_.n_elem; k++) { if (u_[k] \u0026lt; u_lb_) { u_sat_[k] = u_lb_; u_saturated_ = true; } if (u_[k] \u0026gt; u_ub_) { u_sat_[k] = u_ub_; u_saturated_ = true; } } if ((control_type_ \u0026amp; kControlTypeIntY) \u0026amp;\u0026amp; (tau_awu_ \u0026lt; lds::kInf)) { // one-step back-calculation (calculate intE for u=u_sat)  // (Astroem, Rundqwist 1989 warn against using this...)  // int_e_awu_adjust_ =  // solve(Kc_inty_, (u_ - u_sat_)); // pinv(Kc_inty) * (u-uSat);  // gradual: see Astroem, Rundqwist 1989  // this is a fudge for doing MIMO gradual  // n.b., went ahead and multiplied 1/T by dt so don\u0026#39;t have to do that here.  int_e_awu_adjust_ = k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_); // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_);  int_e_ += int_e_awu_adjust_; } // set u to saturated version  u_ = u_sat_; } template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::InitVars(size_t control_type) { // initialize to default values  u_ref_ = Vector(sys_.n_u(), fill::zeros); u_ref_prev_ = Vector(sys_.n_u(), fill::zeros); x_ref_ = Vector(sys_.n_x(), fill::zeros); y_ref_ = Vector(sys_.n_y(), fill::zeros); cx_ref_ = Vector(sys_.n_y(), fill::zeros); u_ = Vector(sys_.n_u(), fill::zeros); u_return_ = Vector(sys_.n_u(), fill::zeros); u_sat_ = Vector(sys_.n_u(), fill::zeros); // Might not need all these, so zero elements until later.  Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros); Kc_u_ = Matrix(0, 0, fill::zeros); Kc_inty_ = Matrix(0, 0, fill::zeros); g_design_ = sys_.g(); // by default, same as model  dv_ = Vector(sys_.n_u(), fill::zeros); v_ = Vector(sys_.n_u(), fill::zeros); du_ref_ = Vector(sys_.n_u(), fill::zeros); dv_ref_ = Vector(sys_.n_u(), fill::zeros); v_ref_ = Vector(sys_.n_u(), fill::zeros); int_e_ = Vector(0, fill::zeros); int_e_awu_adjust_ = Vector(0, fill::zeros); set_control_type(control_type); } } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":53,"href":"/lds-ctrl-est/docs/api/files/lds__fit_8h/","title":"ldsCtrlEst_h/lds_fit.h","section":"Files","content":"ldsCtrlEst_h/lds_fit.h #  LDS base fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::Fit LDS Fit Type.    Detailed Description #  This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.\nSource code #  //===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDS_FIT_HPP #define LDS_FIT_HPP  // namespace #include \u0026#34;lds.h\u0026#34;#include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); virtual ~Fit() = default; // get methods  size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; Q() const { return Q_; }; const Vector\u0026amp; x0() const { return x0_; }; const Matrix\u0026amp; P0() const { return P0_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; // gets measurement noise  virtual const Matrix\u0026amp; R() const = 0; // set methods (e.g., seeding initial fit values)  void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_m(const Vector\u0026amp; m) { Reassign(m_, m); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); ForceSymPD(Q_); }; virtual void set_R(const Matrix\u0026amp; R) = 0; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); ForceSymPD(P0_); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; View f(Matrix\u0026amp; x, const Matrix\u0026amp; u, size_t t) { x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x.col(t); }; View f(Matrix\u0026amp; x_pre, const Matrix\u0026amp; x_post, const Matrix\u0026amp; u, size_t t) { x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x_pre.col(t); }; virtual View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) = 0; protected: data_t dt_{}; // Dynamics  Matrix A_; Matrix B_; Vector g_; Vector m_; Matrix Q_; // Output  Matrix C_; Vector d_; Matrix R_; // initial conditions  Vector x0_; Matrix P0_; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; }; } // namespace lds #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":54,"href":"/lds-ctrl-est/docs/api/files/lds__fit__em_8h/","title":"ldsCtrlEst_h/lds_fit_em.h","section":"Files","content":"ldsCtrlEst_h/lds_fit_em.h #  subspace identification More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::EM    Detailed Description #  This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).\nSource code #  //===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_EMAX_H #define LDSCTRLEST_LDS_EMAX_H  #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class EM { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: EM() = default; EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); virtual ~EM() = default; const Fit\u0026amp; Run(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = true, bool calc_output = true, bool calc_measurement = true, size_t max_iter = 100, data_t tol = 1e-2); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); // auto tuple = std::make_tuple(u_, z_);  u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; x() const { return x_; }; const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; y() const { return y_; }; const Matrix\u0026amp; sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; }; const Matrix\u0026amp; sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; }; const Matrix\u0026amp; sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; }; size_t n_t_tot() { return n_t_tot_; } const Vector\u0026amp; theta() const { return theta_; }; protected: void Expectation(bool force_common_initial = false); void Maximization(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = false, bool calc_output = false, bool calc_measurement = false); void MaximizeDynamics(); void MaximizeQ(); void MaximizeInitial(); virtual void MaximizeOutput() = 0; virtual void MaximizeMeasurement() = 0; void Smooth(bool force_common_initial); virtual void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) = 0; void Reset(); void InitVars(); Vector UpdateTheta(); // input/output training data  UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; std::vector\u0026lt;Matrix\u0026gt; x_; std::vector\u0026lt;Cube\u0026gt; P_; std::vector\u0026lt;Cube\u0026gt; P_t_tm1_; std::vector\u0026lt;Matrix\u0026gt; y_; Matrix diag_y_; // expectations calculated in E-step  Matrix sum_E_x_t_x_t_; Matrix sum_E_xu_tm1_xu_tm1_; Matrix sum_E_xu_t_xu_tm1_; Fit fit_; Vector theta_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; }; template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; fit_ = Fit(n_u_, n_x, n_y_, dt); u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { // make sure fit dims match I/O data  if (fit0.n_u() != u_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and input training data have inconsistent dimensions\u0026#34;); } if (fit0.n_y() != z_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and output training data have inconsistent dimensions\u0026#34;); } fit_ = fit0; u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::InitVars() { // check input/output data dimensions are consistent  if (z_.size() != u_.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_.at(trial).n_cols != u_.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } n_u_ = fit_.n_u(); n_x_ = fit_.n_x(); n_y_ = fit_.n_y(); dt_ = fit_.dt(); x_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); P_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); P_t_tm1_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); y_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros); P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros); } diag_y_ = Matrix(n_y_, n_y_, fill::zeros); // covariances in expectation step  sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); } template \u0026lt;typename Fit\u0026gt; const Fit\u0026amp; EM\u0026lt;Fit\u0026gt;::Run(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement, size_t max_iter, data_t tol) { Reset(); // to initial conditions  size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_; Vector theta(n_params); Vector theta_new(n_params); data_t max_dtheta = 1; // if solving for initial conditions, allow them be varied.  // otherwise, freeze at provided values.  bool force_common_initial = !calc_init; // go until parameter convergence  for (size_t l = 0; l \u0026lt; max_iter; l++) { theta_ = UpdateTheta(); std::cout \u0026lt;\u0026lt; \u0026#34;Iteration \u0026#34; \u0026lt;\u0026lt; l + 1 \u0026lt;\u0026lt; \u0026#34;/\u0026#34; \u0026lt;\u0026lt; max_iter \u0026lt;\u0026lt; \u0026#34; ...\\n\u0026#34;; Expectation(force_common_initial); Maximization(calc_dynamics, calc_Q, calc_init, calc_output, calc_measurement); // check convergence  theta_new = UpdateTheta(); Vector dtheta = abs(theta_new - theta_) / abs(theta_); // some parameters could be zero...  arma::uvec ubi_finite = find_finite(dtheta); max_dtheta = max(dtheta.elem(ubi_finite)); std::cout \u0026lt;\u0026lt; \u0026#34;max dtheta: \u0026#34; \u0026lt;\u0026lt; max_dtheta \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; if (max_dtheta \u0026lt; tol) { std::cout \u0026lt;\u0026lt; \u0026#34;Converged.\\n\u0026#34;; break; } std::cout \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } return fit_; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Smooth(bool force_common_initial) { Matrix k_e(n_x_, n_y_); // estimator gain  Cube k_backfilt; // back-filtering gains  // TODO(mfbolus): this loop could be made parallel  for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { Matrix x_pre(n_x_, n_t_[trial], fill::zeros); Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros); Matrix x_post(n_x_, n_t_[trial], fill::zeros); Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros); if (force_common_initial) // forces all trials to have same initial  // conditions.  { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); } y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); // This *should not* be necessary but make sure P is symmetric.  ForceSymPD(P_[trial].slice(0)); x_pre.col(0) = x_[trial].col(0); p_pre.slice(0) = P_[trial].slice(0); x_post.col(0) = x_[trial].col(0); p_post.slice(0) = P_[trial].slice(0); // filter  for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // predict  fit_.f(x_pre, x_post, u_.at(trial), t); fit_.h(y_[trial], x_pre, t); diag_y_.diag() = y_[trial].col(t); // TODO(mfbolus): change if parallel  // update --\u0026gt; posterior estimation  RecurseKe(k_e, p_pre, p_post, t); x_post.col(t) = x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t)); y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d(); } // backfilter -\u0026gt; Smoothed estimate  // Reference:  // Shumway et Stoffer (1982)  ForceSymPD(p_post.slice(n_t_[trial] - 1)); k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1); P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 0; t--) { // TODO(mfmbolus): should not be necessary to force symm positive def  ForceSymPD(p_pre.slice(t)); ForceSymPD(p_post.slice(t - 1)); ForceSymPD(P_[trial].slice(t)); k_backfilt.slice(t - 1) = p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t)); x_[trial].col(t - 1) = x_post.col(t - 1) + k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t)); P_[trial].slice(t - 1) = p_post.slice(t - 1) + k_backfilt.slice(t - 1) * (P_[trial].slice(t) - p_pre.slice(t)) * k_backfilt.slice(t - 1).t(); } // do the same for P_t_tm1  Matrix id(n_x_, n_x_, fill::eye); P_t_tm1_[trial].slice(n_t_[trial] - 1) = (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 1; t--) { P_t_tm1_[trial].slice(t - 1) = p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() + k_backfilt.slice(t - 1) * (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) * k_backfilt.slice(t - 2).t(); } // finally, get smoothed estimate of output  for (size_t t = 0; t \u0026lt; n_t_[trial]; t++) { fit_.h(y_[trial], x_[trial], t); } // samps loop  } // trial loop } // Smooth  // template \u0026lt;typename Fit\u0026gt; // void EM\u0026lt;Fit\u0026gt;::RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) { // // predict covar // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q();  // // update Ke // Ke = P_pre.slice(t) * fit_.C().t() * // inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R());  // // update cov // // Reference: Ghahramani et Hinton (1996) // P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t);  // // // n.b. for poisson : // // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + // fit_.Q(); // // // update cov // // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ * // // fit_.C()); // // // update Ke // // Ke = P_post.slice(t) * fit_.C(); // }  template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Expectation(bool force_common_initial) { // calculate the mean/cov of state needed for maximizing E[pr(z|theta)]  Smooth(force_common_initial); // now get the various forms of sum(E[xx\u0026#39;]) needed  // n.b. Going to start at t=1 rather than 0 bc most max terms need that.  // so really \u0026#34;n_t_tot_\u0026#34; is (n_t_tot_-1)  n_t_tot_ = 0; sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); Vector xu_tm1(n_x_ + n_u_, fill::zeros); Vector xu_t(n_x_ + n_u_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // ------------ sum_E_x_t_x_t ------------  sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t(); sum_E_x_t_x_t_ += P_[trial].slice(t); // ------------ sum_E_xu_tm1_xu_tm1 ------------  xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1)); sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t(); sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t - 1); // ------------ sum_E_xu_t_xu_tm1 ------------  xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t)); sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t(); sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_t_tm1_[trial].slice(t); n_t_tot_ += 1; } // time  } // trial } // Expectation  template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement) { if (calc_output) { MaximizeOutput(); } if (calc_measurement) { MaximizeMeasurement(); } if (calc_dynamics) { MaximizeDynamics(); } if (calc_Q) { MaximizeQ(); } if (calc_init) { MaximizeInitial(); } } // Maximization  template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeDynamics() { // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996)  Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) * inv_sympd(sum_E_xu_tm1_xu_tm1_); fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1)); std::cout \u0026lt;\u0026lt; \u0026#34;A_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.A()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.B()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeQ() { // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996)  // View sum_e_x_t_xu_tm1 =  // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1);  // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 *  // inv_sympd(sum_E_xu_tm1_xu_tm1_) *  // sum_e_x_t_xu_tm1.t();  // q /= n_t_tot_;  // this way is same as above iff dynamics were just updated:  // View sum_e_x_t_xu_tm1 =  // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1);  // Matrix ab = arma::join_horiz(fit_.A(), fit_.B());  // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t();  // q /= n_t_tot_;  // From scratch method:  // Q is covariance of the error between state and dynamics-predicted state  // (aka process noise)  // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})\u0026#39;]  // t-1 terms:  View sum_e_x_tm1_x_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_u_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1); View sum_e_x_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); // t, t-1 terms:  View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_x_t_u_tm1 = sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); Matrix q = sum_E_x_t_x_t_; q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t(); q -= sum_e_x_t_x_tm1 * fit_.A().t(); q -= fit_.A() * sum_e_x_t_x_tm1.t(); // input-related terms:  q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t(); q -= sum_e_x_t_u_tm1 * fit_.B().t(); q -= fit_.B() * sum_e_x_t_u_tm1.t(); q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t(); q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t(); q /= n_t_tot_; fit_.set_Q(q); std::cout \u0026lt;\u0026lt; \u0026#34;Q_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.Q()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // std::cout \u0026lt;\u0026lt; \u0026#34;Q_new: \\n\u0026#34; \u0026lt;\u0026lt; fit_.Q() \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeInitial() { Vector x0 = fit_.x0(); x0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { x0 += x_[trial].col(0); } x0 /= z_.size(); std::cout \u0026lt;\u0026lt; \u0026#34;x0_new[0]: \u0026#34; \u0026lt;\u0026lt; x0[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // always recalc P0 even if the initial state is fixed (at zero, for  // example)  Matrix e_var(n_x_, n_x_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t(); } e_var /= z_.size(); // go ahead and subtract x0*x0\u0026#39; so don\u0026#39;t have to below.  e_var -= x0 * x0.t(); // To get P0, going to get initial P_ per trial and average.  // (which might be wrong, but need a single number)  Matrix p0 = fit_.P0(); p0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { p0 += (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var; } p0 /= z_.size(); fit_.set_P0(p0); std::cout \u0026lt;\u0026lt; \u0026#34;P0_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.P0()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeOutput() { // solve for C+d:  Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros); Vector x1(n_x_ + 1, fill::zeros); x1[n_x_] = 1.0; // augment with one to solve for bias  Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { x1.subvec(0, n_x_ - 1) = x_[trial].col(t); sum_zx += z_.at(trial).col(t) * x1.t(); sum_e_x1_x1 += x1 * x1.t(); sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t); } } Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1); fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1)); fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_))); std::cout \u0026lt;\u0026lt; \u0026#34;C_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.C()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.d()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeMeasurement() { // Solve for measurement noise covar  size_t n_t_tot = 0; // Ghahgramani, Hinton 1996:  Matrix sum_zz(n_y_, n_y_, fill::zeros); Matrix sum_yz(n_y_, n_y_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t(); // Use Cnew:  sum_yz += (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t(); n_t_tot += 1; } } fit_.set_R((sum_zz - sum_yz) / n_t_tot); std::cout \u0026lt;\u0026lt; \u0026#34;R_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.R()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Reset() { // reset to initial conditions  for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); } } template \u0026lt;typename Fit\u0026gt; Vector EM\u0026lt;Fit\u0026gt;::UpdateTheta() { // TODO(mfbolus): This should include n_y_ more params for d.  size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_; if (fit_.R().n_elem \u0026gt; 0) { n_params += n_y_ * n_y_; } Vector theta(n_params); size_t idx_start = 0; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B()); idx_start += n_x_ * n_u_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0()); idx_start += n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C()); idx_start += n_y_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d()); idx_start += n_y_; if (fit_.R().n_elem \u0026gt; 0) { theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R()); } return theta; } } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":55,"href":"/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_fit_ssid.h","section":"Files","content":"ldsCtrlEst_h/lds_fit_ssid.h #  subspace identification More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::SSID    Detailed Description #  This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)\u0026lt;Fit\u0026gt;).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code #  //===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_FIT_SSID_H #define LDSCTRLEST_LDS_FIT_SSID_H  #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class SSID { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: SSID() = default; SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d = Vector(1).fill(-kInf)); std::tuple\u0026lt;Fit, Vector\u0026gt; Run(SSIDWt ssid_wt); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } protected: void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001); void CreateHankelDataMat(); virtual void DecomposeData() = 0; void CalcSVD(SSIDWt wt); void Solve(data_t wt_dc); void RecomputeExtObs(); // input/output training data  UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; Matrix D_; Fit fit_; Matrix g_dc_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_h_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; Matrix L_; Vector s_; Matrix ext_obs_t_; }; template \u0026lt;typename Fit\u0026gt; SSID\u0026lt;Fit\u0026gt;::SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d) { // check input/output data dimensions are consistent  if (z_train.size() != u_train.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_train.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_train.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } dt_ = dt; n_x_ = n_x; n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; n_h_ = n_h; // dimensionality check for eventual block-hankel data matrix  size_t len = n_t_tot_ - 2 * n_h_ + 1; if (len \u0026lt; (2 * n_h_ * (n_u_ + n_y_))) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;Dataset problem! More rows than columns in block-hankel data \u0026#34; \u0026#34;matrix: 2*(n_u+n_y)*n_h \u0026gt; data-length! Need higher data-length or \u0026#34; \u0026#34;lower n_h.\u0026#34;; throw std::runtime_error(ss.str()); } fit_ = Fit(n_u_, n_x_, n_y_, dt_); u_ = std::move(u_train); z_ = std::move(z_train); if (!d.is_finite() || (d.n_rows != n_y_)) { // TODO(mfbolus): implement least-square solution for impulse response with  // a second input of ones. Data-driven way of accounting for offset *not*  // driven by an input.  //  // For now, calculate output bias (d) as the  // output wherever the stimulus has not been on for some amount of time.  // convolve u with rectangle and take all samples. This is a reasonable  // approach, since often when autonomous systems are fit (i.e., systems with  // no input), they will subtract off the mean of the output. This  // essentially amounts to setting output bias to the mean of the output when  // there is no stimulation.  data_t t_silence = 0.1; data_t thresh_silence = 0.001; CalcD(t_silence, thresh_silence); } else { fit_.set_d(d); } } template \u0026lt;typename Fit\u0026gt; std::tuple\u0026lt;Fit, Vector\u0026gt; SSID\u0026lt;Fit\u0026gt;::Run(SSIDWt ssid_wt) { // the weight on minimizing dc I/O gain only works for gaussian,  // and hopefully not necessary with appropriate dataset.  data_t wt_dc = 0; // std::cout \u0026lt;\u0026lt; \u0026#34;creating hankel mat\\n\u0026#34;;  CreateHankelDataMat(); // std::cout \u0026lt;\u0026lt; \u0026#34;decomposing data\\n\u0026#34;;  DecomposeData(); // std::cout \u0026lt;\u0026lt; \u0026#34;calculating svd\\n\u0026#34;;  CalcSVD(ssid_wt); // std::cout \u0026lt;\u0026lt; \u0026#34;solving for params\\n\u0026#34;;  Solve(wt_dc); // std::cout \u0026lt;\u0026lt; \u0026#34;fin\\n\u0026#34;;  return std::make_tuple(fit_, s_); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcD(data_t t_silence, data_t thresh_silence) { Vector d(z_.at(0).n_rows, fill::zeros); Vector win(static_cast\u0026lt;size_t\u0026gt;(t_silence / dt_), fill::ones); Vector sum_z_silence(n_y_, fill::zeros); size_t n_silence(0); for (size_t trial = 0; trial \u0026lt; u_.size(); trial++) { // find silent samples  // start by convolving with  Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0)); Vector u_conv = conv(sum_u, win, \u0026#34;same\u0026#34;); // get only the samples that are silent...  arma::uvec ubi_silence = find(u_conv \u0026lt;= thresh_silence); if (ubi_silence.n_elem \u0026gt; 0) { sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1); n_silence += ubi_silence.n_elem; } } if (n_silence \u0026gt; 0) { d = sum_z_silence / n_silence; } fit_.set_d(d); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat() { // temporary copy of data  Matrix z(n_y_, n_t_tot_, fill::zeros); Matrix u(n_u_, n_t_tot_, fill::zeros); size_t so_far(0); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial); u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial); so_far += n_t_.at(trial); } // remove output bias  z.each_col() -= fit_.d(); // calculate I/O gain @ DC while data in convenient form  g_dc_ = z * pinv(u); // std::cout \u0026lt;\u0026lt; \u0026#34;G0_data = \u0026#34; \u0026lt;\u0026lt; g_dc_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;;  // create hankel data matrix  size_t len = z.n_cols - 2 * n_h_ + 1; // data length in hankel mat  // block-hankel data matrix  D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros); // past input  auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1); // future input  auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1); // past output  auto y_p = D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1); // future output  auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, len - 1); size_t idx = 0; for (size_t k = 0; k \u0026lt; len; k++) { idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } } D_ /= sqrt(static_cast\u0026lt;data_t\u0026gt;(len)); } // template \u0026lt;typename Fit\u0026gt; // void SSID\u0026lt;Fit\u0026gt;::DecomposeData() { // // do LQ decomp instead of calculating covariance expensive way // // Note that \u0026#34;R\u0026#34; in van Overschee is lower-triangular (L), not \u0026#34;R\u0026#34; in QR // // decomp. Very confusing. // Matrix q_t; // lq(L_, q_t, D_); // // van Overschee zeros out the other elements. // L_ = trimatl(L_); // }  template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcSVD(SSIDWt wt) { // submats that will be needed:  auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1); auto R_23_13 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1); auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_) - 1); auto R_44 = L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14); auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1); auto Luf = Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1); auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); // aka: R_f  Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1); // from van Overschee subid.m:  // Rf = R((2*m+l)*i+1:2*(m+l)*i,:); % Future outputs  Matrix U; Matrix V; switch (wt) { case kSSIDNone: { // No weighting. (what van Overschee calls \u0026#34;N4SID\u0026#34;)  Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14; arma::svd(U, s_, V, O_k_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDMOESP: { // MOESP weighting  // This is what they use in the \u0026#34;robust\u0026#34; algorithm van Overschee, de Moor  // 1996  Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); svd(U, s_, V, O_k_ortho_Uf_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDCVA: { // CVA weighting  // See van Overschee\u0026#39;s matlab code (subid.m):  // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems  Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); Matrix inv_w1; Matrix qt1; lq(inv_w1, qt1, R_56_16); // lq decomp of R_f (future output data)  inv_w1 = trimatl(inv_w1); inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1); Matrix w_o_w = arma::solve( inv_w1, O_k_ortho_Uf_sans_Qt); // alternatively  // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt  svd(U, s_, V, w_o_w, \u0026#34;std\u0026#34;); U = inv_w1 * U; break; } } // Truncate to model order (heart of ssid method)  auto s_hat = s_.subvec(0, n_x_ - 1); Matrix diag_sqrt_s = diagmat(sqrt(s_hat)); auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1); // get extended observability and controllability mats  ext_obs_t_ = u_hat * diag_sqrt_s; // extended observability matrix } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::Solve(data_t wt_dc) { // required submats  auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_56_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); // Solve for params using appropriate algorithm:  // robust deterministic/stochastic algorithm in van Overschee 1996  // algorithm that the authors say \u0026#34;works\u0026#34; in practice.  auto ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix  // This is what textbook (1996) says:  //  // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15);  //  // HOWEVER, do not know why but have to fill the last place with zeros like  // authors\u0026#39; matlab implementation (see `subid.m`)  // Otherwise, get ridiculous covariances (although A,C estimates are close to  // same...)  Matrix Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Matrix S = Tl * pinv(Tr); // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure  // stability.  fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1)); Matrix ext_obs_t_p1 = join_vert( ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1), Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros)); fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1); // At this point, van Overschee \u0026amp; de Moor suggest re-calculating ext_obs_t_,  // ext_obs_tm1 from (A, C) because it was just an approximation. This is  RecomputeExtObs(); ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix  Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); S = Tl * pinv(Tr); Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_); Matrix Mcurly = pinv(ext_obs_tm1); Matrix Pcurly = Tl - Lcurly * R_56_15; Vector Pvec = vectorise(Pcurly); Matrix Qcurly = R_23_15; // Identify [D; B], assuming D=0 and ensuring DC gain is correct  Matrix sum_QcurlyT_kron_Ncurly( (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_), fill::zeros); Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols, fill::eye); eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1, eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1; // van Overschee (1996) p. 126  Matrix N1_Tl = -Lcurly; N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) += join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly); N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) += Matrix(n_y_, n_y_, fill::eye); Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros); Matrix N_k; for (size_t k = 0; k \u0026lt; n_h_; k++) { auto Qcurly_k = Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1); Nk_Tl.zeros(); Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) = N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1); N_k = Nk_Tl * eye_ext_obs_tm1; sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k); } Matrix err_vec; if (wt_dc \u0026gt; 0) { // Constraints enforced by weighted least squares  //  // Reference:  //  // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly  // Excited Industrial Systems. Conference in Decision and Control.  // constraint 1: assume D=0 --\u0026gt; remove the components for Dvec (this is  // actually a hard constraint in that it ignores D)  Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly; sum_QcurlyT_kron_Ncurly = Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_); size_t kkk = 0; for (size_t k = 1; k \u0026lt; (n_u_ + 1); k++) { size_t start_idx = k * (n_y_ + n_x_) - n_x_; for (size_t kk = 0; kk \u0026lt; n_x_; kk++) { sum_QcurlyT_kron_Ncurly.col(kkk) = sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk); kkk++; } } // constraint 2: Make sure DC I/O gain is correct  Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A()); Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_)); Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0); Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 = join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0); // WEIGHTED LS  // Important in practice because I care a lot about at least getting the DC  // gain correct. Put x weight on minimizing error at DC, relative to others  Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye); // Make weight on minimizing DC error immense so at least that  // should be nailed.  size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows; size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows; size_t stop_row = w.n_rows - 1; size_t stop_col = w.n_cols - 1; // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale  // weight with data length?  w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc; Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * sum_QcurlyT_kron_Ncurly_b_to_g0) * sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec; fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_)); // Calculate residuals and their cov.  // Because I\u0026#39;ve added constraints, I need to re-calculate the right term  // with b_vec instead of how van Overschee do in final algorithm.  err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec; } else { // default way: *no* constraint on G0 or D=0  Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec; // TODO(mfbolus) n.b., this gets thrown away...  // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_);  fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_)); err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec; } // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols);  // TODO(mfbolus): Something is wrong with the error calculation above.  // Use the way van overschee does it in `subid.m`  // WARNING: this ignores any above constraints, so Q, R will be approximate...  Matrix err = Tl - S * Tr; Matrix cov_err = err * err.t(); fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1)); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::RecomputeExtObs() { ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C(); for (size_t k = 2; k \u0026lt; (n_h_ + 1); k++) { ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) = ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1, ext_obs_t_.n_cols - 1) * fit_.A(); } } } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":56,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian_8h/","title":"ldsCtrlEst_h/lds_gaussian.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian.h #  glds namespace More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Detailed Description #  This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)).\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_H #define LDSCTRLEST_LDS_GAUSSIAN_H  // namespace #include \u0026#34;lds.h\u0026#34; namespace lds { namespace gaussian { // insert any Gaussian-specific things here... } // namespace gaussian } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":57,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_ctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_ctrl.h #  GLDS Controller. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::Controller Gaussian-observation Controller Type.    Detailed Description #  This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H  // namespace #include \u0026#34;lds_gaussian.h\u0026#34;// system #include \u0026#34;lds_gaussian_sys.h\u0026#34;// controller #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace gaussian { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); cx_ref_ = y_ref - sys_.d(); }; // make sure base class template methods available  using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; }; } // namespace gaussian } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":58,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_fit.h #  GLDS fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::Fit GLDS Fit Type.    Detailed Description #  This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_H  // namespace #include \u0026#34;lds_gaussian.h\u0026#34;// fit type #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace gaussian { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); const Matrix\u0026amp; R() const override { return R_; }; void set_R(const Matrix\u0026amp; R) override { Reassign(R_, R); ForceSymPD(R_); }; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = C_ * x.col(t) + d_; return y.col(t); }; }; }; // namespace gaussian } // namespace lds #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":59,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_em.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_fit_em.h #  GLDS E-M fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::FitEM GLDS E-M Fit Type.    Detailed Description #  This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H  #include \u0026#34;lds_fit_em.h\u0026#34;#include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; }; } // namespace gaussian } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":60,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h #  GLDS SSID fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.    Detailed Description #  This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H  #include \u0026#34;lds_fit_ssid.h\u0026#34;#include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; using SSID\u0026lt;Fit\u0026gt;::Run; private: using SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat; using SSID\u0026lt;Fit\u0026gt;::CalcSVD; using SSID\u0026lt;Fit\u0026gt;::Solve; void DecomposeData() override; void SolveVanOverschee(); }; } // namespace gaussian } // namespace lds #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":61,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_sctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_sctrl.h #  GLDS switched controller type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.    Detailed Description #  This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H  // controller type #include \u0026#34;lds_gaussian_ctrl.h\u0026#34;// switched controller #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace gaussian { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); cx_ref_ = y_ref - sys_.d(); } // make sure base class template methods available  using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; }; // SwitchedController } // namespace gaussian } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":62,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/","title":"ldsCtrlEst_h/lds_gaussian_sys.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_sys.h #  GLDS base type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::System Gaussian LDS Type.    Detailed Description #  This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)).\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H #define LDSCTRLEST_LDS_GAUSSIAN_SYS_H  // namespace #include \u0026#34;lds_gaussian.h\u0026#34;// system #include \u0026#34;lds_sys.h\u0026#34; namespace lds { namespace gaussian { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0, data_t r0 = kDefaultR0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; // get methods  const Matrix\u0026amp; R() const { return R_; }; // set methods  void set_Q(const Matrix\u0026amp; Q) { lds::System::set_Q(Q); do_recurse_Ke_ = true; } void set_R(const Matrix\u0026amp; R) { Reassign(R_,R); do_recurse_Ke_ = true; }; void set_Ke(const Matrix\u0026amp; Ke) { Reassign(Ke_,Ke); // if users have set Ke, they must not want to calculate it online.  do_recurse_Ke_ = false; }; void set_Ke_m(const Matrix\u0026amp; Ke_m) { Reassign(Ke_m_,Ke_m); // if users have set Ke, they must not want to calculate it online.  do_recurse_Ke_ = false; }; void Print(); protected: void h() override { cx_ = C_ * x_; y_ = cx_ + d_; }; void RecurseKe() override; // Gaussian-output-specific  Matrix R_; bool do_recurse_Ke_{}; }; // System } // namespace gaussian } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":63,"href":"/lds-ctrl-est/docs/api/files/lds__poisson_8h/","title":"ldsCtrlEst_h/lds_poisson.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson.h #  plds namespace More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Detailed Description #  This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)).\nSource code #  //===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_H #define LDSCTRLEST_LDS_POISSON_H  #include \u0026#34;lds.h\u0026#34; namespace lds { namespace poisson { // TODO(mfbolus): Not sure if defining these as static here makes the most // sense. Is there a downside to letting multiple poisson System objects share a // common random number generator? static std::random_device rd; static std::mt19937 rng = std::mt19937( rd()); } // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":64,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_ctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_ctrl.h #  PLDS controller type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::Controller PLDS Controller Type.    Detailed Description #  This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/)), including state estimation.\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_CTRL_H #define LDSCTRLEST_LDS_POISSON_CTRL_H  // namespace #include \u0026#34;lds_poisson.h\u0026#34;// system type #include \u0026#34;lds_poisson_sys.h\u0026#34;// control type #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace poisson { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); lds::Limit(y_ref_, kYRefLb, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available  using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; private: constexpr static const data_t kYRefLb = 1e-4; }; } // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":65,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/","title":"ldsCtrlEst_h/lds_poisson_fit.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_fit.h #  PLDS base fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::Fit PLDS Fit Type.    Detailed Description #  This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_FIT_H #define LDSCTRLEST_LDS_POISSON_FIT_H  // namespace #include \u0026#34;lds_poisson.h\u0026#34;// fit #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace poisson { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) : lds::Fit(n_u, n_x, n_y, dt){}; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = exp(C_ * x.col(t) + d_); return y.col(t); }; void set_R(const Matrix\u0026amp; R) override { std::cerr \u0026lt;\u0026lt; \u0026#34;WARNING: Cannot set R (R[0] = \u0026#34; \u0026lt;\u0026lt; R.at(0) \u0026lt;\u0026lt; \u0026#34;). No Gaussian measurement noise in Poisson observation model.\\n\u0026#34;; }; const Matrix\u0026amp; R() const override { return R_; }; }; }; // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":66,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_em.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_fit_em.h #  PLDS E-M fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::FitEM PLDS E-M Fit Type.    Detailed Description #  This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\n[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H #define LDSCTRLEST_LDS_POISSON_FIT_EM_H  #include \u0026#34;lds_fit_em.h\u0026#34;#include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override{}; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; data_t NewtonSolveC(); void AnalyticalSolveD(); }; } // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":67,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_ssid.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_fit_ssid.h #  PLDS SSID fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS.    Detailed Description #  This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitssid/)).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H #define LDSCTRLEST_LDS_POISSON_FIT_SSID_H  #include \u0026#34;lds_fit_ssid.h\u0026#34;#include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; private: void DecomposeData() override; void CalcCov(); void PoissonToGaussianMoments(); Matrix cov_; }; } // namespace poisson } // namespace lds #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":68,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_sctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_sctrl.h #  PLDS switched controller type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::SwitchedController Poisson-observation SwitchedController Type.    Detailed Description #  This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H  #include \u0026#34;lds_poisson_ctrl.h\u0026#34;#include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace poisson { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); lds::Limit(y_ref_, kYRefLB, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available  using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; private: constexpr static data_t kYRefLB = 1e-4; }; } // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":69,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/","title":"ldsCtrlEst_h/lds_poisson_sys.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_sys.h #  PLDS base type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::System Poisson System type.    Detailed Description #  This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)).\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_SYS_H #define LDSCTRLEST_LDS_POISSON_SYS_H  // namespace #include \u0026#34;lds_poisson.h\u0026#34;// system #include \u0026#34;lds_sys.h\u0026#34; // needed for Poisson random number generation #include \u0026lt;random\u0026gt; namespace lds { namespace poisson { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; protected: void h() override { cx_ = C_ * x_; y_ = exp(cx_ + d_); diag_y_.diag() = y_; }; void RecurseKe() override; private: // Poisson-output-specific  Matrix diag_y_; std::poisson_distribution\u0026lt;size_t\u0026gt; pd_; }; // System } // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":70,"href":"/lds-ctrl-est/docs/api/files/lds__sctrl_8h/","title":"ldsCtrlEst_h/lds_sctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_sctrl.h #  SwitchedController type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::SwitchedController SwitchedController Type.    Detailed Description #  This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code #  //===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_SCTRL_H #define LDSCTRLEST_LDS_SCTRL_H  #include \u0026#34;lds_ctrl.h\u0026#34;#include \u0026#34;lds_uniform_mats.h\u0026#34;#include \u0026#34;lds_uniform_vecs.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class SwitchedController : public Controller\u0026lt;System\u0026gt; { public: SwitchedController() = default; SwitchedController(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); SwitchedController(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); void Switch(size_t idx, bool do_force_switch = false); void set_Kc(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc) { Kc_list_ = Kc; Kc_ = Kc_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc) { Kc_list_ = std::move(Kc); Kc_ = Kc_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_inty) { Kc_inty_list_ = Kc_inty; Kc_inty_ = Kc_inty_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_inty) { Kc_inty_list_ = std::move(Kc_inty); Kc_inty_ = Kc_inty_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_u) { Kc_u_list_ = Kc_u; Kc_u_ = Kc_u_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_u) { Kc_u_list_ = std::move(Kc_u); Kc_u_ = Kc_u_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(const UniformVectorList\u0026amp; g) { g_design_list_ = g; g_design_ = g_design_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(UniformVectorList\u0026amp;\u0026amp; g) { g_design_list_ = std::move(g); g_design_ = g_design_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; // make sure base class template methods available  using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; protected: std::vector\u0026lt;System\u0026gt; systems_; size_t n_sys_{}; size_t idx_{}; // controller gains could be different for each  UniformMatrixList\u0026lt;\u0026gt; Kc_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_inty_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_u_list_; // design-phase input gain could also be different  UniformVectorList g_design_list_; // TODO(mfbolus): not sure why I need to do this.  using Controller\u0026lt;System\u0026gt;::Kc_; using Controller\u0026lt;System\u0026gt;::Kc_inty_; using Controller\u0026lt;System\u0026gt;::Kc_u_; using Controller\u0026lt;System\u0026gt;::g_design_; using Controller\u0026lt;System\u0026gt;::sys_; // using Controller\u0026lt;System\u0026gt;::u_ref_;  // using Controller\u0026lt;System\u0026gt;::x_ref_;  // using Controller\u0026lt;System\u0026gt;::y_ref_;  //  using Controller\u0026lt;System\u0026gt;::control_type_; private: void InitVars(); using lds::Controller\u0026lt;System\u0026gt;::set_sys; // using Controller\u0026lt;System\u0026gt;::set_Kc;  // using Controller\u0026lt;System\u0026gt;::set_Kc_inty;  // using Controller\u0026lt;System\u0026gt;::set_Kc_u;  // using Controller\u0026lt;System\u0026gt;::set_g_design; }; template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(systems.at(0), u_lb, u_ub, control_type), systems_(systems) { InitVars(); } template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(System(systems.at(0).n_u(), systems.at(0).n_x(), systems.at(0).n_y(), systems.at(0).dt()), u_lb, u_ub, control_type), systems_(std::move(systems)) { InitVars(); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::InitVars() { n_sys_ = systems_.size(); sys_ = systems_.at(0); Kc_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_)); Kc_inty_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); Kc_u_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); g_design_list_ = UniformVectorList(std::vector\u0026lt;Vector\u0026gt;(n_sys_, g_design_)); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::Switch(size_t idx, bool do_force_switch) { if ((idx == idx_) \u0026amp;\u0026amp; !do_force_switch) { return; // already there.  } // put old up and get new one out  systems_.at(idx_) = std::move(sys_); sys_ = std::move(systems_.at(idx)); // set the state of this system to that of the previous system  // TODO(mfbolus): This will only work as intended if state matrix is the same.  // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp.  sys_.set_m(systems_.at(idx_).m(), true); sys_.set_x(systems_.at(idx_).x()); // swap controller gains  Kc_list_.Swap(Kc_, idx_); Kc_list_.Swap(Kc_, idx); if (control_type_ \u0026amp; kControlTypeIntY) { Kc_inty_list_.Swap(Kc_inty_, idx_); Kc_inty_list_.Swap(Kc_inty_, idx); } if (control_type_ \u0026amp; kControlTypeDeltaU) { Kc_u_list_.Swap(Kc_u_, idx_); Kc_u_list_.Swap(Kc_u_, idx); } g_design_list_.Swap(g_design_, idx_); g_design_list_.Swap(g_design_, idx); idx_ = idx; } // Switch  } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":71,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8h/","title":"ldsCtrlEst_h/lds_sys.h","section":"Files","content":"ldsCtrlEst_h/lds_sys.h #  LDS base type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::System Linear Dynamical System Type.    Detailed Description #  This file declares and partially defines the base type for linear dynamical systems ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code #  //===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_SYS_H #define LDSCTRLEST_LDS_SYS_H  #include \u0026#34;lds.h\u0026#34; namespace lds { class System { public: System() = default; System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); virtual ~System() {} void Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z); virtual const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) = 0; void f(const Vector\u0026amp; u, bool do_add_noise = false) { x_ = A_ * x_ + B_ * (g_ % u) + m_; if (do_add_noise) { x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_); } }; virtual void h() = 0; size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Vector\u0026amp; x() const { return x_; }; const Matrix\u0026amp; P() const { return P_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; P_m() const { return P_m_; }; const Vector\u0026amp; cx() const { return cx_; }; const Vector\u0026amp; y() const { return y_; }; const Vector\u0026amp; x0() const { return x0_; }; const Vector\u0026amp; m0() const { return m0_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; const Matrix\u0026amp; Ke() const { return Ke_; }; const Matrix\u0026amp; Ke_m() const { return Ke_m_; }; const Matrix\u0026amp; Q() { return Q_; }; const Matrix\u0026amp; Q_m() { return Q_m_; }; const Matrix\u0026amp; P0() { return P0_; }; const Matrix\u0026amp; P0_m() { return P0_m_; }; void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_m(const Vector\u0026amp; m, bool do_force_assign=false) { Reassign(m0_, m); if ((!do_adapt_m) || do_force_assign) { Reassign(m_, m); } }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); }; void set_Q_m(const Matrix\u0026amp; Q_m) { Reassign(Q_m_, Q_m); }; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); }; void set_P0_m(const Matrix\u0026amp; P0_m) { Reassign(P0_m_, P0_m); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; void set_x(const Vector\u0026amp; x) { Reassign(x_, x); h(); }; void Reset(); void Print(); // safe to leave this public and non-const  bool do_adapt_m{}; protected: virtual void RecurseKe() = 0; void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); std::size_t n_x_{}; std::size_t n_u_{}; std::size_t n_y_{}; data_t dt_{}; // Signals:  Vector x_; Matrix P_; Vector m_; Matrix P_m_; Vector cx_; Vector y_; Vector z_; // Parameters:  Vector x0_; Matrix P0_; Vector m0_; Matrix P0_m_; Matrix A_; Matrix B_; Vector g_; Matrix Q_; Matrix Q_m_; Matrix C_; Vector d_; Matrix Ke_; Matrix Ke_m_; }; // System  } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":72,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/","title":"ldsCtrlEst_h/lds_uniform_mats.h","section":"Files","content":"ldsCtrlEst_h/lds_uniform_mats.h #  List of uniformly sized matrices. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::UniformMatrixList    Detailed Description #  This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.\nSource code #  //===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H #define LDSCTRLEST_LDS_UNIFORM_MATS_H  #include \u0026lt;array\u0026gt; // std::array#include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { template \u0026lt;MatrixListFreeDim D = kMatFreeDimNone\u0026gt; class UniformMatrixList : public std::vector\u0026lt;Matrix\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion  // using std::vector\u0026lt;Matrix\u0026gt;::vector;  using std::vector\u0026lt;Matrix\u0026gt;::operator=; using std::vector\u0026lt;Matrix\u0026gt;::operator[]; using std::vector\u0026lt;Matrix\u0026gt;::begin; using std::vector\u0026lt;Matrix\u0026gt;::end; using std::vector\u0026lt;Matrix\u0026gt;::size; public: using std::vector\u0026lt;Matrix\u0026gt;::at; UniformMatrixList() = default; explicit UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); explicit UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; ~UniformMatrixList() = default; const std::array\u0026lt;size_t, 2\u0026gt;\u0026amp; dim(size_t n = 0) const { return dim_.at(n); } size_t size() { return std::vector\u0026lt;Matrix\u0026gt;::size(); }; const Matrix\u0026amp; at(size_t n) { return std::vector\u0026lt;Matrix\u0026gt;::at(n); }; void Swap(Matrix\u0026amp; that, size_t n); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim); std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt; dim_; }; template \u0026lt;MatrixListFreeDim D\u0026gt; inline void UniformMatrixList\u0026lt;D\u0026gt;::Swap(Matrix\u0026amp; that, size_t n) { // make sure request in range  if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim  bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.n_cols); } if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap  Matrix tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); if (D == kMatFreeDim1) { this-\u0026gt;dim_[n][0] = (*this)[n].n_rows; } if (D == kMatFreeDim2) { this-\u0026gt;dim_[n][1] = (*this)[n].n_cols; } } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) { // make sure dim_ vector is initialized  if (dim_.empty()) { dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(that.size(), {0, 0}); } // check dimensions  if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices\u0026#34;; throw std::runtime_error(ss.str()); } // if dimensions a not zero and do not match, skip move with error message.  bool dims_nonzero = true; for (auto d : dim_) { if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; d[0] \u0026lt; 1) { dims_nonzero = false; break; } if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; d[1] \u0026lt; 1) { dims_nonzero = false; break; } } if (dims_nonzero) { bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); } if (!does_match) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrices of size \u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; dim_[k] = that.dim(k); } return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept { // // check dimensions  // // if empty, assume a default constructed object and safe to move  // if (!this-\u0026gt;empty()) {  // if (this-\u0026gt;size() != that.size()) {  // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34;  // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices. Skipping.\\n\u0026#34;;  // return (*this);  // }  //  // // if dimensions a not zero and do not match, skip move with error  // message. bool dims_nonzero = true; for (auto d : dim_) {  // if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; (d[0] \u0026lt; 1)) {  // dims_nonzero = false;  // break;  // }  // if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; (d[1] \u0026lt; 1)) {  // dims_nonzero = false;  // break;  // }  // }  //  // if (dims_nonzero) {  // bool does_match = true;  // if (!(D == kMatFreeDim1)) {  // does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows);  // }  //  // if (!(D == kMatFreeDim2)) {  // does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols);  // }  //  // if (!does_match) {  // this-\u0026gt;at(0).print(\u0026#34;this[0] = \u0026#34;);  // that.at(0).print(\u0026#34;that[0] = \u0026#34;);  // std::cerr  // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformMatrixList element of size (\u0026#34; \u0026lt;\u0026lt;  // that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols \u0026lt;\u0026lt; \u0026#34;) for an  // element of size (\u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34;).  // Skipping.\\n\u0026#34;;  // return (*this);  // }  // }  // }  dim_ = that.dim_; std::vector\u0026lt;Matrix\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(std::move(mats)) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) : vector(that) { (*this) = that; } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { std::array\u0026lt;size_t, 2\u0026gt; dim_k({this-\u0026gt;at(k).n_rows, this-\u0026gt;at(k).n_cols}); dim_.push_back(dim_k); } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim) { // change behavior based on free dim D  if ((dim[0] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim1)) { dim[0] = this-\u0026gt;at(0).n_rows; } if ((dim[1] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim2)) { dim[1] = this-\u0026gt;at(0).n_cols; } // make sure dimensiolaties are all uniform  bool does_match(true); for (const Matrix\u0026amp; mat : *this) { if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_rows == dim[0]); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_cols == dim[1]); } if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(this-\u0026gt;size(), dim); for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { dim_[k][0] = (*this)[k].n_rows; dim_[k][1] = (*this)[k].n_cols; } } } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":73,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/","title":"ldsCtrlEst_h/lds_uniform_systems.h","section":"Files","content":"ldsCtrlEst_h/lds_uniform_systems.h #  List of uniformly sized Systems. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::UniformSystemList    Detailed Description #  This file provides a container for uniformly sized Systems.\nSource code #  //===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H  #include \u0026lt;array\u0026gt; // std::array#include \u0026lt;vector\u0026gt; // std::vector // namespace #include \u0026#34;lds.h\u0026#34;// System type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class UniformSystemList : public std::vector\u0026lt;System\u0026gt; { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion  // using std::vector\u0026lt;System\u0026gt;::vector;  using std::vector\u0026lt;System\u0026gt;::operator=; using std::vector\u0026lt;System\u0026gt;::operator[]; using std::vector\u0026lt;System\u0026gt;::at; using std::vector\u0026lt;System\u0026gt;::begin; using std::vector\u0026lt;System\u0026gt;::end; using std::vector\u0026lt;System\u0026gt;::size; public: UniformSystemList() = default; explicit UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); explicit UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(const UniformSystemList\u0026amp; that); UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept; ~UniformSystemList() = default; const std::array\u0026lt;size_t, 3\u0026gt;\u0026amp; dim() const { return dim_; } size_t size() { return std::vector\u0026lt;System\u0026gt;::size(); }; const System\u0026amp; at(size_t n) { return std::vector\u0026lt;System\u0026gt;::at(n); }; void Swap(System\u0026amp; that, size_t n); UniformSystemList\u0026amp; operator=(const UniformSystemList\u0026amp; that); UniformSystemList\u0026amp; operator=(UniformSystemList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim); std::array\u0026lt;size_t, 3\u0026gt; dim_{}; }; template \u0026lt;typename System\u0026gt; inline void UniformSystemList\u0026lt;System\u0026gt;::Swap(System\u0026amp; that, size_t n) { // make sure request in range  if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformSystemList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim  bool does_match = (dim_[0] == that.n_u()) \u0026amp;\u0026amp; (dim_[1] == that.n_x()) \u0026amp;\u0026amp; (dim_[2] == that.n_y()); if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformSystemList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap  System tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( const UniformSystemList\u0026amp; that) { // check dimensions  if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_[0] + dim_[1] + dim_[2]) { std::array\u0026lt;size_t, 3\u0026gt; other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign systems of size \u0026#34; \u0026lt;\u0026lt; dim_[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2] \u0026lt;\u0026lt; \u0026#34; with systems of size \u0026#34; \u0026lt;\u0026lt; other_dim[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other_dim[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2]; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( UniformSystemList\u0026amp;\u0026amp; that) noexcept { // // check dimensions  // // if empty, assume a default constructed object and safe to move  // if (!this-\u0026gt;empty()) {  // if (this-\u0026gt;size() != that.size()) {  // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34;  // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems. Skipping.\\n\u0026#34;;  // return (*this);  // }  //  // // if dimensions a not zero and do not match, skip move with error  // message. if (dim_[0] + dim_[1] + dim_[2]) {  // bool does_match = (dim_[0] == that.at(0).n_u()) \u0026amp;\u0026amp;  // (dim_[1] == that.at(0).n_x()) \u0026amp;\u0026amp;  // (dim_[2] == that.at(0).n_y());  // if (!does_match) {  // std::cerr  // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformSystemList element for an element of \u0026#34;  // \u0026#34;different size. Skipping.\\n\u0026#34;;  // return (*this);  // }  // }  // }  dim_ = that.dim_; std::vector\u0026lt;System\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(std::move(systems)) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList( std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const UniformSystemList\u0026amp; that) : std::vector\u0026lt;System\u0026gt;(that) { (*this) = that; } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept : std::vector\u0026lt;System\u0026gt;(std::move(that)) { this-\u0026gt;dim_[0] = this-\u0026gt;at(0).n_u(); this-\u0026gt;dim_[1] = this-\u0026gt;at(0).n_x(); this-\u0026gt;dim_[2] = this-\u0026gt;at(0).n_y(); } template \u0026lt;typename System\u0026gt; void UniformSystemList\u0026lt;System\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim) { if (dim[0] + dim[1] + dim[2]) { dim_ = dim; } else { dim_[0] = this-\u0026gt;at(0).n_u(); dim_[1] = this-\u0026gt;at(0).n_x(); dim_[2] = this-\u0026gt;at(0).n_y(); } // make sure dimensiolaties are all uniform  bool does_match(true); for (const System\u0026amp; sys : *this) { does_match = does_match \u0026amp;\u0026amp; (sys.n_u() == dim_[0]); does_match = does_match \u0026amp;\u0026amp; (sys.n_x() == dim_[1]); does_match = does_match \u0026amp;\u0026amp; (sys.n_y() == dim_[2]); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input systems are not uniform.\u0026#34;); } } } } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":74,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/","title":"ldsCtrlEst_h/lds_uniform_vecs.h","section":"Files","content":"ldsCtrlEst_h/lds_uniform_vecs.h #  List of uniformly sized vectors. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::UniformVectorList    Detailed Description #  This file provides a container for uniformly sized vectors.\nSource code #  //===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H #define LDSCTRLEST_LDS_UNIFORM_VECS_H  #include \u0026lt;array\u0026gt; // std::array#include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { class UniformVectorList : public std::vector\u0026lt;Vector\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion  // using std::vector\u0026lt;Vector\u0026gt;::vector;  using std::vector\u0026lt;Vector\u0026gt;::operator=; using std::vector\u0026lt;Vector\u0026gt;::operator[]; using std::vector\u0026lt;Vector\u0026gt;::at; using std::vector\u0026lt;Vector\u0026gt;::begin; using std::vector\u0026lt;Vector\u0026gt;::end; using std::vector\u0026lt;Vector\u0026gt;::size; public: UniformVectorList() = default; explicit UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim = 0); explicit UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim = 0); UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim = 0); UniformVectorList(const UniformVectorList\u0026amp; that); UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept; ~UniformVectorList() = default; size_t dim() const { return dim_; } size_t size() { return std::vector\u0026lt;Vector\u0026gt;::size(); }; const Vector\u0026amp; at(size_t n) { return std::vector\u0026lt;Vector\u0026gt;::at(n); }; void Swap(Vector\u0026amp; that, size_t n); UniformVectorList\u0026amp; operator=(const UniformVectorList\u0026amp; that); UniformVectorList\u0026amp; operator=(UniformVectorList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(size_t dim); size_t dim_{}; }; inline void UniformVectorList::Swap(Vector\u0026amp; that, size_t n) { // make sure request in range  if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim  bool does_match = dim_ == that.n_elem; if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap  Vector tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( const UniformVectorList\u0026amp; that) { // check dimensions  if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_) { size_t other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ \u0026lt;\u0026lt; \u0026#34; with vectors of size \u0026#34; \u0026lt;\u0026lt; other_dim; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( UniformVectorList\u0026amp;\u0026amp; that) noexcept { // // check dimensions  // if (!this-\u0026gt;empty()) {  // if (this-\u0026gt;size() != that.size()) {  // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34;  // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors. Skipping.\\n\u0026#34;;  // return (*this);  // }  //  // if (dim_) {  // size_t other_dim(that.dim());  // if (dim_ != other_dim) {  // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_  // \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; other_dim \u0026lt;\u0026lt; \u0026#34;.  // Skipping.\\n\u0026#34;;  // return (*this);  // }  // }  // }  dim_ = that.dim_; std::vector\u0026lt;Vector\u0026gt;::operator=(std::move(that)); return (*this); } } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":75,"href":"/lds-ctrl-est/docs/api/files/mex__c__util_8h/","title":"ldsCtrlEst_h/mex_c_util.h","section":"Files","content":"ldsCtrlEst_h/mex_c_util.h #  arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) More\u0026hellip;\nNamespaces #     Name     armamexc arma/mex interface using Matlab C API    Detailed Description #  This file defines utility functions for interoperability between armadillo and Matlab/Octave\u0026rsquo;s C mex API.\nSource code #  //===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_MEXC_UTIL_H #define LDSCTRLEST_MEXC_UTIL_H  #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.h\u0026#34; // // If Matlab_FOUND, include matrix.h. // // (Octave does not need/have it.) // #ifdef Matlab_FOUND // #include \u0026#34;matrix.h\u0026#34; // #endif  namespace armamexc { template \u0026lt;class T\u0026gt; inline auto m2T_scalar(const mxArray *matlab_scalar) -\u0026gt; T { if (mxGetData(matlab_scalar)) { return static_cast\u0026lt;T\u0026gt;(mxGetScalar(matlab_scalar)); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return 0; } template \u0026lt;class T\u0026gt; inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false, bool strict = true) -\u0026gt; arma::Mat\u0026lt;T\u0026gt; { if (mxGetData(matlab_mat)) { const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat); if (n_dim == 2) { return arma::Mat\u0026lt;T\u0026gt;(static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)), mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem, strict); } mexErrMsgTxt(\u0026#34;Number of dimensions must be 2.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } // TODO(mfbolus): make these templated.  template \u0026lt;typename T\u0026gt; inline auto a2m_mat(arma::Mat\u0026lt;T\u0026gt; const \u0026amp;arma_mat) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_mat.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy.  std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Mat.\u0026#34;); return nullptr; } template \u0026lt;typename T\u0026gt; inline auto a2m_vec(arma::Col\u0026lt;T\u0026gt; const \u0026amp;arma_vec) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_vec.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy.  std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Col.\u0026#34;); return nullptr; } } // namespace armamexc  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":76,"href":"/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/","title":"ldsCtrlEst_h/mex_cpp_util.h","section":"Files","content":"ldsCtrlEst_h/mex_cpp_util.h #  arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) More\u0026hellip;\nNamespaces #     Name     armamexcpp arma/mex interface using Matlab C++ API    Detailed Description #  This file defines utility functions for interoperability between armadillo and Matlab\u0026rsquo;s C++ mex API.\nSource code #  //===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_MEXCPP_UTIL_H #define LDSCTRLEST_MEXCPP_UTIL_H  #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.hpp\u0026#34;#include \u0026#34;mexAdapter.hpp\u0026#34; namespace armamexcpp { template \u0026lt;class T\u0026gt; std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; m2a_cellmat(matlab::data::CellArray\u0026amp; matlab_cell) { size_t n_cells = matlab_cell.getNumberOfElements(); std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; arma_mat(n_cells, arma::Mat\u0026lt;T\u0026gt;(1, 1, arma::fill::zeros)); for (size_t k = 0; k \u0026lt; n_cells; k++) { matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = matlab_cell[k]; auto dims = matlab_mat.getDimensions(); arma_mat[k] = arma::Mat\u0026lt;T\u0026gt;(matlab_mat.release().get(), dims[0], dims[1]); } return arma_mat; }; template \u0026lt;class T\u0026gt; std::vector\u0026lt;T\u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt;T\u0026gt;\u0026amp; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); T* ptr = matlab_array.release().get(); std::vector\u0026lt;T\u0026gt; vec(ptr, ptr + n_elem); return vec; }; template \u0026lt;class T\u0026gt; arma::Col\u0026lt;T\u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); // T* ptr = matlab_array.release().get();  // arma::Col\u0026lt;T\u0026gt; vec(ptr, n_elem); //, false);  // TODO(mfbolus): for some reason, using the above pointer at times leads to  // getting garbage values. matlab array values may be stored in non-contiguous  // memory?  arma::Col\u0026lt;T\u0026gt; vec(n_elem, arma::fill::zeros); for (size_t k = 0; k \u0026lt; n_elem; k++) { vec[k] = matlab_array[k]; } return vec; }; template \u0026lt;class T\u0026gt; arma::Mat\u0026lt;T\u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { // ArrayDimensions == std::vector\u0026lt;size_t\u0026gt;  auto dims = matlab_array.getDimensions(); // T* ptr = matlab_array.release().get();  // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false)  // arma::Mat\u0026lt;T\u0026gt; mat(ptr, dims[0], dims[1]); //, false);  // TODO(mfbolus): for some reason, using the above pointer at times leads to  // getting garbage values. matlab array values may be stored in non-contiguous  // memory?  //  // armadillo and matlab both use column-major ordering, so this should work:  size_t n_elem = dims[0] * dims[1]; arma::Mat\u0026lt;T\u0026gt; mat(dims[0], dims[1], arma::fill::zeros); size_t k(0); for (auto m: matlab_array) { mat[k] = m; k++; } return mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_mat(const arma::Mat\u0026lt;T\u0026gt;\u0026amp; arma_mat, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(), arma_mat.memptr() + arma_mat.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_vec(const arma::Col\u0026lt;T\u0026gt;\u0026amp; arma_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;({arma_vec.n_elem, 1}, arma_vec.memptr(), arma_vec.memptr() + arma_vec.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; s2m_vec(const std::vector\u0026lt;T\u0026gt;\u0026amp; std_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size()); return matlab_mat; }; } // namespace armamexcpp  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":77,"href":"/lds-ctrl-est/docs/terminology/model/","title":"Models","section":"LDS C+E Documentation","content":"Model Definitions #  This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form:   \\[\r\\mathbf{x}_{t\u0026#43;1} = f\\left( \\mathbf{x}_{t}, \\mathbf{v}_{t} \\right) = \\mathbf{A} \\mathbf{x}_{t} \u0026#43; \\mathbf{B} \\mathbf{v}_{t} \u0026#43; \\mathbf{m}_{t} \u0026#43; \\mathbf{w}_{t}\r\\]   \\[\r\\mathbf{y}_{t} = h\\left( \\mathbf{x}_{t} \\right)\r\\]  t : time index\rx : system state\rv = g%u : input (e.g., in physical units used for model fit)\ru : control signal sent to actuator (e.g., in Volts)\ry : system output\rm : process disturbance\rw ~ N(0, Q) : process noise/disturbance\rA : state matrix\rB : input coupling matrix\rg : input gain (e.g., for converting to control signal actuator voltage)\rn.b., assumes this conversion is linear\rQ : process noise covariance\r% : element-wise multiplication\r LDS with Gaussian Observations #  For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.\n \\[\r\\mathbf{y}_{t} = \\mathbf{C} \\mathbf{x}_{t} \u0026#43; \\mathbf{d}\r\\]   \\[\r\\mathbf{z}_{t} \\sim \\mathcal{N}\\left(\\mathbf{y}_{t} , \\mathbf{R} \\right)\r\\]  z : measurement\rC : output matrix\rd : output bias\rR : measurement noise covariance\r LDS with Poisson Observations #  For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.\n \\[\ry_{t}^{i} = \\exp \\left(\\mathbf{c}^i \\mathbf{x}_{t} \u0026#43; d^i\\right)\r\\]   \\[\rz_{t}^i \\sim \\rm{Poisson} \\left(y_{t}^i \\right)\r\\]  i : output index\rz : measurement (count data)\rc : i^th row of output matrix (C)\rd : output bias\r "},{"id":78,"href":"/lds-ctrl-est/docs/api/modules/","title":"Modules","section":"LDS C+E Documentation","content":"Modules #    Control Mode Bit Masks provides fill types for constructing new armadillo vectors, matrices\n  Defaults\n   Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":79,"href":"/lds-ctrl-est/docs/api/namespaces/","title":"Namespaces","section":"LDS C+E Documentation","content":"Namespaces #    armamexc arma/mex interface using Matlab C API\n  armamexcpp arma/mex interface using Matlab C++ API\n  lds::gaussian Linear Dynamical Systems with Gaussian observations.\n  lds::poisson Linear Dynamical Systems with Poisson observations.\n   Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":80,"href":"/lds-ctrl-est/docs/api/pages/","title":"Pages","section":"LDS C+E Documentation","content":"Pages #   Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":81,"href":"/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/","title":"PLDS State Estimation","section":"LDS C+E Examples","content":"PLDS State Estimation Tutorial #  This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.\nThe full code for this can be found here.\nPreamble #  In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system #  A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; // no. inputs  size_t n_x = 1; // no. states  size_t n_y = 1; // no. outputs  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system...  lds::poisson::System system_true(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example,   \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\]   \\[\ry_{t} = \\exp\\left(x_t\\right)\r\\]  where  \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\)  .\nNow, create non-default parameters for this model.\n// Model parameters  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state Finally, assign the parameters using corresponding set-methods.\n// Assign params.  system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); Creating the estimator #  Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.\n// Construct system for estimation  // e.g., will create a model with incorrect disturbance  lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object  system_estimator = system_true; // wrong disturbance  Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. To ensure robust estimates, adaptively re-estimate the process disturbance.\n// turn on adaptive disturbance estimation  system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting  // on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); Simulating estimation #  In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.\n// Simlate the true system.  z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update)  system_estimator.Filter(u.col(t - 1), z.col(t)); Example simulation result #  Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.\nWith this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.\n"},{"id":82,"href":"/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/","title":"PLDS Switched Control","section":"LDS C+E Examples","content":"PLDS Switched Control Tutorial #  This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.\nIn the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.\nThe full code for this can be found here.\nPreamble #  In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating the simulated system #  A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// whether to do switched control  bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); The system\u0026rsquo;s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.\n// for simulating switching  size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2  data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 Initially, the system will be in \u0026ldquo;mode\u0026rdquo; 1, where B = b1.\n// simulated system being controlled  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well  // characterized by two LDS models with different input matrices.**  data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.\nCreating the controller #  Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.\nSimilar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.\nMoreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.\nPutting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.\n// create switched controller  lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains  { // create switched controller sub-systems  // system 1  lds::poisson::System sys1(controlled_system); // set process noise covariance  Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m)  // n.b. using arbitrary default value for process noise if enabled.  sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset:  Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions  // system 2  lds::poisson::System sys2 = sys1; // set parameters  sys2.set_B(b2); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying systems:  Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive.  k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nNow that the SwitchedController is instantiated, assign its parameters.\n// Control variables  size_t control_type = 0; // no integral action, etc  switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); Simulating control #  In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).\nThe control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.\n// Let the controlled system stochastically change gain  // Assume another algorithm decodes this mode change and signals the  // switched_controller  Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1  { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2  if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system.  z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control  u.col(t) = switched_controller.ControlOutputReference(z.col(t)); Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system\u0026rsquo;s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.\nExample simulation result #  Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller\u0026rsquo;s online estimates of the output and state are shown in purple.\nNote that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.\n"},{"id":83,"href":"/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/","title":"src","section":"Files","content":"src #  Files #     Name     src/lds.cpp misc lds namespace functions   src/lds_gaussian_sys.cpp GLDS base type.   src/lds_poisson_sys.cpp PLDS base type.   src/lds_sys.cpp LDS base type.   src/lds_uniform_vecs.cpp Uniformly sized vectors.     Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":84,"href":"/lds-ctrl-est/docs/api/files/lds_8cpp/","title":"src/lds.cpp","section":"Files","content":"src/lds.cpp #  misc lds namespace functions More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Detailed Description #  This file implements miscellaneous lds namespace functions not bound to a class.\nSource code #  //===-- lds.cpp - LDS -----------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds.h\u0026gt; // insert any necessary function definitions here. namespace lds { void ForceSymPD(Matrix\u0026amp; X) { if (X.is_sympd() || !X.is_square()) { return; } // make symmetric  X = (X + X.t()) / 2; // for eigenval decomp  bool did_succeed(true); Vector d; Matrix u; // see first method (which may not be ideal):  // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/  size_t k(1); bool is_sympd = X.is_sympd(); Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye); while (!is_sympd) { if (k \u0026gt; 100) { did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); data_t min_eig = arma::min(d); std::cerr \u0026lt;\u0026lt; \u0026#34;After multiple iterations, min eigen val = \u0026#34; \u0026lt;\u0026lt; min_eig \u0026lt;\u0026lt; \u0026#34;.\\n\u0026#34;; throw std::runtime_error( \u0026#34;Failed to make matrix symmetric positive definite.\u0026#34;); return; } // Limit(d, arma::eps(0), kInf); // force to be positive...  // Matrix d_diag = arma::diagmat(d);  // X = u * d_diag * u.t();  did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymPD failed.\u0026#34;); } data_t min_eig = arma::min(d); X += id * abs(min_eig) + arma::datum::eps; // make sure symm:  X = (X + X.t()) / 2; // double check eigenvals positive after symmetrizing:  arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); min_eig = arma::min(d); is_sympd = min_eig \u0026gt; 0; k++; } } void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min) { if (!X.is_square()) { return; } // make symmetric  X = (X + X.t()) / 2; bool did_succeed(true); Vector d; Matrix u; did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymMinEig failed.\u0026#34;); } Limit(d, eig_min + arma::eps(eig_min), kInf); // enforce lower bound  Matrix d_diag = arma::diagmat(d); X = u * d_diag * u.t(); // double check symmetric  X = (X + X.t()) / 2; } void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X) { bool did_succeed(true); did_succeed = arma::qr_econ(Qt, L, X.t()); if (!did_succeed) { throw std::runtime_error(\u0026#34;LQ decomposition failed.\u0026#34;); } arma::inplace_trans(L); arma::inplace_trans(Qt); } Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B) { // subtract out mean  auto m_a = arma::mean(A, 1); Matrix a0 = A; a0.each_col() -= m_a; auto m_b = arma::mean(B, 1); Matrix b0 = B; b0.each_col() -= m_b; Matrix cov = a0 * b0.t() / a0.n_cols; return cov; } } // namespace lds  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":85,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/","title":"src/lds_gaussian_sys.cpp","section":"Files","content":"src/lds_gaussian_sys.cpp #  GLDS base type. More\u0026hellip;\nDetailed Description #  This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code #  //===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds_gaussian_sys.h\u0026gt; lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0, data_t r0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { R_.zeros(n_y, n_y); R_.diag().fill(r0); do_recurse_Ke_=true; }; // recursively estimate Ke void lds::gaussian::System::RecurseKe() { if (!do_recurse_Ke_) { return; } // predict covariance  P_ = A_ * P_ * A_.t() + Q_; // calc Kalman gain  Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_); // update covariance  // Reference: Ghahramani et Hinton (1996)  P_ = P_ - Ke_ * C_ * P_; if (do_adapt_m) { P_m_ += Q_m_; // A_m = I (i.e., random walk)  Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_); P_m_ = P_m_ - Ke_m_ * C_ * P_m_; } } // Simulate const lds::Vector\u0026amp; lds::gaussian::System::Simulate(const Vector\u0026amp; u_tm1){ f(u_tm1, true);//simulate dynamics with noise added  h();//output  z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure  return z_; } void lds::gaussian::System::Print() { lds::System::Print(); std::cout \u0026lt;\u0026lt; \u0026#34;R: \\n\u0026#34; \u0026lt;\u0026lt; R_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":86,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/","title":"src/lds_poisson_sys.cpp","section":"Files","content":"src/lds_poisson_sys.cpp #  PLDS base type. More\u0026hellip;\nDetailed Description #  This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code #  //===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds_poisson_sys.h\u0026gt; lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { diag_y_ = diagmat(y_); pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(0); }; // Correct: Given measurement (z) and current input (u), update estimate of the // state, covar, output. // // see Eden et al. 2004 void lds::poisson::System::RecurseKe() { // predict covariance  P_ = A_ * P_ * A_.t() + Q_; // update cov  P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_); Ke_ = P_ * C_.t(); if (do_adapt_m) { P_m_ += Q_m_; // predict (A_m = I)  P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_); // update  Ke_m_ = P_m_ * C_.t(); } } // Simulate Measurement: z ~ Poisson(y) const lds::Vector\u0026amp; lds::poisson::System::Simulate(const Vector\u0026amp; u_tm1) { f(u_tm1, true); // simulate dynamics with noise added  h(); // output  z_.zeros(); for (std::size_t k = 0; k \u0026lt; n_y_; k++) { // construct a Poisson distribution object with mean y[k]  pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(y_[k]); // pull random sample from this distribution  z_[k] = pd_(rng); } return z_; } // ******************* SYS_T *******************  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":87,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8cpp/","title":"src/lds_sys.cpp","section":"Files","content":"src/lds_sys.cpp #  LDS base type. More\u0026hellip;\nDetailed Description #  This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code #  //===-- lds_sys.cpp - LDS -------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds_sys.h\u0026gt; lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) { InitVars(p0, q0); } void lds::System::InitVars(data_t p0, data_t q0) { // initial conditions.  x0_ = Vector(n_x_, fill::zeros); // includes bias (nY) and g (nU)  P0_ = p0 * Matrix(n_x_, n_x_, fill::eye); m0_ = x0_; P0_m_ = P0_; // signals  x_ = x0_; P_ = P0_; m_ = m0_; P_m_ = P0_m_; y_ = Vector(n_y_, fill::zeros); cx_ = Vector(n_y_, fill::zeros); z_ = Vector(n_y_, fill::zeros); // By default, random walk where each state is independent  // In this way, provides independent estimates of rate per channel of output.  A_ = Matrix(n_x_, n_x_, fill::eye); B_ = Matrix(n_x_, n_u_, fill::zeros); g_ = Vector(n_u_, fill::ones); Q_ = q0 * Matrix(n_x_, n_x_, fill::eye); Q_m_ = Q_; C_ = Matrix(n_y_, n_x_, fill::eye); // each state will map to an output by  d_ = Vector(n_y_, fill::zeros); Ke_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain.  Ke_m_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain for m adaptation.  do_adapt_m = false; } // Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state // and update estimate of the state, covar, output using Kalman filter void lds::System::Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z_t) { // predict mean  f(u_tm1); // dynamics  h(); // output  // recursively calculate esimator gains (or just keep existing values)  // (also predicts+updates estimate covariance)  RecurseKe(); // update  x_ += Ke_ * (z_t - y_); if (do_adapt_m) { m_ += Ke_m_ * (z_t - y_); // adaptively estimating disturbance  } // With new state, estimate output.  h(); // --\u0026gt; posterior } void lds::System::Reset() { // reset to initial conditions  x_ = x0_; // mean  P_ = P0_; // cov of state estimate  m_ = m0_; // process disturbance  P_m_ = P0_m_; // cov of disturbance estimate  h(); } void lds::System::Print() { std::cout \u0026lt;\u0026lt; \u0026#34;\\n********** SYSTEM ********** \\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;x: \\n\u0026#34; \u0026lt;\u0026lt; x_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;P: \\n\u0026#34; \u0026lt;\u0026lt; P_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;A: \\n\u0026#34; \u0026lt;\u0026lt; A_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B: \\n\u0026#34; \u0026lt;\u0026lt; B_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;g: \\n\u0026#34; \u0026lt;\u0026lt; g_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;m: \\n\u0026#34; \u0026lt;\u0026lt; m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q: \\n\u0026#34; \u0026lt;\u0026lt; Q_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q_m: \\n\u0026#34; \u0026lt;\u0026lt; Q_m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d: \\n\u0026#34; \u0026lt;\u0026lt; d_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;C: \\n\u0026#34; \u0026lt;\u0026lt; C_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;y: \\n\u0026#34; \u0026lt;\u0026lt; y_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } //******************* SYS_T *******************  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":88,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/","title":"src/lds_uniform_vecs.cpp","section":"Files","content":"src/lds_uniform_vecs.cpp #  Uniformly sized vectors. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Detailed Description #  This file provides a container for uniformly sized vectors.\nSource code #  //===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds_uniform_vecs.h\u0026gt; namespace lds { UniformVectorList::UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); } UniformVectorList::UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim) : vector(std::move(vecs)) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(const UniformVectorList\u0026amp; that) : vector(that) { (*this) = that; } UniformVectorList::UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { this-\u0026gt;dim_ = this-\u0026gt;at(0).n_elem; } void UniformVectorList::CheckDimensions(size_t dim) { if (dim) { dim_ = dim; } else { dim_ = this-\u0026gt;at(0).n_elem; } // make sure dimensiolaties are all uniform  bool does_match(true); for (const Vector\u0026amp; vec : *this) { does_match = does_match \u0026amp;\u0026amp; (vec.n_elem == dim_); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } } } // namespace lds  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"}]
\ No newline at end of file
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index 6afe403c..00000000
--- a/docs/en.search-data.min.cdba5d21934f88c8babfbddd59af83b0207389c62d2c3a11b31bc18fbe5810bb.json
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@@ -1 +0,0 @@
-[{"id":0,"href":"/lds-ctrl-est/docs/","title":"LDS C+E Documentation","section":"LDS Control \u0026 Estimation","content":"LDS Control \u0026amp; Estimation Documentation #  "},{"id":1,"href":"/lds-ctrl-est/docs/tutorials/","title":"LDS C+E Examples","section":"LDS C+E Documentation","content":"Examples #  "},{"id":2,"href":"/lds-ctrl-est/acknowledgements/","title":"Acknowledgements","section":"LDS Control \u0026 Estimation","content":"Acknowledgements #  Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.\n"},{"id":3,"href":"/lds-ctrl-est/docs/getting-started/getting-started/","title":"Getting Started","section":"LDS C+E Documentation","content":"Getting Started #  This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.\nldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.\nTested Configurations #  Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the RelWithDebInfo build type in the CMake configure command (-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo):\n Ubuntu 18.04 with GCC 7.5 compiler macOS 11 (Big Sur) with Apple Clang 12 compiler Windows 10 with Visual Studio 16.11 (2019 release) and Clang 12 compiler  That being said, if you want to debug a build for a single platform, here are some things you can try:\n Use different compilers (or even different versions of a single compiler) Use different versions of vcpkg (which you can control by checking out a different commit in the vcpkg submodule)  Mac Pre-requisities #  Xcode Command Line Tools will get you clang, gcc, make, and git:\nxcode-select --install Homebrew is \u0026ldquo;The Missing Package Manager for macOS\u0026rdquo; which will make installing lots of things easy. Install like this:\n/bin/bash -c \u0026#34;$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\u0026#34; You can then use it to install CMake and gfortran:\nbrew install cmake gfortran Linux Pre-requisites #  You\u0026rsquo;ll need Git, CMake, GCC, gfortran, etc.\nsudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential Windows Installation #  Look here for Windows-specific instructions.\nDownloading the Library #  First, clone the repository along with submodules:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est\rgit submodule update --init\rCompilation + Installation #  Now generate the cache and build using your IDE or from the command line as follows.\nmkdir build \u0026amp;\u0026amp; cd build\rcmake ..\rcmake --build .\rThe first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.\nIf you want to use vcpkg set up somewhere besides this repo\u0026rsquo;s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE\u0026rsquo;s settings.\nYou can verify the build is working by running ctest from the build folder, which runs all the example scripts.\nOptions #  This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\n LDSCTRLEST_BUILD_EXAMPLES : [default=ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use  n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake to configure/build the library.\n  For basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build cmake --build #build the project sudo make install #[optional] installs to default location (OS-specific)   To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location cmake --build #build the project make install #install to /your/install/prefix   To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\n LD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool    On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.\nPython bindings package ldsctrlest #  With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython3_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn\u0026rsquo;t use an undesired version. That environment needs to have NumPy installed.\ncmake --build . --target python_modules The bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory (pip install pytest matplotlib first if you need to).\nSee python/ldsctrlest/README.md for usage details.\nAlso, beware that a single build will probably not work for both the standalone library and the Python package, since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.\nCommon issues #   \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo;  If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.\nexport CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix vcpkg fails on configuration  Try running ./bootstrap-vcpkg from the vcpkg folder and try again. If that doesn\u0026rsquo;t work, try updating vcpkg to a newer version (in the source control tab, click on the commit hash by the vcpkg repo then select from the dropdown) and running boostsrap-vcpkg again. You can also try upgrading your system (e.g., apt update, apt upgrade).\n Could not find Python3 (missing: Python3_NumPy_INCLUDE_DIRS NumPy)\nMake sure NumPy is installed in the Python environment you specified. If CMake still can\u0026rsquo;t find it, you may need to tell CMake exactly where to find it by adding an argument to the configure command: -DPython3_NumPy_INCLUDE_DIR=.... You can find that location like this: python -c 'import numpy; print(numpy.get_include())'\n  "},{"id":4,"href":"/lds-ctrl-est/docs/getting-started/windows/","title":"Windows","section":"LDS C+E Documentation","content":"Windows Installation #  Windows Pre-requisites #  Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:\nSet-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time iwr get.scoop.sh | Invoke-Expression Install Git and CMake if you don\u0026rsquo;t already have them:\nscoop install git cmake If that didn\u0026rsquo;t work, follow more detailed instructions here.\nThe easiest way to compile C++ project on Windows is with Visual Studio\u0026rsquo;s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on \u0026ldquo;Desktop development with C++.\u0026rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the \u0026ldquo;Installation details\u0026rdquo; sidebar under optional features.\nAnd the easiest way to use Visual Studio\u0026rsquo;s build tools is with VS Code, along with the CMake Tools extension. Install them and you should be ready to go.\nDownloading the Library #  First, clone the repository, either from VS Code or the command line:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est You\u0026rsquo;ll need to initialize the submodules from the command line after the repo is cloned:\ngit submodule update --init Installation #  When you open the folder in VS Code, you will like be prompted by the CMake Tools extension to configure the project. Make sure you select the kit (you\u0026rsquo;ll be prompted when you configure\u0026ndash;else there\u0026rsquo;s an icon in the bar on the bottom of the window or type Ctrl+Shift+P, then \u0026ldquo;cmake select kit\u0026rdquo;). Choose Clang [latest version] with GNU CLI ... amd64 assuming you are running a 64-bit OS. (MSVC may work okay too if you don\u0026rsquo;t need to build Python bindings.)\nFollow along with the \u0026ldquo;Getting Started\u0026rdquo; instructions, but where you see config options specified as -DLDSCTREST_BUILD_STATIC=OFF or -DPython3_ROOT_DIR=..., you will enter those in settings: open with Ctrl+,, click \u0026ldquo;workspace\u0026rdquo;, then search for \u0026ldquo;CMake: Configure Args\u0026rdquo; and enter each of your desired arguments as a separate item.\nTo configure, use Ctrl+Shift+P and search for the \u0026ldquo;CMake: Configure\u0026rdquo; command. To build, click the \u0026ldquo;Build\u0026rdquo; button on the bottom bar. Then click the \u0026ldquo;CTest\u0026rdquo; button to run the example scripts.\nConsiderations #  Development on Windows has been more prone to bugs than on Unix systems, so if you encounter many problems, consider switching—WSL (Windows Subsystem for Linux) is a good option for Windows users who don\u0026rsquo;t want to work on a different machine.\nCompilation has been successfully tested in VS Code using the following kit, using the \u0026ldquo;RelWithDebInfo\u0026rdquo; config:\nClang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64) Troubleshooting #   The build appears to work, but tests fail with code 0xc0000135 OR \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo;  Have you installed the library? In VS Code, use Shift+F7 to build a specific target, in this case INSTALL. If that doesn\u0026rsquo;t solve your problem, you will likely need to add the build or install folder to your PATH environment variable, which you can do using the settings GUI (search for \u0026ldquo;Edit the system environment variables\u0026rdquo;).\nOn Windows, \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted.  Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":5,"href":"/lds-ctrl-est/issues-contributing/","title":"Issues Contributing","section":"LDS Control \u0026 Estimation","content":"Reporting Issues #  If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.\nContributing #  We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.\n"},{"id":6,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/","title":"armamexc","section":"Namespaces","content":"armamexc #  arma/mex interface using Matlab C API More\u0026hellip; Functions #      Name     template \u0026lt;class T \u0026gt; auto m2T_scalar(const mxArray * matlab_scalar)\nConvert Matlab mxArray to scalar of type T.   template \u0026lt;class T \u0026gt; auto m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)\nConvert matlab matrix to armadillo.   template \u0026lt;typename T \u0026gt; auto a2m_mat(arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat)\nConvert armadillo to matlab matrix.   template \u0026lt;typename T \u0026gt; auto a2m_vec(arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec)\nConvert armadillo to matlab vector.    Detailed Description #  Utilities for arma/mex interface using Matlab C API\nFunction Details #  m2T_scalar #  template \u0026lt;class T \u0026gt; inline auto m2T_scalar( const mxArray * matlab_scalar ) Parameters:\n matlab_scalar matlab scalar  Template Parameters:\n T type  Return: scalar of type T\nm2a_mat #  template \u0026lt;class T \u0026gt; inline auto m2a_mat( const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true ) Parameters:\n matlab_mat matlab matrix copy_aux_mem [optional] whether to copy auxiliary memory strict [optional] strictly enforce the above  Template Parameters:\n T type  Return: armadillo matrix of type T\na2m_mat #  template \u0026lt;typename T \u0026gt; inline auto a2m_mat( arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat ) Parameters:\n arma_mat armadillo matrix  Return: matlab matrix\na2m_vec #  template \u0026lt;typename T \u0026gt; inline auto a2m_vec( arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec ) Parameters:\n arma_vec armadillo vector  Return: matlab vector\n Updated on 19 May 2022 at 17:16:03 Eastern Daylight Time\n"},{"id":7,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/","title":"armamexcpp","section":"Namespaces","content":"armamexcpp #  arma/mex interface using Matlab C++ API More\u0026hellip; Functions #      Name     template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat(matlab::data::CellArray \u0026amp; matlab_cell)\nConvert matlab cell array to vector of armadillo matrices.   template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array)\nConvert matlab matrix to a vector of scalars.   template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo vector.   template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo matrix.   template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat(const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab matrix.   template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec(const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab vector.   template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec(const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert vector of scalar T to matlab matrix.    Detailed Description #  utilities for arma/mex interface using Matlab C++ API\nFunction Details #  m2a_cellmat #  template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat( matlab::data::CellArray \u0026amp; matlab_cell ) Parameters:\n matlab_cell matlab cell  Template Parameters:\n T type  Return: vector of armadillo matrices of type T\nm2s_vec #  template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array ) Parameters:\n matlab_array matlab array  Template Parameters:\n T type  Return: vector of type T\nm2a_vec #  template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\n matlab_array matlab array  Template Parameters:\n T type  Return: armadillo vector of type T\nm2a_mat #  template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\n matlab_array matlab matrix  Template Parameters:\n T type  Return: armadillo matrix of type T\na2m_mat #  template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat( const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\n arma_mat arma matrix factory matlab \u0026ldquo;array factory\u0026rdquo;  Template Parameters:\n T type  Return: matlab matrix\na2m_vec #  template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec( const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\n arma_vec armadillo vector factory matlab \u0026ldquo;array factory\u0026rdquo;  Template Parameters:\n T type  Return: matlab matrix\ns2m_vec #  template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec( const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\n std_vec standard vector factory matlab \u0026ldquo;array factory\u0026rdquo;  Template Parameters:\n T type  Return: matlab matrix\n Updated on 19 May 2022 at 17:16:03 Eastern Daylight Time\n"},{"id":8,"href":"/lds-ctrl-est/docs/terminology/control-estimation/","title":"C\u0026E","section":"LDS C+E Documentation","content":"Control \u0026amp; Estimation #  The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.\nState estimation #  In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by   \\[\r\\widehat{\\mathbf{x}}_{t|t-1} = \\mathbf{A}\\widehat{\\mathbf{x}}_{t-1|t-1} \u0026#43; \\mathbf{B} u_{t-1} \u0026#43; \\mathbf{m}_{t-1} \\;,\r\\]   \\[\r\\widehat{\\mathbf{x}}_{t|t} = \\widehat{\\mathbf{x}}_{t|t-1} \u0026#43; \\mathbf{K}^{\\rm e}_t \\left(\\mathbf{z}_t - \\widehat{\\mathbf{y}}_{t|t-1}\\right)\\;,\r\\]  where  \\( \\hat{\\left(\\cdot\\right)}_{t|j} \\)  indicates an estimate at time  \\( t \\)  given data up to time  \\( j \\)  inclusive,  \\( \\mathbf{K}^{\\rm e} \\)  is the estimator gain, and\n \\[ \\widehat{\\mathbf{y}}_{t|t-1} = h\\left( \\widehat{\\mathbf{x}}_{t|t-1} \\right) \\; .\\]  In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.\nIn the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.\nAdaptive estimation of process disturbance #  Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.\nWhen parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk  \\[\r\\mathbf{m}_{t} = \\mathbf{m}_{t-1} \u0026#43; \\mathbf{w}^m_{t-1} \\;,\r\\]  where  \\( \\mathbf{w}^m \\sim \\mathcal{N}\\left(0, \\mathbf{Q}_m\\right)\\)  . Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.\nControl #  Given the estimated state, the controller updates the inputs to the system according to the following law:  \\[\r\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\r\\]  where  \\( \\left( \\cdot \\right)^{\\rm ref} \\)  correspond to reference/target signals and  \\( \\mathbf{K}^c_x \\)  is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.\nIf users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as\n \\[\r\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right) - \\mathbf{K}^c_{\\rm inty} \\sum_{j=1}^{t}\\left( \\widehat{\\mathbf{y}}_j - \\mathbf{y}^{\\rm ref}_j \\right) \\;.\r\\]  An additional option available to users is a control law that updates the change in u,\n \\[\r\\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\left(\\mathbf{u}_{t-1} - \\mathbf{u}^{\\rm ref}_{t-1} \\right) - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\r\\]   \\[\r\\mathbf{u}_{t} = \\mathbf{u}_{t-1} \u0026#43; \\Delta\\mathbf{u}_{t} \\; .\r\\]  Notice that this takes the form of a first-order difference equation for updating control (i.e.,  \\( \\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\mathbf{u}_{t-1} \u0026#43; \\epsilon_{t-1} \\)  ), effectively low-pass filtering the input depending on the characteristics of  \\( \\mathbf{K}^c_u \\)  . This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.\nIntegral action and the  \\( \\Delta \\mathbf{u} \\)  control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR\u0026rsquo;d to use in combination.\nCalculating reference state-control from output #  In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce  \\( \\mathbf{x}^{\\rm ref} \\)  and  \\( \\mathbf{u}^{\\rm ref} \\)  . Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.\n"},{"id":9,"href":"/lds-ctrl-est/docs/api/classes/","title":"Classes","section":"LDS C+E Documentation","content":"Classes #    lds::Controller\n  lds::EM\n  lds::Fit LDS Fit Type.\n  lds::SSID\n  lds::SwitchedController SwitchedController Type.\n  lds::System Linear Dynamical System Type.\n  lds::UniformMatrixList\n  lds::UniformSystemList\n  lds::UniformVectorList\n  lds::gaussian::Controller Gaussian-observation Controller Type.\n  lds::gaussian::Fit GLDS Fit Type.\n  lds::gaussian::FitEM GLDS E-M Fit Type.\n  lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.\n  lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.\n  lds::gaussian::System Gaussian LDS Type.\n  lds::poisson::Controller PLDS Controller Type.\n  lds::poisson::Fit PLDS Fit Type.\n  lds::poisson::FitEM PLDS E-M Fit Type.\n  lds::poisson::FitSSID Subspace Identification (SSID) for PLDS.\n  lds::poisson::SwitchedController Poisson-observation SwitchedController Type.\n  lds::poisson::System Poisson System type.\n   Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":10,"href":"/lds-ctrl-est/docs/api/modules/group__control__masks/","title":"Control Mode Bit Masks","section":"Modules","content":"Control Mode Bit Masks #  provides fill types for constructing new armadillo vectors, matrices More\u0026hellip; Attributes #      Name     const std::size_t kControlTypeDeltaU control designed to penalize change in input   const std::size_t kControlTypeIntY control using integral action   const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m    Detailed Description #  Control mode bit masks. These can be bit-wise OR\u0026rsquo;d to use in combination.\nAttribute Details #  kControlTypeDeltaU #  static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY #  static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM #  static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\n Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":11,"href":"/lds-ctrl-est/docs/api/modules/group__defaults/","title":"Defaults","section":"Modules","content":"Defaults #  \nMore\u0026hellip; Attributes #      Name     const data_t kDefaultP0 default state estimate covar   const data_t kDefaultQ0 default process noise covar   const data_t kDefaultR0 default output noise covar    Detailed Description #  Default values for common variables (e.g., default diagonal elements of covariances)\nAttribute Details #  kDefaultP0 #  static const data_t kDefaultP0 = 1e-6; kDefaultQ0 #  static const data_t kDefaultQ0 = 1e-6; kDefaultR0 #  static const data_t kDefaultR0 = 1e-2;  Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":12,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/","title":"eg_glds_ctrl.cpp","section":"Examples","content":"eg_glds_ctrl.cpp #  Example GLDS Control\n//===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); // output noise covariance  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb.  data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_x).fill(m_low); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables:  // if following enabled, adapts set point with re-estimated process  // disturbance n.b., should not need integral action if this is enabled as the  // adaptive estimator minimizes DC error  bool do_adaptive_set_point = false; // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err  // setting initial state to target to avoid error at onset:  Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance  control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; } // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(9); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation  // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input  Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals  y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,  // xTrue, mTrue saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; }  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":13,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/","title":"eg_glds_du_plds_ctrl.cpp","section":"Examples","content":"eg_glds_du_plds_ctrl.cpp #  Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).\n//===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb.  data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance  Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables:  // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input  // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du =  // 1e-2.  Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err  Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp  // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control)  control_type = control_type | lds::kControlTypeDeltaU; // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(10); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation  // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input  Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals  y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,  // xTrue, mTrue saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; }  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":14,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/","title":"eg_plds_ctrl.cpp","section":"Examples","content":"eg_plds_ctrl.cpp #  Example PLDS Control\n//===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains  // n.b., Can either use Vector (arma::Col) or std::vector  Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error  Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err  // Set control type bit mask, so controller knows what to do  size_t control_type = lds::kControlTypeIntY; // integral action  // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled...  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions  controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller  lds::poisson::Controller controller; { // Create model used for control.  lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance  Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition  // adaptively re-estimate process disturbance (m)  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type  controller.set_control_type(control_type); // set controller gains  controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above  // target:  data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val  y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference  data_t f = 0.5; // freq [=] Hz  Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system.  z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Notably, it does this in the  // log-linear space (i.e., log(y)).  //  // Therefore, it is only applicable to regulation problems or cases where  // reference trajectory changes slowly compared to system dynamics.  controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; }  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":15,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/","title":"eg_plds_est.cpp","section":"Examples","content":"eg_plds_est.cpp #  Example PLDS Estimation\n//===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; // no. inputs  size_t n_x = 1; // no. states  size_t n_y = 1; // no. outputs  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.  // construct ground truth system...  lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  // Assign params.  system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation  // e.g., will create a model with incorrect disturbance  lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object  system_estimator = system_true; // wrong disturbance  Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.  // turn on adaptive disturbance estimation  system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting  // on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation :  // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus)  Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk  Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in...  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params  Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions  y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system.  z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update)  system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals  y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; }  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":16,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/","title":"eg_plds_switched_ctrl.cpp","section":"Examples","content":"eg_plds_switched_ctrl.cpp #  Example Switched PLDS Control\n//===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control  bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching  size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2  data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1  // simulated system being controlled  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well  // characterized by two LDS models with different input matrices.**  data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions  // reference  Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2  Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller  lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains  { // create switched controller sub-systems  // system 1  lds::poisson::System sys1(controlled_system); // set process noise covariance  Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m)  // n.b. using arbitrary default value for process noise if enabled.  sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset:  Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions  cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2  lds::poisson::System sys2 = sys1; // set parameters  sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s:  Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive.  k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables  size_t control_type = 0; // no integral action, etc  switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements  Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control.  Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in...  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params  Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val  y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain  // Assume another algorithm decodes this mode change and signals the  // switched_controller  Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1  { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2  if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system.  z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control  u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; }  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":17,"href":"/lds-ctrl-est/docs/api/examples/","title":"Examples","section":"LDS C+E Documentation","content":"Examples #    eg_glds_ctrl.cpp\n  eg_glds_du_plds_ctrl.cpp\n  eg_plds_ctrl.cpp\n  eg_plds_est.cpp\n  eg_plds_switched_ctrl.cpp\n   Updated on 19 May 2022 at 17:16:06 Eastern Daylight Time\n"},{"id":18,"href":"/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/","title":"examples","section":"Files","content":"examples #  Files #     Name     examples/eg_glds_ctrl.cpp   examples/eg_glds_du_plds_ctrl.cpp   examples/eg_plds_ctrl.cpp   examples/eg_plds_est.cpp   examples/eg_plds_switched_ctrl.cpp     Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":19,"href":"/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/","title":"examples/eg_glds_ctrl.cpp","section":"Files","content":"examples/eg_glds_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Going to simulate a switching disturbance (m) acting on system\nSource code #  //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); // output noise covariance  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb.  data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_x).fill(m_low); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables:  // if following enabled, adapts set point with re-estimated process  // disturbance n.b., should not need integral action if this is enabled as the  // adaptive estimator minimizes DC error  bool do_adaptive_set_point = false; // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err  // setting initial state to target to avoid error at onset:  Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance  control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; } // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(9); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation  // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input  Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals  y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,  // xTrue, mTrue saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; }  Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":20,"href":"/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/","title":"examples/eg_glds_du_plds_ctrl.cpp","section":"Files","content":"examples/eg_glds_du_plds_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Going to simulate a switching disturbance (m) acting on system\nSource code #  //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb.  data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance  Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables:  // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input  // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du =  // 1e-2.  Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err  Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp  // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control)  control_type = control_type | lds::kControlTypeDeltaU; // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(10); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation  // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input  Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals  y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,  // xTrue, mTrue saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; }  Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":21,"href":"/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/","title":"examples/eg_plds_ctrl.cpp","section":"Files","content":"examples/eg_plds_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Going to simulate a switching disturbance (m) acting on system\nSource code #  //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains  // n.b., Can either use Vector (arma::Col) or std::vector  Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error  Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err  // Set control type bit mask, so controller knows what to do  size_t control_type = lds::kControlTypeIntY; // integral action  // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled...  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions  controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller  lds::poisson::Controller controller; { // Create model used for control.  lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance  Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition  // adaptively re-estimate process disturbance (m)  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type  controller.set_control_type(control_type); // set controller gains  controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above  // target:  data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val  y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference  data_t f = 0.5; // freq [=] Hz  Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system.  z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Notably, it does this in the  // log-linear space (i.e., log(y)).  //  // Therefore, it is only applicable to regulation problems or cases where  // reference trajectory changes slowly compared to system dynamics.  controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; }  Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":22,"href":"/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/","title":"examples/eg_plds_est.cpp","section":"Files","content":"examples/eg_plds_est.cpp #  Functions #      Name     Matrix random_walk(size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0)   int main()    Function Details #  random_walk #  Matrix random_walk( size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0 ) main #  int main() Source code #  //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; // no. inputs  size_t n_x = 1; // no. states  size_t n_y = 1; // no. outputs  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.  // construct ground truth system...  lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  // Assign params.  system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation  // e.g., will create a model with incorrect disturbance  lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object  system_estimator = system_true; // wrong disturbance  Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.  // turn on adaptive disturbance estimation  system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting  // on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation :  // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus)  Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk  Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in...  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params  Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions  y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system.  z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update)  system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals  y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; }  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":23,"href":"/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/","title":"examples/eg_plds_switched_ctrl.cpp","section":"Files","content":"examples/eg_plds_switched_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Source code #  //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control  bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching  size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2  data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1  // simulated system being controlled  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well  // characterized by two LDS models with different input matrices.**  data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions  // reference  Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2  Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller  lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains  { // create switched controller sub-systems  // system 1  lds::poisson::System sys1(controlled_system); // set process noise covariance  Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m)  // n.b. using arbitrary default value for process noise if enabled.  sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset:  Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions  cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2  lds::poisson::System sys2 = sys1; // set parameters  sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s:  Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive.  k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables  size_t control_type = 0; // no integral action, etc  switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements  Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control.  Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in...  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params  Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val  y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain  // Assume another algorithm decodes this mode change and signals the  // switched_controller  Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1  { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2  if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system.  z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control  u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; }  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":24,"href":"/lds-ctrl-est/docs/api/files/","title":"Files","section":"LDS C+E Documentation","content":"Files #    examples/eg_glds_ctrl.cpp\n  examples/eg_glds_du_plds_ctrl.cpp\n  examples/eg_plds_ctrl.cpp\n  examples/eg_plds_est.cpp\n  examples/eg_plds_switched_ctrl.cpp\n  ldsCtrlEst_h/lds.h lds namespace\n  ldsCtrlEst_h/lds_ctrl.h Controller.\n  ldsCtrlEst_h/lds_fit.h LDS base fit type.\n  ldsCtrlEst_h/lds_fit_em.h subspace identification\n  ldsCtrlEst_h/lds_fit_ssid.h subspace identification\n  ldsCtrlEst_h/lds_gaussian.h glds namespace\n  ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.\n  ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.\n  ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.\n  ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.\n  ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.\n  ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.\n  ldsCtrlEst_h/lds_poisson.h plds namespace\n  ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.\n  ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.\n  ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.\n  ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.\n  ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.\n  ldsCtrlEst_h/lds_poisson_sys.h PLDS base type.\n  ldsCtrlEst_h/lds_sctrl.h SwitchedController type.\n  ldsCtrlEst_h/lds_sys.h LDS base type.\n  ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.\n  ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.\n  ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.\n  ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)\n  ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)\n  src/lds.cpp misc lds namespace functions\n  src/lds_gaussian_sys.cpp GLDS base type.\n  src/lds_poisson_sys.cpp PLDS base type.\n  src/lds_sys.cpp LDS base type.\n  src/lds_uniform_vecs.cpp Uniformly sized vectors.\n   Updated on 19 May 2022 at 17:16:06 Eastern Daylight Time\n"},{"id":25,"href":"/lds-ctrl-est/docs/tutorials/eg_glds_control/","title":"GLDS Control","section":"LDS C+E Examples","content":"GLDS Control Tutorial #  This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.\nThe full code for this can be found here.\nPreamble #  In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system #  A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.\n// Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example,   \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\]   \\[\ry_{t} = x_t\r\\]  where  \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\)  .\nNow, create non-default parameters for this model.\n// Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); // output noise covariance  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.\n/// Going to simulate a switching disturbance (m) acting on system  size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb.  data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_y).fill(m_low); Finally, assign the parameters using corresponding set-methods.\n// Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); Creating the controller #  Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.\nFor the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.\n// make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nWith the controller constructed, control variables may be set.\n// Control variables:  // if following enabled, adapts set point with re-estimated process  // disturbance n.b., should not need integral action if this is enabled as the  // adaptive estimator minimizes DC error  bool do_adaptive_set_point = false; // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err  // setting initial state to target to avoid error at onset:  Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance  control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; } // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(9); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); Simulating control #  In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.\nThe control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.\n// Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); Example simulation result #  Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller\u0026rsquo;s online estimates of the output, state, and disturbance are given in purple.\n"},{"id":26,"href":"/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/","title":"include","section":"Files","content":"include #  Directories #     Name     ldsCtrlEst_h     Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":27,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds/","title":"lds","section":"Namespaces","content":"lds #  Linear Dynamical Systems (LDS) namespace. Namespaces #     Name     lds::gaussian Linear Dynamical Systems with Gaussian observations.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::Controller   class lds::EM   class lds::Fit LDS Fit Type.   class lds::SSID   class lds::SwitchedController SwitchedController Type.   class lds::System Linear Dynamical System Type.   class lds::UniformMatrixList   class lds::UniformSystemList   class lds::UniformVectorList    Types #      Name     enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}\nweighting options for SSID   enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2}   using double data_t   using arma::Col\u0026lt; data_t \u0026gt; Vector   using arma::Mat\u0026lt; data_t \u0026gt; Matrix   using arma::Cube\u0026lt; data_t \u0026gt; Cube   using arma::subview\u0026lt; data_t \u0026gt; View    Functions #      Name     void Limit(std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub)   void Limit(Vector \u0026amp; x, data_t lb, data_t ub)   void Limit(Matrix \u0026amp; x, data_t lb, data_t ub)   void Reassign(Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Vector in place   void Reassign(Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Matrix in place   void ForceSymPD(Matrix \u0026amp; X)\nforces matrix to be symmetric positive-definite   void ForceSymMinEig(Matrix \u0026amp; X, data_t eig_min =0)\nforces matrix to be symmetric and have a minimum eigenvalue   void lq(Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X)\nLQ decomposition.   Matrix calcCov(const Matrix \u0026amp; A, const Matrix \u0026amp; B)\nCalculate covariance matrix.    Attributes #      Name     const std::size_t kControlTypeDeltaU control designed to penalize change in input   const std::size_t kControlTypeIntY control using integral action   const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m   const data_t kInf Some useful numbers.   const data_t kPi   const data_t kDefaultP0 default state estimate covar   const data_t kDefaultQ0 default process noise covar   const data_t kDefaultR0 default output noise covar    Type Details #  SSIDWt #     Enumerator Value Description     kSSIDNone  None.   kSSIDMOESP  MOESP (AKA \u0026ldquo;robust method\u0026rdquo; in van Overschee 1996)   kSSIDCVA  CVA \u0026ldquo;Canonical Variate Analysis\u0026rdquo;.    Weighting options for singular value decomposition performed during subspace identification (SSID)\nReference:\nvan Overschee, de Moor. 1996. Subspace Identification for Linear Systems.\nMatrixListFreeDim #     Enumerator Value Description     kMatFreeDimNone  neither dim free to be hetero in mat list   kMatFreeDim1  allow 1st dim of mats in list to be hetero   kMatFreeDim2  allow 2nd dim of mats in list to be hetero    data_t #  using lds::data_t = typedef double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nVector #  using lds::Vector = typedef arma::Col\u0026lt;data_t\u0026gt;; Matrix #  using lds::Matrix = typedef arma::Mat\u0026lt;data_t\u0026gt;; Cube #  using lds::Cube = typedef arma::Cube\u0026lt;data_t\u0026gt;; View #  using lds::View = typedef arma::subview\u0026lt;data_t\u0026gt;; Function Details #  Limit #  inline void Limit( std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub ) Limit #  inline void Limit( Vector \u0026amp; x, data_t lb, data_t ub ) Limit #  inline void Limit( Matrix \u0026amp; x, data_t lb, data_t ub ) Reassign #  inline void Reassign( Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\n some some Vector other other Vector parenthetical optional description provided by caller to ease debugging  Reassign #  inline void Reassign( Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\n some some Matrix other other Matrix parenthetical optional description provided by caller to ease debugging  ForceSymPD #  void ForceSymPD( Matrix \u0026amp; X ) Parameters:\n X mutated matrix  ForceSymMinEig #  void ForceSymMinEig( Matrix \u0026amp; X, data_t eig_min =0 ) Parameters:\n X mutated matrix eig_min [optional] minimum eigen value  lq #  void lq( Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X ) Parameters:\n L lower triangle matrix Qt orthonormal matrix (transposed cf QR decomp) X matrix being decomposed  calcCov #  Matrix calcCov( const Matrix \u0026amp; A, const Matrix \u0026amp; B ) Parameters:\n A some matrix B some other matrix  Return: covariance\nAttribute Details #  kControlTypeDeltaU #  static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY #  static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM #  static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\nkInf #  static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); kPi #  static const data_t kPi = arma::datum::pi; kDefaultP0 #  static const data_t kDefaultP0 = 1e-6; kDefaultQ0 #  static const data_t kDefaultQ0 = 1e-6; kDefaultR0 #  static const data_t kDefaultR0 = 1e-2;  Updated on 19 May 2022 at 17:16:03 Eastern Daylight Time\n"},{"id":28,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1controller/","title":"lds::Controller","section":"Classes","content":"lds::Controller #  More\u0026hellip;\nInherited by lds::gaussian::Controller, lds::poisson::Controller, lds::SwitchedController\u0026lt; System \u0026gt;\nPublic Functions #      Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes #      Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type    Detailed Description #  template \u0026lt;typename System \u0026gt; class lds::Controller;   Public Function Details #  Controller #  Controller() =default  Controller #  inline Controller( const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\n sys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask  Template Parameters:\n System type derived from lds::System   Controller #  inline Controller( System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\n sys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask  Template Parameters:\n System type derived from lds::System   Control #  inline const Vector \u0026amp; Control( const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\n z measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true)  Return: updated control signal\nUpdates the control signal (single-step). This is the most flexible option, but requires user to have set the controller\u0026rsquo;s y_ref, x_ref, and u_ref variables.\n ControlOutputReference #  inline const Vector \u0026amp; ControlOutputReference( const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\n z measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_estimation [optional] whether to update state estimate (if false, effectively open-loop control) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true)  Return: updated control signal\nUpdates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.\n sys #  inline const System \u0026amp; sys() const  Kc #  inline const Matrix \u0026amp; Kc() const  Kc_inty #  inline const Matrix \u0026amp; Kc_inty() const  Kc_u #  inline const Matrix \u0026amp; Kc_u() const  g_design #  inline const Vector \u0026amp; g_design() const  u_ref #  inline const Vector \u0026amp; u_ref() const  x_ref #  inline const Vector \u0026amp; x_ref() const  y_ref #  inline const Vector \u0026amp; y_ref() const  control_type #  inline size_t control_type() const  tau_awu #  inline data_t tau_awu() const  u_lb #  inline data_t u_lb() const  u_ub #  inline data_t u_ub() const  set_sys #  inline void set_sys( const System \u0026amp; sys )  set_g_design #  inline void set_g_design( const Vector \u0026amp; g_design )  set_u_ref #  inline void set_u_ref( const Vector \u0026amp; u_ref )  set_x_ref #  inline void set_x_ref( const Vector \u0026amp; x_ref )  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) Reimplemented by: lds::poisson::SwitchedController::set_y_ref, lds::gaussian::SwitchedController::set_y_ref, lds::poisson::Controller::set_y_ref, lds::gaussian::Controller::set_y_ref\n set_Kc #  inline void set_Kc( const Matrix \u0026amp; Kc )  set_Kc_inty #  inline void set_Kc_inty( const Matrix \u0026amp; Kc_inty )  set_Kc_u #  inline void set_Kc_u( const Matrix \u0026amp; Kc_u )  set_tau_awu #  inline void set_tau_awu( data_t tau )  set_control_type #  inline void set_control_type( size_t control_type ) Parameters:\n control_type control type bit mask  Template Parameters:\n System type derived from lds::System   set_u_lb #  inline void set_u_lb( data_t u_lb ) Parameters:\n u_lb control lower bound   set_u_ub #  inline void set_u_ub( data_t u_ub ) Parameters:\n u_ub control upper bound   Reset #  inline void Reset()  Print #  inline void Print()  Protected Attribute Details #  **sys_** #  System sys_;  **u_** #  Vector u_;  **u_return_** #  Vector u_return_;  **g_design_** #  Vector g_design_;  **u_ref_** #  Vector u_ref_;  **u_ref_prev_** #  Vector u_ref_prev_;  **x_ref_** #  Vector x_ref_;  **y_ref_** #  Vector y_ref_;  **cx_ref_** #  Vector cx_ref_;  **Kc_** #  Matrix Kc_;  **Kc_u_** #  Matrix Kc_u_;  **Kc_inty_** #  Matrix Kc_inty_;  **du_ref_** #  Vector du_ref_;  **dv_ref_** #  Vector dv_ref_;  **v_ref_** #  Vector v_ref_;  **dv_** #  Vector dv_;  **v_** #  Vector v_;  **int_e_** #  Vector int_e_;  **int_e_awu_adjust_** #  Vector int_e_awu_adjust_;  **u_sat_** #  Vector u_sat_;  **do_control_prev_** #  bool do_control_prev_ = false;  **do_lock_control_prev_** #  bool do_lock_control_prev_ = false;  **u_saturated_** #  bool u_saturated_ = false;  **u_lb_** #  data_t u_lb_ {};  **u_ub_** #  data_t u_ub_ {};  **tau_awu_** #  data_t tau_awu_ {};  **k_awu_** #  data_t k_awu_ = 0;  **t_since_control_onset_** #  data_t t_since_control_onset_ = 0;  **control_type_** #  size_t control_type_ {};   Updated on 19 May 2022 at 17:16:03 Eastern Daylight Time\n"},{"id":29,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1em/","title":"lds::EM","section":"Classes","content":"lds::EM #  More\u0026hellip;\nInherited by lds::gaussian::FitEM, lds::poisson::FitEM\nPublic Functions #      Name      EM() =default\nConstructs a new EMFit type.    EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.    EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.   virtual ~EM() =default   const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller.   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time)   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time)   const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance   const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1)   const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance   size_t n_t_tot()\ntotal number of time samples   const Vector \u0026amp; theta() const\ngets parameters updated in M step    Protected Functions #      Name     void Expectation(bool force_common_initial =false)\nExpectation step.   void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step.   void MaximizeDynamics()   void MaximizeQ()   void MaximizeInitial()   virtual void MaximizeOutput() =0   virtual void MaximizeMeasurement() =0   void Smooth(bool force_common_initial)\nget smoothed estimates   virtual void RecurseKe(Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t) =0\nrecursively update estimator gain Ke   void Reset()\nreset to initial conditions   void InitVars()\nInitializes the variables.   Vector UpdateTheta()\nupdates parameter list, theta    Protected Attributes #      Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   std::vector\u0026lt; Matrix \u0026gt; x_ state estimate   std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov   std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance   std::vector\u0026lt; Matrix \u0026gt; y_ output estimate   Matrix diag_y_   Matrix sum_E_x_t_x_t_ state covariance (current time)   Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1)   Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance   Fit fit_   Vector theta_   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials    Detailed Description #  template \u0026lt;typename Fit \u0026gt; class lds::EM;   Public Function Details #  EM #  EM() =default  EM #  EM( size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\n n_x number of states dt sample period u_train input training data z_train measurement training data   EM #  EM( const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\n fit0 initial fit u_train input training data z_train measurement training data   ~EM #  virtual ~EM() =default  Run #  const Fit \u0026amp; Run( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2 ) Parameters:\n calc_dynamics [optional] whether to calculate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law max_iter max number of iterations tol convergence tolerance (max fractional abs change)  Return: Fit\n ReturnData #  inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\n x #  inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const  y #  inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const  sum_E_x_t_x_t #  inline const Matrix \u0026amp; sum_E_x_t_x_t() const  sum_E_xu_tm1_xu_tm1 #  inline const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const  sum_E_xu_t_xu_tm1 #  inline const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const  n_t_tot #  inline size_t n_t_tot()  theta #  inline const Vector \u0026amp; theta() const  Protected Function Details #  Expectation #  void Expectation( bool force_common_initial =false ) Parameters:\n force_common_initial whether to force common initial condition for all trials   Maximization #  void Maximization( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false ) Parameters:\n calc_dynamics [optional] whether to caclulate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law   MaximizeDynamics #  void MaximizeDynamics()  MaximizeQ #  void MaximizeQ()  MaximizeInitial #  void MaximizeInitial()  MaximizeOutput #  virtual void MaximizeOutput() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput\n MaximizeMeasurement #  virtual void MaximizeMeasurement() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement\n Smooth #  void Smooth( bool force_common_initial ) Parameters:\n force_common_initial whether to force common initial conditions   RecurseKe #  virtual void RecurseKe( Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t ) =0 Parameters:\n Ke estimator gain P_pre cov of predicted state est. P_post cov of postior sate est. t time  Reimplemented by: lds::gaussian::FitEM::RecurseKe, lds::poisson::FitEM::RecurseKe\n Reset #  void Reset()  InitVars #  void InitVars()  UpdateTheta #  Vector UpdateTheta() Return: parameter list\n Protected Attribute Details #  **u_** #  UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_;  **z_** #  UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_;  **x_** #  std::vector\u0026lt; Matrix \u0026gt; x_;  **P_** #  std::vector\u0026lt; Cube \u0026gt; P_;  **P_t_tm1_** #  std::vector\u0026lt; Cube \u0026gt; P_t_tm1_;  **y_** #  std::vector\u0026lt; Matrix \u0026gt; y_;  **diag_y_** #  Matrix diag_y_;  **sum_E_x_t_x_t_** #  Matrix sum_E_x_t_x_t_;  **sum_E_xu_tm1_xu_tm1_** #  Matrix sum_E_xu_tm1_xu_tm1_;  **sum_E_xu_t_xu_tm1_** #  Matrix sum_E_xu_t_xu_tm1_;  **fit_** #  Fit fit_;  **theta_** #  Vector theta_;  **dt_** #  data_t dt_ {};  **n_u_** #  size_t n_u_ {};  **n_x_** #  size_t n_x_ {};  **n_y_** #  size_t n_y_ {};  **n_trials_** #  size_t n_trials_ {};  **n_t_** #  std::vector\u0026lt; size_t \u0026gt; n_t_;  **n_t_tot_** #  size_t n_t_tot_ {};   Updated on 19 May 2022 at 17:16:03 Eastern Daylight Time\n"},{"id":30,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1fit/","title":"lds::Fit","section":"Classes","content":"lds::Fit #  LDS Fit Type. #include \u0026lt;lds_fit.h\u0026gt;\nInherited by lds::gaussian::Fit, lds::poisson::Fit\nPublic Functions #      Name      Fit() =default\nConstructs a new Fit.    Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit.   virtual ~Fit() =default   size_t n_u() const\ngets number of inputs   size_t n_x() const\ngets number of states   size_t n_y() const\ngets number of outputs   data_t dt() const\ngets sample period   const Matrix \u0026amp; A() const\ngets state matrix   const Matrix \u0026amp; B() const\ngets input matrix   const Vector \u0026amp; g() const\ngets input gain   const Vector \u0026amp; m() const\ngets process disturbance   const Matrix \u0026amp; Q() const\ngets process noise covariance   const Vector \u0026amp; x0() const\ngets initial state estimate   const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate   const Matrix \u0026amp; C() const\ngets output matrix   const Vector \u0026amp; d() const\ngets output bias   virtual const Matrix \u0026amp; R() const =0   void set_A(const Matrix \u0026amp; A)\nsets state matrix   void set_B(const Matrix \u0026amp; B)\nsets input matrix   void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor   void set_m(const Vector \u0026amp; m)\nsets process disturbance   void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance   virtual void set_R(const Matrix \u0026amp; R) =0\nsets output noise covariance (if any)   void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate   void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance   void set_C(const Matrix \u0026amp; C)\nsets output matrix   void set_d(const Vector \u0026amp; d)\nsets output bias   View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function   View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function   virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) =0\noutput function    Protected Attributes #      Name     data_t dt_ sample period   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Vector m_ process noise mean   Matrix Q_ process noise cov   Matrix C_ output matrix   Vector d_ output bias   Matrix R_ measurement noise   Vector x0_ initial state   Matrix P0_ initial covar   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs      Public Function Details #  Fit #  Fit() =default  Fit #  Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period   ~Fit #  virtual ~Fit() =default  n_u #  inline size_t n_u() const  n_x #  inline size_t n_x() const  n_y #  inline size_t n_y() const  dt #  inline data_t dt() const  A #  inline const Matrix \u0026amp; A() const  B #  inline const Matrix \u0026amp; B() const  g #  inline const Vector \u0026amp; g() const  m #  inline const Vector \u0026amp; m() const  Q #  inline const Matrix \u0026amp; Q() const  x0 #  inline const Vector \u0026amp; x0() const  P0 #  inline const Matrix \u0026amp; P0() const  C #  inline const Matrix \u0026amp; C() const  d #  inline const Vector \u0026amp; d() const  R #  virtual const Matrix \u0026amp; R() const =0 Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R\n set_A #  inline void set_A( const Matrix \u0026amp; A )  set_B #  inline void set_B( const Matrix \u0026amp; B )  set_g #  inline void set_g( const Vector \u0026amp; g )  set_m #  inline void set_m( const Vector \u0026amp; m )  set_Q #  inline void set_Q( const Matrix \u0026amp; Q )  set_R #  virtual void set_R( const Matrix \u0026amp; R ) =0 Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R\n set_x0 #  inline void set_x0( const Vector \u0026amp; x0 )  set_P0 #  inline void set_P0( const Matrix \u0026amp; P0 )  set_C #  inline void set_C( const Matrix \u0026amp; C )  set_d #  inline void set_d( const Vector \u0026amp; d )  f #  inline View f( Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t ) Parameters:\n x state estimate (over time) u input (over time) t time index  Return: view of updated state\n f #  inline View f( Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t ) Parameters:\n x_pre predicted state est. x_post posterior state est. u input (over time) t time index  Return: view of predicted state\n h #  virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) =0 Parameters:\n y output estimate (over time) x state estimate (over time) t time index  Return: output\nReimplemented by: lds::poisson::Fit::h, lds::gaussian::Fit::h\n Protected Attribute Details #  **dt_** #  data_t dt_ {};  **A_** #  Matrix A_;  **B_** #  Matrix B_;  **g_** #  Vector g_;  **m_** #  Vector m_;  **Q_** #  Matrix Q_;  **C_** #  Matrix C_;  **d_** #  Vector d_;  **R_** #  Matrix R_;  **x0_** #  Vector x0_;  **P0_** #  Matrix P0_;  **n_u_** #  size_t n_u_ {};  **n_x_** #  size_t n_x_ {};  **n_y_** #  size_t n_y_ {};   Updated on 19 May 2022 at 17:16:03 Eastern Daylight Time\n"},{"id":31,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/","title":"lds::gaussian","section":"Namespaces","content":"lds::gaussian #  Linear Dynamical Systems with Gaussian observations. Classes #      Name     class lds::gaussian::Controller Gaussian-observation Controller Type.   class lds::gaussian::Fit GLDS Fit Type.   class lds::gaussian::FitEM GLDS E-M Fit Type.   class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.   class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.   class lds::gaussian::System Gaussian LDS Type.     Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":32,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1controller/","title":"lds::gaussian::Controller","section":"Classes","content":"lds::gaussian::Controller #  Gaussian-observation Controller Type. #include \u0026lt;lds_gaussian_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions #      Name     virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output    Additional inherited members #  Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type      Public Function Details #  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\n  Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":33,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/","title":"lds::gaussian::Fit","section":"Classes","content":"lds::gaussian::Fit #  GLDS Fit Type. #include \u0026lt;lds_gaussian_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions #      Name      Fit() =default    Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit.   virtual const Matrix \u0026amp; R() const override\ngets measurement noise covariance   virtual void set_R(const Matrix \u0026amp; R) override\nsets measurement noise covariance   virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function    Additional inherited members #  Public Functions inherited from lds::Fit\n    Name     virtual ~Fit() =default   size_t n_u() const\ngets number of inputs   size_t n_x() const\ngets number of states   size_t n_y() const\ngets number of outputs   data_t dt() const\ngets sample period   const Matrix \u0026amp; A() const\ngets state matrix   const Matrix \u0026amp; B() const\ngets input matrix   const Vector \u0026amp; g() const\ngets input gain   const Vector \u0026amp; m() const\ngets process disturbance   const Matrix \u0026amp; Q() const\ngets process noise covariance   const Vector \u0026amp; x0() const\ngets initial state estimate   const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate   const Matrix \u0026amp; C() const\ngets output matrix   const Vector \u0026amp; d() const\ngets output bias   void set_A(const Matrix \u0026amp; A)\nsets state matrix   void set_B(const Matrix \u0026amp; B)\nsets input matrix   void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor   void set_m(const Vector \u0026amp; m)\nsets process disturbance   void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance   void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate   void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance   void set_C(const Matrix \u0026amp; C)\nsets output matrix   void set_d(const Vector \u0026amp; d)\nsets output bias   View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function   View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function    Protected Attributes inherited from lds::Fit\n    Name     data_t dt_ sample period   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Vector m_ process noise mean   Matrix Q_ process noise cov   Matrix C_ output matrix   Vector d_ output bias   Matrix R_ measurement noise   Vector x0_ initial state   Matrix P0_ initial covar   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs      Public Function Details #  Fit #  Fit() =default  Fit #  Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period   R #  inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\n set_R #  inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\n h #  inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\n y output estimate (over time) x state estimate (over time) t time index  Return: output\nReimplements: lds::Fit::h\n  Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":34,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitem/","title":"lds::gaussian::FitEM","section":"Classes","content":"lds::gaussian::FitEM #  GLDS E-M Fit Type. More\u0026hellip;\n\n#include \u0026lt;lds_gaussian_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members #  Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name      EM() =default\nConstructs a new EMFit type.    EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.    EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.   virtual ~EM() =default   const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller.   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time)   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time)   const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance   const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1)   const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance   size_t n_t_tot()\ntotal number of time samples   const Vector \u0026amp; theta() const\ngets parameters updated in M step    Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name     void Expectation(bool force_common_initial =false)\nExpectation step.   void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step.   void MaximizeDynamics()   void MaximizeQ()   void MaximizeInitial()   void Smooth(bool force_common_initial)\nget smoothed estimates   void Reset()\nreset to initial conditions   void InitVars()\nInitializes the variables.   Vector UpdateTheta()\nupdates parameter list, theta    Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   std::vector\u0026lt; Matrix \u0026gt; x_ state estimate   std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov   std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance   std::vector\u0026lt; Matrix \u0026gt; y_ output estimate   Matrix diag_y_   Matrix sum_E_x_t_x_t_ state covariance (current time)   Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1)   Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance   Fit fit_   Vector theta_   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials    Detailed Description #  class lds::gaussian::FitEM; This type is used in the process of fitting GLDS models by expectation-maximization (EM).\n   Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":35,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitssid/","title":"lds::gaussian::FitSSID","section":"Classes","content":"lds::gaussian::FitSSID #  Subspace Identification (SSID) for GLDS. #include \u0026lt;lds_gaussian_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members #  Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name      SSID() =default\nConstructs a new SSIDFit type.    SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type.   std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller.    Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name     void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d)   void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix.   void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD)   void Solve(data_t wt_dc)\nsolves for LDS parameters   void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C    Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   Matrix D_ block-Hankel I/O data matrix   Fit fit_ fit   Matrix g_dc_ I/O gain @ DC.   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_h_   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials   Matrix L_ lower triangle decomp of covariance matrix   Vector s_ singular values   Matrix ext_obs_t_ extended observability matrix       Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":36,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1switchedcontroller/","title":"lds::gaussian::SwitchedController","section":"Classes","content":"lds::gaussian::SwitchedController #  Gaussian-observation SwitchedController Type. #include \u0026lt;lds_gaussian_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions #      Name     virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output    Additional inherited members #  Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\n    Name      SwitchedController() =default\nConstructs a new SwitchedController.    SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController.    SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems).   void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller.   void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains   void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving)   void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains   void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving)   void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains   void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving)   void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design   void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving)    Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\n    Name     std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between   size_t n_sys_ number of systems   size_t idx_ current system/controller index.   UniformMatrixList Kc_list_   UniformMatrixList Kc_inty_list_   UniformMatrixList Kc_u_list_   UniformVectorList g_design_list_    Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type      Public Function Details #  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\n  Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":37,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/","title":"lds::gaussian::System","section":"Classes","content":"lds::gaussian::System #  Gaussian LDS Type. #include \u0026lt;lds_gaussian_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions #      Name      System() =default\nConstructs a new System.    System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)\nConstructs a new Gaussian System.   virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement.   const Matrix \u0026amp; R() const\nGet output noise covariance.   void set_Q(const Matrix \u0026amp; Q)   void set_R(const Matrix \u0026amp; R)\nSet output noise covariance.   void set_Ke(const Matrix \u0026amp; Ke)\nSet estimator gain.   void set_Ke_m(const Matrix \u0026amp; Ke_m)\nSet disturbance estimator gain.   void Print()\nPrint system variables to stdout.    Protected Functions #      Name     virtual void h() override\nSystem output function.   virtual void RecurseKe() override\nRecursively update estimator gain.    Protected Attributes #      Name     Matrix R_ covariance of output noise   bool do_recurse_Ke_ whether to recursively calculate estimator gain    Additional inherited members #  Public Functions inherited from lds::System\n    Name     virtual ~System()   void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates.   void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function   size_t n_u() const\nGet number of inputs.   size_t n_x() const\nGet number of states.   size_t n_y() const\nGet number of outputs.   data_t dt() const\nGet sample period.   const Vector \u0026amp; x() const\nGet current state.   const Matrix \u0026amp; P() const\nGet covariance of state estimate.   const Vector \u0026amp; m() const\nGet current process disturbance/bias.   const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate.   const Vector \u0026amp; cx() const\nGet C*x.   const Vector \u0026amp; y() const\nGet output.   const Vector \u0026amp; x0() const\nGet initial state.   const Vector \u0026amp; m0() const\nGet initial disturbance.   const Matrix \u0026amp; A() const\nGet state matrix.   const Matrix \u0026amp; B() const\nGet input matrix.   const Vector \u0026amp; g() const\nGet input gain/conversion factor.   const Matrix \u0026amp; C() const\nGet output matrix.   const Vector \u0026amp; d() const\nGet output bias.   const Matrix \u0026amp; Ke() const\nGet estimator gain.   const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m)   const Matrix \u0026amp; Q()\nGet process noise covariance.   const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation.   const Matrix \u0026amp; P0()\nGet covariance of initial state.   const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance.   void set_A(const Matrix \u0026amp; A)\nSet state matrix.   void set_B(const Matrix \u0026amp; B)\nSet input matrix.   void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance.   void set_g(const Vector \u0026amp; g)\nSet input gain.   void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation.   void set_x0(const Vector \u0026amp; x0)\nSet initial state.   void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state.   void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance.   void set_C(const Matrix \u0026amp; C)\nSet output matrix.   void set_d(const Vector \u0026amp; d)\nSet output bias.   void set_x(const Vector \u0026amp; x)\nSet state of system.   void Reset()\nReset system variables.    Protected Functions inherited from lds::System\n    Name     void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)    Public Attributes inherited from lds::System\n    Name     bool do_adapt_m whether to adaptively estimate disturbance m    Protected Attributes inherited from lds::System\n    Name     std::size_t n_x_ number of states   std::size_t n_u_ number of inputs   std::size_t n_y_ number of outputs   data_t dt_ sample period   Vector x_ state   Matrix P_ covariance of state estimate   Vector m_ process disturbance   Matrix P_m_ covariance of disturbance estimate   Vector cx_ C*x.   Vector y_ output   Vector z_ measurement   Vector x0_ initial state   Matrix P0_ covariance of initial state estimate   Vector m0_ initial process disturbance   Matrix P0_m_ covariance of initial disturbance est.   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Matrix Q_ covariance of process noise   Matrix Q_m_ covariance of disturbance random walk   Matrix C_ output matrix   Vector d_ output bias   Matrix Ke_ estimator gain   Matrix Ke_m_ estimator gain for process disturbance      Public Function Details #  System #  System() =default  System #  System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0 ) Parameters:\n n_u number of inputs (u) n_x number of states (x) n_y number of outputs (y) dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) r0 [optional] initial diagonal elements of output noise covariance (R)   Simulate #  virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\n u_tm1 input at t-1  Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\n R #  inline const Matrix \u0026amp; R() const  set_Q #  inline void set_Q( const Matrix \u0026amp; Q )  set_R #  inline void set_R( const Matrix \u0026amp; R )  set_Ke #  inline void set_Ke( const Matrix \u0026amp; Ke )  set_Ke_m #  inline void set_Ke_m( const Matrix \u0026amp; Ke_m )  Print #  void Print()  Protected Function Details #  h #  inline virtual void h() override Reimplements: lds::System::h\n RecurseKe #  virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\n Protected Attribute Details #  **R_** #  Matrix R_;  **do_recurse_Ke_** #  bool do_recurse_Ke_ {};   Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":38,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/","title":"lds::poisson","section":"Namespaces","content":"lds::poisson #  Linear Dynamical Systems with Poisson observations. Classes #      Name     class lds::poisson::Controller PLDS Controller Type.   class lds::poisson::Fit PLDS Fit Type.   class lds::poisson::FitEM PLDS E-M Fit Type.   class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS.   class lds::poisson::SwitchedController Poisson-observation SwitchedController Type.   class lds::poisson::System Poisson System type.    Attributes #      Name     std::random_device rd random device for simulating poisson data   std::mt19937 rng random number generator for simulating poisson data    Attribute Details #  rd #  static std::random_device rd; rng #  static std::mt19937 rng = std::mt19937( rd());  Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":39,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/","title":"lds::poisson::Controller","section":"Classes","content":"lds::poisson::Controller #  PLDS Controller Type. #include \u0026lt;lds_poisson_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions #      Name     virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output.    Additional inherited members #  Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type      Public Function Details #  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\n  Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":40,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/","title":"lds::poisson::Fit","section":"Classes","content":"lds::poisson::Fit #  PLDS Fit Type. #include \u0026lt;lds_poisson_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions #      Name      Fit() =default    Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit.   virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function   virtual void set_R(const Matrix \u0026amp; R) override\nsets output noise covariance (if any)   virtual const Matrix \u0026amp; R() const override    Additional inherited members #  Public Functions inherited from lds::Fit\n    Name     virtual ~Fit() =default   size_t n_u() const\ngets number of inputs   size_t n_x() const\ngets number of states   size_t n_y() const\ngets number of outputs   data_t dt() const\ngets sample period   const Matrix \u0026amp; A() const\ngets state matrix   const Matrix \u0026amp; B() const\ngets input matrix   const Vector \u0026amp; g() const\ngets input gain   const Vector \u0026amp; m() const\ngets process disturbance   const Matrix \u0026amp; Q() const\ngets process noise covariance   const Vector \u0026amp; x0() const\ngets initial state estimate   const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate   const Matrix \u0026amp; C() const\ngets output matrix   const Vector \u0026amp; d() const\ngets output bias   void set_A(const Matrix \u0026amp; A)\nsets state matrix   void set_B(const Matrix \u0026amp; B)\nsets input matrix   void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor   void set_m(const Vector \u0026amp; m)\nsets process disturbance   void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance   void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate   void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance   void set_C(const Matrix \u0026amp; C)\nsets output matrix   void set_d(const Vector \u0026amp; d)\nsets output bias   View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function   View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function    Protected Attributes inherited from lds::Fit\n    Name     data_t dt_ sample period   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Vector m_ process noise mean   Matrix Q_ process noise cov   Matrix C_ output matrix   Vector d_ output bias   Matrix R_ measurement noise   Vector x0_ initial state   Matrix P0_ initial covar   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs      Public Function Details #  Fit #  Fit() =default  Fit #  inline Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period   h #  inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\n y output estimate (over time) x state estimate (over time) t time index  Return: output\nReimplements: lds::Fit::h\n set_R #  inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\n R #  inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\n  Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":41,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitem/","title":"lds::poisson::FitEM","section":"Classes","content":"lds::poisson::FitEM #  PLDS E-M Fit Type. More\u0026hellip;\n\n#include \u0026lt;lds_poisson_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members #  Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name      EM() =default\nConstructs a new EMFit type.    EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.    EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.   virtual ~EM() =default   const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller.   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time)   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time)   const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance   const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1)   const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance   size_t n_t_tot()\ntotal number of time samples   const Vector \u0026amp; theta() const\ngets parameters updated in M step    Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name     void Expectation(bool force_common_initial =false)\nExpectation step.   void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step.   void MaximizeDynamics()   void MaximizeQ()   void MaximizeInitial()   void Smooth(bool force_common_initial)\nget smoothed estimates   void Reset()\nreset to initial conditions   void InitVars()\nInitializes the variables.   Vector UpdateTheta()\nupdates parameter list, theta    Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   std::vector\u0026lt; Matrix \u0026gt; x_ state estimate   std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov   std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance   std::vector\u0026lt; Matrix \u0026gt; y_ output estimate   Matrix diag_y_   Matrix sum_E_x_t_x_t_ state covariance (current time)   Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1)   Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance   Fit fit_   Vector theta_   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials    Detailed Description #  class lds::poisson::FitEM; This type is used in the process of fitting PLDS models by expectation-maximization (EM).\n   Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":42,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitssid/","title":"lds::poisson::FitSSID","section":"Classes","content":"lds::poisson::FitSSID #  Subspace Identification (SSID) for PLDS. #include \u0026lt;lds_poisson_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members #  Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name      SSID() =default\nConstructs a new SSIDFit type.    SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type.   std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller.    Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name     void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d)   void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix.   void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD)   void Solve(data_t wt_dc)\nsolves for LDS parameters   void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C    Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   Matrix D_ block-Hankel I/O data matrix   Fit fit_ fit   Matrix g_dc_ I/O gain @ DC.   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_h_   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials   Matrix L_ lower triangle decomp of covariance matrix   Vector s_ singular values   Matrix ext_obs_t_ extended observability matrix       Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":43,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1switchedcontroller/","title":"lds::poisson::SwitchedController","section":"Classes","content":"lds::poisson::SwitchedController #  Poisson-observation SwitchedController Type. #include \u0026lt;lds_poisson_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions #      Name     virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output.    Additional inherited members #  Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\n    Name      SwitchedController() =default\nConstructs a new SwitchedController.    SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController.    SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems).   void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller.   void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains   void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving)   void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains   void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving)   void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains   void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving)   void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design   void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving)    Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\n    Name     std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between   size_t n_sys_ number of systems   size_t idx_ current system/controller index.   UniformMatrixList Kc_list_   UniformMatrixList Kc_inty_list_   UniformMatrixList Kc_u_list_   UniformVectorList g_design_list_    Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type      Public Function Details #  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\n  Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":44,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/","title":"lds::poisson::System","section":"Classes","content":"lds::poisson::System #  Poisson System type. #include \u0026lt;lds_poisson_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions #      Name      System() =default\nConstructs a new System.    System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nConstructs a new Poisson System.   virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement.    Protected Functions #      Name     virtual void h() override\nSystem output function.   virtual void RecurseKe() override\nRecursively recalculate estimator gain (Ke)    Additional inherited members #  Public Functions inherited from lds::System\n    Name     virtual ~System()   void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates.   void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function   size_t n_u() const\nGet number of inputs.   size_t n_x() const\nGet number of states.   size_t n_y() const\nGet number of outputs.   data_t dt() const\nGet sample period.   const Vector \u0026amp; x() const\nGet current state.   const Matrix \u0026amp; P() const\nGet covariance of state estimate.   const Vector \u0026amp; m() const\nGet current process disturbance/bias.   const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate.   const Vector \u0026amp; cx() const\nGet C*x.   const Vector \u0026amp; y() const\nGet output.   const Vector \u0026amp; x0() const\nGet initial state.   const Vector \u0026amp; m0() const\nGet initial disturbance.   const Matrix \u0026amp; A() const\nGet state matrix.   const Matrix \u0026amp; B() const\nGet input matrix.   const Vector \u0026amp; g() const\nGet input gain/conversion factor.   const Matrix \u0026amp; C() const\nGet output matrix.   const Vector \u0026amp; d() const\nGet output bias.   const Matrix \u0026amp; Ke() const\nGet estimator gain.   const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m)   const Matrix \u0026amp; Q()\nGet process noise covariance.   const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation.   const Matrix \u0026amp; P0()\nGet covariance of initial state.   const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance.   void set_A(const Matrix \u0026amp; A)\nSet state matrix.   void set_B(const Matrix \u0026amp; B)\nSet input matrix.   void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance.   void set_g(const Vector \u0026amp; g)\nSet input gain.   void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance.   void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation.   void set_x0(const Vector \u0026amp; x0)\nSet initial state.   void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state.   void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance.   void set_C(const Matrix \u0026amp; C)\nSet output matrix.   void set_d(const Vector \u0026amp; d)\nSet output bias.   void set_x(const Vector \u0026amp; x)\nSet state of system.   void Reset()\nReset system variables.   void Print()\nPrint system variables to stdout.    Protected Functions inherited from lds::System\n    Name     void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)    Public Attributes inherited from lds::System\n    Name     bool do_adapt_m whether to adaptively estimate disturbance m    Protected Attributes inherited from lds::System\n    Name     std::size_t n_x_ number of states   std::size_t n_u_ number of inputs   std::size_t n_y_ number of outputs   data_t dt_ sample period   Vector x_ state   Matrix P_ covariance of state estimate   Vector m_ process disturbance   Matrix P_m_ covariance of disturbance estimate   Vector cx_ C*x.   Vector y_ output   Vector z_ measurement   Vector x0_ initial state   Matrix P0_ covariance of initial state estimate   Vector m0_ initial process disturbance   Matrix P0_m_ covariance of initial disturbance est.   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Matrix Q_ covariance of process noise   Matrix Q_m_ covariance of disturbance random walk   Matrix C_ output matrix   Vector d_ output bias   Matrix Ke_ estimator gain   Matrix Ke_m_ estimator gain for process disturbance      Public Function Details #  System #  System() =default  System #  System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q)   Simulate #  virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\n u_tm1 input at t-1  Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\n Protected Function Details #  h #  inline virtual void h() override Reimplements: lds::System::h\n RecurseKe #  virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nRecursively recalculate estimator gain (Ke).\nReferences:\nSmith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.\nEden UT, \u0026hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.\n  Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":45,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/","title":"lds::SSID","section":"Classes","content":"lds::SSID #  More\u0026hellip;\nInherited by lds::gaussian::FitSSID, lds::poisson::FitSSID\nPublic Functions #      Name      SSID() =default\nConstructs a new SSIDFit type.    SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type.   std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller.    Protected Functions #      Name     void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d)   void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix.   virtual void DecomposeData() =0\nDecompose data to lower-triangular matrix (used in Solve)   void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD)   void Solve(data_t wt_dc)\nsolves for LDS parameters   void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C    Protected Attributes #      Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   Matrix D_ block-Hankel I/O data matrix   Fit fit_ fit   Matrix g_dc_ I/O gain @ DC.   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_h_   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials   Matrix L_ lower triangle decomp of covariance matrix   Vector s_ singular values   Matrix ext_obs_t_ extended observability matrix    Detailed Description #  template \u0026lt;typename Fit \u0026gt; class lds::SSID;   Public Function Details #  SSID #  SSID() =default  SSID #  SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf) ) Parameters:\n n_x number of states n_h size of block-hankel data matrix dt sample period u_train input training data z_train measurement training data d output bias   Run #  std::tuple\u0026lt; Fit, Vector \u0026gt; Run( SSIDWt ssid_wt ) Parameters:\n ssid_wt weight for singular value decomp  Return: tuple (Fit, singular values)\n ReturnData #  inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\n Protected Function Details #  CalcD #  void CalcD( data_t t_silence =0.1, data_t thresh_silence =0.001 ) Parameters:\n t_silence threshold on period of time that qualifies as \u0026ldquo;silence\u0026rdquo; thresh_silence threshold on input amplitude u that qualifies as \u0026ldquo;silence\u0026rdquo;   CreateHankelDataMat #  void CreateHankelDataMat() Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.\n DecomposeData #  virtual void DecomposeData() =0 Reimplemented by: lds::poisson::FitSSID::DecomposeData, lds::gaussian::FitSSID::DecomposeData\n CalcSVD #  void CalcSVD( SSIDWt wt ) Parameters:\n ssid_wt weight for SVD   Solve #  void Solve( data_t wt_dc ) Parameters:\n wt_dc weight placed on getting correct DC I/O gain   RecomputeExtObs #  void RecomputeExtObs()  Protected Attribute Details #  **u_** #  UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_;  **z_** #  UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_;  **D_** #  Matrix D_;  **fit_** #  Fit fit_;  **g_dc_** #  Matrix g_dc_;  **dt_** #  data_t dt_ {};  **n_u_** #  size_t n_u_ {};  **n_x_** #  size_t n_x_ {};  **n_y_** #  size_t n_y_ {};  **n_h_** #  size_t n_h_ {};  **n_trials_** #  size_t n_trials_ {};  **n_t_** #  std::vector\u0026lt; size_t \u0026gt; n_t_;  **n_t_tot_** #  size_t n_t_tot_ {};  **L_** #  Matrix L_;  **s_** #  Vector s_;  **ext_obs_t_** #  Matrix ext_obs_t_;   Updated on 19 May 2022 at 17:16:03 Eastern Daylight Time\n"},{"id":46,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/","title":"lds::SwitchedController","section":"Classes","content":"lds::SwitchedController #  SwitchedController Type. More\u0026hellip;\n\n#include \u0026lt;lds_sctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nInherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController\nPublic Functions #      Name      SwitchedController() =default\nConstructs a new SwitchedController.    SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController.    SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems).   void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller.   void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains   void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving)   void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains   void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving)   void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains   void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving)   void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design   void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving)    Protected Attributes #      Name     std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between   size_t n_sys_ number of systems   size_t idx_ current system/controller index.   UniformMatrixList Kc_list_   UniformMatrixList Kc_inty_list_   UniformMatrixList Kc_u_list_   UniformVectorList g_design_list_    Additional inherited members #  Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref)   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type    Detailed Description #  template \u0026lt;typename System \u0026gt; class lds::SwitchedController;   Public Function Details #  SwitchedController #  SwitchedController() =default  SwitchedController #  inline SwitchedController( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\n systems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask   SwitchedController #  inline SwitchedController( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\n systems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask   Switch #  inline void Switch( size_t idx, bool do_force_switch =false ) Parameters:\n idx index do_force_switch whether to force a system switch even if already there.   set_Kc #  inline void set_Kc( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc )  set_Kc #  inline void set_Kc( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc )  set_Kc_inty #  inline void set_Kc_inty( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty )  set_Kc_inty #  inline void set_Kc_inty( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty )  set_Kc_u #  inline void set_Kc_u( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u )  set_Kc_u #  inline void set_Kc_u( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u )  set_g_design #  inline void set_g_design( const UniformVectorList \u0026amp; g )  set_g_design #  inline void set_g_design( UniformVectorList \u0026amp;\u0026amp; g )  Protected Attribute Details #  **systems_** #  std::vector\u0026lt; System \u0026gt; systems_;  **n_sys_** #  size_t n_sys_ {};  **idx_** #  size_t idx_ {};  **Kc_list_** #  UniformMatrixList Kc_list_;  **Kc_inty_list_** #  UniformMatrixList Kc_inty_list_;  **Kc_u_list_** #  UniformMatrixList Kc_u_list_;  **g_design_list_** #  UniformVectorList g_design_list_;   Updated on 19 May 2022 at 17:16:03 Eastern Daylight Time\n"},{"id":47,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1system/","title":"lds::System","section":"Classes","content":"lds::System #  Linear Dynamical System Type. #include \u0026lt;lds_sys.h\u0026gt;\nInherited by lds::gaussian::System, lds::poisson::System\nPublic Functions #      Name      System() =default\nConstructs a new System.    System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nconstructs a new System   virtual ~System()   void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates.   virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) =0\nsimulates system (single time step)   void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function   virtual void h() =0\nsystem output function   size_t n_u() const\nGet number of inputs.   size_t n_x() const\nGet number of states.   size_t n_y() const\nGet number of outputs.   data_t dt() const\nGet sample period.   const Vector \u0026amp; x() const\nGet current state.   const Matrix \u0026amp; P() const\nGet covariance of state estimate.   const Vector \u0026amp; m() const\nGet current process disturbance/bias.   const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate.   const Vector \u0026amp; cx() const\nGet C*x.   const Vector \u0026amp; y() const\nGet output.   const Vector \u0026amp; x0() const\nGet initial state.   const Vector \u0026amp; m0() const\nGet initial disturbance.   const Matrix \u0026amp; A() const\nGet state matrix.   const Matrix \u0026amp; B() const\nGet input matrix.   const Vector \u0026amp; g() const\nGet input gain/conversion factor.   const Matrix \u0026amp; C() const\nGet output matrix.   const Vector \u0026amp; d() const\nGet output bias.   const Matrix \u0026amp; Ke() const\nGet estimator gain.   const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m)   const Matrix \u0026amp; Q()\nGet process noise covariance.   const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation.   const Matrix \u0026amp; P0()\nGet covariance of initial state.   const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance.   void set_A(const Matrix \u0026amp; A)\nSet state matrix.   void set_B(const Matrix \u0026amp; B)\nSet input matrix.   void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance.   void set_g(const Vector \u0026amp; g)\nSet input gain.   void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance.   void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation.   void set_x0(const Vector \u0026amp; x0)\nSet initial state.   void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state.   void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance.   void set_C(const Matrix \u0026amp; C)\nSet output matrix.   void set_d(const Vector \u0026amp; d)\nSet output bias.   void set_x(const Vector \u0026amp; x)\nSet state of system.   void Reset()\nReset system variables.   void Print()\nPrint system variables to stdout.    Protected Functions #      Name     virtual void RecurseKe() =0\nRecursively recalculate estimator gain (Ke)   void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)    Public Attributes #      Name     bool do_adapt_m whether to adaptively estimate disturbance m    Protected Attributes #      Name     std::size_t n_x_ number of states   std::size_t n_u_ number of inputs   std::size_t n_y_ number of outputs   data_t dt_ sample period   Vector x_ state   Matrix P_ covariance of state estimate   Vector m_ process disturbance   Matrix P_m_ covariance of disturbance estimate   Vector cx_ C*x.   Vector y_ output   Vector z_ measurement   Vector x0_ initial state   Matrix P0_ covariance of initial state estimate   Vector m0_ initial process disturbance   Matrix P0_m_ covariance of initial disturbance est.   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Matrix Q_ covariance of process noise   Matrix Q_m_ covariance of disturbance random walk   Matrix C_ output matrix   Vector d_ output bias   Matrix Ke_ estimator gain   Matrix Ke_m_ estimator gain for process disturbance      Public Function Details #  System #  System() =default  System #  System( size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period p0 diagonal elements for state estimate covariance q0 diagonal elements for process noise covariance   ~System #  inline virtual ~System()  Filter #  void Filter( const Vector \u0026amp; u_tm1, const Vector \u0026amp; z ) Parameters:\n u_tm1 input at t-minus-1 z_t current measurement  Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.\n Simulate #  virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) =0 Parameters:\n u_tm1 input at time t-1  Return: simulated measurement at time t\nReimplemented by: lds::poisson::System::Simulate, lds::gaussian::System::Simulate\n f #  inline void f( const Vector \u0026amp; u, bool do_add_noise =false ) Parameters:\n u input do_add_noise whether to add simulated process noise   h #  virtual void h() =0 Reimplemented by: lds::poisson::System::h, lds::gaussian::System::h\n n_u #  inline size_t n_u() const  n_x #  inline size_t n_x() const  n_y #  inline size_t n_y() const  dt #  inline data_t dt() const  x #  inline const Vector \u0026amp; x() const  P #  inline const Matrix \u0026amp; P() const  m #  inline const Vector \u0026amp; m() const  P_m #  inline const Matrix \u0026amp; P_m() const  cx #  inline const Vector \u0026amp; cx() const  y #  inline const Vector \u0026amp; y() const  x0 #  inline const Vector \u0026amp; x0() const  m0 #  inline const Vector \u0026amp; m0() const  A #  inline const Matrix \u0026amp; A() const  B #  inline const Matrix \u0026amp; B() const  g #  inline const Vector \u0026amp; g() const  C #  inline const Matrix \u0026amp; C() const  d #  inline const Vector \u0026amp; d() const  Ke #  inline const Matrix \u0026amp; Ke() const  Ke_m #  inline const Matrix \u0026amp; Ke_m() const  Q #  inline const Matrix \u0026amp; Q()  Q_m #  inline const Matrix \u0026amp; Q_m()  P0 #  inline const Matrix \u0026amp; P0()  P0_m #  inline const Matrix \u0026amp; P0_m()  set_A #  inline void set_A( const Matrix \u0026amp; A )  set_B #  inline void set_B( const Matrix \u0026amp; B )  set_m #  inline void set_m( const Vector \u0026amp; m, bool do_force_assign =false )  set_g #  inline void set_g( const Vector \u0026amp; g )  set_Q #  inline void set_Q( const Matrix \u0026amp; Q )  set_Q_m #  inline void set_Q_m( const Matrix \u0026amp; Q_m )  set_x0 #  inline void set_x0( const Vector \u0026amp; x0 )  set_P0 #  inline void set_P0( const Matrix \u0026amp; P0 )  set_P0_m #  inline void set_P0_m( const Matrix \u0026amp; P0_m )  set_C #  inline void set_C( const Matrix \u0026amp; C )  set_d #  inline void set_d( const Vector \u0026amp; d )  set_x #  inline void set_x( const Vector \u0026amp; x )  Reset #  void Reset()  Print #  void Print()  Protected Function Details #  RecurseKe #  virtual void RecurseKe() =0 Reimplemented by: lds::poisson::System::RecurseKe, lds::gaussian::System::RecurseKe\n InitVars #  void InitVars( data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 )  Public Attribute Details #  do_adapt_m #  bool do_adapt_m {};  Protected Attribute Details #  **n_x_** #  std::size_t n_x_ {};  **n_u_** #  std::size_t n_u_ {};  **n_y_** #  std::size_t n_y_ {};  **dt_** #  data_t dt_ {};  **x_** #  Vector x_;  **P_** #  Matrix P_;  **m_** #  Vector m_;  **P_m_** #  Matrix P_m_;  **cx_** #  Vector cx_;  **y_** #  Vector y_;  **z_** #  Vector z_;  **x0_** #  Vector x0_;  **P0_** #  Matrix P0_;  **m0_** #  Vector m0_;  **P0_m_** #  Matrix P0_m_;  **A_** #  Matrix A_;  **B_** #  Matrix B_;  **g_** #  Vector g_;  **Q_** #  Matrix Q_;  **Q_m_** #  Matrix Q_m_;  **C_** #  Matrix C_;  **d_** #  Vector d_;  **Ke_** #  Matrix Ke_;  **Ke_m_** #  Matrix Ke_m_;   Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":48,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/","title":"lds::UniformMatrixList","section":"Classes","content":"lds::UniformMatrixList #  More\u0026hellip;\nInherits from std::vector\u0026lt; Matrix \u0026gt;\nPublic Functions #      Name      UniformMatrixList() =default\nConstructs a new UniformMatrixList.    UniformMatrixList(const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent.    UniformMatrixList(std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent.    UniformMatrixList(std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent.    UniformMatrixList(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nConstructs a new UniformMatrixList (copy).    UniformMatrixList(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nConstructs a new UniformMatrixList (move).    ~UniformMatrixList() =default\nDestroys the object.   const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim(size_t n =0) const\ngets dimensions of uniformly sized matrices   size_t size()\nsize of container   const Matrix \u0026amp; at(size_t n)\ngets reference to n^th element   void Swap(Matrix \u0026amp; that, size_t n)\nswaps input matrix with n^th matrix of list   UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nassigns the contents (copy)   UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nassigns the contents (move)    Detailed Description #  template \u0026lt;MatrixListFreeDim D =kMatFreeDimNone\u0026gt; class lds::UniformMatrixList;   Public Function Details #  UniformMatrixList #  UniformMatrixList() =default  UniformMatrixList #  explicit UniformMatrixList( const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\n mats input matrices dim dimensions   UniformMatrixList #  explicit UniformMatrixList( std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\n mats input matrices dim dimensions   UniformMatrixList #  UniformMatrixList( std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\n mats input matrices dim dimensions   UniformMatrixList #  UniformMatrixList( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\n that another UniformMatrixList   UniformMatrixList #  UniformMatrixList( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\n that another UniformMatrixList   ~UniformMatrixList #  ~UniformMatrixList() =default  dim #  inline const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim( size_t n =0 ) const Parameters:\n n [optional] index in list of matrices  Return: dimensions\n size #  inline size_t size()  at #  inline const Matrix \u0026amp; at( size_t n )  Swap #  inline void Swap( Matrix \u0026amp; that, size_t n ) Parameters:\n that input matrix n index where the matrix is moved   operator= #  inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\n that another UniformMatrixList  Return: reference to object\n operator= #  inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\n that another UniformMatrixList  Return: reference to object\n  Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":49,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/","title":"lds::UniformSystemList","section":"Classes","content":"lds::UniformSystemList #  More\u0026hellip;\nInherits from std::vector\u0026lt; System \u0026gt;\nPublic Functions #      Name      UniformSystemList() =default\nConstructs a new UniformSystemList.    UniformSystemList(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by copying existing vector of System if dimensions consistent.    UniformSystemList(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by moving existing vector of System if dimensions consistent.    UniformSystemList(std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList from initializer_list of System if dimensions consistent.    UniformSystemList(const UniformSystemList \u0026amp; that)\nConstructs a new UniformSystemList (copy).    UniformSystemList(UniformSystemList \u0026amp;\u0026amp; that)\nConstructs a new UniformSystemList (move).    ~UniformSystemList() =default\nDestroys the object.   const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const\ngets dimensions of the uniformly sized systems   size_t size()\nsize of container   const System \u0026amp; at(size_t n)\ngets reference to n^th element   void Swap(System \u0026amp; that, size_t n)\nswaps input system with n^th system of list   UniformSystemList \u0026amp; operator=(const UniformSystemList \u0026amp; that)\nassigns the contents (copy)   UniformSystemList \u0026amp; operator=(UniformSystemList \u0026amp;\u0026amp; that)\nassigns the contents (move)    Detailed Description #  template \u0026lt;typename System\u0026gt; class lds::UniformSystemList;   Public Function Details #  UniformSystemList #  UniformSystemList() =default  UniformSystemList #  explicit UniformSystemList( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\n systems input systems dim dimensions (n_u, n_x, n_y)   UniformSystemList #  explicit UniformSystemList( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\n systems input systems dim dimensions (n_u, n_x, n_y)   UniformSystemList #  UniformSystemList( std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\n systems input systems dim dimensions (n_u, n_x, n_y)   UniformSystemList #  UniformSystemList( const UniformSystemList \u0026amp; that ) Parameters:\n that another UniformSystemList   UniformSystemList #  UniformSystemList( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\n that another UniformSystemList   ~UniformSystemList #  ~UniformSystemList() =default  dim #  inline const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const  size #  inline size_t size()  at #  inline const System \u0026amp; at( size_t n )  Swap #  inline void Swap( System \u0026amp; that, size_t n ) Parameters:\n that input system n index where the system is moved   operator= #  inline UniformSystemList \u0026amp; operator=( const UniformSystemList \u0026amp; that ) Parameters:\n that another UniformSystemList  Return: reference to object\n operator= #  inline UniformSystemList \u0026amp; operator=( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\n that another UniformSystemList  Return: reference to object\n  Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":50,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/","title":"lds::UniformVectorList","section":"Classes","content":"lds::UniformVectorList #  Inherits from std::vector\u0026lt; Vector \u0026gt;\nPublic Functions #      Name      UniformVectorList() =default\nConstructs a new UniformVectorList.    UniformVectorList(const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent.    UniformVectorList(std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent.    UniformVectorList(std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0)\nConstructs a new UniformVectorList from initializer_list of Vector if dimensions consistent.    UniformVectorList(const UniformVectorList \u0026amp; that)\nConstructs a new UniformVectorList (copy)    UniformVectorList(UniformVectorList \u0026amp;\u0026amp; that)\nConstructs a new UniformVectorList (move)    ~UniformVectorList() =default\nDestroys the object.   size_t dim() const\ngets dimensions of the uniformly sized matrices   size_t size()\nsize of container   const Vector \u0026amp; at(size_t n)\ngets reference to n^th element   void Swap(Vector \u0026amp; that, size_t n)\nswaps input matrix with n^th vector of list   UniformVectorList \u0026amp; operator=(const UniformVectorList \u0026amp; that)\nassigns the contents (copy)   UniformVectorList \u0026amp; operator=(UniformVectorList \u0026amp;\u0026amp; that)\nassigns the contents (move)      Public Function Details #  UniformVectorList #  UniformVectorList() =default  UniformVectorList #  explicit UniformVectorList( const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0 ) Parameters:\n vecs input vectors dims dimension   UniformVectorList #  explicit UniformVectorList( std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0 ) Parameters:\n vecs input vectors dim dimension   UniformVectorList #  UniformVectorList( std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0 ) Parameters:\n vecs input vectors dim dimension   UniformVectorList #  UniformVectorList( const UniformVectorList \u0026amp; that ) Parameters:\n that another UniformVectorList   UniformVectorList #  UniformVectorList( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\n that another UniformVectorList   ~UniformVectorList #  ~UniformVectorList() =default  dim #  inline size_t dim() const  size #  inline size_t size()  at #  inline const Vector \u0026amp; at( size_t n )  Swap #  inline void Swap( Vector \u0026amp; that, size_t n ) Parameters:\n that input vector n index where the vector is moved   operator= #  inline UniformVectorList \u0026amp; operator=( const UniformVectorList \u0026amp; that ) Parameters:\n that another UniformVectorList  Return: reference to object\n operator= #  inline UniformVectorList \u0026amp; operator=( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\n that another UniformVectorList  Return: reference to object\n  Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time\n"},{"id":51,"href":"/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/","title":"ldsCtrlEst_h","section":"Files","content":"ldsCtrlEst_h #  Files #     Name     ldsCtrlEst_h/lds.h lds namespace   ldsCtrlEst_h/lds_ctrl.h Controller.   ldsCtrlEst_h/lds_fit.h LDS base fit type.   ldsCtrlEst_h/lds_fit_em.h subspace identification   ldsCtrlEst_h/lds_fit_ssid.h subspace identification   ldsCtrlEst_h/lds_gaussian.h glds namespace   ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.   ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.   ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.   ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.   ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.   ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.   ldsCtrlEst_h/lds_poisson.h plds namespace   ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.   ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.   ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.   ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.   ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.   ldsCtrlEst_h/lds_poisson_sys.h PLDS base type.   ldsCtrlEst_h/lds_sctrl.h SwitchedController type.   ldsCtrlEst_h/lds_sys.h LDS base type.   ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.   ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.   ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.   ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)   ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)     Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":52,"href":"/lds-ctrl-est/docs/api/files/lds_8h/","title":"ldsCtrlEst_h/lds.h","section":"Files","content":"ldsCtrlEst_h/lds.h #  lds namespace More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Detailed Description #  This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)) observations.\nSource code #  //===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_H #define LDSCTRLEST_LDS_H  // #ifndef LDSCTRLEST // #include \u0026lt;ldsCtrlEst\u0026gt; // #endif  #include \u0026lt;armadillo\u0026gt; namespace lds { using data_t = double; // may change to float (but breaks mex functions) using Vector = arma::Col\u0026lt;data_t\u0026gt;; using Matrix = arma::Mat\u0026lt;data_t\u0026gt;; using Cube = arma::Cube\u0026lt;data_t\u0026gt;; using View = arma::subview\u0026lt;data_t\u0026gt;; namespace fill = arma::fill; static const std::size_t kControlTypeDeltaU = 0x1; static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); static const data_t kPi = arma::datum::pi; static const data_t kDefaultP0 = 1e-6; static const data_t kDefaultQ0 = 1e-6; static const data_t kDefaultR0 = 1e-2; enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA }; enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2 }; // TODO(mfbolus): for SwitchedController, may want systems to have differing // numbers of states. Use this enum as template parameter? // enum SystemListFreeDim { // kSysFreeDimNone, // kSysFreeDimX ///\u0026lt; allow state dim (x) of systems in list to be hetero // };  // place hard limits on contents of vecors/mats void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub); void Limit(Vector\u0026amp; x, data_t lb, data_t ub); void Limit(Matrix\u0026amp; x, data_t lb, data_t ub); // in-place assign that errs if there are dimension mismatches: void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); // TODO(mfbolus): this is a fudge, but for some reason, cov mats often going // numerically asymm.  void ForceSymPD(Matrix\u0026amp; X); void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min = 0); void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X); Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B); inline void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Vector\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Matrix\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions  if (other.n_elem != some.n_elem) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vector of size \u0026#34; \u0026lt;\u0026lt; some.n_elem \u0026lt;\u0026lt; \u0026#34; with vector of size \u0026#34; \u0026lt;\u0026lt; other.n_elem \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } inline void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions  if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrix of size \u0026#34; \u0026lt;\u0026lt; some.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; some.n_cols \u0026lt;\u0026lt; \u0026#34; with matrix of size \u0026#34; \u0026lt;\u0026lt; other.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other.n_cols \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } } // namespace lds  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":53,"href":"/lds-ctrl-est/docs/api/files/lds__ctrl_8h/","title":"ldsCtrlEst_h/lds_ctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_ctrl.h #  Controller. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::Controller    Detailed Description #  This file declares the type for control of a linear dynamical system (lds::Controller).\nSource code #  //===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_CTRL_H #define LDSCTRLEST_LDS_CTRL_H  // namespace #include \u0026#34;lds.h\u0026#34;// system type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class Controller { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); public: Controller() = default; Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); const Vector\u0026amp; Control(const Vector\u0026amp; z, bool do_control = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); const Vector\u0026amp; ControlOutputReference(const Vector\u0026amp; z, bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); // get methods:  const System\u0026amp; sys() const { return sys_; }; const Matrix\u0026amp; Kc() const { return Kc_; }; const Matrix\u0026amp; Kc_inty() const { return Kc_inty_; }; const Matrix\u0026amp; Kc_u() const { return Kc_u_; }; const Vector\u0026amp; g_design() const { return g_design_; }; const Vector\u0026amp; u_ref() const { return u_ref_; }; const Vector\u0026amp; x_ref() const { return x_ref_; }; const Vector\u0026amp; y_ref() const { return y_ref_; }; size_t control_type() const { return control_type_; }; data_t tau_awu() const { return tau_awu_; }; data_t u_lb() const { return u_lb_; }; data_t u_ub() const { return u_ub_; }; // set methods  void set_sys(const System\u0026amp; sys) { bool does_match = sys_.n_u() == sys.n_u(); does_match = does_match \u0026amp;\u0026amp; (sys_.n_x() == sys.n_x()); does_match = does_match \u0026amp;\u0026amp; (sys_.n_y() == sys.n_y()); if (does_match) { sys_ = sys; } else { throw std::runtime_error( \u0026#34;new system argument to `set_sys` does not match dimensionality of \u0026#34; \u0026#34;existing system\u0026#34;); } }; void set_g_design(const Vector\u0026amp; g_design) { Reassign(g_design_, g_design); }; void set_u_ref(const Vector\u0026amp; u_ref) { Reassign(u_ref_, u_ref); }; void set_x_ref(const Vector\u0026amp; x_ref) { Reassign(x_ref_, x_ref); cx_ref_ = sys_.C() * x_ref_; }; // y_ref needs to be handled differently depending on output fn.  // (need to populate cx_ref_ too, which depends on output fn)  virtual void set_y_ref(const Vector\u0026amp; y_ref) { Reassign(y_ref_, y_ref); }; void set_Kc(const Matrix\u0026amp; Kc) { Reassign(Kc_, Kc); }; void set_Kc_inty(const Matrix\u0026amp; Kc_inty) { Reassign(Kc_inty_, Kc_inty); }; void set_Kc_u(const Matrix\u0026amp; Kc_u) { Reassign(Kc_u_, Kc_u); }; void set_tau_awu(data_t tau) { tau_awu_ = tau; k_awu_ = sys_.dt() / tau_awu_; }; void set_control_type(size_t control_type); // There is no reason u_lb/ub should not be public, but making set methods  // anyway.  void set_u_lb(data_t u_lb) { u_lb_ = u_lb; }; void set_u_ub(data_t u_ub) { u_ub_ = u_ub; }; void Reset() { sys_.Reset(); u_ref_.zeros(); u_ref_prev_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); u_saturated_ = false; t_since_control_onset_ = 0.0; }; void Print() { sys_.Print(); std::cout \u0026lt;\u0026lt; \u0026#34;g_design : \u0026#34; \u0026lt;\u0026lt; g_design_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_lb : \u0026#34; \u0026lt;\u0026lt; u_lb_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_ub : \u0026#34; \u0026lt;\u0026lt; u_ub_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }; protected: System sys_; Vector u_; Vector u_return_; Vector g_design_; // reference signals  Vector u_ref_; // create no set method for this:  Vector u_ref_prev_; Vector x_ref_; Vector y_ref_; Vector cx_ref_; // Controller gains  Matrix Kc_; Matrix Kc_u_; Matrix Kc_inty_; // control after g inversion  // do not need set methods for these.  Vector du_ref_; Vector dv_ref_; Vector v_ref_; Vector dv_; Vector v_; // integral error  // do not need set method for this  Vector int_e_; Vector int_e_awu_adjust_; Vector u_sat_; bool do_control_prev_ = false; bool do_lock_control_prev_ = false; // whether the g of system has become inverted from what you think it is  // (gain_ref)  bool u_saturated_ = false; // should be safe to have references here bc nothing needs to be done  // (like reset vars) when it changes...  data_t u_lb_{}; data_t u_ub_{}; data_t tau_awu_{}; data_t k_awu_ = 0; data_t t_since_control_onset_ = 0; size_t control_type_{}; private: void CalcControl(bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); void CalcSteadyStateSetPoint(); void AntiWindup(); void InitVars(size_t control_type); }; // Implement the above:  template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(sys), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(std::move(sys)), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::set_control_type(size_t control_type) { if (control_type_ == control_type) { return; } // creating a blank slate...  control_type_ = 0; Kc_inty_.zeros(0, 0); Kc_u_.zeros(0, 0); int_e_.zeros(0, 0); int_e_awu_adjust_.zeros(0, 0); // controller was designed to minimize integral error  if (control_type \u0026amp; kControlTypeIntY) { Kc_inty_.zeros(sys_.n_u(), sys_.n_y()); int_e_.zeros(sys_.n_y()); int_e_awu_adjust_.zeros(sys_.n_u()); control_type_ = control_type_ | kControlTypeIntY; } // controller was designed to minimize deltaU  // (i.e. state augmented with u)  if (control_type \u0026amp; kControlTypeDeltaU) { Kc_u_.zeros(sys_.n_u(), sys_.n_u()); control_type_ = control_type_ | kControlTypeDeltaU; } // whether to adapt set point calculate with (re-estimated) process  // disturbance (m)  if (control_type \u0026amp; kControlTypeAdaptM) { if (sys_.do_adapt_m) // only if adapting m...  { control_type_ = control_type_ | kControlTypeAdaptM; } } } // set_control_type  template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::Control( const Vector\u0026amp; z, bool do_control, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement  sys_.Filter(u_, z); bool do_estimation = true; // always have estimator on in this case  // calculate control signal  CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::ControlOutputReference( const Vector\u0026amp; z, bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement  if (do_estimation) { sys_.Filter(u_, z); } else { sys_.f(u_); } // calculate the set point  // solves for u_ref and x_ref when output is at y_ref at steady state.  if (do_control) { CalcSteadyStateSetPoint(); } // calculate control signal  CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcControl(bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { if (do_control \u0026amp;\u0026amp; do_estimation) { if (!do_control_prev_) { if (do_reset_at_control_onset) { Reset(); } t_since_control_onset_ = 0.0; } else { t_since_control_onset_ += sys_.dt(); } // enforce softstart on control vars.  if (sigma_soft_start \u0026gt; 0) { // half-Gaussian soft-start scaling factor  data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) / (2 * pow(sigma_soft_start, 2))); u_ref_ *= soft_start_sf; // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too  // x_ref_ *= soft_start_sf;  // cx_ref_ *= soft_start_sf;  // y_ref_ *= soft_start_sf;  } if (!do_lock_control) { // first do u -\u0026gt; v change of vars. (v = g.*u)  // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver  // control voltage u[=]V)  v_ref_ = g_design_ % u_ref_; // Given FB, calc. the change in control  if (control_type_ \u0026amp; kControlTypeDeltaU) { // if control designed to minimize not u but deltaU (i.e. state aug with  // u):  // TODO(mfbolus): Commented out for now. See note below.  // du_ref_ = u_ref_ - u_ref_prev_;  // dv_ref_ = g_design_ % du_ref_;  // TODO(mfbolus): Assuming users want *smooth* control signals if using  // kControlTypeDeltaU, it should be the case that dv_ref_ is --\u0026gt; 0. May  // want to revisit, but I am going to force it to be zero unless a  // situation arises that argues for keeping the above.  dv_ref_.zeros(); dv_ = dv_ref_; // nominally-optimal.  dv_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error  dv_ -= Kc_u_ * (v_ - v_ref_); // penalty on amp u (rel to ref)  if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup  // would be to not integrate error when control signal saturated:  // if(!uSaturated)  int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error  dv_ -= Kc_inty_ * int_e_; // control for integrated error  } // update the control  v_ += dv_; } else { v_ = v_ref_; // nominally-optimal.  v_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error  if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup  // would be to not integrate error when control signal saturated:  // if (!uSaturated)  int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error  v_ -= Kc_inty_ * int_e_; // control for integrated error  } } // convert back to control voltage u[=]V  u_ = v_ / sys_.g(); } // else do nothing until lock is low  } else { // if not control  // feed through u_ref in open loop  u_ = u_ref_ % g_design_ / sys_.g(); v_ = sys_.g() % u_; u_ref_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); } // ends do_control  // enforce box constraints (and antiwindup)  AntiWindup(); // add noise to input?  // The value for u that is *returned* to user after addition of any noise,  // while keeping controller/estimator blind to this addition.  u_return_ = u_; if ((sigma_u_noise \u0026gt; 0.0) \u0026amp;\u0026amp; (do_control \u0026amp;\u0026amp; !do_lock_control)) { u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn); Limit(u_return_, u_lb_, u_ub_); }; // For next time step:  u_ref_prev_ = u_ref_; do_control_prev_ = do_control; do_lock_control_prev_ = do_lock_control; } // CalcControl  template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcSteadyStateSetPoint() { // Linearly-constrained least squares (ls).  //  // _reference:  // Boyd \u0026amp; Vandenberghe (2018) Introduction to Applied Linear Algebra  //  Matrix a_ls = join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros)); Vector b_ls = cx_ref_; Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye), sys_.B() * arma::diagmat(sys_.g())); Vector d_ls = -sys_.m0(); if (control_type_ \u0026amp; kControlTypeAdaptM) { d_ls = -sys_.m(); // adapt setpoint calc with disturbance?  } Matrix a_ls_t = a_ls.t(); // TODO(mfbolus): not sure why but causes seg  // fault if I do not do this.  Matrix phi_ls = join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()), join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros))); // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse:  Matrix inv_phi = pinv(phi_ls); Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls); x_ref_ = xulam.subvec(0, sys_.n_x() - 1); u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1); cx_ref_ = sys_.C() * x_ref_; } // CalcSteadyStateSetPoint  template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::AntiWindup() { u_saturated_ = false; u_sat_ = u_; // limit u and flag whether saturated  for (size_t k = 0; k \u0026lt; u_.n_elem; k++) { if (u_[k] \u0026lt; u_lb_) { u_sat_[k] = u_lb_; u_saturated_ = true; } if (u_[k] \u0026gt; u_ub_) { u_sat_[k] = u_ub_; u_saturated_ = true; } } if ((control_type_ \u0026amp; kControlTypeIntY) \u0026amp;\u0026amp; (tau_awu_ \u0026lt; lds::kInf)) { // one-step back-calculation (calculate intE for u=u_sat)  // (Astroem, Rundqwist 1989 warn against using this...)  // int_e_awu_adjust_ =  // solve(Kc_inty_, (u_ - u_sat_)); // pinv(Kc_inty) * (u-uSat);  // gradual: see Astroem, Rundqwist 1989  // this is a fudge for doing MIMO gradual  // n.b., went ahead and multiplied 1/T by dt so don\u0026#39;t have to do that here.  int_e_awu_adjust_ = k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_); // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_);  int_e_ += int_e_awu_adjust_; } // set u to saturated version  u_ = u_sat_; } template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::InitVars(size_t control_type) { // initialize to default values  u_ref_ = Vector(sys_.n_u(), fill::zeros); u_ref_prev_ = Vector(sys_.n_u(), fill::zeros); x_ref_ = Vector(sys_.n_x(), fill::zeros); y_ref_ = Vector(sys_.n_y(), fill::zeros); cx_ref_ = Vector(sys_.n_y(), fill::zeros); u_ = Vector(sys_.n_u(), fill::zeros); u_return_ = Vector(sys_.n_u(), fill::zeros); u_sat_ = Vector(sys_.n_u(), fill::zeros); // Might not need all these, so zero elements until later.  Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros); Kc_u_ = Matrix(0, 0, fill::zeros); Kc_inty_ = Matrix(0, 0, fill::zeros); g_design_ = sys_.g(); // by default, same as model  dv_ = Vector(sys_.n_u(), fill::zeros); v_ = Vector(sys_.n_u(), fill::zeros); du_ref_ = Vector(sys_.n_u(), fill::zeros); dv_ref_ = Vector(sys_.n_u(), fill::zeros); v_ref_ = Vector(sys_.n_u(), fill::zeros); int_e_ = Vector(0, fill::zeros); int_e_awu_adjust_ = Vector(0, fill::zeros); set_control_type(control_type); } } // namespace lds  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":54,"href":"/lds-ctrl-est/docs/api/files/lds__fit_8h/","title":"ldsCtrlEst_h/lds_fit.h","section":"Files","content":"ldsCtrlEst_h/lds_fit.h #  LDS base fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::Fit LDS Fit Type.    Detailed Description #  This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.\nSource code #  //===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDS_FIT_HPP #define LDS_FIT_HPP  // namespace #include \u0026#34;lds.h\u0026#34;#include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); virtual ~Fit() = default; // get methods  size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; Q() const { return Q_; }; const Vector\u0026amp; x0() const { return x0_; }; const Matrix\u0026amp; P0() const { return P0_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; // gets measurement noise  virtual const Matrix\u0026amp; R() const = 0; // set methods (e.g., seeding initial fit values)  void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_m(const Vector\u0026amp; m) { Reassign(m_, m); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); ForceSymPD(Q_); }; virtual void set_R(const Matrix\u0026amp; R) = 0; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); ForceSymPD(P0_); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; View f(Matrix\u0026amp; x, const Matrix\u0026amp; u, size_t t) { x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x.col(t); }; View f(Matrix\u0026amp; x_pre, const Matrix\u0026amp; x_post, const Matrix\u0026amp; u, size_t t) { x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x_pre.col(t); }; virtual View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) = 0; protected: data_t dt_{}; // Dynamics  Matrix A_; Matrix B_; Vector g_; Vector m_; Matrix Q_; // Output  Matrix C_; Vector d_; Matrix R_; // initial conditions  Vector x0_; Matrix P0_; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; }; } // namespace lds #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":55,"href":"/lds-ctrl-est/docs/api/files/lds__fit__em_8h/","title":"ldsCtrlEst_h/lds_fit_em.h","section":"Files","content":"ldsCtrlEst_h/lds_fit_em.h #  subspace identification More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::EM    Detailed Description #  This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).\nSource code #  //===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_EMAX_H #define LDSCTRLEST_LDS_EMAX_H  #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class EM { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: EM() = default; EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); virtual ~EM() = default; const Fit\u0026amp; Run(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = true, bool calc_output = true, bool calc_measurement = true, size_t max_iter = 100, data_t tol = 1e-2); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); // auto tuple = std::make_tuple(u_, z_);  u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; x() const { return x_; }; const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; y() const { return y_; }; const Matrix\u0026amp; sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; }; const Matrix\u0026amp; sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; }; const Matrix\u0026amp; sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; }; size_t n_t_tot() { return n_t_tot_; } const Vector\u0026amp; theta() const { return theta_; }; protected: void Expectation(bool force_common_initial = false); void Maximization(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = false, bool calc_output = false, bool calc_measurement = false); void MaximizeDynamics(); void MaximizeQ(); void MaximizeInitial(); virtual void MaximizeOutput() = 0; virtual void MaximizeMeasurement() = 0; void Smooth(bool force_common_initial); virtual void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) = 0; void Reset(); void InitVars(); Vector UpdateTheta(); // input/output training data  UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; std::vector\u0026lt;Matrix\u0026gt; x_; std::vector\u0026lt;Cube\u0026gt; P_; std::vector\u0026lt;Cube\u0026gt; P_t_tm1_; std::vector\u0026lt;Matrix\u0026gt; y_; Matrix diag_y_; // expectations calculated in E-step  Matrix sum_E_x_t_x_t_; Matrix sum_E_xu_tm1_xu_tm1_; Matrix sum_E_xu_t_xu_tm1_; Fit fit_; Vector theta_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; }; template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; fit_ = Fit(n_u_, n_x, n_y_, dt); u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { // make sure fit dims match I/O data  if (fit0.n_u() != u_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and input training data have inconsistent dimensions\u0026#34;); } if (fit0.n_y() != z_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and output training data have inconsistent dimensions\u0026#34;); } fit_ = fit0; u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::InitVars() { // check input/output data dimensions are consistent  if (z_.size() != u_.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_.at(trial).n_cols != u_.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } n_u_ = fit_.n_u(); n_x_ = fit_.n_x(); n_y_ = fit_.n_y(); dt_ = fit_.dt(); x_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); P_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); P_t_tm1_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); y_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros); P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros); } diag_y_ = Matrix(n_y_, n_y_, fill::zeros); // covariances in expectation step  sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); } template \u0026lt;typename Fit\u0026gt; const Fit\u0026amp; EM\u0026lt;Fit\u0026gt;::Run(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement, size_t max_iter, data_t tol) { Reset(); // to initial conditions  size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_; Vector theta(n_params); Vector theta_new(n_params); data_t max_dtheta = 1; // if solving for initial conditions, allow them be varied.  // otherwise, freeze at provided values.  bool force_common_initial = !calc_init; // go until parameter convergence  for (size_t l = 0; l \u0026lt; max_iter; l++) { theta_ = UpdateTheta(); std::cout \u0026lt;\u0026lt; \u0026#34;Iteration \u0026#34; \u0026lt;\u0026lt; l + 1 \u0026lt;\u0026lt; \u0026#34;/\u0026#34; \u0026lt;\u0026lt; max_iter \u0026lt;\u0026lt; \u0026#34; ...\\n\u0026#34;; Expectation(force_common_initial); Maximization(calc_dynamics, calc_Q, calc_init, calc_output, calc_measurement); // check convergence  theta_new = UpdateTheta(); Vector dtheta = abs(theta_new - theta_) / abs(theta_); // some parameters could be zero...  arma::uvec ubi_finite = find_finite(dtheta); max_dtheta = max(dtheta.elem(ubi_finite)); std::cout \u0026lt;\u0026lt; \u0026#34;max dtheta: \u0026#34; \u0026lt;\u0026lt; max_dtheta \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; if (max_dtheta \u0026lt; tol) { std::cout \u0026lt;\u0026lt; \u0026#34;Converged.\\n\u0026#34;; break; } std::cout \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } return fit_; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Smooth(bool force_common_initial) { Matrix k_e(n_x_, n_y_); // estimator gain  Cube k_backfilt; // back-filtering gains  // TODO(mfbolus): this loop could be made parallel  for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { Matrix x_pre(n_x_, n_t_[trial], fill::zeros); Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros); Matrix x_post(n_x_, n_t_[trial], fill::zeros); Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros); if (force_common_initial) // forces all trials to have same initial  // conditions.  { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); } y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); // This *should not* be necessary but make sure P is symmetric.  ForceSymPD(P_[trial].slice(0)); x_pre.col(0) = x_[trial].col(0); p_pre.slice(0) = P_[trial].slice(0); x_post.col(0) = x_[trial].col(0); p_post.slice(0) = P_[trial].slice(0); // filter  for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // predict  fit_.f(x_pre, x_post, u_.at(trial), t); fit_.h(y_[trial], x_pre, t); diag_y_.diag() = y_[trial].col(t); // TODO(mfbolus): change if parallel  // update --\u0026gt; posterior estimation  RecurseKe(k_e, p_pre, p_post, t); x_post.col(t) = x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t)); y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d(); } // backfilter -\u0026gt; Smoothed estimate  // Reference:  // Shumway et Stoffer (1982)  ForceSymPD(p_post.slice(n_t_[trial] - 1)); k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1); P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 0; t--) { // TODO(mfmbolus): should not be necessary to force symm positive def  ForceSymPD(p_pre.slice(t)); ForceSymPD(p_post.slice(t - 1)); ForceSymPD(P_[trial].slice(t)); k_backfilt.slice(t - 1) = p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t)); x_[trial].col(t - 1) = x_post.col(t - 1) + k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t)); P_[trial].slice(t - 1) = p_post.slice(t - 1) + k_backfilt.slice(t - 1) * (P_[trial].slice(t) - p_pre.slice(t)) * k_backfilt.slice(t - 1).t(); } // do the same for P_t_tm1  Matrix id(n_x_, n_x_, fill::eye); P_t_tm1_[trial].slice(n_t_[trial] - 1) = (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 1; t--) { P_t_tm1_[trial].slice(t - 1) = p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() + k_backfilt.slice(t - 1) * (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) * k_backfilt.slice(t - 2).t(); } // finally, get smoothed estimate of output  for (size_t t = 0; t \u0026lt; n_t_[trial]; t++) { fit_.h(y_[trial], x_[trial], t); } // samps loop  } // trial loop } // Smooth  // template \u0026lt;typename Fit\u0026gt; // void EM\u0026lt;Fit\u0026gt;::RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) { // // predict covar // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q();  // // update Ke // Ke = P_pre.slice(t) * fit_.C().t() * // inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R());  // // update cov // // Reference: Ghahramani et Hinton (1996) // P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t);  // // // n.b. for poisson : // // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + // fit_.Q(); // // // update cov // // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ * // // fit_.C()); // // // update Ke // // Ke = P_post.slice(t) * fit_.C(); // }  template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Expectation(bool force_common_initial) { // calculate the mean/cov of state needed for maximizing E[pr(z|theta)]  Smooth(force_common_initial); // now get the various forms of sum(E[xx\u0026#39;]) needed  // n.b. Going to start at t=1 rather than 0 bc most max terms need that.  // so really \u0026#34;n_t_tot_\u0026#34; is (n_t_tot_-1)  n_t_tot_ = 0; sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); Vector xu_tm1(n_x_ + n_u_, fill::zeros); Vector xu_t(n_x_ + n_u_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // ------------ sum_E_x_t_x_t ------------  sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t(); sum_E_x_t_x_t_ += P_[trial].slice(t); // ------------ sum_E_xu_tm1_xu_tm1 ------------  xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1)); sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t(); sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t - 1); // ------------ sum_E_xu_t_xu_tm1 ------------  xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t)); sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t(); sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_t_tm1_[trial].slice(t); n_t_tot_ += 1; } // time  } // trial } // Expectation  template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement) { if (calc_output) { MaximizeOutput(); } if (calc_measurement) { MaximizeMeasurement(); } if (calc_dynamics) { MaximizeDynamics(); } if (calc_Q) { MaximizeQ(); } if (calc_init) { MaximizeInitial(); } } // Maximization  template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeDynamics() { // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996)  Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) * inv_sympd(sum_E_xu_tm1_xu_tm1_); fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1)); std::cout \u0026lt;\u0026lt; \u0026#34;A_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.A()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.B()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeQ() { // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996)  // View sum_e_x_t_xu_tm1 =  // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1);  // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 *  // inv_sympd(sum_E_xu_tm1_xu_tm1_) *  // sum_e_x_t_xu_tm1.t();  // q /= n_t_tot_;  // this way is same as above iff dynamics were just updated:  // View sum_e_x_t_xu_tm1 =  // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1);  // Matrix ab = arma::join_horiz(fit_.A(), fit_.B());  // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t();  // q /= n_t_tot_;  // From scratch method:  // Q is covariance of the error between state and dynamics-predicted state  // (aka process noise)  // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})\u0026#39;]  // t-1 terms:  View sum_e_x_tm1_x_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_u_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1); View sum_e_x_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); // t, t-1 terms:  View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_x_t_u_tm1 = sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); Matrix q = sum_E_x_t_x_t_; q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t(); q -= sum_e_x_t_x_tm1 * fit_.A().t(); q -= fit_.A() * sum_e_x_t_x_tm1.t(); // input-related terms:  q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t(); q -= sum_e_x_t_u_tm1 * fit_.B().t(); q -= fit_.B() * sum_e_x_t_u_tm1.t(); q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t(); q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t(); q /= n_t_tot_; fit_.set_Q(q); std::cout \u0026lt;\u0026lt; \u0026#34;Q_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.Q()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // std::cout \u0026lt;\u0026lt; \u0026#34;Q_new: \\n\u0026#34; \u0026lt;\u0026lt; fit_.Q() \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeInitial() { Vector x0 = fit_.x0(); x0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { x0 += x_[trial].col(0); } x0 /= z_.size(); std::cout \u0026lt;\u0026lt; \u0026#34;x0_new[0]: \u0026#34; \u0026lt;\u0026lt; x0[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // always recalc P0 even if the initial state is fixed (at zero, for  // example)  Matrix e_var(n_x_, n_x_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t(); } e_var /= z_.size(); // go ahead and subtract x0*x0\u0026#39; so don\u0026#39;t have to below.  e_var -= x0 * x0.t(); // To get P0, going to get initial P_ per trial and average.  // (which might be wrong, but need a single number)  Matrix p0 = fit_.P0(); p0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { p0 += (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var; } p0 /= z_.size(); fit_.set_P0(p0); std::cout \u0026lt;\u0026lt; \u0026#34;P0_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.P0()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeOutput() { // solve for C+d:  Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros); Vector x1(n_x_ + 1, fill::zeros); x1[n_x_] = 1.0; // augment with one to solve for bias  Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { x1.subvec(0, n_x_ - 1) = x_[trial].col(t); sum_zx += z_.at(trial).col(t) * x1.t(); sum_e_x1_x1 += x1 * x1.t(); sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t); } } Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1); fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1)); fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_))); std::cout \u0026lt;\u0026lt; \u0026#34;C_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.C()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.d()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeMeasurement() { // Solve for measurement noise covar  size_t n_t_tot = 0; // Ghahgramani, Hinton 1996:  Matrix sum_zz(n_y_, n_y_, fill::zeros); Matrix sum_yz(n_y_, n_y_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t(); // Use Cnew:  sum_yz += (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t(); n_t_tot += 1; } } fit_.set_R((sum_zz - sum_yz) / n_t_tot); std::cout \u0026lt;\u0026lt; \u0026#34;R_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.R()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Reset() { // reset to initial conditions  for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); } } template \u0026lt;typename Fit\u0026gt; Vector EM\u0026lt;Fit\u0026gt;::UpdateTheta() { // TODO(mfbolus): This should include n_y_ more params for d.  size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_; if (fit_.R().n_elem \u0026gt; 0) { n_params += n_y_ * n_y_; } Vector theta(n_params); size_t idx_start = 0; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B()); idx_start += n_x_ * n_u_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0()); idx_start += n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C()); idx_start += n_y_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d()); idx_start += n_y_; if (fit_.R().n_elem \u0026gt; 0) { theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R()); } return theta; } } // namespace lds  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":56,"href":"/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_fit_ssid.h","section":"Files","content":"ldsCtrlEst_h/lds_fit_ssid.h #  subspace identification More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::SSID    Detailed Description #  This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)\u0026lt;Fit\u0026gt;).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code #  //===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_FIT_SSID_H #define LDSCTRLEST_LDS_FIT_SSID_H  #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class SSID { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: SSID() = default; SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d = Vector(1).fill(-kInf)); std::tuple\u0026lt;Fit, Vector\u0026gt; Run(SSIDWt ssid_wt); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } protected: void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001); void CreateHankelDataMat(); virtual void DecomposeData() = 0; void CalcSVD(SSIDWt wt); void Solve(data_t wt_dc); void RecomputeExtObs(); // input/output training data  UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; Matrix D_; Fit fit_; Matrix g_dc_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_h_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; Matrix L_; Vector s_; Matrix ext_obs_t_; }; template \u0026lt;typename Fit\u0026gt; SSID\u0026lt;Fit\u0026gt;::SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d) { // check input/output data dimensions are consistent  if (z_train.size() != u_train.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_train.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_train.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } dt_ = dt; n_x_ = n_x; n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; n_h_ = n_h; // dimensionality check for eventual block-hankel data matrix  size_t len = n_t_tot_ - 2 * n_h_ + 1; if (len \u0026lt; (2 * n_h_ * (n_u_ + n_y_))) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;Dataset problem! More rows than columns in block-hankel data \u0026#34; \u0026#34;matrix: 2*(n_u+n_y)*n_h \u0026gt; data-length! Need higher data-length or \u0026#34; \u0026#34;lower n_h.\u0026#34;; throw std::runtime_error(ss.str()); } fit_ = Fit(n_u_, n_x_, n_y_, dt_); u_ = std::move(u_train); z_ = std::move(z_train); if (!d.is_finite() || (d.n_rows != n_y_)) { // TODO(mfbolus): implement least-square solution for impulse response with  // a second input of ones. Data-driven way of accounting for offset *not*  // driven by an input.  //  // For now, calculate output bias (d) as the  // output wherever the stimulus has not been on for some amount of time.  // convolve u with rectangle and take all samples. This is a reasonable  // approach, since often when autonomous systems are fit (i.e., systems with  // no input), they will subtract off the mean of the output. This  // essentially amounts to setting output bias to the mean of the output when  // there is no stimulation.  data_t t_silence = 0.1; data_t thresh_silence = 0.001; CalcD(t_silence, thresh_silence); } else { fit_.set_d(d); } } template \u0026lt;typename Fit\u0026gt; std::tuple\u0026lt;Fit, Vector\u0026gt; SSID\u0026lt;Fit\u0026gt;::Run(SSIDWt ssid_wt) { // the weight on minimizing dc I/O gain only works for gaussian,  // and hopefully not necessary with appropriate dataset.  data_t wt_dc = 0; // std::cout \u0026lt;\u0026lt; \u0026#34;creating hankel mat\\n\u0026#34;;  CreateHankelDataMat(); // std::cout \u0026lt;\u0026lt; \u0026#34;decomposing data\\n\u0026#34;;  DecomposeData(); // std::cout \u0026lt;\u0026lt; \u0026#34;calculating svd\\n\u0026#34;;  CalcSVD(ssid_wt); // std::cout \u0026lt;\u0026lt; \u0026#34;solving for params\\n\u0026#34;;  Solve(wt_dc); // std::cout \u0026lt;\u0026lt; \u0026#34;fin\\n\u0026#34;;  return std::make_tuple(fit_, s_); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcD(data_t t_silence, data_t thresh_silence) { Vector d(z_.at(0).n_rows, fill::zeros); Vector win(static_cast\u0026lt;size_t\u0026gt;(t_silence / dt_), fill::ones); Vector sum_z_silence(n_y_, fill::zeros); size_t n_silence(0); for (size_t trial = 0; trial \u0026lt; u_.size(); trial++) { // find silent samples  // start by convolving with  Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0)); Vector u_conv = conv(sum_u, win, \u0026#34;same\u0026#34;); // get only the samples that are silent...  arma::uvec ubi_silence = find(u_conv \u0026lt;= thresh_silence); if (ubi_silence.n_elem \u0026gt; 0) { sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1); n_silence += ubi_silence.n_elem; } } if (n_silence \u0026gt; 0) { d = sum_z_silence / n_silence; } fit_.set_d(d); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat() { // temporary copy of data  Matrix z(n_y_, n_t_tot_, fill::zeros); Matrix u(n_u_, n_t_tot_, fill::zeros); size_t so_far(0); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial); u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial); so_far += n_t_.at(trial); } // remove output bias  z.each_col() -= fit_.d(); // calculate I/O gain @ DC while data in convenient form  g_dc_ = z * pinv(u); // std::cout \u0026lt;\u0026lt; \u0026#34;G0_data = \u0026#34; \u0026lt;\u0026lt; g_dc_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;;  // create hankel data matrix  size_t len = z.n_cols - 2 * n_h_ + 1; // data length in hankel mat  // block-hankel data matrix  D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros); // past input  auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1); // future input  auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1); // past output  auto y_p = D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1); // future output  auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, len - 1); size_t idx = 0; for (size_t k = 0; k \u0026lt; len; k++) { idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } } D_ /= sqrt(static_cast\u0026lt;data_t\u0026gt;(len)); } // template \u0026lt;typename Fit\u0026gt; // void SSID\u0026lt;Fit\u0026gt;::DecomposeData() { // // do LQ decomp instead of calculating covariance expensive way // // Note that \u0026#34;R\u0026#34; in van Overschee is lower-triangular (L), not \u0026#34;R\u0026#34; in QR // // decomp. Very confusing. // Matrix q_t; // lq(L_, q_t, D_); // // van Overschee zeros out the other elements. // L_ = trimatl(L_); // }  template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcSVD(SSIDWt wt) { // submats that will be needed:  auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1); auto R_23_13 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1); auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_) - 1); auto R_44 = L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14); auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1); auto Luf = Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1); auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); // aka: R_f  Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1); // from van Overschee subid.m:  // Rf = R((2*m+l)*i+1:2*(m+l)*i,:); % Future outputs  Matrix U; Matrix V; switch (wt) { case kSSIDNone: { // No weighting. (what van Overschee calls \u0026#34;N4SID\u0026#34;)  Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14; arma::svd(U, s_, V, O_k_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDMOESP: { // MOESP weighting  // This is what they use in the \u0026#34;robust\u0026#34; algorithm van Overschee, de Moor  // 1996  Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); svd(U, s_, V, O_k_ortho_Uf_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDCVA: { // CVA weighting  // See van Overschee\u0026#39;s matlab code (subid.m):  // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems  Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); Matrix inv_w1; Matrix qt1; lq(inv_w1, qt1, R_56_16); // lq decomp of R_f (future output data)  inv_w1 = trimatl(inv_w1); inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1); Matrix w_o_w = arma::solve( inv_w1, O_k_ortho_Uf_sans_Qt); // alternatively  // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt  svd(U, s_, V, w_o_w, \u0026#34;std\u0026#34;); U = inv_w1 * U; break; } } // Truncate to model order (heart of ssid method)  auto s_hat = s_.subvec(0, n_x_ - 1); Matrix diag_sqrt_s = diagmat(sqrt(s_hat)); auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1); // get extended observability and controllability mats  ext_obs_t_ = u_hat * diag_sqrt_s; // extended observability matrix } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::Solve(data_t wt_dc) { // required submats  auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_56_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); // Solve for params using appropriate algorithm:  // robust deterministic/stochastic algorithm in van Overschee 1996  // algorithm that the authors say \u0026#34;works\u0026#34; in practice.  auto ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix  // This is what textbook (1996) says:  //  // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15);  //  // HOWEVER, do not know why but have to fill the last place with zeros like  // authors\u0026#39; matlab implementation (see `subid.m`)  // Otherwise, get ridiculous covariances (although A,C estimates are close to  // same...)  Matrix Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Matrix S = Tl * pinv(Tr); // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure  // stability.  fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1)); Matrix ext_obs_t_p1 = join_vert( ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1), Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros)); fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1); // At this point, van Overschee \u0026amp; de Moor suggest re-calculating ext_obs_t_,  // ext_obs_tm1 from (A, C) because it was just an approximation. This is  RecomputeExtObs(); ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix  Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); S = Tl * pinv(Tr); Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_); Matrix Mcurly = pinv(ext_obs_tm1); Matrix Pcurly = Tl - Lcurly * R_56_15; Vector Pvec = vectorise(Pcurly); Matrix Qcurly = R_23_15; // Identify [D; B], assuming D=0 and ensuring DC gain is correct  Matrix sum_QcurlyT_kron_Ncurly( (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_), fill::zeros); Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols, fill::eye); eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1, eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1; // van Overschee (1996) p. 126  Matrix N1_Tl = -Lcurly; N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) += join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly); N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) += Matrix(n_y_, n_y_, fill::eye); Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros); Matrix N_k; for (size_t k = 0; k \u0026lt; n_h_; k++) { auto Qcurly_k = Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1); Nk_Tl.zeros(); Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) = N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1); N_k = Nk_Tl * eye_ext_obs_tm1; sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k); } Matrix err_vec; if (wt_dc \u0026gt; 0) { // Constraints enforced by weighted least squares  //  // Reference:  //  // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly  // Excited Industrial Systems. Conference in Decision and Control.  // constraint 1: assume D=0 --\u0026gt; remove the components for Dvec (this is  // actually a hard constraint in that it ignores D)  Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly; sum_QcurlyT_kron_Ncurly = Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_); size_t kkk = 0; for (size_t k = 1; k \u0026lt; (n_u_ + 1); k++) { size_t start_idx = k * (n_y_ + n_x_) - n_x_; for (size_t kk = 0; kk \u0026lt; n_x_; kk++) { sum_QcurlyT_kron_Ncurly.col(kkk) = sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk); kkk++; } } // constraint 2: Make sure DC I/O gain is correct  Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A()); Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_)); Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0); Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 = join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0); // WEIGHTED LS  // Important in practice because I care a lot about at least getting the DC  // gain correct. Put x weight on minimizing error at DC, relative to others  Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye); // Make weight on minimizing DC error immense so at least that  // should be nailed.  size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows; size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows; size_t stop_row = w.n_rows - 1; size_t stop_col = w.n_cols - 1; // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale  // weight with data length?  w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc; Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * sum_QcurlyT_kron_Ncurly_b_to_g0) * sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec; fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_)); // Calculate residuals and their cov.  // Because I\u0026#39;ve added constraints, I need to re-calculate the right term  // with b_vec instead of how van Overschee do in final algorithm.  err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec; } else { // default way: *no* constraint on G0 or D=0  Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec; // TODO(mfbolus) n.b., this gets thrown away...  // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_);  fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_)); err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec; } // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols);  // TODO(mfbolus): Something is wrong with the error calculation above.  // Use the way van overschee does it in `subid.m`  // WARNING: this ignores any above constraints, so Q, R will be approximate...  Matrix err = Tl - S * Tr; Matrix cov_err = err * err.t(); fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1)); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::RecomputeExtObs() { ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C(); for (size_t k = 2; k \u0026lt; (n_h_ + 1); k++) { ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) = ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1, ext_obs_t_.n_cols - 1) * fit_.A(); } } } // namespace lds  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":57,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian_8h/","title":"ldsCtrlEst_h/lds_gaussian.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian.h #  glds namespace More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Detailed Description #  This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)).\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_H #define LDSCTRLEST_LDS_GAUSSIAN_H  // namespace #include \u0026#34;lds.h\u0026#34; namespace lds { namespace gaussian { // insert any Gaussian-specific things here... } // namespace gaussian } // namespace lds  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":58,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_ctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_ctrl.h #  GLDS Controller. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::Controller Gaussian-observation Controller Type.    Detailed Description #  This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H  // namespace #include \u0026#34;lds_gaussian.h\u0026#34;// system #include \u0026#34;lds_gaussian_sys.h\u0026#34;// controller #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace gaussian { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); cx_ref_ = y_ref - sys_.d(); }; // make sure base class template methods available  using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; }; } // namespace gaussian } // namespace lds  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":59,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_fit.h #  GLDS fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::Fit GLDS Fit Type.    Detailed Description #  This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_H  // namespace #include \u0026#34;lds_gaussian.h\u0026#34;// fit type #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace gaussian { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); const Matrix\u0026amp; R() const override { return R_; }; void set_R(const Matrix\u0026amp; R) override { Reassign(R_, R); ForceSymPD(R_); }; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = C_ * x.col(t) + d_; return y.col(t); }; }; }; // namespace gaussian } // namespace lds #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":60,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_em.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_fit_em.h #  GLDS E-M fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::FitEM GLDS E-M Fit Type.    Detailed Description #  This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H  #include \u0026#34;lds_fit_em.h\u0026#34;#include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; }; } // namespace gaussian } // namespace lds  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":61,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h #  GLDS SSID fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.    Detailed Description #  This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H  #include \u0026#34;lds_fit_ssid.h\u0026#34;#include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; using SSID\u0026lt;Fit\u0026gt;::Run; private: using SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat; using SSID\u0026lt;Fit\u0026gt;::CalcSVD; using SSID\u0026lt;Fit\u0026gt;::Solve; void DecomposeData() override; void SolveVanOverschee(); }; } // namespace gaussian } // namespace lds #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":62,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_sctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_sctrl.h #  GLDS switched controller type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.    Detailed Description #  This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H  // controller type #include \u0026#34;lds_gaussian_ctrl.h\u0026#34;// switched controller #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace gaussian { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); cx_ref_ = y_ref - sys_.d(); } // make sure base class template methods available  using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; }; // SwitchedController } // namespace gaussian } // namespace lds  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":63,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/","title":"ldsCtrlEst_h/lds_gaussian_sys.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_sys.h #  GLDS base type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::System Gaussian LDS Type.    Detailed Description #  This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)).\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H #define LDSCTRLEST_LDS_GAUSSIAN_SYS_H  // namespace #include \u0026#34;lds_gaussian.h\u0026#34;// system #include \u0026#34;lds_sys.h\u0026#34; namespace lds { namespace gaussian { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0, data_t r0 = kDefaultR0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; // get methods  const Matrix\u0026amp; R() const { return R_; }; // set methods  void set_Q(const Matrix\u0026amp; Q) { lds::System::set_Q(Q); do_recurse_Ke_ = true; } void set_R(const Matrix\u0026amp; R) { Reassign(R_,R); do_recurse_Ke_ = true; }; void set_Ke(const Matrix\u0026amp; Ke) { Reassign(Ke_,Ke); // if users have set Ke, they must not want to calculate it online.  do_recurse_Ke_ = false; }; void set_Ke_m(const Matrix\u0026amp; Ke_m) { Reassign(Ke_m_,Ke_m); // if users have set Ke, they must not want to calculate it online.  do_recurse_Ke_ = false; }; void Print(); protected: void h() override { cx_ = C_ * x_; y_ = cx_ + d_; }; void RecurseKe() override; // Gaussian-output-specific  Matrix R_; bool do_recurse_Ke_{}; }; // System } // namespace gaussian } // namespace lds  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":64,"href":"/lds-ctrl-est/docs/api/files/lds__poisson_8h/","title":"ldsCtrlEst_h/lds_poisson.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson.h #  plds namespace More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Detailed Description #  This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)).\nSource code #  //===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_H #define LDSCTRLEST_LDS_POISSON_H  #include \u0026#34;lds.h\u0026#34; namespace lds { namespace poisson { // TODO(mfbolus): Not sure if defining these as static here makes the most // sense. Is there a downside to letting multiple poisson System objects share a // common random number generator? static std::random_device rd; static std::mt19937 rng = std::mt19937( rd()); } // namespace poisson } // namespace lds  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":65,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_ctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_ctrl.h #  PLDS controller type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::Controller PLDS Controller Type.    Detailed Description #  This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/)), including state estimation.\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_CTRL_H #define LDSCTRLEST_LDS_POISSON_CTRL_H  // namespace #include \u0026#34;lds_poisson.h\u0026#34;// system type #include \u0026#34;lds_poisson_sys.h\u0026#34;// control type #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace poisson { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); lds::Limit(y_ref_, kYRefLb, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available  using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; private: constexpr static const data_t kYRefLb = 1e-4; }; } // namespace poisson } // namespace lds  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":66,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/","title":"ldsCtrlEst_h/lds_poisson_fit.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_fit.h #  PLDS base fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::Fit PLDS Fit Type.    Detailed Description #  This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_FIT_H #define LDSCTRLEST_LDS_POISSON_FIT_H  // namespace #include \u0026#34;lds_poisson.h\u0026#34;// fit #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace poisson { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) : lds::Fit(n_u, n_x, n_y, dt){}; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = exp(C_ * x.col(t) + d_); return y.col(t); }; void set_R(const Matrix\u0026amp; R) override { std::cerr \u0026lt;\u0026lt; \u0026#34;WARNING: Cannot set R (R[0] = \u0026#34; \u0026lt;\u0026lt; R.at(0) \u0026lt;\u0026lt; \u0026#34;). No Gaussian measurement noise in Poisson observation model.\\n\u0026#34;; }; const Matrix\u0026amp; R() const override { return R_; }; }; }; // namespace poisson } // namespace lds  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":67,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_em.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_fit_em.h #  PLDS E-M fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::FitEM PLDS E-M Fit Type.    Detailed Description #  This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\n[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H #define LDSCTRLEST_LDS_POISSON_FIT_EM_H  #include \u0026#34;lds_fit_em.h\u0026#34;#include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override{}; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; data_t NewtonSolveC(); void AnalyticalSolveD(); }; } // namespace poisson } // namespace lds  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":68,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_ssid.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_fit_ssid.h #  PLDS SSID fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS.    Detailed Description #  This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitssid/)).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H #define LDSCTRLEST_LDS_POISSON_FIT_SSID_H  #include \u0026#34;lds_fit_ssid.h\u0026#34;#include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; private: void DecomposeData() override; void CalcCov(); void PoissonToGaussianMoments(); Matrix cov_; }; } // namespace poisson } // namespace lds #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":69,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_sctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_sctrl.h #  PLDS switched controller type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::SwitchedController Poisson-observation SwitchedController Type.    Detailed Description #  This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H  #include \u0026#34;lds_poisson_ctrl.h\u0026#34;#include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace poisson { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); lds::Limit(y_ref_, kYRefLB, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available  using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; private: constexpr static data_t kYRefLB = 1e-4; }; } // namespace poisson } // namespace lds  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":70,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/","title":"ldsCtrlEst_h/lds_poisson_sys.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_sys.h #  PLDS base type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::System Poisson System type.    Detailed Description #  This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)).\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_SYS_H #define LDSCTRLEST_LDS_POISSON_SYS_H  // namespace #include \u0026#34;lds_poisson.h\u0026#34;// system #include \u0026#34;lds_sys.h\u0026#34; // needed for Poisson random number generation #include \u0026lt;random\u0026gt; namespace lds { namespace poisson { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; protected: void h() override { cx_ = C_ * x_; y_ = exp(cx_ + d_); diag_y_.diag() = y_; }; void RecurseKe() override; private: // Poisson-output-specific  Matrix diag_y_; std::poisson_distribution\u0026lt;size_t\u0026gt; pd_; }; // System } // namespace poisson } // namespace lds  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":71,"href":"/lds-ctrl-est/docs/api/files/lds__sctrl_8h/","title":"ldsCtrlEst_h/lds_sctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_sctrl.h #  SwitchedController type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::SwitchedController SwitchedController Type.    Detailed Description #  This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code #  //===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_SCTRL_H #define LDSCTRLEST_LDS_SCTRL_H  #include \u0026#34;lds_ctrl.h\u0026#34;#include \u0026#34;lds_uniform_mats.h\u0026#34;#include \u0026#34;lds_uniform_vecs.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class SwitchedController : public Controller\u0026lt;System\u0026gt; { public: SwitchedController() = default; SwitchedController(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); SwitchedController(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); void Switch(size_t idx, bool do_force_switch = false); void set_Kc(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc) { Kc_list_ = Kc; Kc_ = Kc_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc) { Kc_list_ = std::move(Kc); Kc_ = Kc_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_inty) { Kc_inty_list_ = Kc_inty; Kc_inty_ = Kc_inty_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_inty) { Kc_inty_list_ = std::move(Kc_inty); Kc_inty_ = Kc_inty_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_u) { Kc_u_list_ = Kc_u; Kc_u_ = Kc_u_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_u) { Kc_u_list_ = std::move(Kc_u); Kc_u_ = Kc_u_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(const UniformVectorList\u0026amp; g) { g_design_list_ = g; g_design_ = g_design_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(UniformVectorList\u0026amp;\u0026amp; g) { g_design_list_ = std::move(g); g_design_ = g_design_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; // make sure base class template methods available  using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; protected: std::vector\u0026lt;System\u0026gt; systems_; size_t n_sys_{}; size_t idx_{}; // controller gains could be different for each  UniformMatrixList\u0026lt;\u0026gt; Kc_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_inty_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_u_list_; // design-phase input gain could also be different  UniformVectorList g_design_list_; // TODO(mfbolus): not sure why I need to do this.  using Controller\u0026lt;System\u0026gt;::Kc_; using Controller\u0026lt;System\u0026gt;::Kc_inty_; using Controller\u0026lt;System\u0026gt;::Kc_u_; using Controller\u0026lt;System\u0026gt;::g_design_; using Controller\u0026lt;System\u0026gt;::sys_; // using Controller\u0026lt;System\u0026gt;::u_ref_;  // using Controller\u0026lt;System\u0026gt;::x_ref_;  // using Controller\u0026lt;System\u0026gt;::y_ref_;  //  using Controller\u0026lt;System\u0026gt;::control_type_; private: void InitVars(); using lds::Controller\u0026lt;System\u0026gt;::set_sys; // using Controller\u0026lt;System\u0026gt;::set_Kc;  // using Controller\u0026lt;System\u0026gt;::set_Kc_inty;  // using Controller\u0026lt;System\u0026gt;::set_Kc_u;  // using Controller\u0026lt;System\u0026gt;::set_g_design; }; template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(systems.at(0), u_lb, u_ub, control_type), systems_(systems) { InitVars(); } template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(System(systems.at(0).n_u(), systems.at(0).n_x(), systems.at(0).n_y(), systems.at(0).dt()), u_lb, u_ub, control_type), systems_(std::move(systems)) { InitVars(); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::InitVars() { n_sys_ = systems_.size(); sys_ = systems_.at(0); Kc_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_)); Kc_inty_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); Kc_u_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); g_design_list_ = UniformVectorList(std::vector\u0026lt;Vector\u0026gt;(n_sys_, g_design_)); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::Switch(size_t idx, bool do_force_switch) { if ((idx == idx_) \u0026amp;\u0026amp; !do_force_switch) { return; // already there.  } // put old up and get new one out  systems_.at(idx_) = std::move(sys_); sys_ = std::move(systems_.at(idx)); // set the state of this system to that of the previous system  // TODO(mfbolus): This will only work as intended if state matrix is the same.  // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp.  sys_.set_m(systems_.at(idx_).m(), true); sys_.set_x(systems_.at(idx_).x()); // swap controller gains  Kc_list_.Swap(Kc_, idx_); Kc_list_.Swap(Kc_, idx); if (control_type_ \u0026amp; kControlTypeIntY) { Kc_inty_list_.Swap(Kc_inty_, idx_); Kc_inty_list_.Swap(Kc_inty_, idx); } if (control_type_ \u0026amp; kControlTypeDeltaU) { Kc_u_list_.Swap(Kc_u_, idx_); Kc_u_list_.Swap(Kc_u_, idx); } g_design_list_.Swap(g_design_, idx_); g_design_list_.Swap(g_design_, idx); idx_ = idx; } // Switch  } // namespace lds  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":72,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8h/","title":"ldsCtrlEst_h/lds_sys.h","section":"Files","content":"ldsCtrlEst_h/lds_sys.h #  LDS base type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::System Linear Dynamical System Type.    Detailed Description #  This file declares and partially defines the base type for linear dynamical systems ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code #  //===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_SYS_H #define LDSCTRLEST_LDS_SYS_H  #include \u0026#34;lds.h\u0026#34; namespace lds { class System { public: System() = default; System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); virtual ~System() {} void Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z); virtual const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) = 0; void f(const Vector\u0026amp; u, bool do_add_noise = false) { x_ = A_ * x_ + B_ * (g_ % u) + m_; if (do_add_noise) { x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_); } }; virtual void h() = 0; size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Vector\u0026amp; x() const { return x_; }; const Matrix\u0026amp; P() const { return P_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; P_m() const { return P_m_; }; const Vector\u0026amp; cx() const { return cx_; }; const Vector\u0026amp; y() const { return y_; }; const Vector\u0026amp; x0() const { return x0_; }; const Vector\u0026amp; m0() const { return m0_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; const Matrix\u0026amp; Ke() const { return Ke_; }; const Matrix\u0026amp; Ke_m() const { return Ke_m_; }; const Matrix\u0026amp; Q() { return Q_; }; const Matrix\u0026amp; Q_m() { return Q_m_; }; const Matrix\u0026amp; P0() { return P0_; }; const Matrix\u0026amp; P0_m() { return P0_m_; }; void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_m(const Vector\u0026amp; m, bool do_force_assign=false) { Reassign(m0_, m); if ((!do_adapt_m) || do_force_assign) { Reassign(m_, m); } }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); }; void set_Q_m(const Matrix\u0026amp; Q_m) { Reassign(Q_m_, Q_m); }; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); }; void set_P0_m(const Matrix\u0026amp; P0_m) { Reassign(P0_m_, P0_m); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; void set_x(const Vector\u0026amp; x) { Reassign(x_, x); h(); }; void Reset(); void Print(); // safe to leave this public and non-const  bool do_adapt_m{}; protected: virtual void RecurseKe() = 0; void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); std::size_t n_x_{}; std::size_t n_u_{}; std::size_t n_y_{}; data_t dt_{}; // Signals:  Vector x_; Matrix P_; Vector m_; Matrix P_m_; Vector cx_; Vector y_; Vector z_; // Parameters:  Vector x0_; Matrix P0_; Vector m0_; Matrix P0_m_; Matrix A_; Matrix B_; Vector g_; Matrix Q_; Matrix Q_m_; Matrix C_; Vector d_; Matrix Ke_; Matrix Ke_m_; }; // System  } // namespace lds  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":73,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/","title":"ldsCtrlEst_h/lds_uniform_mats.h","section":"Files","content":"ldsCtrlEst_h/lds_uniform_mats.h #  List of uniformly sized matrices. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::UniformMatrixList    Detailed Description #  This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.\nSource code #  //===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H #define LDSCTRLEST_LDS_UNIFORM_MATS_H  #include \u0026lt;array\u0026gt; // std::array#include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { template \u0026lt;MatrixListFreeDim D = kMatFreeDimNone\u0026gt; class UniformMatrixList : public std::vector\u0026lt;Matrix\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion  // using std::vector\u0026lt;Matrix\u0026gt;::vector;  using std::vector\u0026lt;Matrix\u0026gt;::operator=; using std::vector\u0026lt;Matrix\u0026gt;::operator[]; using std::vector\u0026lt;Matrix\u0026gt;::begin; using std::vector\u0026lt;Matrix\u0026gt;::end; using std::vector\u0026lt;Matrix\u0026gt;::size; public: using std::vector\u0026lt;Matrix\u0026gt;::at; UniformMatrixList() = default; explicit UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); explicit UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; ~UniformMatrixList() = default; const std::array\u0026lt;size_t, 2\u0026gt;\u0026amp; dim(size_t n = 0) const { return dim_.at(n); } size_t size() { return std::vector\u0026lt;Matrix\u0026gt;::size(); }; const Matrix\u0026amp; at(size_t n) { return std::vector\u0026lt;Matrix\u0026gt;::at(n); }; void Swap(Matrix\u0026amp; that, size_t n); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim); std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt; dim_; }; template \u0026lt;MatrixListFreeDim D\u0026gt; inline void UniformMatrixList\u0026lt;D\u0026gt;::Swap(Matrix\u0026amp; that, size_t n) { // make sure request in range  if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim  bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.n_cols); } if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap  Matrix tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); if (D == kMatFreeDim1) { this-\u0026gt;dim_[n][0] = (*this)[n].n_rows; } if (D == kMatFreeDim2) { this-\u0026gt;dim_[n][1] = (*this)[n].n_cols; } } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) { // make sure dim_ vector is initialized  if (dim_.empty()) { dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(that.size(), {0, 0}); } // check dimensions  if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices\u0026#34;; throw std::runtime_error(ss.str()); } // if dimensions a not zero and do not match, skip move with error message.  bool dims_nonzero = true; for (auto d : dim_) { if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; d[0] \u0026lt; 1) { dims_nonzero = false; break; } if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; d[1] \u0026lt; 1) { dims_nonzero = false; break; } } if (dims_nonzero) { bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); } if (!does_match) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrices of size \u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; dim_[k] = that.dim(k); } return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept { // // check dimensions  // // if empty, assume a default constructed object and safe to move  // if (!this-\u0026gt;empty()) {  // if (this-\u0026gt;size() != that.size()) {  // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34;  // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices. Skipping.\\n\u0026#34;;  // return (*this);  // }  //  // // if dimensions a not zero and do not match, skip move with error  // message. bool dims_nonzero = true; for (auto d : dim_) {  // if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; (d[0] \u0026lt; 1)) {  // dims_nonzero = false;  // break;  // }  // if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; (d[1] \u0026lt; 1)) {  // dims_nonzero = false;  // break;  // }  // }  //  // if (dims_nonzero) {  // bool does_match = true;  // if (!(D == kMatFreeDim1)) {  // does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows);  // }  //  // if (!(D == kMatFreeDim2)) {  // does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols);  // }  //  // if (!does_match) {  // this-\u0026gt;at(0).print(\u0026#34;this[0] = \u0026#34;);  // that.at(0).print(\u0026#34;that[0] = \u0026#34;);  // std::cerr  // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformMatrixList element of size (\u0026#34; \u0026lt;\u0026lt;  // that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols \u0026lt;\u0026lt; \u0026#34;) for an  // element of size (\u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34;).  // Skipping.\\n\u0026#34;;  // return (*this);  // }  // }  // }  dim_ = that.dim_; std::vector\u0026lt;Matrix\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(std::move(mats)) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) : vector(that) { (*this) = that; } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { std::array\u0026lt;size_t, 2\u0026gt; dim_k({this-\u0026gt;at(k).n_rows, this-\u0026gt;at(k).n_cols}); dim_.push_back(dim_k); } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim) { // change behavior based on free dim D  if ((dim[0] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim1)) { dim[0] = this-\u0026gt;at(0).n_rows; } if ((dim[1] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim2)) { dim[1] = this-\u0026gt;at(0).n_cols; } // make sure dimensiolaties are all uniform  bool does_match(true); for (const Matrix\u0026amp; mat : *this) { if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_rows == dim[0]); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_cols == dim[1]); } if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(this-\u0026gt;size(), dim); for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { dim_[k][0] = (*this)[k].n_rows; dim_[k][1] = (*this)[k].n_cols; } } } // namespace lds  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":74,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/","title":"ldsCtrlEst_h/lds_uniform_systems.h","section":"Files","content":"ldsCtrlEst_h/lds_uniform_systems.h #  List of uniformly sized Systems. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::UniformSystemList    Detailed Description #  This file provides a container for uniformly sized Systems.\nSource code #  //===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H  #include \u0026lt;array\u0026gt; // std::array#include \u0026lt;vector\u0026gt; // std::vector // namespace #include \u0026#34;lds.h\u0026#34;// System type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class UniformSystemList : public std::vector\u0026lt;System\u0026gt; { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion  // using std::vector\u0026lt;System\u0026gt;::vector;  using std::vector\u0026lt;System\u0026gt;::operator=; using std::vector\u0026lt;System\u0026gt;::operator[]; using std::vector\u0026lt;System\u0026gt;::at; using std::vector\u0026lt;System\u0026gt;::begin; using std::vector\u0026lt;System\u0026gt;::end; using std::vector\u0026lt;System\u0026gt;::size; public: UniformSystemList() = default; explicit UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); explicit UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(const UniformSystemList\u0026amp; that); UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept; ~UniformSystemList() = default; const std::array\u0026lt;size_t, 3\u0026gt;\u0026amp; dim() const { return dim_; } size_t size() { return std::vector\u0026lt;System\u0026gt;::size(); }; const System\u0026amp; at(size_t n) { return std::vector\u0026lt;System\u0026gt;::at(n); }; void Swap(System\u0026amp; that, size_t n); UniformSystemList\u0026amp; operator=(const UniformSystemList\u0026amp; that); UniformSystemList\u0026amp; operator=(UniformSystemList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim); std::array\u0026lt;size_t, 3\u0026gt; dim_{}; }; template \u0026lt;typename System\u0026gt; inline void UniformSystemList\u0026lt;System\u0026gt;::Swap(System\u0026amp; that, size_t n) { // make sure request in range  if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformSystemList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim  bool does_match = (dim_[0] == that.n_u()) \u0026amp;\u0026amp; (dim_[1] == that.n_x()) \u0026amp;\u0026amp; (dim_[2] == that.n_y()); if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformSystemList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap  System tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( const UniformSystemList\u0026amp; that) { // check dimensions  if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_[0] + dim_[1] + dim_[2]) { std::array\u0026lt;size_t, 3\u0026gt; other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign systems of size \u0026#34; \u0026lt;\u0026lt; dim_[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2] \u0026lt;\u0026lt; \u0026#34; with systems of size \u0026#34; \u0026lt;\u0026lt; other_dim[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other_dim[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2]; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( UniformSystemList\u0026amp;\u0026amp; that) noexcept { // // check dimensions  // // if empty, assume a default constructed object and safe to move  // if (!this-\u0026gt;empty()) {  // if (this-\u0026gt;size() != that.size()) {  // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34;  // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems. Skipping.\\n\u0026#34;;  // return (*this);  // }  //  // // if dimensions a not zero and do not match, skip move with error  // message. if (dim_[0] + dim_[1] + dim_[2]) {  // bool does_match = (dim_[0] == that.at(0).n_u()) \u0026amp;\u0026amp;  // (dim_[1] == that.at(0).n_x()) \u0026amp;\u0026amp;  // (dim_[2] == that.at(0).n_y());  // if (!does_match) {  // std::cerr  // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformSystemList element for an element of \u0026#34;  // \u0026#34;different size. Skipping.\\n\u0026#34;;  // return (*this);  // }  // }  // }  dim_ = that.dim_; std::vector\u0026lt;System\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(std::move(systems)) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList( std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const UniformSystemList\u0026amp; that) : std::vector\u0026lt;System\u0026gt;(that) { (*this) = that; } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept : std::vector\u0026lt;System\u0026gt;(std::move(that)) { this-\u0026gt;dim_[0] = this-\u0026gt;at(0).n_u(); this-\u0026gt;dim_[1] = this-\u0026gt;at(0).n_x(); this-\u0026gt;dim_[2] = this-\u0026gt;at(0).n_y(); } template \u0026lt;typename System\u0026gt; void UniformSystemList\u0026lt;System\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim) { if (dim[0] + dim[1] + dim[2]) { dim_ = dim; } else { dim_[0] = this-\u0026gt;at(0).n_u(); dim_[1] = this-\u0026gt;at(0).n_x(); dim_[2] = this-\u0026gt;at(0).n_y(); } // make sure dimensiolaties are all uniform  bool does_match(true); for (const System\u0026amp; sys : *this) { does_match = does_match \u0026amp;\u0026amp; (sys.n_u() == dim_[0]); does_match = does_match \u0026amp;\u0026amp; (sys.n_x() == dim_[1]); does_match = does_match \u0026amp;\u0026amp; (sys.n_y() == dim_[2]); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input systems are not uniform.\u0026#34;); } } } } // namespace lds  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":75,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/","title":"ldsCtrlEst_h/lds_uniform_vecs.h","section":"Files","content":"ldsCtrlEst_h/lds_uniform_vecs.h #  List of uniformly sized vectors. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::UniformVectorList    Detailed Description #  This file provides a container for uniformly sized vectors.\nSource code #  //===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H #define LDSCTRLEST_LDS_UNIFORM_VECS_H  #include \u0026lt;array\u0026gt; // std::array#include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { class UniformVectorList : public std::vector\u0026lt;Vector\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion  // using std::vector\u0026lt;Vector\u0026gt;::vector;  using std::vector\u0026lt;Vector\u0026gt;::operator=; using std::vector\u0026lt;Vector\u0026gt;::operator[]; using std::vector\u0026lt;Vector\u0026gt;::at; using std::vector\u0026lt;Vector\u0026gt;::begin; using std::vector\u0026lt;Vector\u0026gt;::end; using std::vector\u0026lt;Vector\u0026gt;::size; public: UniformVectorList() = default; explicit UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim = 0); explicit UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim = 0); UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim = 0); UniformVectorList(const UniformVectorList\u0026amp; that); UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept; ~UniformVectorList() = default; size_t dim() const { return dim_; } size_t size() { return std::vector\u0026lt;Vector\u0026gt;::size(); }; const Vector\u0026amp; at(size_t n) { return std::vector\u0026lt;Vector\u0026gt;::at(n); }; void Swap(Vector\u0026amp; that, size_t n); UniformVectorList\u0026amp; operator=(const UniformVectorList\u0026amp; that); UniformVectorList\u0026amp; operator=(UniformVectorList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(size_t dim); size_t dim_{}; }; inline void UniformVectorList::Swap(Vector\u0026amp; that, size_t n) { // make sure request in range  if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim  bool does_match = dim_ == that.n_elem; if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap  Vector tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( const UniformVectorList\u0026amp; that) { // check dimensions  if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_) { size_t other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ \u0026lt;\u0026lt; \u0026#34; with vectors of size \u0026#34; \u0026lt;\u0026lt; other_dim; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( UniformVectorList\u0026amp;\u0026amp; that) noexcept { // // check dimensions  // if (!this-\u0026gt;empty()) {  // if (this-\u0026gt;size() != that.size()) {  // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34;  // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors. Skipping.\\n\u0026#34;;  // return (*this);  // }  //  // if (dim_) {  // size_t other_dim(that.dim());  // if (dim_ != other_dim) {  // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_  // \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; other_dim \u0026lt;\u0026lt; \u0026#34;.  // Skipping.\\n\u0026#34;;  // return (*this);  // }  // }  // }  dim_ = that.dim_; std::vector\u0026lt;Vector\u0026gt;::operator=(std::move(that)); return (*this); } } // namespace lds  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":76,"href":"/lds-ctrl-est/docs/api/files/mex__c__util_8h/","title":"ldsCtrlEst_h/mex_c_util.h","section":"Files","content":"ldsCtrlEst_h/mex_c_util.h #  arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) More\u0026hellip;\nNamespaces #     Name     armamexc arma/mex interface using Matlab C API    Detailed Description #  This file defines utility functions for interoperability between armadillo and Matlab/Octave\u0026rsquo;s C mex API.\nSource code #  //===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_MEXC_UTIL_H #define LDSCTRLEST_MEXC_UTIL_H  #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.h\u0026#34; // // If Matlab_FOUND, include matrix.h. // // (Octave does not need/have it.) // #ifdef Matlab_FOUND // #include \u0026#34;matrix.h\u0026#34; // #endif  namespace armamexc { template \u0026lt;class T\u0026gt; inline auto m2T_scalar(const mxArray *matlab_scalar) -\u0026gt; T { if (mxGetData(matlab_scalar)) { return static_cast\u0026lt;T\u0026gt;(mxGetScalar(matlab_scalar)); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return 0; } template \u0026lt;class T\u0026gt; inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false, bool strict = true) -\u0026gt; arma::Mat\u0026lt;T\u0026gt; { if (mxGetData(matlab_mat)) { const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat); if (n_dim == 2) { return arma::Mat\u0026lt;T\u0026gt;(static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)), mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem, strict); } mexErrMsgTxt(\u0026#34;Number of dimensions must be 2.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } // TODO(mfbolus): make these templated.  template \u0026lt;typename T\u0026gt; inline auto a2m_mat(arma::Mat\u0026lt;T\u0026gt; const \u0026amp;arma_mat) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_mat.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy.  std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Mat.\u0026#34;); return nullptr; } template \u0026lt;typename T\u0026gt; inline auto a2m_vec(arma::Col\u0026lt;T\u0026gt; const \u0026amp;arma_vec) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_vec.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy.  std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Col.\u0026#34;); return nullptr; } } // namespace armamexc  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":77,"href":"/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/","title":"ldsCtrlEst_h/mex_cpp_util.h","section":"Files","content":"ldsCtrlEst_h/mex_cpp_util.h #  arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) More\u0026hellip;\nNamespaces #     Name     armamexcpp arma/mex interface using Matlab C++ API    Detailed Description #  This file defines utility functions for interoperability between armadillo and Matlab\u0026rsquo;s C++ mex API.\nSource code #  //===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_MEXCPP_UTIL_H #define LDSCTRLEST_MEXCPP_UTIL_H  #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.hpp\u0026#34;#include \u0026#34;mexAdapter.hpp\u0026#34; namespace armamexcpp { template \u0026lt;class T\u0026gt; std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; m2a_cellmat(matlab::data::CellArray\u0026amp; matlab_cell) { size_t n_cells = matlab_cell.getNumberOfElements(); std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; arma_mat(n_cells, arma::Mat\u0026lt;T\u0026gt;(1, 1, arma::fill::zeros)); for (size_t k = 0; k \u0026lt; n_cells; k++) { matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = matlab_cell[k]; auto dims = matlab_mat.getDimensions(); arma_mat[k] = arma::Mat\u0026lt;T\u0026gt;(matlab_mat.release().get(), dims[0], dims[1]); } return arma_mat; }; template \u0026lt;class T\u0026gt; std::vector\u0026lt;T\u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt;T\u0026gt;\u0026amp; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); T* ptr = matlab_array.release().get(); std::vector\u0026lt;T\u0026gt; vec(ptr, ptr + n_elem); return vec; }; template \u0026lt;class T\u0026gt; arma::Col\u0026lt;T\u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); // T* ptr = matlab_array.release().get();  // arma::Col\u0026lt;T\u0026gt; vec(ptr, n_elem); //, false);  // TODO(mfbolus): for some reason, using the above pointer at times leads to  // getting garbage values. matlab array values may be stored in non-contiguous  // memory?  arma::Col\u0026lt;T\u0026gt; vec(n_elem, arma::fill::zeros); for (size_t k = 0; k \u0026lt; n_elem; k++) { vec[k] = matlab_array[k]; } return vec; }; template \u0026lt;class T\u0026gt; arma::Mat\u0026lt;T\u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { // ArrayDimensions == std::vector\u0026lt;size_t\u0026gt;  auto dims = matlab_array.getDimensions(); // T* ptr = matlab_array.release().get();  // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false)  // arma::Mat\u0026lt;T\u0026gt; mat(ptr, dims[0], dims[1]); //, false);  // TODO(mfbolus): for some reason, using the above pointer at times leads to  // getting garbage values. matlab array values may be stored in non-contiguous  // memory?  //  // armadillo and matlab both use column-major ordering, so this should work:  size_t n_elem = dims[0] * dims[1]; arma::Mat\u0026lt;T\u0026gt; mat(dims[0], dims[1], arma::fill::zeros); size_t k(0); for (auto m: matlab_array) { mat[k] = m; k++; } return mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_mat(const arma::Mat\u0026lt;T\u0026gt;\u0026amp; arma_mat, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(), arma_mat.memptr() + arma_mat.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_vec(const arma::Col\u0026lt;T\u0026gt;\u0026amp; arma_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;({arma_vec.n_elem, 1}, arma_vec.memptr(), arma_vec.memptr() + arma_vec.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; s2m_vec(const std::vector\u0026lt;T\u0026gt;\u0026amp; std_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size()); return matlab_mat; }; } // namespace armamexcpp  #endif  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":78,"href":"/lds-ctrl-est/docs/terminology/model/","title":"Models","section":"LDS C+E Documentation","content":"Model Definitions #  This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form:   \\[\r\\mathbf{x}_{t\u0026#43;1} = f\\left( \\mathbf{x}_{t}, \\mathbf{v}_{t} \\right) = \\mathbf{A} \\mathbf{x}_{t} \u0026#43; \\mathbf{B} \\mathbf{v}_{t} \u0026#43; \\mathbf{m}_{t} \u0026#43; \\mathbf{w}_{t}\r\\]   \\[\r\\mathbf{y}_{t} = h\\left( \\mathbf{x}_{t} \\right)\r\\]  t : time index\rx : system state\rv = g%u : input (e.g., in physical units used for model fit)\ru : control signal sent to actuator (e.g., in Volts)\ry : system output\rm : process disturbance\rw ~ N(0, Q) : process noise/disturbance\rA : state matrix\rB : input coupling matrix\rg : input gain (e.g., for converting to control signal actuator voltage)\rn.b., assumes this conversion is linear\rQ : process noise covariance\r% : element-wise multiplication\r LDS with Gaussian Observations #  For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.\n \\[\r\\mathbf{y}_{t} = \\mathbf{C} \\mathbf{x}_{t} \u0026#43; \\mathbf{d}\r\\]   \\[\r\\mathbf{z}_{t} \\sim \\mathcal{N}\\left(\\mathbf{y}_{t} , \\mathbf{R} \\right)\r\\]  z : measurement\rC : output matrix\rd : output bias\rR : measurement noise covariance\r LDS with Poisson Observations #  For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.\n \\[\ry_{t}^{i} = \\exp \\left(\\mathbf{c}^i \\mathbf{x}_{t} \u0026#43; d^i\\right)\r\\]   \\[\rz_{t}^i \\sim \\rm{Poisson} \\left(y_{t}^i \\right)\r\\]  i : output index\rz : measurement (count data)\rc : i^th row of output matrix (C)\rd : output bias\r "},{"id":79,"href":"/lds-ctrl-est/docs/api/modules/","title":"Modules","section":"LDS C+E Documentation","content":"Modules #    Control Mode Bit Masks provides fill types for constructing new armadillo vectors, matrices\n  Defaults\n   Updated on 19 May 2022 at 17:16:06 Eastern Daylight Time\n"},{"id":80,"href":"/lds-ctrl-est/docs/api/namespaces/","title":"Namespaces","section":"LDS C+E Documentation","content":"Namespaces #    armamexc arma/mex interface using Matlab C API\n  armamexcpp arma/mex interface using Matlab C++ API\n  lds::gaussian Linear Dynamical Systems with Gaussian observations.\n  lds::poisson Linear Dynamical Systems with Poisson observations.\n   Updated on 19 May 2022 at 17:16:06 Eastern Daylight Time\n"},{"id":81,"href":"/lds-ctrl-est/docs/api/pages/","title":"Pages","section":"LDS C+E Documentation","content":"Pages #   Updated on 19 May 2022 at 17:16:06 Eastern Daylight Time\n"},{"id":82,"href":"/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/","title":"PLDS State Estimation","section":"LDS C+E Examples","content":"PLDS State Estimation Tutorial #  This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.\nThe full code for this can be found here.\nPreamble #  In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system #  A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; // no. inputs  size_t n_x = 1; // no. states  size_t n_y = 1; // no. outputs  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system...  lds::poisson::System system_true(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example,   \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\]   \\[\ry_{t} = \\exp\\left(x_t\\right)\r\\]  where  \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\)  .\nNow, create non-default parameters for this model.\n// Model parameters  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state Finally, assign the parameters using corresponding set-methods.\n// Assign params.  system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); Creating the estimator #  Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.\n// Construct system for estimation  // e.g., will create a model with incorrect disturbance  lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object  system_estimator = system_true; // wrong disturbance  Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. To ensure robust estimates, adaptively re-estimate the process disturbance.\n// turn on adaptive disturbance estimation  system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting  // on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); Simulating estimation #  In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.\n// Simlate the true system.  z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update)  system_estimator.Filter(u.col(t - 1), z.col(t)); Example simulation result #  Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.\nWith this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.\n"},{"id":83,"href":"/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/","title":"PLDS Switched Control","section":"LDS C+E Examples","content":"PLDS Switched Control Tutorial #  This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.\nIn the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.\nThe full code for this can be found here.\nPreamble #  In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating the simulated system #  A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// whether to do switched control  bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); The system\u0026rsquo;s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.\n// for simulating switching  size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2  data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 Initially, the system will be in \u0026ldquo;mode\u0026rdquo; 1, where B = b1.\n// simulated system being controlled  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well  // characterized by two LDS models with different input matrices.**  data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.\nCreating the controller #  Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.\nSimilar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.\nMoreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.\nPutting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.\n// create switched controller  lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains  { // create switched controller sub-systems  // system 1  lds::poisson::System sys1(controlled_system); // set process noise covariance  Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m)  // n.b. using arbitrary default value for process noise if enabled.  sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset:  Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions  // system 2  lds::poisson::System sys2 = sys1; // set parameters  sys2.set_B(b2); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying systems:  Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive.  k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nNow that the SwitchedController is instantiated, assign its parameters.\n// Control variables  size_t control_type = 0; // no integral action, etc  switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); Simulating control #  In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).\nThe control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.\n// Let the controlled system stochastically change gain  // Assume another algorithm decodes this mode change and signals the  // switched_controller  Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1  { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2  if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system.  z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control  u.col(t) = switched_controller.ControlOutputReference(z.col(t)); Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system\u0026rsquo;s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.\nExample simulation result #  Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller\u0026rsquo;s online estimates of the output and state are shown in purple.\nNote that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.\n"},{"id":84,"href":"/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/","title":"src","section":"Files","content":"src #  Files #     Name     src/lds.cpp misc lds namespace functions   src/lds_gaussian_sys.cpp GLDS base type.   src/lds_poisson_sys.cpp PLDS base type.   src/lds_sys.cpp LDS base type.   src/lds_uniform_vecs.cpp Uniformly sized vectors.     Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":85,"href":"/lds-ctrl-est/docs/api/files/lds_8cpp/","title":"src/lds.cpp","section":"Files","content":"src/lds.cpp #  misc lds namespace functions More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Detailed Description #  This file implements miscellaneous lds namespace functions not bound to a class.\nSource code #  //===-- lds.cpp - LDS -----------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds.h\u0026gt; // insert any necessary function definitions here. namespace lds { void ForceSymPD(Matrix\u0026amp; X) { if (X.is_sympd() || !X.is_square()) { return; } // make symmetric  X = (X + X.t()) / 2; // for eigenval decomp  bool did_succeed(true); Vector d; Matrix u; // see first method (which may not be ideal):  // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/  size_t k(1); bool is_sympd = X.is_sympd(); Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye); while (!is_sympd) { if (k \u0026gt; 100) { did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); data_t min_eig = arma::min(d); std::cerr \u0026lt;\u0026lt; \u0026#34;After multiple iterations, min eigen val = \u0026#34; \u0026lt;\u0026lt; min_eig \u0026lt;\u0026lt; \u0026#34;.\\n\u0026#34;; throw std::runtime_error( \u0026#34;Failed to make matrix symmetric positive definite.\u0026#34;); return; } // Limit(d, arma::eps(0), kInf); // force to be positive...  // Matrix d_diag = arma::diagmat(d);  // X = u * d_diag * u.t();  did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymPD failed.\u0026#34;); } data_t min_eig = arma::min(d); X += id * abs(min_eig) + arma::datum::eps; // make sure symm:  X = (X + X.t()) / 2; // double check eigenvals positive after symmetrizing:  arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); min_eig = arma::min(d); is_sympd = min_eig \u0026gt; 0; k++; } } void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min) { if (!X.is_square()) { return; } // make symmetric  X = (X + X.t()) / 2; bool did_succeed(true); Vector d; Matrix u; did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymMinEig failed.\u0026#34;); } Limit(d, eig_min + arma::eps(eig_min), kInf); // enforce lower bound  Matrix d_diag = arma::diagmat(d); X = u * d_diag * u.t(); // double check symmetric  X = (X + X.t()) / 2; } void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X) { bool did_succeed(true); did_succeed = arma::qr_econ(Qt, L, X.t()); if (!did_succeed) { throw std::runtime_error(\u0026#34;LQ decomposition failed.\u0026#34;); } arma::inplace_trans(L); arma::inplace_trans(Qt); } Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B) { // subtract out mean  auto m_a = arma::mean(A, 1); Matrix a0 = A; a0.each_col() -= m_a; auto m_b = arma::mean(B, 1); Matrix b0 = B; b0.each_col() -= m_b; Matrix cov = a0 * b0.t() / a0.n_cols; return cov; } } // namespace lds  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":86,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/","title":"src/lds_gaussian_sys.cpp","section":"Files","content":"src/lds_gaussian_sys.cpp #  GLDS base type. More\u0026hellip;\nDetailed Description #  This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code #  //===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds_gaussian_sys.h\u0026gt; lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0, data_t r0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { R_.zeros(n_y, n_y); R_.diag().fill(r0); do_recurse_Ke_=true; }; // recursively estimate Ke void lds::gaussian::System::RecurseKe() { if (!do_recurse_Ke_) { return; } // predict covariance  P_ = A_ * P_ * A_.t() + Q_; // calc Kalman gain  Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_); // update covariance  // Reference: Ghahramani et Hinton (1996)  P_ = P_ - Ke_ * C_ * P_; if (do_adapt_m) { P_m_ += Q_m_; // A_m = I (i.e., random walk)  Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_); P_m_ = P_m_ - Ke_m_ * C_ * P_m_; } } // Simulate const lds::Vector\u0026amp; lds::gaussian::System::Simulate(const Vector\u0026amp; u_tm1){ f(u_tm1, true);//simulate dynamics with noise added  h();//output  z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure  return z_; } void lds::gaussian::System::Print() { lds::System::Print(); std::cout \u0026lt;\u0026lt; \u0026#34;R: \\n\u0026#34; \u0026lt;\u0026lt; R_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":87,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/","title":"src/lds_poisson_sys.cpp","section":"Files","content":"src/lds_poisson_sys.cpp #  PLDS base type. More\u0026hellip;\nDetailed Description #  This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code #  //===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds_poisson_sys.h\u0026gt; lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { diag_y_ = diagmat(y_); pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(0); }; // Correct: Given measurement (z) and current input (u), update estimate of the // state, covar, output. // // see Eden et al. 2004 void lds::poisson::System::RecurseKe() { // predict covariance  P_ = A_ * P_ * A_.t() + Q_; // update cov  P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_); Ke_ = P_ * C_.t(); if (do_adapt_m) { P_m_ += Q_m_; // predict (A_m = I)  P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_); // update  Ke_m_ = P_m_ * C_.t(); } } // Simulate Measurement: z ~ Poisson(y) const lds::Vector\u0026amp; lds::poisson::System::Simulate(const Vector\u0026amp; u_tm1) { f(u_tm1, true); // simulate dynamics with noise added  h(); // output  z_.zeros(); for (std::size_t k = 0; k \u0026lt; n_y_; k++) { // construct a Poisson distribution object with mean y[k]  pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(y_[k]); // pull random sample from this distribution  z_[k] = pd_(rng); } return z_; } // ******************* SYS_T *******************  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":88,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8cpp/","title":"src/lds_sys.cpp","section":"Files","content":"src/lds_sys.cpp #  LDS base type. More\u0026hellip;\nDetailed Description #  This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code #  //===-- lds_sys.cpp - LDS -------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds_sys.h\u0026gt; lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) { InitVars(p0, q0); } void lds::System::InitVars(data_t p0, data_t q0) { // initial conditions.  x0_ = Vector(n_x_, fill::zeros); // includes bias (nY) and g (nU)  P0_ = p0 * Matrix(n_x_, n_x_, fill::eye); m0_ = x0_; P0_m_ = P0_; // signals  x_ = x0_; P_ = P0_; m_ = m0_; P_m_ = P0_m_; y_ = Vector(n_y_, fill::zeros); cx_ = Vector(n_y_, fill::zeros); z_ = Vector(n_y_, fill::zeros); // By default, random walk where each state is independent  // In this way, provides independent estimates of rate per channel of output.  A_ = Matrix(n_x_, n_x_, fill::eye); B_ = Matrix(n_x_, n_u_, fill::zeros); g_ = Vector(n_u_, fill::ones); Q_ = q0 * Matrix(n_x_, n_x_, fill::eye); Q_m_ = Q_; C_ = Matrix(n_y_, n_x_, fill::eye); // each state will map to an output by  d_ = Vector(n_y_, fill::zeros); Ke_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain.  Ke_m_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain for m adaptation.  do_adapt_m = false; } // Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state // and update estimate of the state, covar, output using Kalman filter void lds::System::Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z_t) { // predict mean  f(u_tm1); // dynamics  h(); // output  // recursively calculate esimator gains (or just keep existing values)  // (also predicts+updates estimate covariance)  RecurseKe(); // update  x_ += Ke_ * (z_t - y_); if (do_adapt_m) { m_ += Ke_m_ * (z_t - y_); // adaptively estimating disturbance  } // With new state, estimate output.  h(); // --\u0026gt; posterior } void lds::System::Reset() { // reset to initial conditions  x_ = x0_; // mean  P_ = P0_; // cov of state estimate  m_ = m0_; // process disturbance  P_m_ = P0_m_; // cov of disturbance estimate  h(); } void lds::System::Print() { std::cout \u0026lt;\u0026lt; \u0026#34;\\n********** SYSTEM ********** \\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;x: \\n\u0026#34; \u0026lt;\u0026lt; x_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;P: \\n\u0026#34; \u0026lt;\u0026lt; P_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;A: \\n\u0026#34; \u0026lt;\u0026lt; A_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B: \\n\u0026#34; \u0026lt;\u0026lt; B_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;g: \\n\u0026#34; \u0026lt;\u0026lt; g_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;m: \\n\u0026#34; \u0026lt;\u0026lt; m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q: \\n\u0026#34; \u0026lt;\u0026lt; Q_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q_m: \\n\u0026#34; \u0026lt;\u0026lt; Q_m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d: \\n\u0026#34; \u0026lt;\u0026lt; d_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;C: \\n\u0026#34; \u0026lt;\u0026lt; C_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;y: \\n\u0026#34; \u0026lt;\u0026lt; y_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } //******************* SYS_T *******************  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"},{"id":89,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/","title":"src/lds_uniform_vecs.cpp","section":"Files","content":"src/lds_uniform_vecs.cpp #  Uniformly sized vectors. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Detailed Description #  This file provides a container for uniformly sized vectors.\nSource code #  //===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds_uniform_vecs.h\u0026gt; namespace lds { UniformVectorList::UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); } UniformVectorList::UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim) : vector(std::move(vecs)) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(const UniformVectorList\u0026amp; that) : vector(that) { (*this) = that; } UniformVectorList::UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { this-\u0026gt;dim_ = this-\u0026gt;at(0).n_elem; } void UniformVectorList::CheckDimensions(size_t dim) { if (dim) { dim_ = dim; } else { dim_ = this-\u0026gt;at(0).n_elem; } // make sure dimensiolaties are all uniform  bool does_match(true); for (const Vector\u0026amp; vec : *this) { does_match = does_match \u0026amp;\u0026amp; (vec.n_elem == dim_); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } } } // namespace lds  Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time\n"}]
\ No newline at end of file
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index c6932a81..00000000
--- a/docs/en.search-data.min.dd6d5ec1dc04e75b58c8053ebd9a205c919f3fc365f221e7bb9b64945879058e.json
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@@ -1 +0,0 @@
-[{"id":0,"href":"/lds-ctrl-est/docs/","title":"LDS C+E Documentation","section":"LDS Control \u0026 Estimation","content":"LDS Control \u0026amp; Estimation Documentation #  "},{"id":1,"href":"/lds-ctrl-est/docs/tutorials/","title":"LDS C+E Examples","section":"LDS C+E Documentation","content":"Examples #  "},{"id":2,"href":"/lds-ctrl-est/acknowledgements/","title":"Acknowledgements","section":"LDS Control \u0026 Estimation","content":"Acknowledgements #  Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.\n"},{"id":3,"href":"/lds-ctrl-est/docs/getting-started/getting-started/","title":"Getting Started","section":"LDS C+E Documentation","content":"Getting Started #  This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.\nldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.\nN.B. #  Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably:\n Ubuntu 18.04 with GCC 7.5 compiler macOS 11 (Big Sur) with Apple Clang 12 compiler   Mac pre-requisities #  Xcode Command Line Tools will get you clang, gcc, make, and git:\nxcode-select --install Homebrew is \u0026ldquo;The Missing Package Manager for macOS\u0026rdquo; which will make installing lots of things easy. Install like this:\n/bin/bash -c \u0026#34;$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\u0026#34; You can then use it to install CMake and gfortran:\nbrew install cmake gfortran Linux pre-requisites #  You\u0026rsquo;ll need Git, CMake, GCC, gfortran, etc.\nsudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential Windows installation #  Look here for Windows-specific instructions.\nDownloading the Library #  First, clone the repository along with submodules:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est\rgit submodule update --init\rCompilation + Installation #  Now generate the cache and build using your IDE or from the command line as follows.\nmkdir build \u0026amp;\u0026amp; cd build\rcmake ..\rcmake --build .\rThe first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.\nIf you want to use vcpkg set up somewhere besides this repo\u0026rsquo;s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE\u0026rsquo;s settings.\nYou can verify the build is working by running ctest from the build folder, which runs all the example scripts.\nOptions #  This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\n LDSCTRLEST_BUILD_EXAMPLES : [default=ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use  n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake to configure/build the library.\n  For basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build cmake --build #build the project sudo make install #[optional] installs to default location (OS-specific)   To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location cmake --build #build the project make install #install to /your/install/prefix   To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\n LD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool    On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.\nPython bindings package ldsctrlest #  With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython3_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn\u0026rsquo;t use an undesired version. That environment needs to have NumPy installed.\ncmake --build . --target python_modules The bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory (pip install pytest matplotlib first if you need to).\nSee python/ldsctrlest/README.md for usage details.\nAlso, beware that a single build will probably not work for both the standalone library and the Python package, since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.\nCommon issues #   \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo;  If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.\nexport CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix vcpkg fails on configuration  Try running ./bootstrap-vcpkg from the vcpkg folder and try again. If that doesn\u0026rsquo;t work, try updating vcpkg to a newer version (in the source control tab, click on the commit hash by the vcpkg repo then select from the dropdown) and running boostsrap-vcpkg again.\n Could not find Python3 (missing: Python3_NumPy_INCLUDE_DIRS NumPy)\nMake sure NumPy is installed in the Python environment you specified. If CMake still can\u0026rsquo;t find it, you may need to tell CMake exactly where to find it by adding an argument to the configure command: -DPython3_NumPy_INCLUDE_DIR=.... You can find that location like this: python -c 'import numpy; print(numpy.get_include())'\n  "},{"id":4,"href":"/lds-ctrl-est/docs/getting-started/windows/","title":"Windows","section":"LDS C+E Documentation","content":"Windows installation #  Windows pre-requisites #  Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:\nSet-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time iwr get.scoop.sh | Invoke-Expression Install Git and CMake if you don\u0026rsquo;t already have them:\nscoop install git cmake If that didn\u0026rsquo;t work, follow more detailed instructions here.\nThe easiest way to compile C++ project on Windows is with Visual Studio\u0026rsquo;s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on \u0026ldquo;Desktop development with C++.\u0026rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the \u0026ldquo;Installation details\u0026rdquo; sidebar under optional features.\nAnd the easiest way to use Visual Studio\u0026rsquo;s build tools is with VS Code, along with the CMake Tools extension. Install them and you should be ready to go.\nDownloading the Library #  First, clone the repository, either from VS Code or the command line:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est You\u0026rsquo;ll need to initialize the submodules from the command line after the repo is cloned:\ngit submodule update --init Installation #  When you open the folder in VS Code, you will like be prompted by the CMake Tools extension to configure the project. Make sure you select the kit (you\u0026rsquo;ll be prompted when you configure\u0026ndash;else there\u0026rsquo;s an icon in the bar on the bottom of the window or type Ctrl+Shift+P, then \u0026ldquo;cmake select kit\u0026rdquo;). Choose Clang [latest version] with GNU CLI ... amd64 assuming you are running a 64-bit OS. (MSVC may work okay too if you don\u0026rsquo;t need to build Python bindings.)\nFollow along with the \u0026ldquo;Getting Started\u0026rdquo; instructions, but where you see config options specified as -DLDSCTREST_BUILD_STATIC=OFF or -DPython3_ROOT_DIR=..., you will enter those in settings: open with Ctrl+,, click \u0026ldquo;workspace\u0026rdquo;, then search for \u0026ldquo;CMake: Configure Args\u0026rdquo; and enter each of your desired arguments as a separate item.\nTo configure, use Ctrl+Shift+P and search for the \u0026ldquo;CMake: Configure\u0026rdquo; command. To build, click the \u0026ldquo;Build\u0026rdquo; button on the bottom bar. Then click the \u0026ldquo;CTest\u0026rdquo; button to run the example scripts.\nConsiderations #  Development on Windows has been more prone to bugs than on Unix systems, so if you encounter many problems, consider switching—WSL (Windows Subsystem for Linux) is a good option for Windows users who don\u0026rsquo;t want to work on a different machine.\nCompilation has been successfully tested in VS Code using the following kit, using the \u0026ldquo;RelWithDebInfo\u0026rdquo; config:\nClang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64) Troubleshooting #   \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo;  Have you installed the library? In VS Code, use Shift+F7 to build a specific target, in this case INSTALL. If that doesn\u0026rsquo;t solve your problem, you will likely need to add the build or install folder to your PATH environment variable, which you can do using the settings GUI (search for \u0026ldquo;Edit the system environment variables\u0026rdquo;).\nOn Windows, \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted.  Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":5,"href":"/lds-ctrl-est/issues-contributing/","title":"Issues Contributing","section":"LDS Control \u0026 Estimation","content":"Reporting Issues #  If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.\nContributing #  We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.\n"},{"id":6,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/","title":"armamexc","section":"Namespaces","content":"armamexc #  arma/mex interface using Matlab C API More\u0026hellip; Functions #      Name     template \u0026lt;class T \u0026gt; auto m2T_scalar(const mxArray * matlab_scalar)\nConvert Matlab mxArray to scalar of type T.   template \u0026lt;class T \u0026gt; auto m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)\nConvert matlab matrix to armadillo.   template \u0026lt;typename T \u0026gt; auto a2m_mat(arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat)\nConvert armadillo to matlab matrix.   template \u0026lt;typename T \u0026gt; auto a2m_vec(arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec)\nConvert armadillo to matlab vector.    Detailed Description #  Utilities for arma/mex interface using Matlab C API\nFunction Details #  m2T_scalar #  template \u0026lt;class T \u0026gt; inline auto m2T_scalar( const mxArray * matlab_scalar ) Parameters:\n matlab_scalar matlab scalar  Template Parameters:\n T type  Return: scalar of type T\nm2a_mat #  template \u0026lt;class T \u0026gt; inline auto m2a_mat( const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true ) Parameters:\n matlab_mat matlab matrix copy_aux_mem [optional] whether to copy auxiliary memory strict [optional] strictly enforce the above  Template Parameters:\n T type  Return: armadillo matrix of type T\na2m_mat #  template \u0026lt;typename T \u0026gt; inline auto a2m_mat( arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat ) Parameters:\n arma_mat armadillo matrix  Return: matlab matrix\na2m_vec #  template \u0026lt;typename T \u0026gt; inline auto a2m_vec( arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec ) Parameters:\n arma_vec armadillo vector  Return: matlab vector\n Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":7,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/","title":"armamexcpp","section":"Namespaces","content":"armamexcpp #  arma/mex interface using Matlab C++ API More\u0026hellip; Functions #      Name     template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat(matlab::data::CellArray \u0026amp; matlab_cell)\nConvert matlab cell array to vector of armadillo matrices.   template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array)\nConvert matlab matrix to a vector of scalars.   template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo vector.   template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo matrix.   template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat(const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab matrix.   template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec(const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab vector.   template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec(const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert vector of scalar T to matlab matrix.    Detailed Description #  utilities for arma/mex interface using Matlab C++ API\nFunction Details #  m2a_cellmat #  template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat( matlab::data::CellArray \u0026amp; matlab_cell ) Parameters:\n matlab_cell matlab cell  Template Parameters:\n T type  Return: vector of armadillo matrices of type T\nm2s_vec #  template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array ) Parameters:\n matlab_array matlab array  Template Parameters:\n T type  Return: vector of type T\nm2a_vec #  template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\n matlab_array matlab array  Template Parameters:\n T type  Return: armadillo vector of type T\nm2a_mat #  template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\n matlab_array matlab matrix  Template Parameters:\n T type  Return: armadillo matrix of type T\na2m_mat #  template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat( const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\n arma_mat arma matrix factory matlab \u0026ldquo;array factory\u0026rdquo;  Template Parameters:\n T type  Return: matlab matrix\na2m_vec #  template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec( const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\n arma_vec armadillo vector factory matlab \u0026ldquo;array factory\u0026rdquo;  Template Parameters:\n T type  Return: matlab matrix\ns2m_vec #  template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec( const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\n std_vec standard vector factory matlab \u0026ldquo;array factory\u0026rdquo;  Template Parameters:\n T type  Return: matlab matrix\n Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":8,"href":"/lds-ctrl-est/docs/terminology/control-estimation/","title":"C\u0026E","section":"LDS C+E Documentation","content":"Control \u0026amp; Estimation #  The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.\nState estimation #  In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by   \\[\r\\widehat{\\mathbf{x}}_{t|t-1} = \\mathbf{A}\\widehat{\\mathbf{x}}_{t-1|t-1} \u0026#43; \\mathbf{B} u_{t-1} \u0026#43; \\mathbf{m}_{t-1} \\;,\r\\]   \\[\r\\widehat{\\mathbf{x}}_{t|t} = \\widehat{\\mathbf{x}}_{t|t-1} \u0026#43; \\mathbf{K}^{\\rm e}_t \\left(\\mathbf{z}_t - \\widehat{\\mathbf{y}}_{t|t-1}\\right)\\;,\r\\]  where  \\( \\hat{\\left(\\cdot\\right)}_{t|j} \\)  indicates an estimate at time  \\( t \\)  given data up to time  \\( j \\)  inclusive,  \\( \\mathbf{K}^{\\rm e} \\)  is the estimator gain, and\n \\[ \\widehat{\\mathbf{y}}_{t|t-1} = h\\left( \\widehat{\\mathbf{x}}_{t|t-1} \\right) \\; .\\]  In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.\nIn the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.\nAdaptive estimation of process disturbance #  Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.\nWhen parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk  \\[\r\\mathbf{m}_{t} = \\mathbf{m}_{t-1} \u0026#43; \\mathbf{w}^m_{t-1} \\;,\r\\]  where  \\( \\mathbf{w}^m \\sim \\mathcal{N}\\left(0, \\mathbf{Q}_m\\right)\\)  . Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.\nControl #  Given the estimated state, the controller updates the inputs to the system according to the following law:  \\[\r\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\r\\]  where  \\( \\left( \\cdot \\right)^{\\rm ref} \\)  correspond to reference/target signals and  \\( \\mathbf{K}^c_x \\)  is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.\nIf users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as\n \\[\r\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right) - \\mathbf{K}^c_{\\rm inty} \\sum_{j=1}^{t}\\left( \\widehat{\\mathbf{y}}_j - \\mathbf{y}^{\\rm ref}_j \\right) \\;.\r\\]  An additional option available to users is a control law that updates the change in u,\n \\[\r\\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\left(\\mathbf{u}_{t-1} - \\mathbf{u}^{\\rm ref}_{t-1} \\right) - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\r\\]   \\[\r\\mathbf{u}_{t} = \\mathbf{u}_{t-1} \u0026#43; \\Delta\\mathbf{u}_{t} \\; .\r\\]  Notice that this takes the form of a first-order difference equation for updating control (i.e.,  \\( \\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\mathbf{u}_{t-1} \u0026#43; \\epsilon_{t-1} \\)  ), effectively low-pass filtering the input depending on the characteristics of  \\( \\mathbf{K}^c_u \\)  . This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.\nIntegral action and the  \\( \\Delta \\mathbf{u} \\)  control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR\u0026rsquo;d to use in combination.\nCalculating reference state-control from output #  In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce  \\( \\mathbf{x}^{\\rm ref} \\)  and  \\( \\mathbf{u}^{\\rm ref} \\)  . Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.\n"},{"id":9,"href":"/lds-ctrl-est/docs/api/classes/","title":"Classes","section":"LDS C+E Documentation","content":"Classes #    lds::Controller\n  lds::EM\n  lds::Fit LDS Fit Type.\n  lds::SSID\n  lds::SwitchedController SwitchedController Type.\n  lds::System Linear Dynamical System Type.\n  lds::UniformMatrixList\n  lds::UniformSystemList\n  lds::UniformVectorList\n  lds::gaussian::Controller Gaussian-observation Controller Type.\n  lds::gaussian::Fit GLDS Fit Type.\n  lds::gaussian::FitEM GLDS E-M Fit Type.\n  lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.\n  lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.\n  lds::gaussian::System Gaussian LDS Type.\n  lds::poisson::Controller PLDS Controller Type.\n  lds::poisson::Fit PLDS Fit Type.\n  lds::poisson::FitEM PLDS E-M Fit Type.\n  lds::poisson::FitSSID Subspace Identification (SSID) for PLDS.\n  lds::poisson::SwitchedController Poisson-observation SwitchedController Type.\n  lds::poisson::System Poisson System type.\n   Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":10,"href":"/lds-ctrl-est/docs/api/modules/group__control__masks/","title":"Control Mode Bit Masks","section":"Modules","content":"Control Mode Bit Masks #  provides fill types for constructing new armadillo vectors, matrices More\u0026hellip; Attributes #      Name     const std::size_t kControlTypeDeltaU control designed to penalize change in input   const std::size_t kControlTypeIntY control using integral action   const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m    Detailed Description #  Control mode bit masks. These can be bit-wise OR\u0026rsquo;d to use in combination.\nAttribute Details #  kControlTypeDeltaU #  static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY #  static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM #  static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\n Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":11,"href":"/lds-ctrl-est/docs/api/modules/group__defaults/","title":"Defaults","section":"Modules","content":"Defaults #  \nMore\u0026hellip; Attributes #      Name     const data_t kDefaultP0 default state estimate covar   const data_t kDefaultQ0 default process noise covar   const data_t kDefaultR0 default output noise covar    Detailed Description #  Default values for common variables (e.g., default diagonal elements of covariances)\nAttribute Details #  kDefaultP0 #  static const data_t kDefaultP0 = 1e-6; kDefaultQ0 #  static const data_t kDefaultQ0 = 1e-6; kDefaultR0 #  static const data_t kDefaultR0 = 1e-2;  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":12,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/","title":"eg_glds_ctrl.cpp","section":"Examples","content":"eg_glds_ctrl.cpp #  Example GLDS Control\n//===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); // output noise covariance  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb.  data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_x).fill(m_low); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables:  // if following enabled, adapts set point with re-estimated process  // disturbance n.b., should not need integral action if this is enabled as the  // adaptive estimator minimizes DC error  bool do_adaptive_set_point = false; // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err  // setting initial state to target to avoid error at onset:  Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance  control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; } // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(9); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation  // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input  Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals  y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,  // xTrue, mTrue saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":13,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/","title":"eg_glds_du_plds_ctrl.cpp","section":"Examples","content":"eg_glds_du_plds_ctrl.cpp #  Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).\n//===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb.  data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance  Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables:  // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input  // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du =  // 1e-2.  Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err  Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp  // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control)  control_type = control_type | lds::kControlTypeDeltaU; // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(10); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation  // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input  Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals  y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,  // xTrue, mTrue saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":14,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/","title":"eg_plds_ctrl.cpp","section":"Examples","content":"eg_plds_ctrl.cpp #  Example PLDS Control\n//===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains  // n.b., Can either use Vector (arma::Col) or std::vector  Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error  Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err  // Set control type bit mask, so controller knows what to do  size_t control_type = lds::kControlTypeIntY; // integral action  // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled...  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions  controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller  lds::poisson::Controller controller; { // Create model used for control.  lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance  Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition  // adaptively re-estimate process disturbance (m)  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type  controller.set_control_type(control_type); // set controller gains  controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above  // target:  data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val  y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference  data_t f = 0.5; // freq [=] Hz  Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system.  z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Notably, it does this in the  // log-linear space (i.e., log(y)).  //  // Therefore, it is only applicable to regulation problems or cases where  // reference trajectory changes slowly compared to system dynamics.  controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":15,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/","title":"eg_plds_est.cpp","section":"Examples","content":"eg_plds_est.cpp #  Example PLDS Estimation\n//===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; // no. inputs  size_t n_x = 1; // no. states  size_t n_y = 1; // no. outputs  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.  // construct ground truth system...  lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  // Assign params.  system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation  // e.g., will create a model with incorrect disturbance  lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object  system_estimator = system_true; // wrong disturbance  Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.  // turn on adaptive disturbance estimation  system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting  // on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation :  // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus)  Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk  Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in...  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params  Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions  y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system.  z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update)  system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals  y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":16,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/","title":"eg_plds_switched_ctrl.cpp","section":"Examples","content":"eg_plds_switched_ctrl.cpp #  Example Switched PLDS Control\n//===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control  bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching  size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2  data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1  // simulated system being controlled  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well  // characterized by two LDS models with different input matrices.**  data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions  // reference  Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2  Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller  lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains  { // create switched controller sub-systems  // system 1  lds::poisson::System sys1(controlled_system); // set process noise covariance  Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m)  // n.b. using arbitrary default value for process noise if enabled.  sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset:  Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions  cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2  lds::poisson::System sys2 = sys1; // set parameters  sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s:  Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive.  k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables  size_t control_type = 0; // no integral action, etc  switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements  Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control.  Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in...  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params  Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val  y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain  // Assume another algorithm decodes this mode change and signals the  // switched_controller  Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1  { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2  if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system.  z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control  u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":17,"href":"/lds-ctrl-est/docs/api/examples/","title":"Examples","section":"LDS C+E Documentation","content":"Examples #    eg_glds_ctrl.cpp\n  eg_glds_du_plds_ctrl.cpp\n  eg_plds_ctrl.cpp\n  eg_plds_est.cpp\n  eg_plds_switched_ctrl.cpp\n   Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":18,"href":"/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/","title":"examples","section":"Files","content":"examples #  Files #     Name     examples/eg_glds_ctrl.cpp   examples/eg_glds_du_plds_ctrl.cpp   examples/eg_plds_ctrl.cpp   examples/eg_plds_est.cpp   examples/eg_plds_switched_ctrl.cpp     Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":19,"href":"/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/","title":"examples/eg_glds_ctrl.cpp","section":"Files","content":"examples/eg_glds_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Going to simulate a switching disturbance (m) acting on system\nSource code #  //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); // output noise covariance  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb.  data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_x).fill(m_low); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables:  // if following enabled, adapts set point with re-estimated process  // disturbance n.b., should not need integral action if this is enabled as the  // adaptive estimator minimizes DC error  bool do_adaptive_set_point = false; // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err  // setting initial state to target to avoid error at onset:  Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance  control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; } // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(9); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation  // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input  Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals  y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,  // xTrue, mTrue saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":20,"href":"/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/","title":"examples/eg_glds_du_plds_ctrl.cpp","section":"Files","content":"examples/eg_glds_du_plds_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Going to simulate a switching disturbance (m) acting on system\nSource code #  //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb.  data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance  Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables:  // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input  // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du =  // 1e-2.  Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err  Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp  // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control)  control_type = control_type | lds::kControlTypeDeltaU; // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(10); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation  // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input  Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals  y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,  // xTrue, mTrue saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":21,"href":"/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/","title":"examples/eg_plds_ctrl.cpp","section":"Files","content":"examples/eg_plds_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Going to simulate a switching disturbance (m) acting on system\nSource code #  //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains  // n.b., Can either use Vector (arma::Col) or std::vector  Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error  Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err  // Set control type bit mask, so controller knows what to do  size_t control_type = lds::kControlTypeIntY; // integral action  // Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled...  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions  controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller  lds::poisson::Controller controller; { // Create model used for control.  lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance  Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition  // adaptively re-estimate process disturbance (m)  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type  controller.set_control_type(control_type); // set controller gains  controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above  // target:  data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in...  Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V)  Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params  // *_hat indicates online estimates  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled)  Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val  y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance  Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance  { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance  if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance  m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference  data_t f = 0.5; // freq [=] Hz  Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system.  z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Notably, it does this in the  // log-linear space (i.e., log(y)).  //  // Therefore, it is only applicable to regulation problems or cases where  // reference trajectory changes slowly compared to system dynamics.  controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":22,"href":"/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/","title":"examples/eg_plds_est.cpp","section":"Files","content":"examples/eg_plds_est.cpp #  Functions #      Name     Matrix random_walk(size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0)   int main()    Function Details #  random_walk #  Matrix random_walk( size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0 ) main #  int main() Source code #  //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; // no. inputs  size_t n_x = 1; // no. states  size_t n_y = 1; // no. outputs  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.  // construct ground truth system...  lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  // Assign params.  system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation  // e.g., will create a model with incorrect disturbance  lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object  system_estimator = system_true; // wrong disturbance  Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.  // turn on adaptive disturbance estimation  system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting  // on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation :  // Simulated measurements  Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus)  Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk  Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in...  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params  Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions  y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system.  z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update)  system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals  y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":23,"href":"/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/","title":"examples/eg_plds_switched_ctrl.cpp","section":"Files","content":"examples/eg_plds_switched_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Source code #  //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control  bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching  size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2  data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1  // simulated system being controlled  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well  // characterized by two LDS models with different input matrices.**  data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions  // reference  Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2  Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller  lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains  { // create switched controller sub-systems  // system 1  lds::poisson::System sys1(controlled_system); // set process noise covariance  Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m)  // n.b. using arbitrary default value for process noise if enabled.  sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset:  Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions  cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2  lds::poisson::System sys2 = sys1; // set parameters  sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s:  Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive.  k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables  size_t control_type = 0; // no integral action, etc  switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements  Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control.  Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in...  Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params  Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val  y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain  // Assume another algorithm decodes this mode change and signals the  // switched_controller  Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1  { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2  if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system.  z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control  u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,  // x_true, m_true saving with hdf5 via armadillo  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":24,"href":"/lds-ctrl-est/docs/api/files/","title":"Files","section":"LDS C+E Documentation","content":"Files #    examples/eg_glds_ctrl.cpp\n  examples/eg_glds_du_plds_ctrl.cpp\n  examples/eg_plds_ctrl.cpp\n  examples/eg_plds_est.cpp\n  examples/eg_plds_switched_ctrl.cpp\n  ldsCtrlEst_h/lds.h lds namespace\n  ldsCtrlEst_h/lds_ctrl.h Controller.\n  ldsCtrlEst_h/lds_fit.h LDS base fit type.\n  ldsCtrlEst_h/lds_fit_em.h subspace identification\n  ldsCtrlEst_h/lds_fit_ssid.h subspace identification\n  ldsCtrlEst_h/lds_gaussian.h glds namespace\n  ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.\n  ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.\n  ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.\n  ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.\n  ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.\n  ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.\n  ldsCtrlEst_h/lds_poisson.h plds namespace\n  ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.\n  ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.\n  ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.\n  ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.\n  ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.\n  ldsCtrlEst_h/lds_poisson_sys.h PLDS base type.\n  ldsCtrlEst_h/lds_sctrl.h SwitchedController type.\n  ldsCtrlEst_h/lds_sys.h LDS base type.\n  ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.\n  ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.\n  ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.\n  ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)\n  ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)\n  src/lds.cpp misc lds namespace functions\n  src/lds_gaussian_sys.cpp GLDS base type.\n  src/lds_poisson_sys.cpp PLDS base type.\n  src/lds_sys.cpp LDS base type.\n  src/lds_uniform_vecs.cpp Uniformly sized vectors.\n   Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":25,"href":"/lds-ctrl-est/docs/tutorials/eg_glds_control/","title":"GLDS Control","section":"LDS C+E Examples","content":"GLDS Control Tutorial #  This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.\nThe full code for this can be found here.\nPreamble #  In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system #  A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.\n// Make 1st-order SISO system, sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system to be controlled...  // initializes to random walk model with top-most n_y state observed  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example,   \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\]   \\[\ry_{t} = x_t\r\\]  where  \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\)  .\nNow, create non-default parameters for this model.\n// Ground-truth parameters for the controlled system  // (stand-in for physical system to be controlled)  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2:  Vector g_true = Vector(n_y).fill(10.0); // output noise covariance  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.\n/// Going to simulate a switching disturbance (m) acting on system  size_t which_m = 0; // whether low or high disturbance (0, 1)  data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb.  data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low  Vector m0_true = Vector(n_y).fill(m_low); Finally, assign the parameters using corresponding set-methods.\n// Assign params.  controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); Creating the controller #  Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.\nFor the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.\n// make a controller  lds::gaussian::Controller controller; { // Create **incorrect** model used for control.  // (e.g., imperfect model fitting)  Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially  // (will be re-estimating)  Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R  Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m  // going to adaptively re-estimate the disturbance  controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise  // acting on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller  // lower and upper bounds on control signal (e.g., in Volts)  data_t u_lb = 0.0; // [=] V  data_t u_ub = 5.0; // [=] V  controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nWith the controller constructed, control variables may be set.\n// Control variables:  // if following enabled, adapts set point with re-estimated process  // disturbance n.b., should not need integral action if this is enabled as the  // adaptive estimator minimizes DC error  bool do_adaptive_set_point = false; // Reference/target output, controller gains  Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error  Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err  // setting initial state to target to avoid error at onset:  Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed  size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance  control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error  control_type = control_type | lds::kControlTypeIntY; } // set controller type  controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):  Vector g_design = Vector(n_u).fill(9); // Set params.  // **n.b. using arbitrary defaults for Q, R in this example. Really, these  // should be set by users, as they tune characteristics of Kalman filter.  // Users can also choose not to recursively calculate the estimator gain and  // supply it (setKe) instead of covariances.**  controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); Simulating control #  In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.\nThe control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.\n// Simulate the true system.  z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate  // x_ref, u_ref from reference output y_ref. Therefore, it is only  // applicable to regulation problems or cases where reference trajectory  // changes slowly compared to system dynamics.  u.col(t) = controller.ControlOutputReference(z.col(t)); Example simulation result #  Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller\u0026rsquo;s online estimates of the output, state, and disturbance are given in purple.\n"},{"id":26,"href":"/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/","title":"include","section":"Files","content":"include #  Directories #     Name     ldsCtrlEst_h     Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":27,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds/","title":"lds","section":"Namespaces","content":"lds #  Linear Dynamical Systems (LDS) namespace. Namespaces #     Name     lds::gaussian Linear Dynamical Systems with Gaussian observations.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::Controller   class lds::EM   class lds::Fit LDS Fit Type.   class lds::SSID   class lds::SwitchedController SwitchedController Type.   class lds::System Linear Dynamical System Type.   class lds::UniformMatrixList   class lds::UniformSystemList   class lds::UniformVectorList    Types #      Name     enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}\nweighting options for SSID   enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2}   using double data_t   using arma::Col\u0026lt; data_t \u0026gt; Vector   using arma::Mat\u0026lt; data_t \u0026gt; Matrix   using arma::Cube\u0026lt; data_t \u0026gt; Cube   using arma::subview\u0026lt; data_t \u0026gt; View    Functions #      Name     void Limit(std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub)   void Limit(Vector \u0026amp; x, data_t lb, data_t ub)   void Limit(Matrix \u0026amp; x, data_t lb, data_t ub)   void Reassign(Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Vector in place   void Reassign(Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Matrix in place   void ForceSymPD(Matrix \u0026amp; X)\nforces matrix to be symmetric positive-definite   void ForceSymMinEig(Matrix \u0026amp; X, data_t eig_min =0)\nforces matrix to be symmetric and have a minimum eigenvalue   void lq(Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X)\nLQ decomposition.   Matrix calcCov(const Matrix \u0026amp; A, const Matrix \u0026amp; B)\nCalculate covariance matrix.    Attributes #      Name     const std::size_t kControlTypeDeltaU control designed to penalize change in input   const std::size_t kControlTypeIntY control using integral action   const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m   const data_t kInf Some useful numbers.   const data_t kPi   const data_t kDefaultP0 default state estimate covar   const data_t kDefaultQ0 default process noise covar   const data_t kDefaultR0 default output noise covar    Type Details #  SSIDWt #     Enumerator Value Description     kSSIDNone  None.   kSSIDMOESP  MOESP (AKA \u0026ldquo;robust method\u0026rdquo; in van Overschee 1996)   kSSIDCVA  CVA \u0026ldquo;Canonical Variate Analysis\u0026rdquo;.    Weighting options for singular value decomposition performed during subspace identification (SSID)\nReference:\nvan Overschee, de Moor. 1996. Subspace Identification for Linear Systems.\nMatrixListFreeDim #     Enumerator Value Description     kMatFreeDimNone  neither dim free to be hetero in mat list   kMatFreeDim1  allow 1st dim of mats in list to be hetero   kMatFreeDim2  allow 2nd dim of mats in list to be hetero    data_t #  using lds::data_t = typedef double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nVector #  using lds::Vector = typedef arma::Col\u0026lt;data_t\u0026gt;; Matrix #  using lds::Matrix = typedef arma::Mat\u0026lt;data_t\u0026gt;; Cube #  using lds::Cube = typedef arma::Cube\u0026lt;data_t\u0026gt;; View #  using lds::View = typedef arma::subview\u0026lt;data_t\u0026gt;; Function Details #  Limit #  inline void Limit( std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub ) Limit #  inline void Limit( Vector \u0026amp; x, data_t lb, data_t ub ) Limit #  inline void Limit( Matrix \u0026amp; x, data_t lb, data_t ub ) Reassign #  inline void Reassign( Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\n some some Vector other other Vector parenthetical optional description provided by caller to ease debugging  Reassign #  inline void Reassign( Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\n some some Matrix other other Matrix parenthetical optional description provided by caller to ease debugging  ForceSymPD #  void ForceSymPD( Matrix \u0026amp; X ) Parameters:\n X mutated matrix  ForceSymMinEig #  void ForceSymMinEig( Matrix \u0026amp; X, data_t eig_min =0 ) Parameters:\n X mutated matrix eig_min [optional] minimum eigen value  lq #  void lq( Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X ) Parameters:\n L lower triangle matrix Qt orthonormal matrix (transposed cf QR decomp) X matrix being decomposed  calcCov #  Matrix calcCov( const Matrix \u0026amp; A, const Matrix \u0026amp; B ) Parameters:\n A some matrix B some other matrix  Return: covariance\nAttribute Details #  kControlTypeDeltaU #  static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY #  static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM #  static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\nkInf #  static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); kPi #  static const data_t kPi = arma::datum::pi; kDefaultP0 #  static const data_t kDefaultP0 = 1e-6; kDefaultQ0 #  static const data_t kDefaultQ0 = 1e-6; kDefaultR0 #  static const data_t kDefaultR0 = 1e-2;  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":28,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1controller/","title":"lds::Controller","section":"Classes","content":"lds::Controller #  More\u0026hellip;\nInherited by lds::gaussian::Controller, lds::poisson::Controller, lds::SwitchedController\u0026lt; System \u0026gt;\nPublic Functions #      Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes #      Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type    Detailed Description #  template \u0026lt;typename System \u0026gt; class lds::Controller;   Public Function Details #  Controller #  Controller() =default  Controller #  inline Controller( const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\n sys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask  Template Parameters:\n System type derived from lds::System   Controller #  inline Controller( System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\n sys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask  Template Parameters:\n System type derived from lds::System   Control #  inline const Vector \u0026amp; Control( const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\n z measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true)  Return: updated control signal\nUpdates the control signal (single-step). This is the most flexible option, but requires user to have set the controller\u0026rsquo;s y_ref, x_ref, and u_ref variables.\n ControlOutputReference #  inline const Vector \u0026amp; ControlOutputReference( const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\n z measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_estimation [optional] whether to update state estimate (if false, effectively open-loop control) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true)  Return: updated control signal\nUpdates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.\n sys #  inline const System \u0026amp; sys() const  Kc #  inline const Matrix \u0026amp; Kc() const  Kc_inty #  inline const Matrix \u0026amp; Kc_inty() const  Kc_u #  inline const Matrix \u0026amp; Kc_u() const  g_design #  inline const Vector \u0026amp; g_design() const  u_ref #  inline const Vector \u0026amp; u_ref() const  x_ref #  inline const Vector \u0026amp; x_ref() const  y_ref #  inline const Vector \u0026amp; y_ref() const  control_type #  inline size_t control_type() const  tau_awu #  inline data_t tau_awu() const  u_lb #  inline data_t u_lb() const  u_ub #  inline data_t u_ub() const  set_sys #  inline void set_sys( const System \u0026amp; sys )  set_g_design #  inline void set_g_design( const Vector \u0026amp; g_design )  set_u_ref #  inline void set_u_ref( const Vector \u0026amp; u_ref )  set_x_ref #  inline void set_x_ref( const Vector \u0026amp; x_ref )  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) Reimplemented by: lds::poisson::SwitchedController::set_y_ref, lds::gaussian::SwitchedController::set_y_ref, lds::poisson::Controller::set_y_ref, lds::gaussian::Controller::set_y_ref\n set_Kc #  inline void set_Kc( const Matrix \u0026amp; Kc )  set_Kc_inty #  inline void set_Kc_inty( const Matrix \u0026amp; Kc_inty )  set_Kc_u #  inline void set_Kc_u( const Matrix \u0026amp; Kc_u )  set_tau_awu #  inline void set_tau_awu( data_t tau )  set_control_type #  inline void set_control_type( size_t control_type ) Parameters:\n control_type control type bit mask  Template Parameters:\n System type derived from lds::System   set_u_lb #  inline void set_u_lb( data_t u_lb ) Parameters:\n u_lb control lower bound   set_u_ub #  inline void set_u_ub( data_t u_ub ) Parameters:\n u_ub control upper bound   Reset #  inline void Reset()  Print #  inline void Print()  Protected Attribute Details #  **sys_** #  System sys_;  **u_** #  Vector u_;  **u_return_** #  Vector u_return_;  **g_design_** #  Vector g_design_;  **u_ref_** #  Vector u_ref_;  **u_ref_prev_** #  Vector u_ref_prev_;  **x_ref_** #  Vector x_ref_;  **y_ref_** #  Vector y_ref_;  **cx_ref_** #  Vector cx_ref_;  **Kc_** #  Matrix Kc_;  **Kc_u_** #  Matrix Kc_u_;  **Kc_inty_** #  Matrix Kc_inty_;  **du_ref_** #  Vector du_ref_;  **dv_ref_** #  Vector dv_ref_;  **v_ref_** #  Vector v_ref_;  **dv_** #  Vector dv_;  **v_** #  Vector v_;  **int_e_** #  Vector int_e_;  **int_e_awu_adjust_** #  Vector int_e_awu_adjust_;  **u_sat_** #  Vector u_sat_;  **do_control_prev_** #  bool do_control_prev_ = false;  **do_lock_control_prev_** #  bool do_lock_control_prev_ = false;  **u_saturated_** #  bool u_saturated_ = false;  **u_lb_** #  data_t u_lb_ {};  **u_ub_** #  data_t u_ub_ {};  **tau_awu_** #  data_t tau_awu_ {};  **k_awu_** #  data_t k_awu_ = 0;  **t_since_control_onset_** #  data_t t_since_control_onset_ = 0;  **control_type_** #  size_t control_type_ {};   Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":29,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1em/","title":"lds::EM","section":"Classes","content":"lds::EM #  More\u0026hellip;\nInherited by lds::gaussian::FitEM, lds::poisson::FitEM\nPublic Functions #      Name      EM() =default\nConstructs a new EMFit type.    EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.    EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.   virtual ~EM() =default   const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller.   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time)   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time)   const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance   const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1)   const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance   size_t n_t_tot()\ntotal number of time samples   const Vector \u0026amp; theta() const\ngets parameters updated in M step    Protected Functions #      Name     void Expectation(bool force_common_initial =false)\nExpectation step.   void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step.   void MaximizeDynamics()   void MaximizeQ()   void MaximizeInitial()   virtual void MaximizeOutput() =0   virtual void MaximizeMeasurement() =0   void Smooth(bool force_common_initial)\nget smoothed estimates   virtual void RecurseKe(Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t) =0\nrecursively update estimator gain Ke   void Reset()\nreset to initial conditions   void InitVars()\nInitializes the variables.   Vector UpdateTheta()\nupdates parameter list, theta    Protected Attributes #      Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   std::vector\u0026lt; Matrix \u0026gt; x_ state estimate   std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov   std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance   std::vector\u0026lt; Matrix \u0026gt; y_ output estimate   Matrix diag_y_   Matrix sum_E_x_t_x_t_ state covariance (current time)   Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1)   Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance   Fit fit_   Vector theta_   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials    Detailed Description #  template \u0026lt;typename Fit \u0026gt; class lds::EM;   Public Function Details #  EM #  EM() =default  EM #  EM( size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\n n_x number of states dt sample period u_train input training data z_train measurement training data   EM #  EM( const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\n fit0 initial fit u_train input training data z_train measurement training data   ~EM #  virtual ~EM() =default  Run #  const Fit \u0026amp; Run( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2 ) Parameters:\n calc_dynamics [optional] whether to calculate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law max_iter max number of iterations tol convergence tolerance (max fractional abs change)  Return: Fit\n ReturnData #  inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\n x #  inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const  y #  inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const  sum_E_x_t_x_t #  inline const Matrix \u0026amp; sum_E_x_t_x_t() const  sum_E_xu_tm1_xu_tm1 #  inline const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const  sum_E_xu_t_xu_tm1 #  inline const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const  n_t_tot #  inline size_t n_t_tot()  theta #  inline const Vector \u0026amp; theta() const  Protected Function Details #  Expectation #  void Expectation( bool force_common_initial =false ) Parameters:\n force_common_initial whether to force common initial condition for all trials   Maximization #  void Maximization( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false ) Parameters:\n calc_dynamics [optional] whether to caclulate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law   MaximizeDynamics #  void MaximizeDynamics()  MaximizeQ #  void MaximizeQ()  MaximizeInitial #  void MaximizeInitial()  MaximizeOutput #  virtual void MaximizeOutput() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput\n MaximizeMeasurement #  virtual void MaximizeMeasurement() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement\n Smooth #  void Smooth( bool force_common_initial ) Parameters:\n force_common_initial whether to force common initial conditions   RecurseKe #  virtual void RecurseKe( Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t ) =0 Parameters:\n Ke estimator gain P_pre cov of predicted state est. P_post cov of postior sate est. t time  Reimplemented by: lds::gaussian::FitEM::RecurseKe, lds::poisson::FitEM::RecurseKe\n Reset #  void Reset()  InitVars #  void InitVars()  UpdateTheta #  Vector UpdateTheta() Return: parameter list\n Protected Attribute Details #  **u_** #  UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_;  **z_** #  UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_;  **x_** #  std::vector\u0026lt; Matrix \u0026gt; x_;  **P_** #  std::vector\u0026lt; Cube \u0026gt; P_;  **P_t_tm1_** #  std::vector\u0026lt; Cube \u0026gt; P_t_tm1_;  **y_** #  std::vector\u0026lt; Matrix \u0026gt; y_;  **diag_y_** #  Matrix diag_y_;  **sum_E_x_t_x_t_** #  Matrix sum_E_x_t_x_t_;  **sum_E_xu_tm1_xu_tm1_** #  Matrix sum_E_xu_tm1_xu_tm1_;  **sum_E_xu_t_xu_tm1_** #  Matrix sum_E_xu_t_xu_tm1_;  **fit_** #  Fit fit_;  **theta_** #  Vector theta_;  **dt_** #  data_t dt_ {};  **n_u_** #  size_t n_u_ {};  **n_x_** #  size_t n_x_ {};  **n_y_** #  size_t n_y_ {};  **n_trials_** #  size_t n_trials_ {};  **n_t_** #  std::vector\u0026lt; size_t \u0026gt; n_t_;  **n_t_tot_** #  size_t n_t_tot_ {};   Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":30,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1fit/","title":"lds::Fit","section":"Classes","content":"lds::Fit #  LDS Fit Type. #include \u0026lt;lds_fit.h\u0026gt;\nInherited by lds::gaussian::Fit, lds::poisson::Fit\nPublic Functions #      Name      Fit() =default\nConstructs a new Fit.    Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit.   virtual ~Fit() =default   size_t n_u() const\ngets number of inputs   size_t n_x() const\ngets number of states   size_t n_y() const\ngets number of outputs   data_t dt() const\ngets sample period   const Matrix \u0026amp; A() const\ngets state matrix   const Matrix \u0026amp; B() const\ngets input matrix   const Vector \u0026amp; g() const\ngets input gain   const Vector \u0026amp; m() const\ngets process disturbance   const Matrix \u0026amp; Q() const\ngets process noise covariance   const Vector \u0026amp; x0() const\ngets initial state estimate   const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate   const Matrix \u0026amp; C() const\ngets output matrix   const Vector \u0026amp; d() const\ngets output bias   virtual const Matrix \u0026amp; R() const =0   void set_A(const Matrix \u0026amp; A)\nsets state matrix   void set_B(const Matrix \u0026amp; B)\nsets input matrix   void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor   void set_m(const Vector \u0026amp; m)\nsets process disturbance   void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance   virtual void set_R(const Matrix \u0026amp; R) =0\nsets output noise covariance (if any)   void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate   void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance   void set_C(const Matrix \u0026amp; C)\nsets output matrix   void set_d(const Vector \u0026amp; d)\nsets output bias   View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function   View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function   virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) =0\noutput function    Protected Attributes #      Name     data_t dt_ sample period   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Vector m_ process noise mean   Matrix Q_ process noise cov   Matrix C_ output matrix   Vector d_ output bias   Matrix R_ measurement noise   Vector x0_ initial state   Matrix P0_ initial covar   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs      Public Function Details #  Fit #  Fit() =default  Fit #  Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period   ~Fit #  virtual ~Fit() =default  n_u #  inline size_t n_u() const  n_x #  inline size_t n_x() const  n_y #  inline size_t n_y() const  dt #  inline data_t dt() const  A #  inline const Matrix \u0026amp; A() const  B #  inline const Matrix \u0026amp; B() const  g #  inline const Vector \u0026amp; g() const  m #  inline const Vector \u0026amp; m() const  Q #  inline const Matrix \u0026amp; Q() const  x0 #  inline const Vector \u0026amp; x0() const  P0 #  inline const Matrix \u0026amp; P0() const  C #  inline const Matrix \u0026amp; C() const  d #  inline const Vector \u0026amp; d() const  R #  virtual const Matrix \u0026amp; R() const =0 Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R\n set_A #  inline void set_A( const Matrix \u0026amp; A )  set_B #  inline void set_B( const Matrix \u0026amp; B )  set_g #  inline void set_g( const Vector \u0026amp; g )  set_m #  inline void set_m( const Vector \u0026amp; m )  set_Q #  inline void set_Q( const Matrix \u0026amp; Q )  set_R #  virtual void set_R( const Matrix \u0026amp; R ) =0 Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R\n set_x0 #  inline void set_x0( const Vector \u0026amp; x0 )  set_P0 #  inline void set_P0( const Matrix \u0026amp; P0 )  set_C #  inline void set_C( const Matrix \u0026amp; C )  set_d #  inline void set_d( const Vector \u0026amp; d )  f #  inline View f( Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t ) Parameters:\n x state estimate (over time) u input (over time) t time index  Return: view of updated state\n f #  inline View f( Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t ) Parameters:\n x_pre predicted state est. x_post posterior state est. u input (over time) t time index  Return: view of predicted state\n h #  virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) =0 Parameters:\n y output estimate (over time) x state estimate (over time) t time index  Return: output\nReimplemented by: lds::poisson::Fit::h, lds::gaussian::Fit::h\n Protected Attribute Details #  **dt_** #  data_t dt_ {};  **A_** #  Matrix A_;  **B_** #  Matrix B_;  **g_** #  Vector g_;  **m_** #  Vector m_;  **Q_** #  Matrix Q_;  **C_** #  Matrix C_;  **d_** #  Vector d_;  **R_** #  Matrix R_;  **x0_** #  Vector x0_;  **P0_** #  Matrix P0_;  **n_u_** #  size_t n_u_ {};  **n_x_** #  size_t n_x_ {};  **n_y_** #  size_t n_y_ {};   Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":31,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/","title":"lds::gaussian","section":"Namespaces","content":"lds::gaussian #  Linear Dynamical Systems with Gaussian observations. Classes #      Name     class lds::gaussian::Controller Gaussian-observation Controller Type.   class lds::gaussian::Fit GLDS Fit Type.   class lds::gaussian::FitEM GLDS E-M Fit Type.   class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.   class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.   class lds::gaussian::System Gaussian LDS Type.     Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":32,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1controller/","title":"lds::gaussian::Controller","section":"Classes","content":"lds::gaussian::Controller #  Gaussian-observation Controller Type. #include \u0026lt;lds_gaussian_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions #      Name     virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output    Additional inherited members #  Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type      Public Function Details #  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\n  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":33,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/","title":"lds::gaussian::Fit","section":"Classes","content":"lds::gaussian::Fit #  GLDS Fit Type. #include \u0026lt;lds_gaussian_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions #      Name      Fit() =default    Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit.   virtual const Matrix \u0026amp; R() const override\ngets measurement noise covariance   virtual void set_R(const Matrix \u0026amp; R) override\nsets measurement noise covariance   virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function    Additional inherited members #  Public Functions inherited from lds::Fit\n    Name     virtual ~Fit() =default   size_t n_u() const\ngets number of inputs   size_t n_x() const\ngets number of states   size_t n_y() const\ngets number of outputs   data_t dt() const\ngets sample period   const Matrix \u0026amp; A() const\ngets state matrix   const Matrix \u0026amp; B() const\ngets input matrix   const Vector \u0026amp; g() const\ngets input gain   const Vector \u0026amp; m() const\ngets process disturbance   const Matrix \u0026amp; Q() const\ngets process noise covariance   const Vector \u0026amp; x0() const\ngets initial state estimate   const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate   const Matrix \u0026amp; C() const\ngets output matrix   const Vector \u0026amp; d() const\ngets output bias   void set_A(const Matrix \u0026amp; A)\nsets state matrix   void set_B(const Matrix \u0026amp; B)\nsets input matrix   void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor   void set_m(const Vector \u0026amp; m)\nsets process disturbance   void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance   void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate   void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance   void set_C(const Matrix \u0026amp; C)\nsets output matrix   void set_d(const Vector \u0026amp; d)\nsets output bias   View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function   View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function    Protected Attributes inherited from lds::Fit\n    Name     data_t dt_ sample period   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Vector m_ process noise mean   Matrix Q_ process noise cov   Matrix C_ output matrix   Vector d_ output bias   Matrix R_ measurement noise   Vector x0_ initial state   Matrix P0_ initial covar   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs      Public Function Details #  Fit #  Fit() =default  Fit #  Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period   R #  inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\n set_R #  inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\n h #  inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\n y output estimate (over time) x state estimate (over time) t time index  Return: output\nReimplements: lds::Fit::h\n  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":34,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitem/","title":"lds::gaussian::FitEM","section":"Classes","content":"lds::gaussian::FitEM #  GLDS E-M Fit Type. More\u0026hellip;\n\n#include \u0026lt;lds_gaussian_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members #  Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name      EM() =default\nConstructs a new EMFit type.    EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.    EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.   virtual ~EM() =default   const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller.   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time)   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time)   const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance   const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1)   const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance   size_t n_t_tot()\ntotal number of time samples   const Vector \u0026amp; theta() const\ngets parameters updated in M step    Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name     void Expectation(bool force_common_initial =false)\nExpectation step.   void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step.   void MaximizeDynamics()   void MaximizeQ()   void MaximizeInitial()   void Smooth(bool force_common_initial)\nget smoothed estimates   void Reset()\nreset to initial conditions   void InitVars()\nInitializes the variables.   Vector UpdateTheta()\nupdates parameter list, theta    Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   std::vector\u0026lt; Matrix \u0026gt; x_ state estimate   std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov   std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance   std::vector\u0026lt; Matrix \u0026gt; y_ output estimate   Matrix diag_y_   Matrix sum_E_x_t_x_t_ state covariance (current time)   Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1)   Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance   Fit fit_   Vector theta_   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials    Detailed Description #  class lds::gaussian::FitEM; This type is used in the process of fitting GLDS models by expectation-maximization (EM).\n   Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":35,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitssid/","title":"lds::gaussian::FitSSID","section":"Classes","content":"lds::gaussian::FitSSID #  Subspace Identification (SSID) for GLDS. #include \u0026lt;lds_gaussian_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members #  Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name      SSID() =default\nConstructs a new SSIDFit type.    SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type.   std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller.    Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name     void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d)   void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix.   void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD)   void Solve(data_t wt_dc)\nsolves for LDS parameters   void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C    Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   Matrix D_ block-Hankel I/O data matrix   Fit fit_ fit   Matrix g_dc_ I/O gain @ DC.   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_h_   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials   Matrix L_ lower triangle decomp of covariance matrix   Vector s_ singular values   Matrix ext_obs_t_ extended observability matrix       Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":36,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1switchedcontroller/","title":"lds::gaussian::SwitchedController","section":"Classes","content":"lds::gaussian::SwitchedController #  Gaussian-observation SwitchedController Type. #include \u0026lt;lds_gaussian_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions #      Name     virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output    Additional inherited members #  Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\n    Name      SwitchedController() =default\nConstructs a new SwitchedController.    SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController.    SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems).   void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller.   void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains   void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving)   void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains   void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving)   void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains   void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving)   void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design   void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving)    Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\n    Name     std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between   size_t n_sys_ number of systems   size_t idx_ current system/controller index.   UniformMatrixList Kc_list_   UniformMatrixList Kc_inty_list_   UniformMatrixList Kc_u_list_   UniformVectorList g_design_list_    Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type      Public Function Details #  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\n  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":37,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/","title":"lds::gaussian::System","section":"Classes","content":"lds::gaussian::System #  Gaussian LDS Type. #include \u0026lt;lds_gaussian_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions #      Name      System() =default\nConstructs a new System.    System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)\nConstructs a new Gaussian System.   virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement.   const Matrix \u0026amp; R() const\nGet output noise covariance.   void set_Q(const Matrix \u0026amp; Q)   void set_R(const Matrix \u0026amp; R)\nSet output noise covariance.   void set_Ke(const Matrix \u0026amp; Ke)\nSet estimator gain.   void set_Ke_m(const Matrix \u0026amp; Ke_m)\nSet disturbance estimator gain.   void Print()\nPrint system variables to stdout.    Protected Functions #      Name     virtual void h() override\nSystem output function.   virtual void RecurseKe() override\nRecursively update estimator gain.    Protected Attributes #      Name     Matrix R_ covariance of output noise   bool do_recurse_Ke_ whether to recursively calculate estimator gain    Additional inherited members #  Public Functions inherited from lds::System\n    Name     virtual ~System()   void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates.   void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function   size_t n_u() const\nGet number of inputs.   size_t n_x() const\nGet number of states.   size_t n_y() const\nGet number of outputs.   data_t dt() const\nGet sample period.   const Vector \u0026amp; x() const\nGet current state.   const Matrix \u0026amp; P() const\nGet covariance of state estimate.   const Vector \u0026amp; m() const\nGet current process disturbance/bias.   const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate.   const Vector \u0026amp; cx() const\nGet C*x.   const Vector \u0026amp; y() const\nGet output.   const Vector \u0026amp; x0() const\nGet initial state.   const Vector \u0026amp; m0() const\nGet initial disturbance.   const Matrix \u0026amp; A() const\nGet state matrix.   const Matrix \u0026amp; B() const\nGet input matrix.   const Vector \u0026amp; g() const\nGet input gain/conversion factor.   const Matrix \u0026amp; C() const\nGet output matrix.   const Vector \u0026amp; d() const\nGet output bias.   const Matrix \u0026amp; Ke() const\nGet estimator gain.   const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m)   const Matrix \u0026amp; Q()\nGet process noise covariance.   const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation.   const Matrix \u0026amp; P0()\nGet covariance of initial state.   const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance.   void set_A(const Matrix \u0026amp; A)\nSet state matrix.   void set_B(const Matrix \u0026amp; B)\nSet input matrix.   void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance.   void set_g(const Vector \u0026amp; g)\nSet input gain.   void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation.   void set_x0(const Vector \u0026amp; x0)\nSet initial state.   void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state.   void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance.   void set_C(const Matrix \u0026amp; C)\nSet output matrix.   void set_d(const Vector \u0026amp; d)\nSet output bias.   void set_x(const Vector \u0026amp; x)\nSet state of system.   void Reset()\nReset system variables.    Protected Functions inherited from lds::System\n    Name     void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)    Public Attributes inherited from lds::System\n    Name     bool do_adapt_m whether to adaptively estimate disturbance m    Protected Attributes inherited from lds::System\n    Name     std::size_t n_x_ number of states   std::size_t n_u_ number of inputs   std::size_t n_y_ number of outputs   data_t dt_ sample period   Vector x_ state   Matrix P_ covariance of state estimate   Vector m_ process disturbance   Matrix P_m_ covariance of disturbance estimate   Vector cx_ C*x.   Vector y_ output   Vector z_ measurement   Vector x0_ initial state   Matrix P0_ covariance of initial state estimate   Vector m0_ initial process disturbance   Matrix P0_m_ covariance of initial disturbance est.   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Matrix Q_ covariance of process noise   Matrix Q_m_ covariance of disturbance random walk   Matrix C_ output matrix   Vector d_ output bias   Matrix Ke_ estimator gain   Matrix Ke_m_ estimator gain for process disturbance      Public Function Details #  System #  System() =default  System #  System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0 ) Parameters:\n n_u number of inputs (u) n_x number of states (x) n_y number of outputs (y) dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) r0 [optional] initial diagonal elements of output noise covariance (R)   Simulate #  virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\n u_tm1 input at t-1  Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\n R #  inline const Matrix \u0026amp; R() const  set_Q #  inline void set_Q( const Matrix \u0026amp; Q )  set_R #  inline void set_R( const Matrix \u0026amp; R )  set_Ke #  inline void set_Ke( const Matrix \u0026amp; Ke )  set_Ke_m #  inline void set_Ke_m( const Matrix \u0026amp; Ke_m )  Print #  void Print()  Protected Function Details #  h #  inline virtual void h() override Reimplements: lds::System::h\n RecurseKe #  virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\n Protected Attribute Details #  **R_** #  Matrix R_;  **do_recurse_Ke_** #  bool do_recurse_Ke_ {};   Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":38,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/","title":"lds::poisson","section":"Namespaces","content":"lds::poisson #  Linear Dynamical Systems with Poisson observations. Classes #      Name     class lds::poisson::Controller PLDS Controller Type.   class lds::poisson::Fit PLDS Fit Type.   class lds::poisson::FitEM PLDS E-M Fit Type.   class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS.   class lds::poisson::SwitchedController Poisson-observation SwitchedController Type.   class lds::poisson::System Poisson System type.    Attributes #      Name     std::random_device rd random device for simulating poisson data   std::mt19937 rng random number generator for simulating poisson data    Attribute Details #  rd #  static std::random_device rd; rng #  static std::mt19937 rng = std::mt19937( rd());  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":39,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/","title":"lds::poisson::Controller","section":"Classes","content":"lds::poisson::Controller #  PLDS Controller Type. #include \u0026lt;lds_poisson_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions #      Name     virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output.    Additional inherited members #  Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type      Public Function Details #  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\n  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":40,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/","title":"lds::poisson::Fit","section":"Classes","content":"lds::poisson::Fit #  PLDS Fit Type. #include \u0026lt;lds_poisson_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions #      Name      Fit() =default    Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit.   virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function   virtual void set_R(const Matrix \u0026amp; R) override\nsets output noise covariance (if any)   virtual const Matrix \u0026amp; R() const override    Additional inherited members #  Public Functions inherited from lds::Fit\n    Name     virtual ~Fit() =default   size_t n_u() const\ngets number of inputs   size_t n_x() const\ngets number of states   size_t n_y() const\ngets number of outputs   data_t dt() const\ngets sample period   const Matrix \u0026amp; A() const\ngets state matrix   const Matrix \u0026amp; B() const\ngets input matrix   const Vector \u0026amp; g() const\ngets input gain   const Vector \u0026amp; m() const\ngets process disturbance   const Matrix \u0026amp; Q() const\ngets process noise covariance   const Vector \u0026amp; x0() const\ngets initial state estimate   const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate   const Matrix \u0026amp; C() const\ngets output matrix   const Vector \u0026amp; d() const\ngets output bias   void set_A(const Matrix \u0026amp; A)\nsets state matrix   void set_B(const Matrix \u0026amp; B)\nsets input matrix   void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor   void set_m(const Vector \u0026amp; m)\nsets process disturbance   void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance   void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate   void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance   void set_C(const Matrix \u0026amp; C)\nsets output matrix   void set_d(const Vector \u0026amp; d)\nsets output bias   View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function   View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function    Protected Attributes inherited from lds::Fit\n    Name     data_t dt_ sample period   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Vector m_ process noise mean   Matrix Q_ process noise cov   Matrix C_ output matrix   Vector d_ output bias   Matrix R_ measurement noise   Vector x0_ initial state   Matrix P0_ initial covar   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs      Public Function Details #  Fit #  Fit() =default  Fit #  inline Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period   h #  inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\n y output estimate (over time) x state estimate (over time) t time index  Return: output\nReimplements: lds::Fit::h\n set_R #  inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\n R #  inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\n  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":41,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitem/","title":"lds::poisson::FitEM","section":"Classes","content":"lds::poisson::FitEM #  PLDS E-M Fit Type. More\u0026hellip;\n\n#include \u0026lt;lds_poisson_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members #  Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name      EM() =default\nConstructs a new EMFit type.    EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.    EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type.   virtual ~EM() =default   const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller.   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time)   const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time)   const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance   const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1)   const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance   size_t n_t_tot()\ntotal number of time samples   const Vector \u0026amp; theta() const\ngets parameters updated in M step    Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name     void Expectation(bool force_common_initial =false)\nExpectation step.   void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step.   void MaximizeDynamics()   void MaximizeQ()   void MaximizeInitial()   void Smooth(bool force_common_initial)\nget smoothed estimates   void Reset()\nreset to initial conditions   void InitVars()\nInitializes the variables.   Vector UpdateTheta()\nupdates parameter list, theta    Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\n    Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   std::vector\u0026lt; Matrix \u0026gt; x_ state estimate   std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov   std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance   std::vector\u0026lt; Matrix \u0026gt; y_ output estimate   Matrix diag_y_   Matrix sum_E_x_t_x_t_ state covariance (current time)   Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1)   Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance   Fit fit_   Vector theta_   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials    Detailed Description #  class lds::poisson::FitEM; This type is used in the process of fitting PLDS models by expectation-maximization (EM).\n   Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":42,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitssid/","title":"lds::poisson::FitSSID","section":"Classes","content":"lds::poisson::FitSSID #  Subspace Identification (SSID) for PLDS. #include \u0026lt;lds_poisson_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members #  Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name      SSID() =default\nConstructs a new SSIDFit type.    SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type.   std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller.    Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name     void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d)   void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix.   void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD)   void Solve(data_t wt_dc)\nsolves for LDS parameters   void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C    Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\n    Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   Matrix D_ block-Hankel I/O data matrix   Fit fit_ fit   Matrix g_dc_ I/O gain @ DC.   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_h_   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials   Matrix L_ lower triangle decomp of covariance matrix   Vector s_ singular values   Matrix ext_obs_t_ extended observability matrix       Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":43,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1switchedcontroller/","title":"lds::poisson::SwitchedController","section":"Classes","content":"lds::poisson::SwitchedController #  Poisson-observation SwitchedController Type. #include \u0026lt;lds_poisson_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions #      Name     virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output.    Additional inherited members #  Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\n    Name      SwitchedController() =default\nConstructs a new SwitchedController.    SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController.    SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems).   void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller.   void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains   void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving)   void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains   void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving)   void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains   void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving)   void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design   void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving)    Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\n    Name     std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between   size_t n_sys_ number of systems   size_t idx_ current system/controller index.   UniformMatrixList Kc_list_   UniformMatrixList Kc_inty_list_   UniformMatrixList Kc_u_list_   UniformVectorList g_design_list_    Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design)   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain.   void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain.   void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain.   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type      Public Function Details #  set_y_ref #  inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\n  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":44,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/","title":"lds::poisson::System","section":"Classes","content":"lds::poisson::System #  Poisson System type. #include \u0026lt;lds_poisson_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions #      Name      System() =default\nConstructs a new System.    System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nConstructs a new Poisson System.   virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement.    Protected Functions #      Name     virtual void h() override\nSystem output function.   virtual void RecurseKe() override\nRecursively recalculate estimator gain (Ke)    Additional inherited members #  Public Functions inherited from lds::System\n    Name     virtual ~System()   void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates.   void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function   size_t n_u() const\nGet number of inputs.   size_t n_x() const\nGet number of states.   size_t n_y() const\nGet number of outputs.   data_t dt() const\nGet sample period.   const Vector \u0026amp; x() const\nGet current state.   const Matrix \u0026amp; P() const\nGet covariance of state estimate.   const Vector \u0026amp; m() const\nGet current process disturbance/bias.   const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate.   const Vector \u0026amp; cx() const\nGet C*x.   const Vector \u0026amp; y() const\nGet output.   const Vector \u0026amp; x0() const\nGet initial state.   const Vector \u0026amp; m0() const\nGet initial disturbance.   const Matrix \u0026amp; A() const\nGet state matrix.   const Matrix \u0026amp; B() const\nGet input matrix.   const Vector \u0026amp; g() const\nGet input gain/conversion factor.   const Matrix \u0026amp; C() const\nGet output matrix.   const Vector \u0026amp; d() const\nGet output bias.   const Matrix \u0026amp; Ke() const\nGet estimator gain.   const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m)   const Matrix \u0026amp; Q()\nGet process noise covariance.   const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation.   const Matrix \u0026amp; P0()\nGet covariance of initial state.   const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance.   void set_A(const Matrix \u0026amp; A)\nSet state matrix.   void set_B(const Matrix \u0026amp; B)\nSet input matrix.   void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance.   void set_g(const Vector \u0026amp; g)\nSet input gain.   void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance.   void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation.   void set_x0(const Vector \u0026amp; x0)\nSet initial state.   void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state.   void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance.   void set_C(const Matrix \u0026amp; C)\nSet output matrix.   void set_d(const Vector \u0026amp; d)\nSet output bias.   void set_x(const Vector \u0026amp; x)\nSet state of system.   void Reset()\nReset system variables.   void Print()\nPrint system variables to stdout.    Protected Functions inherited from lds::System\n    Name     void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)    Public Attributes inherited from lds::System\n    Name     bool do_adapt_m whether to adaptively estimate disturbance m    Protected Attributes inherited from lds::System\n    Name     std::size_t n_x_ number of states   std::size_t n_u_ number of inputs   std::size_t n_y_ number of outputs   data_t dt_ sample period   Vector x_ state   Matrix P_ covariance of state estimate   Vector m_ process disturbance   Matrix P_m_ covariance of disturbance estimate   Vector cx_ C*x.   Vector y_ output   Vector z_ measurement   Vector x0_ initial state   Matrix P0_ covariance of initial state estimate   Vector m0_ initial process disturbance   Matrix P0_m_ covariance of initial disturbance est.   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Matrix Q_ covariance of process noise   Matrix Q_m_ covariance of disturbance random walk   Matrix C_ output matrix   Vector d_ output bias   Matrix Ke_ estimator gain   Matrix Ke_m_ estimator gain for process disturbance      Public Function Details #  System #  System() =default  System #  System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q)   Simulate #  virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\n u_tm1 input at t-1  Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\n Protected Function Details #  h #  inline virtual void h() override Reimplements: lds::System::h\n RecurseKe #  virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nRecursively recalculate estimator gain (Ke).\nReferences:\nSmith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.\nEden UT, \u0026hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.\n  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":45,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/","title":"lds::SSID","section":"Classes","content":"lds::SSID #  More\u0026hellip;\nInherited by lds::gaussian::FitSSID, lds::poisson::FitSSID\nPublic Functions #      Name      SSID() =default\nConstructs a new SSIDFit type.    SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type.   std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID)   std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller.    Protected Functions #      Name     void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d)   void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix.   virtual void DecomposeData() =0\nDecompose data to lower-triangular matrix (used in Solve)   void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD)   void Solve(data_t wt_dc)\nsolves for LDS parameters   void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C    Protected Attributes #      Name     UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data   UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data   Matrix D_ block-Hankel I/O data matrix   Fit fit_ fit   Matrix g_dc_ I/O gain @ DC.   data_t dt_ sample period   size_t n_u_ number of inputs   size_t n_x_ number of states   size_t n_y_ number of outputs   size_t n_h_   size_t n_trials_ number of input/output data sequences   std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps   size_t n_t_tot_ total number of time steps across trials   Matrix L_ lower triangle decomp of covariance matrix   Vector s_ singular values   Matrix ext_obs_t_ extended observability matrix    Detailed Description #  template \u0026lt;typename Fit \u0026gt; class lds::SSID;   Public Function Details #  SSID #  SSID() =default  SSID #  SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf) ) Parameters:\n n_x number of states n_h size of block-hankel data matrix dt sample period u_train input training data z_train measurement training data d output bias   Run #  std::tuple\u0026lt; Fit, Vector \u0026gt; Run( SSIDWt ssid_wt ) Parameters:\n ssid_wt weight for singular value decomp  Return: tuple (Fit, singular values)\n ReturnData #  inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\n Protected Function Details #  CalcD #  void CalcD( data_t t_silence =0.1, data_t thresh_silence =0.001 ) Parameters:\n t_silence threshold on period of time that qualifies as \u0026ldquo;silence\u0026rdquo; thresh_silence threshold on input amplitude u that qualifies as \u0026ldquo;silence\u0026rdquo;   CreateHankelDataMat #  void CreateHankelDataMat() Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.\n DecomposeData #  virtual void DecomposeData() =0 Reimplemented by: lds::poisson::FitSSID::DecomposeData, lds::gaussian::FitSSID::DecomposeData\n CalcSVD #  void CalcSVD( SSIDWt wt ) Parameters:\n ssid_wt weight for SVD   Solve #  void Solve( data_t wt_dc ) Parameters:\n wt_dc weight placed on getting correct DC I/O gain   RecomputeExtObs #  void RecomputeExtObs()  Protected Attribute Details #  **u_** #  UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_;  **z_** #  UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_;  **D_** #  Matrix D_;  **fit_** #  Fit fit_;  **g_dc_** #  Matrix g_dc_;  **dt_** #  data_t dt_ {};  **n_u_** #  size_t n_u_ {};  **n_x_** #  size_t n_x_ {};  **n_y_** #  size_t n_y_ {};  **n_h_** #  size_t n_h_ {};  **n_trials_** #  size_t n_trials_ {};  **n_t_** #  std::vector\u0026lt; size_t \u0026gt; n_t_;  **n_t_tot_** #  size_t n_t_tot_ {};  **L_** #  Matrix L_;  **s_** #  Vector s_;  **ext_obs_t_** #  Matrix ext_obs_t_;   Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":46,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/","title":"lds::SwitchedController","section":"Classes","content":"lds::SwitchedController #  SwitchedController Type. More\u0026hellip;\n\n#include \u0026lt;lds_sctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nInherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController\nPublic Functions #      Name      SwitchedController() =default\nConstructs a new SwitchedController.    SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController.    SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems).   void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller.   void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains   void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving)   void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains   void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving)   void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains   void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving)   void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design   void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving)    Protected Attributes #      Name     std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between   size_t n_sys_ number of systems   size_t idx_ current system/controller index.   UniformMatrixList Kc_list_   UniformMatrixList Kc_inty_list_   UniformMatrixList Kc_u_list_   UniformVectorList g_design_list_    Additional inherited members #  Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name      Controller() =default\nConstructs a new Controller.    Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller.    Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object.   const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step)   const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step)   const System \u0026amp; sys() const   const Matrix \u0026amp; Kc() const\nGet state feedback controller gain.   const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain.   const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain.   const Vector \u0026amp; g_design() const\nGet input gain used in controller design.   const Vector \u0026amp; u_ref() const\nGet reference input.   const Vector \u0026amp; x_ref() const\nGet reference state.   const Vector \u0026amp; y_ref() const\nGet reference output.   size_t control_type() const\nGet controller type.   data_t tau_awu() const\nGet time constant of anti-integral-windup.   data_t u_lb() const\nGet control lower bound.   data_t u_ub() const\nGet control upper bound.   void set_sys(const System \u0026amp; sys)\nSet system.   void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref)   void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref)   virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref)   void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup.   void set_control_type(size_t control_type)\nSets the control type.   void set_u_lb(data_t u_lb)\nsets control lower bound   void set_u_ub(data_t u_ub)\nSets control upper bound.   void Reset()\nreset system and control variables.   void Print()\nprints variables to stdout    Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\n    Name     System sys_ underlying LDS   Vector u_ control signal   Vector u_return_ control signal that is returned to user   Vector g_design_ input gain of the system used for controller design   Vector u_ref_ reference input   Vector u_ref_prev_ reference input at previous time step   Vector x_ref_ reference state   Vector y_ref_ reference output   Vector cx_ref_   Matrix Kc_ state controller gain   Matrix Kc_u_ input controller gain (optional when control updates )   Matrix Kc_inty_ integral controller gain   Vector du_ref_   Vector dv_ref_   Vector v_ref_   Vector dv_   Vector v_ Control after g inversion (e.g., control in physical units)   Vector int_e_ integrated error   Vector int_e_awu_adjust_ anti-windup adjustment to intE   Vector u_sat_ control signal after saturation (for antiWindup)   bool do_control_prev_   bool do_lock_control_prev_   bool u_saturated_ whether control signal has reached saturation limits   data_t u_lb_ lower bound on control   data_t u_ub_ upper bound on control   data_t tau_awu_ antiwindup time constant   data_t k_awu_   data_t t_since_control_onset_ time since control epoch onset   size_t control_type_ controller type    Detailed Description #  template \u0026lt;typename System \u0026gt; class lds::SwitchedController;   Public Function Details #  SwitchedController #  SwitchedController() =default  SwitchedController #  inline SwitchedController( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\n systems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask   SwitchedController #  inline SwitchedController( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\n systems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask   Switch #  inline void Switch( size_t idx, bool do_force_switch =false ) Parameters:\n idx index do_force_switch whether to force a system switch even if already there.   set_Kc #  inline void set_Kc( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc )  set_Kc #  inline void set_Kc( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc )  set_Kc_inty #  inline void set_Kc_inty( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty )  set_Kc_inty #  inline void set_Kc_inty( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty )  set_Kc_u #  inline void set_Kc_u( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u )  set_Kc_u #  inline void set_Kc_u( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u )  set_g_design #  inline void set_g_design( const UniformVectorList \u0026amp; g )  set_g_design #  inline void set_g_design( UniformVectorList \u0026amp;\u0026amp; g )  Protected Attribute Details #  **systems_** #  std::vector\u0026lt; System \u0026gt; systems_;  **n_sys_** #  size_t n_sys_ {};  **idx_** #  size_t idx_ {};  **Kc_list_** #  UniformMatrixList Kc_list_;  **Kc_inty_list_** #  UniformMatrixList Kc_inty_list_;  **Kc_u_list_** #  UniformMatrixList Kc_u_list_;  **g_design_list_** #  UniformVectorList g_design_list_;   Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":47,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1system/","title":"lds::System","section":"Classes","content":"lds::System #  Linear Dynamical System Type. #include \u0026lt;lds_sys.h\u0026gt;\nInherited by lds::gaussian::System, lds::poisson::System\nPublic Functions #      Name      System() =default\nConstructs a new System.    System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nconstructs a new System   virtual ~System()   void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates.   virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) =0\nsimulates system (single time step)   void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function   virtual void h() =0\nsystem output function   size_t n_u() const\nGet number of inputs.   size_t n_x() const\nGet number of states.   size_t n_y() const\nGet number of outputs.   data_t dt() const\nGet sample period.   const Vector \u0026amp; x() const\nGet current state.   const Matrix \u0026amp; P() const\nGet covariance of state estimate.   const Vector \u0026amp; m() const\nGet current process disturbance/bias.   const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate.   const Vector \u0026amp; cx() const\nGet C*x.   const Vector \u0026amp; y() const\nGet output.   const Vector \u0026amp; x0() const\nGet initial state.   const Vector \u0026amp; m0() const\nGet initial disturbance.   const Matrix \u0026amp; A() const\nGet state matrix.   const Matrix \u0026amp; B() const\nGet input matrix.   const Vector \u0026amp; g() const\nGet input gain/conversion factor.   const Matrix \u0026amp; C() const\nGet output matrix.   const Vector \u0026amp; d() const\nGet output bias.   const Matrix \u0026amp; Ke() const\nGet estimator gain.   const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m)   const Matrix \u0026amp; Q()\nGet process noise covariance.   const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation.   const Matrix \u0026amp; P0()\nGet covariance of initial state.   const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance.   void set_A(const Matrix \u0026amp; A)\nSet state matrix.   void set_B(const Matrix \u0026amp; B)\nSet input matrix.   void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance.   void set_g(const Vector \u0026amp; g)\nSet input gain.   void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance.   void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation.   void set_x0(const Vector \u0026amp; x0)\nSet initial state.   void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state.   void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance.   void set_C(const Matrix \u0026amp; C)\nSet output matrix.   void set_d(const Vector \u0026amp; d)\nSet output bias.   void set_x(const Vector \u0026amp; x)\nSet state of system.   void Reset()\nReset system variables.   void Print()\nPrint system variables to stdout.    Protected Functions #      Name     virtual void RecurseKe() =0\nRecursively recalculate estimator gain (Ke)   void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)    Public Attributes #      Name     bool do_adapt_m whether to adaptively estimate disturbance m    Protected Attributes #      Name     std::size_t n_x_ number of states   std::size_t n_u_ number of inputs   std::size_t n_y_ number of outputs   data_t dt_ sample period   Vector x_ state   Matrix P_ covariance of state estimate   Vector m_ process disturbance   Matrix P_m_ covariance of disturbance estimate   Vector cx_ C*x.   Vector y_ output   Vector z_ measurement   Vector x0_ initial state   Matrix P0_ covariance of initial state estimate   Vector m0_ initial process disturbance   Matrix P0_m_ covariance of initial disturbance est.   Matrix A_ state matrix   Matrix B_ input matrix   Vector g_ input gain   Matrix Q_ covariance of process noise   Matrix Q_m_ covariance of disturbance random walk   Matrix C_ output matrix   Vector d_ output bias   Matrix Ke_ estimator gain   Matrix Ke_m_ estimator gain for process disturbance      Public Function Details #  System #  System() =default  System #  System( size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\n n_u number of inputs n_x number of states n_y number of outputs dt sample period p0 diagonal elements for state estimate covariance q0 diagonal elements for process noise covariance   ~System #  inline virtual ~System()  Filter #  void Filter( const Vector \u0026amp; u_tm1, const Vector \u0026amp; z ) Parameters:\n u_tm1 input at t-minus-1 z_t current measurement  Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.\n Simulate #  virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) =0 Parameters:\n u_tm1 input at time t-1  Return: simulated measurement at time t\nReimplemented by: lds::poisson::System::Simulate, lds::gaussian::System::Simulate\n f #  inline void f( const Vector \u0026amp; u, bool do_add_noise =false ) Parameters:\n u input do_add_noise whether to add simulated process noise   h #  virtual void h() =0 Reimplemented by: lds::poisson::System::h, lds::gaussian::System::h\n n_u #  inline size_t n_u() const  n_x #  inline size_t n_x() const  n_y #  inline size_t n_y() const  dt #  inline data_t dt() const  x #  inline const Vector \u0026amp; x() const  P #  inline const Matrix \u0026amp; P() const  m #  inline const Vector \u0026amp; m() const  P_m #  inline const Matrix \u0026amp; P_m() const  cx #  inline const Vector \u0026amp; cx() const  y #  inline const Vector \u0026amp; y() const  x0 #  inline const Vector \u0026amp; x0() const  m0 #  inline const Vector \u0026amp; m0() const  A #  inline const Matrix \u0026amp; A() const  B #  inline const Matrix \u0026amp; B() const  g #  inline const Vector \u0026amp; g() const  C #  inline const Matrix \u0026amp; C() const  d #  inline const Vector \u0026amp; d() const  Ke #  inline const Matrix \u0026amp; Ke() const  Ke_m #  inline const Matrix \u0026amp; Ke_m() const  Q #  inline const Matrix \u0026amp; Q()  Q_m #  inline const Matrix \u0026amp; Q_m()  P0 #  inline const Matrix \u0026amp; P0()  P0_m #  inline const Matrix \u0026amp; P0_m()  set_A #  inline void set_A( const Matrix \u0026amp; A )  set_B #  inline void set_B( const Matrix \u0026amp; B )  set_m #  inline void set_m( const Vector \u0026amp; m, bool do_force_assign =false )  set_g #  inline void set_g( const Vector \u0026amp; g )  set_Q #  inline void set_Q( const Matrix \u0026amp; Q )  set_Q_m #  inline void set_Q_m( const Matrix \u0026amp; Q_m )  set_x0 #  inline void set_x0( const Vector \u0026amp; x0 )  set_P0 #  inline void set_P0( const Matrix \u0026amp; P0 )  set_P0_m #  inline void set_P0_m( const Matrix \u0026amp; P0_m )  set_C #  inline void set_C( const Matrix \u0026amp; C )  set_d #  inline void set_d( const Vector \u0026amp; d )  set_x #  inline void set_x( const Vector \u0026amp; x )  Reset #  void Reset()  Print #  void Print()  Protected Function Details #  RecurseKe #  virtual void RecurseKe() =0 Reimplemented by: lds::poisson::System::RecurseKe, lds::gaussian::System::RecurseKe\n InitVars #  void InitVars( data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 )  Public Attribute Details #  do_adapt_m #  bool do_adapt_m {};  Protected Attribute Details #  **n_x_** #  std::size_t n_x_ {};  **n_u_** #  std::size_t n_u_ {};  **n_y_** #  std::size_t n_y_ {};  **dt_** #  data_t dt_ {};  **x_** #  Vector x_;  **P_** #  Matrix P_;  **m_** #  Vector m_;  **P_m_** #  Matrix P_m_;  **cx_** #  Vector cx_;  **y_** #  Vector y_;  **z_** #  Vector z_;  **x0_** #  Vector x0_;  **P0_** #  Matrix P0_;  **m0_** #  Vector m0_;  **P0_m_** #  Matrix P0_m_;  **A_** #  Matrix A_;  **B_** #  Matrix B_;  **g_** #  Vector g_;  **Q_** #  Matrix Q_;  **Q_m_** #  Matrix Q_m_;  **C_** #  Matrix C_;  **d_** #  Vector d_;  **Ke_** #  Matrix Ke_;  **Ke_m_** #  Matrix Ke_m_;   Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":48,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/","title":"lds::UniformMatrixList","section":"Classes","content":"lds::UniformMatrixList #  More\u0026hellip;\nInherits from std::vector\u0026lt; Matrix \u0026gt;\nPublic Functions #      Name      UniformMatrixList() =default\nConstructs a new UniformMatrixList.    UniformMatrixList(const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent.    UniformMatrixList(std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent.    UniformMatrixList(std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent.    UniformMatrixList(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nConstructs a new UniformMatrixList (copy).    UniformMatrixList(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nConstructs a new UniformMatrixList (move).    ~UniformMatrixList() =default\nDestroys the object.   const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim(size_t n =0) const\ngets dimensions of uniformly sized matrices   size_t size()\nsize of container   const Matrix \u0026amp; at(size_t n)\ngets reference to n^th element   void Swap(Matrix \u0026amp; that, size_t n)\nswaps input matrix with n^th matrix of list   UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nassigns the contents (copy)   UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nassigns the contents (move)    Detailed Description #  template \u0026lt;MatrixListFreeDim D =kMatFreeDimNone\u0026gt; class lds::UniformMatrixList;   Public Function Details #  UniformMatrixList #  UniformMatrixList() =default  UniformMatrixList #  explicit UniformMatrixList( const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\n mats input matrices dim dimensions   UniformMatrixList #  explicit UniformMatrixList( std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\n mats input matrices dim dimensions   UniformMatrixList #  UniformMatrixList( std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\n mats input matrices dim dimensions   UniformMatrixList #  UniformMatrixList( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\n that another UniformMatrixList   UniformMatrixList #  UniformMatrixList( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\n that another UniformMatrixList   ~UniformMatrixList #  ~UniformMatrixList() =default  dim #  inline const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim( size_t n =0 ) const Parameters:\n n [optional] index in list of matrices  Return: dimensions\n size #  inline size_t size()  at #  inline const Matrix \u0026amp; at( size_t n )  Swap #  inline void Swap( Matrix \u0026amp; that, size_t n ) Parameters:\n that input matrix n index where the matrix is moved   operator= #  inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\n that another UniformMatrixList  Return: reference to object\n operator= #  inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\n that another UniformMatrixList  Return: reference to object\n  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":49,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/","title":"lds::UniformSystemList","section":"Classes","content":"lds::UniformSystemList #  More\u0026hellip;\nInherits from std::vector\u0026lt; System \u0026gt;\nPublic Functions #      Name      UniformSystemList() =default\nConstructs a new UniformSystemList.    UniformSystemList(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by copying existing vector of System if dimensions consistent.    UniformSystemList(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by moving existing vector of System if dimensions consistent.    UniformSystemList(std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList from initializer_list of System if dimensions consistent.    UniformSystemList(const UniformSystemList \u0026amp; that)\nConstructs a new UniformSystemList (copy).    UniformSystemList(UniformSystemList \u0026amp;\u0026amp; that)\nConstructs a new UniformSystemList (move).    ~UniformSystemList() =default\nDestroys the object.   const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const\ngets dimensions of the uniformly sized systems   size_t size()\nsize of container   const System \u0026amp; at(size_t n)\ngets reference to n^th element   void Swap(System \u0026amp; that, size_t n)\nswaps input system with n^th system of list   UniformSystemList \u0026amp; operator=(const UniformSystemList \u0026amp; that)\nassigns the contents (copy)   UniformSystemList \u0026amp; operator=(UniformSystemList \u0026amp;\u0026amp; that)\nassigns the contents (move)    Detailed Description #  template \u0026lt;typename System\u0026gt; class lds::UniformSystemList;   Public Function Details #  UniformSystemList #  UniformSystemList() =default  UniformSystemList #  explicit UniformSystemList( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\n systems input systems dim dimensions (n_u, n_x, n_y)   UniformSystemList #  explicit UniformSystemList( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\n systems input systems dim dimensions (n_u, n_x, n_y)   UniformSystemList #  UniformSystemList( std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\n systems input systems dim dimensions (n_u, n_x, n_y)   UniformSystemList #  UniformSystemList( const UniformSystemList \u0026amp; that ) Parameters:\n that another UniformSystemList   UniformSystemList #  UniformSystemList( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\n that another UniformSystemList   ~UniformSystemList #  ~UniformSystemList() =default  dim #  inline const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const  size #  inline size_t size()  at #  inline const System \u0026amp; at( size_t n )  Swap #  inline void Swap( System \u0026amp; that, size_t n ) Parameters:\n that input system n index where the system is moved   operator= #  inline UniformSystemList \u0026amp; operator=( const UniformSystemList \u0026amp; that ) Parameters:\n that another UniformSystemList  Return: reference to object\n operator= #  inline UniformSystemList \u0026amp; operator=( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\n that another UniformSystemList  Return: reference to object\n  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":50,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/","title":"lds::UniformVectorList","section":"Classes","content":"lds::UniformVectorList #  Inherits from std::vector\u0026lt; Vector \u0026gt;\nPublic Functions #      Name      UniformVectorList() =default\nConstructs a new UniformVectorList.    UniformVectorList(const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent.    UniformVectorList(std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent.    UniformVectorList(std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0)\nConstructs a new UniformVectorList from initializer_list of Vector if dimensions consistent.    UniformVectorList(const UniformVectorList \u0026amp; that)\nConstructs a new UniformVectorList (copy)    UniformVectorList(UniformVectorList \u0026amp;\u0026amp; that)\nConstructs a new UniformVectorList (move)    ~UniformVectorList() =default\nDestroys the object.   size_t dim() const\ngets dimensions of the uniformly sized matrices   size_t size()\nsize of container   const Vector \u0026amp; at(size_t n)\ngets reference to n^th element   void Swap(Vector \u0026amp; that, size_t n)\nswaps input matrix with n^th vector of list   UniformVectorList \u0026amp; operator=(const UniformVectorList \u0026amp; that)\nassigns the contents (copy)   UniformVectorList \u0026amp; operator=(UniformVectorList \u0026amp;\u0026amp; that)\nassigns the contents (move)      Public Function Details #  UniformVectorList #  UniformVectorList() =default  UniformVectorList #  explicit UniformVectorList( const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0 ) Parameters:\n vecs input vectors dims dimension   UniformVectorList #  explicit UniformVectorList( std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0 ) Parameters:\n vecs input vectors dim dimension   UniformVectorList #  UniformVectorList( std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0 ) Parameters:\n vecs input vectors dim dimension   UniformVectorList #  UniformVectorList( const UniformVectorList \u0026amp; that ) Parameters:\n that another UniformVectorList   UniformVectorList #  UniformVectorList( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\n that another UniformVectorList   ~UniformVectorList #  ~UniformVectorList() =default  dim #  inline size_t dim() const  size #  inline size_t size()  at #  inline const Vector \u0026amp; at( size_t n )  Swap #  inline void Swap( Vector \u0026amp; that, size_t n ) Parameters:\n that input vector n index where the vector is moved   operator= #  inline UniformVectorList \u0026amp; operator=( const UniformVectorList \u0026amp; that ) Parameters:\n that another UniformVectorList  Return: reference to object\n operator= #  inline UniformVectorList \u0026amp; operator=( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\n that another UniformVectorList  Return: reference to object\n  Updated on 4 May 2022 at 16:59:50 Eastern Daylight Time\n"},{"id":51,"href":"/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/","title":"ldsCtrlEst_h","section":"Files","content":"ldsCtrlEst_h #  Files #     Name     ldsCtrlEst_h/lds.h lds namespace   ldsCtrlEst_h/lds_ctrl.h Controller.   ldsCtrlEst_h/lds_fit.h LDS base fit type.   ldsCtrlEst_h/lds_fit_em.h subspace identification   ldsCtrlEst_h/lds_fit_ssid.h subspace identification   ldsCtrlEst_h/lds_gaussian.h glds namespace   ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.   ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.   ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.   ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.   ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.   ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.   ldsCtrlEst_h/lds_poisson.h plds namespace   ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.   ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.   ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.   ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.   ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.   ldsCtrlEst_h/lds_poisson_sys.h PLDS base type.   ldsCtrlEst_h/lds_sctrl.h SwitchedController type.   ldsCtrlEst_h/lds_sys.h LDS base type.   ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.   ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.   ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.   ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)   ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)     Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":52,"href":"/lds-ctrl-est/docs/api/files/lds_8h/","title":"ldsCtrlEst_h/lds.h","section":"Files","content":"ldsCtrlEst_h/lds.h #  lds namespace More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Detailed Description #  This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)) observations.\nSource code #  //===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_H #define LDSCTRLEST_LDS_H  // #ifndef LDSCTRLEST // #include \u0026lt;ldsCtrlEst\u0026gt; // #endif  #include \u0026lt;armadillo\u0026gt; namespace lds { using data_t = double; // may change to float (but breaks mex functions) using Vector = arma::Col\u0026lt;data_t\u0026gt;; using Matrix = arma::Mat\u0026lt;data_t\u0026gt;; using Cube = arma::Cube\u0026lt;data_t\u0026gt;; using View = arma::subview\u0026lt;data_t\u0026gt;; namespace fill = arma::fill; static const std::size_t kControlTypeDeltaU = 0x1; static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); static const data_t kPi = arma::datum::pi; static const data_t kDefaultP0 = 1e-6; static const data_t kDefaultQ0 = 1e-6; static const data_t kDefaultR0 = 1e-2; enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA }; enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2 }; // TODO(mfbolus): for SwitchedController, may want systems to have differing // numbers of states. Use this enum as template parameter? // enum SystemListFreeDim { // kSysFreeDimNone, // kSysFreeDimX ///\u0026lt; allow state dim (x) of systems in list to be hetero // };  // place hard limits on contents of vecors/mats void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub); void Limit(Vector\u0026amp; x, data_t lb, data_t ub); void Limit(Matrix\u0026amp; x, data_t lb, data_t ub); // in-place assign that errs if there are dimension mismatches: void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); // TODO(mfbolus): this is a fudge, but for some reason, cov mats often going // numerically asymm.  void ForceSymPD(Matrix\u0026amp; X); void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min = 0); void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X); Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B); inline void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Vector\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Matrix\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions  if (other.n_elem != some.n_elem) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vector of size \u0026#34; \u0026lt;\u0026lt; some.n_elem \u0026lt;\u0026lt; \u0026#34; with vector of size \u0026#34; \u0026lt;\u0026lt; other.n_elem \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } inline void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions  if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrix of size \u0026#34; \u0026lt;\u0026lt; some.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; some.n_cols \u0026lt;\u0026lt; \u0026#34; with matrix of size \u0026#34; \u0026lt;\u0026lt; other.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other.n_cols \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":53,"href":"/lds-ctrl-est/docs/api/files/lds__ctrl_8h/","title":"ldsCtrlEst_h/lds_ctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_ctrl.h #  Controller. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::Controller    Detailed Description #  This file declares the type for control of a linear dynamical system (lds::Controller).\nSource code #  //===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_CTRL_H #define LDSCTRLEST_LDS_CTRL_H  // namespace #include \u0026#34;lds.h\u0026#34;// system type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class Controller { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); public: Controller() = default; Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); const Vector\u0026amp; Control(const Vector\u0026amp; z, bool do_control = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); const Vector\u0026amp; ControlOutputReference(const Vector\u0026amp; z, bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); // get methods:  const System\u0026amp; sys() const { return sys_; }; const Matrix\u0026amp; Kc() const { return Kc_; }; const Matrix\u0026amp; Kc_inty() const { return Kc_inty_; }; const Matrix\u0026amp; Kc_u() const { return Kc_u_; }; const Vector\u0026amp; g_design() const { return g_design_; }; const Vector\u0026amp; u_ref() const { return u_ref_; }; const Vector\u0026amp; x_ref() const { return x_ref_; }; const Vector\u0026amp; y_ref() const { return y_ref_; }; size_t control_type() const { return control_type_; }; data_t tau_awu() const { return tau_awu_; }; data_t u_lb() const { return u_lb_; }; data_t u_ub() const { return u_ub_; }; // set methods  void set_sys(const System\u0026amp; sys) { bool does_match = sys_.n_u() == sys.n_u(); does_match = does_match \u0026amp;\u0026amp; (sys_.n_x() == sys.n_x()); does_match = does_match \u0026amp;\u0026amp; (sys_.n_y() == sys.n_y()); if (does_match) { sys_ = sys; } else { throw std::runtime_error( \u0026#34;new system argument to `set_sys` does not match dimensionality of \u0026#34; \u0026#34;existing system\u0026#34;); } }; void set_g_design(const Vector\u0026amp; g_design) { Reassign(g_design_, g_design); }; void set_u_ref(const Vector\u0026amp; u_ref) { Reassign(u_ref_, u_ref); }; void set_x_ref(const Vector\u0026amp; x_ref) { Reassign(x_ref_, x_ref); cx_ref_ = sys_.C() * x_ref_; }; // y_ref needs to be handled differently depending on output fn.  // (need to populate cx_ref_ too, which depends on output fn)  virtual void set_y_ref(const Vector\u0026amp; y_ref) { Reassign(y_ref_, y_ref); }; void set_Kc(const Matrix\u0026amp; Kc) { Reassign(Kc_, Kc); }; void set_Kc_inty(const Matrix\u0026amp; Kc_inty) { Reassign(Kc_inty_, Kc_inty); }; void set_Kc_u(const Matrix\u0026amp; Kc_u) { Reassign(Kc_u_, Kc_u); }; void set_tau_awu(data_t tau) { tau_awu_ = tau; k_awu_ = sys_.dt() / tau_awu_; }; void set_control_type(size_t control_type); // There is no reason u_lb/ub should not be public, but making set methods  // anyway.  void set_u_lb(data_t u_lb) { u_lb_ = u_lb; }; void set_u_ub(data_t u_ub) { u_ub_ = u_ub; }; void Reset() { sys_.Reset(); u_ref_.zeros(); u_ref_prev_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); u_saturated_ = false; t_since_control_onset_ = 0.0; }; void Print() { sys_.Print(); std::cout \u0026lt;\u0026lt; \u0026#34;g_design : \u0026#34; \u0026lt;\u0026lt; g_design_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_lb : \u0026#34; \u0026lt;\u0026lt; u_lb_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_ub : \u0026#34; \u0026lt;\u0026lt; u_ub_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }; protected: System sys_; Vector u_; Vector u_return_; Vector g_design_; // reference signals  Vector u_ref_; // create no set method for this:  Vector u_ref_prev_; Vector x_ref_; Vector y_ref_; Vector cx_ref_; // Controller gains  Matrix Kc_; Matrix Kc_u_; Matrix Kc_inty_; // control after g inversion  // do not need set methods for these.  Vector du_ref_; Vector dv_ref_; Vector v_ref_; Vector dv_; Vector v_; // integral error  // do not need set method for this  Vector int_e_; Vector int_e_awu_adjust_; Vector u_sat_; bool do_control_prev_ = false; bool do_lock_control_prev_ = false; // whether the g of system has become inverted from what you think it is  // (gain_ref)  bool u_saturated_ = false; // should be safe to have references here bc nothing needs to be done  // (like reset vars) when it changes...  data_t u_lb_{}; data_t u_ub_{}; data_t tau_awu_{}; data_t k_awu_ = 0; data_t t_since_control_onset_ = 0; size_t control_type_{}; private: void CalcControl(bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); void CalcSteadyStateSetPoint(); void AntiWindup(); void InitVars(size_t control_type); }; // Implement the above:  template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(sys), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(std::move(sys)), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::set_control_type(size_t control_type) { if (control_type_ == control_type) { return; } // creating a blank slate...  control_type_ = 0; Kc_inty_.zeros(0, 0); Kc_u_.zeros(0, 0); int_e_.zeros(0, 0); int_e_awu_adjust_.zeros(0, 0); // controller was designed to minimize integral error  if (control_type \u0026amp; kControlTypeIntY) { Kc_inty_.zeros(sys_.n_u(), sys_.n_y()); int_e_.zeros(sys_.n_y()); int_e_awu_adjust_.zeros(sys_.n_u()); control_type_ = control_type_ | kControlTypeIntY; } // controller was designed to minimize deltaU  // (i.e. state augmented with u)  if (control_type \u0026amp; kControlTypeDeltaU) { Kc_u_.zeros(sys_.n_u(), sys_.n_u()); control_type_ = control_type_ | kControlTypeDeltaU; } // whether to adapt set point calculate with (re-estimated) process  // disturbance (m)  if (control_type \u0026amp; kControlTypeAdaptM) { if (sys_.do_adapt_m) // only if adapting m...  { control_type_ = control_type_ | kControlTypeAdaptM; } } } // set_control_type  template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::Control( const Vector\u0026amp; z, bool do_control, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement  sys_.Filter(u_, z); bool do_estimation = true; // always have estimator on in this case  // calculate control signal  CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::ControlOutputReference( const Vector\u0026amp; z, bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement  if (do_estimation) { sys_.Filter(u_, z); } else { sys_.f(u_); } // calculate the set point  // solves for u_ref and x_ref when output is at y_ref at steady state.  if (do_control) { CalcSteadyStateSetPoint(); } // calculate control signal  CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcControl(bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { if (do_control \u0026amp;\u0026amp; do_estimation) { if (!do_control_prev_) { if (do_reset_at_control_onset) { Reset(); } t_since_control_onset_ = 0.0; } else { t_since_control_onset_ += sys_.dt(); } // enforce softstart on control vars.  if (sigma_soft_start \u0026gt; 0) { // half-Gaussian soft-start scaling factor  data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) / (2 * pow(sigma_soft_start, 2))); u_ref_ *= soft_start_sf; // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too  // x_ref_ *= soft_start_sf;  // cx_ref_ *= soft_start_sf;  // y_ref_ *= soft_start_sf;  } if (!do_lock_control) { // first do u -\u0026gt; v change of vars. (v = g.*u)  // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver  // control voltage u[=]V)  v_ref_ = g_design_ % u_ref_; // Given FB, calc. the change in control  if (control_type_ \u0026amp; kControlTypeDeltaU) { // if control designed to minimize not u but deltaU (i.e. state aug with  // u):  // TODO(mfbolus): Commented out for now. See note below.  // du_ref_ = u_ref_ - u_ref_prev_;  // dv_ref_ = g_design_ % du_ref_;  // TODO(mfbolus): Assuming users want *smooth* control signals if using  // kControlTypeDeltaU, it should be the case that dv_ref_ is --\u0026gt; 0. May  // want to revisit, but I am going to force it to be zero unless a  // situation arises that argues for keeping the above.  dv_ref_.zeros(); dv_ = dv_ref_; // nominally-optimal.  dv_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error  dv_ -= Kc_u_ * (v_ - v_ref_); // penalty on amp u (rel to ref)  if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup  // would be to not integrate error when control signal saturated:  // if(!uSaturated)  int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error  dv_ -= Kc_inty_ * int_e_; // control for integrated error  } // update the control  v_ += dv_; } else { v_ = v_ref_; // nominally-optimal.  v_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error  if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup  // would be to not integrate error when control signal saturated:  // if (!uSaturated)  int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error  v_ -= Kc_inty_ * int_e_; // control for integrated error  } } // convert back to control voltage u[=]V  u_ = v_ / sys_.g(); } // else do nothing until lock is low  } else { // if not control  // feed through u_ref in open loop  u_ = u_ref_ % g_design_ / sys_.g(); v_ = sys_.g() % u_; u_ref_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); } // ends do_control  // enforce box constraints (and antiwindup)  AntiWindup(); // add noise to input?  // The value for u that is *returned* to user after addition of any noise,  // while keeping controller/estimator blind to this addition.  u_return_ = u_; if ((sigma_u_noise \u0026gt; 0.0) \u0026amp;\u0026amp; (do_control \u0026amp;\u0026amp; !do_lock_control)) { u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn); Limit(u_return_, u_lb_, u_ub_); }; // For next time step:  u_ref_prev_ = u_ref_; do_control_prev_ = do_control; do_lock_control_prev_ = do_lock_control; } // CalcControl  template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcSteadyStateSetPoint() { // Linearly-constrained least squares (ls).  //  // _reference:  // Boyd \u0026amp; Vandenberghe (2018) Introduction to Applied Linear Algebra  //  Matrix a_ls = join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros)); Vector b_ls = cx_ref_; Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye), sys_.B() * arma::diagmat(sys_.g())); Vector d_ls = -sys_.m0(); if (control_type_ \u0026amp; kControlTypeAdaptM) { d_ls = -sys_.m(); // adapt setpoint calc with disturbance?  } Matrix a_ls_t = a_ls.t(); // TODO(mfbolus): not sure why but causes seg  // fault if I do not do this.  Matrix phi_ls = join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()), join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros))); // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse:  Matrix inv_phi = pinv(phi_ls); Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls); x_ref_ = xulam.subvec(0, sys_.n_x() - 1); u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1); cx_ref_ = sys_.C() * x_ref_; } // CalcSteadyStateSetPoint  template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::AntiWindup() { u_saturated_ = false; u_sat_ = u_; // limit u and flag whether saturated  for (size_t k = 0; k \u0026lt; u_.n_elem; k++) { if (u_[k] \u0026lt; u_lb_) { u_sat_[k] = u_lb_; u_saturated_ = true; } if (u_[k] \u0026gt; u_ub_) { u_sat_[k] = u_ub_; u_saturated_ = true; } } if ((control_type_ \u0026amp; kControlTypeIntY) \u0026amp;\u0026amp; (tau_awu_ \u0026lt; lds::kInf)) { // one-step back-calculation (calculate intE for u=u_sat)  // (Astroem, Rundqwist 1989 warn against using this...)  // int_e_awu_adjust_ =  // solve(Kc_inty_, (u_ - u_sat_)); // pinv(Kc_inty) * (u-uSat);  // gradual: see Astroem, Rundqwist 1989  // this is a fudge for doing MIMO gradual  // n.b., went ahead and multiplied 1/T by dt so don\u0026#39;t have to do that here.  int_e_awu_adjust_ = k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_); // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_);  int_e_ += int_e_awu_adjust_; } // set u to saturated version  u_ = u_sat_; } template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::InitVars(size_t control_type) { // initialize to default values  u_ref_ = Vector(sys_.n_u(), fill::zeros); u_ref_prev_ = Vector(sys_.n_u(), fill::zeros); x_ref_ = Vector(sys_.n_x(), fill::zeros); y_ref_ = Vector(sys_.n_y(), fill::zeros); cx_ref_ = Vector(sys_.n_y(), fill::zeros); u_ = Vector(sys_.n_u(), fill::zeros); u_return_ = Vector(sys_.n_u(), fill::zeros); u_sat_ = Vector(sys_.n_u(), fill::zeros); // Might not need all these, so zero elements until later.  Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros); Kc_u_ = Matrix(0, 0, fill::zeros); Kc_inty_ = Matrix(0, 0, fill::zeros); g_design_ = sys_.g(); // by default, same as model  dv_ = Vector(sys_.n_u(), fill::zeros); v_ = Vector(sys_.n_u(), fill::zeros); du_ref_ = Vector(sys_.n_u(), fill::zeros); dv_ref_ = Vector(sys_.n_u(), fill::zeros); v_ref_ = Vector(sys_.n_u(), fill::zeros); int_e_ = Vector(0, fill::zeros); int_e_awu_adjust_ = Vector(0, fill::zeros); set_control_type(control_type); } } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":54,"href":"/lds-ctrl-est/docs/api/files/lds__fit_8h/","title":"ldsCtrlEst_h/lds_fit.h","section":"Files","content":"ldsCtrlEst_h/lds_fit.h #  LDS base fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::Fit LDS Fit Type.    Detailed Description #  This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.\nSource code #  //===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDS_FIT_HPP #define LDS_FIT_HPP  // namespace #include \u0026#34;lds.h\u0026#34;#include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); virtual ~Fit() = default; // get methods  size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; Q() const { return Q_; }; const Vector\u0026amp; x0() const { return x0_; }; const Matrix\u0026amp; P0() const { return P0_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; // gets measurement noise  virtual const Matrix\u0026amp; R() const = 0; // set methods (e.g., seeding initial fit values)  void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_m(const Vector\u0026amp; m) { Reassign(m_, m); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); ForceSymPD(Q_); }; virtual void set_R(const Matrix\u0026amp; R) = 0; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); ForceSymPD(P0_); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; View f(Matrix\u0026amp; x, const Matrix\u0026amp; u, size_t t) { x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x.col(t); }; View f(Matrix\u0026amp; x_pre, const Matrix\u0026amp; x_post, const Matrix\u0026amp; u, size_t t) { x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x_pre.col(t); }; virtual View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) = 0; protected: data_t dt_{}; // Dynamics  Matrix A_; Matrix B_; Vector g_; Vector m_; Matrix Q_; // Output  Matrix C_; Vector d_; Matrix R_; // initial conditions  Vector x0_; Matrix P0_; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; }; } // namespace lds #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":55,"href":"/lds-ctrl-est/docs/api/files/lds__fit__em_8h/","title":"ldsCtrlEst_h/lds_fit_em.h","section":"Files","content":"ldsCtrlEst_h/lds_fit_em.h #  subspace identification More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::EM    Detailed Description #  This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).\nSource code #  //===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_EMAX_H #define LDSCTRLEST_LDS_EMAX_H  #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class EM { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: EM() = default; EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); virtual ~EM() = default; const Fit\u0026amp; Run(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = true, bool calc_output = true, bool calc_measurement = true, size_t max_iter = 100, data_t tol = 1e-2); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); // auto tuple = std::make_tuple(u_, z_);  u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; x() const { return x_; }; const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; y() const { return y_; }; const Matrix\u0026amp; sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; }; const Matrix\u0026amp; sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; }; const Matrix\u0026amp; sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; }; size_t n_t_tot() { return n_t_tot_; } const Vector\u0026amp; theta() const { return theta_; }; protected: void Expectation(bool force_common_initial = false); void Maximization(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = false, bool calc_output = false, bool calc_measurement = false); void MaximizeDynamics(); void MaximizeQ(); void MaximizeInitial(); virtual void MaximizeOutput() = 0; virtual void MaximizeMeasurement() = 0; void Smooth(bool force_common_initial); virtual void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) = 0; void Reset(); void InitVars(); Vector UpdateTheta(); // input/output training data  UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; std::vector\u0026lt;Matrix\u0026gt; x_; std::vector\u0026lt;Cube\u0026gt; P_; std::vector\u0026lt;Cube\u0026gt; P_t_tm1_; std::vector\u0026lt;Matrix\u0026gt; y_; Matrix diag_y_; // expectations calculated in E-step  Matrix sum_E_x_t_x_t_; Matrix sum_E_xu_tm1_xu_tm1_; Matrix sum_E_xu_t_xu_tm1_; Fit fit_; Vector theta_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; }; template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; fit_ = Fit(n_u_, n_x, n_y_, dt); u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { // make sure fit dims match I/O data  if (fit0.n_u() != u_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and input training data have inconsistent dimensions\u0026#34;); } if (fit0.n_y() != z_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and output training data have inconsistent dimensions\u0026#34;); } fit_ = fit0; u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::InitVars() { // check input/output data dimensions are consistent  if (z_.size() != u_.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_.at(trial).n_cols != u_.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } n_u_ = fit_.n_u(); n_x_ = fit_.n_x(); n_y_ = fit_.n_y(); dt_ = fit_.dt(); x_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); P_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); P_t_tm1_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); y_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros); P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros); } diag_y_ = Matrix(n_y_, n_y_, fill::zeros); // covariances in expectation step  sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); } template \u0026lt;typename Fit\u0026gt; const Fit\u0026amp; EM\u0026lt;Fit\u0026gt;::Run(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement, size_t max_iter, data_t tol) { Reset(); // to initial conditions  size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_; Vector theta(n_params); Vector theta_new(n_params); data_t max_dtheta = 1; // if solving for initial conditions, allow them be varied.  // otherwise, freeze at provided values.  bool force_common_initial = !calc_init; // go until parameter convergence  for (size_t l = 0; l \u0026lt; max_iter; l++) { theta_ = UpdateTheta(); std::cout \u0026lt;\u0026lt; \u0026#34;Iteration \u0026#34; \u0026lt;\u0026lt; l + 1 \u0026lt;\u0026lt; \u0026#34;/\u0026#34; \u0026lt;\u0026lt; max_iter \u0026lt;\u0026lt; \u0026#34; ...\\n\u0026#34;; Expectation(force_common_initial); Maximization(calc_dynamics, calc_Q, calc_init, calc_output, calc_measurement); // check convergence  theta_new = UpdateTheta(); Vector dtheta = abs(theta_new - theta_) / abs(theta_); // some parameters could be zero...  arma::uvec ubi_finite = find_finite(dtheta); max_dtheta = max(dtheta.elem(ubi_finite)); std::cout \u0026lt;\u0026lt; \u0026#34;max dtheta: \u0026#34; \u0026lt;\u0026lt; max_dtheta \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; if (max_dtheta \u0026lt; tol) { std::cout \u0026lt;\u0026lt; \u0026#34;Converged.\\n\u0026#34;; break; } std::cout \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } return fit_; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Smooth(bool force_common_initial) { Matrix k_e(n_x_, n_y_); // estimator gain  Cube k_backfilt; // back-filtering gains  // TODO(mfbolus): this loop could be made parallel  for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { Matrix x_pre(n_x_, n_t_[trial], fill::zeros); Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros); Matrix x_post(n_x_, n_t_[trial], fill::zeros); Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros); if (force_common_initial) // forces all trials to have same initial  // conditions.  { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); } y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); // This *should not* be necessary but make sure P is symmetric.  ForceSymPD(P_[trial].slice(0)); x_pre.col(0) = x_[trial].col(0); p_pre.slice(0) = P_[trial].slice(0); x_post.col(0) = x_[trial].col(0); p_post.slice(0) = P_[trial].slice(0); // filter  for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // predict  fit_.f(x_pre, x_post, u_.at(trial), t); fit_.h(y_[trial], x_pre, t); diag_y_.diag() = y_[trial].col(t); // TODO(mfbolus): change if parallel  // update --\u0026gt; posterior estimation  RecurseKe(k_e, p_pre, p_post, t); x_post.col(t) = x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t)); y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d(); } // backfilter -\u0026gt; Smoothed estimate  // Reference:  // Shumway et Stoffer (1982)  ForceSymPD(p_post.slice(n_t_[trial] - 1)); k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1); P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 0; t--) { // TODO(mfmbolus): should not be necessary to force symm positive def  ForceSymPD(p_pre.slice(t)); ForceSymPD(p_post.slice(t - 1)); ForceSymPD(P_[trial].slice(t)); k_backfilt.slice(t - 1) = p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t)); x_[trial].col(t - 1) = x_post.col(t - 1) + k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t)); P_[trial].slice(t - 1) = p_post.slice(t - 1) + k_backfilt.slice(t - 1) * (P_[trial].slice(t) - p_pre.slice(t)) * k_backfilt.slice(t - 1).t(); } // do the same for P_t_tm1  Matrix id(n_x_, n_x_, fill::eye); P_t_tm1_[trial].slice(n_t_[trial] - 1) = (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 1; t--) { P_t_tm1_[trial].slice(t - 1) = p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() + k_backfilt.slice(t - 1) * (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) * k_backfilt.slice(t - 2).t(); } // finally, get smoothed estimate of output  for (size_t t = 0; t \u0026lt; n_t_[trial]; t++) { fit_.h(y_[trial], x_[trial], t); } // samps loop  } // trial loop } // Smooth  // template \u0026lt;typename Fit\u0026gt; // void EM\u0026lt;Fit\u0026gt;::RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) { // // predict covar // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q();  // // update Ke // Ke = P_pre.slice(t) * fit_.C().t() * // inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R());  // // update cov // // Reference: Ghahramani et Hinton (1996) // P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t);  // // // n.b. for poisson : // // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + // fit_.Q(); // // // update cov // // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ * // // fit_.C()); // // // update Ke // // Ke = P_post.slice(t) * fit_.C(); // }  template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Expectation(bool force_common_initial) { // calculate the mean/cov of state needed for maximizing E[pr(z|theta)]  Smooth(force_common_initial); // now get the various forms of sum(E[xx\u0026#39;]) needed  // n.b. Going to start at t=1 rather than 0 bc most max terms need that.  // so really \u0026#34;n_t_tot_\u0026#34; is (n_t_tot_-1)  n_t_tot_ = 0; sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); Vector xu_tm1(n_x_ + n_u_, fill::zeros); Vector xu_t(n_x_ + n_u_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // ------------ sum_E_x_t_x_t ------------  sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t(); sum_E_x_t_x_t_ += P_[trial].slice(t); // ------------ sum_E_xu_tm1_xu_tm1 ------------  xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1)); sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t(); sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t - 1); // ------------ sum_E_xu_t_xu_tm1 ------------  xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t)); sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t(); sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_t_tm1_[trial].slice(t); n_t_tot_ += 1; } // time  } // trial } // Expectation  template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement) { if (calc_output) { MaximizeOutput(); } if (calc_measurement) { MaximizeMeasurement(); } if (calc_dynamics) { MaximizeDynamics(); } if (calc_Q) { MaximizeQ(); } if (calc_init) { MaximizeInitial(); } } // Maximization  template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeDynamics() { // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996)  Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) * inv_sympd(sum_E_xu_tm1_xu_tm1_); fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1)); std::cout \u0026lt;\u0026lt; \u0026#34;A_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.A()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.B()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeQ() { // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996)  // View sum_e_x_t_xu_tm1 =  // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1);  // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 *  // inv_sympd(sum_E_xu_tm1_xu_tm1_) *  // sum_e_x_t_xu_tm1.t();  // q /= n_t_tot_;  // this way is same as above iff dynamics were just updated:  // View sum_e_x_t_xu_tm1 =  // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1);  // Matrix ab = arma::join_horiz(fit_.A(), fit_.B());  // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t();  // q /= n_t_tot_;  // From scratch method:  // Q is covariance of the error between state and dynamics-predicted state  // (aka process noise)  // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})\u0026#39;]  // t-1 terms:  View sum_e_x_tm1_x_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_u_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1); View sum_e_x_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); // t, t-1 terms:  View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_x_t_u_tm1 = sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); Matrix q = sum_E_x_t_x_t_; q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t(); q -= sum_e_x_t_x_tm1 * fit_.A().t(); q -= fit_.A() * sum_e_x_t_x_tm1.t(); // input-related terms:  q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t(); q -= sum_e_x_t_u_tm1 * fit_.B().t(); q -= fit_.B() * sum_e_x_t_u_tm1.t(); q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t(); q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t(); q /= n_t_tot_; fit_.set_Q(q); std::cout \u0026lt;\u0026lt; \u0026#34;Q_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.Q()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // std::cout \u0026lt;\u0026lt; \u0026#34;Q_new: \\n\u0026#34; \u0026lt;\u0026lt; fit_.Q() \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeInitial() { Vector x0 = fit_.x0(); x0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { x0 += x_[trial].col(0); } x0 /= z_.size(); std::cout \u0026lt;\u0026lt; \u0026#34;x0_new[0]: \u0026#34; \u0026lt;\u0026lt; x0[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // always recalc P0 even if the initial state is fixed (at zero, for  // example)  Matrix e_var(n_x_, n_x_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t(); } e_var /= z_.size(); // go ahead and subtract x0*x0\u0026#39; so don\u0026#39;t have to below.  e_var -= x0 * x0.t(); // To get P0, going to get initial P_ per trial and average.  // (which might be wrong, but need a single number)  Matrix p0 = fit_.P0(); p0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { p0 += (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var; } p0 /= z_.size(); fit_.set_P0(p0); std::cout \u0026lt;\u0026lt; \u0026#34;P0_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.P0()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeOutput() { // solve for C+d:  Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros); Vector x1(n_x_ + 1, fill::zeros); x1[n_x_] = 1.0; // augment with one to solve for bias  Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { x1.subvec(0, n_x_ - 1) = x_[trial].col(t); sum_zx += z_.at(trial).col(t) * x1.t(); sum_e_x1_x1 += x1 * x1.t(); sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t); } } Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1); fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1)); fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_))); std::cout \u0026lt;\u0026lt; \u0026#34;C_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.C()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.d()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeMeasurement() { // Solve for measurement noise covar  size_t n_t_tot = 0; // Ghahgramani, Hinton 1996:  Matrix sum_zz(n_y_, n_y_, fill::zeros); Matrix sum_yz(n_y_, n_y_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t(); // Use Cnew:  sum_yz += (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t(); n_t_tot += 1; } } fit_.set_R((sum_zz - sum_yz) / n_t_tot); std::cout \u0026lt;\u0026lt; \u0026#34;R_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.R()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Reset() { // reset to initial conditions  for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); } } template \u0026lt;typename Fit\u0026gt; Vector EM\u0026lt;Fit\u0026gt;::UpdateTheta() { // TODO(mfbolus): This should include n_y_ more params for d.  size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_; if (fit_.R().n_elem \u0026gt; 0) { n_params += n_y_ * n_y_; } Vector theta(n_params); size_t idx_start = 0; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B()); idx_start += n_x_ * n_u_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0()); idx_start += n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C()); idx_start += n_y_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d()); idx_start += n_y_; if (fit_.R().n_elem \u0026gt; 0) { theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R()); } return theta; } } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":56,"href":"/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_fit_ssid.h","section":"Files","content":"ldsCtrlEst_h/lds_fit_ssid.h #  subspace identification More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::SSID    Detailed Description #  This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)\u0026lt;Fit\u0026gt;).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code #  //===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_FIT_SSID_H #define LDSCTRLEST_LDS_FIT_SSID_H  #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class SSID { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: SSID() = default; SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d = Vector(1).fill(-kInf)); std::tuple\u0026lt;Fit, Vector\u0026gt; Run(SSIDWt ssid_wt); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } protected: void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001); void CreateHankelDataMat(); virtual void DecomposeData() = 0; void CalcSVD(SSIDWt wt); void Solve(data_t wt_dc); void RecomputeExtObs(); // input/output training data  UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; Matrix D_; Fit fit_; Matrix g_dc_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_h_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; Matrix L_; Vector s_; Matrix ext_obs_t_; }; template \u0026lt;typename Fit\u0026gt; SSID\u0026lt;Fit\u0026gt;::SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d) { // check input/output data dimensions are consistent  if (z_train.size() != u_train.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_train.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_train.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } dt_ = dt; n_x_ = n_x; n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; n_h_ = n_h; // dimensionality check for eventual block-hankel data matrix  size_t len = n_t_tot_ - 2 * n_h_ + 1; if (len \u0026lt; (2 * n_h_ * (n_u_ + n_y_))) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;Dataset problem! More rows than columns in block-hankel data \u0026#34; \u0026#34;matrix: 2*(n_u+n_y)*n_h \u0026gt; data-length! Need higher data-length or \u0026#34; \u0026#34;lower n_h.\u0026#34;; throw std::runtime_error(ss.str()); } fit_ = Fit(n_u_, n_x_, n_y_, dt_); u_ = std::move(u_train); z_ = std::move(z_train); if (!d.is_finite() || (d.n_rows != n_y_)) { // TODO(mfbolus): implement least-square solution for impulse response with  // a second input of ones. Data-driven way of accounting for offset *not*  // driven by an input.  //  // For now, calculate output bias (d) as the  // output wherever the stimulus has not been on for some amount of time.  // convolve u with rectangle and take all samples. This is a reasonable  // approach, since often when autonomous systems are fit (i.e., systems with  // no input), they will subtract off the mean of the output. This  // essentially amounts to setting output bias to the mean of the output when  // there is no stimulation.  data_t t_silence = 0.1; data_t thresh_silence = 0.001; CalcD(t_silence, thresh_silence); } else { fit_.set_d(d); } } template \u0026lt;typename Fit\u0026gt; std::tuple\u0026lt;Fit, Vector\u0026gt; SSID\u0026lt;Fit\u0026gt;::Run(SSIDWt ssid_wt) { // the weight on minimizing dc I/O gain only works for gaussian,  // and hopefully not necessary with appropriate dataset.  data_t wt_dc = 0; // std::cout \u0026lt;\u0026lt; \u0026#34;creating hankel mat\\n\u0026#34;;  CreateHankelDataMat(); // std::cout \u0026lt;\u0026lt; \u0026#34;decomposing data\\n\u0026#34;;  DecomposeData(); // std::cout \u0026lt;\u0026lt; \u0026#34;calculating svd\\n\u0026#34;;  CalcSVD(ssid_wt); // std::cout \u0026lt;\u0026lt; \u0026#34;solving for params\\n\u0026#34;;  Solve(wt_dc); // std::cout \u0026lt;\u0026lt; \u0026#34;fin\\n\u0026#34;;  return std::make_tuple(fit_, s_); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcD(data_t t_silence, data_t thresh_silence) { Vector d(z_.at(0).n_rows, fill::zeros); Vector win(static_cast\u0026lt;size_t\u0026gt;(t_silence / dt_), fill::ones); Vector sum_z_silence(n_y_, fill::zeros); size_t n_silence(0); for (size_t trial = 0; trial \u0026lt; u_.size(); trial++) { // find silent samples  // start by convolving with  Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0)); Vector u_conv = conv(sum_u, win, \u0026#34;same\u0026#34;); // get only the samples that are silent...  arma::uvec ubi_silence = find(u_conv \u0026lt;= thresh_silence); if (ubi_silence.n_elem \u0026gt; 0) { sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1); n_silence += ubi_silence.n_elem; } } if (n_silence \u0026gt; 0) { d = sum_z_silence / n_silence; } fit_.set_d(d); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat() { // temporary copy of data  Matrix z(n_y_, n_t_tot_, fill::zeros); Matrix u(n_u_, n_t_tot_, fill::zeros); size_t so_far(0); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial); u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial); so_far += n_t_.at(trial); } // remove output bias  z.each_col() -= fit_.d(); // calculate I/O gain @ DC while data in convenient form  g_dc_ = z * pinv(u); // std::cout \u0026lt;\u0026lt; \u0026#34;G0_data = \u0026#34; \u0026lt;\u0026lt; g_dc_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;;  // create hankel data matrix  size_t len = z.n_cols - 2 * n_h_ + 1; // data length in hankel mat  // block-hankel data matrix  D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros); // past input  auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1); // future input  auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1); // past output  auto y_p = D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1); // future output  auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, len - 1); size_t idx = 0; for (size_t k = 0; k \u0026lt; len; k++) { idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } } D_ /= sqrt(static_cast\u0026lt;data_t\u0026gt;(len)); } // template \u0026lt;typename Fit\u0026gt; // void SSID\u0026lt;Fit\u0026gt;::DecomposeData() { // // do LQ decomp instead of calculating covariance expensive way // // Note that \u0026#34;R\u0026#34; in van Overschee is lower-triangular (L), not \u0026#34;R\u0026#34; in QR // // decomp. Very confusing. // Matrix q_t; // lq(L_, q_t, D_); // // van Overschee zeros out the other elements. // L_ = trimatl(L_); // }  template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcSVD(SSIDWt wt) { // submats that will be needed:  auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1); auto R_23_13 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1); auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_) - 1); auto R_44 = L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14); auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1); auto Luf = Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1); auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); // aka: R_f  Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1); // from van Overschee subid.m:  // Rf = R((2*m+l)*i+1:2*(m+l)*i,:); % Future outputs  Matrix U; Matrix V; switch (wt) { case kSSIDNone: { // No weighting. (what van Overschee calls \u0026#34;N4SID\u0026#34;)  Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14; arma::svd(U, s_, V, O_k_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDMOESP: { // MOESP weighting  // This is what they use in the \u0026#34;robust\u0026#34; algorithm van Overschee, de Moor  // 1996  Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); svd(U, s_, V, O_k_ortho_Uf_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDCVA: { // CVA weighting  // See van Overschee\u0026#39;s matlab code (subid.m):  // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems  Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); Matrix inv_w1; Matrix qt1; lq(inv_w1, qt1, R_56_16); // lq decomp of R_f (future output data)  inv_w1 = trimatl(inv_w1); inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1); Matrix w_o_w = arma::solve( inv_w1, O_k_ortho_Uf_sans_Qt); // alternatively  // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt  svd(U, s_, V, w_o_w, \u0026#34;std\u0026#34;); U = inv_w1 * U; break; } } // Truncate to model order (heart of ssid method)  auto s_hat = s_.subvec(0, n_x_ - 1); Matrix diag_sqrt_s = diagmat(sqrt(s_hat)); auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1); // get extended observability and controllability mats  ext_obs_t_ = u_hat * diag_sqrt_s; // extended observability matrix } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::Solve(data_t wt_dc) { // required submats  auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_56_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); // Solve for params using appropriate algorithm:  // robust deterministic/stochastic algorithm in van Overschee 1996  // algorithm that the authors say \u0026#34;works\u0026#34; in practice.  auto ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix  // This is what textbook (1996) says:  //  // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15);  //  // HOWEVER, do not know why but have to fill the last place with zeros like  // authors\u0026#39; matlab implementation (see `subid.m`)  // Otherwise, get ridiculous covariances (although A,C estimates are close to  // same...)  Matrix Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Matrix S = Tl * pinv(Tr); // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure  // stability.  fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1)); Matrix ext_obs_t_p1 = join_vert( ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1), Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros)); fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1); // At this point, van Overschee \u0026amp; de Moor suggest re-calculating ext_obs_t_,  // ext_obs_tm1 from (A, C) because it was just an approximation. This is  RecomputeExtObs(); ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix  Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); S = Tl * pinv(Tr); Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_); Matrix Mcurly = pinv(ext_obs_tm1); Matrix Pcurly = Tl - Lcurly * R_56_15; Vector Pvec = vectorise(Pcurly); Matrix Qcurly = R_23_15; // Identify [D; B], assuming D=0 and ensuring DC gain is correct  Matrix sum_QcurlyT_kron_Ncurly( (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_), fill::zeros); Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols, fill::eye); eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1, eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1; // van Overschee (1996) p. 126  Matrix N1_Tl = -Lcurly; N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) += join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly); N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) += Matrix(n_y_, n_y_, fill::eye); Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros); Matrix N_k; for (size_t k = 0; k \u0026lt; n_h_; k++) { auto Qcurly_k = Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1); Nk_Tl.zeros(); Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) = N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1); N_k = Nk_Tl * eye_ext_obs_tm1; sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k); } Matrix err_vec; if (wt_dc \u0026gt; 0) { // Constraints enforced by weighted least squares  //  // Reference:  //  // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly  // Excited Industrial Systems. Conference in Decision and Control.  // constraint 1: assume D=0 --\u0026gt; remove the components for Dvec (this is  // actually a hard constraint in that it ignores D)  Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly; sum_QcurlyT_kron_Ncurly = Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_); size_t kkk = 0; for (size_t k = 1; k \u0026lt; (n_u_ + 1); k++) { size_t start_idx = k * (n_y_ + n_x_) - n_x_; for (size_t kk = 0; kk \u0026lt; n_x_; kk++) { sum_QcurlyT_kron_Ncurly.col(kkk) = sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk); kkk++; } } // constraint 2: Make sure DC I/O gain is correct  Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A()); Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_)); Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0); Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 = join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0); // WEIGHTED LS  // Important in practice because I care a lot about at least getting the DC  // gain correct. Put x weight on minimizing error at DC, relative to others  Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye); // Make weight on minimizing DC error immense so at least that  // should be nailed.  size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows; size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows; size_t stop_row = w.n_rows - 1; size_t stop_col = w.n_cols - 1; // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale  // weight with data length?  w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc; Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * sum_QcurlyT_kron_Ncurly_b_to_g0) * sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec; fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_)); // Calculate residuals and their cov.  // Because I\u0026#39;ve added constraints, I need to re-calculate the right term  // with b_vec instead of how van Overschee do in final algorithm.  err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec; } else { // default way: *no* constraint on G0 or D=0  Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec; // TODO(mfbolus) n.b., this gets thrown away...  // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_);  fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_)); err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec; } // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols);  // TODO(mfbolus): Something is wrong with the error calculation above.  // Use the way van overschee does it in `subid.m`  // WARNING: this ignores any above constraints, so Q, R will be approximate...  Matrix err = Tl - S * Tr; Matrix cov_err = err * err.t(); fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1)); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::RecomputeExtObs() { ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C(); for (size_t k = 2; k \u0026lt; (n_h_ + 1); k++) { ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) = ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1, ext_obs_t_.n_cols - 1) * fit_.A(); } } } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":57,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian_8h/","title":"ldsCtrlEst_h/lds_gaussian.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian.h #  glds namespace More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Detailed Description #  This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)).\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_H #define LDSCTRLEST_LDS_GAUSSIAN_H  // namespace #include \u0026#34;lds.h\u0026#34; namespace lds { namespace gaussian { // insert any Gaussian-specific things here... } // namespace gaussian } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":58,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_ctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_ctrl.h #  GLDS Controller. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::Controller Gaussian-observation Controller Type.    Detailed Description #  This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H  // namespace #include \u0026#34;lds_gaussian.h\u0026#34;// system #include \u0026#34;lds_gaussian_sys.h\u0026#34;// controller #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace gaussian { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); cx_ref_ = y_ref - sys_.d(); }; // make sure base class template methods available  using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; }; } // namespace gaussian } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":59,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_fit.h #  GLDS fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::Fit GLDS Fit Type.    Detailed Description #  This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_H  // namespace #include \u0026#34;lds_gaussian.h\u0026#34;// fit type #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace gaussian { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); const Matrix\u0026amp; R() const override { return R_; }; void set_R(const Matrix\u0026amp; R) override { Reassign(R_, R); ForceSymPD(R_); }; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = C_ * x.col(t) + d_; return y.col(t); }; }; }; // namespace gaussian } // namespace lds #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":60,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_em.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_fit_em.h #  GLDS E-M fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::FitEM GLDS E-M Fit Type.    Detailed Description #  This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H  #include \u0026#34;lds_fit_em.h\u0026#34;#include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; }; } // namespace gaussian } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":61,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h #  GLDS SSID fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.    Detailed Description #  This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H  #include \u0026#34;lds_fit_ssid.h\u0026#34;#include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; using SSID\u0026lt;Fit\u0026gt;::Run; private: using SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat; using SSID\u0026lt;Fit\u0026gt;::CalcSVD; using SSID\u0026lt;Fit\u0026gt;::Solve; void DecomposeData() override; void SolveVanOverschee(); }; } // namespace gaussian } // namespace lds #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":62,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_sctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_sctrl.h #  GLDS switched controller type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.    Detailed Description #  This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H  // controller type #include \u0026#34;lds_gaussian_ctrl.h\u0026#34;// switched controller #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace gaussian { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); cx_ref_ = y_ref - sys_.d(); } // make sure base class template methods available  using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; }; // SwitchedController } // namespace gaussian } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":63,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/","title":"ldsCtrlEst_h/lds_gaussian_sys.h","section":"Files","content":"ldsCtrlEst_h/lds_gaussian_sys.h #  GLDS base type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::gaussian Linear Dynamical Systems with Gaussian observations.    Classes #      Name     class lds::gaussian::System Gaussian LDS Type.    Detailed Description #  This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)).\nSource code #  //===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H #define LDSCTRLEST_LDS_GAUSSIAN_SYS_H  // namespace #include \u0026#34;lds_gaussian.h\u0026#34;// system #include \u0026#34;lds_sys.h\u0026#34; namespace lds { namespace gaussian { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0, data_t r0 = kDefaultR0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; // get methods  const Matrix\u0026amp; R() const { return R_; }; // set methods  void set_Q(const Matrix\u0026amp; Q) { lds::System::set_Q(Q); do_recurse_Ke_ = true; } void set_R(const Matrix\u0026amp; R) { Reassign(R_,R); do_recurse_Ke_ = true; }; void set_Ke(const Matrix\u0026amp; Ke) { Reassign(Ke_,Ke); // if users have set Ke, they must not want to calculate it online.  do_recurse_Ke_ = false; }; void set_Ke_m(const Matrix\u0026amp; Ke_m) { Reassign(Ke_m_,Ke_m); // if users have set Ke, they must not want to calculate it online.  do_recurse_Ke_ = false; }; void Print(); protected: void h() override { cx_ = C_ * x_; y_ = cx_ + d_; }; void RecurseKe() override; // Gaussian-output-specific  Matrix R_; bool do_recurse_Ke_{}; }; // System } // namespace gaussian } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":64,"href":"/lds-ctrl-est/docs/api/files/lds__poisson_8h/","title":"ldsCtrlEst_h/lds_poisson.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson.h #  plds namespace More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Detailed Description #  This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)).\nSource code #  //===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_H #define LDSCTRLEST_LDS_POISSON_H  #include \u0026#34;lds.h\u0026#34; namespace lds { namespace poisson { // TODO(mfbolus): Not sure if defining these as static here makes the most // sense. Is there a downside to letting multiple poisson System objects share a // common random number generator? static std::random_device rd; static std::mt19937 rng = std::mt19937( rd()); } // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":65,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_ctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_ctrl.h #  PLDS controller type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::Controller PLDS Controller Type.    Detailed Description #  This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/)), including state estimation.\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_CTRL_H #define LDSCTRLEST_LDS_POISSON_CTRL_H  // namespace #include \u0026#34;lds_poisson.h\u0026#34;// system type #include \u0026#34;lds_poisson_sys.h\u0026#34;// control type #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace poisson { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); lds::Limit(y_ref_, kYRefLb, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available  using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; private: constexpr static const data_t kYRefLb = 1e-4; }; } // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":66,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/","title":"ldsCtrlEst_h/lds_poisson_fit.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_fit.h #  PLDS base fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::Fit PLDS Fit Type.    Detailed Description #  This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_FIT_H #define LDSCTRLEST_LDS_POISSON_FIT_H  // namespace #include \u0026#34;lds_poisson.h\u0026#34;// fit #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace poisson { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) : lds::Fit(n_u, n_x, n_y, dt){}; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = exp(C_ * x.col(t) + d_); return y.col(t); }; void set_R(const Matrix\u0026amp; R) override { std::cerr \u0026lt;\u0026lt; \u0026#34;WARNING: Cannot set R (R[0] = \u0026#34; \u0026lt;\u0026lt; R.at(0) \u0026lt;\u0026lt; \u0026#34;). No Gaussian measurement noise in Poisson observation model.\\n\u0026#34;; }; const Matrix\u0026amp; R() const override { return R_; }; }; }; // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":67,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_em.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_fit_em.h #  PLDS E-M fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::FitEM PLDS E-M Fit Type.    Detailed Description #  This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\n[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H #define LDSCTRLEST_LDS_POISSON_FIT_EM_H  #include \u0026#34;lds_fit_em.h\u0026#34;#include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override{}; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; data_t NewtonSolveC(); void AnalyticalSolveD(); }; } // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":68,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_ssid.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_fit_ssid.h #  PLDS SSID fit type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS.    Detailed Description #  This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitssid/)).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H #define LDSCTRLEST_LDS_POISSON_FIT_SSID_H  #include \u0026#34;lds_fit_ssid.h\u0026#34;#include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; private: void DecomposeData() override; void CalcCov(); void PoissonToGaussianMoments(); Matrix cov_; }; } // namespace poisson } // namespace lds #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":69,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_sctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_sctrl.h #  PLDS switched controller type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::SwitchedController Poisson-observation SwitchedController Type.    Detailed Description #  This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H  #include \u0026#34;lds_poisson_ctrl.h\u0026#34;#include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace poisson { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); lds::Limit(y_ref_, kYRefLB, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available  using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; private: constexpr static data_t kYRefLB = 1e-4; }; } // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":70,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/","title":"ldsCtrlEst_h/lds_poisson_sys.h","section":"Files","content":"ldsCtrlEst_h/lds_poisson_sys.h #  PLDS base type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.   lds::poisson Linear Dynamical Systems with Poisson observations.    Classes #      Name     class lds::poisson::System Poisson System type.    Detailed Description #  This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)).\nSource code #  //===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_POISSON_SYS_H #define LDSCTRLEST_LDS_POISSON_SYS_H  // namespace #include \u0026#34;lds_poisson.h\u0026#34;// system #include \u0026#34;lds_sys.h\u0026#34; // needed for Poisson random number generation #include \u0026lt;random\u0026gt; namespace lds { namespace poisson { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; protected: void h() override { cx_ = C_ * x_; y_ = exp(cx_ + d_); diag_y_.diag() = y_; }; void RecurseKe() override; private: // Poisson-output-specific  Matrix diag_y_; std::poisson_distribution\u0026lt;size_t\u0026gt; pd_; }; // System } // namespace poisson } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":71,"href":"/lds-ctrl-est/docs/api/files/lds__sctrl_8h/","title":"ldsCtrlEst_h/lds_sctrl.h","section":"Files","content":"ldsCtrlEst_h/lds_sctrl.h #  SwitchedController type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::SwitchedController SwitchedController Type.    Detailed Description #  This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code #  //===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_SCTRL_H #define LDSCTRLEST_LDS_SCTRL_H  #include \u0026#34;lds_ctrl.h\u0026#34;#include \u0026#34;lds_uniform_mats.h\u0026#34;#include \u0026#34;lds_uniform_vecs.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class SwitchedController : public Controller\u0026lt;System\u0026gt; { public: SwitchedController() = default; SwitchedController(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); SwitchedController(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); void Switch(size_t idx, bool do_force_switch = false); void set_Kc(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc) { Kc_list_ = Kc; Kc_ = Kc_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc) { Kc_list_ = std::move(Kc); Kc_ = Kc_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_inty) { Kc_inty_list_ = Kc_inty; Kc_inty_ = Kc_inty_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_inty) { Kc_inty_list_ = std::move(Kc_inty); Kc_inty_ = Kc_inty_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_u) { Kc_u_list_ = Kc_u; Kc_u_ = Kc_u_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_u) { Kc_u_list_ = std::move(Kc_u); Kc_u_ = Kc_u_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(const UniformVectorList\u0026amp; g) { g_design_list_ = g; g_design_ = g_design_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(UniformVectorList\u0026amp;\u0026amp; g) { g_design_list_ = std::move(g); g_design_ = g_design_list_.at(0); // set to first  if (idx_ != 0) { Switch(idx_, true); } }; // make sure base class template methods available  using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; protected: std::vector\u0026lt;System\u0026gt; systems_; size_t n_sys_{}; size_t idx_{}; // controller gains could be different for each  UniformMatrixList\u0026lt;\u0026gt; Kc_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_inty_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_u_list_; // design-phase input gain could also be different  UniformVectorList g_design_list_; // TODO(mfbolus): not sure why I need to do this.  using Controller\u0026lt;System\u0026gt;::Kc_; using Controller\u0026lt;System\u0026gt;::Kc_inty_; using Controller\u0026lt;System\u0026gt;::Kc_u_; using Controller\u0026lt;System\u0026gt;::g_design_; using Controller\u0026lt;System\u0026gt;::sys_; // using Controller\u0026lt;System\u0026gt;::u_ref_;  // using Controller\u0026lt;System\u0026gt;::x_ref_;  // using Controller\u0026lt;System\u0026gt;::y_ref_;  //  using Controller\u0026lt;System\u0026gt;::control_type_; private: void InitVars(); using lds::Controller\u0026lt;System\u0026gt;::set_sys; // using Controller\u0026lt;System\u0026gt;::set_Kc;  // using Controller\u0026lt;System\u0026gt;::set_Kc_inty;  // using Controller\u0026lt;System\u0026gt;::set_Kc_u;  // using Controller\u0026lt;System\u0026gt;::set_g_design; }; template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(systems.at(0), u_lb, u_ub, control_type), systems_(systems) { InitVars(); } template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(System(systems.at(0).n_u(), systems.at(0).n_x(), systems.at(0).n_y(), systems.at(0).dt()), u_lb, u_ub, control_type), systems_(std::move(systems)) { InitVars(); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::InitVars() { n_sys_ = systems_.size(); sys_ = systems_.at(0); Kc_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_)); Kc_inty_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); Kc_u_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); g_design_list_ = UniformVectorList(std::vector\u0026lt;Vector\u0026gt;(n_sys_, g_design_)); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::Switch(size_t idx, bool do_force_switch) { if ((idx == idx_) \u0026amp;\u0026amp; !do_force_switch) { return; // already there.  } // put old up and get new one out  systems_.at(idx_) = std::move(sys_); sys_ = std::move(systems_.at(idx)); // set the state of this system to that of the previous system  // TODO(mfbolus): This will only work as intended if state matrix is the same.  // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp.  sys_.set_m(systems_.at(idx_).m(), true); sys_.set_x(systems_.at(idx_).x()); // swap controller gains  Kc_list_.Swap(Kc_, idx_); Kc_list_.Swap(Kc_, idx); if (control_type_ \u0026amp; kControlTypeIntY) { Kc_inty_list_.Swap(Kc_inty_, idx_); Kc_inty_list_.Swap(Kc_inty_, idx); } if (control_type_ \u0026amp; kControlTypeDeltaU) { Kc_u_list_.Swap(Kc_u_, idx_); Kc_u_list_.Swap(Kc_u_, idx); } g_design_list_.Swap(g_design_, idx_); g_design_list_.Swap(g_design_, idx); idx_ = idx; } // Switch  } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":72,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8h/","title":"ldsCtrlEst_h/lds_sys.h","section":"Files","content":"ldsCtrlEst_h/lds_sys.h #  LDS base type. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::System Linear Dynamical System Type.    Detailed Description #  This file declares and partially defines the base type for linear dynamical systems ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code #  //===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_SYS_H #define LDSCTRLEST_LDS_SYS_H  #include \u0026#34;lds.h\u0026#34; namespace lds { class System { public: System() = default; System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); virtual ~System() {} void Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z); virtual const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) = 0; void f(const Vector\u0026amp; u, bool do_add_noise = false) { x_ = A_ * x_ + B_ * (g_ % u) + m_; if (do_add_noise) { x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_); } }; virtual void h() = 0; size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Vector\u0026amp; x() const { return x_; }; const Matrix\u0026amp; P() const { return P_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; P_m() const { return P_m_; }; const Vector\u0026amp; cx() const { return cx_; }; const Vector\u0026amp; y() const { return y_; }; const Vector\u0026amp; x0() const { return x0_; }; const Vector\u0026amp; m0() const { return m0_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; const Matrix\u0026amp; Ke() const { return Ke_; }; const Matrix\u0026amp; Ke_m() const { return Ke_m_; }; const Matrix\u0026amp; Q() { return Q_; }; const Matrix\u0026amp; Q_m() { return Q_m_; }; const Matrix\u0026amp; P0() { return P0_; }; const Matrix\u0026amp; P0_m() { return P0_m_; }; void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_m(const Vector\u0026amp; m, bool do_force_assign=false) { Reassign(m0_, m); if ((!do_adapt_m) || do_force_assign) { Reassign(m_, m); } }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); }; void set_Q_m(const Matrix\u0026amp; Q_m) { Reassign(Q_m_, Q_m); }; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); }; void set_P0_m(const Matrix\u0026amp; P0_m) { Reassign(P0_m_, P0_m); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; void set_x(const Vector\u0026amp; x) { Reassign(x_, x); h(); }; void Reset(); void Print(); // safe to leave this public and non-const  bool do_adapt_m{}; protected: virtual void RecurseKe() = 0; void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); std::size_t n_x_{}; std::size_t n_u_{}; std::size_t n_y_{}; data_t dt_{}; // Signals:  Vector x_; Matrix P_; Vector m_; Matrix P_m_; Vector cx_; Vector y_; Vector z_; // Parameters:  Vector x0_; Matrix P0_; Vector m0_; Matrix P0_m_; Matrix A_; Matrix B_; Vector g_; Matrix Q_; Matrix Q_m_; Matrix C_; Vector d_; Matrix Ke_; Matrix Ke_m_; }; // System  } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":73,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/","title":"ldsCtrlEst_h/lds_uniform_mats.h","section":"Files","content":"ldsCtrlEst_h/lds_uniform_mats.h #  List of uniformly sized matrices. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::UniformMatrixList    Detailed Description #  This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.\nSource code #  //===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H #define LDSCTRLEST_LDS_UNIFORM_MATS_H  #include \u0026lt;array\u0026gt; // std::array#include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { template \u0026lt;MatrixListFreeDim D = kMatFreeDimNone\u0026gt; class UniformMatrixList : public std::vector\u0026lt;Matrix\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion  // using std::vector\u0026lt;Matrix\u0026gt;::vector;  using std::vector\u0026lt;Matrix\u0026gt;::operator=; using std::vector\u0026lt;Matrix\u0026gt;::operator[]; using std::vector\u0026lt;Matrix\u0026gt;::begin; using std::vector\u0026lt;Matrix\u0026gt;::end; using std::vector\u0026lt;Matrix\u0026gt;::size; public: using std::vector\u0026lt;Matrix\u0026gt;::at; UniformMatrixList() = default; explicit UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); explicit UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; ~UniformMatrixList() = default; const std::array\u0026lt;size_t, 2\u0026gt;\u0026amp; dim(size_t n = 0) const { return dim_.at(n); } size_t size() { return std::vector\u0026lt;Matrix\u0026gt;::size(); }; const Matrix\u0026amp; at(size_t n) { return std::vector\u0026lt;Matrix\u0026gt;::at(n); }; void Swap(Matrix\u0026amp; that, size_t n); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim); std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt; dim_; }; template \u0026lt;MatrixListFreeDim D\u0026gt; inline void UniformMatrixList\u0026lt;D\u0026gt;::Swap(Matrix\u0026amp; that, size_t n) { // make sure request in range  if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim  bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.n_cols); } if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap  Matrix tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); if (D == kMatFreeDim1) { this-\u0026gt;dim_[n][0] = (*this)[n].n_rows; } if (D == kMatFreeDim2) { this-\u0026gt;dim_[n][1] = (*this)[n].n_cols; } } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) { // make sure dim_ vector is initialized  if (dim_.empty()) { dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(that.size(), {0, 0}); } // check dimensions  if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices\u0026#34;; throw std::runtime_error(ss.str()); } // if dimensions a not zero and do not match, skip move with error message.  bool dims_nonzero = true; for (auto d : dim_) { if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; d[0] \u0026lt; 1) { dims_nonzero = false; break; } if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; d[1] \u0026lt; 1) { dims_nonzero = false; break; } } if (dims_nonzero) { bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); } if (!does_match) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrices of size \u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; dim_[k] = that.dim(k); } return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept { // // check dimensions  // // if empty, assume a default constructed object and safe to move  // if (!this-\u0026gt;empty()) {  // if (this-\u0026gt;size() != that.size()) {  // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34;  // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices. Skipping.\\n\u0026#34;;  // return (*this);  // }  //  // // if dimensions a not zero and do not match, skip move with error  // message. bool dims_nonzero = true; for (auto d : dim_) {  // if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; (d[0] \u0026lt; 1)) {  // dims_nonzero = false;  // break;  // }  // if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; (d[1] \u0026lt; 1)) {  // dims_nonzero = false;  // break;  // }  // }  //  // if (dims_nonzero) {  // bool does_match = true;  // if (!(D == kMatFreeDim1)) {  // does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows);  // }  //  // if (!(D == kMatFreeDim2)) {  // does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols);  // }  //  // if (!does_match) {  // this-\u0026gt;at(0).print(\u0026#34;this[0] = \u0026#34;);  // that.at(0).print(\u0026#34;that[0] = \u0026#34;);  // std::cerr  // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformMatrixList element of size (\u0026#34; \u0026lt;\u0026lt;  // that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols \u0026lt;\u0026lt; \u0026#34;) for an  // element of size (\u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34;).  // Skipping.\\n\u0026#34;;  // return (*this);  // }  // }  // }  dim_ = that.dim_; std::vector\u0026lt;Matrix\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(std::move(mats)) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) : vector(that) { (*this) = that; } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { std::array\u0026lt;size_t, 2\u0026gt; dim_k({this-\u0026gt;at(k).n_rows, this-\u0026gt;at(k).n_cols}); dim_.push_back(dim_k); } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim) { // change behavior based on free dim D  if ((dim[0] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim1)) { dim[0] = this-\u0026gt;at(0).n_rows; } if ((dim[1] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim2)) { dim[1] = this-\u0026gt;at(0).n_cols; } // make sure dimensiolaties are all uniform  bool does_match(true); for (const Matrix\u0026amp; mat : *this) { if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_rows == dim[0]); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_cols == dim[1]); } if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(this-\u0026gt;size(), dim); for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { dim_[k][0] = (*this)[k].n_rows; dim_[k][1] = (*this)[k].n_cols; } } } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":74,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/","title":"ldsCtrlEst_h/lds_uniform_systems.h","section":"Files","content":"ldsCtrlEst_h/lds_uniform_systems.h #  List of uniformly sized Systems. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::UniformSystemList    Detailed Description #  This file provides a container for uniformly sized Systems.\nSource code #  //===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H  #include \u0026lt;array\u0026gt; // std::array#include \u0026lt;vector\u0026gt; // std::vector // namespace #include \u0026#34;lds.h\u0026#34;// System type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class UniformSystemList : public std::vector\u0026lt;System\u0026gt; { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion  // using std::vector\u0026lt;System\u0026gt;::vector;  using std::vector\u0026lt;System\u0026gt;::operator=; using std::vector\u0026lt;System\u0026gt;::operator[]; using std::vector\u0026lt;System\u0026gt;::at; using std::vector\u0026lt;System\u0026gt;::begin; using std::vector\u0026lt;System\u0026gt;::end; using std::vector\u0026lt;System\u0026gt;::size; public: UniformSystemList() = default; explicit UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); explicit UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(const UniformSystemList\u0026amp; that); UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept; ~UniformSystemList() = default; const std::array\u0026lt;size_t, 3\u0026gt;\u0026amp; dim() const { return dim_; } size_t size() { return std::vector\u0026lt;System\u0026gt;::size(); }; const System\u0026amp; at(size_t n) { return std::vector\u0026lt;System\u0026gt;::at(n); }; void Swap(System\u0026amp; that, size_t n); UniformSystemList\u0026amp; operator=(const UniformSystemList\u0026amp; that); UniformSystemList\u0026amp; operator=(UniformSystemList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim); std::array\u0026lt;size_t, 3\u0026gt; dim_{}; }; template \u0026lt;typename System\u0026gt; inline void UniformSystemList\u0026lt;System\u0026gt;::Swap(System\u0026amp; that, size_t n) { // make sure request in range  if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformSystemList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim  bool does_match = (dim_[0] == that.n_u()) \u0026amp;\u0026amp; (dim_[1] == that.n_x()) \u0026amp;\u0026amp; (dim_[2] == that.n_y()); if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformSystemList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap  System tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( const UniformSystemList\u0026amp; that) { // check dimensions  if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_[0] + dim_[1] + dim_[2]) { std::array\u0026lt;size_t, 3\u0026gt; other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign systems of size \u0026#34; \u0026lt;\u0026lt; dim_[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2] \u0026lt;\u0026lt; \u0026#34; with systems of size \u0026#34; \u0026lt;\u0026lt; other_dim[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other_dim[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2]; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( UniformSystemList\u0026amp;\u0026amp; that) noexcept { // // check dimensions  // // if empty, assume a default constructed object and safe to move  // if (!this-\u0026gt;empty()) {  // if (this-\u0026gt;size() != that.size()) {  // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34;  // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems. Skipping.\\n\u0026#34;;  // return (*this);  // }  //  // // if dimensions a not zero and do not match, skip move with error  // message. if (dim_[0] + dim_[1] + dim_[2]) {  // bool does_match = (dim_[0] == that.at(0).n_u()) \u0026amp;\u0026amp;  // (dim_[1] == that.at(0).n_x()) \u0026amp;\u0026amp;  // (dim_[2] == that.at(0).n_y());  // if (!does_match) {  // std::cerr  // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformSystemList element for an element of \u0026#34;  // \u0026#34;different size. Skipping.\\n\u0026#34;;  // return (*this);  // }  // }  // }  dim_ = that.dim_; std::vector\u0026lt;System\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(std::move(systems)) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList( std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const UniformSystemList\u0026amp; that) : std::vector\u0026lt;System\u0026gt;(that) { (*this) = that; } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept : std::vector\u0026lt;System\u0026gt;(std::move(that)) { this-\u0026gt;dim_[0] = this-\u0026gt;at(0).n_u(); this-\u0026gt;dim_[1] = this-\u0026gt;at(0).n_x(); this-\u0026gt;dim_[2] = this-\u0026gt;at(0).n_y(); } template \u0026lt;typename System\u0026gt; void UniformSystemList\u0026lt;System\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim) { if (dim[0] + dim[1] + dim[2]) { dim_ = dim; } else { dim_[0] = this-\u0026gt;at(0).n_u(); dim_[1] = this-\u0026gt;at(0).n_x(); dim_[2] = this-\u0026gt;at(0).n_y(); } // make sure dimensiolaties are all uniform  bool does_match(true); for (const System\u0026amp; sys : *this) { does_match = does_match \u0026amp;\u0026amp; (sys.n_u() == dim_[0]); does_match = does_match \u0026amp;\u0026amp; (sys.n_x() == dim_[1]); does_match = does_match \u0026amp;\u0026amp; (sys.n_y() == dim_[2]); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input systems are not uniform.\u0026#34;); } } } } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":75,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/","title":"ldsCtrlEst_h/lds_uniform_vecs.h","section":"Files","content":"ldsCtrlEst_h/lds_uniform_vecs.h #  List of uniformly sized vectors. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Classes #      Name     class lds::UniformVectorList    Detailed Description #  This file provides a container for uniformly sized vectors.\nSource code #  //===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H #define LDSCTRLEST_LDS_UNIFORM_VECS_H  #include \u0026lt;array\u0026gt; // std::array#include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { class UniformVectorList : public std::vector\u0026lt;Vector\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion  // using std::vector\u0026lt;Vector\u0026gt;::vector;  using std::vector\u0026lt;Vector\u0026gt;::operator=; using std::vector\u0026lt;Vector\u0026gt;::operator[]; using std::vector\u0026lt;Vector\u0026gt;::at; using std::vector\u0026lt;Vector\u0026gt;::begin; using std::vector\u0026lt;Vector\u0026gt;::end; using std::vector\u0026lt;Vector\u0026gt;::size; public: UniformVectorList() = default; explicit UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim = 0); explicit UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim = 0); UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim = 0); UniformVectorList(const UniformVectorList\u0026amp; that); UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept; ~UniformVectorList() = default; size_t dim() const { return dim_; } size_t size() { return std::vector\u0026lt;Vector\u0026gt;::size(); }; const Vector\u0026amp; at(size_t n) { return std::vector\u0026lt;Vector\u0026gt;::at(n); }; void Swap(Vector\u0026amp; that, size_t n); UniformVectorList\u0026amp; operator=(const UniformVectorList\u0026amp; that); UniformVectorList\u0026amp; operator=(UniformVectorList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(size_t dim); size_t dim_{}; }; inline void UniformVectorList::Swap(Vector\u0026amp; that, size_t n) { // make sure request in range  if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim  bool does_match = dim_ == that.n_elem; if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap  Vector tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( const UniformVectorList\u0026amp; that) { // check dimensions  if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_) { size_t other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ \u0026lt;\u0026lt; \u0026#34; with vectors of size \u0026#34; \u0026lt;\u0026lt; other_dim; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( UniformVectorList\u0026amp;\u0026amp; that) noexcept { // // check dimensions  // if (!this-\u0026gt;empty()) {  // if (this-\u0026gt;size() != that.size()) {  // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34;  // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors. Skipping.\\n\u0026#34;;  // return (*this);  // }  //  // if (dim_) {  // size_t other_dim(that.dim());  // if (dim_ != other_dim) {  // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_  // \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; other_dim \u0026lt;\u0026lt; \u0026#34;.  // Skipping.\\n\u0026#34;;  // return (*this);  // }  // }  // }  dim_ = that.dim_; std::vector\u0026lt;Vector\u0026gt;::operator=(std::move(that)); return (*this); } } // namespace lds  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":76,"href":"/lds-ctrl-est/docs/api/files/mex__c__util_8h/","title":"ldsCtrlEst_h/mex_c_util.h","section":"Files","content":"ldsCtrlEst_h/mex_c_util.h #  arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) More\u0026hellip;\nNamespaces #     Name     armamexc arma/mex interface using Matlab C API    Detailed Description #  This file defines utility functions for interoperability between armadillo and Matlab/Octave\u0026rsquo;s C mex API.\nSource code #  //===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_MEXC_UTIL_H #define LDSCTRLEST_MEXC_UTIL_H  #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.h\u0026#34; // // If Matlab_FOUND, include matrix.h. // // (Octave does not need/have it.) // #ifdef Matlab_FOUND // #include \u0026#34;matrix.h\u0026#34; // #endif  namespace armamexc { template \u0026lt;class T\u0026gt; inline auto m2T_scalar(const mxArray *matlab_scalar) -\u0026gt; T { if (mxGetData(matlab_scalar)) { return static_cast\u0026lt;T\u0026gt;(mxGetScalar(matlab_scalar)); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return 0; } template \u0026lt;class T\u0026gt; inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false, bool strict = true) -\u0026gt; arma::Mat\u0026lt;T\u0026gt; { if (mxGetData(matlab_mat)) { const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat); if (n_dim == 2) { return arma::Mat\u0026lt;T\u0026gt;(static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)), mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem, strict); } mexErrMsgTxt(\u0026#34;Number of dimensions must be 2.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } // TODO(mfbolus): make these templated.  template \u0026lt;typename T\u0026gt; inline auto a2m_mat(arma::Mat\u0026lt;T\u0026gt; const \u0026amp;arma_mat) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_mat.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy.  std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Mat.\u0026#34;); return nullptr; } template \u0026lt;typename T\u0026gt; inline auto a2m_vec(arma::Col\u0026lt;T\u0026gt; const \u0026amp;arma_vec) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_vec.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy.  std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Col.\u0026#34;); return nullptr; } } // namespace armamexc  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":77,"href":"/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/","title":"ldsCtrlEst_h/mex_cpp_util.h","section":"Files","content":"ldsCtrlEst_h/mex_cpp_util.h #  arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) More\u0026hellip;\nNamespaces #     Name     armamexcpp arma/mex interface using Matlab C++ API    Detailed Description #  This file defines utility functions for interoperability between armadillo and Matlab\u0026rsquo;s C++ mex API.\nSource code #  //===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #ifndef LDSCTRLEST_MEXCPP_UTIL_H #define LDSCTRLEST_MEXCPP_UTIL_H  #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.hpp\u0026#34;#include \u0026#34;mexAdapter.hpp\u0026#34; namespace armamexcpp { template \u0026lt;class T\u0026gt; std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; m2a_cellmat(matlab::data::CellArray\u0026amp; matlab_cell) { size_t n_cells = matlab_cell.getNumberOfElements(); std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; arma_mat(n_cells, arma::Mat\u0026lt;T\u0026gt;(1, 1, arma::fill::zeros)); for (size_t k = 0; k \u0026lt; n_cells; k++) { matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = matlab_cell[k]; auto dims = matlab_mat.getDimensions(); arma_mat[k] = arma::Mat\u0026lt;T\u0026gt;(matlab_mat.release().get(), dims[0], dims[1]); } return arma_mat; }; template \u0026lt;class T\u0026gt; std::vector\u0026lt;T\u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt;T\u0026gt;\u0026amp; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); T* ptr = matlab_array.release().get(); std::vector\u0026lt;T\u0026gt; vec(ptr, ptr + n_elem); return vec; }; template \u0026lt;class T\u0026gt; arma::Col\u0026lt;T\u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); // T* ptr = matlab_array.release().get();  // arma::Col\u0026lt;T\u0026gt; vec(ptr, n_elem); //, false);  // TODO(mfbolus): for some reason, using the above pointer at times leads to  // getting garbage values. matlab array values may be stored in non-contiguous  // memory?  arma::Col\u0026lt;T\u0026gt; vec(n_elem, arma::fill::zeros); for (size_t k = 0; k \u0026lt; n_elem; k++) { vec[k] = matlab_array[k]; } return vec; }; template \u0026lt;class T\u0026gt; arma::Mat\u0026lt;T\u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { // ArrayDimensions == std::vector\u0026lt;size_t\u0026gt;  auto dims = matlab_array.getDimensions(); // T* ptr = matlab_array.release().get();  // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false)  // arma::Mat\u0026lt;T\u0026gt; mat(ptr, dims[0], dims[1]); //, false);  // TODO(mfbolus): for some reason, using the above pointer at times leads to  // getting garbage values. matlab array values may be stored in non-contiguous  // memory?  //  // armadillo and matlab both use column-major ordering, so this should work:  size_t n_elem = dims[0] * dims[1]; arma::Mat\u0026lt;T\u0026gt; mat(dims[0], dims[1], arma::fill::zeros); size_t k(0); for (auto m: matlab_array) { mat[k] = m; k++; } return mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_mat(const arma::Mat\u0026lt;T\u0026gt;\u0026amp; arma_mat, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(), arma_mat.memptr() + arma_mat.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_vec(const arma::Col\u0026lt;T\u0026gt;\u0026amp; arma_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;({arma_vec.n_elem, 1}, arma_vec.memptr(), arma_vec.memptr() + arma_vec.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; s2m_vec(const std::vector\u0026lt;T\u0026gt;\u0026amp; std_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size()); return matlab_mat; }; } // namespace armamexcpp  #endif  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":78,"href":"/lds-ctrl-est/docs/terminology/model/","title":"Models","section":"LDS C+E Documentation","content":"Model Definitions #  This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form:   \\[\r\\mathbf{x}_{t\u0026#43;1} = f\\left( \\mathbf{x}_{t}, \\mathbf{v}_{t} \\right) = \\mathbf{A} \\mathbf{x}_{t} \u0026#43; \\mathbf{B} \\mathbf{v}_{t} \u0026#43; \\mathbf{m}_{t} \u0026#43; \\mathbf{w}_{t}\r\\]   \\[\r\\mathbf{y}_{t} = h\\left( \\mathbf{x}_{t} \\right)\r\\]  t : time index\rx : system state\rv = g%u : input (e.g., in physical units used for model fit)\ru : control signal sent to actuator (e.g., in Volts)\ry : system output\rm : process disturbance\rw ~ N(0, Q) : process noise/disturbance\rA : state matrix\rB : input coupling matrix\rg : input gain (e.g., for converting to control signal actuator voltage)\rn.b., assumes this conversion is linear\rQ : process noise covariance\r% : element-wise multiplication\r LDS with Gaussian Observations #  For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.\n \\[\r\\mathbf{y}_{t} = \\mathbf{C} \\mathbf{x}_{t} \u0026#43; \\mathbf{d}\r\\]   \\[\r\\mathbf{z}_{t} \\sim \\mathcal{N}\\left(\\mathbf{y}_{t} , \\mathbf{R} \\right)\r\\]  z : measurement\rC : output matrix\rd : output bias\rR : measurement noise covariance\r LDS with Poisson Observations #  For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.\n \\[\ry_{t}^{i} = \\exp \\left(\\mathbf{c}^i \\mathbf{x}_{t} \u0026#43; d^i\\right)\r\\]   \\[\rz_{t}^i \\sim \\rm{Poisson} \\left(y_{t}^i \\right)\r\\]  i : output index\rz : measurement (count data)\rc : i^th row of output matrix (C)\rd : output bias\r "},{"id":79,"href":"/lds-ctrl-est/docs/api/modules/","title":"Modules","section":"LDS C+E Documentation","content":"Modules #    Control Mode Bit Masks provides fill types for constructing new armadillo vectors, matrices\n  Defaults\n   Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":80,"href":"/lds-ctrl-est/docs/api/namespaces/","title":"Namespaces","section":"LDS C+E Documentation","content":"Namespaces #    armamexc arma/mex interface using Matlab C API\n  armamexcpp arma/mex interface using Matlab C++ API\n  lds::gaussian Linear Dynamical Systems with Gaussian observations.\n  lds::poisson Linear Dynamical Systems with Poisson observations.\n   Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":81,"href":"/lds-ctrl-est/docs/api/pages/","title":"Pages","section":"LDS C+E Documentation","content":"Pages #   Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":82,"href":"/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/","title":"PLDS State Estimation","section":"LDS C+E Examples","content":"PLDS State Estimation Tutorial #  This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.\nThe full code for this can be found here.\nPreamble #  In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system #  A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; // no. inputs  size_t n_x = 1; // no. states  size_t n_y = 1; // no. outputs  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system...  lds::poisson::System system_true(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example,   \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\]   \\[\ry_{t} = \\exp\\left(x_t\\right)\r\\]  where  \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\)  .\nNow, create non-default parameters for this model.\n// Model parameters  Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state Finally, assign the parameters using corresponding set-methods.\n// Assign params.  system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); Creating the estimator #  Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.\n// Construct system for estimation  // e.g., will create a model with incorrect disturbance  lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object  system_estimator = system_true; // wrong disturbance  Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state  system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. To ensure robust estimates, adaptively re-estimate the process disturbance.\n// turn on adaptive disturbance estimation  system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting  // on random-walk evolution of m  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); Simulating estimation #  In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.\n// Simlate the true system.  z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update)  system_estimator.Filter(u.col(t - 1), z.col(t)); Example simulation result #  Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.\nWith this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.\n"},{"id":83,"href":"/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/","title":"PLDS Switched Control","section":"LDS C+E Examples","content":"PLDS Switched Control Tutorial #  This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.\nIn the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.\nThe full code for this can be found here.\nPreamble #  In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating the simulated system #  A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// whether to do switched control  bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz  data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation.  auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); The system\u0026rsquo;s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.\n// for simulating switching  size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2  data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 Initially, the system will be in \u0026ldquo;mode\u0026rdquo; 1, where B = b1.\n// simulated system being controlled  lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well  // characterized by two LDS models with different input matrices.**  data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.\nCreating the controller #  Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.\nSimilar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.\nMoreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.\nPutting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.\n// create switched controller  lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains  { // create switched controller sub-systems  // system 1  lds::poisson::System sys1(controlled_system); // set process noise covariance  Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m)  // n.b. using arbitrary default value for process noise if enabled.  sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset:  Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions  // system 2  lds::poisson::System sys2 = sys1; // set parameters  sys2.set_B(b2); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying systems:  Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive.  k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nNow that the SwitchedController is instantiated, assign its parameters.\n// Control variables  size_t control_type = 0; // no integral action, etc  switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); Simulating control #  In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).\nThe control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.\n// Let the controlled system stochastically change gain  // Assume another algorithm decodes this mode change and signals the  // switched_controller  Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1  { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2  if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system.  z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control  u.col(t) = switched_controller.ControlOutputReference(z.col(t)); Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system\u0026rsquo;s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.\nExample simulation result #  Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller\u0026rsquo;s online estimates of the output and state are shown in purple.\nNote that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.\n"},{"id":84,"href":"/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/","title":"src","section":"Files","content":"src #  Files #     Name     src/lds.cpp misc lds namespace functions   src/lds_gaussian_sys.cpp GLDS base type.   src/lds_poisson_sys.cpp PLDS base type.   src/lds_sys.cpp LDS base type.   src/lds_uniform_vecs.cpp Uniformly sized vectors.     Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":85,"href":"/lds-ctrl-est/docs/api/files/lds_8cpp/","title":"src/lds.cpp","section":"Files","content":"src/lds.cpp #  misc lds namespace functions More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Detailed Description #  This file implements miscellaneous lds namespace functions not bound to a class.\nSource code #  //===-- lds.cpp - LDS -----------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds.h\u0026gt; // insert any necessary function definitions here. namespace lds { void ForceSymPD(Matrix\u0026amp; X) { if (X.is_sympd() || !X.is_square()) { return; } // make symmetric  X = (X + X.t()) / 2; // for eigenval decomp  bool did_succeed(true); Vector d; Matrix u; // see first method (which may not be ideal):  // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/  size_t k(1); bool is_sympd = X.is_sympd(); Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye); while (!is_sympd) { if (k \u0026gt; 100) { did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); data_t min_eig = arma::min(d); std::cerr \u0026lt;\u0026lt; \u0026#34;After multiple iterations, min eigen val = \u0026#34; \u0026lt;\u0026lt; min_eig \u0026lt;\u0026lt; \u0026#34;.\\n\u0026#34;; throw std::runtime_error( \u0026#34;Failed to make matrix symmetric positive definite.\u0026#34;); return; } // Limit(d, arma::eps(0), kInf); // force to be positive...  // Matrix d_diag = arma::diagmat(d);  // X = u * d_diag * u.t();  did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymPD failed.\u0026#34;); } data_t min_eig = arma::min(d); X += id * abs(min_eig) + arma::datum::eps; // make sure symm:  X = (X + X.t()) / 2; // double check eigenvals positive after symmetrizing:  arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); min_eig = arma::min(d); is_sympd = min_eig \u0026gt; 0; k++; } } void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min) { if (!X.is_square()) { return; } // make symmetric  X = (X + X.t()) / 2; bool did_succeed(true); Vector d; Matrix u; did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymMinEig failed.\u0026#34;); } Limit(d, eig_min + arma::eps(eig_min), kInf); // enforce lower bound  Matrix d_diag = arma::diagmat(d); X = u * d_diag * u.t(); // double check symmetric  X = (X + X.t()) / 2; } void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X) { bool did_succeed(true); did_succeed = arma::qr_econ(Qt, L, X.t()); if (!did_succeed) { throw std::runtime_error(\u0026#34;LQ decomposition failed.\u0026#34;); } arma::inplace_trans(L); arma::inplace_trans(Qt); } Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B) { // subtract out mean  auto m_a = arma::mean(A, 1); Matrix a0 = A; a0.each_col() -= m_a; auto m_b = arma::mean(B, 1); Matrix b0 = B; b0.each_col() -= m_b; Matrix cov = a0 * b0.t() / a0.n_cols; return cov; } } // namespace lds  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":86,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/","title":"src/lds_gaussian_sys.cpp","section":"Files","content":"src/lds_gaussian_sys.cpp #  GLDS base type. More\u0026hellip;\nDetailed Description #  This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code #  //===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds_gaussian_sys.h\u0026gt; lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0, data_t r0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { R_.zeros(n_y, n_y); R_.diag().fill(r0); do_recurse_Ke_=true; }; // recursively estimate Ke void lds::gaussian::System::RecurseKe() { if (!do_recurse_Ke_) { return; } // predict covariance  P_ = A_ * P_ * A_.t() + Q_; // calc Kalman gain  Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_); // update covariance  // Reference: Ghahramani et Hinton (1996)  P_ = P_ - Ke_ * C_ * P_; if (do_adapt_m) { P_m_ += Q_m_; // A_m = I (i.e., random walk)  Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_); P_m_ = P_m_ - Ke_m_ * C_ * P_m_; } } // Simulate const lds::Vector\u0026amp; lds::gaussian::System::Simulate(const Vector\u0026amp; u_tm1){ f(u_tm1, true);//simulate dynamics with noise added  h();//output  z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure  return z_; } void lds::gaussian::System::Print() { lds::System::Print(); std::cout \u0026lt;\u0026lt; \u0026#34;R: \\n\u0026#34; \u0026lt;\u0026lt; R_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":87,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/","title":"src/lds_poisson_sys.cpp","section":"Files","content":"src/lds_poisson_sys.cpp #  PLDS base type. More\u0026hellip;\nDetailed Description #  This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code #  //===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds_poisson_sys.h\u0026gt; lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { diag_y_ = diagmat(y_); pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(0); }; // Correct: Given measurement (z) and current input (u), update estimate of the // state, covar, output. // // see Eden et al. 2004 void lds::poisson::System::RecurseKe() { // predict covariance  P_ = A_ * P_ * A_.t() + Q_; // update cov  P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_); Ke_ = P_ * C_.t(); if (do_adapt_m) { P_m_ += Q_m_; // predict (A_m = I)  P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_); // update  Ke_m_ = P_m_ * C_.t(); } } // Simulate Measurement: z ~ Poisson(y) const lds::Vector\u0026amp; lds::poisson::System::Simulate(const Vector\u0026amp; u_tm1) { f(u_tm1, true); // simulate dynamics with noise added  h(); // output  z_.zeros(); for (std::size_t k = 0; k \u0026lt; n_y_; k++) { // construct a Poisson distribution object with mean y[k]  pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(y_[k]); // pull random sample from this distribution  z_[k] = pd_(rng); } return z_; } // ******************* SYS_T *******************  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":88,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8cpp/","title":"src/lds_sys.cpp","section":"Files","content":"src/lds_sys.cpp #  LDS base type. More\u0026hellip;\nDetailed Description #  This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code #  //===-- lds_sys.cpp - LDS -------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds_sys.h\u0026gt; lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) { InitVars(p0, q0); } void lds::System::InitVars(data_t p0, data_t q0) { // initial conditions.  x0_ = Vector(n_x_, fill::zeros); // includes bias (nY) and g (nU)  P0_ = p0 * Matrix(n_x_, n_x_, fill::eye); m0_ = x0_; P0_m_ = P0_; // signals  x_ = x0_; P_ = P0_; m_ = m0_; P_m_ = P0_m_; y_ = Vector(n_y_, fill::zeros); cx_ = Vector(n_y_, fill::zeros); z_ = Vector(n_y_, fill::zeros); // By default, random walk where each state is independent  // In this way, provides independent estimates of rate per channel of output.  A_ = Matrix(n_x_, n_x_, fill::eye); B_ = Matrix(n_x_, n_u_, fill::zeros); g_ = Vector(n_u_, fill::ones); Q_ = q0 * Matrix(n_x_, n_x_, fill::eye); Q_m_ = Q_; C_ = Matrix(n_y_, n_x_, fill::eye); // each state will map to an output by  d_ = Vector(n_y_, fill::zeros); Ke_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain.  Ke_m_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain for m adaptation.  do_adapt_m = false; } // Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state // and update estimate of the state, covar, output using Kalman filter void lds::System::Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z_t) { // predict mean  f(u_tm1); // dynamics  h(); // output  // recursively calculate esimator gains (or just keep existing values)  // (also predicts+updates estimate covariance)  RecurseKe(); // update  x_ += Ke_ * (z_t - y_); if (do_adapt_m) { m_ += Ke_m_ * (z_t - y_); // adaptively estimating disturbance  } // With new state, estimate output.  h(); // --\u0026gt; posterior } void lds::System::Reset() { // reset to initial conditions  x_ = x0_; // mean  P_ = P0_; // cov of state estimate  m_ = m0_; // process disturbance  P_m_ = P0_m_; // cov of disturbance estimate  h(); } void lds::System::Print() { std::cout \u0026lt;\u0026lt; \u0026#34;\\n********** SYSTEM ********** \\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;x: \\n\u0026#34; \u0026lt;\u0026lt; x_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;P: \\n\u0026#34; \u0026lt;\u0026lt; P_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;A: \\n\u0026#34; \u0026lt;\u0026lt; A_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B: \\n\u0026#34; \u0026lt;\u0026lt; B_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;g: \\n\u0026#34; \u0026lt;\u0026lt; g_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;m: \\n\u0026#34; \u0026lt;\u0026lt; m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q: \\n\u0026#34; \u0026lt;\u0026lt; Q_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q_m: \\n\u0026#34; \u0026lt;\u0026lt; Q_m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d: \\n\u0026#34; \u0026lt;\u0026lt; d_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;C: \\n\u0026#34; \u0026lt;\u0026lt; C_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;y: \\n\u0026#34; \u0026lt;\u0026lt; y_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } //******************* SYS_T *******************  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"},{"id":89,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/","title":"src/lds_uniform_vecs.cpp","section":"Files","content":"src/lds_uniform_vecs.cpp #  Uniformly sized vectors. More\u0026hellip;\nNamespaces #     Name     lds Linear Dynamical Systems (LDS) namespace.    Detailed Description #  This file provides a container for uniformly sized vectors.\nSource code #  //===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//  #include \u0026lt;ldsCtrlEst_h/lds_uniform_vecs.h\u0026gt; namespace lds { UniformVectorList::UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); } UniformVectorList::UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim) : vector(std::move(vecs)) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(const UniformVectorList\u0026amp; that) : vector(that) { (*this) = that; } UniformVectorList::UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { this-\u0026gt;dim_ = this-\u0026gt;at(0).n_elem; } void UniformVectorList::CheckDimensions(size_t dim) { if (dim) { dim_ = dim; } else { dim_ = this-\u0026gt;at(0).n_elem; } // make sure dimensiolaties are all uniform  bool does_match(true); for (const Vector\u0026amp; vec : *this) { does_match = does_match \u0026amp;\u0026amp; (vec.n_elem == dim_); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } } } // namespace lds  Updated on 4 May 2022 at 16:59:51 Eastern Daylight Time\n"}]
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-<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
-<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
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-<p><br /></p>
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-<li><strong>Tutorials</strong>
-<ul>
-<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
-<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
-<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
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-  <article class="markdown"><h1 id="ldsctrlest-lds-control--estimation">
-  <code>ldsCtrlEst</code>: LDS Control &amp; Estimation
-  <a class="anchor" href="#ldsctrlest-lds-control--estimation">#</a>
-</h1>
-<p><code>ldsCtrlEst</code> is a C++ library for estimation and control of linear dynamical systems (LDS) with Gaussian or Poisson observations. It is meant to provide the functionality necessary to implement feedback control of linear dynamical systems experimentally. This library was originally developed for the task of controlling neuronal activity using spike count data as feedback and optogenetic inputs for control. However, the methods are generally applicable.</p>
-<p>This library currently provides three namespaces.</p>
-<ul>
-<li><code>lds</code> : linear dynamical systems (without output/observations)</li>
-<li><code>lds::gaussian</code>: linear dynamical systems with Gaussian observations</li>
-<li><code>lds::poisson</code>: linear dynamical systems with Poisson observations</li>
-</ul>
-<p><em>Future iterations may include an additional namespace for LDS with Bernoulli observations (<code>lds::bernoulli</code>).</em></p>
-<h1 id="project-scope">
-  Project Scope
-  <a class="anchor" href="#project-scope">#</a>
-</h1>
-<p>The goal of this project is to provide necessary functions to implement feedback control of linear dynamical systems experimentally: <em>i.e.</em>, online estimation of state feedback and calculation of control signal updates. Given its intended use in experiments, the library seeks to be <strong>practical</strong> in all things and thus includes optional features such as adaptive estimation of a process disturbance to improve robustness in state estimation and a mechanism for combatting integrator windup with control signal saturation. For cases where the system to be controlled is not adequately modeled as having linear dynamics but has multiple quasi-linear operating modes, a switched control scheme is also implemented. It switches between multiple controllers designed for each operating mode of the physical system as it changes. It also includes options to toggle on/off feedback control and state estimation independently, which can be practically useful when testing the components of the control system. Moreover, to avoid the need for numerical integration of continuous-time models, all state-space models used here are discrete-time.</p>
-<p>Generally, the <code>ldsCtrlEst</code> library does <strong>not</strong> endeavor to provide functionality for things that can be carried out offline/before experiments. For example, it does not design controller gains. Given a model of the system to be controlled, these parameters may be optimized before experimental application in most cases, and there are numerous options available to scientists/engineers in languages such as Matlab and Python for design. An exception to this guiding principle to project scope is the included code for fitting state-space models to data. Currently, this fitting portion of the library is a configurable option, but in future releases this may migrate to a separate project as it is not intended for online use.</p>
-<p>Among other things, this project also does not provide methods for trajectory optimization, linearization of nonlinear models, or other methods related to nonlinear control, with the exception of the nonlinear state estimator for Poisson-output LDS models.</p>
-<h1 id="repository-design">
-  Repository Design
-  <a class="anchor" href="#repository-design">#</a>
-</h1>
-<p><img src="/lds-ctrl-est/classlds_1_1_system__inherit__graph.png" alt="system class hierarchy" /></p>
-<ul>
-<li>All dynamical systems <em>with observations</em> (<code>lds::gaussian::System</code>, <code>lds::poisson::System</code>) are derived from a prototypical linear dynamical system abstract type (<code>lds::System</code>).</li>
-<li>These Gaussian and Poisson system types include user-accessible functions for one-step filtering for one-step simulation, etc.</li>
-</ul>
-<p><img src="/lds-ctrl-est/classlds_1_1_controller__inherit__graph.png" alt="controller class hierarchy" /></p>
-<ul>
-<li>The controller types for Gaussian- and Poisson-output systems (<code>lds::gaussian::Controller</code>, <code>lds::gaussian::SwitchedController</code>, <code>lds::poisson::Controller</code>, <code>lds::poisson::SwitchedController</code>) are derived from an abstract class template (<code>lds::Controller</code>) that is generic over LDS types derived from <code>lds::System</code> (here, <code>lds::gaussian::System</code> and <code>lds::poisson::System</code>). <code>lds::Controller</code> provides functions for one-step updates of the control signal, based on feedback and a target/reference signal. For the common problem of output reference tracking, the controller uses the underlying system model to estimate the control signal required to track the target, effectively providing model-based open-loop control if the estimator is disabled.</li>
-<li>In order to ensure dimensionalities always match internally, every property of a system/controller class is <code>protected</code> or <code>private</code>. Get methods provide read-only references for most signals/parameters. Where appropriate, set methods are defined so users can change hidden parameters if and only if the new parameter has the correct dimensions.</li>
-</ul>
-<h1 id="repository-organization">
-  Repository Organization
-  <a class="anchor" href="#repository-organization">#</a>
-</h1>
-<ul>
-<li>Header files are located under <code>include/ldsEstCtrl_h</code>.</li>
-<li>Source files are located under <code>src/</code> (main source code) and <code>src-fit/</code> (model fitting-related source code).</li>
-<li>Wrappers for exposing functions to Matlab as executables (mex) are located under <code>matlab/</code>. Currently, only fitting functions of the library are exposed for use in Matlab.</li>
-<li>Complimentary Matlab functions for control and estimation are also located under <code>matlab/</code>. They are provided as methods of <code>GLDS</code> and <code>PLDS</code> class definitions.</li>
-<li>Example programs and visualization scripts are located under <code>examples/</code>.</li>
-<li>Example programs that demonstrate how to use ldsCtrlEst in other projects are provided in <code>misc/</code>. See <code>misc/test-cmake-installation</code> for a project that uses <code>cmake</code> to configure your project build and <code>misc/test-pkgconfig-installation</code> which is the same but uses a hand-written Makefile and calls to pkg-config. As the names suggest, building these programs is a simple way to test your installation of ldsCtrlEst.</li>
-</ul>
-</article>
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-    <li><a href="#ldsctrlest-lds-control--estimation"><code>ldsCtrlEst</code>: LDS Control &amp; Estimation</a></li>
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-<?xml version="1.0" encoding="utf-8" standalone="yes"?>
-<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
-  <channel>
-    <title>LDS Control &amp; Estimation on LDS C&amp;E</title>
-    <link>https://stanley-rozell.github.io/lds-ctrl-est/</link>
-    <description>Recent content in LDS Control &amp; Estimation on LDS C&amp;E</description>
-    <generator>Hugo -- gohugo.io</generator><atom:link href="https://stanley-rozell.github.io/lds-ctrl-est/index.xml" rel="self" type="application/rss+xml" />
-    <item>
-      <title></title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/acknowledgements/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/acknowledgements/</guid>
-      <description>Acknowledgements #  Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.</description>
-    </item>
-    
-    <item>
-      <title></title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/getting-started/getting-started/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/getting-started/getting-started/</guid>
-      <description>Getting Started #  This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.
-ldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.
-Tested Configurations #  Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another.</description>
-    </item>
-    
-    <item>
-      <title></title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/getting-started/windows/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/getting-started/windows/</guid>
-      <description>Windows Installation #  Windows Pre-requisites #  Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:
-Set-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time iwr get.scoop.sh | Invoke-Expression Install Git and CMake if you don&amp;rsquo;t already have them:
-scoop install git cmake If that didn&amp;rsquo;t work, follow more detailed instructions here.
-The easiest way to compile C++ project on Windows is with Visual Studio&amp;rsquo;s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.</description>
-    </item>
-    
-    <item>
-      <title></title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/issues-contributing/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/issues-contributing/</guid>
-      <description>Reporting Issues #  If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.
-Contributing #  We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.</description>
-    </item>
-    
-    <item>
-      <title>armamexc</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/</guid>
-      <description>arma/mex interface using Matlab C API</description>
-    </item>
-    
-    <item>
-      <title>armamexcpp</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/</guid>
-      <description>arma/mex interface using Matlab C++ API</description>
-    </item>
-    
-    <item>
-      <title>C&amp;E</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/terminology/control-estimation/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/terminology/control-estimation/</guid>
-      <description>Control &amp;amp; Estimation #  The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.</description>
-    </item>
-    
-    <item>
-      <title>Control Mode Bit Masks</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__control__masks/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__control__masks/</guid>
-      <description>provides fill types for constructing new armadillo vectors, matrices</description>
-    </item>
-    
-    <item>
-      <title>Defaults</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__defaults/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__defaults/</guid>
-      <description>Defaults #  
-More&amp;hellip; Attributes #      Name     const data_t kDefaultP0 default state estimate covar   const data_t kDefaultQ0 default process noise covar   const data_t kDefaultR0 default output noise covar    Detailed Description #  Default values for common variables (e.g., default diagonal elements of covariances)
-Attribute Details #  kDefaultP0 #  static const data_t kDefaultP0 = 1e-6; kDefaultQ0 #  static const data_t kDefaultQ0 = 1e-6; kDefaultR0 #  static const data_t kDefaultR0 = 1e-2;  Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time</description>
-    </item>
-    
-    <item>
-      <title>eg_glds_ctrl.cpp</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/</guid>
-      <description>eg_glds_ctrl.cpp #  Example GLDS Control
-//===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the &amp;#34;License&amp;#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an &amp;#34;AS IS&amp;#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</description>
-    </item>
-    
-    <item>
-      <title>eg_glds_du_plds_ctrl.cpp</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/</guid>
-      <description>eg_glds_du_plds_ctrl.cpp #  Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).
-//===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the &amp;#34;License&amp;#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.</description>
-    </item>
-    
-    <item>
-      <title>eg_plds_ctrl.cpp</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/</guid>
-      <description>eg_plds_ctrl.cpp #  Example PLDS Control
-//===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the &amp;#34;License&amp;#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an &amp;#34;AS IS&amp;#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</description>
-    </item>
-    
-    <item>
-      <title>eg_plds_est.cpp</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/</guid>
-      <description>eg_plds_est.cpp #  Example PLDS Estimation
-//===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the &amp;#34;License&amp;#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an &amp;#34;AS IS&amp;#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</description>
-    </item>
-    
-    <item>
-      <title>eg_plds_switched_ctrl.cpp</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/</guid>
-      <description>eg_plds_switched_ctrl.cpp #  Example Switched PLDS Control
-//===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the &amp;#34;License&amp;#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an &amp;#34;AS IS&amp;#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</description>
-    </item>
-    
-    <item>
-      <title>examples</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/</guid>
-      <description>examples #  Files #     Name     examples/eg_glds_ctrl.cpp   examples/eg_glds_du_plds_ctrl.cpp   examples/eg_plds_ctrl.cpp   examples/eg_plds_est.cpp   examples/eg_plds_switched_ctrl.cpp     Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time</description>
-    </item>
-    
-    <item>
-      <title>examples/eg_glds_ctrl.cpp</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/</guid>
-      <description>examples/eg_glds_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Going to simulate a switching disturbance (m) acting on system
-Source code #  //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.</description>
-    </item>
-    
-    <item>
-      <title>examples/eg_glds_du_plds_ctrl.cpp</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/</guid>
-      <description>examples/eg_glds_du_plds_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Going to simulate a switching disturbance (m) acting on system
-Source code #  //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.</description>
-    </item>
-    
-    <item>
-      <title>examples/eg_plds_ctrl.cpp</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/</guid>
-      <description>examples/eg_plds_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Going to simulate a switching disturbance (m) acting on system
-Source code #  //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.</description>
-    </item>
-    
-    <item>
-      <title>examples/eg_plds_est.cpp</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/</guid>
-      <description>examples/eg_plds_est.cpp #  Functions #      Name     Matrix random_walk(size_t n_t, const Matrix &amp;amp; Q, const Vector &amp;amp; x0)   int main()    Function Details #  random_walk #  Matrix random_walk( size_t n_t, const Matrix &amp;amp; Q, const Vector &amp;amp; x0 ) main #  int main() Source code #  //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.</description>
-    </item>
-    
-    <item>
-      <title>examples/eg_plds_switched_ctrl.cpp</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/</guid>
-      <description>examples/eg_plds_switched_ctrl.cpp #  Functions #      Name     auto main()    Function Details #  main #  auto main() Source code #  //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the &amp;#34;License&amp;#34;); // you may not use this file except in compliance with the License.</description>
-    </item>
-    
-    <item>
-      <title>GLDS Control</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_glds_control/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_glds_control/</guid>
-      <description>GLDS Control Tutorial #  This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system.</description>
-    </item>
-    
-    <item>
-      <title>include</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/</guid>
-      <description>include #  Directories #     Name     ldsCtrlEst_h     Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time</description>
-    </item>
-    
-    <item>
-      <title>lds</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/</guid>
-      <description>Linear Dynamical Systems (LDS) namespace.</description>
-    </item>
-    
-    <item>
-      <title>lds::Controller</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1controller/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1controller/</guid>
-      <description>lds::Controller #  More&amp;hellip;
-Inherited by lds::gaussian::Controller, lds::poisson::Controller, lds::SwitchedController&amp;lt; System &amp;gt;
-Public Functions #      Name      Controller() =default
-Constructs a new Controller.    Controller(const System &amp;amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)
-Constructs a new Controller.    Controller(System &amp;amp;&amp;amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)
-Constructs a new Controller by moving the system object.</description>
-    </item>
-    
-    <item>
-      <title>lds::EM</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1em/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1em/</guid>
-      <description>lds::EM #  More&amp;hellip;
-Inherited by lds::gaussian::FitEM, lds::poisson::FitEM
-Public Functions #      Name      EM() =default
-Constructs a new EMFit type.    EM(size_t n_x, data_t dt, UniformMatrixList&amp;lt; kMatFreeDim2 &amp;gt; &amp;amp;&amp;amp; u_train, UniformMatrixList&amp;lt; kMatFreeDim2 &amp;gt; &amp;amp;&amp;amp; z_train)
-Constructs a new EMFit type.    EM(const Fit &amp;amp; fit0, UniformMatrixList&amp;lt; kMatFreeDim2 &amp;gt; &amp;amp;&amp;amp; u_train, UniformMatrixList&amp;lt; kMatFreeDim2 &amp;gt; &amp;amp;&amp;amp; z_train)
-Constructs a new EMFit type.</description>
-    </item>
-    
-    <item>
-      <title>lds::Fit</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1fit/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1fit/</guid>
-      <description>LDS Fit Type.</description>
-    </item>
-    
-    <item>
-      <title>lds::gaussian</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/</guid>
-      <description>Linear Dynamical Systems with Gaussian observations.</description>
-    </item>
-    
-    <item>
-      <title>lds::gaussian::Controller</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1controller/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1controller/</guid>
-      <description>Gaussian-observation Controller Type.</description>
-    </item>
-    
-    <item>
-      <title>lds::gaussian::Fit</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/</guid>
-      <description>GLDS Fit Type.</description>
-    </item>
-    
-    <item>
-      <title>lds::gaussian::FitEM</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitem/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitem/</guid>
-      <description>GLDS E-M Fit Type.</description>
-    </item>
-    
-    <item>
-      <title>lds::gaussian::FitSSID</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitssid/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitssid/</guid>
-      <description>Subspace Identification (SSID) for GLDS.</description>
-    </item>
-    
-    <item>
-      <title>lds::gaussian::SwitchedController</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1switchedcontroller/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1switchedcontroller/</guid>
-      <description>Gaussian-observation SwitchedController Type.</description>
-    </item>
-    
-    <item>
-      <title>lds::gaussian::System</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/</guid>
-      <description>Gaussian LDS Type.</description>
-    </item>
-    
-    <item>
-      <title>lds::poisson</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/</guid>
-      <description>Linear Dynamical Systems with Poisson observations.</description>
-    </item>
-    
-    <item>
-      <title>lds::poisson::Controller</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/</guid>
-      <description>PLDS Controller Type.</description>
-    </item>
-    
-    <item>
-      <title>lds::poisson::Fit</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/</guid>
-      <description>PLDS Fit Type.</description>
-    </item>
-    
-    <item>
-      <title>lds::poisson::FitEM</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitem/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitem/</guid>
-      <description>PLDS E-M Fit Type.</description>
-    </item>
-    
-    <item>
-      <title>lds::poisson::FitSSID</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitssid/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitssid/</guid>
-      <description>Subspace Identification (SSID) for PLDS.</description>
-    </item>
-    
-    <item>
-      <title>lds::poisson::SwitchedController</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1switchedcontroller/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1switchedcontroller/</guid>
-      <description>Poisson-observation SwitchedController Type.</description>
-    </item>
-    
-    <item>
-      <title>lds::poisson::System</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/</guid>
-      <description>Poisson System type.</description>
-    </item>
-    
-    <item>
-      <title>lds::SSID</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/</guid>
-      <description>lds::SSID #  More&amp;hellip;
-Inherited by lds::gaussian::FitSSID, lds::poisson::FitSSID
-Public Functions #      Name      SSID() =default
-Constructs a new SSIDFit type.    SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList&amp;lt; kMatFreeDim2 &amp;gt; &amp;amp;&amp;amp; u_train, UniformMatrixList&amp;lt; kMatFreeDim2 &amp;gt; &amp;amp;&amp;amp; z_train, const Vector &amp;amp; d =Vector(1).fill(-kInf))
-Constructs a new SSIDFit type.   std::tuple&amp;lt; Fit, Vector &amp;gt; Run(SSIDWt ssid_wt)
-Runs fitting by subspace identification (SSID)   std::tuple&amp;lt; UniformMatrixList&amp;lt; kMatFreeDim2 &amp;gt;, UniformMatrixList&amp;lt; kMatFreeDim2 &amp;gt; &amp;gt; ReturnData()</description>
-    </item>
-    
-    <item>
-      <title>lds::SwitchedController</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/</guid>
-      <description>SwitchedController Type.</description>
-    </item>
-    
-    <item>
-      <title>lds::System</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1system/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1system/</guid>
-      <description>Linear Dynamical System Type.</description>
-    </item>
-    
-    <item>
-      <title>lds::UniformMatrixList</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/</guid>
-      <description>lds::UniformMatrixList #  More&amp;hellip;
-Inherits from std::vector&amp;lt; Matrix &amp;gt;
-Public Functions #      Name      UniformMatrixList() =default
-Constructs a new UniformMatrixList.    UniformMatrixList(const std::vector&amp;lt; Matrix &amp;gt; &amp;amp; mats, std::array&amp;lt; size_t, 2 &amp;gt; dim ={0, 0})
-Constructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent.    UniformMatrixList(std::vector&amp;lt; Matrix &amp;gt; &amp;amp;&amp;amp; mats, std::array&amp;lt; size_t, 2 &amp;gt; dim ={0, 0})</description>
-    </item>
-    
-    <item>
-      <title>lds::UniformSystemList</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/</guid>
-      <description>lds::UniformSystemList #  More&amp;hellip;
-Inherits from std::vector&amp;lt; System &amp;gt;
-Public Functions #      Name      UniformSystemList() =default
-Constructs a new UniformSystemList.    UniformSystemList(const std::vector&amp;lt; System &amp;gt; &amp;amp; systems, std::array&amp;lt; size_t, 3 &amp;gt; dim ={0, 0, 0})
-Constructs a new UniformSystemList by copying existing vector of System if dimensions consistent.    UniformSystemList(std::vector&amp;lt; System &amp;gt; &amp;amp;&amp;amp; systems, std::array&amp;lt; size_t, 3 &amp;gt; dim ={0, 0, 0})</description>
-    </item>
-    
-    <item>
-      <title>lds::UniformVectorList</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/</guid>
-      <description>lds::UniformVectorList #  Inherits from std::vector&amp;lt; Vector &amp;gt;
-Public Functions #      Name      UniformVectorList() =default
-Constructs a new UniformVectorList.    UniformVectorList(const std::vector&amp;lt; Vector &amp;gt; &amp;amp; vecs, size_t dim =0)
-Constructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent.    UniformVectorList(std::vector&amp;lt; Vector &amp;gt; &amp;amp;&amp;amp; vecs, size_t dim =0)
-Constructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent.</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/</guid>
-      <description>ldsCtrlEst_h #  Files #     Name     ldsCtrlEst_h/lds.h lds namespace   ldsCtrlEst_h/lds_ctrl.h Controller.   ldsCtrlEst_h/lds_fit.h LDS base fit type.   ldsCtrlEst_h/lds_fit_em.h subspace identification   ldsCtrlEst_h/lds_fit_ssid.h subspace identification   ldsCtrlEst_h/lds_gaussian.h glds namespace   ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.   ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.   ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.   ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.   ldsCtrlEst_h/lds_gaussian_sctrl.</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds_8h/</guid>
-      <description>lds namespace</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_ctrl.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__ctrl_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__ctrl_8h/</guid>
-      <description>Controller.</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_fit.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit_8h/</guid>
-      <description>LDS base fit type.</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_fit_em.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit__em_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit__em_8h/</guid>
-      <description>subspace identification</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_fit_ssid.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/</guid>
-      <description>subspace identification</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_gaussian.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian_8h/</guid>
-      <description>glds namespace</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_gaussian_ctrl.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/</guid>
-      <description>GLDS Controller.</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_gaussian_fit.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/</guid>
-      <description>GLDS fit type.</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_gaussian_fit_em.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/</guid>
-      <description>GLDS E-M fit type.</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_gaussian_fit_ssid.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/</guid>
-      <description>GLDS SSID fit type.</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_gaussian_sctrl.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/</guid>
-      <description>GLDS switched controller type.</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_gaussian_sys.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/</guid>
-      <description>GLDS base type.</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_poisson.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson_8h/</guid>
-      <description>plds namespace</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_poisson_ctrl.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/</guid>
-      <description>PLDS controller type.</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_poisson_fit.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/</guid>
-      <description>PLDS base fit type.</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_poisson_fit_em.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/</guid>
-      <description>PLDS E-M fit type.</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_poisson_fit_ssid.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/</guid>
-      <description>PLDS SSID fit type.</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_poisson_sctrl.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/</guid>
-      <description>PLDS switched controller type.</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_poisson_sys.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/</guid>
-      <description>PLDS base type.</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_sctrl.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sctrl_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sctrl_8h/</guid>
-      <description>SwitchedController type.</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_sys.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8h/</guid>
-      <description>LDS base type.</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_uniform_mats.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/</guid>
-      <description>List of uniformly sized matrices.</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_uniform_systems.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/</guid>
-      <description>List of uniformly sized Systems.</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/lds_uniform_vecs.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/</guid>
-      <description>List of uniformly sized vectors.</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/mex_c_util.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/mex__c__util_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/mex__c__util_8h/</guid>
-      <description>arma &amp;lt;-&amp;gt; mex interoperability utilities (Matlab C API)</description>
-    </item>
-    
-    <item>
-      <title>ldsCtrlEst_h/mex_cpp_util.h</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/</guid>
-      <description>arma &amp;lt;-&amp;gt; mex interoperability utilities (Matlab C++ API)</description>
-    </item>
-    
-    <item>
-      <title>Models</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/terminology/model/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/terminology/model/</guid>
-      <description>Model Definitions #  This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form:   \[
\mathbf{x}_{t&amp;#43;1} = f\left( \mathbf{x}_{t}, \mathbf{v}_{t} \right) = \mathbf{A} \mathbf{x}_{t} &amp;#43; \mathbf{B} \mathbf{v}_{t} &amp;#43; \mathbf{m}_{t} &amp;#43; \mathbf{w}_{t}
\]   \[
\mathbf{y}_{t} = h\left( \mathbf{x}_{t} \right)
\]  t : time index
x : system state
v = g%u : input (e.g., in physical units used for model fit)
u : control signal sent to actuator (e.</description>
-    </item>
-    
-    <item>
-      <title>PLDS State Estimation</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/</guid>
-      <description>PLDS State Estimation Tutorial #  This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.
-The full code for this can be found here.</description>
-    </item>
-    
-    <item>
-      <title>PLDS Switched Control</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/</guid>
-      <description>PLDS Switched Control Tutorial #  This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.
-In the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system.</description>
-    </item>
-    
-    <item>
-      <title>src</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/</guid>
-      <description>src #  Files #     Name     src/lds.cpp misc lds namespace functions   src/lds_gaussian_sys.cpp GLDS base type.   src/lds_poisson_sys.cpp PLDS base type.   src/lds_sys.cpp LDS base type.   src/lds_uniform_vecs.cpp Uniformly sized vectors.     Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time</description>
-    </item>
-    
-    <item>
-      <title>src/lds.cpp</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds_8cpp/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds_8cpp/</guid>
-      <description>misc lds namespace functions</description>
-    </item>
-    
-    <item>
-      <title>src/lds_gaussian_sys.cpp</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/</guid>
-      <description>GLDS base type.</description>
-    </item>
-    
-    <item>
-      <title>src/lds_poisson_sys.cpp</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/</guid>
-      <description>PLDS base type.</description>
-    </item>
-    
-    <item>
-      <title>src/lds_sys.cpp</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8cpp/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8cpp/</guid>
-      <description>LDS base type.</description>
-    </item>
-    
-    <item>
-      <title>src/lds_uniform_vecs.cpp</title>
-      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/</link>
-      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
-      
-      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/</guid>
-      <description>Uniformly sized vectors.</description>
-    </item>
-    
-  </channel>
-</rss>
diff --git a/docs/issues-contributing/index.html b/docs/issues-contributing/index.html
deleted file mode 100644
index 76734546..00000000
--- a/docs/issues-contributing/index.html
+++ /dev/null
@@ -1,250 +0,0 @@
-<!DOCTYPE html>
-<html lang="en" dir="ltr">
-<head>
-  <meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<meta name="description" content="Reporting Issues #  If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.
-Contributing #  We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.">
-<meta name="theme-color" content="#FFFFFF"><meta property="og:title" content="" />
-<meta property="og:description" content="Reporting Issues #  If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.
-Contributing #  We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository." />
-<meta property="og:type" content="article" />
-<meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/issues-contributing/" /><meta property="article:section" content="" />
-
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-
-<title>Issues Contributing | LDS C&amp;E</title>
-<link rel="manifest" href="/lds-ctrl-est/manifest.json">
-<link rel="icon" href="/lds-ctrl-est/favicon.png" type="image/x-icon">
-<link rel="stylesheet" href="/lds-ctrl-est/book.min.57f7f660871517a5bfcfb5e2de853d806f7e34d94ebd5f3f3bad62e9ddbae209.css" integrity="sha256-V/f2YIcVF6W/z7Xi3oU9gG9&#43;NNlOvV8/O61i6d264gk=" crossorigin="anonymous">
-  <script defer src="/lds-ctrl-est/flexsearch.min.js"></script>
-  <script defer src="/lds-ctrl-est/en.search.min.93b2d0e7fa872893809358a01a9e6e0c0ce7a384100570924726e75a52c21d85.js" integrity="sha256-k7LQ5/qHKJOAk1igGp5uDAzno4QQBXCSRybnWlLCHYU=" crossorigin="anonymous"></script>
-<!--
-Made with Book Theme
-https://github.com/alex-shpak/hugo-book
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-</head>
-<body dir="ltr">
-  <input type="checkbox" class="hidden toggle" id="menu-control" />
-  <input type="checkbox" class="hidden toggle" id="toc-control" />
-  <main class="container flex">
-    <aside class="book-menu">
-      <div class="book-menu-content">
-        
-  <nav>
-<h2 class="book-brand">
-  <a href="/lds-ctrl-est/"><img src="/lds-ctrl-est/ldsctrlest-logo.png" alt="Logo" /><span>LDS C&amp;E</span>
-  </a>
-</h2>
-
-
-<div class="book-search">
-  <input type="text" id="book-search-input" placeholder="Search" aria-label="Search" maxlength="64" data-hotkeys="s/" />
-  <div class="book-search-spinner hidden"></div>
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-</div>
-
-
-
-
-
-
-
-
-
-
-
-  <ul>
-<li><strong>Library Terminology</strong>
-<ul>
-<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
-<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
-</ul>
-</li>
-</ul>
-<p><br /></p>
-<ul>
-<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
-<ul>
-<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
-</ul>
-</li>
-</ul>
-<p><br /></p>
-<ul>
-<li><strong>Tutorials</strong>
-<ul>
-<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
-<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
-<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
-</ul>
-</li>
-</ul>
-<p><br /></p>
-<ul>
-<li><strong>API Reference</strong>
-<ul>
-<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
-<li><a href="/lds-ctrl-est/docs/api/classes/">Classes</a></li>
-<li><a href="/lds-ctrl-est/docs/api/modules/">Modules</a></li>
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-</ul>
-<p><br /></p>
-<ul>
-<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
-</ul>
-<p><br /></p>
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-  <a class="anchor" href="#reporting-issues">#</a>
-</h1>
-<p>If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please <a href="https://github.com/stanley-rozell/lds-ctrl-est/issues">open an issue on GitHub</a> and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.</p>
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-  Contributing
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t(t,e){this.mode=void 0,this.gullet=void 0,this.settings=void 0,this.leftrightDepth=void 0,this.nextToken=void 0,this.mode="math",this.gullet=new wa(t,e,this.mode),this.settings=e,this.leftrightDepth=0}var e=t.prototype;return e.expect=function(t,e){if(void 0===e&&(e=!0),this.fetch().text!==t)throw new o("Expected '"+t+"', got '"+this.fetch().text+"'",this.fetch());e&&this.consume()},e.consume=function(){this.nextToken=null},e.fetch=function(){return null==this.nextToken&&(this.nextToken=this.gullet.expandNextToken()),this.nextToken},e.switchMode=function(t){this.mode=t,this.gullet.switchMode(t)},e.parse=function(){this.gullet.beginGroup(),this.settings.colorIsTextColor&&this.gullet.macros.set("\\color","\\textcolor");var t=this.parseExpression(!1);return this.expect("EOF"),this.gullet.endGroup(),t},e.parseExpression=function(e,r){for(var a=[];;){"math"===this.mode&&this.consumeSpaces();var n=this.fetch();if(-1!==t.endOfExpression.indexOf(n.text))break;if(r&&n.text===r)break;if(e&&ia[n.text]&&ia[n.text].infix)break;var i=this.parseAtom(r);if(!i)break;a.push(i)}return"text"===this.mode&&this.formLigatures(a),this.handleInfixNodes(a)},e.handleInfixNodes=function(t){for(var e,r=-1,a=0;a<t.length;a++){var n=Vt(t[a],"infix");if(n){if(-1!==r)throw new o("only one infix operator per group",n.token);r=a,e=n.replaceWith}}if(-1!==r&&e){var i,s,h=t.slice(0,r),l=t.slice(r+1);return i=1===h.length&&"ordgroup"===h[0].type?h[0]:{type:"ordgroup",mode:this.mode,body:h},s=1===l.length&&"ordgroup"===l[0].type?l[0]:{type:"ordgroup",mode:this.mode,body:l},["\\\\abovefrac"===e?this.callFunction(e,[i,t[r],s],[]):this.callFunction(e,[i,s],[])]}return t},e.handleSupSubscript=function(e){var r=this.fetch(),a=r.text;this.consume();var n=this.parseGroup(e,!1,t.SUPSUB_GREEDINESS,void 0,void 0,!0);if(!n)throw new o("Expected group after '"+a+"'",r);return n},e.formatUnsupportedCmd=function(t){for(var e=[],r=0;r<t.length;r++)e.push({type:"textord",mode:"text",text:t[r]});var a={type:"text",mode:this.mode,body:e};return{type:"color",mode:this.mode,color:this.settings.errorColor,body:[a]}},e.parseAtom=function(t){var e,r,a=this.parseGroup("atom",!1,null,t);if("text"===this.mode)return a;for(;;){this.consumeSpaces();var n=this.fetch();if("\\limits"===n.text||"\\nolimits"===n.text){var i=Vt(a,"op");if(i){var s="\\limits"===n.text;i.limits=s,i.alwaysHandleSupSub=!0}else{if(!(i=Vt(a,"operatorname"))||!i.alwaysHandleSupSub)throw new o("Limit controls must follow a math operator",n);var h="\\limits"===n.text;i.limits=h}this.consume()}else if("^"===n.text){if(e)throw new o("Double superscript",n);e=this.handleSupSubscript("superscript")}else if("_"===n.text){if(r)throw new o("Double subscript",n);r=this.handleSupSubscript("subscript")}else{if("'"!==n.text)break;if(e)throw new o("Double superscript",n);var l={type:"textord",mode:this.mode,text:"\\prime"},m=[l];for(this.consume();"'"===this.fetch().text;)m.push(l),this.consume();"^"===this.fetch().text&&m.push(this.handleSupSubscript("superscript")),e={type:"ordgroup",mode:this.mode,body:m}}}return e||r?{type:"supsub",mode:this.mode,base:a,sup:e,sub:r}:a},e.parseFunction=function(t,e,r){var a=this.fetch(),n=a.text,i=ia[n];if(!i)return null;if(this.consume(),null!=r&&i.greediness<=r)throw new o("Got function '"+n+"' with no arguments"+(e?" as "+e:""),a);if("text"===this.mode&&!i.allowedInText)throw new o("Can't use function '"+n+"' in text mode",a);if("math"===this.mode&&!1===i.allowedInMath)throw new o("Can't use function '"+n+"' in math mode",a);var s=this.parseArguments(n,i),h=s.args,l=s.optArgs;return this.callFunction(n,h,l,a,t)},e.callFunction=function(t,e,r,a,n){var i={funcName:t,parser:this,token:a,breakOnTokenText:n},s=ia[t];if(s&&s.handler)return s.handler(i,e,r);throw new o("No function handler for 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diff --git a/docs/manifest.json b/docs/manifest.json
deleted file mode 100644
index 83ffb796..00000000
--- a/docs/manifest.json
+++ /dev/null
@@ -1,15 +0,0 @@
-{
-  "name": "LDS C&E",
-  "short_name": "LDS C&E",
-  "start_url": "/lds-ctrl-est/",
-  "scope": "/lds-ctrl-est/",
-  "display": "standalone",
-  "background_color": "#000000",
-  "theme_color": "#000000",
-  "icons": [
-    {
-      "src": "/lds-ctrl-est/favicon.svg",
-      "sizes": "512x512"
-    }
-  ]
-}
diff --git a/docs/mermaid.min.js b/docs/mermaid.min.js
deleted file mode 100644
index 48da6d6c..00000000
--- a/docs/mermaid.min.js
+++ /dev/null
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n=new Error(t);throw n.hash=e,n}this.trace(t)},parse:function(t){var e=this,n=[0],r=[],i=[null],a=[],o=this.table,s="",c=0,u=0,l=0,h=2,f=1,d=a.slice.call(arguments,1),p=Object.create(this.lexer),g={yy:{}};for(var y in this.yy)Object.prototype.hasOwnProperty.call(this.yy,y)&&(g.yy[y]=this.yy[y]);p.setInput(t,g.yy),g.yy.lexer=p,g.yy.parser=this,void 0===p.yylloc&&(p.yylloc={});var v=p.yylloc;a.push(v);var m=p.options&&p.options.ranges;function b(){var t;return"number"!=typeof(t=r.pop()||p.lex()||f)&&(t instanceof Array&&(t=(r=t).pop()),t=e.symbols_[t]||t),t}"function"==typeof g.yy.parseError?this.parseError=g.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;for(var x,_,k,w,E,T,C,A,S,M={};;){if(k=n[n.length-1],this.defaultActions[k]?w=this.defaultActions[k]:(null==x&&(x=b()),w=o[k]&&o[k][x]),void 0===w||!w.length||!w[0]){var O="";for(T in S=[],o[k])this.terminals_[T]&&T>h&&S.push("'"+this.terminals_[T]+"'");O=p.showPosition?"Parse error on line 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e=t.length,n=t.split(/(?:\r\n?|\n)/g);this._input=t+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-e),this.offset-=e;var r=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),n.length-1&&(this.yylineno-=n.length-1);var i=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:n?(n.length===r.length?this.yylloc.first_column:0)+r[r.length-n.length].length-n[0].length:this.yylloc.first_column-e},this.options.ranges&&(this.yylloc.range=[i[0],i[0]+this.yyleng-e]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){return this.options.backtrack_lexer?(this._backtrack=!0,this):this.parseError("Lexical error on line "+(this.yylineno+1)+". 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Unrecognized text.\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var t=this.next();return t||this.lex()},begin:function(t){this.conditionStack.push(t)},popState:function(){return this.conditionStack.length-1>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(t){return(t=this.conditionStack.length-1-Math.abs(t||0))>=0?this.conditionStack[t]:"INITIAL"},pushState:function(t){this.begin(t)},stateStackSize:function(){return this.conditionStack.length},options:{"case-insensitive":!0},performAction:function(t,e,n,r){switch(n){case 0:return this.begin("open_directive"),58;case 1:return this.begin("type_directive"),59;case 2:return this.popState(),this.begin("arg_directive"),14;case 3:return this.popState(),this.popState(),61;case 4:return 60;case 5:return 5;case 6:case 7:case 8:case 9:case 10:break;case 11:return this.begin("ID"),16;case 12:return e.yytext=e.yytext.trim(),this.begin("ALIAS"),48;case 13:return this.popState(),this.popState(),this.begin("LINE"),18;case 14:return this.popState(),this.popState(),5;case 15:return this.begin("LINE"),27;case 16:return this.begin("LINE"),29;case 17:return this.begin("LINE"),30;case 18:return this.begin("LINE"),31;case 19:return this.begin("LINE"),36;case 20:return this.begin("LINE"),33;case 21:return this.begin("LINE"),35;case 22:return this.popState(),19;case 23:return 28;case 24:return 43;case 25:return 44;case 26:return 39;case 27:return 37;case 28:return this.begin("ID"),22;case 29:return this.begin("ID"),23;case 30:return 25;case 31:return 7;case 32:return 21;case 33:return 42;case 34:return 5;case 35:return e.yytext=e.yytext.trim(),48;case 36:return 51;case 37:return 52;case 38:return 49;case 39:return 50;case 40:return 53;case 41:return 54;case 42:return 55;case 43:return 56;case 44:return 57;case 45:return 46;case 46:return 47;case 47:return 5;case 48:return"INVALID"}},rules:[/^(?:%%\{)/i,/^(?:((?:(?!\}%%)[^:.])*))/i,/^(?::)/i,/^(?:\}%%)/i,/^(?:((?:(?!\}%%).|\n)*))/i,/^(?:[\n]+)/i,/^(?:\s+)/i,/^(?:((?!\n)\s)+)/i,/^(?:#[^\n]*)/i,/^(?:%(?!\{)[^\n]*)/i,/^(?:[^\}]%%[^\n]*)/i,/^(?:participant\b)/i,/^(?:[^\->:\n,;]+?(?=((?!\n)\s)+as(?!\n)\s|[#\n;]|$))/i,/^(?:as\b)/i,/^(?:(?:))/i,/^(?:loop\b)/i,/^(?:rect\b)/i,/^(?:opt\b)/i,/^(?:alt\b)/i,/^(?:else\b)/i,/^(?:par\b)/i,/^(?:and\b)/i,/^(?:(?:[:]?(?:no)?wrap)?[^#\n;]*)/i,/^(?:end\b)/i,/^(?:left of\b)/i,/^(?:right of\b)/i,/^(?:over\b)/i,/^(?:note\b)/i,/^(?:activate\b)/i,/^(?:deactivate\b)/i,/^(?:title\b)/i,/^(?:sequenceDiagram\b)/i,/^(?:autonumber\b)/i,/^(?:,)/i,/^(?:;)/i,/^(?:[^\+\->:\n,;]+((?!(-x|--x|-\)|--\)))[\-]*[^\+\->:\n,;]+)*)/i,/^(?:->>)/i,/^(?:-->>)/i,/^(?:->)/i,/^(?:-->)/i,/^(?:-[x])/i,/^(?:--[x])/i,/^(?:-[\)])/i,/^(?:--[\)])/i,/^(?::(?:(?:no)?wrap)?[^#\n;]+)/i,/^(?:\+)/i,/^(?:-)/i,/^(?:$)/i,/^(?:.)/i],conditions:{open_directive:{rules:[1,8],inclusive:!1},type_directive:{rules:[2,3,8],inclusive:!1},arg_directive:{rules:[3,4,8],inclusive:!1},ID:{rules:[7,8,12],inclusive:!1},ALIAS:{rules:[7,8,13,14],inclusive:!1},LINE:{rules:[7,8,22],inclusive:!1},INITIAL:{rules:[0,5,6,8,9,10,11,15,16,17,18,19,20,21,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],inclusive:!0}}};function O(){this.yy={}}return S.lexer=M,O.prototype=S,S.Parser=O,new O}();e.parser=i,e.Parser=i.Parser,e.parse=function(){return i.parse.apply(i,arguments)},e.main=function(r){r[1]||(console.log("Usage: "+r[0]+" FILE"),t.exit(1));var i=n(19).readFileSync(n(20).normalize(r[1]),"utf8");return e.parser.parse(i)},n.c[n.s]===r&&e.main(t.argv.slice(1))}).call(this,n(14),n(7)(t))},function(t,e,n){var r=n(198);t.exports={Graph:r.Graph,json:n(301),alg:n(302),version:r.version}},function(t,e,n){var r;try{r={cloneDeep:n(313),constant:n(86),defaults:n(154),each:n(87),filter:n(128),find:n(314),flatten:n(156),forEach:n(126),forIn:n(319),has:n(93),isUndefined:n(139),last:n(320),map:n(140),mapValues:n(321),max:n(322),merge:n(324),min:n(329),minBy:n(330),now:n(331),pick:n(161),range:n(162),reduce:n(142),sortBy:n(338),uniqueId:n(163),values:n(147),zipObject:n(343)}}catch(t){}r||(r=window._),t.exports=r},function(t,e){var n=Array.isArray;t.exports=n},function(t,e,n){
-/**
- * @license
- * Copyright (c) 2012-2013 Chris Pettitt
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in
- * all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
- * THE SOFTWARE.
- */
-t.exports={graphlib:n(311),dagre:n(153),intersect:n(368),render:n(370),util:n(12),version:n(382)}},function(t,e){t.exports=function(t){return t.webpackPolyfill||(t.deprecate=function(){},t.paths=[],t.children||(t.children=[]),Object.defineProperty(t,"loaded",{enumerable:!0,get:function(){return t.l}}),Object.defineProperty(t,"id",{enumerable:!0,get:function(){return t.i}}),t.webpackPolyfill=1),t}},function(t,e,n){"use strict";var r=n(4),i=n(17).Graph;function a(t,e,n,i){var a;do{a=r.uniqueId(i)}while(t.hasNode(a));return n.dummy=e,t.setNode(a,n),a}function o(t){return r.max(r.map(t.nodes(),(function(e){var n=t.node(e).rank;if(!r.isUndefined(n))return n})))}t.exports={addDummyNode:a,simplify:function(t){var e=(new i).setGraph(t.graph());return r.forEach(t.nodes(),(function(n){e.setNode(n,t.node(n))})),r.forEach(t.edges(),(function(n){var 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r.forEach(t,(function(t){e(t)?n.lhs.push(t):n.rhs.push(t)})),n},time:function(t,e){var n=r.now();try{return e()}finally{console.log(t+" time: "+(r.now()-n)+"ms")}},notime:function(t,e){return e()}}},function(t,e,n){"use strict";Object.defineProperty(e,"__esModule",{value:!0});var r=n(173),i=n(174),a=n(175),o={channel:r.default,lang:i.default,unit:a.default};e.default=o},function(t,e,n){var r;try{r={clone:n(199),constant:n(86),each:n(87),filter:n(128),has:n(93),isArray:n(5),isEmpty:n(276),isFunction:n(37),isUndefined:n(139),keys:n(30),map:n(140),reduce:n(142),size:n(279),transform:n(285),union:n(286),values:n(147)}}catch(t){}r||(r=window._),t.exports=r},function(t,e){t.exports=function(t){var e=typeof t;return null!=t&&("object"==e||"function"==e)}},function(t,e,n){var r=n(43);t.exports={isSubgraph:function(t,e){return!!t.children(e).length},edgeToId:function(t){return 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37:this.$={id1:a[s-2],id2:a[s],relation:a[s-1],relationTitle1:"none",relationTitle2:"none"};break;case 38:this.$={id1:a[s-3],id2:a[s],relation:a[s-1],relationTitle1:a[s-2],relationTitle2:"none"};break;case 39:this.$={id1:a[s-3],id2:a[s],relation:a[s-2],relationTitle1:"none",relationTitle2:a[s-1]};break;case 40:this.$={id1:a[s-4],id2:a[s],relation:a[s-2],relationTitle1:a[s-3],relationTitle2:a[s-1]};break;case 41:this.$={type1:a[s-2],type2:a[s],lineType:a[s-1]};break;case 42:this.$={type1:"none",type2:a[s],lineType:a[s-1]};break;case 43:this.$={type1:a[s-1],type2:"none",lineType:a[s]};break;case 44:this.$={type1:"none",type2:"none",lineType:a[s]};break;case 45:this.$=r.relationType.AGGREGATION;break;case 46:this.$=r.relationType.EXTENSION;break;case 47:this.$=r.relationType.COMPOSITION;break;case 48:this.$=r.relationType.DEPENDENCY;break;case 49:this.$=r.lineType.LINE;break;case 50:this.$=r.lineType.DOTTED_LINE;break;case 51:case 57:this.$=a[s-2],r.setClickEvent(a[s-1],a[s]);break;case 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n=new Error(t);throw n.hash=e,n}this.trace(t)},parse:function(t){var e=this,n=[0],r=[],i=[null],a=[],o=this.table,s="",c=0,u=0,l=0,h=2,f=1,d=a.slice.call(arguments,1),p=Object.create(this.lexer),g={yy:{}};for(var y in this.yy)Object.prototype.hasOwnProperty.call(this.yy,y)&&(g.yy[y]=this.yy[y]);p.setInput(t,g.yy),g.yy.lexer=p,g.yy.parser=this,void 0===p.yylloc&&(p.yylloc={});var v=p.yylloc;a.push(v);var m=p.options&&p.options.ranges;function b(){var t;return"number"!=typeof(t=r.pop()||p.lex()||f)&&(t instanceof Array&&(t=(r=t).pop()),t=e.symbols_[t]||t),t}"function"==typeof g.yy.parseError?this.parseError=g.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;for(var x,_,k,w,E,T,C,A,S,M={};;){if(k=n[n.length-1],this.defaultActions[k]?w=this.defaultActions[k]:(null==x&&(x=b()),w=o[k]&&o[k][x]),void 0===w||!w.length||!w[0]){var O="";for(T in S=[],o[k])this.terminals_[T]&&T>h&&S.push("'"+this.terminals_[T]+"'");O=p.showPosition?"Parse error on line 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e=t.length,n=t.split(/(?:\r\n?|\n)/g);this._input=t+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-e),this.offset-=e;var r=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),n.length-1&&(this.yylineno-=n.length-1);var i=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:n?(n.length===r.length?this.yylloc.first_column:0)+r[r.length-n.length].length-n[0].length:this.yylloc.first_column-e},this.options.ranges&&(this.yylloc.range=[i[0],i[0]+this.yyleng-e]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){return this.options.backtrack_lexer?(this._backtrack=!0,this):this.parseError("Lexical error on line "+(this.yylineno+1)+". 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19:this.$={stmt:"state",id:a[s-3],type:"default",description:a[s-5],doc:a[s-1]};break;case 20:this.$={stmt:"state",id:a[s],type:"fork"};break;case 21:this.$={stmt:"state",id:a[s],type:"join"};break;case 22:this.$={stmt:"state",id:r.getDividerId(),type:"divider"};break;case 23:this.$={stmt:"state",id:a[s-1].trim(),note:{position:a[s-2].trim(),text:a[s].trim()}};break;case 30:case 31:this.$=a[s];break;case 34:r.parseDirective("%%{","open_directive");break;case 35:r.parseDirective(a[s],"type_directive");break;case 36:a[s]=a[s].trim().replace(/'/g,'"'),r.parseDirective(a[s],"arg_directive");break;case 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n=new Error(t);throw n.hash=e,n}this.trace(t)},parse:function(t){var e=this,n=[0],r=[],i=[null],a=[],o=this.table,s="",c=0,u=0,l=0,h=2,f=1,d=a.slice.call(arguments,1),p=Object.create(this.lexer),g={yy:{}};for(var y in this.yy)Object.prototype.hasOwnProperty.call(this.yy,y)&&(g.yy[y]=this.yy[y]);p.setInput(t,g.yy),g.yy.lexer=p,g.yy.parser=this,void 0===p.yylloc&&(p.yylloc={});var v=p.yylloc;a.push(v);var m=p.options&&p.options.ranges;function b(){var t;return"number"!=typeof(t=r.pop()||p.lex()||f)&&(t instanceof Array&&(t=(r=t).pop()),t=e.symbols_[t]||t),t}"function"==typeof g.yy.parseError?this.parseError=g.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;for(var x,_,k,w,E,T,C,A,S,M={};;){if(k=n[n.length-1],this.defaultActions[k]?w=this.defaultActions[k]:(null==x&&(x=b()),w=o[k]&&o[k][x]),void 0===w||!w.length||!w[0]){var O="";for(T in S=[],o[k])this.terminals_[T]&&T>h&&S.push("'"+this.terminals_[T]+"'");O=p.showPosition?"Parse error on line 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e=t.length,n=t.split(/(?:\r\n?|\n)/g);this._input=t+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-e),this.offset-=e;var r=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),n.length-1&&(this.yylineno-=n.length-1);var i=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:n?(n.length===r.length?this.yylloc.first_column:0)+r[r.length-n.length].length-n[0].length:this.yylloc.first_column-e},this.options.ranges&&(this.yylloc.range=[i[0],i[0]+this.yyleng-e]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){return this.options.backtrack_lexer?(this._backtrack=!0,this):this.parseError("Lexical error on line "+(this.yylineno+1)+". 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4:return 41;case 5:break;case 6:console.log("Crap after close");break;case 7:return 5;case 8:case 9:case 10:case 11:break;case 12:return this.pushState("SCALE"),15;case 13:return 16;case 14:this.popState();break;case 15:this.pushState("STATE");break;case 16:return this.popState(),e.yytext=e.yytext.slice(0,-8).trim(),23;case 17:return this.popState(),e.yytext=e.yytext.slice(0,-8).trim(),24;case 18:return this.popState(),e.yytext=e.yytext.slice(0,-8).trim(),23;case 19:return this.popState(),e.yytext=e.yytext.slice(0,-8).trim(),24;case 20:this.begin("STATE_STRING");break;case 21:return this.popState(),this.pushState("STATE_ID"),"AS";case 22:return this.popState(),"ID";case 23:this.popState();break;case 24:return"STATE_DESCR";case 25:return 17;case 26:this.popState();break;case 27:return this.popState(),this.pushState("struct"),18;case 28:return this.popState(),19;case 29:break;case 30:return this.begin("NOTE"),26;case 31:return this.popState(),this.pushState("NOTE_ID"),37;case 32:return this.popState(),this.pushState("NOTE_ID"),38;case 33:this.popState(),this.pushState("FLOATING_NOTE");break;case 34:return this.popState(),this.pushState("FLOATING_NOTE_ID"),"AS";case 35:break;case 36:return"NOTE_TEXT";case 37:return this.popState(),"ID";case 38:return this.popState(),this.pushState("NOTE_TEXT"),22;case 39:return this.popState(),e.yytext=e.yytext.substr(2).trim(),28;case 40:return this.popState(),e.yytext=e.yytext.slice(0,-8).trim(),28;case 41:case 42:return 7;case 43:return 14;case 44:return 36;case 45:return 22;case 46:return e.yytext=e.yytext.trim(),12;case 47:return 13;case 48:return 25;case 49:return 5;case 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64:this.$=a[s-3],r.addVertex(a[s-3],a[s-1],"trapezoid");break;case 65:this.$=a[s-3],r.addVertex(a[s-3],a[s-1],"inv_trapezoid");break;case 66:this.$=a[s-3],r.addVertex(a[s-3],a[s-1],"lean_right");break;case 67:this.$=a[s-3],r.addVertex(a[s-3],a[s-1],"lean_left");break;case 68:this.$=a[s],r.addVertex(a[s]);break;case 69:a[s-1].text=a[s],this.$=a[s-1];break;case 70:case 71:a[s-2].text=a[s-1],this.$=a[s-2];break;case 72:this.$=a[s];break;case 73:var c=r.destructLink(a[s],a[s-2]);this.$={type:c.type,stroke:c.stroke,length:c.length,text:a[s-1]};break;case 74:c=r.destructLink(a[s]);this.$={type:c.type,stroke:c.stroke,length:c.length};break;case 75:this.$=a[s-1];break;case 77:case 91:case 147:this.$=a[s-1]+""+a[s];break;case 92:case 93:this.$=a[s-4],r.addClass(a[s-2],a[s]);break;case 94:this.$=a[s-4],r.setClass(a[s-2],a[s]);break;case 95:case 103:this.$=a[s-1],r.setClickEvent(a[s-1],a[s]);break;case 96:case 104:this.$=a[s-3],r.setClickEvent(a[s-3],a[s-2]),r.setTooltip(a[s-3],a[s]);break;case 97:this.$=a[s-2],r.setClickEvent(a[s-2],a[s-1],a[s]);break;case 98:this.$=a[s-4],r.setClickEvent(a[s-4],a[s-3],a[s-2]),r.setTooltip(a[s-4],a[s]);break;case 99:case 105:this.$=a[s-1],r.setLink(a[s-1],a[s]);break;case 100:case 106:this.$=a[s-3],r.setLink(a[s-3],a[s-2]),r.setTooltip(a[s-3],a[s]);break;case 101:case 107:this.$=a[s-3],r.setLink(a[s-3],a[s-2],a[s]);break;case 102:case 108:this.$=a[s-5],r.setLink(a[s-5],a[s-4],a[s]),r.setTooltip(a[s-5],a[s-2]);break;case 109:this.$=a[s-4],r.addVertex(a[s-2],void 0,void 0,a[s]);break;case 110:case 112:this.$=a[s-4],r.updateLink(a[s-2],a[s]);break;case 111:this.$=a[s-4],r.updateLink([a[s-2]],a[s]);break;case 113:this.$=a[s-8],r.updateLinkInterpolate([a[s-6]],a[s-2]),r.updateLink([a[s-6]],a[s]);break;case 114:this.$=a[s-8],r.updateLinkInterpolate(a[s-6],a[s-2]),r.updateLink(a[s-6],a[s]);break;case 115:this.$=a[s-6],r.updateLinkInterpolate([a[s-4]],a[s]);break;case 116:this.$=a[s-6],r.updateLinkInterpolate(a[s-4],a[s]);break;case 117:case 119:this.$=[a[s]];break;case 118:case 120:a[s-2].push(a[s]),this.$=a[s-2];break;case 122:this.$=a[s-1]+a[s];break;case 144:this.$=a[s];break;case 145:this.$=a[s-1]+""+a[s];break;case 150:this.$="v";break;case 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"+(c+1)+":\n"+p.showPosition()+"\nExpecting "+S.join(", ")+", got '"+(this.terminals_[x]||x)+"'":"Parse error on line "+(c+1)+": Unexpected "+(x==f?"end of input":"'"+(this.terminals_[x]||x)+"'"),this.parseError(O,{text:p.match,token:this.terminals_[x]||x,line:p.yylineno,loc:v,expected:S})}if(w[0]instanceof Array&&w.length>1)throw new Error("Parse Error: multiple actions possible at state: "+k+", token: "+x);switch(w[0]){case 1:n.push(x),i.push(p.yytext),a.push(p.yylloc),n.push(w[1]),x=null,_?(x=_,_=null):(u=p.yyleng,s=p.yytext,c=p.yylineno,v=p.yylloc,l>0&&l--);break;case 2:if(C=this.productions_[w[1]][1],M.$=i[i.length-C],M._$={first_line:a[a.length-(C||1)].first_line,last_line:a[a.length-1].last_line,first_column:a[a.length-(C||1)].first_column,last_column:a[a.length-1].last_column},m&&(M._$.range=[a[a.length-(C||1)].range[0],a[a.length-1].range[1]]),void 0!==(E=this.performAction.apply(M,[s,u,c,g.yy,w[1],i,a].concat(d))))return E;C&&(n=n.slice(0,-1*C*2),i=i.slice(0,-1*C),a=a.slice(0,-1*C)),n.push(this.productions_[w[1]][0]),i.push(M.$),a.push(M._$),A=o[n[n.length-2]][n[n.length-1]],n.push(A);break;case 3:return!0}}return!0}},qt={EOF:1,parseError:function(t,e){if(!this.yy.parser)throw new Error(t);this.yy.parser.parseError(t,e)},setInput:function(t,e){return this.yy=e||this.yy||{},this._input=t,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var t=this._input[0];return this.yytext+=t,this.yyleng++,this.offset++,this.match+=t,this.matched+=t,t.match(/(?:\r\n?|\n).*/g)?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),t},unput:function(t){var e=t.length,n=t.split(/(?:\r\n?|\n)/g);this._input=t+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-e),this.offset-=e;var r=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),n.length-1&&(this.yylineno-=n.length-1);var i=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:n?(n.length===r.length?this.yylloc.first_column:0)+r[r.length-n.length].length-n[0].length:this.yylloc.first_column-e},this.options.ranges&&(this.yylloc.range=[i[0],i[0]+this.yyleng-e]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){return this.options.backtrack_lexer?(this._backtrack=!0,this):this.parseError("Lexical error on line "+(this.yylineno+1)+". You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},less:function(t){this.unput(this.match.slice(t))},pastInput:function(){var t=this.matched.substr(0,this.matched.length-this.match.length);return(t.length>20?"...":"")+t.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var t=this.match;return t.length<20&&(t+=this._input.substr(0,20-t.length)),(t.substr(0,20)+(t.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var t=this.pastInput(),e=new Array(t.length+1).join("-");return t+this.upcomingInput()+"\n"+e+"^"},test_match:function(t,e){var n,r,i;if(this.options.backtrack_lexer&&(i={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(i.yylloc.range=this.yylloc.range.slice(0))),(r=t[0].match(/(?:\r\n?|\n).*/g))&&(this.yylineno+=r.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:r?r[r.length-1].length-r[r.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+t[0].length},this.yytext+=t[0],this.match+=t[0],this.matches=t,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(t[0].length),this.matched+=t[0],n=this.performAction.call(this,this.yy,this,e,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),n)return n;if(this._backtrack){for(var a in i)this[a]=i[a];return!1}return!1},next:function(){if(this.done)return this.EOF;var t,e,n,r;this._input||(this.done=!0),this._more||(this.yytext="",this.match="");for(var i=this._currentRules(),a=0;a<i.length;a++)if((n=this._input.match(this.rules[i[a]]))&&(!e||n[0].length>e[0].length)){if(e=n,r=a,this.options.backtrack_lexer){if(!1!==(t=this.test_match(n,i[a])))return t;if(this._backtrack){e=!1;continue}return!1}if(!this.options.flex)break}return e?!1!==(t=this.test_match(e,i[r]))&&t:""===this._input?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+". Unrecognized text.\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var t=this.next();return t||this.lex()},begin:function(t){this.conditionStack.push(t)},popState:function(){return this.conditionStack.length-1>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(t){return(t=this.conditionStack.length-1-Math.abs(t||0))>=0?this.conditionStack[t]:"INITIAL"},pushState:function(t){this.begin(t)},stateStackSize:function(){return this.conditionStack.length},options:{},performAction:function(t,e,n,r){switch(n){case 0:return this.begin("open_directive"),12;case 1:return this.begin("type_directive"),13;case 2:return this.popState(),this.begin("arg_directive"),10;case 3:return this.popState(),this.popState(),15;case 4:return 14;case 5:case 6:break;case 7:this.begin("string");break;case 8:this.popState();break;case 9:return"STR";case 10:return 75;case 11:return 84;case 12:return 76;case 13:return 93;case 14:return 77;case 15:return 78;case 16:this.begin("href");break;case 17:this.popState();break;case 18:return 89;case 19:this.begin("callbackname");break;case 20:this.popState();break;case 21:this.popState(),this.begin("callbackargs");break;case 22:return 87;case 23:this.popState();break;case 24:return 88;case 25:this.begin("click");break;case 26:this.popState();break;case 27:return 79;case 28:case 29:return t.lex.firstGraph()&&this.begin("dir"),24;case 30:return 38;case 31:return 42;case 32:case 33:case 34:case 35:return 90;case 36:return this.popState(),25;case 37:case 38:case 39:case 40:case 41:case 42:case 43:case 44:case 45:case 46:return this.popState(),26;case 47:return 94;case 48:return 102;case 49:return 47;case 50:return 99;case 51:return 46;case 52:return 20;case 53:return 95;case 54:return 113;case 55:case 56:case 57:return 70;case 58:case 59:case 60:return 69;case 61:return 51;case 62:return 52;case 63:return 53;case 64:return 54;case 65:return 55;case 66:return 56;case 67:return 57;case 68:return 58;case 69:return 100;case 70:return 103;case 71:return 114;case 72:return 111;case 73:return 104;case 74:case 75:return 112;case 76:return 105;case 77:return 61;case 78:return 81;case 79:return"SEP";case 80:return 80;case 81:return 98;case 82:return 63;case 83:return 62;case 84:return 65;case 85:return 64;case 86:return 109;case 87:return 110;case 88:return 71;case 89:return 49;case 90:return 50;case 91:return 40;case 92:return 41;case 93:return 59;case 94:return 60;case 95:return 120;case 96:return 21;case 97:return 22;case 98:return 23}},rules:[/^(?:%%\{)/,/^(?:((?:(?!\}%%)[^:.])*))/,/^(?::)/,/^(?:\}%%)/,/^(?:((?:(?!\}%%).|\n)*))/,/^(?:%%(?!\{)[^\n]*)/,/^(?:[^\}]%%[^\n]*)/,/^(?:["])/,/^(?:["])/,/^(?:[^"]*)/,/^(?:style\b)/,/^(?:default\b)/,/^(?:linkStyle\b)/,/^(?:interpolate\b)/,/^(?:classDef\b)/,/^(?:class\b)/,/^(?:href[\s]+["])/,/^(?:["])/,/^(?:[^"]*)/,/^(?:call[\s]+)/,/^(?:\([\s]*\))/,/^(?:\()/,/^(?:[^(]*)/,/^(?:\))/,/^(?:[^)]*)/,/^(?:click[\s]+)/,/^(?:[\s\n])/,/^(?:[^\s\n]*)/,/^(?:graph\b)/,/^(?:flowchart\b)/,/^(?:subgraph\b)/,/^(?:end\b\s*)/,/^(?:_self\b)/,/^(?:_blank\b)/,/^(?:_parent\b)/,/^(?:_top\b)/,/^(?:(\r?\n)*\s*\n)/,/^(?:\s*LR\b)/,/^(?:\s*RL\b)/,/^(?:\s*TB\b)/,/^(?:\s*BT\b)/,/^(?:\s*TD\b)/,/^(?:\s*BR\b)/,/^(?:\s*<)/,/^(?:\s*>)/,/^(?:\s*\^)/,/^(?:\s*v\b)/,/^(?:[0-9]+)/,/^(?:#)/,/^(?::::)/,/^(?::)/,/^(?:&)/,/^(?:;)/,/^(?:,)/,/^(?:\*)/,/^(?:\s*[xo<]?--+[-xo>]\s*)/,/^(?:\s*[xo<]?==+[=xo>]\s*)/,/^(?:\s*[xo<]?-?\.+-[xo>]?\s*)/,/^(?:\s*[xo<]?--\s*)/,/^(?:\s*[xo<]?==\s*)/,/^(?:\s*[xo<]?-\.\s*)/,/^(?:\(-)/,/^(?:-\))/,/^(?:\(\[)/,/^(?:\]\))/,/^(?:\[\[)/,/^(?:\]\])/,/^(?:\[\()/,/^(?:\)\])/,/^(?:-)/,/^(?:\.)/,/^(?:[\_])/,/^(?:\+)/,/^(?:%)/,/^(?:=)/,/^(?:=)/,/^(?:<)/,/^(?:>)/,/^(?:\^)/,/^(?:\\\|)/,/^(?:v\b)/,/^(?:[A-Za-z]+)/,/^(?:\\\])/,/^(?:\[\/)/,/^(?:\/\])/,/^(?:\[\\)/,/^(?:[!"#$%&'*+,-.`?\\_/])/,/^(?:[\u00AA\u00B5\u00BA\u00C0-\u00D6\u00D8-\u00F6]|[\u00F8-\u02C1\u02C6-\u02D1\u02E0-\u02E4\u02EC\u02EE\u0370-\u0374\u0376\u0377]|[\u037A-\u037D\u0386\u0388-\u038A\u038C\u038E-\u03A1\u03A3-\u03F5]|[\u03F7-\u0481\u048A-\u0527\u0531-\u0556\u0559\u0561-\u0587\u05D0-\u05EA]|[\u05F0-\u05F2\u0620-\u064A\u066E\u066F\u0671-\u06D3\u06D5\u06E5\u06E6\u06EE]|[\u06EF\u06FA-\u06FC\u06FF\u0710\u0712-\u072F\u074D-\u07A5\u07B1\u07CA-\u07EA]|[\u07F4\u07F5\u07FA\u0800-\u0815\u081A\u0824\u0828\u0840-\u0858\u08A0]|[\u08A2-\u08AC\u0904-\u0939\u093D\u0950\u0958-\u0961\u0971-\u0977]|[\u0979-\u097F\u0985-\u098C\u098F\u0990\u0993-\u09A8\u09AA-\u09B0\u09B2]|[\u09B6-\u09B9\u09BD\u09CE\u09DC\u09DD\u09DF-\u09E1\u09F0\u09F1\u0A05-\u0A0A]|[\u0A0F\u0A10\u0A13-\u0A28\u0A2A-\u0A30\u0A32\u0A33\u0A35\u0A36\u0A38\u0A39]|[\u0A59-\u0A5C\u0A5E\u0A72-\u0A74\u0A85-\u0A8D\u0A8F-\u0A91\u0A93-\u0AA8]|[\u0AAA-\u0AB0\u0AB2\u0AB3\u0AB5-\u0AB9\u0ABD\u0AD0\u0AE0\u0AE1\u0B05-\u0B0C]|[\u0B0F\u0B10\u0B13-\u0B28\u0B2A-\u0B30\u0B32\u0B33\u0B35-\u0B39\u0B3D\u0B5C]|[\u0B5D\u0B5F-\u0B61\u0B71\u0B83\u0B85-\u0B8A\u0B8E-\u0B90\u0B92-\u0B95\u0B99]|[\u0B9A\u0B9C\u0B9E\u0B9F\u0BA3\u0BA4\u0BA8-\u0BAA\u0BAE-\u0BB9\u0BD0]|[\u0C05-\u0C0C\u0C0E-\u0C10\u0C12-\u0C28\u0C2A-\u0C33\u0C35-\u0C39\u0C3D]|[\u0C58\u0C59\u0C60\u0C61\u0C85-\u0C8C\u0C8E-\u0C90\u0C92-\u0CA8\u0CAA-\u0CB3]|[\u0CB5-\u0CB9\u0CBD\u0CDE\u0CE0\u0CE1\u0CF1\u0CF2\u0D05-\u0D0C\u0D0E-\u0D10]|[\u0D12-\u0D3A\u0D3D\u0D4E\u0D60\u0D61\u0D7A-\u0D7F\u0D85-\u0D96\u0D9A-\u0DB1]|[\u0DB3-\u0DBB\u0DBD\u0DC0-\u0DC6\u0E01-\u0E30\u0E32\u0E33\u0E40-\u0E46\u0E81]|[\u0E82\u0E84\u0E87\u0E88\u0E8A\u0E8D\u0E94-\u0E97\u0E99-\u0E9F\u0EA1-\u0EA3]|[\u0EA5\u0EA7\u0EAA\u0EAB\u0EAD-\u0EB0\u0EB2\u0EB3\u0EBD\u0EC0-\u0EC4\u0EC6]|[\u0EDC-\u0EDF\u0F00\u0F40-\u0F47\u0F49-\u0F6C\u0F88-\u0F8C\u1000-\u102A]|[\u103F\u1050-\u1055\u105A-\u105D\u1061\u1065\u1066\u106E-\u1070\u1075-\u1081]|[\u108E\u10A0-\u10C5\u10C7\u10CD\u10D0-\u10FA\u10FC-\u1248\u124A-\u124D]|[\u1250-\u1256\u1258\u125A-\u125D\u1260-\u1288\u128A-\u128D\u1290-\u12B0]|[\u12B2-\u12B5\u12B8-\u12BE\u12C0\u12C2-\u12C5\u12C8-\u12D6\u12D8-\u1310]|[\u1312-\u1315\u1318-\u135A\u1380-\u138F\u13A0-\u13F4\u1401-\u166C]|[\u166F-\u167F\u1681-\u169A\u16A0-\u16EA\u1700-\u170C\u170E-\u1711]|[\u1720-\u1731\u1740-\u1751\u1760-\u176C\u176E-\u1770\u1780-\u17B3\u17D7]|[\u17DC\u1820-\u1877\u1880-\u18A8\u18AA\u18B0-\u18F5\u1900-\u191C]|[\u1950-\u196D\u1970-\u1974\u1980-\u19AB\u19C1-\u19C7\u1A00-\u1A16]|[\u1A20-\u1A54\u1AA7\u1B05-\u1B33\u1B45-\u1B4B\u1B83-\u1BA0\u1BAE\u1BAF]|[\u1BBA-\u1BE5\u1C00-\u1C23\u1C4D-\u1C4F\u1C5A-\u1C7D\u1CE9-\u1CEC]|[\u1CEE-\u1CF1\u1CF5\u1CF6\u1D00-\u1DBF\u1E00-\u1F15\u1F18-\u1F1D]|[\u1F20-\u1F45\u1F48-\u1F4D\u1F50-\u1F57\u1F59\u1F5B\u1F5D\u1F5F-\u1F7D]|[\u1F80-\u1FB4\u1FB6-\u1FBC\u1FBE\u1FC2-\u1FC4\u1FC6-\u1FCC\u1FD0-\u1FD3]|[\u1FD6-\u1FDB\u1FE0-\u1FEC\u1FF2-\u1FF4\u1FF6-\u1FFC\u2071\u207F]|[\u2090-\u209C\u2102\u2107\u210A-\u2113\u2115\u2119-\u211D\u2124\u2126\u2128]|[\u212A-\u212D\u212F-\u2139\u213C-\u213F\u2145-\u2149\u214E\u2183\u2184]|[\u2C00-\u2C2E\u2C30-\u2C5E\u2C60-\u2CE4\u2CEB-\u2CEE\u2CF2\u2CF3]|[\u2D00-\u2D25\u2D27\u2D2D\u2D30-\u2D67\u2D6F\u2D80-\u2D96\u2DA0-\u2DA6]|[\u2DA8-\u2DAE\u2DB0-\u2DB6\u2DB8-\u2DBE\u2DC0-\u2DC6\u2DC8-\u2DCE]|[\u2DD0-\u2DD6\u2DD8-\u2DDE\u2E2F\u3005\u3006\u3031-\u3035\u303B\u303C]|[\u3041-\u3096\u309D-\u309F\u30A1-\u30FA\u30FC-\u30FF\u3105-\u312D]|[\u3131-\u318E\u31A0-\u31BA\u31F0-\u31FF\u3400-\u4DB5\u4E00-\u9FCC]|[\uA000-\uA48C\uA4D0-\uA4FD\uA500-\uA60C\uA610-\uA61F\uA62A\uA62B]|[\uA640-\uA66E\uA67F-\uA697\uA6A0-\uA6E5\uA717-\uA71F\uA722-\uA788]|[\uA78B-\uA78E\uA790-\uA793\uA7A0-\uA7AA\uA7F8-\uA801\uA803-\uA805]|[\uA807-\uA80A\uA80C-\uA822\uA840-\uA873\uA882-\uA8B3\uA8F2-\uA8F7\uA8FB]|[\uA90A-\uA925\uA930-\uA946\uA960-\uA97C\uA984-\uA9B2\uA9CF\uAA00-\uAA28]|[\uAA40-\uAA42\uAA44-\uAA4B\uAA60-\uAA76\uAA7A\uAA80-\uAAAF\uAAB1\uAAB5]|[\uAAB6\uAAB9-\uAABD\uAAC0\uAAC2\uAADB-\uAADD\uAAE0-\uAAEA\uAAF2-\uAAF4]|[\uAB01-\uAB06\uAB09-\uAB0E\uAB11-\uAB16\uAB20-\uAB26\uAB28-\uAB2E]|[\uABC0-\uABE2\uAC00-\uD7A3\uD7B0-\uD7C6\uD7CB-\uD7FB\uF900-\uFA6D]|[\uFA70-\uFAD9\uFB00-\uFB06\uFB13-\uFB17\uFB1D\uFB1F-\uFB28\uFB2A-\uFB36]|[\uFB38-\uFB3C\uFB3E\uFB40\uFB41\uFB43\uFB44\uFB46-\uFBB1\uFBD3-\uFD3D]|[\uFD50-\uFD8F\uFD92-\uFDC7\uFDF0-\uFDFB\uFE70-\uFE74\uFE76-\uFEFC]|[\uFF21-\uFF3A\uFF41-\uFF5A\uFF66-\uFFBE\uFFC2-\uFFC7\uFFCA-\uFFCF]|[\uFFD2-\uFFD7\uFFDA-\uFFDC])/,/^(?:\|)/,/^(?:\()/,/^(?:\))/,/^(?:\[)/,/^(?:\])/,/^(?:\{)/,/^(?:\})/,/^(?:")/,/^(?:(\r?\n)+)/,/^(?:\s)/,/^(?:$)/],conditions:{close_directive:{rules:[],inclusive:!1},arg_directive:{rules:[3,4],inclusive:!1},type_directive:{rules:[2,3],inclusive:!1},open_directive:{rules:[1],inclusive:!1},callbackargs:{rules:[23,24],inclusive:!1},callbackname:{rules:[20,21,22],inclusive:!1},href:{rules:[17,18],inclusive:!1},click:{rules:[26,27],inclusive:!1},vertex:{rules:[],inclusive:!1},dir:{rules:[36,37,38,39,40,41,42,43,44,45,46],inclusive:!1},string:{rules:[8,9],inclusive:!1},INITIAL:{rules:[0,5,6,7,10,11,12,13,14,15,16,19,25,28,29,30,31,32,33,34,35,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98],inclusive:!0}}};function Xt(){this.yy={}}return Gt.lexer=qt,Xt.prototype=Gt,Gt.Parser=Xt,new Xt}();e.parser=i,e.Parser=i.Parser,e.parse=function(){return i.parse.apply(i,arguments)},e.main=function(r){r[1]||(console.log("Usage: "+r[0]+" FILE"),t.exit(1));var i=n(19).readFileSync(n(20).normalize(r[1]),"utf8");return e.parser.parse(i)},n.c[n.s]===r&&e.main(t.argv.slice(1))}).call(this,n(14),n(7)(t))},function(t,e,n){(function(t,r){var i=function(){var t=function(t,e,n,r){for(n=n||{},r=t.length;r--;n[t[r]]=e);return n},e=[1,3],n=[1,5],r=[7,9,11,12,13,14,15,16,17,18,20,27,32],i=[1,15],a=[1,16],o=[1,17],s=[1,18],c=[1,19],u=[1,20],l=[1,21],h=[1,23],f=[1,25],d=[1,28],p=[5,7,9,11,12,13,14,15,16,17,18,20,27,32],g={trace:function(){},yy:{},symbols_:{error:2,start:3,directive:4,gantt:5,document:6,EOF:7,line:8,SPACE:9,statement:10,NL:11,dateFormat:12,inclusiveEndDates:13,axisFormat:14,excludes:15,todayMarker:16,title:17,section:18,clickStatement:19,taskTxt:20,taskData:21,openDirective:22,typeDirective:23,closeDirective:24,":":25,argDirective:26,click:27,callbackname:28,callbackargs:29,href:30,clickStatementDebug:31,open_directive:32,type_directive:33,arg_directive:34,close_directive:35,$accept:0,$end:1},terminals_:{2:"error",5:"gantt",7:"EOF",9:"SPACE",11:"NL",12:"dateFormat",13:"inclusiveEndDates",14:"axisFormat",15:"excludes",16:"todayMarker",17:"title",18:"section",20:"taskTxt",21:"taskData",25:":",27:"click",28:"callbackname",29:"callbackargs",30:"href",32:"open_directive",33:"type_directive",34:"arg_directive",35:"close_directive"},productions_:[0,[3,2],[3,3],[6,0],[6,2],[8,2],[8,1],[8,1],[8,1],[10,1],[10,1],[10,1],[10,1],[10,1],[10,1],[10,1],[10,1],[10,2],[10,1],[4,4],[4,6],[19,2],[19,3],[19,3],[19,4],[19,3],[19,4],[19,2],[31,2],[31,3],[31,3],[31,4],[31,3],[31,4],[31,2],[22,1],[23,1],[26,1],[24,1]],performAction:function(t,e,n,r,i,a,o){var s=a.length-1;switch(i){case 2:return a[s-1];case 3:this.$=[];break;case 4:a[s-1].push(a[s]),this.$=a[s-1];break;case 5:case 6:this.$=a[s];break;case 7:case 8:this.$=[];break;case 9:r.setDateFormat(a[s].substr(11)),this.$=a[s].substr(11);break;case 10:r.enableInclusiveEndDates(),this.$=a[s].substr(18);break;case 11:r.setAxisFormat(a[s].substr(11)),this.$=a[s].substr(11);break;case 12:r.setExcludes(a[s].substr(9)),this.$=a[s].substr(9);break;case 13:r.setTodayMarker(a[s].substr(12)),this.$=a[s].substr(12);break;case 14:r.setTitle(a[s].substr(6)),this.$=a[s].substr(6);break;case 15:r.addSection(a[s].substr(8)),this.$=a[s].substr(8);break;case 17:r.addTask(a[s-1],a[s]),this.$="task";break;case 21:this.$=a[s-1],r.setClickEvent(a[s-1],a[s],null);break;case 22:this.$=a[s-2],r.setClickEvent(a[s-2],a[s-1],a[s]);break;case 23:this.$=a[s-2],r.setClickEvent(a[s-2],a[s-1],null),r.setLink(a[s-2],a[s]);break;case 24:this.$=a[s-3],r.setClickEvent(a[s-3],a[s-2],a[s-1]),r.setLink(a[s-3],a[s]);break;case 25:this.$=a[s-2],r.setClickEvent(a[s-2],a[s],null),r.setLink(a[s-2],a[s-1]);break;case 26:this.$=a[s-3],r.setClickEvent(a[s-3],a[s-1],a[s]),r.setLink(a[s-3],a[s-2]);break;case 27:this.$=a[s-1],r.setLink(a[s-1],a[s]);break;case 28:case 34:this.$=a[s-1]+" "+a[s];break;case 29:case 30:case 32:this.$=a[s-2]+" "+a[s-1]+" "+a[s];break;case 31:case 33:this.$=a[s-3]+" "+a[s-2]+" "+a[s-1]+" "+a[s];break;case 35:r.parseDirective("%%{","open_directive");break;case 36:r.parseDirective(a[s],"type_directive");break;case 37:a[s]=a[s].trim().replace(/'/g,'"'),r.parseDirective(a[s],"arg_directive");break;case 38:r.parseDirective("}%%","close_directive","gantt")}},table:[{3:1,4:2,5:e,22:4,32:n},{1:[3]},{3:6,4:2,5:e,22:4,32:n},t(r,[2,3],{6:7}),{23:8,33:[1,9]},{33:[2,35]},{1:[2,1]},{4:24,7:[1,10],8:11,9:[1,12],10:13,11:[1,14],12:i,13:a,14:o,15:s,16:c,17:u,18:l,19:22,20:h,22:4,27:f,32:n},{24:26,25:[1,27],35:d},t([25,35],[2,36]),t(r,[2,8],{1:[2,2]}),t(r,[2,4]),{4:24,10:29,12:i,13:a,14:o,15:s,16:c,17:u,18:l,19:22,20:h,22:4,27:f,32:n},t(r,[2,6]),t(r,[2,7]),t(r,[2,9]),t(r,[2,10]),t(r,[2,11]),t(r,[2,12]),t(r,[2,13]),t(r,[2,14]),t(r,[2,15]),t(r,[2,16]),{21:[1,30]},t(r,[2,18]),{28:[1,31],30:[1,32]},{11:[1,33]},{26:34,34:[1,35]},{11:[2,38]},t(r,[2,5]),t(r,[2,17]),t(r,[2,21],{29:[1,36],30:[1,37]}),t(r,[2,27],{28:[1,38]}),t(p,[2,19]),{24:39,35:d},{35:[2,37]},t(r,[2,22],{30:[1,40]}),t(r,[2,23]),t(r,[2,25],{29:[1,41]}),{11:[1,42]},t(r,[2,24]),t(r,[2,26]),t(p,[2,20])],defaultActions:{5:[2,35],6:[2,1],28:[2,38],35:[2,37]},parseError:function(t,e){if(!e.recoverable){var n=new Error(t);throw n.hash=e,n}this.trace(t)},parse:function(t){var e=this,n=[0],r=[],i=[null],a=[],o=this.table,s="",c=0,u=0,l=0,h=2,f=1,d=a.slice.call(arguments,1),p=Object.create(this.lexer),g={yy:{}};for(var y in this.yy)Object.prototype.hasOwnProperty.call(this.yy,y)&&(g.yy[y]=this.yy[y]);p.setInput(t,g.yy),g.yy.lexer=p,g.yy.parser=this,void 0===p.yylloc&&(p.yylloc={});var v=p.yylloc;a.push(v);var m=p.options&&p.options.ranges;function b(){var t;return"number"!=typeof(t=r.pop()||p.lex()||f)&&(t instanceof Array&&(t=(r=t).pop()),t=e.symbols_[t]||t),t}"function"==typeof g.yy.parseError?this.parseError=g.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;for(var x,_,k,w,E,T,C,A,S,M={};;){if(k=n[n.length-1],this.defaultActions[k]?w=this.defaultActions[k]:(null==x&&(x=b()),w=o[k]&&o[k][x]),void 0===w||!w.length||!w[0]){var O="";for(T in S=[],o[k])this.terminals_[T]&&T>h&&S.push("'"+this.terminals_[T]+"'");O=p.showPosition?"Parse error on line "+(c+1)+":\n"+p.showPosition()+"\nExpecting "+S.join(", ")+", got '"+(this.terminals_[x]||x)+"'":"Parse error on line "+(c+1)+": Unexpected "+(x==f?"end of input":"'"+(this.terminals_[x]||x)+"'"),this.parseError(O,{text:p.match,token:this.terminals_[x]||x,line:p.yylineno,loc:v,expected:S})}if(w[0]instanceof Array&&w.length>1)throw new Error("Parse Error: multiple actions possible at state: "+k+", token: "+x);switch(w[0]){case 1:n.push(x),i.push(p.yytext),a.push(p.yylloc),n.push(w[1]),x=null,_?(x=_,_=null):(u=p.yyleng,s=p.yytext,c=p.yylineno,v=p.yylloc,l>0&&l--);break;case 2:if(C=this.productions_[w[1]][1],M.$=i[i.length-C],M._$={first_line:a[a.length-(C||1)].first_line,last_line:a[a.length-1].last_line,first_column:a[a.length-(C||1)].first_column,last_column:a[a.length-1].last_column},m&&(M._$.range=[a[a.length-(C||1)].range[0],a[a.length-1].range[1]]),void 0!==(E=this.performAction.apply(M,[s,u,c,g.yy,w[1],i,a].concat(d))))return E;C&&(n=n.slice(0,-1*C*2),i=i.slice(0,-1*C),a=a.slice(0,-1*C)),n.push(this.productions_[w[1]][0]),i.push(M.$),a.push(M._$),A=o[n[n.length-2]][n[n.length-1]],n.push(A);break;case 3:return!0}}return!0}},y={EOF:1,parseError:function(t,e){if(!this.yy.parser)throw new Error(t);this.yy.parser.parseError(t,e)},setInput:function(t,e){return this.yy=e||this.yy||{},this._input=t,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var t=this._input[0];return this.yytext+=t,this.yyleng++,this.offset++,this.match+=t,this.matched+=t,t.match(/(?:\r\n?|\n).*/g)?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),t},unput:function(t){var e=t.length,n=t.split(/(?:\r\n?|\n)/g);this._input=t+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-e),this.offset-=e;var r=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),n.length-1&&(this.yylineno-=n.length-1);var i=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:n?(n.length===r.length?this.yylloc.first_column:0)+r[r.length-n.length].length-n[0].length:this.yylloc.first_column-e},this.options.ranges&&(this.yylloc.range=[i[0],i[0]+this.yyleng-e]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){return this.options.backtrack_lexer?(this._backtrack=!0,this):this.parseError("Lexical error on line "+(this.yylineno+1)+". You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},less:function(t){this.unput(this.match.slice(t))},pastInput:function(){var t=this.matched.substr(0,this.matched.length-this.match.length);return(t.length>20?"...":"")+t.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var t=this.match;return t.length<20&&(t+=this._input.substr(0,20-t.length)),(t.substr(0,20)+(t.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var t=this.pastInput(),e=new Array(t.length+1).join("-");return t+this.upcomingInput()+"\n"+e+"^"},test_match:function(t,e){var n,r,i;if(this.options.backtrack_lexer&&(i={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(i.yylloc.range=this.yylloc.range.slice(0))),(r=t[0].match(/(?:\r\n?|\n).*/g))&&(this.yylineno+=r.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:r?r[r.length-1].length-r[r.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+t[0].length},this.yytext+=t[0],this.match+=t[0],this.matches=t,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(t[0].length),this.matched+=t[0],n=this.performAction.call(this,this.yy,this,e,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),n)return n;if(this._backtrack){for(var a in i)this[a]=i[a];return!1}return!1},next:function(){if(this.done)return this.EOF;var t,e,n,r;this._input||(this.done=!0),this._more||(this.yytext="",this.match="");for(var i=this._currentRules(),a=0;a<i.length;a++)if((n=this._input.match(this.rules[i[a]]))&&(!e||n[0].length>e[0].length)){if(e=n,r=a,this.options.backtrack_lexer){if(!1!==(t=this.test_match(n,i[a])))return t;if(this._backtrack){e=!1;continue}return!1}if(!this.options.flex)break}return e?!1!==(t=this.test_match(e,i[r]))&&t:""===this._input?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+". Unrecognized text.\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var t=this.next();return t||this.lex()},begin:function(t){this.conditionStack.push(t)},popState:function(){return this.conditionStack.length-1>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(t){return(t=this.conditionStack.length-1-Math.abs(t||0))>=0?this.conditionStack[t]:"INITIAL"},pushState:function(t){this.begin(t)},stateStackSize:function(){return this.conditionStack.length},options:{"case-insensitive":!0},performAction:function(t,e,n,r){switch(n){case 0:return this.begin("open_directive"),32;case 1:return this.begin("type_directive"),33;case 2:return this.popState(),this.begin("arg_directive"),25;case 3:return this.popState(),this.popState(),35;case 4:return 34;case 5:case 6:case 7:break;case 8:return 11;case 9:case 10:case 11:break;case 12:this.begin("href");break;case 13:this.popState();break;case 14:return 30;case 15:this.begin("callbackname");break;case 16:this.popState();break;case 17:this.popState(),this.begin("callbackargs");break;case 18:return 28;case 19:this.popState();break;case 20:return 29;case 21:this.begin("click");break;case 22:this.popState();break;case 23:return 27;case 24:return 5;case 25:return 12;case 26:return 13;case 27:return 14;case 28:return 15;case 29:return 16;case 30:return"date";case 31:return 17;case 32:return 18;case 33:return 20;case 34:return 21;case 35:return 25;case 36:return 7;case 37:return"INVALID"}},rules:[/^(?:%%\{)/i,/^(?:((?:(?!\}%%)[^:.])*))/i,/^(?::)/i,/^(?:\}%%)/i,/^(?:((?:(?!\}%%).|\n)*))/i,/^(?:%%(?!\{)*[^\n]*)/i,/^(?:[^\}]%%*[^\n]*)/i,/^(?:%%*[^\n]*[\n]*)/i,/^(?:[\n]+)/i,/^(?:\s+)/i,/^(?:#[^\n]*)/i,/^(?:%[^\n]*)/i,/^(?:href[\s]+["])/i,/^(?:["])/i,/^(?:[^"]*)/i,/^(?:call[\s]+)/i,/^(?:\([\s]*\))/i,/^(?:\()/i,/^(?:[^(]*)/i,/^(?:\))/i,/^(?:[^)]*)/i,/^(?:click[\s]+)/i,/^(?:[\s\n])/i,/^(?:[^\s\n]*)/i,/^(?:gantt\b)/i,/^(?:dateFormat\s[^#\n;]+)/i,/^(?:inclusiveEndDates\b)/i,/^(?:axisFormat\s[^#\n;]+)/i,/^(?:excludes\s[^#\n;]+)/i,/^(?:todayMarker\s[^\n;]+)/i,/^(?:\d\d\d\d-\d\d-\d\d\b)/i,/^(?:title\s[^#\n;]+)/i,/^(?:section\s[^#:\n;]+)/i,/^(?:[^#:\n;]+)/i,/^(?::[^#\n;]+)/i,/^(?::)/i,/^(?:$)/i,/^(?:.)/i],conditions:{close_directive:{rules:[],inclusive:!1},arg_directive:{rules:[3,4],inclusive:!1},type_directive:{rules:[2,3],inclusive:!1},open_directive:{rules:[1],inclusive:!1},callbackargs:{rules:[19,20],inclusive:!1},callbackname:{rules:[16,17,18],inclusive:!1},href:{rules:[13,14],inclusive:!1},click:{rules:[22,23],inclusive:!1},INITIAL:{rules:[0,5,6,7,8,9,10,11,12,15,21,24,25,26,27,28,29,30,31,32,33,34,35,36,37],inclusive:!0}}};function v(){this.yy={}}return g.lexer=y,v.prototype=g,g.Parser=v,new v}();e.parser=i,e.Parser=i.Parser,e.parse=function(){return i.parse.apply(i,arguments)},e.main=function(r){r[1]||(console.log("Usage: "+r[0]+" FILE"),t.exit(1));var i=n(19).readFileSync(n(20).normalize(r[1]),"utf8");return e.parser.parse(i)},n.c[n.s]===r&&e.main(t.argv.slice(1))}).call(this,n(14),n(7)(t))},function(t,e,n){(function(t,r){var i=function(){var t=function(t,e,n,r){for(n=n||{},r=t.length;r--;n[t[r]]=e);return 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t=function(t,e,n,r){for(n=n||{},r=t.length;r--;n[t[r]]=e);return n},e=[2,3],n=[1,7],r=[7,12,15,17,19,20,21],i=[7,11,12,15,17,19,20,21],a=[2,20],o=[1,32],s={trace:function(){},yy:{},symbols_:{error:2,start:3,GG:4,":":5,document:6,EOF:7,DIR:8,options:9,body:10,OPT:11,NL:12,line:13,statement:14,COMMIT:15,commit_arg:16,BRANCH:17,ID:18,CHECKOUT:19,MERGE:20,RESET:21,reset_arg:22,STR:23,HEAD:24,reset_parents:25,CARET:26,$accept:0,$end:1},terminals_:{2:"error",4:"GG",5:":",7:"EOF",8:"DIR",11:"OPT",12:"NL",15:"COMMIT",17:"BRANCH",18:"ID",19:"CHECKOUT",20:"MERGE",21:"RESET",23:"STR",24:"HEAD",26:"CARET"},productions_:[0,[3,4],[3,5],[6,0],[6,2],[9,2],[9,1],[10,0],[10,2],[13,2],[13,1],[14,2],[14,2],[14,2],[14,2],[14,2],[16,0],[16,1],[22,2],[22,2],[25,0],[25,2]],performAction:function(t,e,n,r,i,a,o){var s=a.length-1;switch(i){case 1:return a[s-1];case 2:return r.setDirection(a[s-3]),a[s-1];case 4:r.setOptions(a[s-1]),this.$=a[s];break;case 5:a[s-1]+=a[s],this.$=a[s-1];break;case 7:this.$=[];break;case 8:a[s-1].push(a[s]),this.$=a[s-1];break;case 9:this.$=a[s-1];break;case 11:r.commit(a[s]);break;case 12:r.branch(a[s]);break;case 13:r.checkout(a[s]);break;case 14:r.merge(a[s]);break;case 15:r.reset(a[s]);break;case 16:this.$="";break;case 17:this.$=a[s];break;case 18:this.$=a[s-1]+":"+a[s];break;case 19:this.$=a[s-1]+":"+r.count,r.count=0;break;case 20:r.count=0;break;case 21:r.count+=1}},table:[{3:1,4:[1,2]},{1:[3]},{5:[1,3],8:[1,4]},{6:5,7:e,9:6,12:n},{5:[1,8]},{7:[1,9]},t(r,[2,7],{10:10,11:[1,11]}),t(i,[2,6]),{6:12,7:e,9:6,12:n},{1:[2,1]},{7:[2,4],12:[1,15],13:13,14:14,15:[1,16],17:[1,17],19:[1,18],20:[1,19],21:[1,20]},t(i,[2,5]),{7:[1,21]},t(r,[2,8]),{12:[1,22]},t(r,[2,10]),{12:[2,16],16:23,23:[1,24]},{18:[1,25]},{18:[1,26]},{18:[1,27]},{18:[1,30],22:28,24:[1,29]},{1:[2,2]},t(r,[2,9]),{12:[2,11]},{12:[2,17]},{12:[2,12]},{12:[2,13]},{12:[2,14]},{12:[2,15]},{12:a,25:31,26:o},{12:a,25:33,26:o},{12:[2,18]},{12:a,25:34,26:o},{12:[2,19]},{12:[2,21]}],defaultActions:{9:[2,1],21:[2,2],23:[2,11],24:[2,17],25:[2,12],26:[2,13],27:[2,14],28:[2,15],31:[2,18],33:[2,19],34:[2,21]},parseError:function(t,e){if(!e.recoverable){var n=new Error(t);throw n.hash=e,n}this.trace(t)},parse:function(t){var e=this,n=[0],r=[],i=[null],a=[],o=this.table,s="",c=0,u=0,l=0,h=2,f=1,d=a.slice.call(arguments,1),p=Object.create(this.lexer),g={yy:{}};for(var y in this.yy)Object.prototype.hasOwnProperty.call(this.yy,y)&&(g.yy[y]=this.yy[y]);p.setInput(t,g.yy),g.yy.lexer=p,g.yy.parser=this,void 0===p.yylloc&&(p.yylloc={});var v=p.yylloc;a.push(v);var m=p.options&&p.options.ranges;function b(){var t;return"number"!=typeof(t=r.pop()||p.lex()||f)&&(t instanceof Array&&(t=(r=t).pop()),t=e.symbols_[t]||t),t}"function"==typeof g.yy.parseError?this.parseError=g.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;for(var x,_,k,w,E,T,C,A,S,M={};;){if(k=n[n.length-1],this.defaultActions[k]?w=this.defaultActions[k]:(null==x&&(x=b()),w=o[k]&&o[k][x]),void 0===w||!w.length||!w[0]){var O="";for(T in S=[],o[k])this.terminals_[T]&&T>h&&S.push("'"+this.terminals_[T]+"'");O=p.showPosition?"Parse error on line "+(c+1)+":\n"+p.showPosition()+"\nExpecting "+S.join(", ")+", got '"+(this.terminals_[x]||x)+"'":"Parse error on line "+(c+1)+": Unexpected "+(x==f?"end of 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3:return!0}}return!0}},c={EOF:1,parseError:function(t,e){if(!this.yy.parser)throw new Error(t);this.yy.parser.parseError(t,e)},setInput:function(t,e){return this.yy=e||this.yy||{},this._input=t,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var t=this._input[0];return this.yytext+=t,this.yyleng++,this.offset++,this.match+=t,this.matched+=t,t.match(/(?:\r\n?|\n).*/g)?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),t},unput:function(t){var e=t.length,n=t.split(/(?:\r\n?|\n)/g);this._input=t+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-e),this.offset-=e;var r=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),n.length-1&&(this.yylineno-=n.length-1);var i=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:n?(n.length===r.length?this.yylloc.first_column:0)+r[r.length-n.length].length-n[0].length:this.yylloc.first_column-e},this.options.ranges&&(this.yylloc.range=[i[0],i[0]+this.yyleng-e]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){return this.options.backtrack_lexer?(this._backtrack=!0,this):this.parseError("Lexical error on line "+(this.yylineno+1)+". You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},less:function(t){this.unput(this.match.slice(t))},pastInput:function(){var t=this.matched.substr(0,this.matched.length-this.match.length);return(t.length>20?"...":"")+t.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var t=this.match;return t.length<20&&(t+=this._input.substr(0,20-t.length)),(t.substr(0,20)+(t.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var t=this.pastInput(),e=new Array(t.length+1).join("-");return t+this.upcomingInput()+"\n"+e+"^"},test_match:function(t,e){var n,r,i;if(this.options.backtrack_lexer&&(i={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(i.yylloc.range=this.yylloc.range.slice(0))),(r=t[0].match(/(?:\r\n?|\n).*/g))&&(this.yylineno+=r.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:r?r[r.length-1].length-r[r.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+t[0].length},this.yytext+=t[0],this.match+=t[0],this.matches=t,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(t[0].length),this.matched+=t[0],n=this.performAction.call(this,this.yy,this,e,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),n)return n;if(this._backtrack){for(var a in i)this[a]=i[a];return!1}return!1},next:function(){if(this.done)return this.EOF;var t,e,n,r;this._input||(this.done=!0),this._more||(this.yytext="",this.match="");for(var i=this._currentRules(),a=0;a<i.length;a++)if((n=this._input.match(this.rules[i[a]]))&&(!e||n[0].length>e[0].length)){if(e=n,r=a,this.options.backtrack_lexer){if(!1!==(t=this.test_match(n,i[a])))return t;if(this._backtrack){e=!1;continue}return!1}if(!this.options.flex)break}return e?!1!==(t=this.test_match(e,i[r]))&&t:""===this._input?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+". Unrecognized text.\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var t=this.next();return t||this.lex()},begin:function(t){this.conditionStack.push(t)},popState:function(){return this.conditionStack.length-1>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(t){return(t=this.conditionStack.length-1-Math.abs(t||0))>=0?this.conditionStack[t]:"INITIAL"},pushState:function(t){this.begin(t)},stateStackSize:function(){return this.conditionStack.length},options:{"case-insensitive":!0},performAction:function(t,e,n,r){switch(n){case 0:return 12;case 1:case 2:case 3:break;case 4:return 4;case 5:return 15;case 6:return 17;case 7:return 20;case 8:return 21;case 9:return 19;case 10:case 11:return 8;case 12:return 5;case 13:return 26;case 14:this.begin("options");break;case 15:this.popState();break;case 16:return 11;case 17:this.begin("string");break;case 18:this.popState();break;case 19:return 23;case 20:return 18;case 21:return 7}},rules:[/^(?:(\r?\n)+)/i,/^(?:\s+)/i,/^(?:#[^\n]*)/i,/^(?:%[^\n]*)/i,/^(?:gitGraph\b)/i,/^(?:commit\b)/i,/^(?:branch\b)/i,/^(?:merge\b)/i,/^(?:reset\b)/i,/^(?:checkout\b)/i,/^(?:LR\b)/i,/^(?:BT\b)/i,/^(?::)/i,/^(?:\^)/i,/^(?:options\r?\n)/i,/^(?:end\r?\n)/i,/^(?:[^\n]+\r?\n)/i,/^(?:["])/i,/^(?:["])/i,/^(?:[^"]*)/i,/^(?:[a-zA-Z][-_\.a-zA-Z0-9]*[-_a-zA-Z0-9])/i,/^(?:$)/i],conditions:{options:{rules:[15,16],inclusive:!1},string:{rules:[18,19],inclusive:!1},INITIAL:{rules:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,17,20,21],inclusive:!0}}};function u(){this.yy={}}return s.lexer=c,u.prototype=s,s.Parser=u,new u}();e.parser=i,e.Parser=i.Parser,e.parse=function(){return i.parse.apply(i,arguments)},e.main=function(r){r[1]||(console.log("Usage: "+r[0]+" FILE"),t.exit(1));var i=n(19).readFileSync(n(20).normalize(r[1]),"utf8");return e.parser.parse(i)},n.c[n.s]===r&&e.main(t.argv.slice(1))}).call(this,n(14),n(7)(t))},function(t,e,n){(function(t,r){var i=function(){var t=function(t,e,n,r){for(n=n||{},r=t.length;r--;n[t[r]]=e);return n},e=[6,9,10],n={trace:function(){},yy:{},symbols_:{error:2,start:3,info:4,document:5,EOF:6,line:7,statement:8,NL:9,showInfo:10,$accept:0,$end:1},terminals_:{2:"error",4:"info",6:"EOF",9:"NL",10:"showInfo"},productions_:[0,[3,3],[5,0],[5,2],[7,1],[7,1],[8,1]],performAction:function(t,e,n,r,i,a,o){a.length;switch(i){case 1:return r;case 4:break;case 6:r.setInfo(!0)}},table:[{3:1,4:[1,2]},{1:[3]},t(e,[2,2],{5:3}),{6:[1,4],7:5,8:6,9:[1,7],10:[1,8]},{1:[2,1]},t(e,[2,3]),t(e,[2,4]),t(e,[2,5]),t(e,[2,6])],defaultActions:{4:[2,1]},parseError:function(t,e){if(!e.recoverable){var n=new Error(t);throw n.hash=e,n}this.trace(t)},parse:function(t){var e=this,n=[0],r=[],i=[null],a=[],o=this.table,s="",c=0,u=0,l=0,h=2,f=1,d=a.slice.call(arguments,1),p=Object.create(this.lexer),g={yy:{}};for(var y in this.yy)Object.prototype.hasOwnProperty.call(this.yy,y)&&(g.yy[y]=this.yy[y]);p.setInput(t,g.yy),g.yy.lexer=p,g.yy.parser=this,void 0===p.yylloc&&(p.yylloc={});var v=p.yylloc;a.push(v);var m=p.options&&p.options.ranges;function b(){var t;return"number"!=typeof(t=r.pop()||p.lex()||f)&&(t instanceof Array&&(t=(r=t).pop()),t=e.symbols_[t]||t),t}"function"==typeof g.yy.parseError?this.parseError=g.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;for(var x,_,k,w,E,T,C,A,S,M={};;){if(k=n[n.length-1],this.defaultActions[k]?w=this.defaultActions[k]:(null==x&&(x=b()),w=o[k]&&o[k][x]),void 0===w||!w.length||!w[0]){var O="";for(T in S=[],o[k])this.terminals_[T]&&T>h&&S.push("'"+this.terminals_[T]+"'");O=p.showPosition?"Parse error on line "+(c+1)+":\n"+p.showPosition()+"\nExpecting "+S.join(", ")+", got '"+(this.terminals_[x]||x)+"'":"Parse error on line "+(c+1)+": Unexpected "+(x==f?"end of input":"'"+(this.terminals_[x]||x)+"'"),this.parseError(O,{text:p.match,token:this.terminals_[x]||x,line:p.yylineno,loc:v,expected:S})}if(w[0]instanceof Array&&w.length>1)throw new Error("Parse Error: multiple actions possible at state: "+k+", token: "+x);switch(w[0]){case 1:n.push(x),i.push(p.yytext),a.push(p.yylloc),n.push(w[1]),x=null,_?(x=_,_=null):(u=p.yyleng,s=p.yytext,c=p.yylineno,v=p.yylloc,l>0&&l--);break;case 2:if(C=this.productions_[w[1]][1],M.$=i[i.length-C],M._$={first_line:a[a.length-(C||1)].first_line,last_line:a[a.length-1].last_line,first_column:a[a.length-(C||1)].first_column,last_column:a[a.length-1].last_column},m&&(M._$.range=[a[a.length-(C||1)].range[0],a[a.length-1].range[1]]),void 0!==(E=this.performAction.apply(M,[s,u,c,g.yy,w[1],i,a].concat(d))))return E;C&&(n=n.slice(0,-1*C*2),i=i.slice(0,-1*C),a=a.slice(0,-1*C)),n.push(this.productions_[w[1]][0]),i.push(M.$),a.push(M._$),A=o[n[n.length-2]][n[n.length-1]],n.push(A);break;case 3:return!0}}return!0}},r={EOF:1,parseError:function(t,e){if(!this.yy.parser)throw new Error(t);this.yy.parser.parseError(t,e)},setInput:function(t,e){return this.yy=e||this.yy||{},this._input=t,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var t=this._input[0];return this.yytext+=t,this.yyleng++,this.offset++,this.match+=t,this.matched+=t,t.match(/(?:\r\n?|\n).*/g)?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),t},unput:function(t){var e=t.length,n=t.split(/(?:\r\n?|\n)/g);this._input=t+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-e),this.offset-=e;var r=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),n.length-1&&(this.yylineno-=n.length-1);var i=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:n?(n.length===r.length?this.yylloc.first_column:0)+r[r.length-n.length].length-n[0].length:this.yylloc.first_column-e},this.options.ranges&&(this.yylloc.range=[i[0],i[0]+this.yyleng-e]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){return this.options.backtrack_lexer?(this._backtrack=!0,this):this.parseError("Lexical error on line "+(this.yylineno+1)+". You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},less:function(t){this.unput(this.match.slice(t))},pastInput:function(){var t=this.matched.substr(0,this.matched.length-this.match.length);return(t.length>20?"...":"")+t.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var t=this.match;return t.length<20&&(t+=this._input.substr(0,20-t.length)),(t.substr(0,20)+(t.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var t=this.pastInput(),e=new Array(t.length+1).join("-");return t+this.upcomingInput()+"\n"+e+"^"},test_match:function(t,e){var n,r,i;if(this.options.backtrack_lexer&&(i={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(i.yylloc.range=this.yylloc.range.slice(0))),(r=t[0].match(/(?:\r\n?|\n).*/g))&&(this.yylineno+=r.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:r?r[r.length-1].length-r[r.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+t[0].length},this.yytext+=t[0],this.match+=t[0],this.matches=t,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(t[0].length),this.matched+=t[0],n=this.performAction.call(this,this.yy,this,e,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),n)return n;if(this._backtrack){for(var a in i)this[a]=i[a];return!1}return!1},next:function(){if(this.done)return this.EOF;var t,e,n,r;this._input||(this.done=!0),this._more||(this.yytext="",this.match="");for(var i=this._currentRules(),a=0;a<i.length;a++)if((n=this._input.match(this.rules[i[a]]))&&(!e||n[0].length>e[0].length)){if(e=n,r=a,this.options.backtrack_lexer){if(!1!==(t=this.test_match(n,i[a])))return t;if(this._backtrack){e=!1;continue}return!1}if(!this.options.flex)break}return e?!1!==(t=this.test_match(e,i[r]))&&t:""===this._input?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+". Unrecognized text.\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var t=this.next();return t||this.lex()},begin:function(t){this.conditionStack.push(t)},popState:function(){return this.conditionStack.length-1>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(t){return(t=this.conditionStack.length-1-Math.abs(t||0))>=0?this.conditionStack[t]:"INITIAL"},pushState:function(t){this.begin(t)},stateStackSize:function(){return this.conditionStack.length},options:{"case-insensitive":!0},performAction:function(t,e,n,r){switch(n){case 0:return 4;case 1:return 9;case 2:return"space";case 3:return 10;case 4:return 6;case 5:return"TXT"}},rules:[/^(?:info\b)/i,/^(?:[\s\n\r]+)/i,/^(?:[\s]+)/i,/^(?:showInfo\b)/i,/^(?:$)/i,/^(?:.)/i],conditions:{INITIAL:{rules:[0,1,2,3,4,5],inclusive:!0}}};function i(){this.yy={}}return n.lexer=r,i.prototype=n,n.Parser=i,new i}();e.parser=i,e.Parser=i.Parser,e.parse=function(){return i.parse.apply(i,arguments)},e.main=function(r){r[1]||(console.log("Usage: "+r[0]+" FILE"),t.exit(1));var i=n(19).readFileSync(n(20).normalize(r[1]),"utf8");return e.parser.parse(i)},n.c[n.s]===r&&e.main(t.argv.slice(1))}).call(this,n(14),n(7)(t))},function(t,e,n){(function(t,r){var i=function(){var t=function(t,e,n,r){for(n=n||{},r=t.length;r--;n[t[r]]=e);return n},e=[1,4],n=[1,5],r=[1,6],i=[1,7],a=[1,9],o=[1,10,12,19,20,21,22],s=[1,6,10,12,19,20,21,22],c=[19,20,21],u=[1,22],l=[6,19,20,21,22],h={trace:function(){},yy:{},symbols_:{error:2,start:3,eol:4,directive:5,PIE:6,document:7,line:8,statement:9,txt:10,value:11,title:12,title_value:13,openDirective:14,typeDirective:15,closeDirective:16,":":17,argDirective:18,NEWLINE:19,";":20,EOF:21,open_directive:22,type_directive:23,arg_directive:24,close_directive:25,$accept:0,$end:1},terminals_:{2:"error",6:"PIE",10:"txt",11:"value",12:"title",13:"title_value",17:":",19:"NEWLINE",20:";",21:"EOF",22:"open_directive",23:"type_directive",24:"arg_directive",25:"close_directive"},productions_:[0,[3,2],[3,2],[3,2],[7,0],[7,2],[8,2],[9,0],[9,2],[9,2],[9,1],[5,3],[5,5],[4,1],[4,1],[4,1],[14,1],[15,1],[18,1],[16,1]],performAction:function(t,e,n,r,i,a,o){var s=a.length-1;switch(i){case 6:this.$=a[s-1];break;case 8:r.addSection(a[s-1],r.cleanupValue(a[s]));break;case 9:this.$=a[s].trim(),r.setTitle(this.$);break;case 16:r.parseDirective("%%{","open_directive");break;case 17:r.parseDirective(a[s],"type_directive");break;case 18:a[s]=a[s].trim().replace(/'/g,'"'),r.parseDirective(a[s],"arg_directive");break;case 19:r.parseDirective("}%%","close_directive","pie")}},table:[{3:1,4:2,5:3,6:e,14:8,19:n,20:r,21:i,22:a},{1:[3]},{3:10,4:2,5:3,6:e,14:8,19:n,20:r,21:i,22:a},{3:11,4:2,5:3,6:e,14:8,19:n,20:r,21:i,22:a},t(o,[2,4],{7:12}),t(s,[2,13]),t(s,[2,14]),t(s,[2,15]),{15:13,23:[1,14]},{23:[2,16]},{1:[2,1]},{1:[2,2]},t(c,[2,7],{14:8,8:15,9:16,5:19,1:[2,3],10:[1,17],12:[1,18],22:a}),{16:20,17:[1,21],25:u},t([17,25],[2,17]),t(o,[2,5]),{4:23,19:n,20:r,21:i},{11:[1,24]},{13:[1,25]},t(c,[2,10]),t(l,[2,11]),{18:26,24:[1,27]},t(l,[2,19]),t(o,[2,6]),t(c,[2,8]),t(c,[2,9]),{16:28,25:u},{25:[2,18]},t(l,[2,12])],defaultActions:{9:[2,16],10:[2,1],11:[2,2],27:[2,18]},parseError:function(t,e){if(!e.recoverable){var n=new Error(t);throw n.hash=e,n}this.trace(t)},parse:function(t){var e=this,n=[0],r=[],i=[null],a=[],o=this.table,s="",c=0,u=0,l=0,h=2,f=1,d=a.slice.call(arguments,1),p=Object.create(this.lexer),g={yy:{}};for(var y in this.yy)Object.prototype.hasOwnProperty.call(this.yy,y)&&(g.yy[y]=this.yy[y]);p.setInput(t,g.yy),g.yy.lexer=p,g.yy.parser=this,void 0===p.yylloc&&(p.yylloc={});var v=p.yylloc;a.push(v);var m=p.options&&p.options.ranges;function b(){var t;return"number"!=typeof(t=r.pop()||p.lex()||f)&&(t instanceof Array&&(t=(r=t).pop()),t=e.symbols_[t]||t),t}"function"==typeof g.yy.parseError?this.parseError=g.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;for(var x,_,k,w,E,T,C,A,S,M={};;){if(k=n[n.length-1],this.defaultActions[k]?w=this.defaultActions[k]:(null==x&&(x=b()),w=o[k]&&o[k][x]),void 0===w||!w.length||!w[0]){var O="";for(T in S=[],o[k])this.terminals_[T]&&T>h&&S.push("'"+this.terminals_[T]+"'");O=p.showPosition?"Parse error on line "+(c+1)+":\n"+p.showPosition()+"\nExpecting "+S.join(", ")+", got '"+(this.terminals_[x]||x)+"'":"Parse error on line "+(c+1)+": Unexpected "+(x==f?"end of input":"'"+(this.terminals_[x]||x)+"'"),this.parseError(O,{text:p.match,token:this.terminals_[x]||x,line:p.yylineno,loc:v,expected:S})}if(w[0]instanceof Array&&w.length>1)throw new Error("Parse Error: multiple actions possible at state: "+k+", token: "+x);switch(w[0]){case 1:n.push(x),i.push(p.yytext),a.push(p.yylloc),n.push(w[1]),x=null,_?(x=_,_=null):(u=p.yyleng,s=p.yytext,c=p.yylineno,v=p.yylloc,l>0&&l--);break;case 2:if(C=this.productions_[w[1]][1],M.$=i[i.length-C],M._$={first_line:a[a.length-(C||1)].first_line,last_line:a[a.length-1].last_line,first_column:a[a.length-(C||1)].first_column,last_column:a[a.length-1].last_column},m&&(M._$.range=[a[a.length-(C||1)].range[0],a[a.length-1].range[1]]),void 0!==(E=this.performAction.apply(M,[s,u,c,g.yy,w[1],i,a].concat(d))))return E;C&&(n=n.slice(0,-1*C*2),i=i.slice(0,-1*C),a=a.slice(0,-1*C)),n.push(this.productions_[w[1]][0]),i.push(M.$),a.push(M._$),A=o[n[n.length-2]][n[n.length-1]],n.push(A);break;case 3:return!0}}return!0}},f={EOF:1,parseError:function(t,e){if(!this.yy.parser)throw new Error(t);this.yy.parser.parseError(t,e)},setInput:function(t,e){return this.yy=e||this.yy||{},this._input=t,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var t=this._input[0];return this.yytext+=t,this.yyleng++,this.offset++,this.match+=t,this.matched+=t,t.match(/(?:\r\n?|\n).*/g)?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),t},unput:function(t){var e=t.length,n=t.split(/(?:\r\n?|\n)/g);this._input=t+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-e),this.offset-=e;var r=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),n.length-1&&(this.yylineno-=n.length-1);var i=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:n?(n.length===r.length?this.yylloc.first_column:0)+r[r.length-n.length].length-n[0].length:this.yylloc.first_column-e},this.options.ranges&&(this.yylloc.range=[i[0],i[0]+this.yyleng-e]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){return this.options.backtrack_lexer?(this._backtrack=!0,this):this.parseError("Lexical error on line "+(this.yylineno+1)+". You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},less:function(t){this.unput(this.match.slice(t))},pastInput:function(){var t=this.matched.substr(0,this.matched.length-this.match.length);return(t.length>20?"...":"")+t.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var t=this.match;return t.length<20&&(t+=this._input.substr(0,20-t.length)),(t.substr(0,20)+(t.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var t=this.pastInput(),e=new Array(t.length+1).join("-");return t+this.upcomingInput()+"\n"+e+"^"},test_match:function(t,e){var n,r,i;if(this.options.backtrack_lexer&&(i={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(i.yylloc.range=this.yylloc.range.slice(0))),(r=t[0].match(/(?:\r\n?|\n).*/g))&&(this.yylineno+=r.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:r?r[r.length-1].length-r[r.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+t[0].length},this.yytext+=t[0],this.match+=t[0],this.matches=t,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(t[0].length),this.matched+=t[0],n=this.performAction.call(this,this.yy,this,e,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),n)return n;if(this._backtrack){for(var a in i)this[a]=i[a];return!1}return!1},next:function(){if(this.done)return this.EOF;var t,e,n,r;this._input||(this.done=!0),this._more||(this.yytext="",this.match="");for(var i=this._currentRules(),a=0;a<i.length;a++)if((n=this._input.match(this.rules[i[a]]))&&(!e||n[0].length>e[0].length)){if(e=n,r=a,this.options.backtrack_lexer){if(!1!==(t=this.test_match(n,i[a])))return t;if(this._backtrack){e=!1;continue}return!1}if(!this.options.flex)break}return e?!1!==(t=this.test_match(e,i[r]))&&t:""===this._input?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+". Unrecognized text.\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var t=this.next();return t||this.lex()},begin:function(t){this.conditionStack.push(t)},popState:function(){return this.conditionStack.length-1>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(t){return(t=this.conditionStack.length-1-Math.abs(t||0))>=0?this.conditionStack[t]:"INITIAL"},pushState:function(t){this.begin(t)},stateStackSize:function(){return this.conditionStack.length},options:{"case-insensitive":!0},performAction:function(t,e,n,r){switch(n){case 0:return this.begin("open_directive"),22;case 1:return this.begin("type_directive"),23;case 2:return this.popState(),this.begin("arg_directive"),17;case 3:return this.popState(),this.popState(),25;case 4:return 24;case 5:case 6:break;case 7:return 19;case 8:case 9:break;case 10:return this.begin("title"),12;case 11:return this.popState(),"title_value";case 12:this.begin("string");break;case 13:this.popState();break;case 14:return"txt";case 15:return 6;case 16:return"value";case 17:return 21}},rules:[/^(?:%%\{)/i,/^(?:((?:(?!\}%%)[^:.])*))/i,/^(?::)/i,/^(?:\}%%)/i,/^(?:((?:(?!\}%%).|\n)*))/i,/^(?:%%(?!\{)[^\n]*)/i,/^(?:[^\}]%%[^\n]*)/i,/^(?:[\n\r]+)/i,/^(?:%%[^\n]*)/i,/^(?:[\s]+)/i,/^(?:title\b)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:["])/i,/^(?:["])/i,/^(?:[^"]*)/i,/^(?:pie\b)/i,/^(?::[\s]*[\d]+(?:\.[\d]+)?)/i,/^(?:$)/i],conditions:{close_directive:{rules:[],inclusive:!1},arg_directive:{rules:[3,4],inclusive:!1},type_directive:{rules:[2,3],inclusive:!1},open_directive:{rules:[1],inclusive:!1},title:{rules:[11],inclusive:!1},string:{rules:[13,14],inclusive:!1},INITIAL:{rules:[0,5,6,7,8,9,10,12,15,16,17],inclusive:!0}}};function d(){this.yy={}}return h.lexer=f,d.prototype=h,h.Parser=d,new d}();e.parser=i,e.Parser=i.Parser,e.parse=function(){return i.parse.apply(i,arguments)},e.main=function(r){r[1]||(console.log("Usage: "+r[0]+" FILE"),t.exit(1));var i=n(19).readFileSync(n(20).normalize(r[1]),"utf8");return e.parser.parse(i)},n.c[n.s]===r&&e.main(t.argv.slice(1))}).call(this,n(14),n(7)(t))},function(t,e,n){(function(t,r){var i=function(){var t=function(t,e,n,r){for(n=n||{},r=t.length;r--;n[t[r]]=e);return n},e=[1,2],n=[1,5],r=[6,9,11,23,37],i=[1,17],a=[1,20],o=[1,25],s=[1,26],c=[1,27],u=[1,28],l=[1,37],h=[23,34,35],f=[4,6,9,11,23,37],d=[30,31,32,33],p=[22,27],g={trace:function(){},yy:{},symbols_:{error:2,start:3,ER_DIAGRAM:4,document:5,EOF:6,directive:7,line:8,SPACE:9,statement:10,NEWLINE:11,openDirective:12,typeDirective:13,closeDirective:14,":":15,argDirective:16,entityName:17,relSpec:18,role:19,BLOCK_START:20,attributes:21,BLOCK_STOP:22,ALPHANUM:23,attribute:24,attributeType:25,attributeName:26,ATTRIBUTE_WORD:27,cardinality:28,relType:29,ZERO_OR_ONE:30,ZERO_OR_MORE:31,ONE_OR_MORE:32,ONLY_ONE:33,NON_IDENTIFYING:34,IDENTIFYING:35,WORD:36,open_directive:37,type_directive:38,arg_directive:39,close_directive:40,$accept:0,$end:1},terminals_:{2:"error",4:"ER_DIAGRAM",6:"EOF",9:"SPACE",11:"NEWLINE",15:":",20:"BLOCK_START",22:"BLOCK_STOP",23:"ALPHANUM",27:"ATTRIBUTE_WORD",30:"ZERO_OR_ONE",31:"ZERO_OR_MORE",32:"ONE_OR_MORE",33:"ONLY_ONE",34:"NON_IDENTIFYING",35:"IDENTIFYING",36:"WORD",37:"open_directive",38:"type_directive",39:"arg_directive",40:"close_directive"},productions_:[0,[3,3],[3,2],[5,0],[5,2],[8,2],[8,1],[8,1],[8,1],[7,4],[7,6],[10,1],[10,5],[10,4],[10,3],[10,1],[17,1],[21,1],[21,2],[24,2],[25,1],[26,1],[18,3],[28,1],[28,1],[28,1],[28,1],[29,1],[29,1],[19,1],[19,1],[12,1],[13,1],[16,1],[14,1]],performAction:function(t,e,n,r,i,a,o){var s=a.length-1;switch(i){case 1:break;case 3:this.$=[];break;case 4:a[s-1].push(a[s]),this.$=a[s-1];break;case 5:case 6:this.$=a[s];break;case 7:case 8:this.$=[];break;case 12:r.addEntity(a[s-4]),r.addEntity(a[s-2]),r.addRelationship(a[s-4],a[s],a[s-2],a[s-3]);break;case 13:r.addEntity(a[s-3]),r.addAttributes(a[s-3],a[s-1]);break;case 14:r.addEntity(a[s-2]);break;case 15:r.addEntity(a[s]);break;case 16:this.$=a[s];break;case 17:this.$=[a[s]];break;case 18:a[s].push(a[s-1]),this.$=a[s];break;case 19:this.$={attributeType:a[s-1],attributeName:a[s]};break;case 20:case 21:this.$=a[s];break;case 22:this.$={cardA:a[s],relType:a[s-1],cardB:a[s-2]};break;case 23:this.$=r.Cardinality.ZERO_OR_ONE;break;case 24:this.$=r.Cardinality.ZERO_OR_MORE;break;case 25:this.$=r.Cardinality.ONE_OR_MORE;break;case 26:this.$=r.Cardinality.ONLY_ONE;break;case 27:this.$=r.Identification.NON_IDENTIFYING;break;case 28:this.$=r.Identification.IDENTIFYING;break;case 29:this.$=a[s].replace(/"/g,"");break;case 30:this.$=a[s];break;case 31:r.parseDirective("%%{","open_directive");break;case 32:r.parseDirective(a[s],"type_directive");break;case 33:a[s]=a[s].trim().replace(/'/g,'"'),r.parseDirective(a[s],"arg_directive");break;case 34:r.parseDirective("}%%","close_directive","er")}},table:[{3:1,4:e,7:3,12:4,37:n},{1:[3]},t(r,[2,3],{5:6}),{3:7,4:e,7:3,12:4,37:n},{13:8,38:[1,9]},{38:[2,31]},{6:[1,10],7:15,8:11,9:[1,12],10:13,11:[1,14],12:4,17:16,23:i,37:n},{1:[2,2]},{14:18,15:[1,19],40:a},t([15,40],[2,32]),t(r,[2,8],{1:[2,1]}),t(r,[2,4]),{7:15,10:21,12:4,17:16,23:i,37:n},t(r,[2,6]),t(r,[2,7]),t(r,[2,11]),t(r,[2,15],{18:22,28:24,20:[1,23],30:o,31:s,32:c,33:u}),t([6,9,11,15,20,23,30,31,32,33,37],[2,16]),{11:[1,29]},{16:30,39:[1,31]},{11:[2,34]},t(r,[2,5]),{17:32,23:i},{21:33,22:[1,34],24:35,25:36,27:l},{29:38,34:[1,39],35:[1,40]},t(h,[2,23]),t(h,[2,24]),t(h,[2,25]),t(h,[2,26]),t(f,[2,9]),{14:41,40:a},{40:[2,33]},{15:[1,42]},{22:[1,43]},t(r,[2,14]),{21:44,22:[2,17],24:35,25:36,27:l},{26:45,27:[1,46]},{27:[2,20]},{28:47,30:o,31:s,32:c,33:u},t(d,[2,27]),t(d,[2,28]),{11:[1,48]},{19:49,23:[1,51],36:[1,50]},t(r,[2,13]),{22:[2,18]},t(p,[2,19]),t(p,[2,21]),{23:[2,22]},t(f,[2,10]),t(r,[2,12]),t(r,[2,29]),t(r,[2,30])],defaultActions:{5:[2,31],7:[2,2],20:[2,34],31:[2,33],37:[2,20],44:[2,18],47:[2,22]},parseError:function(t,e){if(!e.recoverable){var n=new Error(t);throw n.hash=e,n}this.trace(t)},parse:function(t){var e=this,n=[0],r=[],i=[null],a=[],o=this.table,s="",c=0,u=0,l=0,h=2,f=1,d=a.slice.call(arguments,1),p=Object.create(this.lexer),g={yy:{}};for(var y in this.yy)Object.prototype.hasOwnProperty.call(this.yy,y)&&(g.yy[y]=this.yy[y]);p.setInput(t,g.yy),g.yy.lexer=p,g.yy.parser=this,void 0===p.yylloc&&(p.yylloc={});var v=p.yylloc;a.push(v);var m=p.options&&p.options.ranges;function b(){var t;return"number"!=typeof(t=r.pop()||p.lex()||f)&&(t instanceof Array&&(t=(r=t).pop()),t=e.symbols_[t]||t),t}"function"==typeof g.yy.parseError?this.parseError=g.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;for(var x,_,k,w,E,T,C,A,S,M={};;){if(k=n[n.length-1],this.defaultActions[k]?w=this.defaultActions[k]:(null==x&&(x=b()),w=o[k]&&o[k][x]),void 0===w||!w.length||!w[0]){var O="";for(T in S=[],o[k])this.terminals_[T]&&T>h&&S.push("'"+this.terminals_[T]+"'");O=p.showPosition?"Parse error on line "+(c+1)+":\n"+p.showPosition()+"\nExpecting "+S.join(", ")+", got '"+(this.terminals_[x]||x)+"'":"Parse error on line "+(c+1)+": Unexpected "+(x==f?"end of input":"'"+(this.terminals_[x]||x)+"'"),this.parseError(O,{text:p.match,token:this.terminals_[x]||x,line:p.yylineno,loc:v,expected:S})}if(w[0]instanceof Array&&w.length>1)throw new Error("Parse Error: multiple actions possible at state: "+k+", token: "+x);switch(w[0]){case 1:n.push(x),i.push(p.yytext),a.push(p.yylloc),n.push(w[1]),x=null,_?(x=_,_=null):(u=p.yyleng,s=p.yytext,c=p.yylineno,v=p.yylloc,l>0&&l--);break;case 2:if(C=this.productions_[w[1]][1],M.$=i[i.length-C],M._$={first_line:a[a.length-(C||1)].first_line,last_line:a[a.length-1].last_line,first_column:a[a.length-(C||1)].first_column,last_column:a[a.length-1].last_column},m&&(M._$.range=[a[a.length-(C||1)].range[0],a[a.length-1].range[1]]),void 0!==(E=this.performAction.apply(M,[s,u,c,g.yy,w[1],i,a].concat(d))))return E;C&&(n=n.slice(0,-1*C*2),i=i.slice(0,-1*C),a=a.slice(0,-1*C)),n.push(this.productions_[w[1]][0]),i.push(M.$),a.push(M._$),A=o[n[n.length-2]][n[n.length-1]],n.push(A);break;case 3:return!0}}return!0}},y={EOF:1,parseError:function(t,e){if(!this.yy.parser)throw new Error(t);this.yy.parser.parseError(t,e)},setInput:function(t,e){return this.yy=e||this.yy||{},this._input=t,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var t=this._input[0];return this.yytext+=t,this.yyleng++,this.offset++,this.match+=t,this.matched+=t,t.match(/(?:\r\n?|\n).*/g)?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),t},unput:function(t){var e=t.length,n=t.split(/(?:\r\n?|\n)/g);this._input=t+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-e),this.offset-=e;var r=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),n.length-1&&(this.yylineno-=n.length-1);var i=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:n?(n.length===r.length?this.yylloc.first_column:0)+r[r.length-n.length].length-n[0].length:this.yylloc.first_column-e},this.options.ranges&&(this.yylloc.range=[i[0],i[0]+this.yyleng-e]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){return this.options.backtrack_lexer?(this._backtrack=!0,this):this.parseError("Lexical error on line "+(this.yylineno+1)+". You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},less:function(t){this.unput(this.match.slice(t))},pastInput:function(){var t=this.matched.substr(0,this.matched.length-this.match.length);return(t.length>20?"...":"")+t.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var t=this.match;return t.length<20&&(t+=this._input.substr(0,20-t.length)),(t.substr(0,20)+(t.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var t=this.pastInput(),e=new Array(t.length+1).join("-");return t+this.upcomingInput()+"\n"+e+"^"},test_match:function(t,e){var n,r,i;if(this.options.backtrack_lexer&&(i={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(i.yylloc.range=this.yylloc.range.slice(0))),(r=t[0].match(/(?:\r\n?|\n).*/g))&&(this.yylineno+=r.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:r?r[r.length-1].length-r[r.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+t[0].length},this.yytext+=t[0],this.match+=t[0],this.matches=t,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(t[0].length),this.matched+=t[0],n=this.performAction.call(this,this.yy,this,e,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),n)return n;if(this._backtrack){for(var a in i)this[a]=i[a];return!1}return!1},next:function(){if(this.done)return this.EOF;var t,e,n,r;this._input||(this.done=!0),this._more||(this.yytext="",this.match="");for(var i=this._currentRules(),a=0;a<i.length;a++)if((n=this._input.match(this.rules[i[a]]))&&(!e||n[0].length>e[0].length)){if(e=n,r=a,this.options.backtrack_lexer){if(!1!==(t=this.test_match(n,i[a])))return t;if(this._backtrack){e=!1;continue}return!1}if(!this.options.flex)break}return e?!1!==(t=this.test_match(e,i[r]))&&t:""===this._input?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+". Unrecognized text.\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var t=this.next();return t||this.lex()},begin:function(t){this.conditionStack.push(t)},popState:function(){return this.conditionStack.length-1>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(t){return(t=this.conditionStack.length-1-Math.abs(t||0))>=0?this.conditionStack[t]:"INITIAL"},pushState:function(t){this.begin(t)},stateStackSize:function(){return this.conditionStack.length},options:{"case-insensitive":!0},performAction:function(t,e,n,r){switch(n){case 0:return this.begin("open_directive"),37;case 1:return this.begin("type_directive"),38;case 2:return this.popState(),this.begin("arg_directive"),15;case 3:return this.popState(),this.popState(),40;case 4:return 39;case 5:case 6:break;case 7:return 11;case 8:break;case 9:return 9;case 10:return 36;case 11:return 4;case 12:return this.begin("block"),20;case 13:break;case 14:return 27;case 15:break;case 16:return this.popState(),22;case 17:return e.yytext[0];case 18:return 30;case 19:return 31;case 20:return 32;case 21:return 33;case 22:return 30;case 23:return 31;case 24:return 32;case 25:return 34;case 26:return 35;case 27:case 28:return 34;case 29:return 23;case 30:return e.yytext[0];case 31:return 6}},rules:[/^(?:%%\{)/i,/^(?:((?:(?!\}%%)[^:.])*))/i,/^(?::)/i,/^(?:\}%%)/i,/^(?:((?:(?!\}%%).|\n)*))/i,/^(?:%(?!\{)[^\n]*)/i,/^(?:[^\}]%%[^\n]*)/i,/^(?:[\n]+)/i,/^(?:\s+)/i,/^(?:[\s]+)/i,/^(?:"[^"]*")/i,/^(?:erDiagram\b)/i,/^(?:\{)/i,/^(?:\s+)/i,/^(?:[A-Za-z][A-Za-z0-9\-_]*)/i,/^(?:[\n]+)/i,/^(?:\})/i,/^(?:.)/i,/^(?:\|o\b)/i,/^(?:\}o\b)/i,/^(?:\}\|)/i,/^(?:\|\|)/i,/^(?:o\|)/i,/^(?:o\{)/i,/^(?:\|\{)/i,/^(?:\.\.)/i,/^(?:--)/i,/^(?:\.-)/i,/^(?:-\.)/i,/^(?:[A-Za-z][A-Za-z0-9\-_]*)/i,/^(?:.)/i,/^(?:$)/i],conditions:{open_directive:{rules:[1],inclusive:!1},type_directive:{rules:[2,3],inclusive:!1},arg_directive:{rules:[3,4],inclusive:!1},block:{rules:[13,14,15,16,17],inclusive:!1},INITIAL:{rules:[0,5,6,7,8,9,10,11,12,18,19,20,21,22,23,24,25,26,27,28,29,30,31],inclusive:!0}}};function v(){this.yy={}}return g.lexer=y,v.prototype=g,g.Parser=v,new v}();e.parser=i,e.Parser=i.Parser,e.parse=function(){return i.parse.apply(i,arguments)},e.main=function(r){r[1]||(console.log("Usage: "+r[0]+" FILE"),t.exit(1));var i=n(19).readFileSync(n(20).normalize(r[1]),"utf8");return e.parser.parse(i)},n.c[n.s]===r&&e.main(t.argv.slice(1))}).call(this,n(14),n(7)(t))},function(t,e,n){"use strict";var r;Object.defineProperty(e,"__esModule",{value:!0}),function(t){t[t.ALL=0]="ALL",t[t.RGB=1]="RGB",t[t.HSL=2]="HSL"}(r||(r={})),e.TYPE=r},function(t,e,n){"use strict";var r=n(10);t.exports=i;function i(t){this._isDirected=!r.has(t,"directed")||t.directed,this._isMultigraph=!!r.has(t,"multigraph")&&t.multigraph,this._isCompound=!!r.has(t,"compound")&&t.compound,this._label=void 0,this._defaultNodeLabelFn=r.constant(void 0),this._defaultEdgeLabelFn=r.constant(void 0),this._nodes={},this._isCompound&&(this._parent={},this._children={},this._children["\0"]={}),this._in={},this._preds={},this._out={},this._sucs={},this._edgeObjs={},this._edgeLabels={}}function a(t,e){t[e]?t[e]++:t[e]=1}function o(t,e){--t[e]||delete t[e]}function s(t,e,n,i){var a=""+e,o=""+n;if(!t&&a>o){var s=a;a=o,o=s}return a+""+o+""+(r.isUndefined(i)?"\0":i)}function c(t,e,n,r){var i=""+e,a=""+n;if(!t&&i>a){var o=i;i=a,a=o}var s={v:i,w:a};return r&&(s.name=r),s}function u(t,e){return s(t,e.v,e.w,e.name)}i.prototype._nodeCount=0,i.prototype._edgeCount=0,i.prototype.isDirected=function(){return this._isDirected},i.prototype.isMultigraph=function(){return this._isMultigraph},i.prototype.isCompound=function(){return this._isCompound},i.prototype.setGraph=function(t){return this._label=t,this},i.prototype.graph=function(){return this._label},i.prototype.setDefaultNodeLabel=function(t){return r.isFunction(t)||(t=r.constant(t)),this._defaultNodeLabelFn=t,this},i.prototype.nodeCount=function(){return this._nodeCount},i.prototype.nodes=function(){return r.keys(this._nodes)},i.prototype.sources=function(){var t=this;return r.filter(this.nodes(),(function(e){return r.isEmpty(t._in[e])}))},i.prototype.sinks=function(){var t=this;return r.filter(this.nodes(),(function(e){return 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e=t,(n=[{key:"updateColors",value:function(){this.secondBkg=Object(q.lighten)(this.mainBkg,16),this.lineColor=this.mainContrastColor,this.arrowheadColor=this.mainContrastColor,this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.edgeLabelBackground=Object(q.lighten)(this.labelBackground,25),this.actorBorder=this.border1,this.actorBkg=this.mainBkg,this.actorTextColor=this.mainContrastColor,this.actorLineColor=this.mainContrastColor,this.signalColor=this.mainContrastColor,this.signalTextColor=this.mainContrastColor,this.labelBoxBkgColor=this.actorBkg,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.mainContrastColor,this.loopTextColor=this.mainContrastColor,this.noteBorderColor=this.border2,this.noteTextColor=this.mainBkg,this.activationBorderColor=this.border1,this.activationBkgColor=this.secondBkg,this.altSectionBkgColor=this.background,this.taskBkgColor=Object(q.lighten)(this.mainBkg,23),this.taskTextColor=this.darkTextColor,this.taskTextLightColor=this.mainContrastColor,this.taskTextOutsideColor=this.taskTextLightColor,this.gridColor=this.mainContrastColor,this.doneTaskBkgColor=this.mainContrastColor,this.taskTextDarkColor=this.darkTextColor,this.labelColor=this.textColor,this.altBackground=Object(q.lighten)(this.background,20),this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=Object(q.adjust)(this.primaryColor,{h:64}),this.fillType3=Object(q.adjust)(this.secondaryColor,{h:64}),this.fillType4=Object(q.adjust)(this.primaryColor,{h:-64}),this.fillType5=Object(q.adjust)(this.secondaryColor,{h:-64}),this.fillType6=Object(q.adjust)(this.primaryColor,{h:128}),this.fillType7=Object(q.adjust)(this.secondaryColor,{h:128}),this.classText=this.primaryTextColor}},{key:"calculate",value:function(t){var e=this;if("object"===Q(t)){var n=Object.keys(t);n.forEach((function(n){e[n]=t[n]})),this.updateColors(),n.forEach((function(n){e[n]=t[n]}))}else this.updateColors()}}])&&tt(e.prototype,n),r&&tt(e,r),t}();function nt(t){return(nt="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}function rt(t,e){for(var n=0;n<e.length;n++){var r=e[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(t,r.key,r)}}var it=function(){function t(){!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,t),this.background="#f4f4f4",this.primaryColor="#ECECFF",this.secondaryColor=Object(q.adjust)(this.primaryColor,{h:120}),this.secondaryColor="#ffffde",this.tertiaryColor=Object(q.adjust)(this.primaryColor,{h:-160}),this.primaryBorderColor=X(this.primaryColor,this.darkMode),this.secondaryBorderColor=X(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=X(this.tertiaryColor,this.darkMode),this.primaryTextColor=Object(q.invert)(this.primaryColor),this.secondaryTextColor=Object(q.invert)(this.secondaryColor),this.tertiaryTextColor=Object(q.invert)(this.tertiaryColor),this.lineColor=Object(q.invert)(this.background),this.textColor=Object(q.invert)(this.background),this.background="white",this.mainBkg="#ECECFF",this.secondBkg="#ffffde",this.lineColor="#333333",this.border1="#9370DB",this.border2="#aaaa33",this.arrowheadColor="#333333",this.fontFamily='"trebuchet ms", verdana, arial, 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e,n,r;return e=t,(n=[{key:"updateColors",value:function(){this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.titleColor=this.textColor,this.edgeLabelBackground=this.labelBackground,this.actorBorder=Object(q.lighten)(this.border1,23),this.actorBkg=this.mainBkg,this.labelBoxBkgColor=this.actorBkg,this.signalColor=this.textColor,this.signalTextColor=this.textColor,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.actorTextColor,this.loopTextColor=this.actorTextColor,this.noteBorderColor=this.border2,this.noteTextColor=this.actorTextColor,this.taskTextColor=this.taskTextLightColor,this.taskTextOutsideColor=this.taskTextDarkColor,this.classText=this.primaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=Object(q.adjust)(this.primaryColor,{h:64}),this.fillType3=Object(q.adjust)(this.secondaryColor,{h:64}),this.fillType4=Object(q.adjust)(this.primaryColor,{h:-64}),this.fillType5=Object(q.adjust)(this.secondaryColor,{h:-64}),this.fillType6=Object(q.adjust)(this.primaryColor,{h:128}),this.fillType7=Object(q.adjust)(this.secondaryColor,{h:128})}},{key:"calculate",value:function(t){var e=this;if("object"===nt(t)){var n=Object.keys(t);n.forEach((function(n){e[n]=t[n]})),this.updateColors(),n.forEach((function(n){e[n]=t[n]}))}else this.updateColors()}}])&&rt(e.prototype,n),r&&rt(e,r),t}();function at(t){return(at="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}function ot(t,e){for(var n=0;n<e.length;n++){var r=e[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(t,r.key,r)}}var st=function(){function t(){!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,t),this.background="#f4f4f4",this.primaryColor="#cde498",this.secondaryColor="#cdffb2",this.background="white",this.mainBkg="#cde498",this.secondBkg="#cdffb2",this.lineColor="green",this.border1="#13540c",this.border2="#6eaa49",this.arrowheadColor="green",this.fontFamily='"trebuchet ms", verdana, arial, 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e,n,r;return e=t,(n=[{key:"updateColors",value:function(){this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.actorBorder=Object(q.darken)(this.mainBkg,20),this.actorBkg=this.mainBkg,this.labelBoxBkgColor=this.actorBkg,this.labelTextColor=this.actorTextColor,this.loopTextColor=this.actorTextColor,this.noteBorderColor=this.border2,this.noteTextColor=this.actorTextColor,this.taskBorderColor=this.border1,this.taskTextColor=this.taskTextLightColor,this.taskTextOutsideColor=this.taskTextDarkColor,this.activeTaskBorderColor=this.taskBorderColor,this.activeTaskBkgColor=this.mainBkg,this.classText=this.primaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=Object(q.adjust)(this.primaryColor,{h:64}),this.fillType3=Object(q.adjust)(this.secondaryColor,{h:64}),this.fillType4=Object(q.adjust)(this.primaryColor,{h:-64}),this.fillType5=Object(q.adjust)(this.secondaryColor,{h:-64}),this.fillType6=Object(q.adjust)(this.primaryColor,{h:128}),this.fillType7=Object(q.adjust)(this.secondaryColor,{h:128})}},{key:"calculate",value:function(t){var e=this;if("object"===at(t)){var n=Object.keys(t);n.forEach((function(n){e[n]=t[n]})),this.updateColors(),n.forEach((function(n){e[n]=t[n]}))}else this.updateColors()}}])&&ot(e.prototype,n),r&&ot(e,r),t}();function ct(t){return(ct="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}function ut(t,e){for(var n=0;n<e.length;n++){var r=e[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(t,r.key,r)}}var lt=function(){function t(){!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,t),this.primaryColor="#eee",this.contrast="#26a",this.secondaryColor=Object(q.lighten)(this.contrast,55),this.background="#ffffff",this.tertiaryColor=Object(q.adjust)(this.primaryColor,{h:-160}),this.primaryBorderColor=X(this.primaryColor,this.darkMode),this.secondaryBorderColor=X(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=X(this.tertiaryColor,this.darkMode),this.primaryTextColor=Object(q.invert)(this.primaryColor),this.secondaryTextColor=Object(q.invert)(this.secondaryColor),this.tertiaryTextColor=Object(q.invert)(this.tertiaryColor),this.lineColor=Object(q.invert)(this.background),this.textColor=Object(q.invert)(this.background),this.altBackground=Object(q.lighten)(this.contrast,55),this.mainBkg="#eee",this.secondBkg="calculated",this.lineColor="#666",this.border1="#999",this.border2="calculated",this.note="#ffa",this.text="#333",this.critical="#d42",this.done="#bbb",this.arrowheadColor="#333333",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="calculated",this.edgeLabelBackground="white",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="calculated",this.actorLineColor="calculated",this.signalColor="calculated",this.signalTextColor="calculated",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="calculated",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="calculated",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="calculated",this.altSectionBkgColor="white",this.sectionBkgColor2="calculated",this.taskBorderColor="calculated",this.taskBkgColor="calculated",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="calculated",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="calculated",this.doneTaskBkgColor="calculated",this.doneTaskBorderColor="calculated",this.critBkgColor="calculated",this.critBorderColor="calculated",this.todayLineColor="calculated",this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222"}var e,n,r;return e=t,(n=[{key:"updateColors",value:function(){this.secondBkg=Object(q.lighten)(this.contrast,55),this.border2=this.contrast,this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.titleColor=this.text,this.actorBorder=Object(q.lighten)(this.border1,23),this.actorBkg=this.mainBkg,this.actorTextColor=this.text,this.actorLineColor=this.lineColor,this.signalColor=this.text,this.signalTextColor=this.text,this.labelBoxBkgColor=this.actorBkg,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.text,this.loopTextColor=this.text,this.noteBorderColor=Object(q.darken)(this.note,60),this.noteBkgColor=this.note,this.noteTextColor=this.actorTextColor,this.sectionBkgColor=Object(q.lighten)(this.contrast,30),this.sectionBkgColor2=Object(q.lighten)(this.contrast,30),this.taskBorderColor=Object(q.darken)(this.contrast,10),this.taskBkgColor=this.contrast,this.taskTextColor=this.taskTextLightColor,this.taskTextDarkColor=this.text,this.taskTextOutsideColor=this.taskTextDarkColor,this.activeTaskBorderColor=this.taskBorderColor,this.activeTaskBkgColor=this.mainBkg,this.gridColor=Object(q.lighten)(this.border1,30),this.doneTaskBkgColor=this.done,this.doneTaskBorderColor=this.lineColor,this.critBkgColor=this.critical,this.critBorderColor=Object(q.darken)(this.critBkgColor,10),this.todayLineColor=this.critBkgColor,this.classText=this.primaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=Object(q.adjust)(this.primaryColor,{h:64}),this.fillType3=Object(q.adjust)(this.secondaryColor,{h:64}),this.fillType4=Object(q.adjust)(this.primaryColor,{h:-64}),this.fillType5=Object(q.adjust)(this.secondaryColor,{h:-64}),this.fillType6=Object(q.adjust)(this.primaryColor,{h:128}),this.fillType7=Object(q.adjust)(this.secondaryColor,{h:128})}},{key:"calculate",value:function(t){var e=this;if("object"===ct(t)){var n=Object.keys(t);n.forEach((function(n){e[n]=t[n]})),this.updateColors(),n.forEach((function(n){e[n]=t[n]}))}else this.updateColors()}}])&&ut(e.prototype,n),r&&ut(e,r),t}(),ht={base:{getThemeVariables:function(t){var e=new K;return e.calculate(t),e}},dark:{getThemeVariables:function(t){var e=new et;return e.calculate(t),e}},default:{getThemeVariables:function(t){var e=new it;return e.calculate(t),e}},forest:{getThemeVariables:function(t){var e=new st;return e.calculate(t),e}},neutral:{getThemeVariables:function(t){var e=new lt;return e.calculate(t),e}}},ft={theme:"default",themeVariables:ht.default.getThemeVariables(),themeCSS:void 0,maxTextSize:5e4,fontFamily:'"trebuchet ms", verdana, arial, sans-serif;',logLevel:5,securityLevel:"strict",startOnLoad:!0,arrowMarkerAbsolute:!1,secure:["secure","securityLevel","startOnLoad","maxTextSize"],deterministicIds:!1,deterministicIDSeed:void 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0,useMaxWidth:!0},state:{dividerMargin:10,sizeUnit:5,padding:8,textHeight:10,titleShift:-15,noteMargin:10,forkWidth:70,forkHeight:7,miniPadding:2,fontSizeFactor:5.02,fontSize:24,labelHeight:16,edgeLengthFactor:"20",compositTitleSize:35,radius:5,useMaxWidth:!0},er:{diagramPadding:20,layoutDirection:"TB",minEntityWidth:100,minEntityHeight:75,entityPadding:15,stroke:"gray",fill:"honeydew",fontSize:12,useMaxWidth:!0},pie:{useWidth:void 0,useMaxWidth:!0}};ft.class.arrowMarkerAbsolute=ft.arrowMarkerAbsolute,ft.git.arrowMarkerAbsolute=ft.arrowMarkerAbsolute;var dt,pt=ft,gt=Object.freeze(pt),yt=F({},gt),vt=[],mt=F({},gt),bt=function(t,e){for(var n=F({},t),r={},i=0;i<e.length;i++){var a=e[i];kt(a),r=F(r,a)}if(n=F(n,r),r.theme){var o=F(dt.themeVariables||{},r.themeVariables);n.themeVariables=ht[n.theme].getThemeVariables(o)}return mt=n,n},xt=function(){return F({},yt)},_t=function(){return F({},mt)},kt=function(t){Object.keys(yt.secure).forEach((function(e){void 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i=this,a=0;function o(o){return function(s){a++;var c=i.sequenceItems.length-a+1;i.updateVal(s,"starty",e-c*cr.boxMargin,Math.min),i.updateVal(s,"stopy",r+c*cr.boxMargin,Math.max),i.updateVal(ur.data,"startx",t-c*cr.boxMargin,Math.min),i.updateVal(ur.data,"stopx",n+c*cr.boxMargin,Math.max),"activation"!==o&&(i.updateVal(s,"startx",t-c*cr.boxMargin,Math.min),i.updateVal(s,"stopx",n+c*cr.boxMargin,Math.max),i.updateVal(ur.data,"starty",e-c*cr.boxMargin,Math.min),i.updateVal(ur.data,"stopy",r+c*cr.boxMargin,Math.max))}}this.sequenceItems.forEach(o()),this.activations.forEach(o("activation"))},insert:function(t,e,n,r){var i=Math.min(t,n),a=Math.max(t,n),o=Math.min(e,r),s=Math.max(e,r);this.updateVal(ur.data,"startx",i,Math.min),this.updateVal(ur.data,"starty",o,Math.min),this.updateVal(ur.data,"stopx",a,Math.max),this.updateVal(ur.data,"stopy",s,Math.max),this.updateBounds(i,o,a,s)},newActivation:function(t,e,n){var r=n[t.from.actor],i=gr(t.from.actor).length||0,a=r.x+r.width/2+(i-1)*cr.activationWidth/2;this.activations.push({startx:a,starty:this.verticalPos+2,stopx:a+cr.activationWidth,stopy:void 0,actor:t.from.actor,anchored:$n.anchorElement(e)})},endActivation:function(t){var e=this.activations.map((function(t){return t.actor})).lastIndexOf(t.from.actor);return this.activations.splice(e,1)[0]},createLoop:function(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{message:void 0,wrap:!1,width:void 0},e=arguments.length>1?arguments[1]:void 0;return{startx:void 0,starty:this.verticalPos,stopx:void 0,stopy:void 0,title:t.message,wrap:t.wrap,width:t.width,height:0,fill:e}},newLoop:function(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{message:void 0,wrap:!1,width:void 0},e=arguments.length>1?arguments[1]:void 0;this.sequenceItems.push(this.createLoop(t,e))},endLoop:function(){return this.sequenceItems.pop()},addSectionToLoop:function(t){var e=this.sequenceItems.pop();e.sections=e.sections||[],e.sectionTitles=e.sectionTitles||[],e.sections.push({y:ur.getVerticalPos(),height:0}),e.sectionTitles.push(t),this.sequenceItems.push(e)},bumpVerticalPos:function(t){this.verticalPos=this.verticalPos+t,this.data.stopy=this.verticalPos},getVerticalPos:function(){return this.verticalPos},getBounds:function(){return{bounds:this.data,models:this.models}}},lr=function(t){return{fontFamily:t.messageFontFamily,fontSize:t.messageFontSize,fontWeight:t.messageFontWeight}},hr=function(t){return{fontFamily:t.noteFontFamily,fontSize:t.noteFontSize,fontWeight:t.noteFontWeight}},fr=function(t){return{fontFamily:t.actorFontFamily,fontSize:t.actorFontSize,fontWeight:t.actorFontWeight}},dr=function(t,e,n,r){for(var i=0,a=0,o=0;o<n.length;o++){var s=e[n[o]];s.width=s.width||cr.width,s.height=Math.max(s.height||cr.height,cr.height),s.margin=s.margin||cr.actorMargin,s.x=i+a,s.y=r,$n.drawActor(t,s,cr),ur.insert(s.x,r,s.x+s.width,s.height),i+=s.width,a+=s.margin,ur.models.addActor(s)}ur.bumpVerticalPos(cr.height)},pr=function(t){F(cr,t),t.fontFamily&&(cr.actorFontFamily=cr.noteFontFamily=cr.messageFontFamily=t.fontFamily),t.fontSize&&(cr.actorFontSize=cr.noteFontSize=cr.messageFontSize=t.fontSize),t.fontWeight&&(cr.actorFontWeight=cr.noteFontWeight=cr.messageFontWeight=t.fontWeight)},gr=function(t){return ur.activations.filter((function(e){return e.actor===t}))},yr=function(t,e){var n=e[t],r=gr(t);return[r.reduce((function(t,e){return Math.min(t,e.startx)}),n.x+n.width/2),r.reduce((function(t,e){return Math.max(t,e.stopx)}),n.x+n.width/2)]};function vr(t,e,n,r,i){ur.bumpVerticalPos(n);var a=r;if(e.id&&e.message&&t[e.id]){var o=t[e.id].width,s=lr(cr);e.message=H.wrapLabel("[".concat(e.message,"]"),o-2*cr.wrapPadding,s),e.width=o,e.wrap=!0;var u=H.calculateTextDimensions(e.message,s),l=Math.max(u.height,cr.labelBoxHeight);a=r+l,c.debug("".concat(l," - ").concat(e.message))}i(e),ur.bumpVerticalPos(a)}var mr=function(t,e){var n={};return e.forEach((function(e){if(t[e.to]&&t[e.from]){var r=t[e.to];if(e.placement===Wn.parser.yy.PLACEMENT.LEFTOF&&!r.prevActor)return;if(e.placement===Wn.parser.yy.PLACEMENT.RIGHTOF&&!r.nextActor)return;var i=void 0!==e.placement,a=!i,o=i?hr(cr):lr(cr),s=e.wrap?H.wrapLabel(e.message,cr.width-2*cr.wrapPadding,o):e.message,c=H.calculateTextDimensions(s,o).width+2*cr.wrapPadding;a&&e.from===r.nextActor?n[e.to]=Math.max(n[e.to]||0,c):a&&e.from===r.prevActor?n[e.from]=Math.max(n[e.from]||0,c):a&&e.from===e.to?(n[e.from]=Math.max(n[e.from]||0,c/2),n[e.to]=Math.max(n[e.to]||0,c/2)):e.placement===Wn.parser.yy.PLACEMENT.RIGHTOF?n[e.from]=Math.max(n[e.from]||0,c):e.placement===Wn.parser.yy.PLACEMENT.LEFTOF?n[r.prevActor]=Math.max(n[r.prevActor]||0,c):e.placement===Wn.parser.yy.PLACEMENT.OVER&&(r.prevActor&&(n[r.prevActor]=Math.max(n[r.prevActor]||0,c/2)),r.nextActor&&(n[e.from]=Math.max(n[e.from]||0,c/2)))}})),c.debug("maxMessageWidthPerActor:",n),n},br=function(t,e){var n=0;for(var r in Object.keys(t).forEach((function(e){var r=t[e];r.wrap&&(r.description=H.wrapLabel(r.description,cr.width-2*cr.wrapPadding,fr(cr)));var i=H.calculateTextDimensions(r.description,fr(cr));r.width=r.wrap?cr.width:Math.max(cr.width,i.width+2*cr.wrapPadding),r.height=r.wrap?Math.max(i.height,cr.height):cr.height,n=Math.max(n,r.height)})),e){var i=t[r];if(i){var a=t[i.nextActor];if(a){var o=e[r]+cr.actorMargin-i.width/2-a.width/2;i.margin=Math.max(o,cr.actorMargin)}}}return Math.max(n,cr.height)},xr=function(t,e){var n,r,i,a={},o=[];return t.forEach((function(t){switch(t.id=H.random({length:10}),t.type){case Wn.parser.yy.LINETYPE.LOOP_START:case Wn.parser.yy.LINETYPE.ALT_START:case Wn.parser.yy.LINETYPE.OPT_START:case Wn.parser.yy.LINETYPE.PAR_START:o.push({id:t.id,msg:t.message,from:Number.MAX_SAFE_INTEGER,to:Number.MIN_SAFE_INTEGER,width:0});break;case Wn.parser.yy.LINETYPE.ALT_ELSE:case Wn.parser.yy.LINETYPE.PAR_AND:t.message&&(n=o.pop(),a[n.id]=n,a[t.id]=n,o.push(n));break;case Wn.parser.yy.LINETYPE.LOOP_END:case Wn.parser.yy.LINETYPE.ALT_END:case Wn.parser.yy.LINETYPE.OPT_END:case Wn.parser.yy.LINETYPE.PAR_END:n=o.pop(),a[n.id]=n;break;case Wn.parser.yy.LINETYPE.ACTIVE_START:var s=e[t.from?t.from.actor:t.to.actor],u=gr(t.from?t.from.actor:t.to.actor).length,l=s.x+s.width/2+(u-1)*cr.activationWidth/2,h={startx:l,stopx:l+cr.activationWidth,actor:t.from.actor,enabled:!0};ur.activations.push(h);break;case Wn.parser.yy.LINETYPE.ACTIVE_END:var f=ur.activations.map((function(t){return t.actor})).lastIndexOf(t.from.actor);delete ur.activations.splice(f,1)[0]}void 0!==t.placement?(r=function(t,e){var n=e[t.from].x,r=e[t.to].x,i=t.wrap&&t.message,a=H.calculateTextDimensions(i?H.wrapLabel(t.message,cr.width,hr(cr)):t.message,hr(cr)),o={width:i?cr.width:Math.max(cr.width,a.width+2*cr.noteMargin),height:0,startx:e[t.from].x,stopx:0,starty:0,stopy:0,message:t.message};return t.placement===Wn.parser.yy.PLACEMENT.RIGHTOF?(o.width=i?Math.max(cr.width,a.width):Math.max(e[t.from].width/2+e[t.to].width/2,a.width+2*cr.noteMargin),o.startx=n+(e[t.from].width+cr.actorMargin)/2):t.placement===Wn.parser.yy.PLACEMENT.LEFTOF?(o.width=i?Math.max(cr.width,a.width+2*cr.noteMargin):Math.max(e[t.from].width/2+e[t.to].width/2,a.width+2*cr.noteMargin),o.startx=n-o.width+(e[t.from].width-cr.actorMargin)/2):t.to===t.from?(a=H.calculateTextDimensions(i?H.wrapLabel(t.message,Math.max(cr.width,e[t.from].width),hr(cr)):t.message,hr(cr)),o.width=i?Math.max(cr.width,e[t.from].width):Math.max(e[t.from].width,cr.width,a.width+2*cr.noteMargin),o.startx=n+(e[t.from].width-o.width)/2):(o.width=Math.abs(n+e[t.from].width/2-(r+e[t.to].width/2))+cr.actorMargin,o.startx=n<r?n+e[t.from].width/2-cr.actorMargin/2:r+e[t.to].width/2-cr.actorMargin/2),i&&(o.message=H.wrapLabel(t.message,o.width-2*cr.wrapPadding,hr(cr))),c.debug("NM:[".concat(o.startx,",").concat(o.stopx,",").concat(o.starty,",").concat(o.stopy,":").concat(o.width,",").concat(o.height,"=").concat(t.message,"]")),o}(t,e),t.noteModel=r,o.forEach((function(t){(n=t).from=Math.min(n.from,r.startx),n.to=Math.max(n.to,r.startx+r.width),n.width=Math.max(n.width,Math.abs(n.from-n.to))-cr.labelBoxWidth}))):(i=function(t,e){var n=!1;if([Wn.parser.yy.LINETYPE.SOLID_OPEN,Wn.parser.yy.LINETYPE.DOTTED_OPEN,Wn.parser.yy.LINETYPE.SOLID,Wn.parser.yy.LINETYPE.DOTTED,Wn.parser.yy.LINETYPE.SOLID_CROSS,Wn.parser.yy.LINETYPE.DOTTED_CROSS,Wn.parser.yy.LINETYPE.SOLID_POINT,Wn.parser.yy.LINETYPE.DOTTED_POINT].includes(t.type)&&(n=!0),!n)return{};var r=yr(t.from,e),i=yr(t.to,e),a=r[0]<=i[0]?1:0,o=r[0]<i[0]?0:1,s=r.concat(i),c=Math.abs(i[o]-r[a]);t.wrap&&t.message&&(t.message=H.wrapLabel(t.message,Math.max(c+2*cr.wrapPadding,cr.width),lr(cr)));var u=H.calculateTextDimensions(t.message,lr(cr));return{width:Math.max(t.wrap?0:u.width+2*cr.wrapPadding,c+2*cr.wrapPadding,cr.width),height:0,startx:r[a],stopx:i[o],starty:0,stopy:0,message:t.message,type:t.type,wrap:t.wrap,fromBounds:Math.min.apply(null,s),toBounds:Math.max.apply(null,s)}}(t,e),t.msgModel=i,i.startx&&i.stopx&&o.length>0&&o.forEach((function(r){if(n=r,i.startx===i.stopx){var a=e[t.from],o=e[t.to];n.from=Math.min(a.x-i.width/2,a.x-a.width/2,n.from),n.to=Math.max(o.x+i.width/2,o.x+a.width/2,n.to),n.width=Math.max(n.width,Math.abs(n.to-n.from))-cr.labelBoxWidth}else n.from=Math.min(i.startx,n.from),n.to=Math.max(i.stopx,n.to),n.width=Math.max(n.width,i.width)-cr.labelBoxWidth})))})),ur.activations=[],c.debug("Loop type widths:",a),a},_r={bounds:ur,drawActors:dr,setConf:pr,draw:function(t,e){cr=_t().sequence,Wn.parser.yy.clear(),Wn.parser.yy.setWrap(cr.wrap),Wn.parser.parse(t+"\n"),ur.init(),c.debug("C:".concat(JSON.stringify(cr,null,2)));var n=Object(d.select)('[id="'.concat(e,'"]')),r=Wn.parser.yy.getActors(),i=Wn.parser.yy.getActorKeys(),a=Wn.parser.yy.getMessages(),o=Wn.parser.yy.getTitle(),s=mr(r,a);cr.height=br(r,s),dr(n,r,i,0);var u=xr(a,r,s);$n.insertArrowHead(n),$n.insertArrowCrossHead(n),$n.insertArrowFilledHead(n),$n.insertSequenceNumber(n);var l=1;a.forEach((function(t){var e,i,a;switch(t.type){case Wn.parser.yy.LINETYPE.NOTE:i=t.noteModel,function(t,e){ur.bumpVerticalPos(cr.boxMargin),e.height=cr.boxMargin,e.starty=ur.getVerticalPos();var n=$n.getNoteRect();n.x=e.startx,n.y=e.starty,n.width=e.width||cr.width,n.class="note";var r=t.append("g"),i=$n.drawRect(r,n),a=$n.getTextObj();a.x=e.startx,a.y=e.starty,a.width=n.width,a.dy="1em",a.text=e.message,a.class="noteText",a.fontFamily=cr.noteFontFamily,a.fontSize=cr.noteFontSize,a.fontWeight=cr.noteFontWeight,a.anchor=cr.noteAlign,a.textMargin=cr.noteMargin,a.valign=cr.noteAlign;var o=Fn(r,a),s=Math.round(o.map((function(t){return(t._groups||t)[0][0].getBBox().height})).reduce((function(t,e){return t+e})));i.attr("height",s+2*cr.noteMargin),e.height+=s+2*cr.noteMargin,ur.bumpVerticalPos(s+2*cr.noteMargin),e.stopy=e.starty+s+2*cr.noteMargin,e.stopx=e.startx+n.width,ur.insert(e.startx,e.starty,e.stopx,e.stopy),ur.models.addNote(e)}(n,i);break;case Wn.parser.yy.LINETYPE.ACTIVE_START:ur.newActivation(t,n,r);break;case Wn.parser.yy.LINETYPE.ACTIVE_END:!function(t,e){var r=ur.endActivation(t);r.starty+18>e&&(r.starty=e-6,e+=12),$n.drawActivation(n,r,e,cr,gr(t.from.actor).length),ur.insert(r.startx,e-10,r.stopx,e)}(t,ur.getVerticalPos());break;case Wn.parser.yy.LINETYPE.LOOP_START:vr(u,t,cr.boxMargin,cr.boxMargin+cr.boxTextMargin,(function(t){return ur.newLoop(t)}));break;case Wn.parser.yy.LINETYPE.LOOP_END:e=ur.endLoop(),$n.drawLoop(n,e,"loop",cr),ur.bumpVerticalPos(e.stopy-ur.getVerticalPos()),ur.models.addLoop(e);break;case Wn.parser.yy.LINETYPE.RECT_START:vr(u,t,cr.boxMargin,cr.boxMargin,(function(t){return ur.newLoop(void 0,t.message)}));break;case Wn.parser.yy.LINETYPE.RECT_END:e=ur.endLoop(),$n.drawBackgroundRect(n,e),ur.models.addLoop(e),ur.bumpVerticalPos(e.stopy-ur.getVerticalPos());break;case Wn.parser.yy.LINETYPE.OPT_START:vr(u,t,cr.boxMargin,cr.boxMargin+cr.boxTextMargin,(function(t){return ur.newLoop(t)}));break;case Wn.parser.yy.LINETYPE.OPT_END:e=ur.endLoop(),$n.drawLoop(n,e,"opt",cr),ur.bumpVerticalPos(e.stopy-ur.getVerticalPos()),ur.models.addLoop(e);break;case Wn.parser.yy.LINETYPE.ALT_START:vr(u,t,cr.boxMargin,cr.boxMargin+cr.boxTextMargin,(function(t){return ur.newLoop(t)}));break;case Wn.parser.yy.LINETYPE.ALT_ELSE:vr(u,t,cr.boxMargin+cr.boxTextMargin,cr.boxMargin,(function(t){return ur.addSectionToLoop(t)}));break;case Wn.parser.yy.LINETYPE.ALT_END:e=ur.endLoop(),$n.drawLoop(n,e,"alt",cr),ur.bumpVerticalPos(e.stopy-ur.getVerticalPos()),ur.models.addLoop(e);break;case Wn.parser.yy.LINETYPE.PAR_START:vr(u,t,cr.boxMargin,cr.boxMargin+cr.boxTextMargin,(function(t){return ur.newLoop(t)}));break;case Wn.parser.yy.LINETYPE.PAR_AND:vr(u,t,cr.boxMargin+cr.boxTextMargin,cr.boxMargin,(function(t){return ur.addSectionToLoop(t)}));break;case Wn.parser.yy.LINETYPE.PAR_END:e=ur.endLoop(),$n.drawLoop(n,e,"par",cr),ur.bumpVerticalPos(e.stopy-ur.getVerticalPos()),ur.models.addLoop(e);break;default:try{(a=t.msgModel).starty=ur.getVerticalPos(),a.sequenceIndex=l,function(t,e){ur.bumpVerticalPos(10);var n=e.startx,r=e.stopx,i=e.starty,a=e.message,o=e.type,s=e.sequenceIndex,c=x.splitBreaks(a).length,u=H.calculateTextDimensions(a,lr(cr)),l=u.height/c;e.height+=l,ur.bumpVerticalPos(l);var h=$n.getTextObj();h.x=n,h.y=i+10,h.width=r-n,h.class="messageText",h.dy="1em",h.text=a,h.fontFamily=cr.messageFontFamily,h.fontSize=cr.messageFontSize,h.fontWeight=cr.messageFontWeight,h.anchor=cr.messageAlign,h.valign=cr.messageAlign,h.textMargin=cr.wrapPadding,h.tspan=!1,Fn(t,h);var f,d,p=u.height-10,g=u.width;if(n===r){d=ur.getVerticalPos()+p,cr.rightAngles?f=t.append("path").attr("d","M  ".concat(n,",").concat(d," H ").concat(n+Math.max(cr.width/2,g/2)," V ").concat(d+25," H ").concat(n)):(p+=cr.boxMargin,d=ur.getVerticalPos()+p,f=t.append("path").attr("d","M "+n+","+d+" C "+(n+60)+","+(d-10)+" "+(n+60)+","+(d+30)+" "+n+","+(d+20))),p+=30;var y=Math.max(g/2,cr.width/2);ur.insert(n-y,ur.getVerticalPos()-10+p,r+y,ur.getVerticalPos()+30+p)}else p+=cr.boxMargin,d=ur.getVerticalPos()+p,(f=t.append("line")).attr("x1",n),f.attr("y1",d),f.attr("x2",r),f.attr("y2",d),ur.insert(n,d-10,r,d);o===Wn.parser.yy.LINETYPE.DOTTED||o===Wn.parser.yy.LINETYPE.DOTTED_CROSS||o===Wn.parser.yy.LINETYPE.DOTTED_POINT||o===Wn.parser.yy.LINETYPE.DOTTED_OPEN?(f.style("stroke-dasharray","3, 3"),f.attr("class","messageLine1")):f.attr("class","messageLine0");var v="";cr.arrowMarkerAbsolute&&(v=(v=(v=window.location.protocol+"//"+window.location.host+window.location.pathname+window.location.search).replace(/\(/g,"\\(")).replace(/\)/g,"\\)")),f.attr("stroke-width",2),f.attr("stroke","none"),f.style("fill","none"),o!==Wn.parser.yy.LINETYPE.SOLID&&o!==Wn.parser.yy.LINETYPE.DOTTED||f.attr("marker-end","url("+v+"#arrowhead)"),o!==Wn.parser.yy.LINETYPE.SOLID_POINT&&o!==Wn.parser.yy.LINETYPE.DOTTED_POINT||f.attr("marker-end","url("+v+"#filled-head)"),o!==Wn.parser.yy.LINETYPE.SOLID_CROSS&&o!==Wn.parser.yy.LINETYPE.DOTTED_CROSS||f.attr("marker-end","url("+v+"#crosshead)"),(sr.showSequenceNumbers()||cr.showSequenceNumbers)&&(f.attr("marker-start","url("+v+"#sequencenumber)"),t.append("text").attr("x",n).attr("y",d+4).attr("font-family","sans-serif").attr("font-size","12px").attr("text-anchor","middle").attr("textLength","16px").attr("class","sequenceNumber").text(s)),ur.bumpVerticalPos(p),e.height+=p,e.stopy=e.starty+e.height,ur.insert(e.fromBounds,e.starty,e.toBounds,e.stopy)}(n,a),ur.models.addMessage(a)}catch(t){c.error("error while drawing message",t)}}[Wn.parser.yy.LINETYPE.SOLID_OPEN,Wn.parser.yy.LINETYPE.DOTTED_OPEN,Wn.parser.yy.LINETYPE.SOLID,Wn.parser.yy.LINETYPE.DOTTED,Wn.parser.yy.LINETYPE.SOLID_CROSS,Wn.parser.yy.LINETYPE.DOTTED_CROSS,Wn.parser.yy.LINETYPE.SOLID_POINT,Wn.parser.yy.LINETYPE.DOTTED_POINT].includes(t.type)&&l++})),cr.mirrorActors&&(ur.bumpVerticalPos(2*cr.boxMargin),dr(n,r,i,ur.getVerticalPos()));var h=ur.getBounds().bounds;c.debug("For line height fix Querying: #"+e+" .actor-line"),Object(d.selectAll)("#"+e+" .actor-line").attr("y2",h.stopy);var f=h.stopy-h.starty+2*cr.diagramMarginY;cr.mirrorActors&&(f=f-cr.boxMargin+cr.bottomMarginAdj);var p=h.stopx-h.startx+2*cr.diagramMarginX;o&&n.append("text").text(o).attr("x",(h.stopx-h.startx)/2-2*cr.diagramMarginX).attr("y",-25),W(n,f,p,cr.useMaxWidth);var g=o?40:0;n.attr("viewBox",h.startx-cr.diagramMarginX+" -"+(cr.diagramMarginY+g)+" "+p+" "+(f+g)),c.debug("models:",ur.models)}},kr=n(27),wr=n.n(kr);function Er(t){return 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p=o.height+2*r,g="attributeBoxOdd";s.forEach((function(e){var n=p+r+Math.max(e.tn.node().getBBox().height,e.nn.node().getBBox().height)/2;e.tn.attr("transform","translate("+i+","+n+")");var a=t.insert("rect","#"+e.tn.node().id).attr("class","er ".concat(g)).attr("fill",Qa.fill).attr("fill-opacity","100%").attr("stroke",Qa.stroke).attr("x",0).attr("y",p).attr("width",c+2*i+d/2).attr("height",e.tn.node().getBBox().height+2*r);e.nn.attr("transform","translate("+(parseFloat(a.attr("width"))+i)+","+n+")"),t.insert("rect","#"+e.nn.node().id).attr("class","er ".concat(g)).attr("fill",Qa.fill).attr("fill-opacity","100%").attr("stroke",Qa.stroke).attr("x","".concat(a.attr("x")+a.attr("width"))).attr("y",p).attr("width",u+2*i+d/2).attr("height",e.nn.node().getBBox().height+2*r),p+=Math.max(e.tn.node().getBBox().height,e.nn.node().getBBox().height)+2*r,g="attributeBoxOdd"==g?"attributeBoxEven":"attributeBoxOdd"}))}else 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a,o,s=to(i,Ga.getEntities(),r),u=function(t,e){return t.forEach((function(t){e.setEdge(t.entityA,t.entityB,{relationship:t},eo(t))})),t}(Ga.getRelationships(),r);ke.a.layout(r),a=i,(o=r).nodes().forEach((function(t){void 0!==t&&void 0!==o.node(t)&&a.select("#"+t).attr("transform","translate("+(o.node(t).x-o.node(t).width/2)+","+(o.node(t).y-o.node(t).height/2)+" )")})),u.forEach((function(t){!function(t,e,n,r){no++;var i=n.edge(e.entityA,e.entityB,eo(e)),a=Object(d.line)().x((function(t){return t.x})).y((function(t){return t.y})).curve(d.curveBasis),o=t.insert("path","#"+r).attr("class","er relationshipLine").attr("d",a(i.points)).attr("stroke",Qa.stroke).attr("fill","none");e.relSpec.relType===Ga.Identification.NON_IDENTIFYING&&o.attr("stroke-dasharray","8,8");var s="";switch(Qa.arrowMarkerAbsolute&&(s=(s=(s=window.location.protocol+"//"+window.location.host+window.location.pathname+window.location.search).replace(/\(/g,"\\(")).replace(/\)/g,"\\)")),e.relSpec.cardA){case 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e=Object(d.arc)().startAngle(3*Math.PI/2).endAngle(Math.PI/2*5).innerRadius(7.5).outerRadius(15/2.2);t.append("path").attr("class","mouth").attr("d",e).attr("transform","translate("+o.cx+","+(o.cy+7)+")")}(s):function(t){t.append("line").attr("class","mouth").attr("stroke",2).attr("x1",o.cx-5).attr("y1",o.cy+7).attr("x2",o.cx+5).attr("y2",o.cy+7).attr("class","mouth").attr("stroke-width","1px").attr("stroke","#666")}(s);var c=xo();c.x=e.x,c.y=e.y,c.fill=e.fill,c.width=n.width,c.height=n.height,c.class="task task-type-"+e.num,c.rx=3,c.ry=3,yo(i,c);var u=e.x+14;e.people.forEach((function(t){var n=e.actors[t],r={cx:u,cy:e.y,r:7,fill:n,stroke:"#000",title:t};vo(i,r),u+=10})),_o(n)(e.task,i,c.x,c.y,c.width,c.height,{class:"task"},n,e.colour)},Co=function(t){t.append("defs").append("marker").attr("id","arrowhead").attr("refX",5).attr("refY",2).attr("markerWidth",6).attr("markerHeight",4).attr("orient","auto").append("path").attr("d","M 0,0 V 4 L6,2 Z")};ao.parser.yy=go;var Ao={leftMargin:150,diagramMarginX:50,diagramMarginY:20,taskMargin:50,width:150,height:50,taskFontSize:14,taskFontFamily:'"Open-Sans", "sans-serif"',boxMargin:10,boxTextMargin:5,noteMargin:10,messageMargin:35,messageAlign:"center",bottomMarginAdj:1,activationWidth:10,textPlacement:"fo",actorColours:["#8FBC8F","#7CFC00","#00FFFF","#20B2AA","#B0E0E6","#FFFFE0"],sectionFills:["#191970","#8B008B","#4B0082","#2F4F4F","#800000","#8B4513","#00008B"],sectionColours:["#fff"]},So={};var Mo=Ao.leftMargin,Oo={data:{startx:void 0,stopx:void 0,starty:void 0,stopy:void 0},verticalPos:0,sequenceItems:[],init:function(){this.sequenceItems=[],this.data={startx:void 0,stopx:void 0,starty:void 0,stopy:void 0},this.verticalPos=0},updateVal:function(t,e,n,r){void 0===t[e]?t[e]=n:t[e]=r(n,t[e])},updateBounds:function(t,e,n,r){var i,a=this,o=0;this.sequenceItems.forEach((function(s){o++;var 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this.data}},Do=Ao.sectionFills,No=Ao.sectionColours,Bo=function(t,e,n){for(var r="",i=n+(2*Ao.height+Ao.diagramMarginY),a=0,o="#CCC",s="black",c=0,u=0;u<e.length;u++){var l=e[u];if(r!==l.section){o=Do[a%Do.length],c=a%Do.length,s=No[a%No.length];var h={x:u*Ao.taskMargin+u*Ao.width+Mo,y:50,text:l.section,fill:o,num:c,colour:s};wo(t,h,Ao),r=l.section,a++}var f=l.people.reduce((function(t,e){return So[e]&&(t[e]=So[e]),t}),{});l.x=u*Ao.taskMargin+u*Ao.width+Mo,l.y=i,l.width=Ao.diagramMarginX,l.height=Ao.diagramMarginY,l.colour=s,l.fill=o,l.num=c,l.actors=f,To(t,l,Ao),Oo.insert(l.x,l.y,l.x+l.width+Ao.taskMargin,450)}},Lo=function(t){Object.keys(t).forEach((function(e){Ao[e]=t[e]}))},Po=function(t,e){ao.parser.yy.clear(),ao.parser.parse(t+"\n"),Oo.init();var n=Object(d.select)("#"+e);n.attr("xmlns:xlink","http://www.w3.org/1999/xlink"),Co(n);var r=ao.parser.yy.getTasks(),i=ao.parser.yy.getTitle(),a=ao.parser.yy.getActors();for(var o in So)delete So[o];var s=0;a.forEach((function(t){So[t]=Ao.actorColours[s%Ao.actorColours.length],s++})),function(t){var e=60;Object.keys(So).forEach((function(n){var r=So[n];ko(t,{cx:20,cy:e,r:7,fill:r,stroke:"#000"});var i={x:40,y:e+7,fill:"#666",text:n,textMargin:5|Ao.boxTextMargin};Eo(t,i),e+=20}))}(n),Oo.insert(0,0,Mo,50*Object.keys(So).length),Bo(n,r,0);var c=Oo.getBounds();i&&n.append("text").text(i).attr("x",Mo).attr("font-size","4ex").attr("font-weight","bold").attr("y",25);var u=c.stopy-c.starty+2*Ao.diagramMarginY,l=Mo+c.stopx+2*Ao.diagramMarginX;W(n,u,l,Ao.useMaxWidth),n.append("line").attr("x1",Mo).attr("y1",4*Ao.height).attr("x2",l-Mo-4).attr("y2",4*Ao.height).attr("stroke-width",4).attr("stroke","black").attr("marker-end","url(#arrowhead)");var h=i?70:0;n.attr("viewBox","".concat(c.startx," -25 ").concat(l," ").concat(u+h)),n.attr("preserveAspectRatio","xMinYMin meet")},Io=function(t){return"g.classGroup text {\n  fill: ".concat(t.nodeBorder,";\n  fill: ").concat(t.classText,";\n  stroke: 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fill: ").concat(t.lineColor," !important;\n  stroke: ").concat(t.lineColor," !important;\n  stroke-width: 1;\n}\n\n#compositionEnd, .composition {\n  fill: ").concat(t.lineColor," !important;\n  stroke: ").concat(t.lineColor," !important;\n  stroke-width: 1;\n}\n\n#dependencyStart, .dependency {\n  fill: ").concat(t.lineColor," !important;\n  stroke: ").concat(t.lineColor," !important;\n  stroke-width: 1;\n}\n\n#dependencyStart, .dependency {\n  fill: ").concat(t.lineColor," !important;\n  stroke: ").concat(t.lineColor," !important;\n  stroke-width: 1;\n}\n\n#extensionStart, .extension {\n  fill: ").concat(t.lineColor," !important;\n  stroke: ").concat(t.lineColor," !important;\n  stroke-width: 1;\n}\n\n#extensionEnd, .extension {\n  fill: ").concat(t.lineColor," !important;\n  stroke: ").concat(t.lineColor," !important;\n  stroke-width: 1;\n}\n\n#aggregationStart, .aggregation {\n  fill: ").concat(t.mainBkg," !important;\n  stroke: ").concat(t.lineColor," !important;\n  stroke-width: 1;\n}\n\n#aggregationEnd, .aggregation {\n  fill: ").concat(t.mainBkg," !important;\n  stroke: ").concat(t.lineColor," !important;\n  stroke-width: 1;\n}\n\n.edgeTerminals {\n  font-size: 11px;\n}\n\n")},Fo=function(t){return".label {\n    font-family: ".concat(t.fontFamily,";\n    color: ").concat(t.nodeTextColor||t.textColor,";\n  }\n\n  .label text {\n    fill: ").concat(t.nodeTextColor||t.textColor,";\n  }\n\n  .node rect,\n  .node circle,\n  .node ellipse,\n  .node polygon,\n  .node path {\n    fill: ").concat(t.mainBkg,";\n    stroke: ").concat(t.nodeBorder,";\n    stroke-width: 1px;\n  }\n\n  .node .label {\n    text-align: center;\n  }\n  .node.clickable {\n    cursor: pointer;\n  }\n\n  .arrowheadPath {\n    fill: ").concat(t.arrowheadColor,";\n  }\n\n  .edgePath .path {\n    stroke: ").concat(t.lineColor,";\n    stroke-width: 1.5px;\n  }\n\n  .flowchart-link {\n    stroke: ").concat(t.lineColor,";\n    fill: none;\n  }\n\n  .edgeLabel {\n    background-color: ").concat(t.edgeLabelBackground,";\n    rect {\n      opacity: 0.5;\n      background-color: ").concat(t.edgeLabelBackground,";\n      fill: ").concat(t.edgeLabelBackground,";\n    }\n    text-align: center;\n  }\n\n  .cluster rect {\n    fill: ").concat(t.clusterBkg,";\n    stroke: ").concat(t.clusterBorder,";\n    stroke-width: 1px;\n  }\n\n  .cluster text {\n    fill: ").concat(t.titleColor,";\n  }\n\n  div.mermaidTooltip {\n    position: absolute;\n    text-align: center;\n    max-width: 200px;\n    padding: 2px;\n    font-family: ").concat(t.fontFamily,";\n    font-size: 12px;\n    background: ").concat(t.tertiaryColor,";\n    border: 1px solid ").concat(t.border2,";\n    border-radius: 2px;\n    pointer-events: none;\n    z-index: 100;\n  }\n")},jo=function(t){return"g.stateGroup text {\n  fill: ".concat(t.nodeBorder,";\n  stroke: none;\n  font-size: 10px;\n}\ng.stateGroup text {\n  fill: ").concat(t.textColor,";\n  stroke: none;\n  font-size: 10px;\n\n}\ng.stateGroup .state-title {\n  font-weight: bolder;\n  fill: ").concat(t.labelColor,";\n}\n\ng.stateGroup rect {\n  fill: ").concat(t.mainBkg,";\n  stroke: ").concat(t.nodeBorder,";\n}\n\ng.stateGroup line {\n  stroke: ").concat(t.lineColor,";\n  stroke-width: 1;\n}\n\n.transition {\n  stroke: ").concat(t.lineColor,";\n  stroke-width: 1;\n  fill: none;\n}\n\n.stateGroup .composit {\n  fill: ").concat(t.background,";\n  border-bottom: 1px\n}\n\n.stateGroup .alt-composit {\n  fill: #e0e0e0;\n  border-bottom: 1px\n}\n\n.state-note {\n  stroke: ").concat(t.noteBorderColor,";\n  fill: ").concat(t.noteBkgColor,";\n\n  text {\n    fill: black;\n    stroke: none;\n    font-size: 10px;\n  }\n}\n\n.stateLabel .box {\n  stroke: none;\n  stroke-width: 0;\n  fill: ").concat(t.mainBkg,";\n  opacity: 0.5;\n}\n\n.edgeLabel .label rect {\n  fill: ").concat(t.tertiaryColor,";\n  opacity: 0.5;\n}\n.edgeLabel .label text {\n  fill: ").concat(t.tertiaryTextColor,";\n}\n.label div .edgeLabel {\n  color: ").concat(t.tertiaryTextColor,";\n}\n\n.stateLabel text {\n  fill: ").concat(t.labelColor,";\n  font-size: 10px;\n  font-weight: bold;\n}\n\n.node circle.state-start {\n  fill: ").concat(t.lineColor,";\n  stroke: black;\n}\n.node circle.state-end {\n  fill: ").concat(t.primaryBorderColor,";\n  stroke: ").concat(t.background,";\n  stroke-width: 1.5\n}\n.end-state-inner {\n  fill: ").concat(t.background,";\n  // stroke: ").concat(t.background,";\n  stroke-width: 1.5\n}\n\n.node rect {\n  fill: ").concat(t.mainBkg,";\n  stroke: ").concat(t.nodeBorder,";\n  stroke-width: 1px;\n}\n#statediagram-barbEnd {\n  fill: ").concat(t.lineColor,";\n}\n\n.statediagram-cluster rect {\n  fill: ").concat(t.mainBkg,";\n  stroke: ").concat(t.nodeBorder,";\n  stroke-width: 1px;\n}\n\n.cluster-label, .nodeLabel {\n  color: ").concat(t.textColor,";\n}\n\n.statediagram-cluster rect.outer {\n  rx: 5px;\n  ry: 5px;\n}\n.statediagram-state .divider {\n  stroke: ").concat(t.nodeBorder,";\n}\n\n.statediagram-state .title-state {\n  rx: 5px;\n  ry: 5px;\n}\n.statediagram-cluster.statediagram-cluster .inner {\n  fill: ").concat(t.background,";\n}\n.statediagram-cluster.statediagram-cluster-alt .inner {\n  fill: #e0e0e0;\n}\n\n.statediagram-cluster .inner {\n  rx:0;\n  ry:0;\n}\n\n.statediagram-state rect.basic {\n  rx: 5px;\n  ry: 5px;\n}\n.statediagram-state rect.divider {\n  stroke-dasharray: 10,10;\n  fill: ").concat(t.altBackground?t.altBackground:"#efefef",";\n}\n\n.note-edge {\n  stroke-dasharray: 5;\n}\n\n.statediagram-note rect {\n  fill: ").concat(t.noteBkgColor,";\n  stroke: ").concat(t.noteBorderColor,";\n  stroke-width: 1px;\n  rx: 0;\n  ry: 0;\n}\n.statediagram-note rect {\n  fill: ").concat(t.noteBkgColor,";\n  stroke: ").concat(t.noteBorderColor,";\n  stroke-width: 1px;\n  rx: 0;\n  ry: 0;\n}\n\n.statediagram-note text {\n  fill: ").concat(t.noteTextColor,";\n}\n\n.statediagram-note .nodeLabel {\n  color: ").concat(t.noteTextColor,";\n}\n\n#dependencyStart, #dependencyEnd {\n  fill: ").concat(t.lineColor,";\n  stroke: ").concat(t.lineColor,";\n  stroke-width: 1;\n}\n")},Ro={flowchart:Fo,"flowchart-v2":Fo,sequence:function(t){return".actor {\n    stroke: ".concat(t.actorBorder,";\n    fill: ").concat(t.actorBkg,";\n  }\n\n  text.actor > tspan {\n    fill: ").concat(t.actorTextColor,";\n    stroke: none;\n  }\n\n  .actor-line {\n    stroke: ").concat(t.actorLineColor,";\n  }\n\n  .messageLine0 {\n    stroke-width: 1.5;\n    stroke-dasharray: none;\n    stroke: ").concat(t.signalColor,";\n  }\n\n  .messageLine1 {\n    stroke-width: 1.5;\n    stroke-dasharray: 2, 2;\n    stroke: ").concat(t.signalColor,";\n  }\n\n  #arrowhead path {\n    fill: ").concat(t.signalColor,";\n    stroke: ").concat(t.signalColor,";\n  }\n\n  .sequenceNumber {\n    fill: ").concat(t.sequenceNumberColor,";\n  }\n\n  #sequencenumber {\n    fill: ").concat(t.signalColor,";\n  }\n\n  #crosshead path {\n    fill: ").concat(t.signalColor,";\n    stroke: ").concat(t.signalColor,";\n  }\n\n  .messageText {\n    fill: ").concat(t.signalTextColor,";\n    stroke: ").concat(t.signalTextColor,";\n  }\n\n  .labelBox {\n    stroke: ").concat(t.labelBoxBorderColor,";\n    fill: ").concat(t.labelBoxBkgColor,";\n  }\n\n  .labelText, .labelText > tspan {\n    fill: ").concat(t.labelTextColor,";\n    stroke: none;\n  }\n\n  .loopText, .loopText > tspan {\n    fill: ").concat(t.loopTextColor,";\n    stroke: none;\n  }\n\n  .loopLine {\n    stroke-width: 2px;\n    stroke-dasharray: 2, 2;\n    stroke: ").concat(t.labelBoxBorderColor,";\n    fill: ").concat(t.labelBoxBorderColor,";\n  }\n\n  .note {\n    //stroke: #decc93;\n    stroke: ").concat(t.noteBorderColor,";\n    fill: ").concat(t.noteBkgColor,";\n  }\n\n  .noteText, .noteText > tspan {\n    fill: ").concat(t.noteTextColor,";\n    stroke: none;\n  }\n\n  .activation0 {\n    fill: ").concat(t.activationBkgColor,";\n    stroke: ").concat(t.activationBorderColor,";\n  }\n\n  .activation1 {\n    fill: ").concat(t.activationBkgColor,";\n    stroke: ").concat(t.activationBorderColor,";\n  }\n\n  .activation2 {\n    fill: ").concat(t.activationBkgColor,";\n    stroke: ").concat(t.activationBorderColor,";\n  }\n")},gantt:function(t){return'\n  .mermaid-main-font {\n    font-family: "trebuchet ms", verdana, arial, sans-serif;\n    font-family: var(--mermaid-font-family);\n  }\n\n  .section {\n    stroke: none;\n    opacity: 0.2;\n  }\n\n  .section0 {\n    fill: '.concat(t.sectionBkgColor,";\n  }\n\n  .section2 {\n    fill: ").concat(t.sectionBkgColor2,";\n  }\n\n  .section1,\n  .section3 {\n    fill: ").concat(t.altSectionBkgColor,";\n    opacity: 0.2;\n  }\n\n  .sectionTitle0 {\n    fill: ").concat(t.titleColor,";\n  }\n\n  .sectionTitle1 {\n    fill: ").concat(t.titleColor,";\n  }\n\n  .sectionTitle2 {\n    fill: ").concat(t.titleColor,";\n  }\n\n  .sectionTitle3 {\n    fill: ").concat(t.titleColor,";\n  }\n\n  .sectionTitle {\n    text-anchor: start;\n    font-size: 11px;\n    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-    <loc>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8cpp/</loc>
-  </url><url>
-    <loc>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/</loc>
-  </url><url>
-    <loc>https://stanley-rozell.github.io/lds-ctrl-est/tags/</loc>
-  </url>
-</urlset>
diff --git a/docs/svg/calendar.svg b/docs/svg/calendar.svg
deleted file mode 100644
index f8481120..00000000
--- a/docs/svg/calendar.svg
+++ /dev/null
@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24"><path d="M20 3h-1V1h-2v2H7V1H5v2H4c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h16c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zm0 18H4V8h16v13z"/><path fill="none" d="M0 0h24v24H0z"/></svg>
\ No newline at end of file
diff --git a/docs/svg/edit.svg b/docs/svg/edit.svg
deleted file mode 100644
index 5b54e693..00000000
--- a/docs/svg/edit.svg
+++ /dev/null
@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24"><path d="M3 17.25V21h3.75L17.81 9.94l-3.75-3.75L3 17.25zM20.71 7.04c.39-.39.39-1.02 0-1.41l-2.34-2.34c-.39-.39-1.02-.39-1.41 0l-1.83 1.83 3.75 3.75 1.83-1.83z"/><path d="M0 0h24v24H0z" fill="none"/></svg>
\ No newline at end of file
diff --git a/docs/svg/menu.svg b/docs/svg/menu.svg
deleted file mode 100644
index 770b1923..00000000
--- a/docs/svg/menu.svg
+++ /dev/null
@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24"><path fill="none" d="M0 0h24v24H0V0z"/><path d="M3 18h18v-2H3v2zm0-5h18v-2H3v2zm0-7v2h18V6H3z"/></svg>
\ No newline at end of file
diff --git a/docs/svg/toc.svg b/docs/svg/toc.svg
deleted file mode 100644
index 1889904e..00000000
--- a/docs/svg/toc.svg
+++ /dev/null
@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24"><path d="M3 9h14V7H3v2zm0 4h14v-2H3v2zm0 4h14v-2H3v2zm16 0h2v-2h-2v2zm0-10v2h2V7h-2zm0 6h2v-2h-2v2z"/><path d="M0 0h24v24H0z" fill="none"/></svg>
\ No newline at end of file
diff --git a/docs/svg/translate.svg b/docs/svg/translate.svg
deleted file mode 100644
index a1bbe166..00000000
--- a/docs/svg/translate.svg
+++ /dev/null
@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24"><path d="M0 0h24v24H0z" fill="none"/><path d="M12.87 15.07l-2.54-2.51.03-.03c1.74-1.94 2.98-4.17 3.71-6.53H17V4h-7V2H8v2H1v1.99h11.17C11.5 7.92 10.44 9.75 9 11.35 8.07 10.32 7.3 9.19 6.69 8h-2c.73 1.63 1.73 3.17 2.98 4.56l-5.09 5.02L4 19l5-5 3.11 3.11.76-2.04zM18.5 10h-2L12 22h2l1.12-3h4.75L21 22h2l-4.5-12zm-2.62 7l1.62-4.33L19.12 17h-3.24z"/></svg>
\ No newline at end of file
diff --git a/docs/tags/index.html b/docs/tags/index.html
deleted file mode 100644
index 756d8684..00000000
--- a/docs/tags/index.html
+++ /dev/null
@@ -1,269 +0,0 @@
-<!DOCTYPE html>
-<html lang="en" dir="ltr">
-<head>
-  <meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<meta name="description" content="">
-<meta name="theme-color" content="#FFFFFF"><meta property="og:title" content="Tags" />
-<meta property="og:description" content="" />
-<meta property="og:type" content="website" />
-<meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/tags/" />
-
-<title>Tags | LDS C&amp;E</title>
-<link rel="manifest" href="/lds-ctrl-est/manifest.json">
-<link rel="icon" href="/lds-ctrl-est/favicon.png" type="image/x-icon">
-<link rel="stylesheet" href="/lds-ctrl-est/book.min.57f7f660871517a5bfcfb5e2de853d806f7e34d94ebd5f3f3bad62e9ddbae209.css" integrity="sha256-V/f2YIcVF6W/z7Xi3oU9gG9&#43;NNlOvV8/O61i6d264gk=" crossorigin="anonymous">
-  <script defer src="/lds-ctrl-est/flexsearch.min.js"></script>
-  <script defer src="/lds-ctrl-est/en.search.min.93b2d0e7fa872893809358a01a9e6e0c0ce7a384100570924726e75a52c21d85.js" integrity="sha256-k7LQ5/qHKJOAk1igGp5uDAzno4QQBXCSRybnWlLCHYU=" crossorigin="anonymous"></script>
-<link rel="alternate" type="application/rss+xml" href="https://stanley-rozell.github.io/lds-ctrl-est/tags/index.xml" title="LDS C&E" />
-<!--
-Made with Book Theme
-https://github.com/alex-shpak/hugo-book
--->
-  
-</head>
-<body dir="ltr">
-  <input type="checkbox" class="hidden toggle" id="menu-control" />
-  <input type="checkbox" class="hidden toggle" id="toc-control" />
-  <main class="container flex">
-    <aside class="book-menu">
-      <div class="book-menu-content">
-        
-  <nav>
-<h2 class="book-brand">
-  <a href="/lds-ctrl-est/"><img src="/lds-ctrl-est/ldsctrlest-logo.png" alt="Logo" /><span>LDS C&amp;E</span>
-  </a>
-</h2>
-
-
-<div class="book-search">
-  <input type="text" id="book-search-input" placeholder="Search" aria-label="Search" maxlength="64" data-hotkeys="s/" />
-  <div class="book-search-spinner hidden"></div>
-  <ul id="book-search-results"></ul>
-</div>
-
-
-
-
-
-
-
-
-
-
-
-  <ul>
-<li><strong>Library Terminology</strong>
-<ul>
-<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
-<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
-</ul>
-</li>
-</ul>
-<p><br /></p>
-<ul>
-<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
-<ul>
-<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
-</ul>
-</li>
-</ul>
-<p><br /></p>
-<ul>
-<li><strong>Tutorials</strong>
-<ul>
-<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
-<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
-<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
-</ul>
-</li>
-</ul>
-<p><br /></p>
-<ul>
-<li><strong>API Reference</strong>
-<ul>
-<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
-<li><a href="/lds-ctrl-est/docs/api/classes/">Classes</a></li>
-<li><a href="/lds-ctrl-est/docs/api/modules/">Modules</a></li>
-<li><a href="/lds-ctrl-est/docs/api/files/">Files</a></li>
-<li><a href="/lds-ctrl-est/docs/api/examples/">Examples</a></li>
-</ul>
-</li>
-</ul>
-<p><br /></p>
-<ul>
-<li><a href="/lds-ctrl-est/issues-contributing/"><strong>Reporting Issues &amp; Contributing</strong></a></li>
-</ul>
-<p><br /></p>
-<ul>
-<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
-</ul>
-<p><br /></p>
-
-
-
-
-
-
-  
-<ul>
-  
-  <li>
-    <a href="https://github.com/stanley-rozell/lds-ctrl-est" target="_blank" rel="noopener">
-        Github
-      </a>
-  </li>
-  
-</ul>
-
-
-
-
-
-
-</nav>
-
-
-
-
-  <script>(function(){var a=document.querySelector("aside .book-menu-content");addEventListener("beforeunload",function(b){localStorage.setItem("menu.scrollTop",a.scrollTop)}),a.scrollTop=localStorage.getItem("menu.scrollTop")})()</script>
-
-
- 
-      </div>
-    </aside>
-
-    <div class="book-page">
-      <header class="book-header">
-        
-  <div class="flex align-center justify-between">
-  <label for="menu-control">
-    <img src="/lds-ctrl-est/svg/menu.svg" class="book-icon" alt="Menu" />
-  </label>
-
-  <strong>Tags</strong>
-
-  <label for="toc-control">
-    
-    <img src="/lds-ctrl-est/svg/toc.svg" class="book-icon" alt="Table of Contents" />
-    
-  </label>
-</div>
-
-
-  
-  <aside class="hidden clearfix">
-    
-  <nav>
-  <ul>
-  
-    
-    <li class="book-section-flat">
-      <strong>Categories</strong>
-      <ul>
-      
-      </ul>
-    </li>
-    
-  
-    
-    <li class="book-section-flat">
-      <strong>Tags</strong>
-      <ul>
-      
-      </ul>
-    </li>
-    
-  
-  </ul>
-</nav>
-
-
-  </aside>
-  
- 
-      </header>
-
-      
-      
-  
-
-  
- 
-      
-
-      <footer class="book-footer">
-        
-  <div class="flex flex-wrap justify-between">
-
-
-
-
-
-</div>
-
-
-
-  <script>(function(){function a(c){const a=window.getSelection(),b=document.createRange();b.selectNodeContents(c),a.removeAllRanges(),a.addRange(b)}document.querySelectorAll("pre code").forEach(b=>{b.addEventListener("click",function(c){a(b.parentElement),navigator.clipboard&&navigator.clipboard.writeText(b.parentElement.textContent)})})})()</script>
-
-
- 
-        
-      </footer>
-
-      
-  
- 
-
-      <label for="menu-control" class="hidden book-menu-overlay"></label>
-    </div>
-
-    
-    <aside class="book-toc">
-      <div class="book-toc-content">
-        
-  <nav>
-  <ul>
-  
-    
-    <li class="book-section-flat">
-      <strong>Categories</strong>
-      <ul>
-      
-      </ul>
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-    
-  
-    
-    <li class="book-section-flat">
-      <strong>Tags</strong>
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-      </div>
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-    
-  </main>
-
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-</body>
-</html>
-
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diff --git a/docs/tags/index.xml b/docs/tags/index.xml
deleted file mode 100644
index 32090c56..00000000
--- a/docs/tags/index.xml
+++ /dev/null
@@ -1,9 +0,0 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?>
-<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
-  <channel>
-    <title>Tags on LDS C&amp;E</title>
-    <link>https://stanley-rozell.github.io/lds-ctrl-est/tags/</link>
-    <description>Recent content in Tags on LDS C&amp;E</description>
-    <generator>Hugo -- gohugo.io</generator><atom:link href="https://stanley-rozell.github.io/lds-ctrl-est/tags/index.xml" rel="self" type="application/rss+xml" />
-  </channel>
-</rss>
diff --git a/docs/tags/page/1/index.html b/docs/tags/page/1/index.html
deleted file mode 100644
index cadd04da..00000000
--- a/docs/tags/page/1/index.html
+++ /dev/null
@@ -1 +0,0 @@
-<!DOCTYPE html><html><head><title>https://stanley-rozell.github.io/lds-ctrl-est/tags/</title><link rel="canonical" href="https://stanley-rozell.github.io/lds-ctrl-est/tags/"/><meta name="robots" content="noindex"><meta charset="utf-8" /><meta http-equiv="refresh" content="0; url=https://stanley-rozell.github.io/lds-ctrl-est/tags/" /></head></html>
\ No newline at end of file
diff --git a/misc/docs-hugo/.hugo_build.lock b/misc/docs-hugo/.hugo_build.lock
new file mode 100644
index 00000000..e69de29b
diff --git a/misc/docs-hugo/content/docs/api/Classes/_index.md b/misc/docs-hugo/content/docs/api/Classes/_index.md
index 330aef00..e4e0dab9 100644
--- a/misc/docs-hugo/content/docs/api/Classes/_index.md
+++ b/misc/docs-hugo/content/docs/api/Classes/_index.md
@@ -1,126 +1,130 @@
----
-title: Classes
-
----
-
-# Classes
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-- **[lds::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/)** 
-
-
-
-- **[lds::EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)** 
-
-
-
-- **[lds::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/)** <br>LDS [Fit]() Type. 
-
-
-
-- **[lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)** 
-
-
-
-- **[lds::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/)** <br>[SwitchedController]() Type. 
-
-
-
-- **[lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)** <br>Linear Dynamical [System]() Type. 
-
-
-
-- **[lds::UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)** 
-
-
-
-- **[lds::UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/)** 
-
-
-
-- **[lds::UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/)** 
-
-
-
-
-
-
-- **[lds::gaussian::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1controller/)** <br>Gaussian-observation [Controller]() Type. 
-
-
-
-- **[lds::gaussian::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/)** <br>GLDS [Fit]() Type. 
-
-
-
-- **[lds::gaussian::FitEM](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitem/)** <br>GLDS E-M [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/) Type. 
-
-
-
-- **[lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitssid/)** <br>Subspace Identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)) for GLDS. 
-
-
-
-- **[lds::gaussian::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1switchedcontroller/)** <br>Gaussian-observation [SwitchedController]() Type. 
-
-
-
-- **[lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/)** <br>Gaussian LDS Type. 
-
-
-
-
-
-
-
-
-
-- **[lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/)** <br>PLDS [Controller]() Type. 
-
-
-
-- **[lds::poisson::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/)** <br>PLDS [Fit]() Type. 
-
-
-
-- **[lds::poisson::FitEM](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitem/)** <br>PLDS E-M [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/) Type. 
-
-
-
-- **[lds::poisson::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitssid/)** <br>Subspace Identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)) for PLDS. 
-
-
-
-- **[lds::poisson::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1switchedcontroller/)** <br>Poisson-observation [SwitchedController]() Type. 
-
-
-
-- **[lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/)** <br>Poisson [System]() type. 
-
-
-
-
-
-
-
-
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: Classes
+
+---
+
+# Classes
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+- **[lds::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/)** 
+
+
+
+- **[lds::EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/)** 
+
+
+
+- **[lds::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/)** <br>LDS [Fit]() Type. 
+
+
+
+- **[lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)** 
+
+
+
+- **[lds::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/)** <br>[SwitchedController]() Type. 
+
+
+
+- **[lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)** <br>Linear Dynamical [System]() Type. 
+
+
+
+- **[lds::UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)** 
+
+
+
+- **[lds::UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/)** 
+
+
+
+- **[lds::UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/)** 
+
+
+
+
+
+
+- **[lds::gaussian::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/)** <br>Gaussian-observation [Controller]() Type. 
+
+
+
+- **[lds::gaussian::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/)** <br>GLDS [Fit]() Type. 
+
+
+
+- **[lds::gaussian::FitEM](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/)** <br>GLDS E-M [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/) Type. 
+
+
+
+- **[lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/)** <br>Subspace Identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)) for GLDS. 
+
+
+
+- **[lds::gaussian::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/)** <br>Gaussian-observation [SwitchedController]() Type. 
+
+
+
+- **[lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/)** <br>Gaussian LDS Type. 
+
+
+
+
+
+
+
+
+
+- **[lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/)** <br>PLDS [Controller]() Type. 
+
+
+
+- **[lds::poisson::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/)** <br>PLDS [Fit]() Type. 
+
+
+
+- **[lds::poisson::FitEM](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/)** <br>PLDS E-M [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/) Type. 
+
+
+
+- **[lds::poisson::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/)** <br>Subspace Identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)) for PLDS. 
+
+
+
+- **[lds::poisson::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/)** <br>Poisson-observation [SwitchedController]() Type. 
+
+
+
+- **[lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)** <br>Poisson [System]() type. 
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1Fit.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1Fit.md
deleted file mode 100644
index 7dd0ce98..00000000
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1Fit.md
+++ /dev/null
@@ -1,561 +0,0 @@
----
-title: lds::Fit
-summary: LDS Fit Type. 
-
----
-
-# lds::Fit
-
-
-
-LDS [Fit]() Type. 
-<br /> `#include <lds_fit.h>`
-
-Inherited by [lds::gaussian::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/), [lds::poisson::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/)
-
-## Public Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-fit)**() =default<br>Constructs a new [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/).  |
-| | **[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-fit)**(size_t n_u, size_t n_x, size_t n_y, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt)<br>Constructs a new [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/).  |
-| virtual | **[~Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-~fit)**() =default |
-| size_t | **[n_u](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-n-u)**() const<br>gets number of inputs  |
-| size_t | **[n_x](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-n-x)**() const<br>gets number of states  |
-| size_t | **[n_y](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-n-y)**() const<br>gets number of outputs  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-dt)**() const<br>gets sample period  |
-| const Matrix & | **[A](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-a)**() const<br>gets state matrix  |
-| const Matrix & | **[B](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-b)**() const<br>gets input matrix  |
-| const Vector & | **[g](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-g)**() const<br>gets input gain  |
-| const Vector & | **[m](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-m)**() const<br>gets process disturbance  |
-| const Matrix & | **[Q](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-q)**() const<br>gets process noise covariance  |
-| const Vector & | **[x0](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-x0)**() const<br>gets initial state estimate  |
-| const Matrix & | **[P0](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-p0)**() const<br>gets covariance of initial state estimate  |
-| const Matrix & | **[C](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-c)**() const<br>gets output matrix  |
-| const Vector & | **[d](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-d)**() const<br>gets output bias  |
-| virtual const Matrix & | **[R](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-r)**() const =0 |
-| void | **[set_A](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-a)**(const Matrix & A)<br>sets state matrix  |
-| void | **[set_B](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-b)**(const Matrix & B)<br>sets input matrix  |
-| void | **[set_g](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-g)**(const Vector & g)<br>sets input gain/conversion factor  |
-| void | **[set_m](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-m)**(const Vector & m)<br>sets process disturbance  |
-| void | **[set_Q](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-q)**(const Matrix & Q)<br>sets process noise covariance  |
-| virtual void | **[set_R](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-r)**(const Matrix & R) =0<br>sets output noise covariance (if any)  |
-| void | **[set_x0](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-x0)**(const Vector & x0)<br>sets initial state estimate  |
-| void | **[set_P0](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-p0)**(const Matrix & P0)<br>sets initial state estimate covariance  |
-| void | **[set_C](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-c)**(const Matrix & C)<br>sets output matrix  |
-| void | **[set_d](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-d)**(const Vector & d)<br>sets output bias  |
-| View | **[f](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-f)**(Matrix & x, const Matrix & u, size_t t)<br>system dynamics function  |
-| View | **[f](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-f)**(Matrix & x_pre, const Matrix & x_post, const Matrix & u, size_t t)<br>system dynamics function  |
-| virtual View | **[h](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-h)**(Matrix & y, const Matrix & x, size_t t) =0<br>output function  |
-
-## Protected Attributes
-
-|                | Name           |
-| -------------- | -------------- |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-dt-)** <br>sample period  |
-| Matrix | **[A_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-a-)** <br>state matrix  |
-| Matrix | **[B_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-b-)** <br>input matrix  |
-| Vector | **[g_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-g-)** <br>input gain  |
-| Vector | **[m_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-m-)** <br>process noise mean  |
-| Matrix | **[Q_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-q-)** <br>process noise cov  |
-| Matrix | **[C_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-c-)** <br>output matrix  |
-| Vector | **[d_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-d-)** <br>output bias  |
-| Matrix | **[R_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-r-)** <br>measurement noise  |
-| Vector | **[x0_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-x0-)** <br>initial state  |
-| Matrix | **[P0_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-p0-)** <br>initial covar  |
-| size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-n-u-)** <br>number of inputs  |
-| size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-n-x-)** <br>number of states  |
-| size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-n-y-)** <br>number of outputs  |
-
----
----
-## Public Function Details
-
-### **Fit**
-
-```cpp
-Fit() =default
-```
-
-
-
----
-### **Fit**
-
-```cpp
-Fit(
-    size_t n_u,
-    size_t n_x,
-    size_t n_y,
-    data_t dt
-)
-```
-
-
-
-**Parameters**:
-
-  * **n_u** number of inputs 
-  * **n_x** number of states 
-  * **n_y** number of outputs 
-  * **dt** sample period 
-
-
----
-### **~Fit**
-
-```cpp
-virtual ~Fit() =default
-```
-
-
-
----
-### **n_u**
-
-```cpp
-inline size_t n_u() const
-```
-
-
-
----
-### **n_x**
-
-```cpp
-inline size_t n_x() const
-```
-
-
-
----
-### **n_y**
-
-```cpp
-inline size_t n_y() const
-```
-
-
-
----
-### **dt**
-
-```cpp
-inline data_t dt() const
-```
-
-
-
----
-### **A**
-
-```cpp
-inline const Matrix & A() const
-```
-
-
-
----
-### **B**
-
-```cpp
-inline const Matrix & B() const
-```
-
-
-
----
-### **g**
-
-```cpp
-inline const Vector & g() const
-```
-
-
-
----
-### **m**
-
-```cpp
-inline const Vector & m() const
-```
-
-
-
----
-### **Q**
-
-```cpp
-inline const Matrix & Q() const
-```
-
-
-
----
-### **x0**
-
-```cpp
-inline const Vector & x0() const
-```
-
-
-
----
-### **P0**
-
-```cpp
-inline const Matrix & P0() const
-```
-
-
-
----
-### **C**
-
-```cpp
-inline const Matrix & C() const
-```
-
-
-
----
-### **d**
-
-```cpp
-inline const Vector & d() const
-```
-
-
-
----
-### **R**
-
-```cpp
-virtual const Matrix & R() const =0
-```
-
-
-
-**Reimplemented by**: [lds::gaussian::Fit::R](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/#function-r), [lds::poisson::Fit::R](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/#function-r)
-
-
----
-### **set_A**
-
-```cpp
-inline void set_A(
-    const Matrix & A
-)
-```
-
-
-
----
-### **set_B**
-
-```cpp
-inline void set_B(
-    const Matrix & B
-)
-```
-
-
-
----
-### **set_g**
-
-```cpp
-inline void set_g(
-    const Vector & g
-)
-```
-
-
-
----
-### **set_m**
-
-```cpp
-inline void set_m(
-    const Vector & m
-)
-```
-
-
-
----
-### **set_Q**
-
-```cpp
-inline void set_Q(
-    const Matrix & Q
-)
-```
-
-
-
----
-### **set_R**
-
-```cpp
-virtual void set_R(
-    const Matrix & R
-) =0
-```
-
-
-
-**Reimplemented by**: [lds::gaussian::Fit::set_R](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/#function-set-r), [lds::poisson::Fit::set_R](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/#function-set-r)
-
-
----
-### **set_x0**
-
-```cpp
-inline void set_x0(
-    const Vector & x0
-)
-```
-
-
-
----
-### **set_P0**
-
-```cpp
-inline void set_P0(
-    const Matrix & P0
-)
-```
-
-
-
----
-### **set_C**
-
-```cpp
-inline void set_C(
-    const Matrix & C
-)
-```
-
-
-
----
-### **set_d**
-
-```cpp
-inline void set_d(
-    const Vector & d
-)
-```
-
-
-
----
-### **f**
-
-```cpp
-inline View f(
-    Matrix & x,
-    const Matrix & u,
-    size_t t
-)
-```
-
-
-
-**Parameters**:
-
-  * **x** state estimate (over time) 
-  * **u** input (over time) 
-  * **t** time index
-
-
-**Return**: view of updated state 
-
----
-### **f**
-
-```cpp
-inline View f(
-    Matrix & x_pre,
-    const Matrix & x_post,
-    const Matrix & u,
-    size_t t
-)
-```
-
-
-
-**Parameters**:
-
-  * **x_pre** predicted state est. 
-  * **x_post** posterior state est. 
-  * **u** input (over time) 
-  * **t** time index
-
-
-**Return**: view of predicted state 
-
----
-### **h**
-
-```cpp
-virtual View h(
-    Matrix & y,
-    const Matrix & x,
-    size_t t
-) =0
-```
-
-
-
-**Parameters**:
-
-  * **y** output estimate (over time) 
-  * **x** state estimate (over time) 
-  * **t** time index
-
-
-**Return**: output 
-
-**Reimplemented by**: [lds::poisson::Fit::h](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/#function-h), [lds::gaussian::Fit::h](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/#function-h)
-
-
----
-
-
-## Protected Attribute Details
-
-### **dt_**
-
-```cpp
-data_t dt_ {};
-```
-
-
-
----
-### **A_**
-
-```cpp
-Matrix A_;
-```
-
-
-
----
-### **B_**
-
-```cpp
-Matrix B_;
-```
-
-
-
----
-### **g_**
-
-```cpp
-Vector g_;
-```
-
-
-
----
-### **m_**
-
-```cpp
-Vector m_;
-```
-
-
-
----
-### **Q_**
-
-```cpp
-Matrix Q_;
-```
-
-
-
----
-### **C_**
-
-```cpp
-Matrix C_;
-```
-
-
-
----
-### **d_**
-
-```cpp
-Vector d_;
-```
-
-
-
----
-### **R_**
-
-```cpp
-Matrix R_;
-```
-
-
-
----
-### **x0_**
-
-```cpp
-Vector x0_;
-```
-
-
-
----
-### **P0_**
-
-```cpp
-Matrix P0_;
-```
-
-
-
----
-### **n_u_**
-
-```cpp
-size_t n_u_ {};
-```
-
-
-
----
-### **n_x_**
-
-```cpp
-size_t n_x_ {};
-```
-
-
-
----
-### **n_y_**
-
-```cpp
-size_t n_y_ {};
-```
-
-
-
----
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:03 Eastern Daylight Time
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1SSID.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1SSID.md
deleted file mode 100644
index 3da039f5..00000000
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1SSID.md
+++ /dev/null
@@ -1,370 +0,0 @@
----
-title: lds::SSID
-
----
-
-# lds::SSID
-
-
-
- [More...](#detailed-description)
-
-Inherited by [lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitssid/), [lds::poisson::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitssid/)
-
-## Public Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-ssid)**() =default<br>Constructs a new [SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/) type.  |
-| | **[SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-ssid)**(size_t n_x, size_t n_h, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && u_train, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && z_train, const Vector & d =Vector(1).fill(-kInf))<br>Constructs a new [SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/) type.  |
-| std::tuple< [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/), Vector > | **[Run](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-run)**([SSIDWt](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt) ssid_wt)<br>Runs fitting by subspace identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/))  |
-| std::tuple< [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) >, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > > | **[ReturnData](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-returndata)**()<br>Returns the I/O data to caller.  |
-
-## Protected Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| void | **[CalcD](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-calcd)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) t_silence =0.1, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) thresh_silence =0.001)<br>Using periods of silence in inputs (u), calculates the output \ bias (d)  |
-| void | **[CreateHankelDataMat](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-createhankeldatamat)**()<br>Creates the block-hankel I/O data matrix.  |
-| virtual void | **[DecomposeData](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-decomposedata)**() =0<br>Decompose data to lower-triangular matrix (used in Solve)  |
-| void | **[CalcSVD](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-calcsvd)**([SSIDWt](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt) wt)<br>performs the singular value decomposition (SVD)  |
-| void | **[Solve](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-solve)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) wt_dc)<br>solves for LDS parameters  |
-| void | **[RecomputeExtObs](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-recomputeextobs)**()<br>recompute extended observability matrix from estimates of A, C  |
-
-## Protected Attributes
-
-|                | Name           |
-| -------------- | -------------- |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-u-)** <br>input training data  |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[z_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-z-)** <br>measurement training data  |
-| Matrix | **[D_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-d-)** <br>block-Hankel I/O data matrix  |
-| [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/) | **[fit_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-fit-)** <br>fit  |
-| Matrix | **[g_dc_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-g-dc-)** <br>I/O gain @ DC.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-dt-)** <br>sample period  |
-| size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-n-u-)** <br>number of inputs  |
-| size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-n-x-)** <br>number of states  |
-| size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-n-y-)** <br>number of outputs  |
-| size_t | **[n_h_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-n-h-)**  |
-| size_t | **[n_trials_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-n-trials-)** <br>number of input/output data sequences  |
-| std::vector< size_t > | **[n_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-n-t-)** <br>number of time steps  |
-| size_t | **[n_t_tot_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-n-t-tot-)** <br>total number of time steps across trials  |
-| Matrix | **[L_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-l-)** <br>lower triangle decomp of covariance matrix  |
-| Vector | **[s_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-s-)** <br>singular values  |
-| Matrix | **[ext_obs_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-ext-obs-t-)** <br>extended observability matrix  |
-
-## Detailed Description
-
-```cpp
-template <typename Fit >
-class lds::SSID;
-```
-
-
----
----
-## Public Function Details
-
-### **SSID**
-
-```cpp
-SSID() =default
-```
-
-
-
----
-### **SSID**
-
-```cpp
-SSID(
-    size_t n_x,
-    size_t n_h,
-    data_t dt,
-    UniformMatrixList< kMatFreeDim2 > && u_train,
-    UniformMatrixList< kMatFreeDim2 > && z_train,
-    const Vector & d =Vector(1).fill(-kInf)
-)
-```
-
-
-
-**Parameters**:
-
-  * **n_x** number of states 
-  * **n_h** size of block-hankel data matrix 
-  * **dt** sample period 
-  * **u_train** input training data 
-  * **z_train** measurement training data 
-  * **d** output bias 
-
-
----
-### **Run**
-
-```cpp
-std::tuple< Fit, Vector > Run(
-    SSIDWt ssid_wt
-)
-```
-
-
-
-**Parameters**:
-
-  * **ssid_wt** weight for singular value decomp
-
-
-**Return**: tuple ([Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/), singular values) 
-
----
-### **ReturnData**
-
-```cpp
-inline std::tuple< UniformMatrixList< kMatFreeDim2 >, UniformMatrixList< kMatFreeDim2 > > ReturnData()
-```
-
-
-
-**Return**: tuple(input data, output data) 
-
----
-
-
-## Protected Function Details
-
-### **CalcD**
-
-```cpp
-void CalcD(
-    data_t t_silence =0.1,
-    data_t thresh_silence =0.001
-)
-```
-
-
-
-**Parameters**:
-
-  * **t_silence** threshold on period of time that qualifies as "silence" 
-  * **thresh_silence** threshold on input amplitude u that qualifies as "silence" 
-
-
----
-### **CreateHankelDataMat**
-
-```cpp
-void CreateHankelDataMat()
-```
-
-
-
-Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC. 
-
-
----
-### **DecomposeData**
-
-```cpp
-virtual void DecomposeData() =0
-```
-
-
-
-**Reimplemented by**: [lds::poisson::FitSSID::DecomposeData](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitssid/#function-decomposedata), [lds::gaussian::FitSSID::DecomposeData](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitssid/#function-decomposedata)
-
-
----
-### **CalcSVD**
-
-```cpp
-void CalcSVD(
-    SSIDWt wt
-)
-```
-
-
-
-**Parameters**:
-
-  * **ssid_wt** weight for SVD 
-
-
----
-### **Solve**
-
-```cpp
-void Solve(
-    data_t wt_dc
-)
-```
-
-
-
-**Parameters**:
-
-  * **wt_dc** weight placed on getting correct DC I/O gain 
-
-
----
-### **RecomputeExtObs**
-
-```cpp
-void RecomputeExtObs()
-```
-
-
-
----
-
-
-## Protected Attribute Details
-
-### **u_**
-
-```cpp
-UniformMatrixList< kMatFreeDim2 > u_;
-```
-
-
-
----
-### **z_**
-
-```cpp
-UniformMatrixList< kMatFreeDim2 > z_;
-```
-
-
-
----
-### **D_**
-
-```cpp
-Matrix D_;
-```
-
-
-
----
-### **fit_**
-
-```cpp
-Fit fit_;
-```
-
-
-
----
-### **g_dc_**
-
-```cpp
-Matrix g_dc_;
-```
-
-
-
----
-### **dt_**
-
-```cpp
-data_t dt_ {};
-```
-
-
-
----
-### **n_u_**
-
-```cpp
-size_t n_u_ {};
-```
-
-
-
----
-### **n_x_**
-
-```cpp
-size_t n_x_ {};
-```
-
-
-
----
-### **n_y_**
-
-```cpp
-size_t n_y_ {};
-```
-
-
-
----
-### **n_h_**
-
-```cpp
-size_t n_h_ {};
-```
-
-
-
----
-### **n_trials_**
-
-```cpp
-size_t n_trials_ {};
-```
-
-
-
----
-### **n_t_**
-
-```cpp
-std::vector< size_t > n_t_;
-```
-
-
-
----
-### **n_t_tot_**
-
-```cpp
-size_t n_t_tot_ {};
-```
-
-
-
----
-### **L_**
-
-```cpp
-Matrix L_;
-```
-
-
-
----
-### **s_**
-
-```cpp
-Vector s_;
-```
-
-
-
----
-### **ext_obs_t_**
-
-```cpp
-Matrix ext_obs_t_;
-```
-
-
-
----
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:03 Eastern Daylight Time
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1SwitchedController.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1SwitchedController.md
deleted file mode 100644
index a66dd9b5..00000000
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1SwitchedController.md
+++ /dev/null
@@ -1,360 +0,0 @@
----
-title: lds::SwitchedController
-summary: SwitchedController Type. 
-
----
-
-# lds::SwitchedController
-
-
-
-[SwitchedController]() Type.  [More...](#detailed-description)
-
-
-<br /> `#include <lds_sctrl.h>`
-
-Inherits from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/)
-
-Inherited by [lds::gaussian::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1switchedcontroller/), [lds::poisson::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1switchedcontroller/)
-
-## Public Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-switchedcontroller)**() =default<br>Constructs a new [SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/).  |
-| | **[SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-switchedcontroller)**(const std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) > & systems, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/).  |
-| | **[SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-switchedcontroller)**(std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) > && systems, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/) (moves systems).  |
-| void | **[Switch](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-switch)**(size_t idx, bool do_force_switch =false)<br>Switch to a different sub-system/controller.  |
-| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-kc)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)<> & Kc)<br>sets state feedback gains  |
-| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-kc)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)<> && Kc)<br>sets state feedback gains (moving)  |
-| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-kc-inty)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)<> & Kc_inty)<br>sets integral feedback gains  |
-| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-kc-inty)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)<> && Kc_inty)<br>sets integral feedback gains (moving)  |
-| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-kc-u)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)<> & Kc_u)<br>sets input feedback gains  |
-| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-kc-u)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)<> && Kc_u)<br>sets input feedback gains (moving)  |
-| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-g-design)**(const [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/) & g)<br>sets input gain used during controller design  |
-| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-g-design)**([UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/) && g)<br>sets input gain used during controller design (moving)  |
-
-## Protected Attributes
-
-|                | Name           |
-| -------------- | -------------- |
-| std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) > | **[systems_](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#variable-systems-)** <br>underlying sub-systems which are switched between  |
-| size_t | **[n_sys_](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#variable-n-sys-)** <br>number of systems  |
-| size_t | **[idx_](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#variable-idx-)** <br>current system/controller index.  |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/) | **[Kc_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#variable-kc-list-)**  |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/) | **[Kc_inty_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#variable-kc-inty-list-)**  |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/) | **[Kc_u_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#variable-kc-u-list-)**  |
-| [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/) | **[g_design_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#variable-g-design-list-)**  |
-
-## Additional inherited members
-
-**Public Functions inherited from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controller)**() =default<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/).  |
-| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controller)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) & sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/).  |
-| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controller)**([System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) && sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/) by moving the system object.  |
-| const Vector & | **[Control](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-control)**(const Vector & z, bool do_control =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal (single-step)  |
-| const Vector & | **[ControlOutputReference](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controloutputreference)**(const Vector & z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal, given previously-set (single-step)  |
-| const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) & | **[sys](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-sys)**() const |
-| const Matrix & | **[Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-kc)**() const<br>Get state feedback controller gain.  |
-| const Matrix & | **[Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-kc-inty)**() const<br>Get integral controller gain.  |
-| const Matrix & | **[Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-kc-u)**() const<br>Get input feedback controller gain.  |
-| const Vector & | **[g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-g-design)**() const<br>Get input gain used in controller design.  |
-| const Vector & | **[u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-u-ref)**() const<br>Get reference input.  |
-| const Vector & | **[x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-x-ref)**() const<br>Get reference state.  |
-| const Vector & | **[y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-y-ref)**() const<br>Get reference output.  |
-| size_t | **[control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-control-type)**() const<br>Get controller type.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-tau-awu)**() const<br>Get time constant of anti-integral-windup.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-u-lb)**() const<br>Get control lower bound.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-u-ub)**() const<br>Get control upper bound.  |
-| void | **[set_sys](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-sys)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) & sys)<br>Set system.  |
-| void | **[set_u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-u-ref)**(const Vector & u_ref)<br>Set reference input (u_ref)  |
-| void | **[set_x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-x-ref)**(const Vector & x_ref)<br>Set reference state (x_ref)  |
-| virtual void | **[set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-y-ref)**(const Vector & y_ref)<br>Set reference output (y_ref)  |
-| void | **[set_tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-tau-awu)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) tau)<br>Set time constant of anti-integral-windup.  |
-| void | **[set_control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-control-type)**(size_t control_type)<br>Sets the control type.  |
-| void | **[set_u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-u-lb)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb)<br>sets control lower bound  |
-| void | **[set_u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-u-ub)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub)<br>Sets control upper bound.  |
-| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-reset)**()<br>reset system and control variables.  |
-| void | **[Print](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-print)**()<br>prints variables to stdout  |
-
-**Protected Attributes inherited from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) | **[sys_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-sys-)** <br>underlying LDS  |
-| Vector | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-)** <br>control signal  |
-| Vector | **[u_return_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-return-)** <br>control signal that is _returned_ to user  |
-| Vector | **[g_design_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-g-design-)** <br>input gain of the system used for controller design  |
-| Vector | **[u_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-ref-)** <br>reference input  |
-| Vector | **[u_ref_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-ref-prev-)** <br>reference input at previous time step  |
-| Vector | **[x_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-x-ref-)** <br>reference state  |
-| Vector | **[y_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-y-ref-)** <br>reference output  |
-| Vector | **[cx_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-cx-ref-)**  |
-| Matrix | **[Kc_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-kc-)** <br>state controller gain  |
-| Matrix | **[Kc_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-kc-u-)** <br>input controller gain (optional when control updates )  |
-| Matrix | **[Kc_inty_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-kc-inty-)** <br>integral controller gain  |
-| Vector | **[du_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-du-ref-)**  |
-| Vector | **[dv_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-dv-ref-)**  |
-| Vector | **[v_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-v-ref-)**  |
-| Vector | **[dv_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-dv-)**  |
-| Vector | **[v_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-v-)** <br>Control after g inversion (e.g., control in physical units)  |
-| Vector | **[int_e_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-int-e-)** <br>integrated error  |
-| Vector | **[int_e_awu_adjust_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-int-e-awu-adjust-)** <br>anti-windup adjustment to intE  |
-| Vector | **[u_sat_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-sat-)** <br>control signal after saturation (for antiWindup)  |
-| bool | **[do_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-do-control-prev-)**  |
-| bool | **[do_lock_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-do-lock-control-prev-)**  |
-| bool | **[u_saturated_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-saturated-)** <br>whether control signal has reached saturation limits  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-lb-)** <br>lower bound on control  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-ub-)** <br>upper bound on control  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-tau-awu-)** <br>antiwindup time constant  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[k_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-k-awu-)**  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[t_since_control_onset_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-t-since-control-onset-)** <br>time since control epoch onset  |
-| size_t | **[control_type_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-control-type-)** <br>controller type  |
-
-
-## Detailed Description
-
-```cpp
-template <typename System >
-class lds::SwitchedController;
-```
-
-
----
----
-## Public Function Details
-
-### **SwitchedController**
-
-```cpp
-SwitchedController() =default
-```
-
-
-
----
-### **SwitchedController**
-
-```cpp
-inline SwitchedController(
-    const std::vector< System > & systems,
-    data_t u_lb,
-    data_t u_ub,
-    size_t control_type =0
-)
-```
-
-
-
-**Parameters**:
-
-  * **systems** vector of sub-systems 
-  * **u_lb** lower bound on control (u) 
-  * **u_ub** upper bound on control (u) 
-  * **control_type** [optional] control type bit mask 
-
-
----
-### **SwitchedController**
-
-```cpp
-inline SwitchedController(
-    std::vector< System > && systems,
-    data_t u_lb,
-    data_t u_ub,
-    size_t control_type =0
-)
-```
-
-
-
-**Parameters**:
-
-  * **systems** vector of sub-systems 
-  * **u_lb** lower bound on control (u) 
-  * **u_ub** upper bound on control (u) 
-  * **control_type** [optional] control type bit mask 
-
-
----
-### **Switch**
-
-```cpp
-inline void Switch(
-    size_t idx,
-    bool do_force_switch =false
-)
-```
-
-
-
-**Parameters**:
-
-  * **idx** index 
-  * **do_force_switch** whether to force a system switch even if already there. 
-
-
----
-### **set_Kc**
-
-```cpp
-inline void set_Kc(
-    const UniformMatrixList<> & Kc
-)
-```
-
-
-
----
-### **set_Kc**
-
-```cpp
-inline void set_Kc(
-    UniformMatrixList<> && Kc
-)
-```
-
-
-
----
-### **set_Kc_inty**
-
-```cpp
-inline void set_Kc_inty(
-    const UniformMatrixList<> & Kc_inty
-)
-```
-
-
-
----
-### **set_Kc_inty**
-
-```cpp
-inline void set_Kc_inty(
-    UniformMatrixList<> && Kc_inty
-)
-```
-
-
-
----
-### **set_Kc_u**
-
-```cpp
-inline void set_Kc_u(
-    const UniformMatrixList<> & Kc_u
-)
-```
-
-
-
----
-### **set_Kc_u**
-
-```cpp
-inline void set_Kc_u(
-    UniformMatrixList<> && Kc_u
-)
-```
-
-
-
----
-### **set_g_design**
-
-```cpp
-inline void set_g_design(
-    const UniformVectorList & g
-)
-```
-
-
-
----
-### **set_g_design**
-
-```cpp
-inline void set_g_design(
-    UniformVectorList && g
-)
-```
-
-
-
----
-
-
-## Protected Attribute Details
-
-### **systems_**
-
-```cpp
-std::vector< System > systems_;
-```
-
-
-
----
-### **n_sys_**
-
-```cpp
-size_t n_sys_ {};
-```
-
-
-
----
-### **idx_**
-
-```cpp
-size_t idx_ {};
-```
-
-
-
----
-### **Kc_list_**
-
-```cpp
-UniformMatrixList Kc_list_;
-```
-
-
-
----
-### **Kc_inty_list_**
-
-```cpp
-UniformMatrixList Kc_inty_list_;
-```
-
-
-
----
-### **Kc_u_list_**
-
-```cpp
-UniformMatrixList Kc_u_list_;
-```
-
-
-
----
-### **g_design_list_**
-
-```cpp
-UniformVectorList g_design_list_;
-```
-
-
-
----
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:03 Eastern Daylight Time
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1System.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1System.md
deleted file mode 100644
index ef818466..00000000
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1System.md
+++ /dev/null
@@ -1,850 +0,0 @@
----
-title: lds::System
-summary: Linear Dynamical System Type. 
-
----
-
-# lds::System
-
-
-
-Linear Dynamical [System]() Type. 
-<br /> `#include <lds_sys.h>`
-
-Inherited by [lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/), [lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/)
-
-## Public Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-system)**() =default<br>Constructs a new [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/).  |
-| | **[System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-system)**(size_t n_u, size_t n_x, size_t n_y, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) p0 =kDefaultP0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) q0 =kDefaultQ0)<br>constructs a new [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) |
-| virtual | **[~System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-~system)**() |
-| void | **[Filter](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-filter)**(const Vector & u_tm1, const Vector & z)<br>Filter data to produce causal state estimates.  |
-| virtual const Vector & | **[Simulate](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-simulate)**(const Vector & u_tm1) =0<br>simulates system (single time step)  |
-| void | **[f](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-f)**(const Vector & u, bool do_add_noise =false)<br>system dynamics function  |
-| virtual void | **[h](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-h)**() =0<br>system output function  |
-| size_t | **[n_u](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-n-u)**() const<br>Get number of inputs.  |
-| size_t | **[n_x](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-n-x)**() const<br>Get number of states.  |
-| size_t | **[n_y](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-n-y)**() const<br>Get number of outputs.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-dt)**() const<br>Get sample period.  |
-| const Vector & | **[x](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-x)**() const<br>Get current state.  |
-| const Matrix & | **[P](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-p)**() const<br>Get covariance of state estimate.  |
-| const Vector & | **[m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-m)**() const<br>Get current process disturbance/bias.  |
-| const Matrix & | **[P_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-p-m)**() const<br>Get covariance of process disturbance estimate.  |
-| const Vector & | **[cx](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-cx)**() const<br>Get C*x.  |
-| const Vector & | **[y](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-y)**() const<br>Get output.  |
-| const Vector & | **[x0](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-x0)**() const<br>Get initial state.  |
-| const Vector & | **[m0](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-m0)**() const<br>Get initial disturbance.  |
-| const Matrix & | **[A](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-a)**() const<br>Get state matrix.  |
-| const Matrix & | **[B](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-b)**() const<br>Get input matrix.  |
-| const Vector & | **[g](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-g)**() const<br>Get input gain/conversion factor.  |
-| const Matrix & | **[C](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-c)**() const<br>Get output matrix.  |
-| const Vector & | **[d](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-d)**() const<br>Get output bias.  |
-| const Matrix & | **[Ke](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-ke)**() const<br>Get estimator gain.  |
-| const Matrix & | **[Ke_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-ke-m)**() const<br>Get estimator gain for process disturbance (m)  |
-| const Matrix & | **[Q](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-q)**()<br>Get process noise covariance.  |
-| const Matrix & | **[Q_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-q-m)**()<br>Get process noise covariance of disturbance evoluation.  |
-| const Matrix & | **[P0](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-p0)**()<br>Get covariance of initial state.  |
-| const Matrix & | **[P0_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-p0-m)**()<br>Get covariance of initial process disturbance.  |
-| void | **[set_A](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-a)**(const Matrix & A)<br>Set state matrix.  |
-| void | **[set_B](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-b)**(const Matrix & B)<br>Set input matrix.  |
-| void | **[set_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-m)**(const Vector & m, bool do_force_assign =false)<br>Set process disturbance.  |
-| void | **[set_g](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-g)**(const Vector & g)<br>Set input gain.  |
-| void | **[set_Q](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-q)**(const Matrix & Q)<br>Set process noise covariance.  |
-| void | **[set_Q_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-q-m)**(const Matrix & Q_m)<br>Set process noise covariance of disturbance evoluation.  |
-| void | **[set_x0](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-x0)**(const Vector & x0)<br>Set initial state.  |
-| void | **[set_P0](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-p0)**(const Matrix & P0)<br>Set covariance of initial state.  |
-| void | **[set_P0_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-p0-m)**(const Matrix & P0_m)<br>Set covariance of initial process disturbance.  |
-| void | **[set_C](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-c)**(const Matrix & C)<br>Set output matrix.  |
-| void | **[set_d](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-d)**(const Vector & d)<br>Set output bias.  |
-| void | **[set_x](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-x)**(const Vector & x)<br>Set state of system.  |
-| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-reset)**()<br>Reset system variables.  |
-| void | **[Print](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-print)**()<br>Print system variables to stdout.  |
-
-## Protected Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| virtual void | **[RecurseKe](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-recurseke)**() =0<br>Recursively recalculate estimator gain (Ke)  |
-| void | **[InitVars](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-initvars)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) p0 =kDefaultP0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) q0 =kDefaultQ0) |
-
-## Public Attributes
-
-|                | Name           |
-| -------------- | -------------- |
-| bool | **[do_adapt_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-do-adapt-m)** <br>whether to adaptively estimate disturbance m  |
-
-## Protected Attributes
-
-|                | Name           |
-| -------------- | -------------- |
-| std::size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-n-x-)** <br>number of states  |
-| std::size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-n-u-)** <br>number of inputs  |
-| std::size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-n-y-)** <br>number of outputs  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-dt-)** <br>sample period  |
-| Vector | **[x_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-x-)** <br>state  |
-| Matrix | **[P_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-p-)** <br>covariance of state estimate  |
-| Vector | **[m_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-m-)** <br>process disturbance  |
-| Matrix | **[P_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-p-m-)** <br>covariance of disturbance estimate  |
-| Vector | **[cx_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-cx-)** <br>C*x.  |
-| Vector | **[y_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-y-)** <br>output  |
-| Vector | **[z_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-z-)** <br>measurement  |
-| Vector | **[x0_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-x0-)** <br>initial state  |
-| Matrix | **[P0_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-p0-)** <br>covariance of initial state estimate  |
-| Vector | **[m0_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-m0-)** <br>initial process disturbance  |
-| Matrix | **[P0_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-p0-m-)** <br>covariance of initial disturbance est.  |
-| Matrix | **[A_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-a-)** <br>state matrix  |
-| Matrix | **[B_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-b-)** <br>input matrix  |
-| Vector | **[g_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-g-)** <br>input gain  |
-| Matrix | **[Q_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-q-)** <br>covariance of process noise  |
-| Matrix | **[Q_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-q-m-)** <br>covariance of disturbance random walk  |
-| Matrix | **[C_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-c-)** <br>output matrix  |
-| Vector | **[d_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-d-)** <br>output bias  |
-| Matrix | **[Ke_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-ke-)** <br>estimator gain  |
-| Matrix | **[Ke_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-ke-m-)** <br>estimator gain for process disturbance  |
-
----
----
-## Public Function Details
-
-### **System**
-
-```cpp
-System() =default
-```
-
-
-
----
-### **System**
-
-```cpp
-System(
-    size_t n_u,
-    size_t n_x,
-    size_t n_y,
-    data_t dt,
-    data_t p0 =kDefaultP0,
-    data_t q0 =kDefaultQ0
-)
-```
-
-
-
-**Parameters**:
-
-  * **n_u** number of inputs 
-  * **n_x** number of states 
-  * **n_y** number of outputs 
-  * **dt** sample period 
-  * **p0** diagonal elements for state estimate covariance 
-  * **q0** diagonal elements for process noise covariance 
-
-
----
-### **~System**
-
-```cpp
-inline virtual ~System()
-```
-
-
-
----
-### **Filter**
-
-```cpp
-void Filter(
-    const Vector & u_tm1,
-    const Vector & z
-)
-```
-
-
-
-**Parameters**:
-
-  * **u_tm1** input at t-minus-1 
-  * **z_t** current measurement 
-
-
-Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.
-
-
----
-### **Simulate**
-
-```cpp
-virtual const Vector & Simulate(
-    const Vector & u_tm1
-) =0
-```
-
-
-
-**Parameters**:
-
-  * **u_tm1** input at time t-1
-
-
-**Return**: simulated measurement at time t 
-
-**Reimplemented by**: [lds::poisson::System::Simulate](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/#function-simulate), [lds::gaussian::System::Simulate](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/#function-simulate)
-
-
----
-### **f**
-
-```cpp
-inline void f(
-    const Vector & u,
-    bool do_add_noise =false
-)
-```
-
-
-
-**Parameters**:
-
-  * **u** input 
-  * **do_add_noise** whether to add simulated process noise 
-
-
----
-### **h**
-
-```cpp
-virtual void h() =0
-```
-
-
-
-**Reimplemented by**: [lds::poisson::System::h](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/#function-h), [lds::gaussian::System::h](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/#function-h)
-
-
----
-### **n_u**
-
-```cpp
-inline size_t n_u() const
-```
-
-
-
----
-### **n_x**
-
-```cpp
-inline size_t n_x() const
-```
-
-
-
----
-### **n_y**
-
-```cpp
-inline size_t n_y() const
-```
-
-
-
----
-### **dt**
-
-```cpp
-inline data_t dt() const
-```
-
-
-
----
-### **x**
-
-```cpp
-inline const Vector & x() const
-```
-
-
-
----
-### **P**
-
-```cpp
-inline const Matrix & P() const
-```
-
-
-
----
-### **m**
-
-```cpp
-inline const Vector & m() const
-```
-
-
-
----
-### **P_m**
-
-```cpp
-inline const Matrix & P_m() const
-```
-
-
-
----
-### **cx**
-
-```cpp
-inline const Vector & cx() const
-```
-
-
-
----
-### **y**
-
-```cpp
-inline const Vector & y() const
-```
-
-
-
----
-### **x0**
-
-```cpp
-inline const Vector & x0() const
-```
-
-
-
----
-### **m0**
-
-```cpp
-inline const Vector & m0() const
-```
-
-
-
----
-### **A**
-
-```cpp
-inline const Matrix & A() const
-```
-
-
-
----
-### **B**
-
-```cpp
-inline const Matrix & B() const
-```
-
-
-
----
-### **g**
-
-```cpp
-inline const Vector & g() const
-```
-
-
-
----
-### **C**
-
-```cpp
-inline const Matrix & C() const
-```
-
-
-
----
-### **d**
-
-```cpp
-inline const Vector & d() const
-```
-
-
-
----
-### **Ke**
-
-```cpp
-inline const Matrix & Ke() const
-```
-
-
-
----
-### **Ke_m**
-
-```cpp
-inline const Matrix & Ke_m() const
-```
-
-
-
----
-### **Q**
-
-```cpp
-inline const Matrix & Q()
-```
-
-
-
----
-### **Q_m**
-
-```cpp
-inline const Matrix & Q_m()
-```
-
-
-
----
-### **P0**
-
-```cpp
-inline const Matrix & P0()
-```
-
-
-
----
-### **P0_m**
-
-```cpp
-inline const Matrix & P0_m()
-```
-
-
-
----
-### **set_A**
-
-```cpp
-inline void set_A(
-    const Matrix & A
-)
-```
-
-
-
----
-### **set_B**
-
-```cpp
-inline void set_B(
-    const Matrix & B
-)
-```
-
-
-
----
-### **set_m**
-
-```cpp
-inline void set_m(
-    const Vector & m,
-    bool do_force_assign =false
-)
-```
-
-
-
----
-### **set_g**
-
-```cpp
-inline void set_g(
-    const Vector & g
-)
-```
-
-
-
----
-### **set_Q**
-
-```cpp
-inline void set_Q(
-    const Matrix & Q
-)
-```
-
-
-
----
-### **set_Q_m**
-
-```cpp
-inline void set_Q_m(
-    const Matrix & Q_m
-)
-```
-
-
-
----
-### **set_x0**
-
-```cpp
-inline void set_x0(
-    const Vector & x0
-)
-```
-
-
-
----
-### **set_P0**
-
-```cpp
-inline void set_P0(
-    const Matrix & P0
-)
-```
-
-
-
----
-### **set_P0_m**
-
-```cpp
-inline void set_P0_m(
-    const Matrix & P0_m
-)
-```
-
-
-
----
-### **set_C**
-
-```cpp
-inline void set_C(
-    const Matrix & C
-)
-```
-
-
-
----
-### **set_d**
-
-```cpp
-inline void set_d(
-    const Vector & d
-)
-```
-
-
-
----
-### **set_x**
-
-```cpp
-inline void set_x(
-    const Vector & x
-)
-```
-
-
-
----
-### **Reset**
-
-```cpp
-void Reset()
-```
-
-
-
----
-### **Print**
-
-```cpp
-void Print()
-```
-
-
-
----
-
-
-## Protected Function Details
-
-### **RecurseKe**
-
-```cpp
-virtual void RecurseKe() =0
-```
-
-
-
-**Reimplemented by**: [lds::poisson::System::RecurseKe](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/#function-recurseke), [lds::gaussian::System::RecurseKe](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/#function-recurseke)
-
-
----
-### **InitVars**
-
-```cpp
-void InitVars(
-    data_t p0 =kDefaultP0,
-    data_t q0 =kDefaultQ0
-)
-```
-
-
-
----
-
-
-## Public Attribute Details
-
-### **do_adapt_m**
-
-```cpp
-bool do_adapt_m {};
-```
-
-
-
----
-
-
-## Protected Attribute Details
-
-### **n_x_**
-
-```cpp
-std::size_t n_x_ {};
-```
-
-
-
----
-### **n_u_**
-
-```cpp
-std::size_t n_u_ {};
-```
-
-
-
----
-### **n_y_**
-
-```cpp
-std::size_t n_y_ {};
-```
-
-
-
----
-### **dt_**
-
-```cpp
-data_t dt_ {};
-```
-
-
-
----
-### **x_**
-
-```cpp
-Vector x_;
-```
-
-
-
----
-### **P_**
-
-```cpp
-Matrix P_;
-```
-
-
-
----
-### **m_**
-
-```cpp
-Vector m_;
-```
-
-
-
----
-### **P_m_**
-
-```cpp
-Matrix P_m_;
-```
-
-
-
----
-### **cx_**
-
-```cpp
-Vector cx_;
-```
-
-
-
----
-### **y_**
-
-```cpp
-Vector y_;
-```
-
-
-
----
-### **z_**
-
-```cpp
-Vector z_;
-```
-
-
-
----
-### **x0_**
-
-```cpp
-Vector x0_;
-```
-
-
-
----
-### **P0_**
-
-```cpp
-Matrix P0_;
-```
-
-
-
----
-### **m0_**
-
-```cpp
-Vector m0_;
-```
-
-
-
----
-### **P0_m_**
-
-```cpp
-Matrix P0_m_;
-```
-
-
-
----
-### **A_**
-
-```cpp
-Matrix A_;
-```
-
-
-
----
-### **B_**
-
-```cpp
-Matrix B_;
-```
-
-
-
----
-### **g_**
-
-```cpp
-Vector g_;
-```
-
-
-
----
-### **Q_**
-
-```cpp
-Matrix Q_;
-```
-
-
-
----
-### **Q_m_**
-
-```cpp
-Matrix Q_m_;
-```
-
-
-
----
-### **C_**
-
-```cpp
-Matrix C_;
-```
-
-
-
----
-### **d_**
-
-```cpp
-Vector d_;
-```
-
-
-
----
-### **Ke_**
-
-```cpp
-Matrix Ke_;
-```
-
-
-
----
-### **Ke_m_**
-
-```cpp
-Matrix Ke_m_;
-```
-
-
-
----
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1UniformSystemList.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1UniformSystemList.md
deleted file mode 100644
index 37b04282..00000000
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1UniformSystemList.md
+++ /dev/null
@@ -1,235 +0,0 @@
----
-title: lds::UniformSystemList
-
----
-
-# lds::UniformSystemList
-
-
-
- [More...](#detailed-description)
-
-Inherits from std::vector< System >
-
-## Public Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/#function-uniformsystemlist)**() =default<br>Constructs a new [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/).  |
-| | **[UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/#function-uniformsystemlist)**(const std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) > & systems, std::array< size_t, 3 > dim ={0, 0, 0})<br>Constructs a new [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/) by copying existing vector of [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) if dimensions consistent.  |
-| | **[UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/#function-uniformsystemlist)**(std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) > && systems, std::array< size_t, 3 > dim ={0, 0, 0})<br>Constructs a new [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/) by moving existing vector of [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) if dimensions consistent.  |
-| | **[UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/#function-uniformsystemlist)**(std::initializer_list< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) > systems, std::array< size_t, 3 > dim ={0, 0, 0})<br>Constructs a new [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/) from initializer_list of [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) if dimensions consistent.  |
-| | **[UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/#function-uniformsystemlist)**(const [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/) & that)<br>Constructs a new [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/) (copy).  |
-| | **[UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/#function-uniformsystemlist)**([UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/) && that)<br>Constructs a new [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/) (move).  |
-| | **[~UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/#function-~uniformsystemlist)**() =default<br>Destroys the object.  |
-| const std::array< size_t, 3 > & | **[dim](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/#function-dim)**() const<br>gets dimensions of the uniformly sized systems  |
-| size_t | **[size](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/#function-size)**()<br>size of container  |
-| const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) & | **[at](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/#function-at)**(size_t n)<br>gets reference to n^th element  |
-| void | **[Swap](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/#function-swap)**([System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) & that, size_t n)<br>swaps input system with n^th system of list  |
-| [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/) & | **[operator=](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/#function-operator=)**(const [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/) & that)<br>assigns the contents (copy)  |
-| [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/) & | **[operator=](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/#function-operator=)**([UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/) && that)<br>assigns the contents (move)  |
-
-## Detailed Description
-
-```cpp
-template <typename System>
-class lds::UniformSystemList;
-```
-
-
----
----
-## Public Function Details
-
-### **UniformSystemList**
-
-```cpp
-UniformSystemList() =default
-```
-
-
-
----
-### **UniformSystemList**
-
-```cpp
-explicit UniformSystemList(
-    const std::vector< System > & systems,
-    std::array< size_t, 3 > dim ={0, 0, 0}
-)
-```
-
-
-
-**Parameters**:
-
-  * **systems** input systems 
-  * **dim** dimensions (n_u, n_x, n_y) 
-
-
----
-### **UniformSystemList**
-
-```cpp
-explicit UniformSystemList(
-    std::vector< System > && systems,
-    std::array< size_t, 3 > dim ={0, 0, 0}
-)
-```
-
-
-
-**Parameters**:
-
-  * **systems** input systems 
-  * **dim** dimensions (n_u, n_x, n_y) 
-
-
----
-### **UniformSystemList**
-
-```cpp
-UniformSystemList(
-    std::initializer_list< System > systems,
-    std::array< size_t, 3 > dim ={0, 0, 0}
-)
-```
-
-
-
-**Parameters**:
-
-  * **systems** input systems 
-  * **dim** dimensions (n_u, n_x, n_y) 
-
-
----
-### **UniformSystemList**
-
-```cpp
-UniformSystemList(
-    const UniformSystemList & that
-)
-```
-
-
-
-**Parameters**:
-
-  * **that** another [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/)
-
-
----
-### **UniformSystemList**
-
-```cpp
-UniformSystemList(
-    UniformSystemList && that
-)
-```
-
-
-
-**Parameters**:
-
-  * **that** another [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/)
-
-
----
-### **~UniformSystemList**
-
-```cpp
-~UniformSystemList() =default
-```
-
-
-
----
-### **dim**
-
-```cpp
-inline const std::array< size_t, 3 > & dim() const
-```
-
-
-
----
-### **size**
-
-```cpp
-inline size_t size()
-```
-
-
-
----
-### **at**
-
-```cpp
-inline const System & at(
-    size_t n
-)
-```
-
-
-
----
-### **Swap**
-
-```cpp
-inline void Swap(
-    System & that,
-    size_t n
-)
-```
-
-
-
-**Parameters**:
-
-  * **that** input system 
-  * **n** index where the system is moved 
-
-
----
-### **operator=**
-
-```cpp
-inline UniformSystemList & operator=(
-    const UniformSystemList & that
-)
-```
-
-
-
-**Parameters**:
-
-  * **that** another [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/)
-
-
-**Return**: reference to object 
-
----
-### **operator=**
-
-```cpp
-inline UniformSystemList & operator=(
-    UniformSystemList && that
-)
-```
-
-
-
-**Parameters**:
-
-  * **that** another [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/)
-
-
-**Return**: reference to object 
-
----
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1UniformVectorList.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1UniformVectorList.md
deleted file mode 100644
index a483504b..00000000
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1UniformVectorList.md
+++ /dev/null
@@ -1,225 +0,0 @@
----
-title: lds::UniformVectorList
-
----
-
-# lds::UniformVectorList
-
-
-
-Inherits from std::vector< Vector >
-
-## Public Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/#function-uniformvectorlist)**() =default<br>Constructs a new [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/).  |
-| | **[UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/#function-uniformvectorlist)**(const std::vector< Vector > & vecs, size_t dim =0)<br>Constructs a new [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/) by copying existing vector of Vector if dimensions consistent.  |
-| | **[UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/#function-uniformvectorlist)**(std::vector< Vector > && vecs, size_t dim =0)<br>Constructs a new [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/) by moving existing vector of Vector if dimensions consistent.  |
-| | **[UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/#function-uniformvectorlist)**(std::initializer_list< Vector > vecs, size_t dim =0)<br>Constructs a new [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/) from initializer_list of Vector if dimensions consistent.  |
-| | **[UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/#function-uniformvectorlist)**(const [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/) & that)<br>Constructs a new [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/) (copy)  |
-| | **[UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/#function-uniformvectorlist)**([UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/) && that)<br>Constructs a new [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/) (move)  |
-| | **[~UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/#function-~uniformvectorlist)**() =default<br>Destroys the object.  |
-| size_t | **[dim](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/#function-dim)**() const<br>gets dimensions of the uniformly sized matrices  |
-| size_t | **[size](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/#function-size)**()<br>size of container  |
-| const Vector & | **[at](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/#function-at)**(size_t n)<br>gets reference to n^th element  |
-| void | **[Swap](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/#function-swap)**(Vector & that, size_t n)<br>swaps input matrix with n^th vector of list  |
-| [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/) & | **[operator=](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/#function-operator=)**(const [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/) & that)<br>assigns the contents (copy)  |
-| [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/) & | **[operator=](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/#function-operator=)**([UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/) && that)<br>assigns the contents (move)  |
-
----
----
-## Public Function Details
-
-### **UniformVectorList**
-
-```cpp
-UniformVectorList() =default
-```
-
-
-
----
-### **UniformVectorList**
-
-```cpp
-explicit UniformVectorList(
-    const std::vector< Vector > & vecs,
-    size_t dim =0
-)
-```
-
-
-
-**Parameters**:
-
-  * **vecs** input vectors 
-  * **dims** dimension 
-
-
----
-### **UniformVectorList**
-
-```cpp
-explicit UniformVectorList(
-    std::vector< Vector > && vecs,
-    size_t dim =0
-)
-```
-
-
-
-**Parameters**:
-
-  * **vecs** input vectors 
-  * **dim** dimension 
-
-
----
-### **UniformVectorList**
-
-```cpp
-UniformVectorList(
-    std::initializer_list< Vector > vecs,
-    size_t dim =0
-)
-```
-
-
-
-**Parameters**:
-
-  * **vecs** input vectors 
-  * **dim** dimension 
-
-
----
-### **UniformVectorList**
-
-```cpp
-UniformVectorList(
-    const UniformVectorList & that
-)
-```
-
-
-
-**Parameters**:
-
-  * **that** another [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/)
-
-
----
-### **UniformVectorList**
-
-```cpp
-UniformVectorList(
-    UniformVectorList && that
-)
-```
-
-
-
-**Parameters**:
-
-  * **that** another [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/)
-
-
----
-### **~UniformVectorList**
-
-```cpp
-~UniformVectorList() =default
-```
-
-
-
----
-### **dim**
-
-```cpp
-inline size_t dim() const
-```
-
-
-
----
-### **size**
-
-```cpp
-inline size_t size()
-```
-
-
-
----
-### **at**
-
-```cpp
-inline const Vector & at(
-    size_t n
-)
-```
-
-
-
----
-### **Swap**
-
-```cpp
-inline void Swap(
-    Vector & that,
-    size_t n
-)
-```
-
-
-
-**Parameters**:
-
-  * **that** input vector 
-  * **n** index where the vector is moved 
-
-
----
-### **operator=**
-
-```cpp
-inline UniformVectorList & operator=(
-    const UniformVectorList & that
-)
-```
-
-
-
-**Parameters**:
-
-  * **that** another [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/)
-
-
-**Return**: reference to object 
-
----
-### **operator=**
-
-```cpp
-inline UniformVectorList & operator=(
-    UniformVectorList && that
-)
-```
-
-
-
-**Parameters**:
-
-  * **that** another [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/)
-
-
-**Return**: reference to object 
-
----
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1Controller.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_controller.md
similarity index 54%
rename from misc/docs-hugo/content/docs/api/Classes/classlds_1_1Controller.md
rename to misc/docs-hugo/content/docs/api/Classes/classlds_1_1_controller.md
index 9b78fe9d..5b8c85f5 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1Controller.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_controller.md
@@ -1,774 +1,774 @@
----
-title: lds::Controller
-
----
-
-# lds::Controller
-
-
-
- [More...](#detailed-description)
-
-Inherited by [lds::gaussian::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1controller/), [lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/), [lds::SwitchedController< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/)
-
-## Public Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controller)**() =default<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/).  |
-| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controller)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) & sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/).  |
-| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controller)**([System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) && sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/) by moving the system object.  |
-| const Vector & | **[Control](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-control)**(const Vector & z, bool do_control =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal (single-step)  |
-| const Vector & | **[ControlOutputReference](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controloutputreference)**(const Vector & z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal, given previously-set (single-step)  |
-| const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) & | **[sys](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-sys)**() const |
-| const Matrix & | **[Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-kc)**() const<br>Get state feedback controller gain.  |
-| const Matrix & | **[Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-kc-inty)**() const<br>Get integral controller gain.  |
-| const Matrix & | **[Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-kc-u)**() const<br>Get input feedback controller gain.  |
-| const Vector & | **[g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-g-design)**() const<br>Get input gain used in controller design.  |
-| const Vector & | **[u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-u-ref)**() const<br>Get reference input.  |
-| const Vector & | **[x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-x-ref)**() const<br>Get reference state.  |
-| const Vector & | **[y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-y-ref)**() const<br>Get reference output.  |
-| size_t | **[control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-control-type)**() const<br>Get controller type.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-tau-awu)**() const<br>Get time constant of anti-integral-windup.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-u-lb)**() const<br>Get control lower bound.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-u-ub)**() const<br>Get control upper bound.  |
-| void | **[set_sys](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-sys)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) & sys)<br>Set system.  |
-| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-g-design)**(const Vector & g_design)<br>Set input gain used in controller design (g_design)  |
-| void | **[set_u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-u-ref)**(const Vector & u_ref)<br>Set reference input (u_ref)  |
-| void | **[set_x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-x-ref)**(const Vector & x_ref)<br>Set reference state (x_ref)  |
-| virtual void | **[set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-y-ref)**(const Vector & y_ref)<br>Set reference output (y_ref)  |
-| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-kc)**(const Matrix & Kc)<br>Set state controller gain.  |
-| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-kc-inty)**(const Matrix & Kc_inty)<br>Set integral controller gain.  |
-| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-kc-u)**(const Matrix & Kc_u)<br>Set input controller gain.  |
-| void | **[set_tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-tau-awu)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) tau)<br>Set time constant of anti-integral-windup.  |
-| void | **[set_control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-control-type)**(size_t control_type)<br>Sets the control type.  |
-| void | **[set_u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-u-lb)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb)<br>sets control lower bound  |
-| void | **[set_u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-u-ub)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub)<br>Sets control upper bound.  |
-| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-reset)**()<br>reset system and control variables.  |
-| void | **[Print](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-print)**()<br>prints variables to stdout  |
-
-## Protected Attributes
-
-|                | Name           |
-| -------------- | -------------- |
-| [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) | **[sys_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-sys-)** <br>underlying LDS  |
-| Vector | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-)** <br>control signal  |
-| Vector | **[u_return_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-return-)** <br>control signal that is _returned_ to user  |
-| Vector | **[g_design_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-g-design-)** <br>input gain of the system used for controller design  |
-| Vector | **[u_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-ref-)** <br>reference input  |
-| Vector | **[u_ref_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-ref-prev-)** <br>reference input at previous time step  |
-| Vector | **[x_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-x-ref-)** <br>reference state  |
-| Vector | **[y_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-y-ref-)** <br>reference output  |
-| Vector | **[cx_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-cx-ref-)**  |
-| Matrix | **[Kc_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-kc-)** <br>state controller gain  |
-| Matrix | **[Kc_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-kc-u-)** <br>input controller gain (optional when control updates )  |
-| Matrix | **[Kc_inty_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-kc-inty-)** <br>integral controller gain  |
-| Vector | **[du_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-du-ref-)**  |
-| Vector | **[dv_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-dv-ref-)**  |
-| Vector | **[v_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-v-ref-)**  |
-| Vector | **[dv_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-dv-)**  |
-| Vector | **[v_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-v-)** <br>Control after g inversion (e.g., control in physical units)  |
-| Vector | **[int_e_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-int-e-)** <br>integrated error  |
-| Vector | **[int_e_awu_adjust_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-int-e-awu-adjust-)** <br>anti-windup adjustment to intE  |
-| Vector | **[u_sat_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-sat-)** <br>control signal after saturation (for antiWindup)  |
-| bool | **[do_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-do-control-prev-)**  |
-| bool | **[do_lock_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-do-lock-control-prev-)**  |
-| bool | **[u_saturated_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-saturated-)** <br>whether control signal has reached saturation limits  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-lb-)** <br>lower bound on control  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-ub-)** <br>upper bound on control  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-tau-awu-)** <br>antiwindup time constant  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[k_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-k-awu-)**  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[t_since_control_onset_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-t-since-control-onset-)** <br>time since control epoch onset  |
-| size_t | **[control_type_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-control-type-)** <br>controller type  |
-
-## Detailed Description
-
-```cpp
-template <typename System >
-class lds::Controller;
-```
-
-
----
----
-## Public Function Details
-
-### **Controller**
-
-```cpp
-Controller() =default
-```
-
-
-
----
-### **Controller**
-
-```cpp
-inline Controller(
-    const System & sys,
-    data_t u_lb,
-    data_t u_ub,
-    size_t control_type =0
-)
-```
-
-
-
-**Parameters**:
-
-  * **sys** [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) (derived from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)) 
-  * **u_lb** lower bound on control (u) 
-  * **u_ub** upper bound on control (u) 
-  * **control_type** [optional] control type bit mask
-
-
-**Template Parameters**:
-
-  * **[System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)** type derived from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)
-
-
----
-### **Controller**
-
-```cpp
-inline Controller(
-    System && sys,
-    data_t u_lb,
-    data_t u_ub,
-    size_t control_type =0
-)
-```
-
-
-
-**Parameters**:
-
-  * **sys** [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) (derived from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)) 
-  * **u_lb** lower bound on control (u) 
-  * **u_ub** upper bound on control (u) 
-  * **control_type** [optional] control type bit mask
-
-
-**Template Parameters**:
-
-  * **[System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)** type derived from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)
-
-
----
-### **Control**
-
-```cpp
-inline const Vector & Control(
-    const Vector & z,
-    bool do_control =true,
-    bool do_lock_control =false,
-    data_t sigma_soft_start =0,
-    data_t sigma_u_noise =0,
-    bool do_reset_at_control_onset =true
-)
-```
-
-
-
-**Parameters**:
-
-  * **z** measurement 
-  * **do_control** [optional] whether to update control (true) or simply feed through u_ref (false) 
-  * **do_lock_control** [optional] whether to lock control at its current value 
-  * **sigma_soft_start** [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) 
-  * **sigma_u_noise** [optional] standard deviation (sigma) of Gaussian noise added on top of control signal 
-  * **do_reset_at_control_onset** [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true)
-
-
-**Return**: updated control signal 
-
-Updates the control signal (single-step). This is the most flexible option, but requires user to have set the controller's y_ref, x_ref, and u_ref variables.
-
-
----
-### **ControlOutputReference**
-
-```cpp
-inline const Vector & ControlOutputReference(
-    const Vector & z,
-    bool do_control =true,
-    bool do_estimation =true,
-    bool do_lock_control =false,
-    data_t sigma_soft_start =0,
-    data_t sigma_u_noise =0,
-    bool do_reset_at_control_onset =true
-)
-```
-
-
-
-**Parameters**:
-
-  * **z** measurement 
-  * **do_control** [optional] whether to update control (true) or simply feed through u_ref (false) 
-  * **do_estimation** [optional] whether to update state estimate (if false, effectively open-loop control) 
-  * **do_lock_control** [optional] whether to lock control at its current value 
-  * **sigma_soft_start** [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) 
-  * **sigma_u_noise** [optional] standard deviation (sigma) of Gaussian noise added on top of control signal 
-  * **do_reset_at_control_onset** [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true)
-
-
-**Return**: updated control signal 
-
-Updates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.
-
-
----
-### **sys**
-
-```cpp
-inline const System & sys() const
-```
-
-
-
----
-### **Kc**
-
-```cpp
-inline const Matrix & Kc() const
-```
-
-
-
----
-### **Kc_inty**
-
-```cpp
-inline const Matrix & Kc_inty() const
-```
-
-
-
----
-### **Kc_u**
-
-```cpp
-inline const Matrix & Kc_u() const
-```
-
-
-
----
-### **g_design**
-
-```cpp
-inline const Vector & g_design() const
-```
-
-
-
----
-### **u_ref**
-
-```cpp
-inline const Vector & u_ref() const
-```
-
-
-
----
-### **x_ref**
-
-```cpp
-inline const Vector & x_ref() const
-```
-
-
-
----
-### **y_ref**
-
-```cpp
-inline const Vector & y_ref() const
-```
-
-
-
----
-### **control_type**
-
-```cpp
-inline size_t control_type() const
-```
-
-
-
----
-### **tau_awu**
-
-```cpp
-inline data_t tau_awu() const
-```
-
-
-
----
-### **u_lb**
-
-```cpp
-inline data_t u_lb() const
-```
-
-
-
----
-### **u_ub**
-
-```cpp
-inline data_t u_ub() const
-```
-
-
-
----
-### **set_sys**
-
-```cpp
-inline void set_sys(
-    const System & sys
-)
-```
-
-
-
----
-### **set_g_design**
-
-```cpp
-inline void set_g_design(
-    const Vector & g_design
-)
-```
-
-
-
----
-### **set_u_ref**
-
-```cpp
-inline void set_u_ref(
-    const Vector & u_ref
-)
-```
-
-
-
----
-### **set_x_ref**
-
-```cpp
-inline void set_x_ref(
-    const Vector & x_ref
-)
-```
-
-
-
----
-### **set_y_ref**
-
-```cpp
-inline virtual void set_y_ref(
-    const Vector & y_ref
-)
-```
-
-
-
-**Reimplemented by**: [lds::poisson::SwitchedController::set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1switchedcontroller/#function-set-y-ref), [lds::gaussian::SwitchedController::set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1switchedcontroller/#function-set-y-ref), [lds::poisson::Controller::set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/#function-set-y-ref), [lds::gaussian::Controller::set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1controller/#function-set-y-ref)
-
-
----
-### **set_Kc**
-
-```cpp
-inline void set_Kc(
-    const Matrix & Kc
-)
-```
-
-
-
----
-### **set_Kc_inty**
-
-```cpp
-inline void set_Kc_inty(
-    const Matrix & Kc_inty
-)
-```
-
-
-
----
-### **set_Kc_u**
-
-```cpp
-inline void set_Kc_u(
-    const Matrix & Kc_u
-)
-```
-
-
-
----
-### **set_tau_awu**
-
-```cpp
-inline void set_tau_awu(
-    data_t tau
-)
-```
-
-
-
----
-### **set_control_type**
-
-```cpp
-inline void set_control_type(
-    size_t control_type
-)
-```
-
-
-
-**Parameters**:
-
-  * **control_type** control type bit mask
-
-
-**Template Parameters**:
-
-  * **[System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)** type derived from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)
-
-
----
-### **set_u_lb**
-
-```cpp
-inline void set_u_lb(
-    data_t u_lb
-)
-```
-
-
-
-**Parameters**:
-
-  * **u_lb** control lower bound 
-
-
----
-### **set_u_ub**
-
-```cpp
-inline void set_u_ub(
-    data_t u_ub
-)
-```
-
-
-
-**Parameters**:
-
-  * **u_ub** control upper bound 
-
-
----
-### **Reset**
-
-```cpp
-inline void Reset()
-```
-
-
-
----
-### **Print**
-
-```cpp
-inline void Print()
-```
-
-
-
----
-
-
-## Protected Attribute Details
-
-### **sys_**
-
-```cpp
-System sys_;
-```
-
-
-
----
-### **u_**
-
-```cpp
-Vector u_;
-```
-
-
-
----
-### **u_return_**
-
-```cpp
-Vector u_return_;
-```
-
-
-
----
-### **g_design_**
-
-```cpp
-Vector g_design_;
-```
-
-
-
----
-### **u_ref_**
-
-```cpp
-Vector u_ref_;
-```
-
-
-
----
-### **u_ref_prev_**
-
-```cpp
-Vector u_ref_prev_;
-```
-
-
-
----
-### **x_ref_**
-
-```cpp
-Vector x_ref_;
-```
-
-
-
----
-### **y_ref_**
-
-```cpp
-Vector y_ref_;
-```
-
-
-
----
-### **cx_ref_**
-
-```cpp
-Vector cx_ref_;
-```
-
-
-
----
-### **Kc_**
-
-```cpp
-Matrix Kc_;
-```
-
-
-
----
-### **Kc_u_**
-
-```cpp
-Matrix Kc_u_;
-```
-
-
-
----
-### **Kc_inty_**
-
-```cpp
-Matrix Kc_inty_;
-```
-
-
-
----
-### **du_ref_**
-
-```cpp
-Vector du_ref_;
-```
-
-
-
----
-### **dv_ref_**
-
-```cpp
-Vector dv_ref_;
-```
-
-
-
----
-### **v_ref_**
-
-```cpp
-Vector v_ref_;
-```
-
-
-
----
-### **dv_**
-
-```cpp
-Vector dv_;
-```
-
-
-
----
-### **v_**
-
-```cpp
-Vector v_;
-```
-
-
-
----
-### **int_e_**
-
-```cpp
-Vector int_e_;
-```
-
-
-
----
-### **int_e_awu_adjust_**
-
-```cpp
-Vector int_e_awu_adjust_;
-```
-
-
-
----
-### **u_sat_**
-
-```cpp
-Vector u_sat_;
-```
-
-
-
----
-### **do_control_prev_**
-
-```cpp
-bool do_control_prev_ = false;
-```
-
-
-
----
-### **do_lock_control_prev_**
-
-```cpp
-bool do_lock_control_prev_ = false;
-```
-
-
-
----
-### **u_saturated_**
-
-```cpp
-bool u_saturated_ =
-      false;
-```
-
-
-
----
-### **u_lb_**
-
-```cpp
-data_t u_lb_ {};
-```
-
-
-
----
-### **u_ub_**
-
-```cpp
-data_t u_ub_ {};
-```
-
-
-
----
-### **tau_awu_**
-
-```cpp
-data_t tau_awu_ {};
-```
-
-
-
----
-### **k_awu_**
-
-```cpp
-data_t k_awu_ = 0;
-```
-
-
-
----
-### **t_since_control_onset_**
-
-```cpp
-data_t t_since_control_onset_ = 0;
-```
-
-
-
----
-### **control_type_**
-
-```cpp
-size_t control_type_ {};
-```
-
-
-
----
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:03 Eastern Daylight Time
+---
+title: lds::Controller
+
+---
+
+# lds::Controller
+
+
+
+ [More...](#detailed-description)
+
+Inherited by [lds::SwitchedController< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/), [lds::gaussian::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/), [lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/)
+
+## Public Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller)**() =default<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/).  |
+| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) & sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/).  |
+| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller)**([System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) && sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/) by moving the system object.  |
+| const Vector & | **[Control](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control)**(const Vector & z, bool do_control =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal (single-step)  |
+| const Vector & | **[ControlOutputReference](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controloutputreference)**(const Vector & z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal, given previously-set (single-step)  |
+| const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) & | **[sys](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-sys)**() const |
+| const Matrix & | **[Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc)**() const<br>Get state feedback controller gain.  |
+| const Matrix & | **[Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-inty)**() const<br>Get integral controller gain.  |
+| const Matrix & | **[Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-u)**() const<br>Get input feedback controller gain.  |
+| const Vector & | **[g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-g-design)**() const<br>Get input gain used in controller design.  |
+| const Vector & | **[u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ref)**() const<br>Get reference input.  |
+| const Vector & | **[x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-x-ref)**() const<br>Get reference state.  |
+| const Vector & | **[y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-y-ref)**() const<br>Get reference output.  |
+| size_t | **[control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control-type)**() const<br>Get controller type.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-tau-awu)**() const<br>Get time constant of anti-integral-windup.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-lb)**() const<br>Get control lower bound.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ub)**() const<br>Get control upper bound.  |
+| void | **[set_sys](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-sys)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) & sys)<br>Set system.  |
+| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-g-design)**(const Vector & g_design)<br>Set input gain used in controller design (g_design)  |
+| void | **[set_u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ref)**(const Vector & u_ref)<br>Set reference input (u_ref)  |
+| void | **[set_x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-x-ref)**(const Vector & x_ref)<br>Set reference state (x_ref)  |
+| virtual void | **[set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-y-ref)**(const Vector & y_ref)<br>Set reference output (y_ref)  |
+| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc)**(const Matrix & Kc)<br>Set state controller gain.  |
+| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-inty)**(const Matrix & Kc_inty)<br>Set integral controller gain.  |
+| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-u)**(const Matrix & Kc_u)<br>Set input controller gain.  |
+| void | **[set_tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-tau-awu)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) tau)<br>Set time constant of anti-integral-windup.  |
+| void | **[set_control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-control-type)**(size_t control_type)<br>Sets the control type.  |
+| void | **[set_u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-lb)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb)<br>sets control lower bound  |
+| void | **[set_u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ub)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub)<br>Sets control upper bound.  |
+| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-reset)**()<br>reset system and control variables.  |
+| void | **[Print](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-print)**()<br>prints variables to stdout  |
+
+## Protected Attributes
+
+|                | Name           |
+| -------------- | -------------- |
+| [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) | **[sys_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-sys-)** <br>underlying LDS  |
+| Vector | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-)** <br>control signal  |
+| Vector | **[u_return_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-return-)** <br>control signal that is _returned_ to user  |
+| Vector | **[g_design_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-g-design-)** <br>input gain of the system used for controller design  |
+| Vector | **[u_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-)** <br>reference input  |
+| Vector | **[u_ref_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-prev-)** <br>reference input at previous time step  |
+| Vector | **[x_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-x-ref-)** <br>reference state  |
+| Vector | **[y_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-y-ref-)** <br>reference output  |
+| Vector | **[cx_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-cx-ref-)**  |
+| Matrix | **[Kc_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-)** <br>state controller gain  |
+| Matrix | **[Kc_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-u-)** <br>input controller gain (optional when control updates \deltaU)  |
+| Matrix | **[Kc_inty_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-inty-)** <br>integral controller gain  |
+| Vector | **[du_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-du-ref-)**  |
+| Vector | **[dv_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-ref-)**  |
+| Vector | **[v_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-ref-)**  |
+| Vector | **[dv_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-)**  |
+| Vector | **[v_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-)** <br>Control after g inversion (e.g., control in physical units)  |
+| Vector | **[int_e_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-)** <br>integrated error  |
+| Vector | **[int_e_awu_adjust_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-awu-adjust-)** <br>anti-windup adjustment to intE  |
+| Vector | **[u_sat_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-sat-)** <br>control signal after saturation (for antiWindup)  |
+| bool | **[do_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-control-prev-)**  |
+| bool | **[do_lock_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-lock-control-prev-)**  |
+| bool | **[u_saturated_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-saturated-)** <br>whether control signal has reached saturation limits  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-lb-)** <br>lower bound on control  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ub-)** <br>upper bound on control  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-tau-awu-)** <br>antiwindup time constant  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[k_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-k-awu-)**  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[t_since_control_onset_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-t-since-control-onset-)** <br>time since control epoch onset  |
+| size_t | **[control_type_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-control-type-)** <br>controller type  |
+
+## Detailed Description
+
+```cpp
+template <typename System >
+class lds::Controller;
+```
+
+
+---
+---
+## Public Function Details
+
+### **Controller**
+
+```cpp
+Controller() =default
+```
+
+
+
+---
+### **Controller**
+
+```cpp
+inline Controller(
+    const System & sys,
+    data_t u_lb,
+    data_t u_ub,
+    size_t control_type =0
+)
+```
+
+
+
+**Parameters**:
+
+  * **sys** [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) (derived from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)) 
+  * **u_lb** lower bound on control (u) 
+  * **u_ub** upper bound on control (u) 
+  * **control_type** [optional] control type bit mask
+
+
+**Template Parameters**:
+
+  * **[System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)** type derived from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)
+
+
+---
+### **Controller**
+
+```cpp
+inline Controller(
+    System && sys,
+    data_t u_lb,
+    data_t u_ub,
+    size_t control_type =0
+)
+```
+
+
+
+**Parameters**:
+
+  * **sys** [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) (derived from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)) 
+  * **u_lb** lower bound on control (u) 
+  * **u_ub** upper bound on control (u) 
+  * **control_type** [optional] control type bit mask
+
+
+**Template Parameters**:
+
+  * **[System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)** type derived from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)
+
+
+---
+### **Control**
+
+```cpp
+inline const Vector & Control(
+    const Vector & z,
+    bool do_control =true,
+    bool do_lock_control =false,
+    data_t sigma_soft_start =0,
+    data_t sigma_u_noise =0,
+    bool do_reset_at_control_onset =true
+)
+```
+
+
+
+**Parameters**:
+
+  * **z** measurement 
+  * **do_control** [optional] whether to update control (true) or simply feed through u_ref (false) 
+  * **do_lock_control** [optional] whether to lock control at its current value 
+  * **sigma_soft_start** [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) 
+  * **sigma_u_noise** [optional] standard deviation (sigma) of Gaussian noise added on top of control signal 
+  * **do_reset_at_control_onset** [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true)
+
+
+**Return**: updated control signal 
+
+Updates the control signal (single-step). This is the most flexible option, but requires user to have set the controller's y_ref, x_ref, and u_ref variables.
+
+
+---
+### **ControlOutputReference**
+
+```cpp
+inline const Vector & ControlOutputReference(
+    const Vector & z,
+    bool do_control =true,
+    bool do_estimation =true,
+    bool do_lock_control =false,
+    data_t sigma_soft_start =0,
+    data_t sigma_u_noise =0,
+    bool do_reset_at_control_onset =true
+)
+```
+
+
+
+**Parameters**:
+
+  * **z** measurement 
+  * **do_control** [optional] whether to update control (true) or simply feed through u_ref (false) 
+  * **do_estimation** [optional] whether to update state estimate (if false, effectively open-loop control) 
+  * **do_lock_control** [optional] whether to lock control at its current value 
+  * **sigma_soft_start** [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) 
+  * **sigma_u_noise** [optional] standard deviation (sigma) of Gaussian noise added on top of control signal 
+  * **do_reset_at_control_onset** [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true)
+
+
+**Return**: updated control signal 
+
+Updates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.
+
+
+---
+### **sys**
+
+```cpp
+inline const System & sys() const
+```
+
+
+
+---
+### **Kc**
+
+```cpp
+inline const Matrix & Kc() const
+```
+
+
+
+---
+### **Kc_inty**
+
+```cpp
+inline const Matrix & Kc_inty() const
+```
+
+
+
+---
+### **Kc_u**
+
+```cpp
+inline const Matrix & Kc_u() const
+```
+
+
+
+---
+### **g_design**
+
+```cpp
+inline const Vector & g_design() const
+```
+
+
+
+---
+### **u_ref**
+
+```cpp
+inline const Vector & u_ref() const
+```
+
+
+
+---
+### **x_ref**
+
+```cpp
+inline const Vector & x_ref() const
+```
+
+
+
+---
+### **y_ref**
+
+```cpp
+inline const Vector & y_ref() const
+```
+
+
+
+---
+### **control_type**
+
+```cpp
+inline size_t control_type() const
+```
+
+
+
+---
+### **tau_awu**
+
+```cpp
+inline data_t tau_awu() const
+```
+
+
+
+---
+### **u_lb**
+
+```cpp
+inline data_t u_lb() const
+```
+
+
+
+---
+### **u_ub**
+
+```cpp
+inline data_t u_ub() const
+```
+
+
+
+---
+### **set_sys**
+
+```cpp
+inline void set_sys(
+    const System & sys
+)
+```
+
+
+
+---
+### **set_g_design**
+
+```cpp
+inline void set_g_design(
+    const Vector & g_design
+)
+```
+
+
+
+---
+### **set_u_ref**
+
+```cpp
+inline void set_u_ref(
+    const Vector & u_ref
+)
+```
+
+
+
+---
+### **set_x_ref**
+
+```cpp
+inline void set_x_ref(
+    const Vector & x_ref
+)
+```
+
+
+
+---
+### **set_y_ref**
+
+```cpp
+inline virtual void set_y_ref(
+    const Vector & y_ref
+)
+```
+
+
+
+**Reimplemented by**: [lds::gaussian::Controller::set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/#function-set-y-ref), [lds::gaussian::SwitchedController::set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/#function-set-y-ref), [lds::poisson::Controller::set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/#function-set-y-ref), [lds::poisson::SwitchedController::set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/#function-set-y-ref)
+
+
+---
+### **set_Kc**
+
+```cpp
+inline void set_Kc(
+    const Matrix & Kc
+)
+```
+
+
+
+---
+### **set_Kc_inty**
+
+```cpp
+inline void set_Kc_inty(
+    const Matrix & Kc_inty
+)
+```
+
+
+
+---
+### **set_Kc_u**
+
+```cpp
+inline void set_Kc_u(
+    const Matrix & Kc_u
+)
+```
+
+
+
+---
+### **set_tau_awu**
+
+```cpp
+inline void set_tau_awu(
+    data_t tau
+)
+```
+
+
+
+---
+### **set_control_type**
+
+```cpp
+inline void set_control_type(
+    size_t control_type
+)
+```
+
+
+
+**Parameters**:
+
+  * **control_type** control type bit mask
+
+
+**Template Parameters**:
+
+  * **[System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)** type derived from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)
+
+
+---
+### **set_u_lb**
+
+```cpp
+inline void set_u_lb(
+    data_t u_lb
+)
+```
+
+
+
+**Parameters**:
+
+  * **u_lb** control lower bound 
+
+
+---
+### **set_u_ub**
+
+```cpp
+inline void set_u_ub(
+    data_t u_ub
+)
+```
+
+
+
+**Parameters**:
+
+  * **u_ub** control upper bound 
+
+
+---
+### **Reset**
+
+```cpp
+inline void Reset()
+```
+
+
+
+---
+### **Print**
+
+```cpp
+inline void Print()
+```
+
+
+
+---
+
+
+## Protected Attribute Details
+
+### **sys_**
+
+```cpp
+System sys_;
+```
+
+
+
+---
+### **u_**
+
+```cpp
+Vector u_;
+```
+
+
+
+---
+### **u_return_**
+
+```cpp
+Vector u_return_;
+```
+
+
+
+---
+### **g_design_**
+
+```cpp
+Vector g_design_;
+```
+
+
+
+---
+### **u_ref_**
+
+```cpp
+Vector u_ref_;
+```
+
+
+
+---
+### **u_ref_prev_**
+
+```cpp
+Vector u_ref_prev_;
+```
+
+
+
+---
+### **x_ref_**
+
+```cpp
+Vector x_ref_;
+```
+
+
+
+---
+### **y_ref_**
+
+```cpp
+Vector y_ref_;
+```
+
+
+
+---
+### **cx_ref_**
+
+```cpp
+Vector cx_ref_;
+```
+
+
+
+---
+### **Kc_**
+
+```cpp
+Matrix Kc_;
+```
+
+
+
+---
+### **Kc_u_**
+
+```cpp
+Matrix Kc_u_;
+```
+
+
+
+---
+### **Kc_inty_**
+
+```cpp
+Matrix Kc_inty_;
+```
+
+
+
+---
+### **du_ref_**
+
+```cpp
+Vector du_ref_;
+```
+
+
+
+---
+### **dv_ref_**
+
+```cpp
+Vector dv_ref_;
+```
+
+
+
+---
+### **v_ref_**
+
+```cpp
+Vector v_ref_;
+```
+
+
+
+---
+### **dv_**
+
+```cpp
+Vector dv_;
+```
+
+
+
+---
+### **v_**
+
+```cpp
+Vector v_;
+```
+
+
+
+---
+### **int_e_**
+
+```cpp
+Vector int_e_;
+```
+
+
+
+---
+### **int_e_awu_adjust_**
+
+```cpp
+Vector int_e_awu_adjust_;
+```
+
+
+
+---
+### **u_sat_**
+
+```cpp
+Vector u_sat_;
+```
+
+
+
+---
+### **do_control_prev_**
+
+```cpp
+bool do_control_prev_ = false;
+```
+
+
+
+---
+### **do_lock_control_prev_**
+
+```cpp
+bool do_lock_control_prev_ = false;
+```
+
+
+
+---
+### **u_saturated_**
+
+```cpp
+bool u_saturated_ =
+      false;
+```
+
+
+
+---
+### **u_lb_**
+
+```cpp
+data_t u_lb_ {};
+```
+
+
+
+---
+### **u_ub_**
+
+```cpp
+data_t u_ub_ {};
+```
+
+
+
+---
+### **tau_awu_**
+
+```cpp
+data_t tau_awu_ {};
+```
+
+
+
+---
+### **k_awu_**
+
+```cpp
+data_t k_awu_ = 0;
+```
+
+
+
+---
+### **t_since_control_onset_**
+
+```cpp
+data_t t_since_control_onset_ = 0;
+```
+
+
+
+---
+### **control_type_**
+
+```cpp
+size_t control_type_ {};
+```
+
+
+
+---
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1EM.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_e_m.md
similarity index 52%
rename from misc/docs-hugo/content/docs/api/Classes/classlds_1_1EM.md
rename to misc/docs-hugo/content/docs/api/Classes/classlds_1_1_e_m.md
index c48ea2a0..9e4b61d6 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1EM.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_e_m.md
@@ -1,593 +1,593 @@
----
-title: lds::EM
-
----
-
-# lds::EM
-
-
-
- [More...](#detailed-description)
-
-Inherited by [lds::gaussian::FitEM](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitem/), [lds::poisson::FitEM](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitem/)
-
-## Public Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-em)**() =default<br>Constructs a new [EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)[Fit]() type.  |
-| | **[EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-em)**(size_t n_x, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && u_train, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && z_train)<br>Constructs a new [EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)[Fit]() type.  |
-| | **[EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-em)**(const [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/) & fit0, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && u_train, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && z_train)<br>Constructs a new [EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)[Fit]() type.  |
-| virtual | **[~EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-~em)**() =default |
-| const [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/) & | **[Run](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-run)**(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) tol =1e-2)<br>Runs fitting by Expectation(E)-Maximization(M)  |
-| std::tuple< [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) >, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > > | **[ReturnData](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-returndata)**()<br>Returns the input/output data to caller.  |
-| const std::vector< Matrix > & | **[x](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-x)**() const<br>gets estimated state (over time)  |
-| const std::vector< Matrix > & | **[y](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-y)**() const<br>gets estimated output (over time)  |
-| const Matrix & | **[sum_E_x_t_x_t](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-sum-e-x-t-x-t)**() const<br>gets state-input covariance  |
-| const Matrix & | **[sum_E_xu_tm1_xu_tm1](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-sum-e-xu-tm1-xu-tm1)**() const<br>gets state-input covariance (t-minus-1)  |
-| const Matrix & | **[sum_E_xu_t_xu_tm1](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-sum-e-xu-t-xu-tm1)**() const<br>gets single lag state-input covariance  |
-| size_t | **[n_t_tot](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-n-t-tot)**()<br>total number of time samples  |
-| const Vector & | **[theta](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-theta)**() const<br>gets parameters updated in M step  |
-
-## Protected Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| void | **[Expectation](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-expectation)**(bool force_common_initial =false)<br>Expectation step.  |
-| void | **[Maximization](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-maximization)**(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)<br>Maximization step.  |
-| void | **[MaximizeDynamics](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-maximizedynamics)**() |
-| void | **[MaximizeQ](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-maximizeq)**() |
-| void | **[MaximizeInitial](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-maximizeinitial)**() |
-| virtual void | **[MaximizeOutput](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-maximizeoutput)**() =0 |
-| virtual void | **[MaximizeMeasurement](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-maximizemeasurement)**() =0 |
-| void | **[Smooth](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-smooth)**(bool force_common_initial)<br>get smoothed estimates  |
-| virtual void | **[RecurseKe](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-recurseke)**(Matrix & Ke, Cube & P_pre, Cube & P_post, size_t t) =0<br>recursively update estimator gain Ke  |
-| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-reset)**()<br>reset to initial conditions  |
-| void | **[InitVars](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-initvars)**()<br>Initializes the variables.  |
-| Vector | **[UpdateTheta](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-updatetheta)**()<br>updates parameter list, theta  |
-
-## Protected Attributes
-
-|                | Name           |
-| -------------- | -------------- |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-u-)** <br>input training data  |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[z_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-z-)** <br>measurement training data  |
-| std::vector< Matrix > | **[x_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-x-)** <br>state estimate  |
-| std::vector< Cube > | **[P_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-p-)** <br>state estimate cov  |
-| std::vector< Cube > | **[P_t_tm1_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-p-t-tm1-)** <br>single-lag state covariance  |
-| std::vector< Matrix > | **[y_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-y-)** <br>output estimate  |
-| Matrix | **[diag_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-diag-y-)**  |
-| Matrix | **[sum_E_x_t_x_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-sum-e-x-t-x-t-)** <br>state covariance (current time)  |
-| Matrix | **[sum_E_xu_tm1_xu_tm1_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-sum-e-xu-tm1-xu-tm1-)** <br>state-input covariance (t-minus-1)  |
-| Matrix | **[sum_E_xu_t_xu_tm1_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-sum-e-xu-t-xu-tm1-)** <br>single lag state-input covariance  |
-| [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/) | **[fit_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-fit-)**  |
-| Vector | **[theta_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-theta-)**  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-dt-)** <br>sample period  |
-| size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-n-u-)** <br>number of inputs  |
-| size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-n-x-)** <br>number of states  |
-| size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-n-y-)** <br>number of outputs  |
-| size_t | **[n_trials_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-n-trials-)** <br>number of input/output data sequences  |
-| std::vector< size_t > | **[n_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-n-t-)** <br>number of time steps  |
-| size_t | **[n_t_tot_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-n-t-tot-)** <br>total number of time steps across trials  |
-
-## Detailed Description
-
-```cpp
-template <typename Fit >
-class lds::EM;
-```
-
-
----
----
-## Public Function Details
-
-### **EM**
-
-```cpp
-EM() =default
-```
-
-
-
----
-### **EM**
-
-```cpp
-EM(
-    size_t n_x,
-    data_t dt,
-    UniformMatrixList< kMatFreeDim2 > && u_train,
-    UniformMatrixList< kMatFreeDim2 > && z_train
-)
-```
-
-
-
-**Parameters**:
-
-  * **n_x** number of states 
-  * **dt** sample period 
-  * **u_train** input training data 
-  * **z_train** measurement training data 
-
-
----
-### **EM**
-
-```cpp
-EM(
-    const Fit & fit0,
-    UniformMatrixList< kMatFreeDim2 > && u_train,
-    UniformMatrixList< kMatFreeDim2 > && z_train
-)
-```
-
-
-
-**Parameters**:
-
-  * **fit0** initial fit 
-  * **u_train** input training data 
-  * **z_train** measurement training data 
-
-
----
-### **~EM**
-
-```cpp
-virtual ~EM() =default
-```
-
-
-
----
-### **Run**
-
-```cpp
-const Fit & Run(
-    bool calc_dynamics =true,
-    bool calc_Q =true,
-    bool calc_init =true,
-    bool calc_output =true,
-    bool calc_measurement =true,
-    size_t max_iter =100,
-    data_t tol =1e-2
-)
-```
-
-
-
-**Parameters**:
-
-  * **calc_dynamics** [optional] whether to calculate dynamics (A, B) 
-  * **calc_Q** [optional] whether to calculate process noise covariance 
-  * **calc_init** [optional] whether to calculate initial conditions 
-  * **calc_output** [optional] whether to calculate output function 
-  * **calc_measurement** [optional] whether to calculate parameters for measurement/observation law 
-  * **max_iter** max number of iterations 
-  * **tol** convergence tolerance (max fractional abs change)
-
-
-**Return**: [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/)
-
----
-### **ReturnData**
-
-```cpp
-inline std::tuple< UniformMatrixList< kMatFreeDim2 >, UniformMatrixList< kMatFreeDim2 > > ReturnData()
-```
-
-
-
-**Return**: tuple(input data, output data) 
-
----
-### **x**
-
-```cpp
-inline const std::vector< Matrix > & x() const
-```
-
-
-
----
-### **y**
-
-```cpp
-inline const std::vector< Matrix > & y() const
-```
-
-
-
----
-### **sum_E_x_t_x_t**
-
-```cpp
-inline const Matrix & sum_E_x_t_x_t() const
-```
-
-
-
----
-### **sum_E_xu_tm1_xu_tm1**
-
-```cpp
-inline const Matrix & sum_E_xu_tm1_xu_tm1() const
-```
-
-
-
----
-### **sum_E_xu_t_xu_tm1**
-
-```cpp
-inline const Matrix & sum_E_xu_t_xu_tm1() const
-```
-
-
-
----
-### **n_t_tot**
-
-```cpp
-inline size_t n_t_tot()
-```
-
-
-
----
-### **theta**
-
-```cpp
-inline const Vector & theta() const
-```
-
-
-
----
-
-
-## Protected Function Details
-
-### **Expectation**
-
-```cpp
-void Expectation(
-    bool force_common_initial =false
-)
-```
-
-
-
-**Parameters**:
-
-  * **force_common_initial** whether to force common initial condition for all trials 
-
-
----
-### **Maximization**
-
-```cpp
-void Maximization(
-    bool calc_dynamics =true,
-    bool calc_Q =true,
-    bool calc_init =false,
-    bool calc_output =false,
-    bool calc_measurement =false
-)
-```
-
-
-
-**Parameters**:
-
-  * **calc_dynamics** [optional] whether to caclulate dynamics (A, B) 
-  * **calc_Q** [optional] whether to calculate process noise covariance 
-  * **calc_init** [optional] whether to calculate initial conditions 
-  * **calc_output** [optional] whether to calculate output function 
-  * **calc_measurement** [optional] whether to calculate parameters for measurement/observation law 
-
-
----
-### **MaximizeDynamics**
-
-```cpp
-void MaximizeDynamics()
-```
-
-
-
----
-### **MaximizeQ**
-
-```cpp
-void MaximizeQ()
-```
-
-
-
----
-### **MaximizeInitial**
-
-```cpp
-void MaximizeInitial()
-```
-
-
-
----
-### **MaximizeOutput**
-
-```cpp
-virtual void MaximizeOutput() =0
-```
-
-
-
-**Reimplemented by**: [lds::gaussian::FitEM::MaximizeOutput](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitem/#function-maximizeoutput), [lds::poisson::FitEM::MaximizeOutput](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitem/#function-maximizeoutput)
-
-
----
-### **MaximizeMeasurement**
-
-```cpp
-virtual void MaximizeMeasurement() =0
-```
-
-
-
-**Reimplemented by**: [lds::gaussian::FitEM::MaximizeMeasurement](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitem/#function-maximizemeasurement), [lds::poisson::FitEM::MaximizeMeasurement](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitem/#function-maximizemeasurement)
-
-
----
-### **Smooth**
-
-```cpp
-void Smooth(
-    bool force_common_initial
-)
-```
-
-
-
-**Parameters**:
-
-  * **force_common_initial** whether to force common initial conditions 
-
-
----
-### **RecurseKe**
-
-```cpp
-virtual void RecurseKe(
-    Matrix & Ke,
-    Cube & P_pre,
-    Cube & P_post,
-    size_t t
-) =0
-```
-
-
-
-**Parameters**:
-
-  * **Ke** estimator gain 
-  * **P_pre** cov of predicted state est. 
-  * **P_post** cov of postior sate est. 
-  * **t** time 
-
-
-**Reimplemented by**: [lds::gaussian::FitEM::RecurseKe](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitem/#function-recurseke), [lds::poisson::FitEM::RecurseKe](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitem/#function-recurseke)
-
-
----
-### **Reset**
-
-```cpp
-void Reset()
-```
-
-
-
----
-### **InitVars**
-
-```cpp
-void InitVars()
-```
-
-
-
----
-### **UpdateTheta**
-
-```cpp
-Vector UpdateTheta()
-```
-
-
-
-**Return**: parameter list 
-
----
-
-
-## Protected Attribute Details
-
-### **u_**
-
-```cpp
-UniformMatrixList< kMatFreeDim2 > u_;
-```
-
-
-
----
-### **z_**
-
-```cpp
-UniformMatrixList< kMatFreeDim2 > z_;
-```
-
-
-
----
-### **x_**
-
-```cpp
-std::vector< Matrix > x_;
-```
-
-
-
----
-### **P_**
-
-```cpp
-std::vector< Cube > P_;
-```
-
-
-
----
-### **P_t_tm1_**
-
-```cpp
-std::vector< Cube > P_t_tm1_;
-```
-
-
-
----
-### **y_**
-
-```cpp
-std::vector< Matrix > y_;
-```
-
-
-
----
-### **diag_y_**
-
-```cpp
-Matrix diag_y_;
-```
-
-
-
----
-### **sum_E_x_t_x_t_**
-
-```cpp
-Matrix sum_E_x_t_x_t_;
-```
-
-
-
----
-### **sum_E_xu_tm1_xu_tm1_**
-
-```cpp
-Matrix sum_E_xu_tm1_xu_tm1_;
-```
-
-
-
----
-### **sum_E_xu_t_xu_tm1_**
-
-```cpp
-Matrix sum_E_xu_t_xu_tm1_;
-```
-
-
-
----
-### **fit_**
-
-```cpp
-Fit fit_;
-```
-
-
-
----
-### **theta_**
-
-```cpp
-Vector theta_;
-```
-
-
-
----
-### **dt_**
-
-```cpp
-data_t dt_ {};
-```
-
-
-
----
-### **n_u_**
-
-```cpp
-size_t n_u_ {};
-```
-
-
-
----
-### **n_x_**
-
-```cpp
-size_t n_x_ {};
-```
-
-
-
----
-### **n_y_**
-
-```cpp
-size_t n_y_ {};
-```
-
-
-
----
-### **n_trials_**
-
-```cpp
-size_t n_trials_ {};
-```
-
-
-
----
-### **n_t_**
-
-```cpp
-std::vector< size_t > n_t_;
-```
-
-
-
----
-### **n_t_tot_**
-
-```cpp
-size_t n_t_tot_ {};
-```
-
-
-
----
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:03 Eastern Daylight Time
+---
+title: lds::EM
+
+---
+
+# lds::EM
+
+
+
+ [More...](#detailed-description)
+
+Inherited by [lds::gaussian::FitEM](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/), [lds::poisson::FitEM](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/)
+
+## Public Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em)**() =default<br>Constructs a new [EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/)[Fit]() type.  |
+| | **[EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em)**(size_t n_x, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && u_train, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && z_train)<br>Constructs a new [EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/)[Fit]() type.  |
+| | **[EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em)**(const [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/) & fit0, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && u_train, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && z_train)<br>Constructs a new [EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/)[Fit]() type.  |
+| virtual | **[~EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-~em)**() =default |
+| const [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/) & | **[Run](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-run)**(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) tol =1e-2)<br>Runs fitting by Expectation(E)-Maximization(M)  |
+| std::tuple< [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) >, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > > | **[ReturnData](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-returndata)**()<br>Returns the input/output data to caller.  |
+| const std::vector< Matrix > & | **[x](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-x)**() const<br>gets estimated state (over time)  |
+| const std::vector< Matrix > & | **[y](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-y)**() const<br>gets estimated output (over time)  |
+| const Matrix & | **[sum_E_x_t_x_t](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-x-t-x-t)**() const<br>gets state-input covariance  |
+| const Matrix & | **[sum_E_xu_tm1_xu_tm1](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-xu-tm1-xu-tm1)**() const<br>gets state-input covariance (t-minus-1)  |
+| const Matrix & | **[sum_E_xu_t_xu_tm1](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-xu-t-xu-tm1)**() const<br>gets single lag state-input covariance  |
+| size_t | **[n_t_tot](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-n-t-tot)**()<br>total number of time samples  |
+| const Vector & | **[theta](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-theta)**() const<br>gets parameters updated in M step  |
+
+## Protected Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| void | **[Expectation](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-expectation)**(bool force_common_initial =false)<br>Expectation step.  |
+| void | **[Maximization](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximization)**(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)<br>Maximization step.  |
+| void | **[MaximizeDynamics](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizedynamics)**() |
+| void | **[MaximizeQ](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizeq)**() |
+| void | **[MaximizeInitial](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizeinitial)**() |
+| virtual void | **[MaximizeOutput](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizeoutput)**() =0 |
+| virtual void | **[MaximizeMeasurement](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizemeasurement)**() =0 |
+| void | **[Smooth](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-smooth)**(bool force_common_initial)<br>get smoothed estimates  |
+| virtual void | **[RecurseKe](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-recurseke)**(Matrix & Ke, Cube & P_pre, Cube & P_post, size_t t) =0<br>recursively update estimator gain Ke  |
+| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-reset)**()<br>reset to initial conditions  |
+| void | **[InitVars](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-initvars)**()<br>Initializes the variables.  |
+| Vector | **[UpdateTheta](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-updatetheta)**()<br>updates parameter list, theta  |
+
+## Protected Attributes
+
+|                | Name           |
+| -------------- | -------------- |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-u-)** <br>input training data  |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[z_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-z-)** <br>measurement training data  |
+| std::vector< Matrix > | **[x_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-x-)** <br>state estimate  |
+| std::vector< Cube > | **[P_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-p-)** <br>state estimate cov  |
+| std::vector< Cube > | **[P_t_tm1_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-p-t-tm1-)** <br>single-lag state covariance  |
+| std::vector< Matrix > | **[y_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-y-)** <br>output estimate  |
+| Matrix | **[diag_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-diag-y-)**  |
+| Matrix | **[sum_E_x_t_x_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-x-t-x-t-)** <br>state covariance (current time)  |
+| Matrix | **[sum_E_xu_tm1_xu_tm1_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-xu-tm1-xu-tm1-)** <br>state-input covariance (t-minus-1)  |
+| Matrix | **[sum_E_xu_t_xu_tm1_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-xu-t-xu-tm1-)** <br>single lag state-input covariance  |
+| [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/) | **[fit_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-fit-)**  |
+| Vector | **[theta_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-theta-)**  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-dt-)** <br>sample period  |
+| size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-u-)** <br>number of inputs  |
+| size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-x-)** <br>number of states  |
+| size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-y-)** <br>number of outputs  |
+| size_t | **[n_trials_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-trials-)** <br>number of input/output data sequences  |
+| std::vector< size_t > | **[n_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-t-)** <br>number of time steps  |
+| size_t | **[n_t_tot_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-t-tot-)** <br>total number of time steps across trials  |
+
+## Detailed Description
+
+```cpp
+template <typename Fit >
+class lds::EM;
+```
+
+
+---
+---
+## Public Function Details
+
+### **EM**
+
+```cpp
+EM() =default
+```
+
+
+
+---
+### **EM**
+
+```cpp
+EM(
+    size_t n_x,
+    data_t dt,
+    UniformMatrixList< kMatFreeDim2 > && u_train,
+    UniformMatrixList< kMatFreeDim2 > && z_train
+)
+```
+
+
+
+**Parameters**:
+
+  * **n_x** number of states 
+  * **dt** sample period 
+  * **u_train** input training data 
+  * **z_train** measurement training data 
+
+
+---
+### **EM**
+
+```cpp
+EM(
+    const Fit & fit0,
+    UniformMatrixList< kMatFreeDim2 > && u_train,
+    UniformMatrixList< kMatFreeDim2 > && z_train
+)
+```
+
+
+
+**Parameters**:
+
+  * **fit0** initial fit 
+  * **u_train** input training data 
+  * **z_train** measurement training data 
+
+
+---
+### **~EM**
+
+```cpp
+virtual ~EM() =default
+```
+
+
+
+---
+### **Run**
+
+```cpp
+const Fit & Run(
+    bool calc_dynamics =true,
+    bool calc_Q =true,
+    bool calc_init =true,
+    bool calc_output =true,
+    bool calc_measurement =true,
+    size_t max_iter =100,
+    data_t tol =1e-2
+)
+```
+
+
+
+**Parameters**:
+
+  * **calc_dynamics** [optional] whether to calculate dynamics (A, B) 
+  * **calc_Q** [optional] whether to calculate process noise covariance 
+  * **calc_init** [optional] whether to calculate initial conditions 
+  * **calc_output** [optional] whether to calculate output function 
+  * **calc_measurement** [optional] whether to calculate parameters for measurement/observation law 
+  * **max_iter** max number of iterations 
+  * **tol** convergence tolerance (max fractional abs change)
+
+
+**Return**: [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/)
+
+---
+### **ReturnData**
+
+```cpp
+inline std::tuple< UniformMatrixList< kMatFreeDim2 >, UniformMatrixList< kMatFreeDim2 > > ReturnData()
+```
+
+
+
+**Return**: tuple(input data, output data) 
+
+---
+### **x**
+
+```cpp
+inline const std::vector< Matrix > & x() const
+```
+
+
+
+---
+### **y**
+
+```cpp
+inline const std::vector< Matrix > & y() const
+```
+
+
+
+---
+### **sum_E_x_t_x_t**
+
+```cpp
+inline const Matrix & sum_E_x_t_x_t() const
+```
+
+
+
+---
+### **sum_E_xu_tm1_xu_tm1**
+
+```cpp
+inline const Matrix & sum_E_xu_tm1_xu_tm1() const
+```
+
+
+
+---
+### **sum_E_xu_t_xu_tm1**
+
+```cpp
+inline const Matrix & sum_E_xu_t_xu_tm1() const
+```
+
+
+
+---
+### **n_t_tot**
+
+```cpp
+inline size_t n_t_tot()
+```
+
+
+
+---
+### **theta**
+
+```cpp
+inline const Vector & theta() const
+```
+
+
+
+---
+
+
+## Protected Function Details
+
+### **Expectation**
+
+```cpp
+void Expectation(
+    bool force_common_initial =false
+)
+```
+
+
+
+**Parameters**:
+
+  * **force_common_initial** whether to force common initial condition for all trials 
+
+
+---
+### **Maximization**
+
+```cpp
+void Maximization(
+    bool calc_dynamics =true,
+    bool calc_Q =true,
+    bool calc_init =false,
+    bool calc_output =false,
+    bool calc_measurement =false
+)
+```
+
+
+
+**Parameters**:
+
+  * **calc_dynamics** [optional] whether to caclulate dynamics (A, B) 
+  * **calc_Q** [optional] whether to calculate process noise covariance 
+  * **calc_init** [optional] whether to calculate initial conditions 
+  * **calc_output** [optional] whether to calculate output function 
+  * **calc_measurement** [optional] whether to calculate parameters for measurement/observation law 
+
+
+---
+### **MaximizeDynamics**
+
+```cpp
+void MaximizeDynamics()
+```
+
+
+
+---
+### **MaximizeQ**
+
+```cpp
+void MaximizeQ()
+```
+
+
+
+---
+### **MaximizeInitial**
+
+```cpp
+void MaximizeInitial()
+```
+
+
+
+---
+### **MaximizeOutput**
+
+```cpp
+virtual void MaximizeOutput() =0
+```
+
+
+
+**Reimplemented by**: [lds::gaussian::FitEM::MaximizeOutput](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/#function-maximizeoutput), [lds::poisson::FitEM::MaximizeOutput](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/#function-maximizeoutput)
+
+
+---
+### **MaximizeMeasurement**
+
+```cpp
+virtual void MaximizeMeasurement() =0
+```
+
+
+
+**Reimplemented by**: [lds::gaussian::FitEM::MaximizeMeasurement](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/#function-maximizemeasurement), [lds::poisson::FitEM::MaximizeMeasurement](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/#function-maximizemeasurement)
+
+
+---
+### **Smooth**
+
+```cpp
+void Smooth(
+    bool force_common_initial
+)
+```
+
+
+
+**Parameters**:
+
+  * **force_common_initial** whether to force common initial conditions 
+
+
+---
+### **RecurseKe**
+
+```cpp
+virtual void RecurseKe(
+    Matrix & Ke,
+    Cube & P_pre,
+    Cube & P_post,
+    size_t t
+) =0
+```
+
+
+
+**Parameters**:
+
+  * **Ke** estimator gain 
+  * **P_pre** cov of predicted state est. 
+  * **P_post** cov of postior sate est. 
+  * **t** time 
+
+
+**Reimplemented by**: [lds::gaussian::FitEM::RecurseKe](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/#function-recurseke), [lds::poisson::FitEM::RecurseKe](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/#function-recurseke)
+
+
+---
+### **Reset**
+
+```cpp
+void Reset()
+```
+
+
+
+---
+### **InitVars**
+
+```cpp
+void InitVars()
+```
+
+
+
+---
+### **UpdateTheta**
+
+```cpp
+Vector UpdateTheta()
+```
+
+
+
+**Return**: parameter list 
+
+---
+
+
+## Protected Attribute Details
+
+### **u_**
+
+```cpp
+UniformMatrixList< kMatFreeDim2 > u_;
+```
+
+
+
+---
+### **z_**
+
+```cpp
+UniformMatrixList< kMatFreeDim2 > z_;
+```
+
+
+
+---
+### **x_**
+
+```cpp
+std::vector< Matrix > x_;
+```
+
+
+
+---
+### **P_**
+
+```cpp
+std::vector< Cube > P_;
+```
+
+
+
+---
+### **P_t_tm1_**
+
+```cpp
+std::vector< Cube > P_t_tm1_;
+```
+
+
+
+---
+### **y_**
+
+```cpp
+std::vector< Matrix > y_;
+```
+
+
+
+---
+### **diag_y_**
+
+```cpp
+Matrix diag_y_;
+```
+
+
+
+---
+### **sum_E_x_t_x_t_**
+
+```cpp
+Matrix sum_E_x_t_x_t_;
+```
+
+
+
+---
+### **sum_E_xu_tm1_xu_tm1_**
+
+```cpp
+Matrix sum_E_xu_tm1_xu_tm1_;
+```
+
+
+
+---
+### **sum_E_xu_t_xu_tm1_**
+
+```cpp
+Matrix sum_E_xu_t_xu_tm1_;
+```
+
+
+
+---
+### **fit_**
+
+```cpp
+Fit fit_;
+```
+
+
+
+---
+### **theta_**
+
+```cpp
+Vector theta_;
+```
+
+
+
+---
+### **dt_**
+
+```cpp
+data_t dt_ {};
+```
+
+
+
+---
+### **n_u_**
+
+```cpp
+size_t n_u_ {};
+```
+
+
+
+---
+### **n_x_**
+
+```cpp
+size_t n_x_ {};
+```
+
+
+
+---
+### **n_y_**
+
+```cpp
+size_t n_y_ {};
+```
+
+
+
+---
+### **n_trials_**
+
+```cpp
+size_t n_trials_ {};
+```
+
+
+
+---
+### **n_t_**
+
+```cpp
+std::vector< size_t > n_t_;
+```
+
+
+
+---
+### **n_t_tot_**
+
+```cpp
+size_t n_t_tot_ {};
+```
+
+
+
+---
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_fit.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_fit.md
new file mode 100644
index 00000000..47139854
--- /dev/null
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_fit.md
@@ -0,0 +1,561 @@
+---
+title: lds::Fit
+summary: LDS Fit Type. 
+
+---
+
+# lds::Fit
+
+
+
+LDS [Fit]() Type. 
+<br /> `#include <lds_fit.h>`
+
+Inherited by [lds::gaussian::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/), [lds::poisson::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/)
+
+## Public Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-fit)**() =default<br>Constructs a new [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/).  |
+| | **[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-fit)**(size_t n_u, size_t n_x, size_t n_y, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt)<br>Constructs a new [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/).  |
+| virtual | **[~Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-~fit)**() =default |
+| size_t | **[n_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-n-u)**() const<br>gets number of inputs  |
+| size_t | **[n_x](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-n-x)**() const<br>gets number of states  |
+| size_t | **[n_y](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-n-y)**() const<br>gets number of outputs  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-dt)**() const<br>gets sample period  |
+| const Matrix & | **[A](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-a)**() const<br>gets state matrix  |
+| const Matrix & | **[B](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-b)**() const<br>gets input matrix  |
+| const Vector & | **[g](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-g)**() const<br>gets input gain  |
+| const Vector & | **[m](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-m)**() const<br>gets process disturbance  |
+| const Matrix & | **[Q](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-q)**() const<br>gets process noise covariance  |
+| const Vector & | **[x0](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-x0)**() const<br>gets initial state estimate  |
+| const Matrix & | **[P0](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-p0)**() const<br>gets covariance of initial state estimate  |
+| const Matrix & | **[C](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-c)**() const<br>gets output matrix  |
+| const Vector & | **[d](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-d)**() const<br>gets output bias  |
+| virtual const Matrix & | **[R](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-r)**() const =0 |
+| void | **[set_A](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-a)**(const Matrix & A)<br>sets state matrix  |
+| void | **[set_B](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-b)**(const Matrix & B)<br>sets input matrix  |
+| void | **[set_g](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-g)**(const Vector & g)<br>sets input gain/conversion factor  |
+| void | **[set_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-m)**(const Vector & m)<br>sets process disturbance  |
+| void | **[set_Q](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-q)**(const Matrix & Q)<br>sets process noise covariance  |
+| virtual void | **[set_R](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-r)**(const Matrix & R) =0<br>sets output noise covariance (if any)  |
+| void | **[set_x0](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-x0)**(const Vector & x0)<br>sets initial state estimate  |
+| void | **[set_P0](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-p0)**(const Matrix & P0)<br>sets initial state estimate covariance  |
+| void | **[set_C](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-c)**(const Matrix & C)<br>sets output matrix  |
+| void | **[set_d](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-d)**(const Vector & d)<br>sets output bias  |
+| View | **[f](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-f)**(Matrix & x, const Matrix & u, size_t t)<br>system dynamics function  |
+| View | **[f](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-f)**(Matrix & x_pre, const Matrix & x_post, const Matrix & u, size_t t)<br>system dynamics function  |
+| virtual View | **[h](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-h)**(Matrix & y, const Matrix & x, size_t t) =0<br>output function  |
+
+## Protected Attributes
+
+|                | Name           |
+| -------------- | -------------- |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-dt-)** <br>sample period  |
+| Matrix | **[A_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-a-)** <br>state matrix  |
+| Matrix | **[B_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-b-)** <br>input matrix  |
+| Vector | **[g_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-g-)** <br>input gain  |
+| Vector | **[m_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-m-)** <br>process noise mean  |
+| Matrix | **[Q_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-q-)** <br>process noise cov  |
+| Matrix | **[C_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-c-)** <br>output matrix  |
+| Vector | **[d_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-d-)** <br>output bias  |
+| Matrix | **[R_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-r-)** <br>measurement noise  |
+| Vector | **[x0_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-x0-)** <br>initial state  |
+| Matrix | **[P0_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-p0-)** <br>initial covar  |
+| size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-n-u-)** <br>number of inputs  |
+| size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-n-x-)** <br>number of states  |
+| size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-n-y-)** <br>number of outputs  |
+
+---
+---
+## Public Function Details
+
+### **Fit**
+
+```cpp
+Fit() =default
+```
+
+
+
+---
+### **Fit**
+
+```cpp
+Fit(
+    size_t n_u,
+    size_t n_x,
+    size_t n_y,
+    data_t dt
+)
+```
+
+
+
+**Parameters**:
+
+  * **n_u** number of inputs 
+  * **n_x** number of states 
+  * **n_y** number of outputs 
+  * **dt** sample period 
+
+
+---
+### **~Fit**
+
+```cpp
+virtual ~Fit() =default
+```
+
+
+
+---
+### **n_u**
+
+```cpp
+inline size_t n_u() const
+```
+
+
+
+---
+### **n_x**
+
+```cpp
+inline size_t n_x() const
+```
+
+
+
+---
+### **n_y**
+
+```cpp
+inline size_t n_y() const
+```
+
+
+
+---
+### **dt**
+
+```cpp
+inline data_t dt() const
+```
+
+
+
+---
+### **A**
+
+```cpp
+inline const Matrix & A() const
+```
+
+
+
+---
+### **B**
+
+```cpp
+inline const Matrix & B() const
+```
+
+
+
+---
+### **g**
+
+```cpp
+inline const Vector & g() const
+```
+
+
+
+---
+### **m**
+
+```cpp
+inline const Vector & m() const
+```
+
+
+
+---
+### **Q**
+
+```cpp
+inline const Matrix & Q() const
+```
+
+
+
+---
+### **x0**
+
+```cpp
+inline const Vector & x0() const
+```
+
+
+
+---
+### **P0**
+
+```cpp
+inline const Matrix & P0() const
+```
+
+
+
+---
+### **C**
+
+```cpp
+inline const Matrix & C() const
+```
+
+
+
+---
+### **d**
+
+```cpp
+inline const Vector & d() const
+```
+
+
+
+---
+### **R**
+
+```cpp
+virtual const Matrix & R() const =0
+```
+
+
+
+**Reimplemented by**: [lds::gaussian::Fit::R](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/#function-r), [lds::poisson::Fit::R](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/#function-r)
+
+
+---
+### **set_A**
+
+```cpp
+inline void set_A(
+    const Matrix & A
+)
+```
+
+
+
+---
+### **set_B**
+
+```cpp
+inline void set_B(
+    const Matrix & B
+)
+```
+
+
+
+---
+### **set_g**
+
+```cpp
+inline void set_g(
+    const Vector & g
+)
+```
+
+
+
+---
+### **set_m**
+
+```cpp
+inline void set_m(
+    const Vector & m
+)
+```
+
+
+
+---
+### **set_Q**
+
+```cpp
+inline void set_Q(
+    const Matrix & Q
+)
+```
+
+
+
+---
+### **set_R**
+
+```cpp
+virtual void set_R(
+    const Matrix & R
+) =0
+```
+
+
+
+**Reimplemented by**: [lds::gaussian::Fit::set_R](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/#function-set-r), [lds::poisson::Fit::set_R](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/#function-set-r)
+
+
+---
+### **set_x0**
+
+```cpp
+inline void set_x0(
+    const Vector & x0
+)
+```
+
+
+
+---
+### **set_P0**
+
+```cpp
+inline void set_P0(
+    const Matrix & P0
+)
+```
+
+
+
+---
+### **set_C**
+
+```cpp
+inline void set_C(
+    const Matrix & C
+)
+```
+
+
+
+---
+### **set_d**
+
+```cpp
+inline void set_d(
+    const Vector & d
+)
+```
+
+
+
+---
+### **f**
+
+```cpp
+inline View f(
+    Matrix & x,
+    const Matrix & u,
+    size_t t
+)
+```
+
+
+
+**Parameters**:
+
+  * **x** state estimate (over time) 
+  * **u** input (over time) 
+  * **t** time index
+
+
+**Return**: view of updated state 
+
+---
+### **f**
+
+```cpp
+inline View f(
+    Matrix & x_pre,
+    const Matrix & x_post,
+    const Matrix & u,
+    size_t t
+)
+```
+
+
+
+**Parameters**:
+
+  * **x_pre** predicted state est. 
+  * **x_post** posterior state est. 
+  * **u** input (over time) 
+  * **t** time index
+
+
+**Return**: view of predicted state 
+
+---
+### **h**
+
+```cpp
+virtual View h(
+    Matrix & y,
+    const Matrix & x,
+    size_t t
+) =0
+```
+
+
+
+**Parameters**:
+
+  * **y** output estimate (over time) 
+  * **x** state estimate (over time) 
+  * **t** time index
+
+
+**Return**: output 
+
+**Reimplemented by**: [lds::gaussian::Fit::h](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/#function-h), [lds::poisson::Fit::h](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/#function-h)
+
+
+---
+
+
+## Protected Attribute Details
+
+### **dt_**
+
+```cpp
+data_t dt_ {};
+```
+
+
+
+---
+### **A_**
+
+```cpp
+Matrix A_;
+```
+
+
+
+---
+### **B_**
+
+```cpp
+Matrix B_;
+```
+
+
+
+---
+### **g_**
+
+```cpp
+Vector g_;
+```
+
+
+
+---
+### **m_**
+
+```cpp
+Vector m_;
+```
+
+
+
+---
+### **Q_**
+
+```cpp
+Matrix Q_;
+```
+
+
+
+---
+### **C_**
+
+```cpp
+Matrix C_;
+```
+
+
+
+---
+### **d_**
+
+```cpp
+Vector d_;
+```
+
+
+
+---
+### **R_**
+
+```cpp
+Matrix R_;
+```
+
+
+
+---
+### **x0_**
+
+```cpp
+Vector x0_;
+```
+
+
+
+---
+### **P0_**
+
+```cpp
+Matrix P0_;
+```
+
+
+
+---
+### **n_u_**
+
+```cpp
+size_t n_u_ {};
+```
+
+
+
+---
+### **n_x_**
+
+```cpp
+size_t n_x_ {};
+```
+
+
+
+---
+### **n_y_**
+
+```cpp
+size_t n_y_ {};
+```
+
+
+
+---
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_s_s_i_d.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_s_s_i_d.md
new file mode 100644
index 00000000..e38934fa
--- /dev/null
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_s_s_i_d.md
@@ -0,0 +1,370 @@
+---
+title: lds::SSID
+
+---
+
+# lds::SSID
+
+
+
+ [More...](#detailed-description)
+
+Inherited by [lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/), [lds::poisson::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/)
+
+## Public Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-ssid)**() =default<br>Constructs a new [SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/) type.  |
+| | **[SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-ssid)**(size_t n_x, size_t n_h, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && u_train, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && z_train, const Vector & d =Vector(1).fill(-kInf))<br>Constructs a new [SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/) type.  |
+| std::tuple< [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/), Vector > | **[Run](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-run)**([SSIDWt](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt) ssid_wt)<br>Runs fitting by subspace identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/))  |
+| std::tuple< [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) >, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > > | **[ReturnData](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-returndata)**()<br>Returns the I/O data to caller.  |
+
+## Protected Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| void | **[CalcD](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-calcd)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) t_silence =0.1, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) thresh_silence =0.001)<br>Using periods of silence in inputs (u), calculates the output \ bias (d)  |
+| void | **[CreateHankelDataMat](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-createhankeldatamat)**()<br>Creates the block-hankel I/O data matrix.  |
+| virtual void | **[DecomposeData](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-decomposedata)**() =0<br>Decompose data to lower-triangular matrix (used in Solve)  |
+| void | **[CalcSVD](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-calcsvd)**([SSIDWt](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt) wt)<br>performs the singular value decomposition (SVD)  |
+| void | **[Solve](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-solve)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) wt_dc)<br>solves for LDS parameters  |
+| void | **[RecomputeExtObs](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-recomputeextobs)**()<br>recompute extended observability matrix from estimates of A, C  |
+
+## Protected Attributes
+
+|                | Name           |
+| -------------- | -------------- |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-u-)** <br>input training data  |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[z_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-z-)** <br>measurement training data  |
+| Matrix | **[D_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-d-)** <br>block-Hankel I/O data matrix  |
+| [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/) | **[fit_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-fit-)** <br>fit  |
+| Matrix | **[g_dc_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-g-dc-)** <br>I/O gain @ DC.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-dt-)** <br>sample period  |
+| size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-u-)** <br>number of inputs  |
+| size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-x-)** <br>number of states  |
+| size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-y-)** <br>number of outputs  |
+| size_t | **[n_h_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-h-)**  |
+| size_t | **[n_trials_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-trials-)** <br>number of input/output data sequences  |
+| std::vector< size_t > | **[n_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-t-)** <br>number of time steps  |
+| size_t | **[n_t_tot_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-t-tot-)** <br>total number of time steps across trials  |
+| Matrix | **[L_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-l-)** <br>lower triangle decomp of covariance matrix  |
+| Vector | **[s_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-s-)** <br>singular values  |
+| Matrix | **[ext_obs_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-ext-obs-t-)** <br>extended observability matrix  |
+
+## Detailed Description
+
+```cpp
+template <typename Fit >
+class lds::SSID;
+```
+
+
+---
+---
+## Public Function Details
+
+### **SSID**
+
+```cpp
+SSID() =default
+```
+
+
+
+---
+### **SSID**
+
+```cpp
+SSID(
+    size_t n_x,
+    size_t n_h,
+    data_t dt,
+    UniformMatrixList< kMatFreeDim2 > && u_train,
+    UniformMatrixList< kMatFreeDim2 > && z_train,
+    const Vector & d =Vector(1).fill(-kInf)
+)
+```
+
+
+
+**Parameters**:
+
+  * **n_x** number of states 
+  * **n_h** size of block-hankel data matrix 
+  * **dt** sample period 
+  * **u_train** input training data 
+  * **z_train** measurement training data 
+  * **d** output bias 
+
+
+---
+### **Run**
+
+```cpp
+std::tuple< Fit, Vector > Run(
+    SSIDWt ssid_wt
+)
+```
+
+
+
+**Parameters**:
+
+  * **ssid_wt** weight for singular value decomp
+
+
+**Return**: tuple ([Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/), singular values) 
+
+---
+### **ReturnData**
+
+```cpp
+inline std::tuple< UniformMatrixList< kMatFreeDim2 >, UniformMatrixList< kMatFreeDim2 > > ReturnData()
+```
+
+
+
+**Return**: tuple(input data, output data) 
+
+---
+
+
+## Protected Function Details
+
+### **CalcD**
+
+```cpp
+void CalcD(
+    data_t t_silence =0.1,
+    data_t thresh_silence =0.001
+)
+```
+
+
+
+**Parameters**:
+
+  * **t_silence** threshold on period of time that qualifies as "silence" 
+  * **thresh_silence** threshold on input amplitude u that qualifies as "silence" 
+
+
+---
+### **CreateHankelDataMat**
+
+```cpp
+void CreateHankelDataMat()
+```
+
+
+
+Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC. 
+
+
+---
+### **DecomposeData**
+
+```cpp
+virtual void DecomposeData() =0
+```
+
+
+
+**Reimplemented by**: [lds::gaussian::FitSSID::DecomposeData](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/#function-decomposedata), [lds::poisson::FitSSID::DecomposeData](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/#function-decomposedata)
+
+
+---
+### **CalcSVD**
+
+```cpp
+void CalcSVD(
+    SSIDWt wt
+)
+```
+
+
+
+**Parameters**:
+
+  * **ssid_wt** weight for SVD 
+
+
+---
+### **Solve**
+
+```cpp
+void Solve(
+    data_t wt_dc
+)
+```
+
+
+
+**Parameters**:
+
+  * **wt_dc** weight placed on getting correct DC I/O gain 
+
+
+---
+### **RecomputeExtObs**
+
+```cpp
+void RecomputeExtObs()
+```
+
+
+
+---
+
+
+## Protected Attribute Details
+
+### **u_**
+
+```cpp
+UniformMatrixList< kMatFreeDim2 > u_;
+```
+
+
+
+---
+### **z_**
+
+```cpp
+UniformMatrixList< kMatFreeDim2 > z_;
+```
+
+
+
+---
+### **D_**
+
+```cpp
+Matrix D_;
+```
+
+
+
+---
+### **fit_**
+
+```cpp
+Fit fit_;
+```
+
+
+
+---
+### **g_dc_**
+
+```cpp
+Matrix g_dc_;
+```
+
+
+
+---
+### **dt_**
+
+```cpp
+data_t dt_ {};
+```
+
+
+
+---
+### **n_u_**
+
+```cpp
+size_t n_u_ {};
+```
+
+
+
+---
+### **n_x_**
+
+```cpp
+size_t n_x_ {};
+```
+
+
+
+---
+### **n_y_**
+
+```cpp
+size_t n_y_ {};
+```
+
+
+
+---
+### **n_h_**
+
+```cpp
+size_t n_h_ {};
+```
+
+
+
+---
+### **n_trials_**
+
+```cpp
+size_t n_trials_ {};
+```
+
+
+
+---
+### **n_t_**
+
+```cpp
+std::vector< size_t > n_t_;
+```
+
+
+
+---
+### **n_t_tot_**
+
+```cpp
+size_t n_t_tot_ {};
+```
+
+
+
+---
+### **L_**
+
+```cpp
+Matrix L_;
+```
+
+
+
+---
+### **s_**
+
+```cpp
+Vector s_;
+```
+
+
+
+---
+### **ext_obs_t_**
+
+```cpp
+Matrix ext_obs_t_;
+```
+
+
+
+---
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_switched_controller.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_switched_controller.md
new file mode 100644
index 00000000..f1cdc293
--- /dev/null
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_switched_controller.md
@@ -0,0 +1,360 @@
+---
+title: lds::SwitchedController
+summary: SwitchedController Type. 
+
+---
+
+# lds::SwitchedController
+
+
+
+[SwitchedController]() Type.  [More...](#detailed-description)
+
+
+<br /> `#include <lds_sctrl.h>`
+
+Inherits from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/)
+
+Inherited by [lds::gaussian::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/), [lds::poisson::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/)
+
+## Public Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switchedcontroller)**() =default<br>Constructs a new [SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/).  |
+| | **[SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switchedcontroller)**(const std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) > & systems, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/).  |
+| | **[SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switchedcontroller)**(std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) > && systems, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/) (moves systems).  |
+| void | **[Switch](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switch)**(size_t idx, bool do_force_switch =false)<br>Switch to a different sub-system/controller.  |
+| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)<> & Kc)<br>sets state feedback gains  |
+| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)<> && Kc)<br>sets state feedback gains (moving)  |
+| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-inty)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)<> & Kc_inty)<br>sets integral feedback gains  |
+| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-inty)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)<> && Kc_inty)<br>sets integral feedback gains (moving)  |
+| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-u)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)<> & Kc_u)<br>sets input feedback gains  |
+| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-u)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)<> && Kc_u)<br>sets input feedback gains (moving)  |
+| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-g-design)**(const [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/) & g)<br>sets input gain used during controller design  |
+| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-g-design)**([UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/) && g)<br>sets input gain used during controller design (moving)  |
+
+## Protected Attributes
+
+|                | Name           |
+| -------------- | -------------- |
+| std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) > | **[systems_](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-systems-)** <br>underlying sub-systems which are switched between  |
+| size_t | **[n_sys_](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-n-sys-)** <br>number of systems  |
+| size_t | **[idx_](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-idx-)** <br>current system/controller index.  |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/) | **[Kc_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-kc-list-)**  |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/) | **[Kc_inty_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-kc-inty-list-)**  |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/) | **[Kc_u_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-kc-u-list-)**  |
+| [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/) | **[g_design_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-g-design-list-)**  |
+
+## Additional inherited members
+
+**Public Functions inherited from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller)**() =default<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/).  |
+| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) & sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/).  |
+| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller)**([System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) && sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/) by moving the system object.  |
+| const Vector & | **[Control](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control)**(const Vector & z, bool do_control =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal (single-step)  |
+| const Vector & | **[ControlOutputReference](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controloutputreference)**(const Vector & z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal, given previously-set (single-step)  |
+| const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) & | **[sys](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-sys)**() const |
+| const Matrix & | **[Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc)**() const<br>Get state feedback controller gain.  |
+| const Matrix & | **[Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-inty)**() const<br>Get integral controller gain.  |
+| const Matrix & | **[Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-u)**() const<br>Get input feedback controller gain.  |
+| const Vector & | **[g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-g-design)**() const<br>Get input gain used in controller design.  |
+| const Vector & | **[u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ref)**() const<br>Get reference input.  |
+| const Vector & | **[x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-x-ref)**() const<br>Get reference state.  |
+| const Vector & | **[y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-y-ref)**() const<br>Get reference output.  |
+| size_t | **[control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control-type)**() const<br>Get controller type.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-tau-awu)**() const<br>Get time constant of anti-integral-windup.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-lb)**() const<br>Get control lower bound.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ub)**() const<br>Get control upper bound.  |
+| void | **[set_sys](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-sys)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) & sys)<br>Set system.  |
+| void | **[set_u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ref)**(const Vector & u_ref)<br>Set reference input (u_ref)  |
+| void | **[set_x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-x-ref)**(const Vector & x_ref)<br>Set reference state (x_ref)  |
+| virtual void | **[set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-y-ref)**(const Vector & y_ref)<br>Set reference output (y_ref)  |
+| void | **[set_tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-tau-awu)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) tau)<br>Set time constant of anti-integral-windup.  |
+| void | **[set_control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-control-type)**(size_t control_type)<br>Sets the control type.  |
+| void | **[set_u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-lb)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb)<br>sets control lower bound  |
+| void | **[set_u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ub)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub)<br>Sets control upper bound.  |
+| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-reset)**()<br>reset system and control variables.  |
+| void | **[Print](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-print)**()<br>prints variables to stdout  |
+
+**Protected Attributes inherited from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) | **[sys_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-sys-)** <br>underlying LDS  |
+| Vector | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-)** <br>control signal  |
+| Vector | **[u_return_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-return-)** <br>control signal that is _returned_ to user  |
+| Vector | **[g_design_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-g-design-)** <br>input gain of the system used for controller design  |
+| Vector | **[u_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-)** <br>reference input  |
+| Vector | **[u_ref_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-prev-)** <br>reference input at previous time step  |
+| Vector | **[x_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-x-ref-)** <br>reference state  |
+| Vector | **[y_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-y-ref-)** <br>reference output  |
+| Vector | **[cx_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-cx-ref-)**  |
+| Matrix | **[Kc_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-)** <br>state controller gain  |
+| Matrix | **[Kc_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-u-)** <br>input controller gain (optional when control updates \deltaU)  |
+| Matrix | **[Kc_inty_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-inty-)** <br>integral controller gain  |
+| Vector | **[du_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-du-ref-)**  |
+| Vector | **[dv_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-ref-)**  |
+| Vector | **[v_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-ref-)**  |
+| Vector | **[dv_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-)**  |
+| Vector | **[v_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-)** <br>Control after g inversion (e.g., control in physical units)  |
+| Vector | **[int_e_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-)** <br>integrated error  |
+| Vector | **[int_e_awu_adjust_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-awu-adjust-)** <br>anti-windup adjustment to intE  |
+| Vector | **[u_sat_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-sat-)** <br>control signal after saturation (for antiWindup)  |
+| bool | **[do_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-control-prev-)**  |
+| bool | **[do_lock_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-lock-control-prev-)**  |
+| bool | **[u_saturated_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-saturated-)** <br>whether control signal has reached saturation limits  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-lb-)** <br>lower bound on control  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ub-)** <br>upper bound on control  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-tau-awu-)** <br>antiwindup time constant  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[k_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-k-awu-)**  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[t_since_control_onset_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-t-since-control-onset-)** <br>time since control epoch onset  |
+| size_t | **[control_type_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-control-type-)** <br>controller type  |
+
+
+## Detailed Description
+
+```cpp
+template <typename System >
+class lds::SwitchedController;
+```
+
+
+---
+---
+## Public Function Details
+
+### **SwitchedController**
+
+```cpp
+SwitchedController() =default
+```
+
+
+
+---
+### **SwitchedController**
+
+```cpp
+inline SwitchedController(
+    const std::vector< System > & systems,
+    data_t u_lb,
+    data_t u_ub,
+    size_t control_type =0
+)
+```
+
+
+
+**Parameters**:
+
+  * **systems** vector of sub-systems 
+  * **u_lb** lower bound on control (u) 
+  * **u_ub** upper bound on control (u) 
+  * **control_type** [optional] control type bit mask 
+
+
+---
+### **SwitchedController**
+
+```cpp
+inline SwitchedController(
+    std::vector< System > && systems,
+    data_t u_lb,
+    data_t u_ub,
+    size_t control_type =0
+)
+```
+
+
+
+**Parameters**:
+
+  * **systems** vector of sub-systems 
+  * **u_lb** lower bound on control (u) 
+  * **u_ub** upper bound on control (u) 
+  * **control_type** [optional] control type bit mask 
+
+
+---
+### **Switch**
+
+```cpp
+inline void Switch(
+    size_t idx,
+    bool do_force_switch =false
+)
+```
+
+
+
+**Parameters**:
+
+  * **idx** index 
+  * **do_force_switch** whether to force a system switch even if already there. 
+
+
+---
+### **set_Kc**
+
+```cpp
+inline void set_Kc(
+    const UniformMatrixList<> & Kc
+)
+```
+
+
+
+---
+### **set_Kc**
+
+```cpp
+inline void set_Kc(
+    UniformMatrixList<> && Kc
+)
+```
+
+
+
+---
+### **set_Kc_inty**
+
+```cpp
+inline void set_Kc_inty(
+    const UniformMatrixList<> & Kc_inty
+)
+```
+
+
+
+---
+### **set_Kc_inty**
+
+```cpp
+inline void set_Kc_inty(
+    UniformMatrixList<> && Kc_inty
+)
+```
+
+
+
+---
+### **set_Kc_u**
+
+```cpp
+inline void set_Kc_u(
+    const UniformMatrixList<> & Kc_u
+)
+```
+
+
+
+---
+### **set_Kc_u**
+
+```cpp
+inline void set_Kc_u(
+    UniformMatrixList<> && Kc_u
+)
+```
+
+
+
+---
+### **set_g_design**
+
+```cpp
+inline void set_g_design(
+    const UniformVectorList & g
+)
+```
+
+
+
+---
+### **set_g_design**
+
+```cpp
+inline void set_g_design(
+    UniformVectorList && g
+)
+```
+
+
+
+---
+
+
+## Protected Attribute Details
+
+### **systems_**
+
+```cpp
+std::vector< System > systems_;
+```
+
+
+
+---
+### **n_sys_**
+
+```cpp
+size_t n_sys_ {};
+```
+
+
+
+---
+### **idx_**
+
+```cpp
+size_t idx_ {};
+```
+
+
+
+---
+### **Kc_list_**
+
+```cpp
+UniformMatrixList Kc_list_;
+```
+
+
+
+---
+### **Kc_inty_list_**
+
+```cpp
+UniformMatrixList Kc_inty_list_;
+```
+
+
+
+---
+### **Kc_u_list_**
+
+```cpp
+UniformMatrixList Kc_u_list_;
+```
+
+
+
+---
+### **g_design_list_**
+
+```cpp
+UniformVectorList g_design_list_;
+```
+
+
+
+---
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_system.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_system.md
new file mode 100644
index 00000000..0b29291a
--- /dev/null
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_system.md
@@ -0,0 +1,886 @@
+---
+title: lds::System
+summary: Linear Dynamical System Type. 
+
+---
+
+# lds::System
+
+
+
+Linear Dynamical [System]() Type. 
+<br /> `#include <lds_sys.h>`
+
+Inherited by [lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/), [lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)
+
+## Public Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-system)**() =default<br>Constructs a new [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/).  |
+| | **[System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-system)**(size_t n_u, size_t n_x, size_t n_y, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) p0 =kDefaultP0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) q0 =kDefaultQ0)<br>constructs a new [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) |
+| virtual | **[~System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-~system)**() |
+| void | **[Filter](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-filter)**(const Vector & u_tm1, const Vector & z)<br>Filter data to produce causal state estimates.  |
+| virtual const Vector & | **[Simulate](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-simulate)**(const Vector & u_tm1) =0<br>simulates system (single time step)  |
+| void | **[f](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-f)**(const Vector & u, bool do_add_noise =false)<br>system dynamics function  |
+| virtual void | **[h](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-h)**() =0<br>system output function  |
+| virtual Vector | **[h_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-h-)**(Vector x) =0<br>system output function (stateless)  |
+| size_t | **[n_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-n-u)**() const<br>Get number of inputs.  |
+| size_t | **[n_x](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-n-x)**() const<br>Get number of states.  |
+| size_t | **[n_y](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-n-y)**() const<br>Get number of outputs.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-dt)**() const<br>Get sample period.  |
+| const Vector & | **[x](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-x)**() const<br>Get current state.  |
+| const Matrix & | **[P](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p)**() const<br>Get covariance of state estimate.  |
+| const Vector & | **[m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-m)**() const<br>Get current process disturbance/bias.  |
+| const Matrix & | **[P_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p-m)**() const<br>Get covariance of process disturbance estimate.  |
+| const Vector & | **[cx](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-cx)**() const<br>Get C*x.  |
+| const Vector & | **[y](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-y)**() const<br>Get output.  |
+| const Vector & | **[x0](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-x0)**() const<br>Get initial state.  |
+| const Vector & | **[m0](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-m0)**() const<br>Get initial disturbance.  |
+| const Matrix & | **[A](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-a)**() const<br>Get state matrix.  |
+| const Matrix & | **[B](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-b)**() const<br>Get input matrix.  |
+| const Vector & | **[g](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-g)**() const<br>Get input gain/conversion factor.  |
+| const Matrix & | **[C](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-c)**() const<br>Get output matrix.  |
+| const Vector & | **[d](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-d)**() const<br>Get output bias.  |
+| const Matrix & | **[Ke](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-ke)**() const<br>Get estimator gain.  |
+| const Matrix & | **[Ke_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-ke-m)**() const<br>Get estimator gain for process disturbance (m)  |
+| const Matrix & | **[Q](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-q)**()<br>Get process noise covariance.  |
+| const Matrix & | **[Q_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-q-m)**()<br>Get process noise covariance of disturbance evoluation.  |
+| const Matrix & | **[P0](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p0)**()<br>Get covariance of initial state.  |
+| const Matrix & | **[P0_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p0-m)**()<br>Get covariance of initial process disturbance.  |
+| void | **[set_A](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-a)**(const Matrix & A)<br>Set state matrix.  |
+| void | **[set_B](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-b)**(const Matrix & B)<br>Set input matrix.  |
+| void | **[set_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-m)**(const Vector & m, bool do_force_assign =false)<br>Set process disturbance.  |
+| void | **[set_g](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-g)**(const Vector & g)<br>Set input gain.  |
+| void | **[set_Q](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-q)**(const Matrix & Q)<br>Set process noise covariance.  |
+| void | **[set_Q_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-q-m)**(const Matrix & Q_m)<br>Set process noise covariance of disturbance evoluation.  |
+| void | **[set_x0](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-x0)**(const Vector & x0)<br>Set initial state.  |
+| void | **[set_P0](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-p0)**(const Matrix & P0)<br>Set covariance of initial state.  |
+| void | **[set_P0_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-p0-m)**(const Matrix & P0_m)<br>Set covariance of initial process disturbance.  |
+| void | **[set_C](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-c)**(const Matrix & C)<br>Set output matrix.  |
+| void | **[set_d](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-d)**(const Vector & d)<br>Set output bias.  |
+| void | **[set_x](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-x)**(const Vector & x)<br>Set state of system.  |
+| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-reset)**()<br>Reset system variables.  |
+| std::vector< [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > > | **[nstep_pred_block](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-nstep-pred-block)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > u, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > z, size_t n_pred =1) |
+| void | **[Print](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-print)**()<br>Print system variables to stdout.  |
+
+## Protected Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| virtual void | **[RecurseKe](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-recurseke)**() =0<br>Recursively recalculate estimator gain (Ke)  |
+| void | **[InitVars](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-initvars)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) p0 =kDefaultP0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) q0 =kDefaultQ0) |
+
+## Public Attributes
+
+|                | Name           |
+| -------------- | -------------- |
+| bool | **[do_adapt_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-do-adapt-m)** <br>whether to adaptively estimate disturbance m  |
+
+## Protected Attributes
+
+|                | Name           |
+| -------------- | -------------- |
+| std::size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-n-x-)** <br>number of states  |
+| std::size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-n-u-)** <br>number of inputs  |
+| std::size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-n-y-)** <br>number of outputs  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-dt-)** <br>sample period  |
+| Vector | **[x_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-x-)** <br>state  |
+| Matrix | **[P_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p-)** <br>covariance of state estimate  |
+| Vector | **[m_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-m-)** <br>process disturbance  |
+| Matrix | **[P_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p-m-)** <br>covariance of disturbance estimate  |
+| Vector | **[cx_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-cx-)** <br>C*x.  |
+| Vector | **[y_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-y-)** <br>output  |
+| Vector | **[z_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-z-)** <br>measurement  |
+| Vector | **[x0_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-x0-)** <br>initial state  |
+| Matrix | **[P0_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p0-)** <br>covariance of initial state estimate  |
+| Vector | **[m0_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-m0-)** <br>initial process disturbance  |
+| Matrix | **[P0_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p0-m-)** <br>covariance of initial disturbance est.  |
+| Matrix | **[A_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-a-)** <br>state matrix  |
+| Matrix | **[B_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-b-)** <br>input matrix  |
+| Vector | **[g_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-g-)** <br>input gain  |
+| Matrix | **[Q_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-q-)** <br>covariance of process noise  |
+| Matrix | **[Q_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-q-m-)** <br>covariance of disturbance random walk  |
+| Matrix | **[C_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-c-)** <br>output matrix  |
+| Vector | **[d_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-d-)** <br>output bias  |
+| Matrix | **[Ke_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-ke-)** <br>estimator gain  |
+| Matrix | **[Ke_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-ke-m-)** <br>estimator gain for process disturbance  |
+
+---
+---
+## Public Function Details
+
+### **System**
+
+```cpp
+System() =default
+```
+
+
+
+---
+### **System**
+
+```cpp
+System(
+    size_t n_u,
+    size_t n_x,
+    size_t n_y,
+    data_t dt,
+    data_t p0 =kDefaultP0,
+    data_t q0 =kDefaultQ0
+)
+```
+
+
+
+**Parameters**:
+
+  * **n_u** number of inputs 
+  * **n_x** number of states 
+  * **n_y** number of outputs 
+  * **dt** sample period 
+  * **p0** diagonal elements for state estimate covariance 
+  * **q0** diagonal elements for process noise covariance 
+
+
+---
+### **~System**
+
+```cpp
+inline virtual ~System()
+```
+
+
+
+---
+### **Filter**
+
+```cpp
+void Filter(
+    const Vector & u_tm1,
+    const Vector & z
+)
+```
+
+
+
+**Parameters**:
+
+  * **u_tm1** input at t-minus-1 
+  * **z_t** current measurement 
+
+
+Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.
+
+
+---
+### **Simulate**
+
+```cpp
+virtual const Vector & Simulate(
+    const Vector & u_tm1
+) =0
+```
+
+
+
+**Parameters**:
+
+  * **u_tm1** input at time t-1
+
+
+**Return**: simulated measurement at time t 
+
+**Reimplemented by**: [lds::gaussian::System::Simulate](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-simulate), [lds::poisson::System::Simulate](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/#function-simulate)
+
+
+---
+### **f**
+
+```cpp
+inline void f(
+    const Vector & u,
+    bool do_add_noise =false
+)
+```
+
+
+
+**Parameters**:
+
+  * **u** input 
+  * **do_add_noise** whether to add simulated process noise 
+
+
+---
+### **h**
+
+```cpp
+virtual void h() =0
+```
+
+
+
+**Reimplemented by**: [lds::gaussian::System::h](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-h), [lds::poisson::System::h](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/#function-h)
+
+
+---
+### **h_**
+
+```cpp
+virtual Vector h_(
+    Vector x
+) =0
+```
+
+
+
+**Parameters**:
+
+  * **x_t** state at time t 
+
+
+**Return**: predicted state at time t + 1 
+
+**Reimplemented by**: [lds::gaussian::System::h_](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-h-), [lds::poisson::System::h_](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/#function-h-)
+
+
+---
+### **n_u**
+
+```cpp
+inline size_t n_u() const
+```
+
+
+
+---
+### **n_x**
+
+```cpp
+inline size_t n_x() const
+```
+
+
+
+---
+### **n_y**
+
+```cpp
+inline size_t n_y() const
+```
+
+
+
+---
+### **dt**
+
+```cpp
+inline data_t dt() const
+```
+
+
+
+---
+### **x**
+
+```cpp
+inline const Vector & x() const
+```
+
+
+
+---
+### **P**
+
+```cpp
+inline const Matrix & P() const
+```
+
+
+
+---
+### **m**
+
+```cpp
+inline const Vector & m() const
+```
+
+
+
+---
+### **P_m**
+
+```cpp
+inline const Matrix & P_m() const
+```
+
+
+
+---
+### **cx**
+
+```cpp
+inline const Vector & cx() const
+```
+
+
+
+---
+### **y**
+
+```cpp
+inline const Vector & y() const
+```
+
+
+
+---
+### **x0**
+
+```cpp
+inline const Vector & x0() const
+```
+
+
+
+---
+### **m0**
+
+```cpp
+inline const Vector & m0() const
+```
+
+
+
+---
+### **A**
+
+```cpp
+inline const Matrix & A() const
+```
+
+
+
+---
+### **B**
+
+```cpp
+inline const Matrix & B() const
+```
+
+
+
+---
+### **g**
+
+```cpp
+inline const Vector & g() const
+```
+
+
+
+---
+### **C**
+
+```cpp
+inline const Matrix & C() const
+```
+
+
+
+---
+### **d**
+
+```cpp
+inline const Vector & d() const
+```
+
+
+
+---
+### **Ke**
+
+```cpp
+inline const Matrix & Ke() const
+```
+
+
+
+---
+### **Ke_m**
+
+```cpp
+inline const Matrix & Ke_m() const
+```
+
+
+
+---
+### **Q**
+
+```cpp
+inline const Matrix & Q()
+```
+
+
+
+---
+### **Q_m**
+
+```cpp
+inline const Matrix & Q_m()
+```
+
+
+
+---
+### **P0**
+
+```cpp
+inline const Matrix & P0()
+```
+
+
+
+---
+### **P0_m**
+
+```cpp
+inline const Matrix & P0_m()
+```
+
+
+
+---
+### **set_A**
+
+```cpp
+inline void set_A(
+    const Matrix & A
+)
+```
+
+
+
+---
+### **set_B**
+
+```cpp
+inline void set_B(
+    const Matrix & B
+)
+```
+
+
+
+---
+### **set_m**
+
+```cpp
+inline void set_m(
+    const Vector & m,
+    bool do_force_assign =false
+)
+```
+
+
+
+---
+### **set_g**
+
+```cpp
+inline void set_g(
+    const Vector & g
+)
+```
+
+
+
+---
+### **set_Q**
+
+```cpp
+inline void set_Q(
+    const Matrix & Q
+)
+```
+
+
+
+---
+### **set_Q_m**
+
+```cpp
+inline void set_Q_m(
+    const Matrix & Q_m
+)
+```
+
+
+
+---
+### **set_x0**
+
+```cpp
+inline void set_x0(
+    const Vector & x0
+)
+```
+
+
+
+---
+### **set_P0**
+
+```cpp
+inline void set_P0(
+    const Matrix & P0
+)
+```
+
+
+
+---
+### **set_P0_m**
+
+```cpp
+inline void set_P0_m(
+    const Matrix & P0_m
+)
+```
+
+
+
+---
+### **set_C**
+
+```cpp
+inline void set_C(
+    const Matrix & C
+)
+```
+
+
+
+---
+### **set_d**
+
+```cpp
+inline void set_d(
+    const Vector & d
+)
+```
+
+
+
+---
+### **set_x**
+
+```cpp
+inline void set_x(
+    const Vector & x
+)
+```
+
+
+
+---
+### **Reset**
+
+```cpp
+void Reset()
+```
+
+
+
+---
+### **nstep_pred_block**
+
+```cpp
+std::vector< UniformMatrixList< kMatFreeDim2 > > nstep_pred_block(
+    UniformMatrixList< kMatFreeDim2 > u,
+    UniformMatrixList< kMatFreeDim2 > z,
+    size_t n_pred =1
+)
+```
+
+
+
+---
+### **Print**
+
+```cpp
+void Print()
+```
+
+
+
+---
+
+
+## Protected Function Details
+
+### **RecurseKe**
+
+```cpp
+virtual void RecurseKe() =0
+```
+
+
+
+**Reimplemented by**: [lds::gaussian::System::RecurseKe](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-recurseke), [lds::poisson::System::RecurseKe](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/#function-recurseke)
+
+
+---
+### **InitVars**
+
+```cpp
+void InitVars(
+    data_t p0 =kDefaultP0,
+    data_t q0 =kDefaultQ0
+)
+```
+
+
+
+---
+
+
+## Public Attribute Details
+
+### **do_adapt_m**
+
+```cpp
+bool do_adapt_m {};
+```
+
+
+
+---
+
+
+## Protected Attribute Details
+
+### **n_x_**
+
+```cpp
+std::size_t n_x_ {};
+```
+
+
+
+---
+### **n_u_**
+
+```cpp
+std::size_t n_u_ {};
+```
+
+
+
+---
+### **n_y_**
+
+```cpp
+std::size_t n_y_ {};
+```
+
+
+
+---
+### **dt_**
+
+```cpp
+data_t dt_ {};
+```
+
+
+
+---
+### **x_**
+
+```cpp
+Vector x_;
+```
+
+
+
+---
+### **P_**
+
+```cpp
+Matrix P_;
+```
+
+
+
+---
+### **m_**
+
+```cpp
+Vector m_;
+```
+
+
+
+---
+### **P_m_**
+
+```cpp
+Matrix P_m_;
+```
+
+
+
+---
+### **cx_**
+
+```cpp
+Vector cx_;
+```
+
+
+
+---
+### **y_**
+
+```cpp
+Vector y_;
+```
+
+
+
+---
+### **z_**
+
+```cpp
+Vector z_;
+```
+
+
+
+---
+### **x0_**
+
+```cpp
+Vector x0_;
+```
+
+
+
+---
+### **P0_**
+
+```cpp
+Matrix P0_;
+```
+
+
+
+---
+### **m0_**
+
+```cpp
+Vector m0_;
+```
+
+
+
+---
+### **P0_m_**
+
+```cpp
+Matrix P0_m_;
+```
+
+
+
+---
+### **A_**
+
+```cpp
+Matrix A_;
+```
+
+
+
+---
+### **B_**
+
+```cpp
+Matrix B_;
+```
+
+
+
+---
+### **g_**
+
+```cpp
+Vector g_;
+```
+
+
+
+---
+### **Q_**
+
+```cpp
+Matrix Q_;
+```
+
+
+
+---
+### **Q_m_**
+
+```cpp
+Matrix Q_m_;
+```
+
+
+
+---
+### **C_**
+
+```cpp
+Matrix C_;
+```
+
+
+
+---
+### **d_**
+
+```cpp
+Vector d_;
+```
+
+
+
+---
+### **Ke_**
+
+```cpp
+Matrix Ke_;
+```
+
+
+
+---
+### **Ke_m_**
+
+```cpp
+Matrix Ke_m_;
+```
+
+
+
+---
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1UniformMatrixList.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_uniform_matrix_list.md
similarity index 50%
rename from misc/docs-hugo/content/docs/api/Classes/classlds_1_1UniformMatrixList.md
rename to misc/docs-hugo/content/docs/api/Classes/classlds_1_1_uniform_matrix_list.md
index b7acbf92..437ac7f7 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1UniformMatrixList.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_uniform_matrix_list.md
@@ -1,244 +1,261 @@
----
-title: lds::UniformMatrixList
-
----
-
-# lds::UniformMatrixList
-
-
-
- [More...](#detailed-description)
-
-Inherits from std::vector< Matrix >
-
-## Public Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/#function-uniformmatrixlist)**() =default<br>Constructs a new [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/).  |
-| | **[UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/#function-uniformmatrixlist)**(const std::vector< Matrix > & mats, std::array< size_t, 2 > dim ={0, 0})<br>Constructs a new [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/) by copying existing vector of Matrix if dimensions consistent.  |
-| | **[UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/#function-uniformmatrixlist)**(std::vector< Matrix > && mats, std::array< size_t, 2 > dim ={0, 0})<br>Constructs a new [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/) by moving existing vector of Matrix if dimensions consistent.  |
-| | **[UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/#function-uniformmatrixlist)**(std::initializer_list< Matrix > mats, std::array< size_t, 2 > dim ={0, 0})<br>Constructs a new [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/) from initializer_list of Matrix if dimensions consistent.  |
-| | **[UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/#function-uniformmatrixlist)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< D > & that)<br>Constructs a new [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/) (copy).  |
-| | **[UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/#function-uniformmatrixlist)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< D > && that)<br>Constructs a new [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/) (move).  |
-| | **[~UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/#function-~uniformmatrixlist)**() =default<br>Destroys the object.  |
-| const std::array< size_t, 2 > & | **[dim](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/#function-dim)**(size_t n =0) const<br>gets dimensions of uniformly sized matrices  |
-| size_t | **[size](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/#function-size)**()<br>size of container  |
-| const Matrix & | **[at](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/#function-at)**(size_t n)<br>gets reference to n^th element  |
-| void | **[Swap](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/#function-swap)**(Matrix & that, size_t n)<br>swaps input matrix with n^th matrix of list  |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< D > & | **[operator=](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/#function-operator=)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< D > & that)<br>assigns the contents (copy)  |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< D > & | **[operator=](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/#function-operator=)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< D > && that)<br>assigns the contents (move)  |
-
-## Detailed Description
-
-```cpp
-template <MatrixListFreeDim D =kMatFreeDimNone>
-class lds::UniformMatrixList;
-```
-
-
----
----
-## Public Function Details
-
-### **UniformMatrixList**
-
-```cpp
-UniformMatrixList() =default
-```
-
-
-
----
-### **UniformMatrixList**
-
-```cpp
-explicit UniformMatrixList(
-    const std::vector< Matrix > & mats,
-    std::array< size_t, 2 > dim ={0, 0}
-)
-```
-
-
-
-**Parameters**:
-
-  * **mats** input matrices 
-  * **dim** dimensions 
-
-
----
-### **UniformMatrixList**
-
-```cpp
-explicit UniformMatrixList(
-    std::vector< Matrix > && mats,
-    std::array< size_t, 2 > dim ={0, 0}
-)
-```
-
-
-
-**Parameters**:
-
-  * **mats** input matrices 
-  * **dim** dimensions 
-
-
----
-### **UniformMatrixList**
-
-```cpp
-UniformMatrixList(
-    std::initializer_list< Matrix > mats,
-    std::array< size_t, 2 > dim ={0, 0}
-)
-```
-
-
-
-**Parameters**:
-
-  * **mats** input matrices 
-  * **dim** dimensions 
-
-
----
-### **UniformMatrixList**
-
-```cpp
-UniformMatrixList(
-    const UniformMatrixList< D > & that
-)
-```
-
-
-
-**Parameters**:
-
-  * **that** another [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)
-
-
----
-### **UniformMatrixList**
-
-```cpp
-UniformMatrixList(
-    UniformMatrixList< D > && that
-)
-```
-
-
-
-**Parameters**:
-
-  * **that** another [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)
-
-
----
-### **~UniformMatrixList**
-
-```cpp
-~UniformMatrixList() =default
-```
-
-
-
----
-### **dim**
-
-```cpp
-inline const std::array< size_t, 2 > & dim(
-    size_t n =0
-) const
-```
-
-
-
-**Parameters**:
-
-  * **n** [optional] index in list of matrices
-
-
-**Return**: dimensions 
-
----
-### **size**
-
-```cpp
-inline size_t size()
-```
-
-
-
----
-### **at**
-
-```cpp
-inline const Matrix & at(
-    size_t n
-)
-```
-
-
-
----
-### **Swap**
-
-```cpp
-inline void Swap(
-    Matrix & that,
-    size_t n
-)
-```
-
-
-
-**Parameters**:
-
-  * **that** input matrix 
-  * **n** index where the matrix is moved 
-
-
----
-### **operator=**
-
-```cpp
-inline UniformMatrixList< D > & operator=(
-    const UniformMatrixList< D > & that
-)
-```
-
-
-
-**Parameters**:
-
-  * **that** another [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)
-
-
-**Return**: reference to object 
-
----
-### **operator=**
-
-```cpp
-inline UniformMatrixList< D > & operator=(
-    UniformMatrixList< D > && that
-)
-```
-
-
-
-**Parameters**:
-
-  * **that** another [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)
-
-
-**Return**: reference to object 
-
----
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
+---
+title: lds::UniformMatrixList
+
+---
+
+# lds::UniformMatrixList
+
+
+
+ [More...](#detailed-description)
+
+Inherits from std::vector< Matrix >
+
+## Public Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist)**() =default<br>Constructs a new [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/).  |
+| | **[UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist)**(const std::vector< Matrix > & mats, std::array< size_t, 2 > dim ={0, 0})<br>Constructs a new [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/) by copying existing vector of Matrix if dimensions consistent.  |
+| | **[UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist)**(std::vector< Matrix > && mats, std::array< size_t, 2 > dim ={0, 0})<br>Constructs a new [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/) by moving existing vector of Matrix if dimensions consistent.  |
+| | **[UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist)**(std::initializer_list< Matrix > mats, std::array< size_t, 2 > dim ={0, 0})<br>Constructs a new [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/) from initializer_list of Matrix if dimensions consistent.  |
+| | **[UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< D > & that)<br>Constructs a new [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/) (copy).  |
+| | **[UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< D > && that)<br>Constructs a new [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/) (move).  |
+| | **[~UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-~uniformmatrixlist)**() =default<br>Destroys the object.  |
+| const std::array< size_t, 2 > & | **[dim](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-dim)**(size_t n =0) const<br>gets dimensions of uniformly sized matrices  |
+| size_t | **[size](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-size)**()<br>size of container  |
+| const Matrix & | **[at](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-at)**(size_t n)<br>gets reference to n^th element  |
+| void | **[Swap](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-swap)**(Matrix & that, size_t n)<br>swaps input matrix with n^th matrix of list  |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< D > & | **[operator=](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-operator=)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< D > & that)<br>assigns the contents (copy)  |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< D > & | **[operator=](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-operator=)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< D > && that)<br>assigns the contents (move)  |
+| void | **[append](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-append)**(const Matrix & mat)<br>appends a matrix to the list  |
+
+## Detailed Description
+
+```cpp
+template <MatrixListFreeDim D =kMatFreeDimNone>
+class lds::UniformMatrixList;
+```
+
+
+---
+---
+## Public Function Details
+
+### **UniformMatrixList**
+
+```cpp
+UniformMatrixList() =default
+```
+
+
+
+---
+### **UniformMatrixList**
+
+```cpp
+explicit UniformMatrixList(
+    const std::vector< Matrix > & mats,
+    std::array< size_t, 2 > dim ={0, 0}
+)
+```
+
+
+
+**Parameters**:
+
+  * **mats** input matrices 
+  * **dim** dimensions 
+
+
+---
+### **UniformMatrixList**
+
+```cpp
+explicit UniformMatrixList(
+    std::vector< Matrix > && mats,
+    std::array< size_t, 2 > dim ={0, 0}
+)
+```
+
+
+
+**Parameters**:
+
+  * **mats** input matrices 
+  * **dim** dimensions 
+
+
+---
+### **UniformMatrixList**
+
+```cpp
+UniformMatrixList(
+    std::initializer_list< Matrix > mats,
+    std::array< size_t, 2 > dim ={0, 0}
+)
+```
+
+
+
+**Parameters**:
+
+  * **mats** input matrices 
+  * **dim** dimensions 
+
+
+---
+### **UniformMatrixList**
+
+```cpp
+UniformMatrixList(
+    const UniformMatrixList< D > & that
+)
+```
+
+
+
+**Parameters**:
+
+  * **that** another [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)
+
+
+---
+### **UniformMatrixList**
+
+```cpp
+UniformMatrixList(
+    UniformMatrixList< D > && that
+)
+```
+
+
+
+**Parameters**:
+
+  * **that** another [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)
+
+
+---
+### **~UniformMatrixList**
+
+```cpp
+~UniformMatrixList() =default
+```
+
+
+
+---
+### **dim**
+
+```cpp
+inline const std::array< size_t, 2 > & dim(
+    size_t n =0
+) const
+```
+
+
+
+**Parameters**:
+
+  * **n** [optional] index in list of matrices
+
+
+**Return**: dimensions 
+
+---
+### **size**
+
+```cpp
+inline size_t size()
+```
+
+
+
+---
+### **at**
+
+```cpp
+inline const Matrix & at(
+    size_t n
+)
+```
+
+
+
+---
+### **Swap**
+
+```cpp
+inline void Swap(
+    Matrix & that,
+    size_t n
+)
+```
+
+
+
+**Parameters**:
+
+  * **that** input matrix 
+  * **n** index where the matrix is moved 
+
+
+---
+### **operator=**
+
+```cpp
+inline UniformMatrixList< D > & operator=(
+    const UniformMatrixList< D > & that
+)
+```
+
+
+
+**Parameters**:
+
+  * **that** another [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)
+
+
+**Return**: reference to object 
+
+---
+### **operator=**
+
+```cpp
+inline UniformMatrixList< D > & operator=(
+    UniformMatrixList< D > && that
+)
+```
+
+
+
+**Parameters**:
+
+  * **that** another [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)
+
+
+**Return**: reference to object 
+
+---
+### **append**
+
+```cpp
+void append(
+    const Matrix & mat
+)
+```
+
+
+
+**Parameters**:
+
+  * **mat** input matrix 
+
+
+---
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_uniform_system_list.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_uniform_system_list.md
new file mode 100644
index 00000000..936fae46
--- /dev/null
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_uniform_system_list.md
@@ -0,0 +1,235 @@
+---
+title: lds::UniformSystemList
+
+---
+
+# lds::UniformSystemList
+
+
+
+ [More...](#detailed-description)
+
+Inherits from std::vector< System >
+
+## Public Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist)**() =default<br>Constructs a new [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/).  |
+| | **[UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist)**(const std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) > & systems, std::array< size_t, 3 > dim ={0, 0, 0})<br>Constructs a new [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/) by copying existing vector of [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) if dimensions consistent.  |
+| | **[UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist)**(std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) > && systems, std::array< size_t, 3 > dim ={0, 0, 0})<br>Constructs a new [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/) by moving existing vector of [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) if dimensions consistent.  |
+| | **[UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist)**(std::initializer_list< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) > systems, std::array< size_t, 3 > dim ={0, 0, 0})<br>Constructs a new [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/) from initializer_list of [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) if dimensions consistent.  |
+| | **[UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist)**(const [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/) & that)<br>Constructs a new [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/) (copy).  |
+| | **[UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist)**([UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/) && that)<br>Constructs a new [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/) (move).  |
+| | **[~UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-~uniformsystemlist)**() =default<br>Destroys the object.  |
+| const std::array< size_t, 3 > & | **[dim](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-dim)**() const<br>gets dimensions of the uniformly sized systems  |
+| size_t | **[size](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-size)**()<br>size of container  |
+| const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) & | **[at](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-at)**(size_t n)<br>gets reference to n^th element  |
+| void | **[Swap](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-swap)**([System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) & that, size_t n)<br>swaps input system with n^th system of list  |
+| [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/) & | **[operator=](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-operator=)**(const [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/) & that)<br>assigns the contents (copy)  |
+| [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/) & | **[operator=](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-operator=)**([UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/) && that)<br>assigns the contents (move)  |
+
+## Detailed Description
+
+```cpp
+template <typename System >
+class lds::UniformSystemList;
+```
+
+
+---
+---
+## Public Function Details
+
+### **UniformSystemList**
+
+```cpp
+UniformSystemList() =default
+```
+
+
+
+---
+### **UniformSystemList**
+
+```cpp
+explicit UniformSystemList(
+    const std::vector< System > & systems,
+    std::array< size_t, 3 > dim ={0, 0, 0}
+)
+```
+
+
+
+**Parameters**:
+
+  * **systems** input systems 
+  * **dim** dimensions (n_u, n_x, n_y) 
+
+
+---
+### **UniformSystemList**
+
+```cpp
+explicit UniformSystemList(
+    std::vector< System > && systems,
+    std::array< size_t, 3 > dim ={0, 0, 0}
+)
+```
+
+
+
+**Parameters**:
+
+  * **systems** input systems 
+  * **dim** dimensions (n_u, n_x, n_y) 
+
+
+---
+### **UniformSystemList**
+
+```cpp
+UniformSystemList(
+    std::initializer_list< System > systems,
+    std::array< size_t, 3 > dim ={0, 0, 0}
+)
+```
+
+
+
+**Parameters**:
+
+  * **systems** input systems 
+  * **dim** dimensions (n_u, n_x, n_y) 
+
+
+---
+### **UniformSystemList**
+
+```cpp
+UniformSystemList(
+    const UniformSystemList & that
+)
+```
+
+
+
+**Parameters**:
+
+  * **that** another [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/)
+
+
+---
+### **UniformSystemList**
+
+```cpp
+UniformSystemList(
+    UniformSystemList && that
+)
+```
+
+
+
+**Parameters**:
+
+  * **that** another [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/)
+
+
+---
+### **~UniformSystemList**
+
+```cpp
+~UniformSystemList() =default
+```
+
+
+
+---
+### **dim**
+
+```cpp
+inline const std::array< size_t, 3 > & dim() const
+```
+
+
+
+---
+### **size**
+
+```cpp
+inline size_t size()
+```
+
+
+
+---
+### **at**
+
+```cpp
+inline const System & at(
+    size_t n
+)
+```
+
+
+
+---
+### **Swap**
+
+```cpp
+inline void Swap(
+    System & that,
+    size_t n
+)
+```
+
+
+
+**Parameters**:
+
+  * **that** input system 
+  * **n** index where the system is moved 
+
+
+---
+### **operator=**
+
+```cpp
+inline UniformSystemList & operator=(
+    const UniformSystemList & that
+)
+```
+
+
+
+**Parameters**:
+
+  * **that** another [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/)
+
+
+**Return**: reference to object 
+
+---
+### **operator=**
+
+```cpp
+inline UniformSystemList & operator=(
+    UniformSystemList && that
+)
+```
+
+
+
+**Parameters**:
+
+  * **that** another [UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/)
+
+
+**Return**: reference to object 
+
+---
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_uniform_vector_list.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_uniform_vector_list.md
new file mode 100644
index 00000000..7f443dd4
--- /dev/null
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_uniform_vector_list.md
@@ -0,0 +1,225 @@
+---
+title: lds::UniformVectorList
+
+---
+
+# lds::UniformVectorList
+
+
+
+Inherits from std::vector< Vector >
+
+## Public Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist)**() =default<br>Constructs a new [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/).  |
+| | **[UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist)**(const std::vector< Vector > & vecs, size_t dim =0)<br>Constructs a new [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/) by copying existing vector of Vector if dimensions consistent.  |
+| | **[UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist)**(std::vector< Vector > && vecs, size_t dim =0)<br>Constructs a new [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/) by moving existing vector of Vector if dimensions consistent.  |
+| | **[UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist)**(std::initializer_list< Vector > vecs, size_t dim =0)<br>Constructs a new [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/) from initializer_list of Vector if dimensions consistent.  |
+| | **[UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist)**(const [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/) & that)<br>Constructs a new [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/) (copy)  |
+| | **[UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist)**([UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/) && that)<br>Constructs a new [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/) (move)  |
+| | **[~UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-~uniformvectorlist)**() =default<br>Destroys the object.  |
+| size_t | **[dim](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-dim)**() const<br>gets dimensions of the uniformly sized matrices  |
+| size_t | **[size](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-size)**()<br>size of container  |
+| const Vector & | **[at](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-at)**(size_t n)<br>gets reference to n^th element  |
+| void | **[Swap](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-swap)**(Vector & that, size_t n)<br>swaps input matrix with n^th vector of list  |
+| [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/) & | **[operator=](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-operator=)**(const [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/) & that)<br>assigns the contents (copy)  |
+| [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/) & | **[operator=](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-operator=)**([UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/) && that)<br>assigns the contents (move)  |
+
+---
+---
+## Public Function Details
+
+### **UniformVectorList**
+
+```cpp
+UniformVectorList() =default
+```
+
+
+
+---
+### **UniformVectorList**
+
+```cpp
+explicit UniformVectorList(
+    const std::vector< Vector > & vecs,
+    size_t dim =0
+)
+```
+
+
+
+**Parameters**:
+
+  * **vecs** input vectors 
+  * **dims** dimension 
+
+
+---
+### **UniformVectorList**
+
+```cpp
+explicit UniformVectorList(
+    std::vector< Vector > && vecs,
+    size_t dim =0
+)
+```
+
+
+
+**Parameters**:
+
+  * **vecs** input vectors 
+  * **dim** dimension 
+
+
+---
+### **UniformVectorList**
+
+```cpp
+UniformVectorList(
+    std::initializer_list< Vector > vecs,
+    size_t dim =0
+)
+```
+
+
+
+**Parameters**:
+
+  * **vecs** input vectors 
+  * **dim** dimension 
+
+
+---
+### **UniformVectorList**
+
+```cpp
+UniformVectorList(
+    const UniformVectorList & that
+)
+```
+
+
+
+**Parameters**:
+
+  * **that** another [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/)
+
+
+---
+### **UniformVectorList**
+
+```cpp
+UniformVectorList(
+    UniformVectorList && that
+)
+```
+
+
+
+**Parameters**:
+
+  * **that** another [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/)
+
+
+---
+### **~UniformVectorList**
+
+```cpp
+~UniformVectorList() =default
+```
+
+
+
+---
+### **dim**
+
+```cpp
+inline size_t dim() const
+```
+
+
+
+---
+### **size**
+
+```cpp
+inline size_t size()
+```
+
+
+
+---
+### **at**
+
+```cpp
+inline const Vector & at(
+    size_t n
+)
+```
+
+
+
+---
+### **Swap**
+
+```cpp
+inline void Swap(
+    Vector & that,
+    size_t n
+)
+```
+
+
+
+**Parameters**:
+
+  * **that** input vector 
+  * **n** index where the vector is moved 
+
+
+---
+### **operator=**
+
+```cpp
+inline UniformVectorList & operator=(
+    const UniformVectorList & that
+)
+```
+
+
+
+**Parameters**:
+
+  * **that** another [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/)
+
+
+**Return**: reference to object 
+
+---
+### **operator=**
+
+```cpp
+inline UniformVectorList & operator=(
+    UniformVectorList && that
+)
+```
+
+
+
+**Parameters**:
+
+  * **that** another [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/)
+
+
+**Return**: reference to object 
+
+---
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1Controller.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1Controller.md
deleted file mode 100644
index e8f65930..00000000
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1Controller.md
+++ /dev/null
@@ -1,116 +0,0 @@
----
-title: lds::gaussian::Controller
-summary: Gaussian-observation Controller Type. 
-
----
-
-# lds::gaussian::Controller
-
-
-
-Gaussian-observation [Controller]() Type. 
-<br /> `#include <lds_gaussian_ctrl.h>`
-
-Inherits from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/)
-
-## Public Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| virtual void | **[set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1controller/#function-set-y-ref)**(const Vector & y_ref) override<br>sets reference output  |
-
-## Additional inherited members
-
-**Public Functions inherited from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controller)**() =default<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/).  |
-| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controller)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) & sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/).  |
-| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controller)**([System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) && sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/) by moving the system object.  |
-| const Vector & | **[Control](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-control)**(const Vector & z, bool do_control =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal (single-step)  |
-| const Vector & | **[ControlOutputReference](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controloutputreference)**(const Vector & z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal, given previously-set (single-step)  |
-| const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) & | **[sys](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-sys)**() const |
-| const Matrix & | **[Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-kc)**() const<br>Get state feedback controller gain.  |
-| const Matrix & | **[Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-kc-inty)**() const<br>Get integral controller gain.  |
-| const Matrix & | **[Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-kc-u)**() const<br>Get input feedback controller gain.  |
-| const Vector & | **[g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-g-design)**() const<br>Get input gain used in controller design.  |
-| const Vector & | **[u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-u-ref)**() const<br>Get reference input.  |
-| const Vector & | **[x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-x-ref)**() const<br>Get reference state.  |
-| const Vector & | **[y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-y-ref)**() const<br>Get reference output.  |
-| size_t | **[control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-control-type)**() const<br>Get controller type.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-tau-awu)**() const<br>Get time constant of anti-integral-windup.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-u-lb)**() const<br>Get control lower bound.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-u-ub)**() const<br>Get control upper bound.  |
-| void | **[set_sys](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-sys)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) & sys)<br>Set system.  |
-| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-g-design)**(const Vector & g_design)<br>Set input gain used in controller design (g_design)  |
-| void | **[set_u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-u-ref)**(const Vector & u_ref)<br>Set reference input (u_ref)  |
-| void | **[set_x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-x-ref)**(const Vector & x_ref)<br>Set reference state (x_ref)  |
-| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-kc)**(const Matrix & Kc)<br>Set state controller gain.  |
-| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-kc-inty)**(const Matrix & Kc_inty)<br>Set integral controller gain.  |
-| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-kc-u)**(const Matrix & Kc_u)<br>Set input controller gain.  |
-| void | **[set_tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-tau-awu)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) tau)<br>Set time constant of anti-integral-windup.  |
-| void | **[set_control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-control-type)**(size_t control_type)<br>Sets the control type.  |
-| void | **[set_u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-u-lb)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb)<br>sets control lower bound  |
-| void | **[set_u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-u-ub)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub)<br>Sets control upper bound.  |
-| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-reset)**()<br>reset system and control variables.  |
-| void | **[Print](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-print)**()<br>prints variables to stdout  |
-
-**Protected Attributes inherited from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) | **[sys_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-sys-)** <br>underlying LDS  |
-| Vector | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-)** <br>control signal  |
-| Vector | **[u_return_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-return-)** <br>control signal that is _returned_ to user  |
-| Vector | **[g_design_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-g-design-)** <br>input gain of the system used for controller design  |
-| Vector | **[u_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-ref-)** <br>reference input  |
-| Vector | **[u_ref_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-ref-prev-)** <br>reference input at previous time step  |
-| Vector | **[x_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-x-ref-)** <br>reference state  |
-| Vector | **[y_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-y-ref-)** <br>reference output  |
-| Vector | **[cx_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-cx-ref-)**  |
-| Matrix | **[Kc_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-kc-)** <br>state controller gain  |
-| Matrix | **[Kc_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-kc-u-)** <br>input controller gain (optional when control updates )  |
-| Matrix | **[Kc_inty_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-kc-inty-)** <br>integral controller gain  |
-| Vector | **[du_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-du-ref-)**  |
-| Vector | **[dv_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-dv-ref-)**  |
-| Vector | **[v_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-v-ref-)**  |
-| Vector | **[dv_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-dv-)**  |
-| Vector | **[v_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-v-)** <br>Control after g inversion (e.g., control in physical units)  |
-| Vector | **[int_e_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-int-e-)** <br>integrated error  |
-| Vector | **[int_e_awu_adjust_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-int-e-awu-adjust-)** <br>anti-windup adjustment to intE  |
-| Vector | **[u_sat_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-sat-)** <br>control signal after saturation (for antiWindup)  |
-| bool | **[do_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-do-control-prev-)**  |
-| bool | **[do_lock_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-do-lock-control-prev-)**  |
-| bool | **[u_saturated_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-saturated-)** <br>whether control signal has reached saturation limits  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-lb-)** <br>lower bound on control  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-ub-)** <br>upper bound on control  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-tau-awu-)** <br>antiwindup time constant  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[k_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-k-awu-)**  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[t_since_control_onset_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-t-since-control-onset-)** <br>time since control epoch onset  |
-| size_t | **[control_type_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-control-type-)** <br>controller type  |
-
-
----
----
-## Public Function Details
-
-### **set_y_ref**
-
-```cpp
-inline virtual void set_y_ref(
-    const Vector & y_ref
-) override
-```
-
-
-
-**Reimplements**: [lds::Controller::set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-y-ref)
-
-
----
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1Fit.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1Fit.md
deleted file mode 100644
index 5334c351..00000000
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1Fit.md
+++ /dev/null
@@ -1,168 +0,0 @@
----
-title: lds::gaussian::Fit
-summary: GLDS Fit Type. 
-
----
-
-# lds::gaussian::Fit
-
-
-
-GLDS [Fit]() Type. 
-<br /> `#include <lds_gaussian_fit.h>`
-
-Inherits from [lds::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/)
-
-## Public Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/#function-fit)**() =default |
-| | **[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/#function-fit)**(size_t n_u, size_t n_x, size_t n_y, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt)<br>Constructs a new [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/).  |
-| virtual const Matrix & | **[R](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/#function-r)**() const override<br>gets measurement noise covariance  |
-| virtual void | **[set_R](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/#function-set-r)**(const Matrix & R) override<br>sets measurement noise covariance  |
-| virtual View | **[h](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/#function-h)**(Matrix & y, const Matrix & x, size_t t) override<br>output function  |
-
-## Additional inherited members
-
-**Public Functions inherited from [lds::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| virtual | **[~Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-~fit)**() =default |
-| size_t | **[n_u](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-n-u)**() const<br>gets number of inputs  |
-| size_t | **[n_x](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-n-x)**() const<br>gets number of states  |
-| size_t | **[n_y](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-n-y)**() const<br>gets number of outputs  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-dt)**() const<br>gets sample period  |
-| const Matrix & | **[A](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-a)**() const<br>gets state matrix  |
-| const Matrix & | **[B](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-b)**() const<br>gets input matrix  |
-| const Vector & | **[g](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-g)**() const<br>gets input gain  |
-| const Vector & | **[m](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-m)**() const<br>gets process disturbance  |
-| const Matrix & | **[Q](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-q)**() const<br>gets process noise covariance  |
-| const Vector & | **[x0](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-x0)**() const<br>gets initial state estimate  |
-| const Matrix & | **[P0](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-p0)**() const<br>gets covariance of initial state estimate  |
-| const Matrix & | **[C](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-c)**() const<br>gets output matrix  |
-| const Vector & | **[d](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-d)**() const<br>gets output bias  |
-| void | **[set_A](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-a)**(const Matrix & A)<br>sets state matrix  |
-| void | **[set_B](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-b)**(const Matrix & B)<br>sets input matrix  |
-| void | **[set_g](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-g)**(const Vector & g)<br>sets input gain/conversion factor  |
-| void | **[set_m](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-m)**(const Vector & m)<br>sets process disturbance  |
-| void | **[set_Q](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-q)**(const Matrix & Q)<br>sets process noise covariance  |
-| void | **[set_x0](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-x0)**(const Vector & x0)<br>sets initial state estimate  |
-| void | **[set_P0](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-p0)**(const Matrix & P0)<br>sets initial state estimate covariance  |
-| void | **[set_C](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-c)**(const Matrix & C)<br>sets output matrix  |
-| void | **[set_d](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-d)**(const Vector & d)<br>sets output bias  |
-| View | **[f](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-f)**(Matrix & x, const Matrix & u, size_t t)<br>system dynamics function  |
-| View | **[f](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-f)**(Matrix & x_pre, const Matrix & x_post, const Matrix & u, size_t t)<br>system dynamics function  |
-
-**Protected Attributes inherited from [lds::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-dt-)** <br>sample period  |
-| Matrix | **[A_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-a-)** <br>state matrix  |
-| Matrix | **[B_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-b-)** <br>input matrix  |
-| Vector | **[g_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-g-)** <br>input gain  |
-| Vector | **[m_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-m-)** <br>process noise mean  |
-| Matrix | **[Q_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-q-)** <br>process noise cov  |
-| Matrix | **[C_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-c-)** <br>output matrix  |
-| Vector | **[d_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-d-)** <br>output bias  |
-| Matrix | **[R_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-r-)** <br>measurement noise  |
-| Vector | **[x0_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-x0-)** <br>initial state  |
-| Matrix | **[P0_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-p0-)** <br>initial covar  |
-| size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-n-u-)** <br>number of inputs  |
-| size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-n-x-)** <br>number of states  |
-| size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-n-y-)** <br>number of outputs  |
-
-
----
----
-## Public Function Details
-
-### **Fit**
-
-```cpp
-Fit() =default
-```
-
-
-
----
-### **Fit**
-
-```cpp
-Fit(
-    size_t n_u,
-    size_t n_x,
-    size_t n_y,
-    data_t dt
-)
-```
-
-
-
-**Parameters**:
-
-  * **n_u** number of inputs 
-  * **n_x** number of states 
-  * **n_y** number of outputs 
-  * **dt** sample period 
-
-
----
-### **R**
-
-```cpp
-inline virtual const Matrix & R() const override
-```
-
-
-
-**Reimplements**: [lds::Fit::R](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-r)
-
-
----
-### **set_R**
-
-```cpp
-inline virtual void set_R(
-    const Matrix & R
-) override
-```
-
-
-
-**Reimplements**: [lds::Fit::set_R](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-r)
-
-
----
-### **h**
-
-```cpp
-inline virtual View h(
-    Matrix & y,
-    const Matrix & x,
-    size_t t
-) override
-```
-
-
-
-**Parameters**:
-
-  * **y** output estimate (over time) 
-  * **x** state estimate (over time) 
-  * **t** time index
-
-
-**Return**: output 
-
-**Reimplements**: [lds::Fit::h](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-h)
-
-
----
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1FitEM.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1FitEM.md
deleted file mode 100644
index 2a3d68db..00000000
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1FitEM.md
+++ /dev/null
@@ -1,91 +0,0 @@
----
-title: lds::gaussian::FitEM
-summary: GLDS E-M Fit Type. 
-
----
-
-# lds::gaussian::FitEM
-
-
-
-GLDS E-M [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/) Type.  [More...](#detailed-description)
-
-
-<br /> `#include <lds_gaussian_fit_em.h>`
-
-Inherits from [lds::EM< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)
-
-## Additional inherited members
-
-**Public Functions inherited from [lds::EM< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-em)**() =default<br>Constructs a new [EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)[Fit]() type.  |
-| | **[EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-em)**(size_t n_x, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && u_train, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && z_train)<br>Constructs a new [EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)[Fit]() type.  |
-| | **[EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-em)**(const [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/) & fit0, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && u_train, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && z_train)<br>Constructs a new [EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)[Fit]() type.  |
-| virtual | **[~EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-~em)**() =default |
-| const [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/) & | **[Run](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-run)**(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) tol =1e-2)<br>Runs fitting by Expectation(E)-Maximization(M)  |
-| std::tuple< [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) >, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > > | **[ReturnData](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-returndata)**()<br>Returns the input/output data to caller.  |
-| const std::vector< Matrix > & | **[x](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-x)**() const<br>gets estimated state (over time)  |
-| const std::vector< Matrix > & | **[y](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-y)**() const<br>gets estimated output (over time)  |
-| const Matrix & | **[sum_E_x_t_x_t](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-sum-e-x-t-x-t)**() const<br>gets state-input covariance  |
-| const Matrix & | **[sum_E_xu_tm1_xu_tm1](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-sum-e-xu-tm1-xu-tm1)**() const<br>gets state-input covariance (t-minus-1)  |
-| const Matrix & | **[sum_E_xu_t_xu_tm1](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-sum-e-xu-t-xu-tm1)**() const<br>gets single lag state-input covariance  |
-| size_t | **[n_t_tot](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-n-t-tot)**()<br>total number of time samples  |
-| const Vector & | **[theta](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-theta)**() const<br>gets parameters updated in M step  |
-
-**Protected Functions inherited from [lds::EM< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| void | **[Expectation](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-expectation)**(bool force_common_initial =false)<br>Expectation step.  |
-| void | **[Maximization](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-maximization)**(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)<br>Maximization step.  |
-| void | **[MaximizeDynamics](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-maximizedynamics)**() |
-| void | **[MaximizeQ](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-maximizeq)**() |
-| void | **[MaximizeInitial](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-maximizeinitial)**() |
-| void | **[Smooth](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-smooth)**(bool force_common_initial)<br>get smoothed estimates  |
-| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-reset)**()<br>reset to initial conditions  |
-| void | **[InitVars](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-initvars)**()<br>Initializes the variables.  |
-| Vector | **[UpdateTheta](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-updatetheta)**()<br>updates parameter list, theta  |
-
-**Protected Attributes inherited from [lds::EM< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-u-)** <br>input training data  |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[z_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-z-)** <br>measurement training data  |
-| std::vector< Matrix > | **[x_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-x-)** <br>state estimate  |
-| std::vector< Cube > | **[P_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-p-)** <br>state estimate cov  |
-| std::vector< Cube > | **[P_t_tm1_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-p-t-tm1-)** <br>single-lag state covariance  |
-| std::vector< Matrix > | **[y_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-y-)** <br>output estimate  |
-| Matrix | **[diag_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-diag-y-)**  |
-| Matrix | **[sum_E_x_t_x_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-sum-e-x-t-x-t-)** <br>state covariance (current time)  |
-| Matrix | **[sum_E_xu_tm1_xu_tm1_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-sum-e-xu-tm1-xu-tm1-)** <br>state-input covariance (t-minus-1)  |
-| Matrix | **[sum_E_xu_t_xu_tm1_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-sum-e-xu-t-xu-tm1-)** <br>single lag state-input covariance  |
-| [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/) | **[fit_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-fit-)**  |
-| Vector | **[theta_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-theta-)**  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-dt-)** <br>sample period  |
-| size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-n-u-)** <br>number of inputs  |
-| size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-n-x-)** <br>number of states  |
-| size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-n-y-)** <br>number of outputs  |
-| size_t | **[n_trials_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-n-trials-)** <br>number of input/output data sequences  |
-| std::vector< size_t > | **[n_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-n-t-)** <br>number of time steps  |
-| size_t | **[n_t_tot_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-n-t-tot-)** <br>total number of time steps across trials  |
-
-
-## Detailed Description
-
-```cpp
-class lds::gaussian::FitEM;
-```
-
-
-
-This type is used in the process of fitting GLDS models by expectation-maximization ([EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)). 
-
----
----
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1FitSSID.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1FitSSID.md
deleted file mode 100644
index 026a136c..00000000
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1FitSSID.md
+++ /dev/null
@@ -1,63 +0,0 @@
----
-title: lds::gaussian::FitSSID
-summary: Subspace Identification (SSID) for GLDS. 
-
----
-
-# lds::gaussian::FitSSID
-
-
-
-Subspace Identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)) for GLDS. 
-<br /> `#include <lds_gaussian_fit_ssid.h>`
-
-Inherits from [lds::SSID< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)
-
-## Additional inherited members
-
-**Public Functions inherited from [lds::SSID< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-ssid)**() =default<br>Constructs a new [SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/) type.  |
-| | **[SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-ssid)**(size_t n_x, size_t n_h, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && u_train, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && z_train, const Vector & d =Vector(1).fill(-kInf))<br>Constructs a new [SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/) type.  |
-| std::tuple< [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/), Vector > | **[Run](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-run)**([SSIDWt](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt) ssid_wt)<br>Runs fitting by subspace identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/))  |
-| std::tuple< [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) >, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > > | **[ReturnData](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-returndata)**()<br>Returns the I/O data to caller.  |
-
-**Protected Functions inherited from [lds::SSID< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| void | **[CalcD](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-calcd)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) t_silence =0.1, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) thresh_silence =0.001)<br>Using periods of silence in inputs (u), calculates the output \ bias (d)  |
-| void | **[CreateHankelDataMat](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-createhankeldatamat)**()<br>Creates the block-hankel I/O data matrix.  |
-| void | **[CalcSVD](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-calcsvd)**([SSIDWt](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt) wt)<br>performs the singular value decomposition (SVD)  |
-| void | **[Solve](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-solve)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) wt_dc)<br>solves for LDS parameters  |
-| void | **[RecomputeExtObs](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-recomputeextobs)**()<br>recompute extended observability matrix from estimates of A, C  |
-
-**Protected Attributes inherited from [lds::SSID< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-u-)** <br>input training data  |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[z_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-z-)** <br>measurement training data  |
-| Matrix | **[D_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-d-)** <br>block-Hankel I/O data matrix  |
-| [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/) | **[fit_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-fit-)** <br>fit  |
-| Matrix | **[g_dc_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-g-dc-)** <br>I/O gain @ DC.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-dt-)** <br>sample period  |
-| size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-n-u-)** <br>number of inputs  |
-| size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-n-x-)** <br>number of states  |
-| size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-n-y-)** <br>number of outputs  |
-| size_t | **[n_h_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-n-h-)**  |
-| size_t | **[n_trials_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-n-trials-)** <br>number of input/output data sequences  |
-| std::vector< size_t > | **[n_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-n-t-)** <br>number of time steps  |
-| size_t | **[n_t_tot_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-n-t-tot-)** <br>total number of time steps across trials  |
-| Matrix | **[L_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-l-)** <br>lower triangle decomp of covariance matrix  |
-| Vector | **[s_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-s-)** <br>singular values  |
-| Matrix | **[ext_obs_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-ext-obs-t-)** <br>extended observability matrix  |
-
-
----
----
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1SwitchedController.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1SwitchedController.md
deleted file mode 100644
index bddd9804..00000000
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1SwitchedController.md
+++ /dev/null
@@ -1,145 +0,0 @@
----
-title: lds::gaussian::SwitchedController
-summary: Gaussian-observation SwitchedController Type. 
-
----
-
-# lds::gaussian::SwitchedController
-
-
-
-Gaussian-observation [SwitchedController]() Type. 
-<br /> `#include <lds_gaussian_sctrl.h>`
-
-Inherits from [lds::SwitchedController< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/), [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/)
-
-## Public Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| virtual void | **[set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1switchedcontroller/#function-set-y-ref)**(const Vector & y_ref) override<br>sets reference output  |
-
-## Additional inherited members
-
-**Public Functions inherited from [lds::SwitchedController< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-switchedcontroller)**() =default<br>Constructs a new [SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/).  |
-| | **[SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-switchedcontroller)**(const std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) > & systems, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/).  |
-| | **[SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-switchedcontroller)**(std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) > && systems, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/) (moves systems).  |
-| void | **[Switch](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-switch)**(size_t idx, bool do_force_switch =false)<br>Switch to a different sub-system/controller.  |
-| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-kc)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)<> & Kc)<br>sets state feedback gains  |
-| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-kc)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)<> && Kc)<br>sets state feedback gains (moving)  |
-| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-kc-inty)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)<> & Kc_inty)<br>sets integral feedback gains  |
-| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-kc-inty)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)<> && Kc_inty)<br>sets integral feedback gains (moving)  |
-| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-kc-u)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)<> & Kc_u)<br>sets input feedback gains  |
-| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-kc-u)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)<> && Kc_u)<br>sets input feedback gains (moving)  |
-| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-g-design)**(const [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/) & g)<br>sets input gain used during controller design  |
-| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-g-design)**([UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/) && g)<br>sets input gain used during controller design (moving)  |
-
-**Protected Attributes inherited from [lds::SwitchedController< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) > | **[systems_](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#variable-systems-)** <br>underlying sub-systems which are switched between  |
-| size_t | **[n_sys_](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#variable-n-sys-)** <br>number of systems  |
-| size_t | **[idx_](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#variable-idx-)** <br>current system/controller index.  |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/) | **[Kc_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#variable-kc-list-)**  |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/) | **[Kc_inty_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#variable-kc-inty-list-)**  |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/) | **[Kc_u_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#variable-kc-u-list-)**  |
-| [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/) | **[g_design_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#variable-g-design-list-)**  |
-
-**Public Functions inherited from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controller)**() =default<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/).  |
-| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controller)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) & sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/).  |
-| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controller)**([System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) && sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/) by moving the system object.  |
-| const Vector & | **[Control](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-control)**(const Vector & z, bool do_control =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal (single-step)  |
-| const Vector & | **[ControlOutputReference](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controloutputreference)**(const Vector & z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal, given previously-set (single-step)  |
-| const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) & | **[sys](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-sys)**() const |
-| const Matrix & | **[Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-kc)**() const<br>Get state feedback controller gain.  |
-| const Matrix & | **[Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-kc-inty)**() const<br>Get integral controller gain.  |
-| const Matrix & | **[Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-kc-u)**() const<br>Get input feedback controller gain.  |
-| const Vector & | **[g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-g-design)**() const<br>Get input gain used in controller design.  |
-| const Vector & | **[u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-u-ref)**() const<br>Get reference input.  |
-| const Vector & | **[x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-x-ref)**() const<br>Get reference state.  |
-| const Vector & | **[y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-y-ref)**() const<br>Get reference output.  |
-| size_t | **[control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-control-type)**() const<br>Get controller type.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-tau-awu)**() const<br>Get time constant of anti-integral-windup.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-u-lb)**() const<br>Get control lower bound.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-u-ub)**() const<br>Get control upper bound.  |
-| void | **[set_sys](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-sys)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) & sys)<br>Set system.  |
-| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-g-design)**(const Vector & g_design)<br>Set input gain used in controller design (g_design)  |
-| void | **[set_u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-u-ref)**(const Vector & u_ref)<br>Set reference input (u_ref)  |
-| void | **[set_x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-x-ref)**(const Vector & x_ref)<br>Set reference state (x_ref)  |
-| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-kc)**(const Matrix & Kc)<br>Set state controller gain.  |
-| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-kc-inty)**(const Matrix & Kc_inty)<br>Set integral controller gain.  |
-| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-kc-u)**(const Matrix & Kc_u)<br>Set input controller gain.  |
-| void | **[set_tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-tau-awu)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) tau)<br>Set time constant of anti-integral-windup.  |
-| void | **[set_control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-control-type)**(size_t control_type)<br>Sets the control type.  |
-| void | **[set_u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-u-lb)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb)<br>sets control lower bound  |
-| void | **[set_u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-u-ub)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub)<br>Sets control upper bound.  |
-| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-reset)**()<br>reset system and control variables.  |
-| void | **[Print](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-print)**()<br>prints variables to stdout  |
-
-**Protected Attributes inherited from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) | **[sys_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-sys-)** <br>underlying LDS  |
-| Vector | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-)** <br>control signal  |
-| Vector | **[u_return_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-return-)** <br>control signal that is _returned_ to user  |
-| Vector | **[g_design_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-g-design-)** <br>input gain of the system used for controller design  |
-| Vector | **[u_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-ref-)** <br>reference input  |
-| Vector | **[u_ref_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-ref-prev-)** <br>reference input at previous time step  |
-| Vector | **[x_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-x-ref-)** <br>reference state  |
-| Vector | **[y_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-y-ref-)** <br>reference output  |
-| Vector | **[cx_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-cx-ref-)**  |
-| Matrix | **[Kc_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-kc-)** <br>state controller gain  |
-| Matrix | **[Kc_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-kc-u-)** <br>input controller gain (optional when control updates )  |
-| Matrix | **[Kc_inty_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-kc-inty-)** <br>integral controller gain  |
-| Vector | **[du_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-du-ref-)**  |
-| Vector | **[dv_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-dv-ref-)**  |
-| Vector | **[v_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-v-ref-)**  |
-| Vector | **[dv_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-dv-)**  |
-| Vector | **[v_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-v-)** <br>Control after g inversion (e.g., control in physical units)  |
-| Vector | **[int_e_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-int-e-)** <br>integrated error  |
-| Vector | **[int_e_awu_adjust_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-int-e-awu-adjust-)** <br>anti-windup adjustment to intE  |
-| Vector | **[u_sat_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-sat-)** <br>control signal after saturation (for antiWindup)  |
-| bool | **[do_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-do-control-prev-)**  |
-| bool | **[do_lock_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-do-lock-control-prev-)**  |
-| bool | **[u_saturated_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-saturated-)** <br>whether control signal has reached saturation limits  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-lb-)** <br>lower bound on control  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-ub-)** <br>upper bound on control  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-tau-awu-)** <br>antiwindup time constant  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[k_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-k-awu-)**  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[t_since_control_onset_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-t-since-control-onset-)** <br>time since control epoch onset  |
-| size_t | **[control_type_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-control-type-)** <br>controller type  |
-
-
----
----
-## Public Function Details
-
-### **set_y_ref**
-
-```cpp
-inline virtual void set_y_ref(
-    const Vector & y_ref
-) override
-```
-
-
-
-**Reimplements**: [lds::Controller::set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-y-ref)
-
-
----
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1System.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1System.md
deleted file mode 100644
index 79fe52f8..00000000
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1System.md
+++ /dev/null
@@ -1,312 +0,0 @@
----
-title: lds::gaussian::System
-summary: Gaussian LDS Type. 
-
----
-
-# lds::gaussian::System
-
-
-
-Gaussian LDS Type. 
-<br /> `#include <lds_gaussian_sys.h>`
-
-Inherits from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)
-
-## Public Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/#function-system)**() =default<br>Constructs a new [System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/).  |
-| | **[System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/#function-system)**(std::size_t n_u, std::size_t n_x, std::size_t n_y, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) p0 =kDefaultP0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) q0 =kDefaultQ0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) r0 =kDefaultR0)<br>Constructs a new Gaussian [System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/).  |
-| virtual const Vector & | **[Simulate](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/#function-simulate)**(const Vector & u_tm1) override<br>Simulate system measurement.  |
-| const Matrix & | **[R](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/#function-r)**() const<br>Get output noise covariance.  |
-| void | **[set_Q](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/#function-set-q)**(const Matrix & Q) |
-| void | **[set_R](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/#function-set-r)**(const Matrix & R)<br>Set output noise covariance.  |
-| void | **[set_Ke](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/#function-set-ke)**(const Matrix & Ke)<br>Set estimator gain.  |
-| void | **[set_Ke_m](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/#function-set-ke-m)**(const Matrix & Ke_m)<br>Set disturbance estimator gain.  |
-| void | **[Print](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/#function-print)**()<br>Print system variables to stdout.  |
-
-## Protected Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| virtual void | **[h](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/#function-h)**() override<br>[System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/) output function.  |
-| virtual void | **[RecurseKe](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/#function-recurseke)**() override<br>Recursively update estimator gain.  |
-
-## Protected Attributes
-
-|                | Name           |
-| -------------- | -------------- |
-| Matrix | **[R_](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/#variable-r-)** <br>covariance of output noise  |
-| bool | **[do_recurse_Ke_](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/#variable-do-recurse-ke-)** <br>whether to recursively calculate estimator gain  |
-
-## Additional inherited members
-
-**Public Functions inherited from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| virtual | **[~System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-~system)**() |
-| void | **[Filter](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-filter)**(const Vector & u_tm1, const Vector & z)<br>Filter data to produce causal state estimates.  |
-| void | **[f](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-f)**(const Vector & u, bool do_add_noise =false)<br>system dynamics function  |
-| size_t | **[n_u](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-n-u)**() const<br>Get number of inputs.  |
-| size_t | **[n_x](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-n-x)**() const<br>Get number of states.  |
-| size_t | **[n_y](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-n-y)**() const<br>Get number of outputs.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-dt)**() const<br>Get sample period.  |
-| const Vector & | **[x](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-x)**() const<br>Get current state.  |
-| const Matrix & | **[P](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-p)**() const<br>Get covariance of state estimate.  |
-| const Vector & | **[m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-m)**() const<br>Get current process disturbance/bias.  |
-| const Matrix & | **[P_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-p-m)**() const<br>Get covariance of process disturbance estimate.  |
-| const Vector & | **[cx](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-cx)**() const<br>Get C*x.  |
-| const Vector & | **[y](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-y)**() const<br>Get output.  |
-| const Vector & | **[x0](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-x0)**() const<br>Get initial state.  |
-| const Vector & | **[m0](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-m0)**() const<br>Get initial disturbance.  |
-| const Matrix & | **[A](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-a)**() const<br>Get state matrix.  |
-| const Matrix & | **[B](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-b)**() const<br>Get input matrix.  |
-| const Vector & | **[g](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-g)**() const<br>Get input gain/conversion factor.  |
-| const Matrix & | **[C](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-c)**() const<br>Get output matrix.  |
-| const Vector & | **[d](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-d)**() const<br>Get output bias.  |
-| const Matrix & | **[Ke](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-ke)**() const<br>Get estimator gain.  |
-| const Matrix & | **[Ke_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-ke-m)**() const<br>Get estimator gain for process disturbance (m)  |
-| const Matrix & | **[Q](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-q)**()<br>Get process noise covariance.  |
-| const Matrix & | **[Q_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-q-m)**()<br>Get process noise covariance of disturbance evoluation.  |
-| const Matrix & | **[P0](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-p0)**()<br>Get covariance of initial state.  |
-| const Matrix & | **[P0_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-p0-m)**()<br>Get covariance of initial process disturbance.  |
-| void | **[set_A](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-a)**(const Matrix & A)<br>Set state matrix.  |
-| void | **[set_B](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-b)**(const Matrix & B)<br>Set input matrix.  |
-| void | **[set_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-m)**(const Vector & m, bool do_force_assign =false)<br>Set process disturbance.  |
-| void | **[set_g](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-g)**(const Vector & g)<br>Set input gain.  |
-| void | **[set_Q_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-q-m)**(const Matrix & Q_m)<br>Set process noise covariance of disturbance evoluation.  |
-| void | **[set_x0](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-x0)**(const Vector & x0)<br>Set initial state.  |
-| void | **[set_P0](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-p0)**(const Matrix & P0)<br>Set covariance of initial state.  |
-| void | **[set_P0_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-p0-m)**(const Matrix & P0_m)<br>Set covariance of initial process disturbance.  |
-| void | **[set_C](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-c)**(const Matrix & C)<br>Set output matrix.  |
-| void | **[set_d](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-d)**(const Vector & d)<br>Set output bias.  |
-| void | **[set_x](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-x)**(const Vector & x)<br>Set state of system.  |
-| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-reset)**()<br>Reset system variables.  |
-
-**Protected Functions inherited from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| void | **[InitVars](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-initvars)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) p0 =kDefaultP0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) q0 =kDefaultQ0) |
-
-**Public Attributes inherited from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| bool | **[do_adapt_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-do-adapt-m)** <br>whether to adaptively estimate disturbance m  |
-
-**Protected Attributes inherited from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| std::size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-n-x-)** <br>number of states  |
-| std::size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-n-u-)** <br>number of inputs  |
-| std::size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-n-y-)** <br>number of outputs  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-dt-)** <br>sample period  |
-| Vector | **[x_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-x-)** <br>state  |
-| Matrix | **[P_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-p-)** <br>covariance of state estimate  |
-| Vector | **[m_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-m-)** <br>process disturbance  |
-| Matrix | **[P_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-p-m-)** <br>covariance of disturbance estimate  |
-| Vector | **[cx_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-cx-)** <br>C*x.  |
-| Vector | **[y_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-y-)** <br>output  |
-| Vector | **[z_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-z-)** <br>measurement  |
-| Vector | **[x0_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-x0-)** <br>initial state  |
-| Matrix | **[P0_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-p0-)** <br>covariance of initial state estimate  |
-| Vector | **[m0_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-m0-)** <br>initial process disturbance  |
-| Matrix | **[P0_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-p0-m-)** <br>covariance of initial disturbance est.  |
-| Matrix | **[A_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-a-)** <br>state matrix  |
-| Matrix | **[B_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-b-)** <br>input matrix  |
-| Vector | **[g_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-g-)** <br>input gain  |
-| Matrix | **[Q_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-q-)** <br>covariance of process noise  |
-| Matrix | **[Q_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-q-m-)** <br>covariance of disturbance random walk  |
-| Matrix | **[C_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-c-)** <br>output matrix  |
-| Vector | **[d_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-d-)** <br>output bias  |
-| Matrix | **[Ke_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-ke-)** <br>estimator gain  |
-| Matrix | **[Ke_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-ke-m-)** <br>estimator gain for process disturbance  |
-
-
----
----
-## Public Function Details
-
-### **System**
-
-```cpp
-System() =default
-```
-
-
-
----
-### **System**
-
-```cpp
-System(
-    std::size_t n_u,
-    std::size_t n_x,
-    std::size_t n_y,
-    data_t dt,
-    data_t p0 =kDefaultP0,
-    data_t q0 =kDefaultQ0,
-    data_t r0 =kDefaultR0
-)
-```
-
-
-
-**Parameters**:
-
-  * **n_u** number of inputs (u) 
-  * **n_x** number of states (x) 
-  * **n_y** number of outputs (y) 
-  * **dt** sample period 
-  * **p0** [optional] initial diagonal elements of state estimate covariance (P) 
-  * **q0** [optional] initial diagonal elements of process noise covariance (Q) 
-  * **r0** [optional] initial diagonal elements of output noise covariance (R) 
-
-
----
-### **Simulate**
-
-```cpp
-virtual const Vector & Simulate(
-    const Vector & u_tm1
-) override
-```
-
-
-
-**Parameters**:
-
-  * **u_tm1** input at t-1
-
-
-**Return**: z measurement 
-
-**Reimplements**: [lds::System::Simulate](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-simulate)
-
-
-Simulate system and produce measurement
-
-
----
-### **R**
-
-```cpp
-inline const Matrix & R() const
-```
-
-
-
----
-### **set_Q**
-
-```cpp
-inline void set_Q(
-    const Matrix & Q
-)
-```
-
-
-
----
-### **set_R**
-
-```cpp
-inline void set_R(
-    const Matrix & R
-)
-```
-
-
-
----
-### **set_Ke**
-
-```cpp
-inline void set_Ke(
-    const Matrix & Ke
-)
-```
-
-
-
----
-### **set_Ke_m**
-
-```cpp
-inline void set_Ke_m(
-    const Matrix & Ke_m
-)
-```
-
-
-
----
-### **Print**
-
-```cpp
-void Print()
-```
-
-
-
----
-
-
-## Protected Function Details
-
-### **h**
-
-```cpp
-inline virtual void h() override
-```
-
-
-
-**Reimplements**: [lds::System::h](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-h)
-
-
----
-### **RecurseKe**
-
-```cpp
-virtual void RecurseKe() override
-```
-
-
-
-**Reimplements**: [lds::System::RecurseKe](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-recurseke)
-
-
----
-
-
-## Protected Attribute Details
-
-### **R_**
-
-```cpp
-Matrix R_;
-```
-
-
-
----
-### **do_recurse_Ke_**
-
-```cpp
-bool do_recurse_Ke_ {};
-```
-
-
-
----
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_controller.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_controller.md
new file mode 100644
index 00000000..8650210a
--- /dev/null
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_controller.md
@@ -0,0 +1,116 @@
+---
+title: lds::gaussian::Controller
+summary: Gaussian-observation Controller Type. 
+
+---
+
+# lds::gaussian::Controller
+
+
+
+Gaussian-observation [Controller]() Type. 
+<br /> `#include <lds_gaussian_ctrl.h>`
+
+Inherits from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/)
+
+## Public Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| virtual void | **[set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/#function-set-y-ref)**(const Vector & y_ref) override<br>sets reference output  |
+
+## Additional inherited members
+
+**Public Functions inherited from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller)**() =default<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/).  |
+| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) & sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/).  |
+| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller)**([System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) && sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/) by moving the system object.  |
+| const Vector & | **[Control](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control)**(const Vector & z, bool do_control =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal (single-step)  |
+| const Vector & | **[ControlOutputReference](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controloutputreference)**(const Vector & z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal, given previously-set (single-step)  |
+| const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) & | **[sys](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-sys)**() const |
+| const Matrix & | **[Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc)**() const<br>Get state feedback controller gain.  |
+| const Matrix & | **[Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-inty)**() const<br>Get integral controller gain.  |
+| const Matrix & | **[Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-u)**() const<br>Get input feedback controller gain.  |
+| const Vector & | **[g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-g-design)**() const<br>Get input gain used in controller design.  |
+| const Vector & | **[u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ref)**() const<br>Get reference input.  |
+| const Vector & | **[x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-x-ref)**() const<br>Get reference state.  |
+| const Vector & | **[y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-y-ref)**() const<br>Get reference output.  |
+| size_t | **[control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control-type)**() const<br>Get controller type.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-tau-awu)**() const<br>Get time constant of anti-integral-windup.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-lb)**() const<br>Get control lower bound.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ub)**() const<br>Get control upper bound.  |
+| void | **[set_sys](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-sys)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) & sys)<br>Set system.  |
+| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-g-design)**(const Vector & g_design)<br>Set input gain used in controller design (g_design)  |
+| void | **[set_u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ref)**(const Vector & u_ref)<br>Set reference input (u_ref)  |
+| void | **[set_x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-x-ref)**(const Vector & x_ref)<br>Set reference state (x_ref)  |
+| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc)**(const Matrix & Kc)<br>Set state controller gain.  |
+| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-inty)**(const Matrix & Kc_inty)<br>Set integral controller gain.  |
+| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-u)**(const Matrix & Kc_u)<br>Set input controller gain.  |
+| void | **[set_tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-tau-awu)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) tau)<br>Set time constant of anti-integral-windup.  |
+| void | **[set_control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-control-type)**(size_t control_type)<br>Sets the control type.  |
+| void | **[set_u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-lb)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb)<br>sets control lower bound  |
+| void | **[set_u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ub)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub)<br>Sets control upper bound.  |
+| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-reset)**()<br>reset system and control variables.  |
+| void | **[Print](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-print)**()<br>prints variables to stdout  |
+
+**Protected Attributes inherited from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) | **[sys_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-sys-)** <br>underlying LDS  |
+| Vector | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-)** <br>control signal  |
+| Vector | **[u_return_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-return-)** <br>control signal that is _returned_ to user  |
+| Vector | **[g_design_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-g-design-)** <br>input gain of the system used for controller design  |
+| Vector | **[u_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-)** <br>reference input  |
+| Vector | **[u_ref_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-prev-)** <br>reference input at previous time step  |
+| Vector | **[x_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-x-ref-)** <br>reference state  |
+| Vector | **[y_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-y-ref-)** <br>reference output  |
+| Vector | **[cx_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-cx-ref-)**  |
+| Matrix | **[Kc_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-)** <br>state controller gain  |
+| Matrix | **[Kc_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-u-)** <br>input controller gain (optional when control updates \deltaU)  |
+| Matrix | **[Kc_inty_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-inty-)** <br>integral controller gain  |
+| Vector | **[du_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-du-ref-)**  |
+| Vector | **[dv_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-ref-)**  |
+| Vector | **[v_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-ref-)**  |
+| Vector | **[dv_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-)**  |
+| Vector | **[v_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-)** <br>Control after g inversion (e.g., control in physical units)  |
+| Vector | **[int_e_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-)** <br>integrated error  |
+| Vector | **[int_e_awu_adjust_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-awu-adjust-)** <br>anti-windup adjustment to intE  |
+| Vector | **[u_sat_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-sat-)** <br>control signal after saturation (for antiWindup)  |
+| bool | **[do_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-control-prev-)**  |
+| bool | **[do_lock_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-lock-control-prev-)**  |
+| bool | **[u_saturated_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-saturated-)** <br>whether control signal has reached saturation limits  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-lb-)** <br>lower bound on control  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ub-)** <br>upper bound on control  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-tau-awu-)** <br>antiwindup time constant  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[k_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-k-awu-)**  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[t_since_control_onset_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-t-since-control-onset-)** <br>time since control epoch onset  |
+| size_t | **[control_type_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-control-type-)** <br>controller type  |
+
+
+---
+---
+## Public Function Details
+
+### **set_y_ref**
+
+```cpp
+inline virtual void set_y_ref(
+    const Vector & y_ref
+) override
+```
+
+
+
+**Reimplements**: [lds::Controller::set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-y-ref)
+
+
+---
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_fit.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_fit.md
new file mode 100644
index 00000000..e7a6c3fb
--- /dev/null
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_fit.md
@@ -0,0 +1,168 @@
+---
+title: lds::gaussian::Fit
+summary: GLDS Fit Type. 
+
+---
+
+# lds::gaussian::Fit
+
+
+
+GLDS [Fit]() Type. 
+<br /> `#include <lds_gaussian_fit.h>`
+
+Inherits from [lds::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/)
+
+## Public Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/#function-fit)**() =default |
+| | **[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/#function-fit)**(size_t n_u, size_t n_x, size_t n_y, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt)<br>Constructs a new [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/).  |
+| virtual const Matrix & | **[R](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/#function-r)**() const override<br>gets measurement noise covariance  |
+| virtual void | **[set_R](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/#function-set-r)**(const Matrix & R) override<br>sets measurement noise covariance  |
+| virtual View | **[h](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/#function-h)**(Matrix & y, const Matrix & x, size_t t) override<br>output function  |
+
+## Additional inherited members
+
+**Public Functions inherited from [lds::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| virtual | **[~Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-~fit)**() =default |
+| size_t | **[n_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-n-u)**() const<br>gets number of inputs  |
+| size_t | **[n_x](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-n-x)**() const<br>gets number of states  |
+| size_t | **[n_y](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-n-y)**() const<br>gets number of outputs  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-dt)**() const<br>gets sample period  |
+| const Matrix & | **[A](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-a)**() const<br>gets state matrix  |
+| const Matrix & | **[B](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-b)**() const<br>gets input matrix  |
+| const Vector & | **[g](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-g)**() const<br>gets input gain  |
+| const Vector & | **[m](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-m)**() const<br>gets process disturbance  |
+| const Matrix & | **[Q](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-q)**() const<br>gets process noise covariance  |
+| const Vector & | **[x0](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-x0)**() const<br>gets initial state estimate  |
+| const Matrix & | **[P0](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-p0)**() const<br>gets covariance of initial state estimate  |
+| const Matrix & | **[C](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-c)**() const<br>gets output matrix  |
+| const Vector & | **[d](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-d)**() const<br>gets output bias  |
+| void | **[set_A](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-a)**(const Matrix & A)<br>sets state matrix  |
+| void | **[set_B](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-b)**(const Matrix & B)<br>sets input matrix  |
+| void | **[set_g](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-g)**(const Vector & g)<br>sets input gain/conversion factor  |
+| void | **[set_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-m)**(const Vector & m)<br>sets process disturbance  |
+| void | **[set_Q](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-q)**(const Matrix & Q)<br>sets process noise covariance  |
+| void | **[set_x0](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-x0)**(const Vector & x0)<br>sets initial state estimate  |
+| void | **[set_P0](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-p0)**(const Matrix & P0)<br>sets initial state estimate covariance  |
+| void | **[set_C](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-c)**(const Matrix & C)<br>sets output matrix  |
+| void | **[set_d](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-d)**(const Vector & d)<br>sets output bias  |
+| View | **[f](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-f)**(Matrix & x, const Matrix & u, size_t t)<br>system dynamics function  |
+| View | **[f](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-f)**(Matrix & x_pre, const Matrix & x_post, const Matrix & u, size_t t)<br>system dynamics function  |
+
+**Protected Attributes inherited from [lds::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-dt-)** <br>sample period  |
+| Matrix | **[A_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-a-)** <br>state matrix  |
+| Matrix | **[B_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-b-)** <br>input matrix  |
+| Vector | **[g_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-g-)** <br>input gain  |
+| Vector | **[m_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-m-)** <br>process noise mean  |
+| Matrix | **[Q_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-q-)** <br>process noise cov  |
+| Matrix | **[C_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-c-)** <br>output matrix  |
+| Vector | **[d_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-d-)** <br>output bias  |
+| Matrix | **[R_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-r-)** <br>measurement noise  |
+| Vector | **[x0_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-x0-)** <br>initial state  |
+| Matrix | **[P0_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-p0-)** <br>initial covar  |
+| size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-n-u-)** <br>number of inputs  |
+| size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-n-x-)** <br>number of states  |
+| size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-n-y-)** <br>number of outputs  |
+
+
+---
+---
+## Public Function Details
+
+### **Fit**
+
+```cpp
+Fit() =default
+```
+
+
+
+---
+### **Fit**
+
+```cpp
+Fit(
+    size_t n_u,
+    size_t n_x,
+    size_t n_y,
+    data_t dt
+)
+```
+
+
+
+**Parameters**:
+
+  * **n_u** number of inputs 
+  * **n_x** number of states 
+  * **n_y** number of outputs 
+  * **dt** sample period 
+
+
+---
+### **R**
+
+```cpp
+inline virtual const Matrix & R() const override
+```
+
+
+
+**Reimplements**: [lds::Fit::R](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-r)
+
+
+---
+### **set_R**
+
+```cpp
+inline virtual void set_R(
+    const Matrix & R
+) override
+```
+
+
+
+**Reimplements**: [lds::Fit::set_R](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-r)
+
+
+---
+### **h**
+
+```cpp
+inline virtual View h(
+    Matrix & y,
+    const Matrix & x,
+    size_t t
+) override
+```
+
+
+
+**Parameters**:
+
+  * **y** output estimate (over time) 
+  * **x** state estimate (over time) 
+  * **t** time index
+
+
+**Return**: output 
+
+**Reimplements**: [lds::Fit::h](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-h)
+
+
+---
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_fit_e_m.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_fit_e_m.md
new file mode 100644
index 00000000..73c6f4b0
--- /dev/null
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_fit_e_m.md
@@ -0,0 +1,96 @@
+---
+title: lds::gaussian::FitEM
+summary: GLDS E-M Fit Type. 
+
+---
+
+# lds::gaussian::FitEM
+
+
+
+GLDS E-M [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/) Type.  [More...](#detailed-description)
+
+
+<br /> `#include <lds_gaussian_fit_em.h>`
+
+Inherits from [lds::EM< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/)
+
+## Additional inherited members
+
+**Public Functions inherited from [lds::EM< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em)**() =default<br>Constructs a new [EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/)[Fit]() type.  |
+| | **[EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em)**(size_t n_x, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && u_train, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && z_train)<br>Constructs a new [EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/)[Fit]() type.  |
+| | **[EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em)**(const [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/) & fit0, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && u_train, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && z_train)<br>Constructs a new [EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/)[Fit]() type.  |
+| virtual | **[~EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-~em)**() =default |
+| const [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/) & | **[Run](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-run)**(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) tol =1e-2)<br>Runs fitting by Expectation(E)-Maximization(M)  |
+| std::tuple< [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) >, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > > | **[ReturnData](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-returndata)**()<br>Returns the input/output data to caller.  |
+| const std::vector< Matrix > & | **[x](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-x)**() const<br>gets estimated state (over time)  |
+| const std::vector< Matrix > & | **[y](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-y)**() const<br>gets estimated output (over time)  |
+| const Matrix & | **[sum_E_x_t_x_t](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-x-t-x-t)**() const<br>gets state-input covariance  |
+| const Matrix & | **[sum_E_xu_tm1_xu_tm1](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-xu-tm1-xu-tm1)**() const<br>gets state-input covariance (t-minus-1)  |
+| const Matrix & | **[sum_E_xu_t_xu_tm1](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-xu-t-xu-tm1)**() const<br>gets single lag state-input covariance  |
+| size_t | **[n_t_tot](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-n-t-tot)**()<br>total number of time samples  |
+| const Vector & | **[theta](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-theta)**() const<br>gets parameters updated in M step  |
+
+**Protected Functions inherited from [lds::EM< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| void | **[Expectation](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-expectation)**(bool force_common_initial =false)<br>Expectation step.  |
+| void | **[Maximization](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximization)**(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)<br>Maximization step.  |
+| void | **[MaximizeDynamics](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizedynamics)**() |
+| void | **[MaximizeQ](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizeq)**() |
+| void | **[MaximizeInitial](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizeinitial)**() |
+| void | **[Smooth](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-smooth)**(bool force_common_initial)<br>get smoothed estimates  |
+| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-reset)**()<br>reset to initial conditions  |
+| void | **[InitVars](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-initvars)**()<br>Initializes the variables.  |
+| Vector | **[UpdateTheta](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-updatetheta)**()<br>updates parameter list, theta  |
+
+**Protected Attributes inherited from [lds::EM< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-u-)** <br>input training data  |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[z_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-z-)** <br>measurement training data  |
+| std::vector< Matrix > | **[x_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-x-)** <br>state estimate  |
+| std::vector< Cube > | **[P_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-p-)** <br>state estimate cov  |
+| std::vector< Cube > | **[P_t_tm1_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-p-t-tm1-)** <br>single-lag state covariance  |
+| std::vector< Matrix > | **[y_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-y-)** <br>output estimate  |
+| Matrix | **[diag_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-diag-y-)**  |
+| Matrix | **[sum_E_x_t_x_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-x-t-x-t-)** <br>state covariance (current time)  |
+| Matrix | **[sum_E_xu_tm1_xu_tm1_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-xu-tm1-xu-tm1-)** <br>state-input covariance (t-minus-1)  |
+| Matrix | **[sum_E_xu_t_xu_tm1_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-xu-t-xu-tm1-)** <br>single lag state-input covariance  |
+| [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/) | **[fit_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-fit-)**  |
+| Vector | **[theta_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-theta-)**  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-dt-)** <br>sample period  |
+| size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-u-)** <br>number of inputs  |
+| size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-x-)** <br>number of states  |
+| size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-y-)** <br>number of outputs  |
+| size_t | **[n_trials_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-trials-)** <br>number of input/output data sequences  |
+| std::vector< size_t > | **[n_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-t-)** <br>number of time steps  |
+| size_t | **[n_t_tot_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-t-tot-)** <br>total number of time steps across trials  |
+
+
+## Detailed Description
+
+```cpp
+class lds::gaussian::FitEM;
+```
+
+
+
+
+
+```
+        This type is used in the process of fitting GLDS models by
+        expectation-maximization (EM).
+```
+
+---
+---
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_fit_s_s_i_d.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_fit_s_s_i_d.md
new file mode 100644
index 00000000..c14c9b7e
--- /dev/null
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_fit_s_s_i_d.md
@@ -0,0 +1,63 @@
+---
+title: lds::gaussian::FitSSID
+summary: Subspace Identification (SSID) for GLDS. 
+
+---
+
+# lds::gaussian::FitSSID
+
+
+
+Subspace Identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)) for GLDS. 
+<br /> `#include <lds_gaussian_fit_ssid.h>`
+
+Inherits from [lds::SSID< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)
+
+## Additional inherited members
+
+**Public Functions inherited from [lds::SSID< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-ssid)**() =default<br>Constructs a new [SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/) type.  |
+| | **[SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-ssid)**(size_t n_x, size_t n_h, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && u_train, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && z_train, const Vector & d =Vector(1).fill(-kInf))<br>Constructs a new [SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/) type.  |
+| std::tuple< [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/), Vector > | **[Run](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-run)**([SSIDWt](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt) ssid_wt)<br>Runs fitting by subspace identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/))  |
+| std::tuple< [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) >, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > > | **[ReturnData](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-returndata)**()<br>Returns the I/O data to caller.  |
+
+**Protected Functions inherited from [lds::SSID< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| void | **[CalcD](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-calcd)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) t_silence =0.1, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) thresh_silence =0.001)<br>Using periods of silence in inputs (u), calculates the output \ bias (d)  |
+| void | **[CreateHankelDataMat](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-createhankeldatamat)**()<br>Creates the block-hankel I/O data matrix.  |
+| void | **[CalcSVD](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-calcsvd)**([SSIDWt](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt) wt)<br>performs the singular value decomposition (SVD)  |
+| void | **[Solve](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-solve)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) wt_dc)<br>solves for LDS parameters  |
+| void | **[RecomputeExtObs](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-recomputeextobs)**()<br>recompute extended observability matrix from estimates of A, C  |
+
+**Protected Attributes inherited from [lds::SSID< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-u-)** <br>input training data  |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[z_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-z-)** <br>measurement training data  |
+| Matrix | **[D_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-d-)** <br>block-Hankel I/O data matrix  |
+| [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/) | **[fit_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-fit-)** <br>fit  |
+| Matrix | **[g_dc_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-g-dc-)** <br>I/O gain @ DC.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-dt-)** <br>sample period  |
+| size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-u-)** <br>number of inputs  |
+| size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-x-)** <br>number of states  |
+| size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-y-)** <br>number of outputs  |
+| size_t | **[n_h_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-h-)**  |
+| size_t | **[n_trials_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-trials-)** <br>number of input/output data sequences  |
+| std::vector< size_t > | **[n_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-t-)** <br>number of time steps  |
+| size_t | **[n_t_tot_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-t-tot-)** <br>total number of time steps across trials  |
+| Matrix | **[L_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-l-)** <br>lower triangle decomp of covariance matrix  |
+| Vector | **[s_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-s-)** <br>singular values  |
+| Matrix | **[ext_obs_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-ext-obs-t-)** <br>extended observability matrix  |
+
+
+---
+---
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_switched_controller.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_switched_controller.md
new file mode 100644
index 00000000..74faeb8e
--- /dev/null
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_switched_controller.md
@@ -0,0 +1,145 @@
+---
+title: lds::gaussian::SwitchedController
+summary: Gaussian-observation SwitchedController Type. 
+
+---
+
+# lds::gaussian::SwitchedController
+
+
+
+Gaussian-observation [SwitchedController]() Type. 
+<br /> `#include <lds_gaussian_sctrl.h>`
+
+Inherits from [lds::SwitchedController< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/), [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/)
+
+## Public Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| virtual void | **[set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/#function-set-y-ref)**(const Vector & y_ref) override<br>sets reference output  |
+
+## Additional inherited members
+
+**Public Functions inherited from [lds::SwitchedController< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switchedcontroller)**() =default<br>Constructs a new [SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/).  |
+| | **[SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switchedcontroller)**(const std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) > & systems, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/).  |
+| | **[SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switchedcontroller)**(std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) > && systems, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/) (moves systems).  |
+| void | **[Switch](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switch)**(size_t idx, bool do_force_switch =false)<br>Switch to a different sub-system/controller.  |
+| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)<> & Kc)<br>sets state feedback gains  |
+| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)<> && Kc)<br>sets state feedback gains (moving)  |
+| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-inty)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)<> & Kc_inty)<br>sets integral feedback gains  |
+| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-inty)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)<> && Kc_inty)<br>sets integral feedback gains (moving)  |
+| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-u)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)<> & Kc_u)<br>sets input feedback gains  |
+| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-u)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)<> && Kc_u)<br>sets input feedback gains (moving)  |
+| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-g-design)**(const [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/) & g)<br>sets input gain used during controller design  |
+| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-g-design)**([UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/) && g)<br>sets input gain used during controller design (moving)  |
+
+**Protected Attributes inherited from [lds::SwitchedController< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) > | **[systems_](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-systems-)** <br>underlying sub-systems which are switched between  |
+| size_t | **[n_sys_](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-n-sys-)** <br>number of systems  |
+| size_t | **[idx_](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-idx-)** <br>current system/controller index.  |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/) | **[Kc_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-kc-list-)**  |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/) | **[Kc_inty_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-kc-inty-list-)**  |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/) | **[Kc_u_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-kc-u-list-)**  |
+| [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/) | **[g_design_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-g-design-list-)**  |
+
+**Public Functions inherited from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller)**() =default<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/).  |
+| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) & sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/).  |
+| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller)**([System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) && sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/) by moving the system object.  |
+| const Vector & | **[Control](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control)**(const Vector & z, bool do_control =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal (single-step)  |
+| const Vector & | **[ControlOutputReference](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controloutputreference)**(const Vector & z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal, given previously-set (single-step)  |
+| const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) & | **[sys](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-sys)**() const |
+| const Matrix & | **[Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc)**() const<br>Get state feedback controller gain.  |
+| const Matrix & | **[Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-inty)**() const<br>Get integral controller gain.  |
+| const Matrix & | **[Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-u)**() const<br>Get input feedback controller gain.  |
+| const Vector & | **[g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-g-design)**() const<br>Get input gain used in controller design.  |
+| const Vector & | **[u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ref)**() const<br>Get reference input.  |
+| const Vector & | **[x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-x-ref)**() const<br>Get reference state.  |
+| const Vector & | **[y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-y-ref)**() const<br>Get reference output.  |
+| size_t | **[control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control-type)**() const<br>Get controller type.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-tau-awu)**() const<br>Get time constant of anti-integral-windup.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-lb)**() const<br>Get control lower bound.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ub)**() const<br>Get control upper bound.  |
+| void | **[set_sys](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-sys)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) & sys)<br>Set system.  |
+| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-g-design)**(const Vector & g_design)<br>Set input gain used in controller design (g_design)  |
+| void | **[set_u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ref)**(const Vector & u_ref)<br>Set reference input (u_ref)  |
+| void | **[set_x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-x-ref)**(const Vector & x_ref)<br>Set reference state (x_ref)  |
+| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc)**(const Matrix & Kc)<br>Set state controller gain.  |
+| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-inty)**(const Matrix & Kc_inty)<br>Set integral controller gain.  |
+| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-u)**(const Matrix & Kc_u)<br>Set input controller gain.  |
+| void | **[set_tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-tau-awu)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) tau)<br>Set time constant of anti-integral-windup.  |
+| void | **[set_control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-control-type)**(size_t control_type)<br>Sets the control type.  |
+| void | **[set_u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-lb)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb)<br>sets control lower bound  |
+| void | **[set_u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ub)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub)<br>Sets control upper bound.  |
+| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-reset)**()<br>reset system and control variables.  |
+| void | **[Print](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-print)**()<br>prints variables to stdout  |
+
+**Protected Attributes inherited from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) | **[sys_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-sys-)** <br>underlying LDS  |
+| Vector | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-)** <br>control signal  |
+| Vector | **[u_return_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-return-)** <br>control signal that is _returned_ to user  |
+| Vector | **[g_design_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-g-design-)** <br>input gain of the system used for controller design  |
+| Vector | **[u_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-)** <br>reference input  |
+| Vector | **[u_ref_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-prev-)** <br>reference input at previous time step  |
+| Vector | **[x_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-x-ref-)** <br>reference state  |
+| Vector | **[y_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-y-ref-)** <br>reference output  |
+| Vector | **[cx_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-cx-ref-)**  |
+| Matrix | **[Kc_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-)** <br>state controller gain  |
+| Matrix | **[Kc_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-u-)** <br>input controller gain (optional when control updates \deltaU)  |
+| Matrix | **[Kc_inty_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-inty-)** <br>integral controller gain  |
+| Vector | **[du_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-du-ref-)**  |
+| Vector | **[dv_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-ref-)**  |
+| Vector | **[v_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-ref-)**  |
+| Vector | **[dv_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-)**  |
+| Vector | **[v_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-)** <br>Control after g inversion (e.g., control in physical units)  |
+| Vector | **[int_e_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-)** <br>integrated error  |
+| Vector | **[int_e_awu_adjust_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-awu-adjust-)** <br>anti-windup adjustment to intE  |
+| Vector | **[u_sat_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-sat-)** <br>control signal after saturation (for antiWindup)  |
+| bool | **[do_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-control-prev-)**  |
+| bool | **[do_lock_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-lock-control-prev-)**  |
+| bool | **[u_saturated_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-saturated-)** <br>whether control signal has reached saturation limits  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-lb-)** <br>lower bound on control  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ub-)** <br>upper bound on control  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-tau-awu-)** <br>antiwindup time constant  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[k_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-k-awu-)**  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[t_since_control_onset_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-t-since-control-onset-)** <br>time since control epoch onset  |
+| size_t | **[control_type_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-control-type-)** <br>controller type  |
+
+
+---
+---
+## Public Function Details
+
+### **set_y_ref**
+
+```cpp
+inline virtual void set_y_ref(
+    const Vector & y_ref
+) override
+```
+
+
+
+**Reimplements**: [lds::Controller::set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-y-ref)
+
+
+---
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_system.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_system.md
new file mode 100644
index 00000000..8cecdcfe
--- /dev/null
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_system.md
@@ -0,0 +1,328 @@
+---
+title: lds::gaussian::System
+summary: Gaussian LDS Type. 
+
+---
+
+# lds::gaussian::System
+
+
+
+Gaussian LDS Type. 
+<br /> `#include <lds_gaussian_sys.h>`
+
+Inherits from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)
+
+## Public Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-system)**() =default<br>Constructs a new [System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/).  |
+| | **[System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-system)**(std::size_t n_u, std::size_t n_x, std::size_t n_y, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) p0 =kDefaultP0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) q0 =kDefaultQ0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) r0 =kDefaultR0)<br>Constructs a new Gaussian [System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/).  |
+| virtual const Vector & | **[Simulate](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-simulate)**(const Vector & u_tm1) override<br>Simulate system measurement.  |
+| const Matrix & | **[R](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-r)**() const<br>Get output noise covariance.  |
+| void | **[set_Q](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-set-q)**(const Matrix & Q) |
+| void | **[set_R](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-set-r)**(const Matrix & R)<br>Set output noise covariance.  |
+| void | **[set_Ke](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-set-ke)**(const Matrix & Ke)<br>Set estimator gain.  |
+| void | **[set_Ke_m](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-set-ke-m)**(const Matrix & Ke_m)<br>Set disturbance estimator gain.  |
+| void | **[Print](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-print)**()<br>Print system variables to stdout.  |
+
+## Protected Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| virtual void | **[h](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-h)**() override<br>[System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/) output function.  |
+| virtual Vector | **[h_](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-h-)**(Vector x) override<br>[System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/) output function: stateless.  |
+| virtual void | **[RecurseKe](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-recurseke)**() override<br>Recursively update estimator gain.  |
+
+## Protected Attributes
+
+|                | Name           |
+| -------------- | -------------- |
+| Matrix | **[R_](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#variable-r-)** <br>covariance of output noise  |
+| bool | **[do_recurse_Ke_](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#variable-do-recurse-ke-)** <br>whether to recursively calculate estimator gain  |
+
+## Additional inherited members
+
+**Public Functions inherited from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| virtual | **[~System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-~system)**() |
+| void | **[Filter](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-filter)**(const Vector & u_tm1, const Vector & z)<br>Filter data to produce causal state estimates.  |
+| void | **[f](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-f)**(const Vector & u, bool do_add_noise =false)<br>system dynamics function  |
+| size_t | **[n_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-n-u)**() const<br>Get number of inputs.  |
+| size_t | **[n_x](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-n-x)**() const<br>Get number of states.  |
+| size_t | **[n_y](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-n-y)**() const<br>Get number of outputs.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-dt)**() const<br>Get sample period.  |
+| const Vector & | **[x](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-x)**() const<br>Get current state.  |
+| const Matrix & | **[P](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p)**() const<br>Get covariance of state estimate.  |
+| const Vector & | **[m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-m)**() const<br>Get current process disturbance/bias.  |
+| const Matrix & | **[P_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p-m)**() const<br>Get covariance of process disturbance estimate.  |
+| const Vector & | **[cx](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-cx)**() const<br>Get C*x.  |
+| const Vector & | **[y](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-y)**() const<br>Get output.  |
+| const Vector & | **[x0](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-x0)**() const<br>Get initial state.  |
+| const Vector & | **[m0](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-m0)**() const<br>Get initial disturbance.  |
+| const Matrix & | **[A](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-a)**() const<br>Get state matrix.  |
+| const Matrix & | **[B](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-b)**() const<br>Get input matrix.  |
+| const Vector & | **[g](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-g)**() const<br>Get input gain/conversion factor.  |
+| const Matrix & | **[C](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-c)**() const<br>Get output matrix.  |
+| const Vector & | **[d](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-d)**() const<br>Get output bias.  |
+| const Matrix & | **[Ke](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-ke)**() const<br>Get estimator gain.  |
+| const Matrix & | **[Ke_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-ke-m)**() const<br>Get estimator gain for process disturbance (m)  |
+| const Matrix & | **[Q](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-q)**()<br>Get process noise covariance.  |
+| const Matrix & | **[Q_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-q-m)**()<br>Get process noise covariance of disturbance evoluation.  |
+| const Matrix & | **[P0](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p0)**()<br>Get covariance of initial state.  |
+| const Matrix & | **[P0_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p0-m)**()<br>Get covariance of initial process disturbance.  |
+| void | **[set_A](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-a)**(const Matrix & A)<br>Set state matrix.  |
+| void | **[set_B](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-b)**(const Matrix & B)<br>Set input matrix.  |
+| void | **[set_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-m)**(const Vector & m, bool do_force_assign =false)<br>Set process disturbance.  |
+| void | **[set_g](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-g)**(const Vector & g)<br>Set input gain.  |
+| void | **[set_Q_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-q-m)**(const Matrix & Q_m)<br>Set process noise covariance of disturbance evoluation.  |
+| void | **[set_x0](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-x0)**(const Vector & x0)<br>Set initial state.  |
+| void | **[set_P0](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-p0)**(const Matrix & P0)<br>Set covariance of initial state.  |
+| void | **[set_P0_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-p0-m)**(const Matrix & P0_m)<br>Set covariance of initial process disturbance.  |
+| void | **[set_C](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-c)**(const Matrix & C)<br>Set output matrix.  |
+| void | **[set_d](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-d)**(const Vector & d)<br>Set output bias.  |
+| void | **[set_x](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-x)**(const Vector & x)<br>Set state of system.  |
+| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-reset)**()<br>Reset system variables.  |
+| std::vector< [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > > | **[nstep_pred_block](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-nstep-pred-block)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > u, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > z, size_t n_pred =1) |
+
+**Protected Functions inherited from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| void | **[InitVars](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-initvars)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) p0 =kDefaultP0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) q0 =kDefaultQ0) |
+
+**Public Attributes inherited from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| bool | **[do_adapt_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-do-adapt-m)** <br>whether to adaptively estimate disturbance m  |
+
+**Protected Attributes inherited from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| std::size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-n-x-)** <br>number of states  |
+| std::size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-n-u-)** <br>number of inputs  |
+| std::size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-n-y-)** <br>number of outputs  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-dt-)** <br>sample period  |
+| Vector | **[x_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-x-)** <br>state  |
+| Matrix | **[P_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p-)** <br>covariance of state estimate  |
+| Vector | **[m_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-m-)** <br>process disturbance  |
+| Matrix | **[P_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p-m-)** <br>covariance of disturbance estimate  |
+| Vector | **[cx_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-cx-)** <br>C*x.  |
+| Vector | **[y_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-y-)** <br>output  |
+| Vector | **[z_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-z-)** <br>measurement  |
+| Vector | **[x0_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-x0-)** <br>initial state  |
+| Matrix | **[P0_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p0-)** <br>covariance of initial state estimate  |
+| Vector | **[m0_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-m0-)** <br>initial process disturbance  |
+| Matrix | **[P0_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p0-m-)** <br>covariance of initial disturbance est.  |
+| Matrix | **[A_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-a-)** <br>state matrix  |
+| Matrix | **[B_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-b-)** <br>input matrix  |
+| Vector | **[g_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-g-)** <br>input gain  |
+| Matrix | **[Q_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-q-)** <br>covariance of process noise  |
+| Matrix | **[Q_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-q-m-)** <br>covariance of disturbance random walk  |
+| Matrix | **[C_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-c-)** <br>output matrix  |
+| Vector | **[d_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-d-)** <br>output bias  |
+| Matrix | **[Ke_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-ke-)** <br>estimator gain  |
+| Matrix | **[Ke_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-ke-m-)** <br>estimator gain for process disturbance  |
+
+
+---
+---
+## Public Function Details
+
+### **System**
+
+```cpp
+System() =default
+```
+
+
+
+---
+### **System**
+
+```cpp
+System(
+    std::size_t n_u,
+    std::size_t n_x,
+    std::size_t n_y,
+    data_t dt,
+    data_t p0 =kDefaultP0,
+    data_t q0 =kDefaultQ0,
+    data_t r0 =kDefaultR0
+)
+```
+
+
+
+**Parameters**:
+
+  * **n_u** number of inputs (u) 
+  * **n_x** number of states (x) 
+  * **n_y** number of outputs (y) 
+  * **dt** sample period 
+  * **p0** [optional] initial diagonal elements of state estimate covariance (P) 
+  * **q0** [optional] initial diagonal elements of process noise covariance (Q) 
+  * **r0** [optional] initial diagonal elements of output noise covariance (R) 
+
+
+---
+### **Simulate**
+
+```cpp
+virtual const Vector & Simulate(
+    const Vector & u_tm1
+) override
+```
+
+
+
+**Parameters**:
+
+  * **u_tm1** input at t-1
+
+
+**Return**: z measurement 
+
+**Reimplements**: [lds::System::Simulate](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-simulate)
+
+
+Simulate system and produce measurement
+
+
+---
+### **R**
+
+```cpp
+inline const Matrix & R() const
+```
+
+
+
+---
+### **set_Q**
+
+```cpp
+inline void set_Q(
+    const Matrix & Q
+)
+```
+
+
+
+---
+### **set_R**
+
+```cpp
+inline void set_R(
+    const Matrix & R
+)
+```
+
+
+
+---
+### **set_Ke**
+
+```cpp
+inline void set_Ke(
+    const Matrix & Ke
+)
+```
+
+
+
+---
+### **set_Ke_m**
+
+```cpp
+inline void set_Ke_m(
+    const Matrix & Ke_m
+)
+```
+
+
+
+---
+### **Print**
+
+```cpp
+void Print()
+```
+
+
+
+---
+
+
+## Protected Function Details
+
+### **h**
+
+```cpp
+inline virtual void h() override
+```
+
+
+
+**Reimplements**: [lds::System::h](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-h)
+
+
+---
+### **h_**
+
+```cpp
+inline virtual Vector h_(
+    Vector x
+) override
+```
+
+
+
+**Reimplements**: [lds::System::h_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-h-)
+
+
+---
+### **RecurseKe**
+
+```cpp
+virtual void RecurseKe() override
+```
+
+
+
+**Reimplements**: [lds::System::RecurseKe](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-recurseke)
+
+
+---
+
+
+## Protected Attribute Details
+
+### **R_**
+
+```cpp
+Matrix R_;
+```
+
+
+
+---
+### **do_recurse_Ke_**
+
+```cpp
+bool do_recurse_Ke_ {};
+```
+
+
+
+---
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1Controller.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1Controller.md
deleted file mode 100644
index fa222cf4..00000000
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1Controller.md
+++ /dev/null
@@ -1,116 +0,0 @@
----
-title: lds::poisson::Controller
-summary: PLDS Controller Type. 
-
----
-
-# lds::poisson::Controller
-
-
-
-PLDS [Controller]() Type. 
-<br /> `#include <lds_poisson_ctrl.h>`
-
-Inherits from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/)
-
-## Public Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| virtual void | **[set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/#function-set-y-ref)**(const Vector & y_ref) override<br>Set reference output.  |
-
-## Additional inherited members
-
-**Public Functions inherited from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controller)**() =default<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/).  |
-| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controller)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) & sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/).  |
-| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controller)**([System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) && sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/) by moving the system object.  |
-| const Vector & | **[Control](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-control)**(const Vector & z, bool do_control =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal (single-step)  |
-| const Vector & | **[ControlOutputReference](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controloutputreference)**(const Vector & z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal, given previously-set (single-step)  |
-| const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) & | **[sys](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-sys)**() const |
-| const Matrix & | **[Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-kc)**() const<br>Get state feedback controller gain.  |
-| const Matrix & | **[Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-kc-inty)**() const<br>Get integral controller gain.  |
-| const Matrix & | **[Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-kc-u)**() const<br>Get input feedback controller gain.  |
-| const Vector & | **[g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-g-design)**() const<br>Get input gain used in controller design.  |
-| const Vector & | **[u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-u-ref)**() const<br>Get reference input.  |
-| const Vector & | **[x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-x-ref)**() const<br>Get reference state.  |
-| const Vector & | **[y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-y-ref)**() const<br>Get reference output.  |
-| size_t | **[control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-control-type)**() const<br>Get controller type.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-tau-awu)**() const<br>Get time constant of anti-integral-windup.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-u-lb)**() const<br>Get control lower bound.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-u-ub)**() const<br>Get control upper bound.  |
-| void | **[set_sys](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-sys)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) & sys)<br>Set system.  |
-| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-g-design)**(const Vector & g_design)<br>Set input gain used in controller design (g_design)  |
-| void | **[set_u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-u-ref)**(const Vector & u_ref)<br>Set reference input (u_ref)  |
-| void | **[set_x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-x-ref)**(const Vector & x_ref)<br>Set reference state (x_ref)  |
-| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-kc)**(const Matrix & Kc)<br>Set state controller gain.  |
-| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-kc-inty)**(const Matrix & Kc_inty)<br>Set integral controller gain.  |
-| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-kc-u)**(const Matrix & Kc_u)<br>Set input controller gain.  |
-| void | **[set_tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-tau-awu)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) tau)<br>Set time constant of anti-integral-windup.  |
-| void | **[set_control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-control-type)**(size_t control_type)<br>Sets the control type.  |
-| void | **[set_u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-u-lb)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb)<br>sets control lower bound  |
-| void | **[set_u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-u-ub)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub)<br>Sets control upper bound.  |
-| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-reset)**()<br>reset system and control variables.  |
-| void | **[Print](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-print)**()<br>prints variables to stdout  |
-
-**Protected Attributes inherited from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) | **[sys_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-sys-)** <br>underlying LDS  |
-| Vector | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-)** <br>control signal  |
-| Vector | **[u_return_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-return-)** <br>control signal that is _returned_ to user  |
-| Vector | **[g_design_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-g-design-)** <br>input gain of the system used for controller design  |
-| Vector | **[u_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-ref-)** <br>reference input  |
-| Vector | **[u_ref_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-ref-prev-)** <br>reference input at previous time step  |
-| Vector | **[x_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-x-ref-)** <br>reference state  |
-| Vector | **[y_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-y-ref-)** <br>reference output  |
-| Vector | **[cx_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-cx-ref-)**  |
-| Matrix | **[Kc_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-kc-)** <br>state controller gain  |
-| Matrix | **[Kc_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-kc-u-)** <br>input controller gain (optional when control updates )  |
-| Matrix | **[Kc_inty_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-kc-inty-)** <br>integral controller gain  |
-| Vector | **[du_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-du-ref-)**  |
-| Vector | **[dv_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-dv-ref-)**  |
-| Vector | **[v_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-v-ref-)**  |
-| Vector | **[dv_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-dv-)**  |
-| Vector | **[v_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-v-)** <br>Control after g inversion (e.g., control in physical units)  |
-| Vector | **[int_e_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-int-e-)** <br>integrated error  |
-| Vector | **[int_e_awu_adjust_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-int-e-awu-adjust-)** <br>anti-windup adjustment to intE  |
-| Vector | **[u_sat_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-sat-)** <br>control signal after saturation (for antiWindup)  |
-| bool | **[do_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-do-control-prev-)**  |
-| bool | **[do_lock_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-do-lock-control-prev-)**  |
-| bool | **[u_saturated_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-saturated-)** <br>whether control signal has reached saturation limits  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-lb-)** <br>lower bound on control  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-ub-)** <br>upper bound on control  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-tau-awu-)** <br>antiwindup time constant  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[k_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-k-awu-)**  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[t_since_control_onset_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-t-since-control-onset-)** <br>time since control epoch onset  |
-| size_t | **[control_type_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-control-type-)** <br>controller type  |
-
-
----
----
-## Public Function Details
-
-### **set_y_ref**
-
-```cpp
-inline virtual void set_y_ref(
-    const Vector & y_ref
-) override
-```
-
-
-
-**Reimplements**: [lds::Controller::set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-y-ref)
-
-
----
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1Fit.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1Fit.md
deleted file mode 100644
index 3e58f45f..00000000
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1Fit.md
+++ /dev/null
@@ -1,168 +0,0 @@
----
-title: lds::poisson::Fit
-summary: PLDS Fit Type. 
-
----
-
-# lds::poisson::Fit
-
-
-
-PLDS [Fit]() Type. 
-<br /> `#include <lds_poisson_fit.h>`
-
-Inherits from [lds::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/)
-
-## Public Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/#function-fit)**() =default |
-| | **[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/#function-fit)**(size_t n_u, size_t n_x, size_t n_y, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt)<br>Constructs a new [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/).  |
-| virtual View | **[h](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/#function-h)**(Matrix & y, const Matrix & x, size_t t) override<br>output function  |
-| virtual void | **[set_R](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/#function-set-r)**(const Matrix & R) override<br>sets output noise covariance (if any)  |
-| virtual const Matrix & | **[R](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/#function-r)**() const override |
-
-## Additional inherited members
-
-**Public Functions inherited from [lds::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| virtual | **[~Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-~fit)**() =default |
-| size_t | **[n_u](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-n-u)**() const<br>gets number of inputs  |
-| size_t | **[n_x](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-n-x)**() const<br>gets number of states  |
-| size_t | **[n_y](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-n-y)**() const<br>gets number of outputs  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-dt)**() const<br>gets sample period  |
-| const Matrix & | **[A](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-a)**() const<br>gets state matrix  |
-| const Matrix & | **[B](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-b)**() const<br>gets input matrix  |
-| const Vector & | **[g](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-g)**() const<br>gets input gain  |
-| const Vector & | **[m](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-m)**() const<br>gets process disturbance  |
-| const Matrix & | **[Q](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-q)**() const<br>gets process noise covariance  |
-| const Vector & | **[x0](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-x0)**() const<br>gets initial state estimate  |
-| const Matrix & | **[P0](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-p0)**() const<br>gets covariance of initial state estimate  |
-| const Matrix & | **[C](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-c)**() const<br>gets output matrix  |
-| const Vector & | **[d](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-d)**() const<br>gets output bias  |
-| void | **[set_A](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-a)**(const Matrix & A)<br>sets state matrix  |
-| void | **[set_B](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-b)**(const Matrix & B)<br>sets input matrix  |
-| void | **[set_g](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-g)**(const Vector & g)<br>sets input gain/conversion factor  |
-| void | **[set_m](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-m)**(const Vector & m)<br>sets process disturbance  |
-| void | **[set_Q](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-q)**(const Matrix & Q)<br>sets process noise covariance  |
-| void | **[set_x0](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-x0)**(const Vector & x0)<br>sets initial state estimate  |
-| void | **[set_P0](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-p0)**(const Matrix & P0)<br>sets initial state estimate covariance  |
-| void | **[set_C](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-c)**(const Matrix & C)<br>sets output matrix  |
-| void | **[set_d](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-d)**(const Vector & d)<br>sets output bias  |
-| View | **[f](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-f)**(Matrix & x, const Matrix & u, size_t t)<br>system dynamics function  |
-| View | **[f](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-f)**(Matrix & x_pre, const Matrix & x_post, const Matrix & u, size_t t)<br>system dynamics function  |
-
-**Protected Attributes inherited from [lds::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-dt-)** <br>sample period  |
-| Matrix | **[A_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-a-)** <br>state matrix  |
-| Matrix | **[B_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-b-)** <br>input matrix  |
-| Vector | **[g_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-g-)** <br>input gain  |
-| Vector | **[m_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-m-)** <br>process noise mean  |
-| Matrix | **[Q_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-q-)** <br>process noise cov  |
-| Matrix | **[C_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-c-)** <br>output matrix  |
-| Vector | **[d_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-d-)** <br>output bias  |
-| Matrix | **[R_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-r-)** <br>measurement noise  |
-| Vector | **[x0_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-x0-)** <br>initial state  |
-| Matrix | **[P0_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-p0-)** <br>initial covar  |
-| size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-n-u-)** <br>number of inputs  |
-| size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-n-x-)** <br>number of states  |
-| size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#variable-n-y-)** <br>number of outputs  |
-
-
----
----
-## Public Function Details
-
-### **Fit**
-
-```cpp
-Fit() =default
-```
-
-
-
----
-### **Fit**
-
-```cpp
-inline Fit(
-    size_t n_u,
-    size_t n_x,
-    size_t n_y,
-    data_t dt
-)
-```
-
-
-
-**Parameters**:
-
-  * **n_u** number of inputs 
-  * **n_x** number of states 
-  * **n_y** number of outputs 
-  * **dt** sample period 
-
-
----
-### **h**
-
-```cpp
-inline virtual View h(
-    Matrix & y,
-    const Matrix & x,
-    size_t t
-) override
-```
-
-
-
-**Parameters**:
-
-  * **y** output estimate (over time) 
-  * **x** state estimate (over time) 
-  * **t** time index
-
-
-**Return**: output 
-
-**Reimplements**: [lds::Fit::h](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-h)
-
-
----
-### **set_R**
-
-```cpp
-inline virtual void set_R(
-    const Matrix & R
-) override
-```
-
-
-
-**Reimplements**: [lds::Fit::set_R](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-set-r)
-
-
----
-### **R**
-
-```cpp
-inline virtual const Matrix & R() const override
-```
-
-
-
-**Reimplements**: [lds::Fit::R](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/#function-r)
-
-
----
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1FitEM.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1FitEM.md
deleted file mode 100644
index 14af83b9..00000000
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1FitEM.md
+++ /dev/null
@@ -1,91 +0,0 @@
----
-title: lds::poisson::FitEM
-summary: PLDS E-M Fit Type. 
-
----
-
-# lds::poisson::FitEM
-
-
-
-PLDS E-M [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/) Type.  [More...](#detailed-description)
-
-
-<br /> `#include <lds_poisson_fit_em.h>`
-
-Inherits from [lds::EM< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)
-
-## Additional inherited members
-
-**Public Functions inherited from [lds::EM< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-em)**() =default<br>Constructs a new [EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)[Fit]() type.  |
-| | **[EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-em)**(size_t n_x, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && u_train, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && z_train)<br>Constructs a new [EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)[Fit]() type.  |
-| | **[EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-em)**(const [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/) & fit0, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && u_train, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && z_train)<br>Constructs a new [EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)[Fit]() type.  |
-| virtual | **[~EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-~em)**() =default |
-| const [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/) & | **[Run](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-run)**(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) tol =1e-2)<br>Runs fitting by Expectation(E)-Maximization(M)  |
-| std::tuple< [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) >, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > > | **[ReturnData](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-returndata)**()<br>Returns the input/output data to caller.  |
-| const std::vector< Matrix > & | **[x](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-x)**() const<br>gets estimated state (over time)  |
-| const std::vector< Matrix > & | **[y](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-y)**() const<br>gets estimated output (over time)  |
-| const Matrix & | **[sum_E_x_t_x_t](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-sum-e-x-t-x-t)**() const<br>gets state-input covariance  |
-| const Matrix & | **[sum_E_xu_tm1_xu_tm1](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-sum-e-xu-tm1-xu-tm1)**() const<br>gets state-input covariance (t-minus-1)  |
-| const Matrix & | **[sum_E_xu_t_xu_tm1](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-sum-e-xu-t-xu-tm1)**() const<br>gets single lag state-input covariance  |
-| size_t | **[n_t_tot](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-n-t-tot)**()<br>total number of time samples  |
-| const Vector & | **[theta](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-theta)**() const<br>gets parameters updated in M step  |
-
-**Protected Functions inherited from [lds::EM< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| void | **[Expectation](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-expectation)**(bool force_common_initial =false)<br>Expectation step.  |
-| void | **[Maximization](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-maximization)**(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)<br>Maximization step.  |
-| void | **[MaximizeDynamics](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-maximizedynamics)**() |
-| void | **[MaximizeQ](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-maximizeq)**() |
-| void | **[MaximizeInitial](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-maximizeinitial)**() |
-| void | **[Smooth](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-smooth)**(bool force_common_initial)<br>get smoothed estimates  |
-| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-reset)**()<br>reset to initial conditions  |
-| void | **[InitVars](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-initvars)**()<br>Initializes the variables.  |
-| Vector | **[UpdateTheta](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#function-updatetheta)**()<br>updates parameter list, theta  |
-
-**Protected Attributes inherited from [lds::EM< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-u-)** <br>input training data  |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[z_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-z-)** <br>measurement training data  |
-| std::vector< Matrix > | **[x_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-x-)** <br>state estimate  |
-| std::vector< Cube > | **[P_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-p-)** <br>state estimate cov  |
-| std::vector< Cube > | **[P_t_tm1_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-p-t-tm1-)** <br>single-lag state covariance  |
-| std::vector< Matrix > | **[y_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-y-)** <br>output estimate  |
-| Matrix | **[diag_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-diag-y-)**  |
-| Matrix | **[sum_E_x_t_x_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-sum-e-x-t-x-t-)** <br>state covariance (current time)  |
-| Matrix | **[sum_E_xu_tm1_xu_tm1_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-sum-e-xu-tm1-xu-tm1-)** <br>state-input covariance (t-minus-1)  |
-| Matrix | **[sum_E_xu_t_xu_tm1_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-sum-e-xu-t-xu-tm1-)** <br>single lag state-input covariance  |
-| [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/) | **[fit_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-fit-)**  |
-| Vector | **[theta_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-theta-)**  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-dt-)** <br>sample period  |
-| size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-n-u-)** <br>number of inputs  |
-| size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-n-x-)** <br>number of states  |
-| size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-n-y-)** <br>number of outputs  |
-| size_t | **[n_trials_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-n-trials-)** <br>number of input/output data sequences  |
-| std::vector< size_t > | **[n_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-n-t-)** <br>number of time steps  |
-| size_t | **[n_t_tot_](/lds-ctrl-est/docs/api/classes/classlds_1_1em/#variable-n-t-tot-)** <br>total number of time steps across trials  |
-
-
-## Detailed Description
-
-```cpp
-class lds::poisson::FitEM;
-```
-
-
-
-This type is used in the process of fitting PLDS models by expectation-maximization ([EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)). 
-
----
----
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1FitSSID.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1FitSSID.md
deleted file mode 100644
index ab728c5c..00000000
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1FitSSID.md
+++ /dev/null
@@ -1,63 +0,0 @@
----
-title: lds::poisson::FitSSID
-summary: Subspace Identification (SSID) for PLDS. 
-
----
-
-# lds::poisson::FitSSID
-
-
-
-Subspace Identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)) for PLDS. 
-<br /> `#include <lds_poisson_fit_ssid.h>`
-
-Inherits from [lds::SSID< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)
-
-## Additional inherited members
-
-**Public Functions inherited from [lds::SSID< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-ssid)**() =default<br>Constructs a new [SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/) type.  |
-| | **[SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-ssid)**(size_t n_x, size_t n_h, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && u_train, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && z_train, const Vector & d =Vector(1).fill(-kInf))<br>Constructs a new [SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/) type.  |
-| std::tuple< [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/), Vector > | **[Run](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-run)**([SSIDWt](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt) ssid_wt)<br>Runs fitting by subspace identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/))  |
-| std::tuple< [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) >, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > > | **[ReturnData](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-returndata)**()<br>Returns the I/O data to caller.  |
-
-**Protected Functions inherited from [lds::SSID< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| void | **[CalcD](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-calcd)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) t_silence =0.1, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) thresh_silence =0.001)<br>Using periods of silence in inputs (u), calculates the output \ bias (d)  |
-| void | **[CreateHankelDataMat](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-createhankeldatamat)**()<br>Creates the block-hankel I/O data matrix.  |
-| void | **[CalcSVD](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-calcsvd)**([SSIDWt](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt) wt)<br>performs the singular value decomposition (SVD)  |
-| void | **[Solve](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-solve)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) wt_dc)<br>solves for LDS parameters  |
-| void | **[RecomputeExtObs](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#function-recomputeextobs)**()<br>recompute extended observability matrix from estimates of A, C  |
-
-**Protected Attributes inherited from [lds::SSID< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-u-)** <br>input training data  |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[z_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-z-)** <br>measurement training data  |
-| Matrix | **[D_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-d-)** <br>block-Hankel I/O data matrix  |
-| [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/) | **[fit_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-fit-)** <br>fit  |
-| Matrix | **[g_dc_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-g-dc-)** <br>I/O gain @ DC.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-dt-)** <br>sample period  |
-| size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-n-u-)** <br>number of inputs  |
-| size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-n-x-)** <br>number of states  |
-| size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-n-y-)** <br>number of outputs  |
-| size_t | **[n_h_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-n-h-)**  |
-| size_t | **[n_trials_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-n-trials-)** <br>number of input/output data sequences  |
-| std::vector< size_t > | **[n_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-n-t-)** <br>number of time steps  |
-| size_t | **[n_t_tot_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-n-t-tot-)** <br>total number of time steps across trials  |
-| Matrix | **[L_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-l-)** <br>lower triangle decomp of covariance matrix  |
-| Vector | **[s_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-s-)** <br>singular values  |
-| Matrix | **[ext_obs_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/#variable-ext-obs-t-)** <br>extended observability matrix  |
-
-
----
----
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1SwitchedController.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1SwitchedController.md
deleted file mode 100644
index a5c7f33c..00000000
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1SwitchedController.md
+++ /dev/null
@@ -1,145 +0,0 @@
----
-title: lds::poisson::SwitchedController
-summary: Poisson-observation SwitchedController Type. 
-
----
-
-# lds::poisson::SwitchedController
-
-
-
-Poisson-observation [SwitchedController]() Type. 
-<br /> `#include <lds_poisson_sctrl.h>`
-
-Inherits from [lds::SwitchedController< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/), [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/)
-
-## Public Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| virtual void | **[set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1switchedcontroller/#function-set-y-ref)**(const Vector & y_ref) override<br>Set reference output.  |
-
-## Additional inherited members
-
-**Public Functions inherited from [lds::SwitchedController< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-switchedcontroller)**() =default<br>Constructs a new [SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/).  |
-| | **[SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-switchedcontroller)**(const std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) > & systems, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/).  |
-| | **[SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-switchedcontroller)**(std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) > && systems, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/) (moves systems).  |
-| void | **[Switch](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-switch)**(size_t idx, bool do_force_switch =false)<br>Switch to a different sub-system/controller.  |
-| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-kc)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)<> & Kc)<br>sets state feedback gains  |
-| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-kc)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)<> && Kc)<br>sets state feedback gains (moving)  |
-| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-kc-inty)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)<> & Kc_inty)<br>sets integral feedback gains  |
-| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-kc-inty)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)<> && Kc_inty)<br>sets integral feedback gains (moving)  |
-| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-kc-u)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)<> & Kc_u)<br>sets input feedback gains  |
-| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-kc-u)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)<> && Kc_u)<br>sets input feedback gains (moving)  |
-| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-g-design)**(const [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/) & g)<br>sets input gain used during controller design  |
-| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#function-set-g-design)**([UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/) && g)<br>sets input gain used during controller design (moving)  |
-
-**Protected Attributes inherited from [lds::SwitchedController< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) > | **[systems_](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#variable-systems-)** <br>underlying sub-systems which are switched between  |
-| size_t | **[n_sys_](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#variable-n-sys-)** <br>number of systems  |
-| size_t | **[idx_](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#variable-idx-)** <br>current system/controller index.  |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/) | **[Kc_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#variable-kc-list-)**  |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/) | **[Kc_inty_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#variable-kc-inty-list-)**  |
-| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/) | **[Kc_u_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#variable-kc-u-list-)**  |
-| [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/) | **[g_design_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/#variable-g-design-list-)**  |
-
-**Public Functions inherited from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controller)**() =default<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/).  |
-| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controller)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) & sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/).  |
-| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controller)**([System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) && sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/) by moving the system object.  |
-| const Vector & | **[Control](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-control)**(const Vector & z, bool do_control =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal (single-step)  |
-| const Vector & | **[ControlOutputReference](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-controloutputreference)**(const Vector & z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal, given previously-set (single-step)  |
-| const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) & | **[sys](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-sys)**() const |
-| const Matrix & | **[Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-kc)**() const<br>Get state feedback controller gain.  |
-| const Matrix & | **[Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-kc-inty)**() const<br>Get integral controller gain.  |
-| const Matrix & | **[Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-kc-u)**() const<br>Get input feedback controller gain.  |
-| const Vector & | **[g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-g-design)**() const<br>Get input gain used in controller design.  |
-| const Vector & | **[u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-u-ref)**() const<br>Get reference input.  |
-| const Vector & | **[x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-x-ref)**() const<br>Get reference state.  |
-| const Vector & | **[y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-y-ref)**() const<br>Get reference output.  |
-| size_t | **[control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-control-type)**() const<br>Get controller type.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-tau-awu)**() const<br>Get time constant of anti-integral-windup.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-u-lb)**() const<br>Get control lower bound.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-u-ub)**() const<br>Get control upper bound.  |
-| void | **[set_sys](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-sys)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) & sys)<br>Set system.  |
-| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-g-design)**(const Vector & g_design)<br>Set input gain used in controller design (g_design)  |
-| void | **[set_u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-u-ref)**(const Vector & u_ref)<br>Set reference input (u_ref)  |
-| void | **[set_x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-x-ref)**(const Vector & x_ref)<br>Set reference state (x_ref)  |
-| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-kc)**(const Matrix & Kc)<br>Set state controller gain.  |
-| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-kc-inty)**(const Matrix & Kc_inty)<br>Set integral controller gain.  |
-| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-kc-u)**(const Matrix & Kc_u)<br>Set input controller gain.  |
-| void | **[set_tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-tau-awu)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) tau)<br>Set time constant of anti-integral-windup.  |
-| void | **[set_control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-control-type)**(size_t control_type)<br>Sets the control type.  |
-| void | **[set_u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-u-lb)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb)<br>sets control lower bound  |
-| void | **[set_u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-u-ub)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub)<br>Sets control upper bound.  |
-| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-reset)**()<br>reset system and control variables.  |
-| void | **[Print](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-print)**()<br>prints variables to stdout  |
-
-**Protected Attributes inherited from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| [System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/) | **[sys_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-sys-)** <br>underlying LDS  |
-| Vector | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-)** <br>control signal  |
-| Vector | **[u_return_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-return-)** <br>control signal that is _returned_ to user  |
-| Vector | **[g_design_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-g-design-)** <br>input gain of the system used for controller design  |
-| Vector | **[u_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-ref-)** <br>reference input  |
-| Vector | **[u_ref_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-ref-prev-)** <br>reference input at previous time step  |
-| Vector | **[x_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-x-ref-)** <br>reference state  |
-| Vector | **[y_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-y-ref-)** <br>reference output  |
-| Vector | **[cx_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-cx-ref-)**  |
-| Matrix | **[Kc_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-kc-)** <br>state controller gain  |
-| Matrix | **[Kc_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-kc-u-)** <br>input controller gain (optional when control updates )  |
-| Matrix | **[Kc_inty_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-kc-inty-)** <br>integral controller gain  |
-| Vector | **[du_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-du-ref-)**  |
-| Vector | **[dv_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-dv-ref-)**  |
-| Vector | **[v_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-v-ref-)**  |
-| Vector | **[dv_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-dv-)**  |
-| Vector | **[v_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-v-)** <br>Control after g inversion (e.g., control in physical units)  |
-| Vector | **[int_e_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-int-e-)** <br>integrated error  |
-| Vector | **[int_e_awu_adjust_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-int-e-awu-adjust-)** <br>anti-windup adjustment to intE  |
-| Vector | **[u_sat_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-sat-)** <br>control signal after saturation (for antiWindup)  |
-| bool | **[do_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-do-control-prev-)**  |
-| bool | **[do_lock_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-do-lock-control-prev-)**  |
-| bool | **[u_saturated_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-saturated-)** <br>whether control signal has reached saturation limits  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-lb-)** <br>lower bound on control  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-u-ub-)** <br>upper bound on control  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-tau-awu-)** <br>antiwindup time constant  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[k_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-k-awu-)**  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[t_since_control_onset_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-t-since-control-onset-)** <br>time since control epoch onset  |
-| size_t | **[control_type_](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#variable-control-type-)** <br>controller type  |
-
-
----
----
-## Public Function Details
-
-### **set_y_ref**
-
-```cpp
-inline virtual void set_y_ref(
-    const Vector & y_ref
-) override
-```
-
-
-
-**Reimplements**: [lds::Controller::set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/#function-set-y-ref)
-
-
----
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1System.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1System.md
deleted file mode 100644
index dd54d6b2..00000000
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1System.md
+++ /dev/null
@@ -1,224 +0,0 @@
----
-title: lds::poisson::System
-summary: Poisson System type. 
-
----
-
-# lds::poisson::System
-
-
-
-Poisson [System]() type. 
-<br /> `#include <lds_poisson_sys.h>`
-
-Inherits from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)
-
-## Public Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| | **[System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/#function-system)**() =default<br>Constructs a new [System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/).  |
-| | **[System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/#function-system)**(std::size_t n_u, std::size_t n_x, std::size_t n_y, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) p0 =kDefaultP0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) q0 =kDefaultQ0)<br>Constructs a new Poisson [System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/).  |
-| virtual const Vector & | **[Simulate](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/#function-simulate)**(const Vector & u_tm1) override<br>Simulate system measurement.  |
-
-## Protected Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| virtual void | **[h](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/#function-h)**() override<br>[System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/) output function.  |
-| virtual void | **[RecurseKe](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/#function-recurseke)**() override<br>Recursively recalculate estimator gain (Ke)  |
-
-## Additional inherited members
-
-**Public Functions inherited from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| virtual | **[~System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-~system)**() |
-| void | **[Filter](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-filter)**(const Vector & u_tm1, const Vector & z)<br>Filter data to produce causal state estimates.  |
-| void | **[f](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-f)**(const Vector & u, bool do_add_noise =false)<br>system dynamics function  |
-| size_t | **[n_u](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-n-u)**() const<br>Get number of inputs.  |
-| size_t | **[n_x](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-n-x)**() const<br>Get number of states.  |
-| size_t | **[n_y](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-n-y)**() const<br>Get number of outputs.  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-dt)**() const<br>Get sample period.  |
-| const Vector & | **[x](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-x)**() const<br>Get current state.  |
-| const Matrix & | **[P](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-p)**() const<br>Get covariance of state estimate.  |
-| const Vector & | **[m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-m)**() const<br>Get current process disturbance/bias.  |
-| const Matrix & | **[P_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-p-m)**() const<br>Get covariance of process disturbance estimate.  |
-| const Vector & | **[cx](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-cx)**() const<br>Get C*x.  |
-| const Vector & | **[y](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-y)**() const<br>Get output.  |
-| const Vector & | **[x0](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-x0)**() const<br>Get initial state.  |
-| const Vector & | **[m0](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-m0)**() const<br>Get initial disturbance.  |
-| const Matrix & | **[A](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-a)**() const<br>Get state matrix.  |
-| const Matrix & | **[B](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-b)**() const<br>Get input matrix.  |
-| const Vector & | **[g](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-g)**() const<br>Get input gain/conversion factor.  |
-| const Matrix & | **[C](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-c)**() const<br>Get output matrix.  |
-| const Vector & | **[d](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-d)**() const<br>Get output bias.  |
-| const Matrix & | **[Ke](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-ke)**() const<br>Get estimator gain.  |
-| const Matrix & | **[Ke_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-ke-m)**() const<br>Get estimator gain for process disturbance (m)  |
-| const Matrix & | **[Q](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-q)**()<br>Get process noise covariance.  |
-| const Matrix & | **[Q_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-q-m)**()<br>Get process noise covariance of disturbance evoluation.  |
-| const Matrix & | **[P0](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-p0)**()<br>Get covariance of initial state.  |
-| const Matrix & | **[P0_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-p0-m)**()<br>Get covariance of initial process disturbance.  |
-| void | **[set_A](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-a)**(const Matrix & A)<br>Set state matrix.  |
-| void | **[set_B](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-b)**(const Matrix & B)<br>Set input matrix.  |
-| void | **[set_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-m)**(const Vector & m, bool do_force_assign =false)<br>Set process disturbance.  |
-| void | **[set_g](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-g)**(const Vector & g)<br>Set input gain.  |
-| void | **[set_Q](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-q)**(const Matrix & Q)<br>Set process noise covariance.  |
-| void | **[set_Q_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-q-m)**(const Matrix & Q_m)<br>Set process noise covariance of disturbance evoluation.  |
-| void | **[set_x0](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-x0)**(const Vector & x0)<br>Set initial state.  |
-| void | **[set_P0](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-p0)**(const Matrix & P0)<br>Set covariance of initial state.  |
-| void | **[set_P0_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-p0-m)**(const Matrix & P0_m)<br>Set covariance of initial process disturbance.  |
-| void | **[set_C](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-c)**(const Matrix & C)<br>Set output matrix.  |
-| void | **[set_d](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-d)**(const Vector & d)<br>Set output bias.  |
-| void | **[set_x](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-set-x)**(const Vector & x)<br>Set state of system.  |
-| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-reset)**()<br>Reset system variables.  |
-| void | **[Print](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-print)**()<br>Print system variables to stdout.  |
-
-**Protected Functions inherited from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| void | **[InitVars](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-initvars)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) p0 =kDefaultP0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) q0 =kDefaultQ0) |
-
-**Public Attributes inherited from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| bool | **[do_adapt_m](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-do-adapt-m)** <br>whether to adaptively estimate disturbance m  |
-
-**Protected Attributes inherited from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)**
-
-|                | Name           |
-| -------------- | -------------- |
-| std::size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-n-x-)** <br>number of states  |
-| std::size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-n-u-)** <br>number of inputs  |
-| std::size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-n-y-)** <br>number of outputs  |
-| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-dt-)** <br>sample period  |
-| Vector | **[x_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-x-)** <br>state  |
-| Matrix | **[P_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-p-)** <br>covariance of state estimate  |
-| Vector | **[m_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-m-)** <br>process disturbance  |
-| Matrix | **[P_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-p-m-)** <br>covariance of disturbance estimate  |
-| Vector | **[cx_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-cx-)** <br>C*x.  |
-| Vector | **[y_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-y-)** <br>output  |
-| Vector | **[z_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-z-)** <br>measurement  |
-| Vector | **[x0_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-x0-)** <br>initial state  |
-| Matrix | **[P0_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-p0-)** <br>covariance of initial state estimate  |
-| Vector | **[m0_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-m0-)** <br>initial process disturbance  |
-| Matrix | **[P0_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-p0-m-)** <br>covariance of initial disturbance est.  |
-| Matrix | **[A_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-a-)** <br>state matrix  |
-| Matrix | **[B_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-b-)** <br>input matrix  |
-| Vector | **[g_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-g-)** <br>input gain  |
-| Matrix | **[Q_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-q-)** <br>covariance of process noise  |
-| Matrix | **[Q_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-q-m-)** <br>covariance of disturbance random walk  |
-| Matrix | **[C_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-c-)** <br>output matrix  |
-| Vector | **[d_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-d-)** <br>output bias  |
-| Matrix | **[Ke_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-ke-)** <br>estimator gain  |
-| Matrix | **[Ke_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#variable-ke-m-)** <br>estimator gain for process disturbance  |
-
-
----
----
-## Public Function Details
-
-### **System**
-
-```cpp
-System() =default
-```
-
-
-
----
-### **System**
-
-```cpp
-System(
-    std::size_t n_u,
-    std::size_t n_x,
-    std::size_t n_y,
-    data_t dt,
-    data_t p0 =kDefaultP0,
-    data_t q0 =kDefaultQ0
-)
-```
-
-
-
-**Parameters**:
-
-  * **n_u** number of inputs 
-  * **n_x** number of states 
-  * **n_y** number of outputs 
-  * **dt** sample period 
-  * **p0** [optional] initial diagonal elements of state estimate covariance (P) 
-  * **q0** [optional] initial diagonal elements of process noise covariance (Q) 
-
-
----
-### **Simulate**
-
-```cpp
-virtual const Vector & Simulate(
-    const Vector & u_tm1
-) override
-```
-
-
-
-**Parameters**:
-
-  * **u_tm1** input at t-1
-
-
-**Return**: z measurement 
-
-**Reimplements**: [lds::System::Simulate](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-simulate)
-
-
-Simulate system and produce measurement
-
-
----
-
-
-## Protected Function Details
-
-### **h**
-
-```cpp
-inline virtual void h() override
-```
-
-
-
-**Reimplements**: [lds::System::h](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-h)
-
-
----
-### **RecurseKe**
-
-```cpp
-virtual void RecurseKe() override
-```
-
-
-
-**Reimplements**: [lds::System::RecurseKe](/lds-ctrl-est/docs/api/classes/classlds_1_1system/#function-recurseke)
-
-
-Recursively recalculate estimator gain (Ke).
-
-References:
-
-Smith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.
-
-Eden UT, ..., Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16. 
-
-
----
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_controller.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_controller.md
new file mode 100644
index 00000000..ddf5c236
--- /dev/null
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_controller.md
@@ -0,0 +1,116 @@
+---
+title: lds::poisson::Controller
+summary: PLDS Controller Type. 
+
+---
+
+# lds::poisson::Controller
+
+
+
+PLDS [Controller]() Type. 
+<br /> `#include <lds_poisson_ctrl.h>`
+
+Inherits from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/)
+
+## Public Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| virtual void | **[set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/#function-set-y-ref)**(const Vector & y_ref) override<br>Set reference output.  |
+
+## Additional inherited members
+
+**Public Functions inherited from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller)**() =default<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/).  |
+| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) & sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/).  |
+| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller)**([System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) && sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/) by moving the system object.  |
+| const Vector & | **[Control](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control)**(const Vector & z, bool do_control =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal (single-step)  |
+| const Vector & | **[ControlOutputReference](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controloutputreference)**(const Vector & z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal, given previously-set (single-step)  |
+| const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) & | **[sys](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-sys)**() const |
+| const Matrix & | **[Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc)**() const<br>Get state feedback controller gain.  |
+| const Matrix & | **[Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-inty)**() const<br>Get integral controller gain.  |
+| const Matrix & | **[Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-u)**() const<br>Get input feedback controller gain.  |
+| const Vector & | **[g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-g-design)**() const<br>Get input gain used in controller design.  |
+| const Vector & | **[u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ref)**() const<br>Get reference input.  |
+| const Vector & | **[x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-x-ref)**() const<br>Get reference state.  |
+| const Vector & | **[y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-y-ref)**() const<br>Get reference output.  |
+| size_t | **[control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control-type)**() const<br>Get controller type.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-tau-awu)**() const<br>Get time constant of anti-integral-windup.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-lb)**() const<br>Get control lower bound.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ub)**() const<br>Get control upper bound.  |
+| void | **[set_sys](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-sys)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) & sys)<br>Set system.  |
+| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-g-design)**(const Vector & g_design)<br>Set input gain used in controller design (g_design)  |
+| void | **[set_u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ref)**(const Vector & u_ref)<br>Set reference input (u_ref)  |
+| void | **[set_x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-x-ref)**(const Vector & x_ref)<br>Set reference state (x_ref)  |
+| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc)**(const Matrix & Kc)<br>Set state controller gain.  |
+| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-inty)**(const Matrix & Kc_inty)<br>Set integral controller gain.  |
+| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-u)**(const Matrix & Kc_u)<br>Set input controller gain.  |
+| void | **[set_tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-tau-awu)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) tau)<br>Set time constant of anti-integral-windup.  |
+| void | **[set_control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-control-type)**(size_t control_type)<br>Sets the control type.  |
+| void | **[set_u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-lb)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb)<br>sets control lower bound  |
+| void | **[set_u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ub)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub)<br>Sets control upper bound.  |
+| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-reset)**()<br>reset system and control variables.  |
+| void | **[Print](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-print)**()<br>prints variables to stdout  |
+
+**Protected Attributes inherited from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) | **[sys_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-sys-)** <br>underlying LDS  |
+| Vector | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-)** <br>control signal  |
+| Vector | **[u_return_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-return-)** <br>control signal that is _returned_ to user  |
+| Vector | **[g_design_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-g-design-)** <br>input gain of the system used for controller design  |
+| Vector | **[u_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-)** <br>reference input  |
+| Vector | **[u_ref_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-prev-)** <br>reference input at previous time step  |
+| Vector | **[x_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-x-ref-)** <br>reference state  |
+| Vector | **[y_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-y-ref-)** <br>reference output  |
+| Vector | **[cx_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-cx-ref-)**  |
+| Matrix | **[Kc_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-)** <br>state controller gain  |
+| Matrix | **[Kc_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-u-)** <br>input controller gain (optional when control updates \deltaU)  |
+| Matrix | **[Kc_inty_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-inty-)** <br>integral controller gain  |
+| Vector | **[du_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-du-ref-)**  |
+| Vector | **[dv_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-ref-)**  |
+| Vector | **[v_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-ref-)**  |
+| Vector | **[dv_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-)**  |
+| Vector | **[v_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-)** <br>Control after g inversion (e.g., control in physical units)  |
+| Vector | **[int_e_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-)** <br>integrated error  |
+| Vector | **[int_e_awu_adjust_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-awu-adjust-)** <br>anti-windup adjustment to intE  |
+| Vector | **[u_sat_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-sat-)** <br>control signal after saturation (for antiWindup)  |
+| bool | **[do_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-control-prev-)**  |
+| bool | **[do_lock_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-lock-control-prev-)**  |
+| bool | **[u_saturated_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-saturated-)** <br>whether control signal has reached saturation limits  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-lb-)** <br>lower bound on control  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ub-)** <br>upper bound on control  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-tau-awu-)** <br>antiwindup time constant  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[k_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-k-awu-)**  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[t_since_control_onset_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-t-since-control-onset-)** <br>time since control epoch onset  |
+| size_t | **[control_type_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-control-type-)** <br>controller type  |
+
+
+---
+---
+## Public Function Details
+
+### **set_y_ref**
+
+```cpp
+inline virtual void set_y_ref(
+    const Vector & y_ref
+) override
+```
+
+
+
+**Reimplements**: [lds::Controller::set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-y-ref)
+
+
+---
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_fit.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_fit.md
new file mode 100644
index 00000000..b875bbeb
--- /dev/null
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_fit.md
@@ -0,0 +1,168 @@
+---
+title: lds::poisson::Fit
+summary: PLDS Fit Type. 
+
+---
+
+# lds::poisson::Fit
+
+
+
+PLDS [Fit]() Type. 
+<br /> `#include <lds_poisson_fit.h>`
+
+Inherits from [lds::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/)
+
+## Public Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/#function-fit)**() =default |
+| | **[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/#function-fit)**(size_t n_u, size_t n_x, size_t n_y, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt)<br>Constructs a new [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/).  |
+| virtual View | **[h](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/#function-h)**(Matrix & y, const Matrix & x, size_t t) override<br>output function  |
+| virtual void | **[set_R](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/#function-set-r)**(const Matrix & R) override<br>sets output noise covariance (if any)  |
+| virtual const Matrix & | **[R](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/#function-r)**() const override |
+
+## Additional inherited members
+
+**Public Functions inherited from [lds::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| virtual | **[~Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-~fit)**() =default |
+| size_t | **[n_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-n-u)**() const<br>gets number of inputs  |
+| size_t | **[n_x](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-n-x)**() const<br>gets number of states  |
+| size_t | **[n_y](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-n-y)**() const<br>gets number of outputs  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-dt)**() const<br>gets sample period  |
+| const Matrix & | **[A](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-a)**() const<br>gets state matrix  |
+| const Matrix & | **[B](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-b)**() const<br>gets input matrix  |
+| const Vector & | **[g](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-g)**() const<br>gets input gain  |
+| const Vector & | **[m](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-m)**() const<br>gets process disturbance  |
+| const Matrix & | **[Q](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-q)**() const<br>gets process noise covariance  |
+| const Vector & | **[x0](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-x0)**() const<br>gets initial state estimate  |
+| const Matrix & | **[P0](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-p0)**() const<br>gets covariance of initial state estimate  |
+| const Matrix & | **[C](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-c)**() const<br>gets output matrix  |
+| const Vector & | **[d](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-d)**() const<br>gets output bias  |
+| void | **[set_A](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-a)**(const Matrix & A)<br>sets state matrix  |
+| void | **[set_B](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-b)**(const Matrix & B)<br>sets input matrix  |
+| void | **[set_g](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-g)**(const Vector & g)<br>sets input gain/conversion factor  |
+| void | **[set_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-m)**(const Vector & m)<br>sets process disturbance  |
+| void | **[set_Q](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-q)**(const Matrix & Q)<br>sets process noise covariance  |
+| void | **[set_x0](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-x0)**(const Vector & x0)<br>sets initial state estimate  |
+| void | **[set_P0](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-p0)**(const Matrix & P0)<br>sets initial state estimate covariance  |
+| void | **[set_C](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-c)**(const Matrix & C)<br>sets output matrix  |
+| void | **[set_d](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-d)**(const Vector & d)<br>sets output bias  |
+| View | **[f](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-f)**(Matrix & x, const Matrix & u, size_t t)<br>system dynamics function  |
+| View | **[f](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-f)**(Matrix & x_pre, const Matrix & x_post, const Matrix & u, size_t t)<br>system dynamics function  |
+
+**Protected Attributes inherited from [lds::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-dt-)** <br>sample period  |
+| Matrix | **[A_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-a-)** <br>state matrix  |
+| Matrix | **[B_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-b-)** <br>input matrix  |
+| Vector | **[g_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-g-)** <br>input gain  |
+| Vector | **[m_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-m-)** <br>process noise mean  |
+| Matrix | **[Q_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-q-)** <br>process noise cov  |
+| Matrix | **[C_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-c-)** <br>output matrix  |
+| Vector | **[d_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-d-)** <br>output bias  |
+| Matrix | **[R_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-r-)** <br>measurement noise  |
+| Vector | **[x0_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-x0-)** <br>initial state  |
+| Matrix | **[P0_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-p0-)** <br>initial covar  |
+| size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-n-u-)** <br>number of inputs  |
+| size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-n-x-)** <br>number of states  |
+| size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-n-y-)** <br>number of outputs  |
+
+
+---
+---
+## Public Function Details
+
+### **Fit**
+
+```cpp
+Fit() =default
+```
+
+
+
+---
+### **Fit**
+
+```cpp
+inline Fit(
+    size_t n_u,
+    size_t n_x,
+    size_t n_y,
+    data_t dt
+)
+```
+
+
+
+**Parameters**:
+
+  * **n_u** number of inputs 
+  * **n_x** number of states 
+  * **n_y** number of outputs 
+  * **dt** sample period 
+
+
+---
+### **h**
+
+```cpp
+inline virtual View h(
+    Matrix & y,
+    const Matrix & x,
+    size_t t
+) override
+```
+
+
+
+**Parameters**:
+
+  * **y** output estimate (over time) 
+  * **x** state estimate (over time) 
+  * **t** time index
+
+
+**Return**: output 
+
+**Reimplements**: [lds::Fit::h](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-h)
+
+
+---
+### **set_R**
+
+```cpp
+inline virtual void set_R(
+    const Matrix & R
+) override
+```
+
+
+
+**Reimplements**: [lds::Fit::set_R](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-r)
+
+
+---
+### **R**
+
+```cpp
+inline virtual const Matrix & R() const override
+```
+
+
+
+**Reimplements**: [lds::Fit::R](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-r)
+
+
+---
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_fit_e_m.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_fit_e_m.md
new file mode 100644
index 00000000..bbf0f88b
--- /dev/null
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_fit_e_m.md
@@ -0,0 +1,96 @@
+---
+title: lds::poisson::FitEM
+summary: PLDS E-M Fit Type. 
+
+---
+
+# lds::poisson::FitEM
+
+
+
+PLDS E-M [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/) Type.  [More...](#detailed-description)
+
+
+<br /> `#include <lds_poisson_fit_em.h>`
+
+Inherits from [lds::EM< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/)
+
+## Additional inherited members
+
+**Public Functions inherited from [lds::EM< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em)**() =default<br>Constructs a new [EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/)[Fit]() type.  |
+| | **[EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em)**(size_t n_x, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && u_train, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && z_train)<br>Constructs a new [EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/)[Fit]() type.  |
+| | **[EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em)**(const [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/) & fit0, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && u_train, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && z_train)<br>Constructs a new [EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/)[Fit]() type.  |
+| virtual | **[~EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-~em)**() =default |
+| const [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/) & | **[Run](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-run)**(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) tol =1e-2)<br>Runs fitting by Expectation(E)-Maximization(M)  |
+| std::tuple< [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) >, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > > | **[ReturnData](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-returndata)**()<br>Returns the input/output data to caller.  |
+| const std::vector< Matrix > & | **[x](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-x)**() const<br>gets estimated state (over time)  |
+| const std::vector< Matrix > & | **[y](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-y)**() const<br>gets estimated output (over time)  |
+| const Matrix & | **[sum_E_x_t_x_t](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-x-t-x-t)**() const<br>gets state-input covariance  |
+| const Matrix & | **[sum_E_xu_tm1_xu_tm1](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-xu-tm1-xu-tm1)**() const<br>gets state-input covariance (t-minus-1)  |
+| const Matrix & | **[sum_E_xu_t_xu_tm1](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-xu-t-xu-tm1)**() const<br>gets single lag state-input covariance  |
+| size_t | **[n_t_tot](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-n-t-tot)**()<br>total number of time samples  |
+| const Vector & | **[theta](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-theta)**() const<br>gets parameters updated in M step  |
+
+**Protected Functions inherited from [lds::EM< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| void | **[Expectation](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-expectation)**(bool force_common_initial =false)<br>Expectation step.  |
+| void | **[Maximization](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximization)**(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)<br>Maximization step.  |
+| void | **[MaximizeDynamics](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizedynamics)**() |
+| void | **[MaximizeQ](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizeq)**() |
+| void | **[MaximizeInitial](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizeinitial)**() |
+| void | **[Smooth](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-smooth)**(bool force_common_initial)<br>get smoothed estimates  |
+| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-reset)**()<br>reset to initial conditions  |
+| void | **[InitVars](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-initvars)**()<br>Initializes the variables.  |
+| Vector | **[UpdateTheta](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-updatetheta)**()<br>updates parameter list, theta  |
+
+**Protected Attributes inherited from [lds::EM< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-u-)** <br>input training data  |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[z_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-z-)** <br>measurement training data  |
+| std::vector< Matrix > | **[x_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-x-)** <br>state estimate  |
+| std::vector< Cube > | **[P_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-p-)** <br>state estimate cov  |
+| std::vector< Cube > | **[P_t_tm1_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-p-t-tm1-)** <br>single-lag state covariance  |
+| std::vector< Matrix > | **[y_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-y-)** <br>output estimate  |
+| Matrix | **[diag_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-diag-y-)**  |
+| Matrix | **[sum_E_x_t_x_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-x-t-x-t-)** <br>state covariance (current time)  |
+| Matrix | **[sum_E_xu_tm1_xu_tm1_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-xu-tm1-xu-tm1-)** <br>state-input covariance (t-minus-1)  |
+| Matrix | **[sum_E_xu_t_xu_tm1_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-xu-t-xu-tm1-)** <br>single lag state-input covariance  |
+| [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/) | **[fit_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-fit-)**  |
+| Vector | **[theta_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-theta-)**  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-dt-)** <br>sample period  |
+| size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-u-)** <br>number of inputs  |
+| size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-x-)** <br>number of states  |
+| size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-y-)** <br>number of outputs  |
+| size_t | **[n_trials_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-trials-)** <br>number of input/output data sequences  |
+| std::vector< size_t > | **[n_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-t-)** <br>number of time steps  |
+| size_t | **[n_t_tot_](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-t-tot-)** <br>total number of time steps across trials  |
+
+
+## Detailed Description
+
+```cpp
+class lds::poisson::FitEM;
+```
+
+
+
+
+
+```
+        This type is used in the process of fitting PLDS models by
+        expectation-maximization (EM).
+```
+
+---
+---
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_fit_s_s_i_d.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_fit_s_s_i_d.md
new file mode 100644
index 00000000..3f33c77d
--- /dev/null
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_fit_s_s_i_d.md
@@ -0,0 +1,63 @@
+---
+title: lds::poisson::FitSSID
+summary: Subspace Identification (SSID) for PLDS. 
+
+---
+
+# lds::poisson::FitSSID
+
+
+
+Subspace Identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)) for PLDS. 
+<br /> `#include <lds_poisson_fit_ssid.h>`
+
+Inherits from [lds::SSID< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)
+
+## Additional inherited members
+
+**Public Functions inherited from [lds::SSID< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-ssid)**() =default<br>Constructs a new [SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/) type.  |
+| | **[SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-ssid)**(size_t n_x, size_t n_h, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && u_train, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > && z_train, const Vector & d =Vector(1).fill(-kInf))<br>Constructs a new [SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)[Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/) type.  |
+| std::tuple< [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/), Vector > | **[Run](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-run)**([SSIDWt](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt) ssid_wt)<br>Runs fitting by subspace identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/))  |
+| std::tuple< [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) >, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > > | **[ReturnData](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-returndata)**()<br>Returns the I/O data to caller.  |
+
+**Protected Functions inherited from [lds::SSID< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| void | **[CalcD](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-calcd)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) t_silence =0.1, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) thresh_silence =0.001)<br>Using periods of silence in inputs (u), calculates the output \ bias (d)  |
+| void | **[CreateHankelDataMat](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-createhankeldatamat)**()<br>Creates the block-hankel I/O data matrix.  |
+| void | **[CalcSVD](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-calcsvd)**([SSIDWt](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt) wt)<br>performs the singular value decomposition (SVD)  |
+| void | **[Solve](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-solve)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) wt_dc)<br>solves for LDS parameters  |
+| void | **[RecomputeExtObs](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-recomputeextobs)**()<br>recompute extended observability matrix from estimates of A, C  |
+
+**Protected Attributes inherited from [lds::SSID< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-u-)** <br>input training data  |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > | **[z_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-z-)** <br>measurement training data  |
+| Matrix | **[D_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-d-)** <br>block-Hankel I/O data matrix  |
+| [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/) | **[fit_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-fit-)** <br>fit  |
+| Matrix | **[g_dc_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-g-dc-)** <br>I/O gain @ DC.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-dt-)** <br>sample period  |
+| size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-u-)** <br>number of inputs  |
+| size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-x-)** <br>number of states  |
+| size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-y-)** <br>number of outputs  |
+| size_t | **[n_h_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-h-)**  |
+| size_t | **[n_trials_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-trials-)** <br>number of input/output data sequences  |
+| std::vector< size_t > | **[n_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-t-)** <br>number of time steps  |
+| size_t | **[n_t_tot_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-t-tot-)** <br>total number of time steps across trials  |
+| Matrix | **[L_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-l-)** <br>lower triangle decomp of covariance matrix  |
+| Vector | **[s_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-s-)** <br>singular values  |
+| Matrix | **[ext_obs_t_](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-ext-obs-t-)** <br>extended observability matrix  |
+
+
+---
+---
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_switched_controller.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_switched_controller.md
new file mode 100644
index 00000000..f20892f1
--- /dev/null
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_switched_controller.md
@@ -0,0 +1,145 @@
+---
+title: lds::poisson::SwitchedController
+summary: Poisson-observation SwitchedController Type. 
+
+---
+
+# lds::poisson::SwitchedController
+
+
+
+Poisson-observation [SwitchedController]() Type. 
+<br /> `#include <lds_poisson_sctrl.h>`
+
+Inherits from [lds::SwitchedController< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/), [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/)
+
+## Public Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| virtual void | **[set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/#function-set-y-ref)**(const Vector & y_ref) override<br>Set reference output.  |
+
+## Additional inherited members
+
+**Public Functions inherited from [lds::SwitchedController< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switchedcontroller)**() =default<br>Constructs a new [SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/).  |
+| | **[SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switchedcontroller)**(const std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) > & systems, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/).  |
+| | **[SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switchedcontroller)**(std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) > && systems, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/) (moves systems).  |
+| void | **[Switch](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switch)**(size_t idx, bool do_force_switch =false)<br>Switch to a different sub-system/controller.  |
+| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)<> & Kc)<br>sets state feedback gains  |
+| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)<> && Kc)<br>sets state feedback gains (moving)  |
+| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-inty)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)<> & Kc_inty)<br>sets integral feedback gains  |
+| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-inty)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)<> && Kc_inty)<br>sets integral feedback gains (moving)  |
+| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-u)**(const [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)<> & Kc_u)<br>sets input feedback gains  |
+| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-u)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)<> && Kc_u)<br>sets input feedback gains (moving)  |
+| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-g-design)**(const [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/) & g)<br>sets input gain used during controller design  |
+| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-g-design)**([UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/) && g)<br>sets input gain used during controller design (moving)  |
+
+**Protected Attributes inherited from [lds::SwitchedController< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| std::vector< [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) > | **[systems_](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-systems-)** <br>underlying sub-systems which are switched between  |
+| size_t | **[n_sys_](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-n-sys-)** <br>number of systems  |
+| size_t | **[idx_](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-idx-)** <br>current system/controller index.  |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/) | **[Kc_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-kc-list-)**  |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/) | **[Kc_inty_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-kc-inty-list-)**  |
+| [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/) | **[Kc_u_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-kc-u-list-)**  |
+| [UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/) | **[g_design_list_](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-g-design-list-)**  |
+
+**Public Functions inherited from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller)**() =default<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/).  |
+| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) & sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/).  |
+| | **[Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller)**([System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) && sys, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub, size_t control_type =0)<br>Constructs a new [Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/) by moving the system object.  |
+| const Vector & | **[Control](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control)**(const Vector & z, bool do_control =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal (single-step)  |
+| const Vector & | **[ControlOutputReference](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controloutputreference)**(const Vector & z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_soft_start =0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal, given previously-set (single-step)  |
+| const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) & | **[sys](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-sys)**() const |
+| const Matrix & | **[Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc)**() const<br>Get state feedback controller gain.  |
+| const Matrix & | **[Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-inty)**() const<br>Get integral controller gain.  |
+| const Matrix & | **[Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-u)**() const<br>Get input feedback controller gain.  |
+| const Vector & | **[g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-g-design)**() const<br>Get input gain used in controller design.  |
+| const Vector & | **[u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ref)**() const<br>Get reference input.  |
+| const Vector & | **[x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-x-ref)**() const<br>Get reference state.  |
+| const Vector & | **[y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-y-ref)**() const<br>Get reference output.  |
+| size_t | **[control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control-type)**() const<br>Get controller type.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-tau-awu)**() const<br>Get time constant of anti-integral-windup.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-lb)**() const<br>Get control lower bound.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ub)**() const<br>Get control upper bound.  |
+| void | **[set_sys](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-sys)**(const [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) & sys)<br>Set system.  |
+| void | **[set_g_design](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-g-design)**(const Vector & g_design)<br>Set input gain used in controller design (g_design)  |
+| void | **[set_u_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ref)**(const Vector & u_ref)<br>Set reference input (u_ref)  |
+| void | **[set_x_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-x-ref)**(const Vector & x_ref)<br>Set reference state (x_ref)  |
+| void | **[set_Kc](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc)**(const Matrix & Kc)<br>Set state controller gain.  |
+| void | **[set_Kc_inty](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-inty)**(const Matrix & Kc_inty)<br>Set integral controller gain.  |
+| void | **[set_Kc_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-u)**(const Matrix & Kc_u)<br>Set input controller gain.  |
+| void | **[set_tau_awu](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-tau-awu)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) tau)<br>Set time constant of anti-integral-windup.  |
+| void | **[set_control_type](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-control-type)**(size_t control_type)<br>Sets the control type.  |
+| void | **[set_u_lb](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-lb)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_lb)<br>sets control lower bound  |
+| void | **[set_u_ub](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ub)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) u_ub)<br>Sets control upper bound.  |
+| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-reset)**()<br>reset system and control variables.  |
+| void | **[Print](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-print)**()<br>prints variables to stdout  |
+
+**Protected Attributes inherited from [lds::Controller< System >](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| [System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/) | **[sys_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-sys-)** <br>underlying LDS  |
+| Vector | **[u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-)** <br>control signal  |
+| Vector | **[u_return_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-return-)** <br>control signal that is _returned_ to user  |
+| Vector | **[g_design_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-g-design-)** <br>input gain of the system used for controller design  |
+| Vector | **[u_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-)** <br>reference input  |
+| Vector | **[u_ref_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-prev-)** <br>reference input at previous time step  |
+| Vector | **[x_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-x-ref-)** <br>reference state  |
+| Vector | **[y_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-y-ref-)** <br>reference output  |
+| Vector | **[cx_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-cx-ref-)**  |
+| Matrix | **[Kc_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-)** <br>state controller gain  |
+| Matrix | **[Kc_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-u-)** <br>input controller gain (optional when control updates \deltaU)  |
+| Matrix | **[Kc_inty_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-inty-)** <br>integral controller gain  |
+| Vector | **[du_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-du-ref-)**  |
+| Vector | **[dv_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-ref-)**  |
+| Vector | **[v_ref_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-ref-)**  |
+| Vector | **[dv_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-)**  |
+| Vector | **[v_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-)** <br>Control after g inversion (e.g., control in physical units)  |
+| Vector | **[int_e_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-)** <br>integrated error  |
+| Vector | **[int_e_awu_adjust_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-awu-adjust-)** <br>anti-windup adjustment to intE  |
+| Vector | **[u_sat_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-sat-)** <br>control signal after saturation (for antiWindup)  |
+| bool | **[do_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-control-prev-)**  |
+| bool | **[do_lock_control_prev_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-lock-control-prev-)**  |
+| bool | **[u_saturated_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-saturated-)** <br>whether control signal has reached saturation limits  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_lb_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-lb-)** <br>lower bound on control  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[u_ub_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ub-)** <br>upper bound on control  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[tau_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-tau-awu-)** <br>antiwindup time constant  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[k_awu_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-k-awu-)**  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[t_since_control_onset_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-t-since-control-onset-)** <br>time since control epoch onset  |
+| size_t | **[control_type_](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-control-type-)** <br>controller type  |
+
+
+---
+---
+## Public Function Details
+
+### **set_y_ref**
+
+```cpp
+inline virtual void set_y_ref(
+    const Vector & y_ref
+) override
+```
+
+
+
+**Reimplements**: [lds::Controller::set_y_ref](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-y-ref)
+
+
+---
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_system.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_system.md
new file mode 100644
index 00000000..e4e2d313
--- /dev/null
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_system.md
@@ -0,0 +1,240 @@
+---
+title: lds::poisson::System
+summary: Poisson System type. 
+
+---
+
+# lds::poisson::System
+
+
+
+Poisson [System]() type. 
+<br /> `#include <lds_poisson_sys.h>`
+
+Inherits from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)
+
+## Public Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| | **[System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/#function-system)**() =default<br>Constructs a new [System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/).  |
+| | **[System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/#function-system)**(std::size_t n_u, std::size_t n_x, std::size_t n_y, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) dt, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) p0 =kDefaultP0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) q0 =kDefaultQ0)<br>Constructs a new Poisson [System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/).  |
+| virtual const Vector & | **[Simulate](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/#function-simulate)**(const Vector & u_tm1) override<br>Simulate system measurement.  |
+
+## Protected Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| virtual void | **[h](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/#function-h)**() override<br>[System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/) output function.  |
+| virtual Vector | **[h_](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/#function-h-)**(Vector x) override<br>[System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/) output function: stateless.  |
+| virtual void | **[RecurseKe](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/#function-recurseke)**() override<br>Recursively recalculate estimator gain (Ke)  |
+
+## Additional inherited members
+
+**Public Functions inherited from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| virtual | **[~System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-~system)**() |
+| void | **[Filter](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-filter)**(const Vector & u_tm1, const Vector & z)<br>Filter data to produce causal state estimates.  |
+| void | **[f](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-f)**(const Vector & u, bool do_add_noise =false)<br>system dynamics function  |
+| size_t | **[n_u](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-n-u)**() const<br>Get number of inputs.  |
+| size_t | **[n_x](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-n-x)**() const<br>Get number of states.  |
+| size_t | **[n_y](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-n-y)**() const<br>Get number of outputs.  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-dt)**() const<br>Get sample period.  |
+| const Vector & | **[x](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-x)**() const<br>Get current state.  |
+| const Matrix & | **[P](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p)**() const<br>Get covariance of state estimate.  |
+| const Vector & | **[m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-m)**() const<br>Get current process disturbance/bias.  |
+| const Matrix & | **[P_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p-m)**() const<br>Get covariance of process disturbance estimate.  |
+| const Vector & | **[cx](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-cx)**() const<br>Get C*x.  |
+| const Vector & | **[y](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-y)**() const<br>Get output.  |
+| const Vector & | **[x0](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-x0)**() const<br>Get initial state.  |
+| const Vector & | **[m0](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-m0)**() const<br>Get initial disturbance.  |
+| const Matrix & | **[A](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-a)**() const<br>Get state matrix.  |
+| const Matrix & | **[B](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-b)**() const<br>Get input matrix.  |
+| const Vector & | **[g](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-g)**() const<br>Get input gain/conversion factor.  |
+| const Matrix & | **[C](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-c)**() const<br>Get output matrix.  |
+| const Vector & | **[d](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-d)**() const<br>Get output bias.  |
+| const Matrix & | **[Ke](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-ke)**() const<br>Get estimator gain.  |
+| const Matrix & | **[Ke_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-ke-m)**() const<br>Get estimator gain for process disturbance (m)  |
+| const Matrix & | **[Q](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-q)**()<br>Get process noise covariance.  |
+| const Matrix & | **[Q_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-q-m)**()<br>Get process noise covariance of disturbance evoluation.  |
+| const Matrix & | **[P0](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p0)**()<br>Get covariance of initial state.  |
+| const Matrix & | **[P0_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p0-m)**()<br>Get covariance of initial process disturbance.  |
+| void | **[set_A](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-a)**(const Matrix & A)<br>Set state matrix.  |
+| void | **[set_B](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-b)**(const Matrix & B)<br>Set input matrix.  |
+| void | **[set_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-m)**(const Vector & m, bool do_force_assign =false)<br>Set process disturbance.  |
+| void | **[set_g](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-g)**(const Vector & g)<br>Set input gain.  |
+| void | **[set_Q](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-q)**(const Matrix & Q)<br>Set process noise covariance.  |
+| void | **[set_Q_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-q-m)**(const Matrix & Q_m)<br>Set process noise covariance of disturbance evoluation.  |
+| void | **[set_x0](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-x0)**(const Vector & x0)<br>Set initial state.  |
+| void | **[set_P0](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-p0)**(const Matrix & P0)<br>Set covariance of initial state.  |
+| void | **[set_P0_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-p0-m)**(const Matrix & P0_m)<br>Set covariance of initial process disturbance.  |
+| void | **[set_C](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-c)**(const Matrix & C)<br>Set output matrix.  |
+| void | **[set_d](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-d)**(const Vector & d)<br>Set output bias.  |
+| void | **[set_x](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-x)**(const Vector & x)<br>Set state of system.  |
+| void | **[Reset](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-reset)**()<br>Reset system variables.  |
+| std::vector< [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > > | **[nstep_pred_block](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-nstep-pred-block)**([UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > u, [UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)< [kMatFreeDim2](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2) > z, size_t n_pred =1) |
+| void | **[Print](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-print)**()<br>Print system variables to stdout.  |
+
+**Protected Functions inherited from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| void | **[InitVars](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-initvars)**([data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) p0 =kDefaultP0, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) q0 =kDefaultQ0) |
+
+**Public Attributes inherited from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| bool | **[do_adapt_m](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-do-adapt-m)** <br>whether to adaptively estimate disturbance m  |
+
+**Protected Attributes inherited from [lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)**
+
+|                | Name           |
+| -------------- | -------------- |
+| std::size_t | **[n_x_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-n-x-)** <br>number of states  |
+| std::size_t | **[n_u_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-n-u-)** <br>number of inputs  |
+| std::size_t | **[n_y_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-n-y-)** <br>number of outputs  |
+| [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[dt_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-dt-)** <br>sample period  |
+| Vector | **[x_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-x-)** <br>state  |
+| Matrix | **[P_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p-)** <br>covariance of state estimate  |
+| Vector | **[m_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-m-)** <br>process disturbance  |
+| Matrix | **[P_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p-m-)** <br>covariance of disturbance estimate  |
+| Vector | **[cx_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-cx-)** <br>C*x.  |
+| Vector | **[y_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-y-)** <br>output  |
+| Vector | **[z_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-z-)** <br>measurement  |
+| Vector | **[x0_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-x0-)** <br>initial state  |
+| Matrix | **[P0_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p0-)** <br>covariance of initial state estimate  |
+| Vector | **[m0_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-m0-)** <br>initial process disturbance  |
+| Matrix | **[P0_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p0-m-)** <br>covariance of initial disturbance est.  |
+| Matrix | **[A_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-a-)** <br>state matrix  |
+| Matrix | **[B_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-b-)** <br>input matrix  |
+| Vector | **[g_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-g-)** <br>input gain  |
+| Matrix | **[Q_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-q-)** <br>covariance of process noise  |
+| Matrix | **[Q_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-q-m-)** <br>covariance of disturbance random walk  |
+| Matrix | **[C_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-c-)** <br>output matrix  |
+| Vector | **[d_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-d-)** <br>output bias  |
+| Matrix | **[Ke_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-ke-)** <br>estimator gain  |
+| Matrix | **[Ke_m_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-ke-m-)** <br>estimator gain for process disturbance  |
+
+
+---
+---
+## Public Function Details
+
+### **System**
+
+```cpp
+System() =default
+```
+
+
+
+---
+### **System**
+
+```cpp
+System(
+    std::size_t n_u,
+    std::size_t n_x,
+    std::size_t n_y,
+    data_t dt,
+    data_t p0 =kDefaultP0,
+    data_t q0 =kDefaultQ0
+)
+```
+
+
+
+**Parameters**:
+
+  * **n_u** number of inputs 
+  * **n_x** number of states 
+  * **n_y** number of outputs 
+  * **dt** sample period 
+  * **p0** [optional] initial diagonal elements of state estimate covariance (P) 
+  * **q0** [optional] initial diagonal elements of process noise covariance (Q) 
+
+
+---
+### **Simulate**
+
+```cpp
+virtual const Vector & Simulate(
+    const Vector & u_tm1
+) override
+```
+
+
+
+**Parameters**:
+
+  * **u_tm1** input at t-1
+
+
+**Return**: z measurement 
+
+**Reimplements**: [lds::System::Simulate](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-simulate)
+
+
+Simulate system and produce measurement
+
+
+---
+
+
+## Protected Function Details
+
+### **h**
+
+```cpp
+inline virtual void h() override
+```
+
+
+
+**Reimplements**: [lds::System::h](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-h)
+
+
+---
+### **h_**
+
+```cpp
+inline virtual Vector h_(
+    Vector x
+) override
+```
+
+
+
+**Reimplements**: [lds::System::h_](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-h-)
+
+
+---
+### **RecurseKe**
+
+```cpp
+virtual void RecurseKe() override
+```
+
+
+
+**Reimplements**: [lds::System::RecurseKe](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-recurseke)
+
+
+Recursively recalculate estimator gain (Ke).
+
+References:
+
+Smith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.
+
+Eden UT, ..., Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16. 
+
+
+---
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Examples/_index.md b/misc/docs-hugo/content/docs/api/Examples/_index.md
index 359dbc8c..3bea99c0 100644
--- a/misc/docs-hugo/content/docs/api/Examples/_index.md
+++ b/misc/docs-hugo/content/docs/api/Examples/_index.md
@@ -1,46 +1,46 @@
----
-title: Examples
-
----
-
-# Examples
-
-
-
-
-
-
-
-- **[eg_glds_ctrl.cpp](/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/#example-eg-glds-ctrl.cpp)** 
-
-
-
-
-
-- **[eg_glds_du_plds_ctrl.cpp](/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/#example-eg-glds-du-plds-ctrl.cpp)** 
-
-
-
-
-
-- **[eg_plds_ctrl.cpp](/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/#example-eg-plds-ctrl.cpp)** 
-
-
-
-
-
-- **[eg_plds_est.cpp](/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/#example-eg-plds-est.cpp)** 
-
-
-
-
-
-- **[eg_plds_switched_ctrl.cpp](/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/#example-eg-plds-switched-ctrl.cpp)** 
-
-
-
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:06 Eastern Daylight Time
+---
+title: Examples
+
+---
+
+# Examples
+
+
+
+
+
+
+
+- **[eg_glds_ctrl.cpp](/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/#example-eg-glds-ctrl.cpp)** <br>Example GLDS Control. 
+
+
+
+
+
+- **[eg_glds_du_plds_ctrl.cpp](/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/#example-eg-glds-du-plds-ctrl.cpp)** <br>Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u). 
+
+
+
+
+
+- **[eg_plds_ctrl.cpp](/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/#example-eg-plds-ctrl.cpp)** <br>Example PLDS Control. 
+
+
+
+
+
+- **[eg_plds_est.cpp](/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/#example-eg-plds-est.cpp)** <br>Example PLDS Estimation. 
+
+
+
+
+
+- **[eg_plds_switched_ctrl.cpp](/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/#example-eg-plds-switched-ctrl.cpp)** <br>Example Switched PLDS Control. 
+
+
+
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Examples/eg_glds_ctrl_8cpp-example.md b/misc/docs-hugo/content/docs/api/Examples/eg_glds_ctrl_8cpp-example.md
index 6a17bad8..55f80a7d 100644
--- a/misc/docs-hugo/content/docs/api/Examples/eg_glds_ctrl_8cpp-example.md
+++ b/misc/docs-hugo/content/docs/api/Examples/eg_glds_ctrl_8cpp-example.md
@@ -1,273 +1,275 @@
----
-title: eg_glds_ctrl.cpp
-
----
-
-# eg_glds_ctrl.cpp
-
-
-
-Example GLDS Control 
-
-```cpp
-//===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst>
-
-using lds::Matrix;
-using lds::Vector;
-using lds::data_t;
-using std::cout;
-
-auto main() -> int {
-  cout << " ********** Example Gaussian LDS Control ********** \n\n";
-
-  // Make 1st-order SISO system, sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;
-  size_t n_x = 1;
-  size_t n_y = 1;
-
-  // no time steps for simulation.
-  auto n_t = static_cast<size_t>(5.0 / dt);
-
-  // construct ground truth system to be controlled...
-  // initializes to random walk model with top-most n_y state observed
-  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);
-
-  // Ground-truth parameters for the controlled system
-  // (stand-in for physical system to be controlled)
-  Matrix a_true(n_x, n_x, arma::fill::eye);
-  a_true[0] = exp(-dt / 0.01);
-  Matrix b_true = Matrix(n_x, n_u).fill(2e-4);
-  // control signal to model input unit conversion e.g., V -> mW/mm2:
-  Vector g_true = Vector(n_y).fill(10.0);
-
-  // output noise covariance
-  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;
-
-  size_t which_m = 0;  // whether low or high disturbance (0, 1)
-  data_t m_low = 5 * dt * (1 - a_true[0]);
-  data_t pr_lo2hi = 1e-3;  // probability of going from low to high disturb.
-  data_t m_high = 20 * dt * (1 - a_true[0]);
-  data_t pr_hi2lo = pr_lo2hi;
-
-  // initially let m be low
-  Vector m0_true = Vector(n_x).fill(m_low);
-
-  // Assign params.
-  controlled_system.set_A(a_true);
-  controlled_system.set_B(b_true);
-  controlled_system.set_m(m0_true);
-  controlled_system.set_g(g_true);
-  controlled_system.set_R(r_true);
-
-  cout << ".....................................\n";
-  cout << "controlled_system:\n";
-  cout << ".....................................\n";
-  controlled_system.Print();
-  cout << ".....................................\n";
-
-  // make a controller
-  lds::gaussian::Controller controller;
-  {
-    // Create **incorrect** model used for control.
-    // (e.g., imperfect model fitting)
-    Matrix b_controller = b_true / 2;
-
-    // let's assume zero process disturbance initially
-    // (will be re-estimating)
-    Vector m_controller = Vector(n_x, arma::fill::zeros);
-
-    // for this demo, just use arbitrary default R
-    Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0;
-
-    lds::gaussian::System controller_system(controlled_system);
-    controller_system.set_B(b_controller);
-    controller_system.set_m(m_controller);
-    controller_system.set_R(r_controller);
-    controller_system.Reset();  // reset to new m
-
-    // going to adaptively re-estimate the disturbance
-    controller_system.do_adapt_m = true;
-
-    // set adaptation rate by changing covariance of assumed process noise
-    // acting on random-walk evolution of m
-    Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6;
-    controller_system.set_Q_m(q_m);
-
-    // create controller
-    // lower and upper bounds on control signal (e.g., in Volts)
-    data_t u_lb = 0.0;  // [=] V
-    data_t u_ub = 5.0;  // [=] V
-    controller = std::move(
-        lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub));
-  }
-
-  // Control variables:
-  // if following enabled, adapts set point with re-estimated process
-  // disturbance n.b., should not need integral action if this is enabled as the
-  // adaptive estimator minimizes DC error
-  bool do_adaptive_set_point = false;
-
-  // Reference/target output, controller gains
-  Vector y_ref0 = Vector(n_y).fill(20.0 * dt);
-  Matrix k_x = Matrix(n_u, n_x).fill(100);     // gains on state error
-  Matrix k_inty = Matrix(n_u, n_y).fill(1e3);  // gains on integrated err
-
-  // setting initial state to target to avoid error at onset:
-  Vector x0 = Vector(n_x).fill(y_ref0[0]);
-
-  // set up controller type bit mask so controller knows how to proceed
-  size_t control_type = 0;
-  if (do_adaptive_set_point) {
-    // adapt set point with estimated disturbance
-    control_type = control_type | lds::kControlTypeAdaptM;
-  } else {
-    // use integral action to minimize DC error
-    control_type = control_type | lds::kControlTypeIntY;
-  }
-
-  // set controller type
-  controller.set_control_type(control_type);
-
-  // Let's say these controller gains were designed assuming g was 9 V/(mW/mm2):
-  Vector g_design = Vector(n_u).fill(9);
-
-  // Set params.
-  // **n.b. using arbitrary defaults for Q, R in this example. Really, these
-  // should be set by users, as they tune characteristics of Kalman filter.
-  // Users can also choose not to recursively calculate the estimator gain and
-  // supply it (setKe) instead of covariances.**
-  controller.set_y_ref(y_ref0);
-  controller.set_Kc(k_x);
-  controller.set_Kc_inty(k_inty);
-  controller.set_g_design(g_design);
-
-  cout << ".....................................\n";
-  cout << "control system:\n";
-  cout << ".....................................\n";
-  controller.Print();
-  cout << ".....................................\n";
-
-  // set up variables for simulation
-  // create Matrix to save outputs in...
-  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];
-
-  // Simulated measurements
-  Matrix z(n_y, n_t, arma::fill::zeros);
-
-  // simulated control signal ([=] V)
-  Matrix u(n_u, n_t, arma::fill::zeros);
-
-  // outputs, states and gain/disturbance params
-  // *_hat indicates online estimates
-  Matrix y_hat(n_y, n_t, arma::fill::zeros);
-  Matrix x_hat(n_x, n_t, arma::fill::zeros);
-  Matrix m_hat(n_x, n_t, arma::fill::zeros);
-
-  // *_true indicates ground truth (system being controlled)
-  Matrix y_true(n_y, n_t, arma::fill::zeros);
-  Matrix x_true(n_x, n_t, arma::fill::zeros);
-  Matrix m_true(n_x, n_t, arma::fill::zeros);
-
-  // set initial val
-  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y();
-  y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();
-
-  x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x();
-  x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();
-
-  m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m();
-  m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();
-
-  cout << "Starting " << n_t * dt << " sec simulation ... \n";
-  auto start = std::chrono::high_resolution_clock::now();
-  for (size_t t = 1; t < n_t; t++) {
-    // simulate a stochastically switched disturbance
-    Vector chance = arma::randu<Vector>(1);
-    if (which_m == 0)  // low disturbance
-    {
-      if (chance[0] < pr_lo2hi) {  // switches low -> high disturbance
-        m0_true = std::vector<data_t>(n_x, m_high);
-        which_m = 1;
-      }
-    } else {                       // high disturbance
-      if (chance[0] < pr_hi2lo) {  // swithces high -> low disturbance
-        m0_true = std::vector<data_t>(n_x, m_low);
-        which_m = 0;
-      }
-    }
-    controlled_system.set_m(m0_true);
-
-    // input
-    Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true);
-
-    // Simulate the true system.
-    z.col(t) = controlled_system.Simulate(u_tm1);
-
-    // This method uses a steady-state solution to control problem to calculate
-    // x_ref, u_ref from reference output y_ref. Therefore, it is only
-    // applicable to regulation problems or cases where reference trajectory
-    // changes slowly compared to system dynamics.
-    u.col(t) = controller.ControlOutputReference(z.col(t));
-
-    // save the signals
-    y_true.col(t) = controlled_system.y();
-    x_true.col(t) = controlled_system.x();
-    m_true.col(t) = controlled_system.m();
-
-    y_hat.col(t) = controller.sys().y();
-    x_hat.col(t) = controller.sys().x();
-    m_hat.col(t) = controller.sys().m();
-  }
-
-  auto finish = std::chrono::high_resolution_clock::now();
-  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
-  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
-  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
-
-  cout << "Saving simulation data to disk.\n";
-
-  // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,
-  // xTrue, mTrue saving with hdf5 via armadillo
-  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
-
-  auto dt_vec = Vector(1).fill(dt);
-  dt_vec.save(arma::hdf5_name("eg_glds_ctrl.h5", "dt"));
-  y_ref.save(arma::hdf5_name("eg_glds_ctrl.h5", "y_ref", replace));
-  u.save(arma::hdf5_name("eg_glds_ctrl.h5", "u", replace));
-  z.save(arma::hdf5_name("eg_glds_ctrl.h5", "z", replace));
-  x_true.save(arma::hdf5_name("eg_glds_ctrl.h5", "x_true", replace));
-  m_true.save(arma::hdf5_name("eg_glds_ctrl.h5", "m_true", replace));
-  y_true.save(arma::hdf5_name("eg_glds_ctrl.h5", "y_true", replace));
-  x_hat.save(arma::hdf5_name("eg_glds_ctrl.h5", "x_hat", replace));
-  m_hat.save(arma::hdf5_name("eg_glds_ctrl.h5", "m_hat", replace));
-  y_hat.save(arma::hdf5_name("eg_glds_ctrl.h5", "y_hat", replace));
-
-  cout << "fin.\n";
-  return 0;
-}
-```
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: eg_glds_ctrl.cpp
+summary: Example GLDS Control. 
+
+---
+
+# eg_glds_ctrl.cpp
+
+
+
+Example GLDS Control ```cpp
+
+//===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#include <ldsCtrlEst>
+
+using lds::Matrix;
+using lds::Vector;
+using lds::data_t;
+using std::cout;
+
+auto main() -> int {
+  cout << " ********** Example Gaussian LDS Control ********** \n\n";
+
+  // Make 1st-order SISO system, sampled at 1kHz
+  data_t dt = 1e-3;
+  size_t n_u = 1;
+  size_t n_x = 1;
+  size_t n_y = 1;
+
+  // no time steps for simulation.
+  auto n_t = static_cast<size_t>(5.0 / dt);
+
+  // construct ground truth system to be controlled...
+  // initializes to random walk model with top-most n_y state observed
+  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);
+
+  // Ground-truth parameters for the controlled system
+  // (stand-in for physical system to be controlled)
+  Matrix a_true(n_x, n_x, arma::fill::eye);
+  a_true[0] = exp(-dt / 0.01);
+  Matrix b_true = Matrix(n_x, n_u).fill(2e-4);
+  // control signal to model input unit conversion e.g., V -> mW/mm2:
+  Vector g_true = Vector(n_y).fill(10.0);
+
+  // output noise covariance
+  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;
+
+  size_t which_m = 0;  // whether low or high disturbance (0, 1)
+  data_t m_low = 5 * dt * (1 - a_true[0]);
+  data_t pr_lo2hi = 1e-3;  // probability of going from low to high disturb.
+  data_t m_high = 20 * dt * (1 - a_true[0]);
+  data_t pr_hi2lo = pr_lo2hi;
+
+  // initially let m be low
+  Vector m0_true = Vector(n_x).fill(m_low);
+
+  // Assign params.
+  controlled_system.set_A(a_true);
+  controlled_system.set_B(b_true);
+  controlled_system.set_m(m0_true);
+  controlled_system.set_g(g_true);
+  controlled_system.set_R(r_true);
+
+  cout << ".....................................\n";
+  cout << "controlled_system:\n";
+  cout << ".....................................\n";
+  controlled_system.Print();
+  cout << ".....................................\n";
+
+  // make a controller
+  lds::gaussian::Controller controller;
+  {
+    // Create **incorrect** model used for control.
+    // (e.g., imperfect model fitting)
+    Matrix b_controller = b_true / 2;
+
+    // let's assume zero process disturbance initially
+    // (will be re-estimating)
+    Vector m_controller = Vector(n_x, arma::fill::zeros);
+
+    // for this demo, just use arbitrary default R
+    Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0;
+
+    lds::gaussian::System controller_system(controlled_system);
+    controller_system.set_B(b_controller);
+    controller_system.set_m(m_controller);
+    controller_system.set_R(r_controller);
+    controller_system.Reset();  // reset to new m
+
+    // going to adaptively re-estimate the disturbance
+    controller_system.do_adapt_m = true;
+
+    // set adaptation rate by changing covariance of assumed process noise
+    // acting on random-walk evolution of m
+    Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6;
+    controller_system.set_Q_m(q_m);
+
+    // create controller
+    // lower and upper bounds on control signal (e.g., in Volts)
+    data_t u_lb = 0.0;  // [=] V
+    data_t u_ub = 5.0;  // [=] V
+    controller = std::move(
+        lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub));
+  }
+
+  // Control variables:
+  // if following enabled, adapts set point with re-estimated process
+  // disturbance n.b., should not need integral action if this is enabled as the
+  // adaptive estimator minimizes DC error
+  bool do_adaptive_set_point = false;
+
+  // Reference/target output, controller gains
+  Vector y_ref0 = Vector(n_y).fill(20.0 * dt);
+  Matrix k_x = Matrix(n_u, n_x).fill(100);     // gains on state error
+  Matrix k_inty = Matrix(n_u, n_y).fill(1e3);  // gains on integrated err
+
+  // setting initial state to target to avoid error at onset:
+  Vector x0 = Vector(n_x).fill(y_ref0[0]);
+
+  // set up controller type bit mask so controller knows how to proceed
+  size_t control_type = 0;
+  if (do_adaptive_set_point) {
+    // adapt set point with estimated disturbance
+    control_type = control_type | lds::kControlTypeAdaptM;
+  } else {
+    // use integral action to minimize DC error
+    control_type = control_type | lds::kControlTypeIntY;
+  }
+
+  // set controller type
+  controller.set_control_type(control_type);
+
+  // Let's say these controller gains were designed assuming g was 9 V/(mW/mm2):
+  Vector g_design = Vector(n_u).fill(9);
+
+  // Set params.
+  // **n.b. using arbitrary defaults for Q, R in this example. Really, these
+  // should be set by users, as they tune characteristics of Kalman filter.
+  // Users can also choose not to recursively calculate the estimator gain and
+  // supply it (setKe) instead of covariances.**
+  controller.set_y_ref(y_ref0);
+  controller.set_Kc(k_x);
+  controller.set_Kc_inty(k_inty);
+  controller.set_g_design(g_design);
+
+  cout << ".....................................\n";
+  cout << "control system:\n";
+  cout << ".....................................\n";
+  controller.Print();
+  cout << ".....................................\n";
+
+  // set up variables for simulation
+  // create Matrix to save outputs in...
+  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];
+
+  // Simulated measurements
+  Matrix z(n_y, n_t, arma::fill::zeros);
+
+  // simulated control signal ([=] V)
+  Matrix u(n_u, n_t, arma::fill::zeros);
+
+  // outputs, states and gain/disturbance params
+  // *_hat indicates online estimates
+  Matrix y_hat(n_y, n_t, arma::fill::zeros);
+  Matrix x_hat(n_x, n_t, arma::fill::zeros);
+  Matrix m_hat(n_x, n_t, arma::fill::zeros);
+
+  // *_true indicates ground truth (system being controlled)
+  Matrix y_true(n_y, n_t, arma::fill::zeros);
+  Matrix x_true(n_x, n_t, arma::fill::zeros);
+  Matrix m_true(n_x, n_t, arma::fill::zeros);
+
+  // set initial val
+  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y();
+  y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();
+
+  x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x();
+  x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();
+
+  m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m();
+  m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();
+
+  cout << "Starting " << n_t * dt << " sec simulation ... \n";
+  auto start = std::chrono::high_resolution_clock::now();
+  for (size_t t = 1; t < n_t; t++) {
+    // simulate a stochastically switched disturbance
+    Vector chance = arma::randu<Vector>(1);
+    if (which_m == 0)  // low disturbance
+    {
+      if (chance[0] < pr_lo2hi) {  // switches low -> high disturbance
+        m0_true = std::vector<data_t>(n_x, m_high);
+        which_m = 1;
+      }
+    } else {                       // high disturbance
+      if (chance[0] < pr_hi2lo) {  // swithces high -> low disturbance
+        m0_true = std::vector<data_t>(n_x, m_low);
+        which_m = 0;
+      }
+    }
+    controlled_system.set_m(m0_true);
+
+    // input
+    Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true);
+
+    // Simulate the true system.
+    z.col(t) = controlled_system.Simulate(u_tm1);
+
+    // This method uses a steady-state solution to control problem to calculate
+    // x_ref, u_ref from reference output y_ref. Therefore, it is only
+    // applicable to regulation problems or cases where reference trajectory
+    // changes slowly compared to system dynamics.
+    u.col(t) = controller.ControlOutputReference(z.col(t));
+
+    // save the signals
+    y_true.col(t) = controlled_system.y();
+    x_true.col(t) = controlled_system.x();
+    m_true.col(t) = controlled_system.m();
+
+    y_hat.col(t) = controller.sys().y();
+    x_hat.col(t) = controller.sys().x();
+    m_hat.col(t) = controller.sys().m();
+  }
+
+  auto finish = std::chrono::high_resolution_clock::now();
+  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
+  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
+  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
+
+  cout << "Saving simulation data to disk.\n";
+
+  // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,
+  // xTrue, mTrue saving with hdf5 via armadillo
+  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
+
+  auto dt_vec = Vector(1).fill(dt);
+  dt_vec.save(arma::hdf5_name("eg_glds_ctrl.h5", "dt"));
+  y_ref.save(arma::hdf5_name("eg_glds_ctrl.h5", "y_ref", replace));
+  u.save(arma::hdf5_name("eg_glds_ctrl.h5", "u", replace));
+  z.save(arma::hdf5_name("eg_glds_ctrl.h5", "z", replace));
+  x_true.save(arma::hdf5_name("eg_glds_ctrl.h5", "x_true", replace));
+  m_true.save(arma::hdf5_name("eg_glds_ctrl.h5", "m_true", replace));
+  y_true.save(arma::hdf5_name("eg_glds_ctrl.h5", "y_true", replace));
+  x_hat.save(arma::hdf5_name("eg_glds_ctrl.h5", "x_hat", replace));
+  m_hat.save(arma::hdf5_name("eg_glds_ctrl.h5", "m_hat", replace));
+  y_hat.save(arma::hdf5_name("eg_glds_ctrl.h5", "y_hat", replace));
+
+  cout << "fin.\n";
+  return 0;
+}
+```
+
+_Filename: eg_glds_ctrl.cpp_
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Examples/eg_glds_du_plds_ctrl_8cpp-example.md b/misc/docs-hugo/content/docs/api/Examples/eg_glds_du_plds_ctrl_8cpp-example.md
index 0a5b7ea0..33b998e0 100644
--- a/misc/docs-hugo/content/docs/api/Examples/eg_glds_du_plds_ctrl_8cpp-example.md
+++ b/misc/docs-hugo/content/docs/api/Examples/eg_glds_du_plds_ctrl_8cpp-example.md
@@ -1,274 +1,276 @@
----
-title: eg_glds_du_plds_ctrl.cpp
-
----
-
-# eg_glds_du_plds_ctrl.cpp
-
-
-
-Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u). 
-
-```cpp
-//===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst>
-
-using lds::data_t;
-using lds::Matrix;
-using lds::Vector;
-using std::cout;
-
-auto main() -> int {
-  cout << " ********** Example Gaussian LDS du Control of PLDS ********** \n\n";
-
-  // Make 1st-order SISO system, sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;
-  size_t n_x = 1;
-  size_t n_y = 1;
-
-  // no time steps for simulation.
-  auto n_t = static_cast<size_t>(5.0 / dt);
-
-  // construct ground truth system to be controlled...
-  // initializes to random walk model with top-most n_y state observed
-  lds::poisson::System controlled_system(n_u, n_x, n_y, dt);
-
-  // Ground-truth parameters for the controlled system
-  // (stand-in for physical system to be controlled)
-  Matrix a_true(n_x, n_x, arma::fill::eye);
-  a_true[0] = exp(-dt / 0.01);
-  Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2);
-  // control signal to model input unit conversion e.g., V -> mW/mm2:
-  Vector g_true = Vector(n_y).fill(10.0);
-
-  size_t which_m = 0;  // whether low or high disturbance (0, 1)
-  data_t m_low = log(1 * dt) * (1 - a_true[0]);
-  data_t pr_lo2hi =
-      0;  // 1e-3;  // probability of going from low to high disturb.
-  data_t m_high = log(20 * dt) * (1 - a_true[0]);
-  data_t pr_hi2lo = pr_lo2hi;
-
-  // initially let m be low
-  Vector m0_true = Vector(n_x).fill(m_low);
-  Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);
-
-  // Assign params.
-  controlled_system.set_A(a_true);
-  controlled_system.set_B(b_true);
-  controlled_system.set_m(m0_true);
-  controlled_system.set_g(g_true);
-  controlled_system.set_x0(x0_true);
-  controlled_system.Reset();
-
-  cout << ".....................................\n";
-  cout << "controlled_system:\n";
-  cout << ".....................................\n";
-  controlled_system.Print();
-  cout << ".....................................\n";
-
-  // make a controller
-  lds::gaussian::Controller controller;
-  {
-    // Create **incorrect** model used for control.
-    // (e.g., imperfect model fitting)
-    Matrix b_controller = b_true / 50;
-
-    // let's assume zero process disturbance initially
-    // (will be re-estimating)
-    Vector m_controller = Vector(n_x, arma::fill::zeros);
-
-    // process noise covariance
-    Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8;
-
-    // output noise covariance
-    Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2;
-
-    lds::gaussian::System controller_system(n_u, n_x, n_y, dt);
-    controller_system.set_A(a_true);
-    controller_system.set_B(b_controller);
-    controller_system.set_g(g_true);
-    controller_system.set_m(m_controller);
-    controller_system.set_Q(q_controller);
-    controller_system.set_R(r_controller);
-    controller_system.Reset();  // reset to new m
-
-    // going to adaptively re-estimate the disturbance
-    controller_system.do_adapt_m = true;
-
-    // set adaptation rate by changing covariance of assumed process noise
-    // acting on random-walk evolution of m
-    Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8;
-    controller_system.set_Q_m(q_m);
-
-    // create controller
-    // lower and upper bounds on control signal (e.g., in Volts)
-    data_t u_lb = 0.0;  // [=] V
-    data_t u_ub = 5.0;  // [=] V
-    controller = std::move(
-        lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub));
-  }
-
-  // Control variables:
-  // Reference/target output, controller gains
-  Vector y_ref0 = Vector(n_y).fill(20.0 * dt);
-
-  // to design for this example, augmented state with control and made the input
-  // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du =
-  // 1e-2.
-  Matrix k_x = Matrix(n_u, n_x).fill(2.44);     // gains on state error
-  Matrix k_inty = Matrix(n_u, n_y).fill(97.4);  // gains on integrated err
-  Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2);  // gains on input amp
-
-  // set up controller type bit mask so controller knows how to proceed
-  size_t control_type = 0;
-  // use integral action to minimize DC error
-  control_type = control_type | lds::kControlTypeIntY;
-  // update change in control (LP filters control)
-  control_type = control_type | lds::kControlTypeDeltaU;
-
-  // set controller type
-  controller.set_control_type(control_type);
-
-  // Let's say these controller gains were designed assuming g was 9 V/(mW/mm2):
-  Vector g_design = Vector(n_u).fill(10);
-
-  // Set params.
-  // **n.b. using arbitrary defaults for Q, R in this example. Really, these
-  // should be set by users, as they tune characteristics of Kalman filter.
-  // Users can also choose not to recursively calculate the estimator gain and
-  // supply it (setKe) instead of covariances.**
-  controller.set_y_ref(y_ref0);
-  controller.set_Kc(k_x);
-  controller.set_Kc_inty(k_inty);
-  controller.set_Kc_u(k_u);
-  controller.set_g_design(g_design);
-
-  cout << ".....................................\n";
-  cout << "control system:\n";
-  cout << ".....................................\n";
-  controller.Print();
-  cout << ".....................................\n";
-
-  // set up variables for simulation
-  // create Matrix to save outputs in...
-  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];
-
-  // Simulated measurements
-  Matrix z(n_y, n_t, arma::fill::zeros);
-
-  // simulated control signal ([=] V)
-  Matrix u(n_u, n_t, arma::fill::zeros);
-
-  // outputs, states and gain/disturbance params
-  // *_hat indicates online estimates
-  Matrix y_hat(n_y, n_t, arma::fill::zeros);
-  Matrix x_hat(n_x, n_t, arma::fill::zeros);
-  Matrix m_hat(n_x, n_t, arma::fill::zeros);
-
-  // *_true indicates ground truth (system being controlled)
-  Matrix y_true(n_y, n_t, arma::fill::zeros);
-  Matrix x_true(n_x, n_t, arma::fill::zeros);
-  Matrix m_true(n_x, n_t, arma::fill::zeros);
-
-  // get initial val
-  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y();
-  y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();
-
-  x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x();
-  x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();
-
-  m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m();
-  m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();
-
-  cout << "Starting " << n_t * dt << " sec simulation ... \n";
-  auto start = std::chrono::high_resolution_clock::now();
-  for (size_t t = 1; t < n_t; t++) {
-    // simulate a stochastically switched disturbance
-    Vector chance = arma::randu<Vector>(1);
-    if (which_m == 0)  // low disturbance
-    {
-      if (chance[0] < pr_lo2hi) {  // switches low -> high disturbance
-        m0_true = std::vector<data_t>(n_x, m_high);
-        which_m = 1;
-      }
-    } else {                       // high disturbance
-      if (chance[0] < pr_hi2lo) {  // swithces high -> low disturbance
-        m0_true = std::vector<data_t>(n_x, m_low);
-        which_m = 0;
-      }
-    }
-    controlled_system.set_m(m0_true);
-
-    // input
-    Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true);
-
-    // Simulate the true system.
-    z.col(t) = controlled_system.Simulate(u_tm1);
-
-    // This method uses a steady-state solution to control problem to calculate
-    // x_ref, u_ref from reference output y_ref. Therefore, it is only
-    // applicable to regulation problems or cases where reference trajectory
-    // changes slowly compared to system dynamics.
-    u.col(t) = controller.ControlOutputReference(z.col(t));
-
-    // save the signals
-    y_true.col(t) = controlled_system.y();
-    x_true.col(t) = controlled_system.x();
-    m_true.col(t) = controlled_system.m();
-
-    y_hat.col(t) = controller.sys().y();
-    x_hat.col(t) = controller.sys().x();
-    m_hat.col(t) = controller.sys().m();
-  }
-
-  auto finish = std::chrono::high_resolution_clock::now();
-  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
-  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
-  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
-
-  cout << "Saving simulation data to disk.\n";
-
-  // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,
-  // xTrue, mTrue saving with hdf5 via armadillo
-  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
-
-  auto dt_vec = Vector(1).fill(dt);
-  dt_vec.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "dt"));
-  y_ref.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "y_ref", replace));
-  u.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "u", replace));
-  z.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "z", replace));
-  x_true.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "x_true", replace));
-  m_true.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "m_true", replace));
-  y_true.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "y_true", replace));
-  x_hat.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "x_hat", replace));
-  m_hat.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "m_hat", replace));
-  y_hat.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "y_hat", replace));
-
-  cout << "fin.\n";
-  return 0;
-}
-```
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: eg_glds_du_plds_ctrl.cpp
+summary: Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u). 
+
+---
+
+# eg_glds_du_plds_ctrl.cpp
+
+
+
+Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u). ```cpp
+
+//===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#include <ldsCtrlEst>
+
+using lds::data_t;
+using lds::Matrix;
+using lds::Vector;
+using std::cout;
+
+auto main() -> int {
+  cout << " ********** Example Gaussian LDS du Control of PLDS ********** \n\n";
+
+  // Make 1st-order SISO system, sampled at 1kHz
+  data_t dt = 1e-3;
+  size_t n_u = 1;
+  size_t n_x = 1;
+  size_t n_y = 1;
+
+  // no time steps for simulation.
+  auto n_t = static_cast<size_t>(5.0 / dt);
+
+  // construct ground truth system to be controlled...
+  // initializes to random walk model with top-most n_y state observed
+  lds::poisson::System controlled_system(n_u, n_x, n_y, dt);
+
+  // Ground-truth parameters for the controlled system
+  // (stand-in for physical system to be controlled)
+  Matrix a_true(n_x, n_x, arma::fill::eye);
+  a_true[0] = exp(-dt / 0.01);
+  Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2);
+  // control signal to model input unit conversion e.g., V -> mW/mm2:
+  Vector g_true = Vector(n_y).fill(10.0);
+
+  size_t which_m = 0;  // whether low or high disturbance (0, 1)
+  data_t m_low = log(1 * dt) * (1 - a_true[0]);
+  data_t pr_lo2hi =
+      0;  // 1e-3;  // probability of going from low to high disturb.
+  data_t m_high = log(20 * dt) * (1 - a_true[0]);
+  data_t pr_hi2lo = pr_lo2hi;
+
+  // initially let m be low
+  Vector m0_true = Vector(n_x).fill(m_low);
+  Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);
+
+  // Assign params.
+  controlled_system.set_A(a_true);
+  controlled_system.set_B(b_true);
+  controlled_system.set_m(m0_true);
+  controlled_system.set_g(g_true);
+  controlled_system.set_x0(x0_true);
+  controlled_system.Reset();
+
+  cout << ".....................................\n";
+  cout << "controlled_system:\n";
+  cout << ".....................................\n";
+  controlled_system.Print();
+  cout << ".....................................\n";
+
+  // make a controller
+  lds::gaussian::Controller controller;
+  {
+    // Create **incorrect** model used for control.
+    // (e.g., imperfect model fitting)
+    Matrix b_controller = b_true / 50;
+
+    // let's assume zero process disturbance initially
+    // (will be re-estimating)
+    Vector m_controller = Vector(n_x, arma::fill::zeros);
+
+    // process noise covariance
+    Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8;
+
+    // output noise covariance
+    Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2;
+
+    lds::gaussian::System controller_system(n_u, n_x, n_y, dt);
+    controller_system.set_A(a_true);
+    controller_system.set_B(b_controller);
+    controller_system.set_g(g_true);
+    controller_system.set_m(m_controller);
+    controller_system.set_Q(q_controller);
+    controller_system.set_R(r_controller);
+    controller_system.Reset();  // reset to new m
+
+    // going to adaptively re-estimate the disturbance
+    controller_system.do_adapt_m = true;
+
+    // set adaptation rate by changing covariance of assumed process noise
+    // acting on random-walk evolution of m
+    Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8;
+    controller_system.set_Q_m(q_m);
+
+    // create controller
+    // lower and upper bounds on control signal (e.g., in Volts)
+    data_t u_lb = 0.0;  // [=] V
+    data_t u_ub = 5.0;  // [=] V
+    controller = std::move(
+        lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub));
+  }
+
+  // Control variables:
+  // Reference/target output, controller gains
+  Vector y_ref0 = Vector(n_y).fill(20.0 * dt);
+
+  // to design for this example, augmented state with control and made the input
+  // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du =
+  // 1e-2.
+  Matrix k_x = Matrix(n_u, n_x).fill(2.44);     // gains on state error
+  Matrix k_inty = Matrix(n_u, n_y).fill(97.4);  // gains on integrated err
+  Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2);  // gains on input amp
+
+  // set up controller type bit mask so controller knows how to proceed
+  size_t control_type = 0;
+  // use integral action to minimize DC error
+  control_type = control_type | lds::kControlTypeIntY;
+  // update change in control (LP filters control)
+  control_type = control_type | lds::kControlTypeDeltaU;
+
+  // set controller type
+  controller.set_control_type(control_type);
+
+  // Let's say these controller gains were designed assuming g was 9 V/(mW/mm2):
+  Vector g_design = Vector(n_u).fill(10);
+
+  // Set params.
+  // **n.b. using arbitrary defaults for Q, R in this example. Really, these
+  // should be set by users, as they tune characteristics of Kalman filter.
+  // Users can also choose not to recursively calculate the estimator gain and
+  // supply it (setKe) instead of covariances.**
+  controller.set_y_ref(y_ref0);
+  controller.set_Kc(k_x);
+  controller.set_Kc_inty(k_inty);
+  controller.set_Kc_u(k_u);
+  controller.set_g_design(g_design);
+
+  cout << ".....................................\n";
+  cout << "control system:\n";
+  cout << ".....................................\n";
+  controller.Print();
+  cout << ".....................................\n";
+
+  // set up variables for simulation
+  // create Matrix to save outputs in...
+  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];
+
+  // Simulated measurements
+  Matrix z(n_y, n_t, arma::fill::zeros);
+
+  // simulated control signal ([=] V)
+  Matrix u(n_u, n_t, arma::fill::zeros);
+
+  // outputs, states and gain/disturbance params
+  // *_hat indicates online estimates
+  Matrix y_hat(n_y, n_t, arma::fill::zeros);
+  Matrix x_hat(n_x, n_t, arma::fill::zeros);
+  Matrix m_hat(n_x, n_t, arma::fill::zeros);
+
+  // *_true indicates ground truth (system being controlled)
+  Matrix y_true(n_y, n_t, arma::fill::zeros);
+  Matrix x_true(n_x, n_t, arma::fill::zeros);
+  Matrix m_true(n_x, n_t, arma::fill::zeros);
+
+  // get initial val
+  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y();
+  y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();
+
+  x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x();
+  x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();
+
+  m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m();
+  m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();
+
+  cout << "Starting " << n_t * dt << " sec simulation ... \n";
+  auto start = std::chrono::high_resolution_clock::now();
+  for (size_t t = 1; t < n_t; t++) {
+    // simulate a stochastically switched disturbance
+    Vector chance = arma::randu<Vector>(1);
+    if (which_m == 0)  // low disturbance
+    {
+      if (chance[0] < pr_lo2hi) {  // switches low -> high disturbance
+        m0_true = std::vector<data_t>(n_x, m_high);
+        which_m = 1;
+      }
+    } else {                       // high disturbance
+      if (chance[0] < pr_hi2lo) {  // swithces high -> low disturbance
+        m0_true = std::vector<data_t>(n_x, m_low);
+        which_m = 0;
+      }
+    }
+    controlled_system.set_m(m0_true);
+
+    // input
+    Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true);
+
+    // Simulate the true system.
+    z.col(t) = controlled_system.Simulate(u_tm1);
+
+    // This method uses a steady-state solution to control problem to calculate
+    // x_ref, u_ref from reference output y_ref. Therefore, it is only
+    // applicable to regulation problems or cases where reference trajectory
+    // changes slowly compared to system dynamics.
+    u.col(t) = controller.ControlOutputReference(z.col(t));
+
+    // save the signals
+    y_true.col(t) = controlled_system.y();
+    x_true.col(t) = controlled_system.x();
+    m_true.col(t) = controlled_system.m();
+
+    y_hat.col(t) = controller.sys().y();
+    x_hat.col(t) = controller.sys().x();
+    m_hat.col(t) = controller.sys().m();
+  }
+
+  auto finish = std::chrono::high_resolution_clock::now();
+  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
+  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
+  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
+
+  cout << "Saving simulation data to disk.\n";
+
+  // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,
+  // xTrue, mTrue saving with hdf5 via armadillo
+  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
+
+  auto dt_vec = Vector(1).fill(dt);
+  dt_vec.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "dt"));
+  y_ref.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "y_ref", replace));
+  u.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "u", replace));
+  z.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "z", replace));
+  x_true.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "x_true", replace));
+  m_true.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "m_true", replace));
+  y_true.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "y_true", replace));
+  x_hat.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "x_hat", replace));
+  m_hat.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "m_hat", replace));
+  y_hat.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "y_hat", replace));
+
+  cout << "fin.\n";
+  return 0;
+}
+```
+
+_Filename: eg_glds_du_plds_ctrl.cpp_
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Examples/eg_plds_ctrl_8cpp-example.md b/misc/docs-hugo/content/docs/api/Examples/eg_plds_ctrl_8cpp-example.md
index da605224..bee4b5d6 100644
--- a/misc/docs-hugo/content/docs/api/Examples/eg_plds_ctrl_8cpp-example.md
+++ b/misc/docs-hugo/content/docs/api/Examples/eg_plds_ctrl_8cpp-example.md
@@ -1,243 +1,245 @@
----
-title: eg_plds_ctrl.cpp
-
----
-
-# eg_plds_ctrl.cpp
-
-
-
-Example PLDS Control 
-
-```cpp
-//===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst>
-
-using lds::Matrix;
-using lds::Vector;
-using lds::data_t;
-using std::cout;
-
-auto main() -> int {
-  cout << " ********** Example Poisson LDS Control ********** \n\n";
-
-  // Make SISO system sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;
-  size_t n_x = 1;
-  size_t n_y = 1;
-
-  // no time steps for simulation.
-  auto n_t = static_cast<size_t>(10.0 / dt);
-
-  // Control variables: _reference/target output, controller gains
-  // n.b., Can either use Vector (arma::Col) or std::vector
-  Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt;
-  Matrix k_x =
-      Matrix(n_u, n_x, arma::fill::zeros) + 1;  // gains on state error
-  Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) +
-                   10;  // gains on integrated output err
-
-  // Set control type bit mask, so controller knows what to do
-  size_t control_type = lds::kControlTypeIntY;  // integral action
-
-  // Ground-truth parameters for the controlled system
-  // (stand-in for physical system to be controlled)
-  Matrix a_true(n_x, n_x, arma::fill::eye);
-  a_true[0] = 0.986;
-  Matrix b_true(n_x, n_u, arma::fill::zeros);
-  b_true[0] = 0.054;
-  Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);
-
-  size_t which_m = 0;
-  data_t m_low = log(1 * dt) * (1 - a_true[0]);
-  data_t pr_lo2hi = 1e-3;
-  data_t m_high = log(20 * dt) * (1 - a_true[0]);
-  data_t pr_hi2lo = pr_lo2hi;
-
-  Vector m0_true = Vector(n_x, arma::fill::ones) * m_low;
-  // construct ground truth system to be controlled...
-  lds::poisson::System controlled_system(n_u, n_x, n_y, dt);
-
-  // Assign params.
-  controlled_system.set_A(a_true);
-  controlled_system.set_B(b_true);
-  controlled_system.set_m(m0_true);
-  controlled_system.set_x0(x0_true);
-  // reset to initial conditions
-  controlled_system.Reset();
-
-  cout << ".....................................\n";
-  cout << "controlled_system:\n";
-  cout << ".....................................\n";
-  controlled_system.Print();
-  cout << ".....................................\n";
-
-  // Create the controller
-  lds::poisson::Controller controller;
-  {
-    // Create model used for control.
-    lds::poisson::System controller_system(controlled_system);
-
-    // for this example, assume model correct, except disturbance
-    Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low;
-    Vector x0_controller = arma::log(y_ref0);
-    controller_system.set_m(m0_controller);
-    controller_system.set_x0(x0_controller);
-    controller_system.Reset(); //reset to new init condition
-
-    // adaptively re-estimate process disturbance (m)
-    controller_system.do_adapt_m = true;
-
-    // set adaptation rate by changing covariance of assumed process noise
-    // acting on random-walk evolution of m
-    Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5;
-    controller_system.set_Q_m(q_m);
-
-    data_t u_lb = 0.0;
-    data_t u_ub = 5.0;
-    controller = std::move(
-        lds::poisson::Controller(std::move(controller_system), u_lb, u_ub));
-  }
-  // set controller type
-  controller.set_control_type(control_type);
-
-  // set controller gains
-  controller.set_Kc(k_x);
-  controller.set_Kc_inty(k_inty);
-
-  // to protect against integral windup when output is consistently above
-  // target:
-  data_t tau_awu(0.1);
-  controller.set_tau_awu(tau_awu);
-
-  cout << ".....................................\n";
-  cout << "controller:\n";
-  cout << ".....................................\n";
-  controller.Print();
-  cout << ".....................................\n";
-
-  // create Matrix to save outputs in...
-  Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros);
-  y_ref.each_col() += y_ref0;
-
-  // Simulated measurements
-  Matrix z(n_y, n_t, arma::fill::zeros);
-
-  // simulated control signal ([=] V)
-  Matrix u(n_u, n_t, arma::fill::zeros);
-
-  // outputs, states and gain/disturbance params
-  // *_hat indicates online estimates
-  Matrix y_hat(n_y, n_t, arma::fill::zeros);
-  Matrix x_hat(n_x, n_t, arma::fill::zeros);
-  Matrix m_hat(n_y, n_t, arma::fill::zeros);
-
-  // *_true indicates ground truth (system being controlled)
-  Matrix y_true(n_y, n_t, arma::fill::zeros);
-  Matrix x_true(n_x, n_t, arma::fill::zeros);
-  Matrix m_true(n_y, n_t, arma::fill::zeros);
-
-  // set initial val
-  y_hat.col(0) = controller.sys().y();
-  y_true.col(0) = controlled_system.y();
-
-  x_hat.col(0) = controller.sys().x();
-  x_true.col(0) = controlled_system.x();
-
-  m_hat.col(0) = controller.sys().m();
-  m_true.col(0) = controlled_system.m();
-
-  cout << "Starting " << n_t * dt << " sec simulation ... \n";
-  auto start = std::chrono::high_resolution_clock::now();
-  for (size_t t = 1; t < n_t; t++) {
-    // simulate a stochastically switched disturbance
-    Vector chance = arma::randu<Vector>(1);
-    if (which_m == 0)  // low disturbance
-    {
-      if (chance[0] < pr_lo2hi) {  // switches low -> high disturbance
-        m0_true = std::vector<data_t>(n_x, m_high);
-        which_m = 1;
-      }
-    } else {                       // high disturbance
-      if (chance[0] < pr_hi2lo) {  // swithces high -> low disturbance
-        m0_true = std::vector<data_t>(n_x, m_low);
-        which_m = 0;
-      }
-    }
-    controlled_system.set_m(m0_true);
-
-    // e.g., use sinusoidal reference
-    data_t f = 0.5;  // freq [=] Hz
-    Vector t_vec = Vector(n_y, arma::fill::ones) * t;
-    y_ref.col(t) +=
-        y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4);
-
-    // Simulate the true system.
-    z.col(t)=controlled_system.Simulate(u.col(t-1));
-
-    // This method uses a steady-state solution to control problem to calculate
-    // x_ref, u_ref from reference output y_ref. Notably, it does this in the
-    // log-linear space (i.e., log(y)).
-    //
-    // Therefore, it is only applicable to regulation problems or cases where
-    // reference trajectory changes slowly compared to system dynamics.
-    controller.set_y_ref(y_ref.col(t));
-    u.col(t)=controller.ControlOutputReference(z.col(t));
-
-    y_true.col(t) = controlled_system.y();
-    x_true.col(t) = controlled_system.x();
-    m_true.col(t) = controlled_system.m();
-
-    y_hat.col(t) = controller.sys().y();
-    x_hat.col(t) = controller.sys().x();
-    m_hat.col(t) = controller.sys().m();
-  }
-
-  auto finish = std::chrono::high_resolution_clock::now();
-  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
-  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
-  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
-
-  // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,
-  // x_true, m_true saving with hdf5 via armadillo
-  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
-
-  auto dt_vec = Vector(1).fill(dt);
-  dt_vec.save(arma::hdf5_name("eg_plds_ctrl.h5", "dt"));
-  y_ref.save(arma::hdf5_name("eg_plds_ctrl.h5", "y_ref", replace));
-  u.save(arma::hdf5_name("eg_plds_ctrl.h5", "u", replace));
-  z.save(arma::hdf5_name("eg_plds_ctrl.h5", "z", replace));
-  x_true.save(arma::hdf5_name("eg_plds_ctrl.h5", "x_true", replace));
-  m_true.save(arma::hdf5_name("eg_plds_ctrl.h5", "m_true", replace));
-  y_true.save(arma::hdf5_name("eg_plds_ctrl.h5", "y_true", replace));
-  x_hat.save(arma::hdf5_name("eg_plds_ctrl.h5", "x_hat", replace));
-  m_hat.save(arma::hdf5_name("eg_plds_ctrl.h5", "m_hat", replace));
-  y_hat.save(arma::hdf5_name("eg_plds_ctrl.h5", "y_hat", replace));
-
-  return 0;
-}
-```
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: eg_plds_ctrl.cpp
+summary: Example PLDS Control. 
+
+---
+
+# eg_plds_ctrl.cpp
+
+
+
+Example PLDS Control ```cpp
+
+//===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#include <ldsCtrlEst>
+
+using lds::Matrix;
+using lds::Vector;
+using lds::data_t;
+using std::cout;
+
+auto main() -> int {
+  cout << " ********** Example Poisson LDS Control ********** \n\n";
+
+  // Make SISO system sampled at 1kHz
+  data_t dt = 1e-3;
+  size_t n_u = 1;
+  size_t n_x = 1;
+  size_t n_y = 1;
+
+  // no time steps for simulation.
+  auto n_t = static_cast<size_t>(10.0 / dt);
+
+  // Control variables: _reference/target output, controller gains
+  // n.b., Can either use Vector (arma::Col) or std::vector
+  Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt;
+  Matrix k_x =
+      Matrix(n_u, n_x, arma::fill::zeros) + 1;  // gains on state error
+  Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) +
+                   10;  // gains on integrated output err
+
+  // Set control type bit mask, so controller knows what to do
+  size_t control_type = lds::kControlTypeIntY;  // integral action
+
+  // Ground-truth parameters for the controlled system
+  // (stand-in for physical system to be controlled)
+  Matrix a_true(n_x, n_x, arma::fill::eye);
+  a_true[0] = 0.986;
+  Matrix b_true(n_x, n_u, arma::fill::zeros);
+  b_true[0] = 0.054;
+  Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);
+
+  size_t which_m = 0;
+  data_t m_low = log(1 * dt) * (1 - a_true[0]);
+  data_t pr_lo2hi = 1e-3;
+  data_t m_high = log(20 * dt) * (1 - a_true[0]);
+  data_t pr_hi2lo = pr_lo2hi;
+
+  Vector m0_true = Vector(n_x, arma::fill::ones) * m_low;
+  // construct ground truth system to be controlled...
+  lds::poisson::System controlled_system(n_u, n_x, n_y, dt);
+
+  // Assign params.
+  controlled_system.set_A(a_true);
+  controlled_system.set_B(b_true);
+  controlled_system.set_m(m0_true);
+  controlled_system.set_x0(x0_true);
+  // reset to initial conditions
+  controlled_system.Reset();
+
+  cout << ".....................................\n";
+  cout << "controlled_system:\n";
+  cout << ".....................................\n";
+  controlled_system.Print();
+  cout << ".....................................\n";
+
+  // Create the controller
+  lds::poisson::Controller controller;
+  {
+    // Create model used for control.
+    lds::poisson::System controller_system(controlled_system);
+
+    // for this example, assume model correct, except disturbance
+    Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low;
+    Vector x0_controller = arma::log(y_ref0);
+    controller_system.set_m(m0_controller);
+    controller_system.set_x0(x0_controller);
+    controller_system.Reset(); //reset to new init condition
+
+    // adaptively re-estimate process disturbance (m)
+    controller_system.do_adapt_m = true;
+
+    // set adaptation rate by changing covariance of assumed process noise
+    // acting on random-walk evolution of m
+    Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5;
+    controller_system.set_Q_m(q_m);
+
+    data_t u_lb = 0.0;
+    data_t u_ub = 5.0;
+    controller = std::move(
+        lds::poisson::Controller(std::move(controller_system), u_lb, u_ub));
+  }
+  // set controller type
+  controller.set_control_type(control_type);
+
+  // set controller gains
+  controller.set_Kc(k_x);
+  controller.set_Kc_inty(k_inty);
+
+  // to protect against integral windup when output is consistently above
+  // target:
+  data_t tau_awu(0.1);
+  controller.set_tau_awu(tau_awu);
+
+  cout << ".....................................\n";
+  cout << "controller:\n";
+  cout << ".....................................\n";
+  controller.Print();
+  cout << ".....................................\n";
+
+  // create Matrix to save outputs in...
+  Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros);
+  y_ref.each_col() += y_ref0;
+
+  // Simulated measurements
+  Matrix z(n_y, n_t, arma::fill::zeros);
+
+  // simulated control signal ([=] V)
+  Matrix u(n_u, n_t, arma::fill::zeros);
+
+  // outputs, states and gain/disturbance params
+  // *_hat indicates online estimates
+  Matrix y_hat(n_y, n_t, arma::fill::zeros);
+  Matrix x_hat(n_x, n_t, arma::fill::zeros);
+  Matrix m_hat(n_y, n_t, arma::fill::zeros);
+
+  // *_true indicates ground truth (system being controlled)
+  Matrix y_true(n_y, n_t, arma::fill::zeros);
+  Matrix x_true(n_x, n_t, arma::fill::zeros);
+  Matrix m_true(n_y, n_t, arma::fill::zeros);
+
+  // set initial val
+  y_hat.col(0) = controller.sys().y();
+  y_true.col(0) = controlled_system.y();
+
+  x_hat.col(0) = controller.sys().x();
+  x_true.col(0) = controlled_system.x();
+
+  m_hat.col(0) = controller.sys().m();
+  m_true.col(0) = controlled_system.m();
+
+  cout << "Starting " << n_t * dt << " sec simulation ... \n";
+  auto start = std::chrono::high_resolution_clock::now();
+  for (size_t t = 1; t < n_t; t++) {
+    // simulate a stochastically switched disturbance
+    Vector chance = arma::randu<Vector>(1);
+    if (which_m == 0)  // low disturbance
+    {
+      if (chance[0] < pr_lo2hi) {  // switches low -> high disturbance
+        m0_true = std::vector<data_t>(n_x, m_high);
+        which_m = 1;
+      }
+    } else {                       // high disturbance
+      if (chance[0] < pr_hi2lo) {  // swithces high -> low disturbance
+        m0_true = std::vector<data_t>(n_x, m_low);
+        which_m = 0;
+      }
+    }
+    controlled_system.set_m(m0_true);
+
+    // e.g., use sinusoidal reference
+    data_t f = 0.5;  // freq [=] Hz
+    Vector t_vec = Vector(n_y, arma::fill::ones) * t;
+    y_ref.col(t) +=
+        y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4);
+
+    // Simulate the true system.
+    z.col(t)=controlled_system.Simulate(u.col(t-1));
+
+    // This method uses a steady-state solution to control problem to calculate
+    // x_ref, u_ref from reference output y_ref. Notably, it does this in the
+    // log-linear space (i.e., log(y)).
+    //
+    // Therefore, it is only applicable to regulation problems or cases where
+    // reference trajectory changes slowly compared to system dynamics.
+    controller.set_y_ref(y_ref.col(t));
+    u.col(t)=controller.ControlOutputReference(z.col(t));
+
+    y_true.col(t) = controlled_system.y();
+    x_true.col(t) = controlled_system.x();
+    m_true.col(t) = controlled_system.m();
+
+    y_hat.col(t) = controller.sys().y();
+    x_hat.col(t) = controller.sys().x();
+    m_hat.col(t) = controller.sys().m();
+  }
+
+  auto finish = std::chrono::high_resolution_clock::now();
+  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
+  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
+  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
+
+  // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,
+  // x_true, m_true saving with hdf5 via armadillo
+  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
+
+  auto dt_vec = Vector(1).fill(dt);
+  dt_vec.save(arma::hdf5_name("eg_plds_ctrl.h5", "dt"));
+  y_ref.save(arma::hdf5_name("eg_plds_ctrl.h5", "y_ref", replace));
+  u.save(arma::hdf5_name("eg_plds_ctrl.h5", "u", replace));
+  z.save(arma::hdf5_name("eg_plds_ctrl.h5", "z", replace));
+  x_true.save(arma::hdf5_name("eg_plds_ctrl.h5", "x_true", replace));
+  m_true.save(arma::hdf5_name("eg_plds_ctrl.h5", "m_true", replace));
+  y_true.save(arma::hdf5_name("eg_plds_ctrl.h5", "y_true", replace));
+  x_hat.save(arma::hdf5_name("eg_plds_ctrl.h5", "x_hat", replace));
+  m_hat.save(arma::hdf5_name("eg_plds_ctrl.h5", "m_hat", replace));
+  y_hat.save(arma::hdf5_name("eg_plds_ctrl.h5", "y_hat", replace));
+
+  return 0;
+}
+```
+
+_Filename: eg_plds_ctrl.cpp_
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Examples/eg_plds_est_8cpp-example.md b/misc/docs-hugo/content/docs/api/Examples/eg_plds_est_8cpp-example.md
index 55ef8d12..a8ad636f 100644
--- a/misc/docs-hugo/content/docs/api/Examples/eg_plds_est_8cpp-example.md
+++ b/misc/docs-hugo/content/docs/api/Examples/eg_plds_est_8cpp-example.md
@@ -1,193 +1,195 @@
----
-title: eg_plds_est.cpp
-
----
-
-# eg_plds_est.cpp
-
-
-
-Example PLDS Estimation 
-
-```cpp
-//===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst>
-
-using lds::Matrix;
-using lds::Vector;
-using lds::data_t;
-using std::cout;
-
-// for generating random input
-Matrix random_walk(size_t n_t, const Matrix& Q, const Vector& x0);
-
-int main() {
-  cout << " ********** Example Poisson LDS Estimation ********** \n\n";
-
-  // Make SISO system sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;                           // no. inputs
-  size_t n_x = 1;                           // no. states
-  size_t n_y = 1;                           // no. outputs
-  auto n_t = static_cast<size_t>(30 / dt);  // no time steps for simulation.
-
-  // construct ground truth system...
-  lds::poisson::System system_true(n_u, n_x, n_y, dt);
-
-  // Model parameters
-  Matrix a_true(n_x, n_x, arma::fill::eye);
-  a_true[0] = exp(-dt / 0.075);
-  Matrix b_true = Matrix(n_x, n_u).fill(1e-2);
-  Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2);  // disturbance
-  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) -
-                                        a_true);  // initial state
-
-  // Assign params.
-  system_true.set_A(a_true);
-  system_true.set_B(b_true);
-  system_true.set_x0(x0_true);
-  system_true.set_m(m0_true);
-  system_true.Reset();
-
-  // Construct system for estimation
-  // e.g., will create a model with incorrect disturbance
-  lds::poisson::System system_estimator(n_u, n_x, n_y, dt);
-
-  // Can copy parameters from another system object
-  system_estimator = system_true;
-
-  // wrong disturbance
-  Vector m0_est = m0_true * 2;
-  system_estimator.set_m(m0_est);
-
-  // set new initial conditions
-  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) -
-                                      a_true);  // initial state
-  system_estimator.set_x0(x0_est);
-  system_estimator.Reset();  // reset to initial condition.
-
-  // turn on adaptive disturbance estimation
-  system_estimator.do_adapt_m = true;
-
-  // set adaptation rate by changing covariance of assumed process noise acting
-  // on random-walk evolution of m
-  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6;
-  system_estimator.set_Q_m(q_m);
-
-  cout << ".....................................\n";
-  cout << "estimator:\n";
-  cout << ".....................................\n";
-  system_estimator.Print();
-  cout << ".....................................\n";
-
-  // Set up simulation :
-  // Simulated measurements
-  Matrix z(n_y, n_t, arma::fill::zeros);
-
-  // Stimulus (generate random stimulus)
-  Matrix q_u =
-      Matrix(n_u, n_u, arma::fill::eye) * 1e-3;  // cov of random walk
-  Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));
-
-  // create matrix to save outputs in...
-  Matrix y_hat(n_y, n_t, arma::fill::zeros);
-  Matrix y_true(n_y, n_t, arma::fill::zeros);
-
-  // states and disturbance params
-  Matrix x_hat(n_x, n_t, arma::fill::zeros);
-  Matrix m_hat(n_x, n_t, arma::fill::zeros);
-
-  Matrix x_true(n_x, n_t, arma::fill::zeros);
-  Matrix m_true(n_x, n_t, arma::fill::zeros);
-
-  // initial conditions
-  y_hat.col(0) = system_estimator.y();
-  y_true.col(0) = system_true.y();
-  x_hat.col(0) = system_estimator.x();
-  x_true.col(0) = system_true.x();
-  m_hat.col(0) = system_estimator.m();
-  m_true.col(0) = system_true.m();
-
-  cout << "Starting " << n_t * dt << " sec simlation ... \n";
-  auto start = std::chrono::high_resolution_clock::now();
-  for (size_t t = 1; t < n_t; t++) {
-    // Simlate the true system.
-    z.col(t) = system_true.Simulate(u.col(t - 1));
-
-    // Filter (predict -> update)
-    system_estimator.Filter(u.col(t - 1), z.col(t));
-
-    // save signals
-    y_hat.col(t) = system_estimator.y();
-    y_true.col(t) = system_true.y();
-
-    x_true.col(t) = system_true.x();
-    m_true.col(t) = system_true.m();
-
-    x_hat.col(t) = system_estimator.x();
-    m_hat.col(t) = system_estimator.m();
-  }
-
-  auto finish = std::chrono::high_resolution_clock::now();
-  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
-  cout << "Finished simlation in " << sim_time_ms.count() << " ms.\n";
-  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
-
-  // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true,
-  // x_true, m_true saving with hdf5 via armadillo
-  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
-
-  auto dt_vec = Vector(1).fill(dt);
-  dt_vec.save(arma::hdf5_name("eg_plds_est.h5", "dt"));
-  u.save(arma::hdf5_name("eg_plds_est.h5", "u", replace));
-  z.save(arma::hdf5_name("eg_plds_est.h5", "z", replace));
-  x_true.save(arma::hdf5_name("eg_plds_est.h5", "x_true", replace));
-  m_true.save(arma::hdf5_name("eg_plds_est.h5", "m_true", replace));
-  y_true.save(arma::hdf5_name("eg_plds_est.h5", "y_true", replace));
-  x_hat.save(arma::hdf5_name("eg_plds_est.h5", "x_hat", replace));
-  m_hat.save(arma::hdf5_name("eg_plds_est.h5", "m_hat", replace));
-  y_hat.save(arma::hdf5_name("eg_plds_est.h5", "y_hat", replace));
-
-  return 0;
-}
-
-// for generating random input
-Matrix random_walk(size_t n_t, const Matrix& Q, const Vector& x0) {
-  size_t n = Q.n_rows;
-
-  if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) {
-    throw std::logic_error("Q must be `n` x `n`.");
-  }
-
-  Matrix x(n, n_t, arma::fill::zeros);
-  x.col(0) = x0;
-  for (size_t t = 1; t < n_t; t++) {
-    x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q);
-  }
-
-  return x;
-}
-```
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: eg_plds_est.cpp
+summary: Example PLDS Estimation. 
+
+---
+
+# eg_plds_est.cpp
+
+
+
+Example PLDS Estimation ```cpp
+
+//===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#include <ldsCtrlEst>
+
+using lds::Matrix;
+using lds::Vector;
+using lds::data_t;
+using std::cout;
+
+// for generating random input
+Matrix random_walk(size_t n_t, const Matrix& Q, const Vector& x0);
+
+int main() {
+  cout << " ********** Example Poisson LDS Estimation ********** \n\n";
+
+  // Make SISO system sampled at 1kHz
+  data_t dt = 1e-3;
+  size_t n_u = 1;                           // no. inputs
+  size_t n_x = 1;                           // no. states
+  size_t n_y = 1;                           // no. outputs
+  auto n_t = static_cast<size_t>(30 / dt);  // no time steps for simulation.
+
+  // construct ground truth system...
+  lds::poisson::System system_true(n_u, n_x, n_y, dt);
+
+  // Model parameters
+  Matrix a_true(n_x, n_x, arma::fill::eye);
+  a_true[0] = exp(-dt / 0.075);
+  Matrix b_true = Matrix(n_x, n_u).fill(1e-2);
+  Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2);  // disturbance
+  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) -
+                                        a_true);  // initial state
+
+  // Assign params.
+  system_true.set_A(a_true);
+  system_true.set_B(b_true);
+  system_true.set_x0(x0_true);
+  system_true.set_m(m0_true);
+  system_true.Reset();
+
+  // Construct system for estimation
+  // e.g., will create a model with incorrect disturbance
+  lds::poisson::System system_estimator(n_u, n_x, n_y, dt);
+
+  // Can copy parameters from another system object
+  system_estimator = system_true;
+
+  // wrong disturbance
+  Vector m0_est = m0_true * 2;
+  system_estimator.set_m(m0_est);
+
+  // set new initial conditions
+  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) -
+                                      a_true);  // initial state
+  system_estimator.set_x0(x0_est);
+  system_estimator.Reset();  // reset to initial condition.
+
+  // turn on adaptive disturbance estimation
+  system_estimator.do_adapt_m = true;
+
+  // set adaptation rate by changing covariance of assumed process noise acting
+  // on random-walk evolution of m
+  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6;
+  system_estimator.set_Q_m(q_m);
+
+  cout << ".....................................\n";
+  cout << "estimator:\n";
+  cout << ".....................................\n";
+  system_estimator.Print();
+  cout << ".....................................\n";
+
+  // Set up simulation :
+  // Simulated measurements
+  Matrix z(n_y, n_t, arma::fill::zeros);
+
+  // Stimulus (generate random stimulus)
+  Matrix q_u =
+      Matrix(n_u, n_u, arma::fill::eye) * 1e-3;  // cov of random walk
+  Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));
+
+  // create matrix to save outputs in...
+  Matrix y_hat(n_y, n_t, arma::fill::zeros);
+  Matrix y_true(n_y, n_t, arma::fill::zeros);
+
+  // states and disturbance params
+  Matrix x_hat(n_x, n_t, arma::fill::zeros);
+  Matrix m_hat(n_x, n_t, arma::fill::zeros);
+
+  Matrix x_true(n_x, n_t, arma::fill::zeros);
+  Matrix m_true(n_x, n_t, arma::fill::zeros);
+
+  // initial conditions
+  y_hat.col(0) = system_estimator.y();
+  y_true.col(0) = system_true.y();
+  x_hat.col(0) = system_estimator.x();
+  x_true.col(0) = system_true.x();
+  m_hat.col(0) = system_estimator.m();
+  m_true.col(0) = system_true.m();
+
+  cout << "Starting " << n_t * dt << " sec simlation ... \n";
+  auto start = std::chrono::high_resolution_clock::now();
+  for (size_t t = 1; t < n_t; t++) {
+    // Simlate the true system.
+    z.col(t) = system_true.Simulate(u.col(t - 1));
+
+    // Filter (predict -> update)
+    system_estimator.Filter(u.col(t - 1), z.col(t));
+
+    // save signals
+    y_hat.col(t) = system_estimator.y();
+    y_true.col(t) = system_true.y();
+
+    x_true.col(t) = system_true.x();
+    m_true.col(t) = system_true.m();
+
+    x_hat.col(t) = system_estimator.x();
+    m_hat.col(t) = system_estimator.m();
+  }
+
+  auto finish = std::chrono::high_resolution_clock::now();
+  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
+  cout << "Finished simlation in " << sim_time_ms.count() << " ms.\n";
+  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
+
+  // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true,
+  // x_true, m_true saving with hdf5 via armadillo
+  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
+
+  auto dt_vec = Vector(1).fill(dt);
+  dt_vec.save(arma::hdf5_name("eg_plds_est.h5", "dt"));
+  u.save(arma::hdf5_name("eg_plds_est.h5", "u", replace));
+  z.save(arma::hdf5_name("eg_plds_est.h5", "z", replace));
+  x_true.save(arma::hdf5_name("eg_plds_est.h5", "x_true", replace));
+  m_true.save(arma::hdf5_name("eg_plds_est.h5", "m_true", replace));
+  y_true.save(arma::hdf5_name("eg_plds_est.h5", "y_true", replace));
+  x_hat.save(arma::hdf5_name("eg_plds_est.h5", "x_hat", replace));
+  m_hat.save(arma::hdf5_name("eg_plds_est.h5", "m_hat", replace));
+  y_hat.save(arma::hdf5_name("eg_plds_est.h5", "y_hat", replace));
+
+  return 0;
+}
+
+// for generating random input
+Matrix random_walk(size_t n_t, const Matrix& Q, const Vector& x0) {
+  size_t n = Q.n_rows;
+
+  if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) {
+    throw std::logic_error("Q must be `n` x `n`.");
+  }
+
+  Matrix x(n, n_t, arma::fill::zeros);
+  x.col(0) = x0;
+  for (size_t t = 1; t < n_t; t++) {
+    x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q);
+  }
+
+  return x;
+}
+```
+
+_Filename: eg_plds_est.cpp_
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Examples/eg_plds_switched_ctrl_8cpp-example.md b/misc/docs-hugo/content/docs/api/Examples/eg_plds_switched_ctrl_8cpp-example.md
index e151a9dd..43596029 100644
--- a/misc/docs-hugo/content/docs/api/Examples/eg_plds_switched_ctrl_8cpp-example.md
+++ b/misc/docs-hugo/content/docs/api/Examples/eg_plds_switched_ctrl_8cpp-example.md
@@ -1,238 +1,240 @@
----
-title: eg_plds_switched_ctrl.cpp
-
----
-
-# eg_plds_switched_ctrl.cpp
-
-
-
-Example Switched PLDS Control 
-
-```cpp
-//===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst>
-
-using lds::data_t;
-using lds::Matrix;
-using lds::Vector;
-using std::cout;
-
-auto main() -> int {
-  cout << " ********** Example Switched Poisson LDS Control ********** \n\n";
-
-  // whether to do switched control
-  bool do_switch_ctrl = true;
-
-  // Make SISO system sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;
-  size_t n_x = 1;
-  size_t n_y = 1;
-
-  // no time steps for simulation.
-  auto n_t = static_cast<size_t>(30.0 / dt);
-
-  // for simulating switching
-  size_t which_mode = 1;
-  data_t pr_21 = 1e-3;   // prob mode 1 -> 2
-  data_t pr_12 = pr_21;  // prob mode 2 -> 1
-
-  // simulated system being controlled
-  lds::poisson::System controlled_system(n_u, n_x, n_y, dt);
-
-  // **Assume the system is not well characterized by one LDS, but is well
-  // characterized by two LDS models with different input matrices.**
-  data_t scale_sys_b = 2;
-
-  Matrix a(n_x, n_x, arma::fill::eye);
-  a[0] = 0.985;
-  Matrix b1 = Matrix(n_x, n_u).fill(0.05);
-  Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));
-
-  controlled_system.set_A(a);
-  controlled_system.set_B(b1);
-  controlled_system.set_d(d);
-  controlled_system.Reset();  // reset to initial conditions
-
-  // reference
-  Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);
-
-  // Let underlying system 1 be more sensitive than system 2
-  Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);
-
-  // create switched controller
-  lds::poisson::SwitchedController switched_controller;
-  lds::UniformMatrixList<> k_x;  // feedback controller gains
-  {
-    // create switched controller sub-systems
-    // system 1
-    lds::poisson::System sys1(controlled_system);
-
-    // set process noise covariance
-    Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3;
-    sys1.set_Q(q_controller);
-
-    // adaptively estimate process disturbance (m)
-    // n.b. using arbitrary default value for process noise if enabled.
-    sys1.do_adapt_m = true;
-
-    // setting initial mode to target to avoid large error at onset:
-    Vector x0_controller = arma::log(y_ref0) - d;
-    sys1.set_x0(x0_controller);
-    sys1.Reset();  // reset to initial conditions
-
-    cout << ".....................................\n";
-    cout << "sys1:\n";
-    cout << ".....................................\n";
-    sys1.Print();
-    cout << ".....................................\n";
-
-    // system 2
-    lds::poisson::System sys2 = sys1;
-
-    // set parameters
-    sys2.set_B(b2);
-
-    cout << ".....................................\n";
-    cout << "sys2:\n";
-    cout << ".....................................\n";
-    sys2.Print();
-    cout << ".....................................\n";
-
-    lds::UniformSystemList<lds::poisson::System> systems({sys1, sys2});
-
-    // controller gains for underlying system s:
-    Matrix k_x1(n_u, n_x, arma::fill::ones);
-    Matrix k_x2 = scale_sys_b * k_x1;  // system2 is x-times less sensitive.
-    k_x = lds::UniformMatrixList<>({k_x1, k_x2});
-
-    data_t u_lb = 0.0;
-    data_t u_ub = 5.0;
-    switched_controller = std::move(
-        lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub));
-  }
-  // Control variables
-  size_t control_type = 0;  // no integral action, etc
-  switched_controller.set_control_type(control_type);
-  switched_controller.set_Kc(std::move(k_x));
-
-  switched_controller.set_y_ref(y_ref0);
-
-  std::vector<lds::poisson::System> systems_vec(3, lds::poisson::System());
-  lds::UniformSystemList<lds::poisson::System> systems(std::move(systems_vec));
-
-  cout << ".....................................\n";
-  cout << "switched_controller:\n";
-  cout << ".....................................\n";
-  switched_controller.Print();
-  cout << ".....................................\n";
-
-  // Fake measurements
-  Matrix z(n_y, n_t, arma::fill::zeros);
-
-  // Will later contain control.
-  Matrix u(n_u, n_t, arma::fill::zeros);
-
-  // create Matrix to save outputs in...
-  Matrix y_hat(n_y, n_t, arma::fill::zeros);
-  Matrix y_true(n_y, n_t, arma::fill::zeros);
-  Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);
-
-  // modes and gain/disturbance params
-  Matrix x_hat(n_x, n_t, arma::fill::zeros);
-  Matrix x_true(n_x, n_t, arma::fill::zeros);
-  Matrix mode(1, n_t, arma::fill::ones);
-
-  // set initial val
-  y_hat.col(0) = switched_controller.sys().y();
-  y_true.col(0) = controlled_system.y();
-  x_hat.col(0) = switched_controller.sys().x();
-  x_true.col(0) = controlled_system.x();
-
-  cout << "Starting " << n_t * dt << " sec simulation ... \n";
-  auto start = std::chrono::high_resolution_clock::now();
-  for (size_t t = 1; t < n_t; t++) {
-    // Let the controlled system stochastically change gain
-    // Assume another algorithm decodes this mode change and signals the
-    // switched_controller
-    Vector chance(1, arma::fill::randu);
-    if (which_mode == 1)  // mode1
-    {
-      if (chance[0] < pr_21) {
-        which_mode = 2;
-        controlled_system.set_B(b2);
-        if (do_switch_ctrl) {
-          switched_controller.Switch(1);
-        }
-      }
-    } else {  // mode2
-      if (chance[0] < pr_12) {
-        which_mode = 1;
-        controlled_system.set_B(b1);
-        if (do_switch_ctrl) {
-          switched_controller.Switch(0);
-        }
-      }
-    }
-
-    // Simulate the true system.
-    z.col(t) = controlled_system.Simulate(u.col(t - 1));
-
-    // perform control
-    u.col(t) = switched_controller.ControlOutputReference(z.col(t));
-
-    mode.col(t) = which_mode;
-    y_ref.col(t) = y_ref0;
-    y_true.col(t) = controlled_system.y();
-    x_true.col(t) = controlled_system.x();
-    y_hat.col(t) = switched_controller.sys().y();
-    x_hat.col(t) = switched_controller.sys().x();
-  }
-
-  auto finish = std::chrono::high_resolution_clock::now();
-  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
-  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
-  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
-
-  // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,
-  // x_true, m_true saving with hdf5 via armadillo
-  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
-
-  auto dt_vec = Vector(1).fill(dt);
-  dt_vec.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "dt"));
-  y_ref.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "y_ref", replace));
-  u.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "u", replace));
-  z.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "z", replace));
-  x_true.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "x_true", replace));
-  y_true.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "y_true", replace));
-  x_hat.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "x_hat", replace));
-  y_hat.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "y_hat", replace));
-  mode.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "mode", replace));
-
-  return 0;
-}
-```
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: eg_plds_switched_ctrl.cpp
+summary: Example Switched PLDS Control. 
+
+---
+
+# eg_plds_switched_ctrl.cpp
+
+
+
+Example Switched PLDS Control ```cpp
+
+//===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#include <ldsCtrlEst>
+
+using lds::data_t;
+using lds::Matrix;
+using lds::Vector;
+using std::cout;
+
+auto main() -> int {
+  cout << " ********** Example Switched Poisson LDS Control ********** \n\n";
+
+  // whether to do switched control
+  bool do_switch_ctrl = true;
+
+  // Make SISO system sampled at 1kHz
+  data_t dt = 1e-3;
+  size_t n_u = 1;
+  size_t n_x = 1;
+  size_t n_y = 1;
+
+  // no time steps for simulation.
+  auto n_t = static_cast<size_t>(30.0 / dt);
+
+  // for simulating switching
+  size_t which_mode = 1;
+  data_t pr_21 = 1e-3;   // prob mode 1 -> 2
+  data_t pr_12 = pr_21;  // prob mode 2 -> 1
+
+  // simulated system being controlled
+  lds::poisson::System controlled_system(n_u, n_x, n_y, dt);
+
+  // **Assume the system is not well characterized by one LDS, but is well
+  // characterized by two LDS models with different input matrices.**
+  data_t scale_sys_b = 2;
+
+  Matrix a(n_x, n_x, arma::fill::eye);
+  a[0] = 0.985;
+  Matrix b1 = Matrix(n_x, n_u).fill(0.05);
+  Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));
+
+  controlled_system.set_A(a);
+  controlled_system.set_B(b1);
+  controlled_system.set_d(d);
+  controlled_system.Reset();  // reset to initial conditions
+
+  // reference
+  Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);
+
+  // Let underlying system 1 be more sensitive than system 2
+  Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);
+
+  // create switched controller
+  lds::poisson::SwitchedController switched_controller;
+  lds::UniformMatrixList<> k_x;  // feedback controller gains
+  {
+    // create switched controller sub-systems
+    // system 1
+    lds::poisson::System sys1(controlled_system);
+
+    // set process noise covariance
+    Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3;
+    sys1.set_Q(q_controller);
+
+    // adaptively estimate process disturbance (m)
+    // n.b. using arbitrary default value for process noise if enabled.
+    sys1.do_adapt_m = true;
+
+    // setting initial mode to target to avoid large error at onset:
+    Vector x0_controller = arma::log(y_ref0) - d;
+    sys1.set_x0(x0_controller);
+    sys1.Reset();  // reset to initial conditions
+
+    cout << ".....................................\n";
+    cout << "sys1:\n";
+    cout << ".....................................\n";
+    sys1.Print();
+    cout << ".....................................\n";
+
+    // system 2
+    lds::poisson::System sys2 = sys1;
+
+    // set parameters
+    sys2.set_B(b2);
+
+    cout << ".....................................\n";
+    cout << "sys2:\n";
+    cout << ".....................................\n";
+    sys2.Print();
+    cout << ".....................................\n";
+
+    lds::UniformSystemList<lds::poisson::System> systems({sys1, sys2});
+
+    // controller gains for underlying system s:
+    Matrix k_x1(n_u, n_x, arma::fill::ones);
+    Matrix k_x2 = scale_sys_b * k_x1;  // system2 is x-times less sensitive.
+    k_x = lds::UniformMatrixList<>({k_x1, k_x2});
+
+    data_t u_lb = 0.0;
+    data_t u_ub = 5.0;
+    switched_controller = std::move(
+        lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub));
+  }
+  // Control variables
+  size_t control_type = 0;  // no integral action, etc
+  switched_controller.set_control_type(control_type);
+  switched_controller.set_Kc(std::move(k_x));
+
+  switched_controller.set_y_ref(y_ref0);
+
+  std::vector<lds::poisson::System> systems_vec(3, lds::poisson::System());
+  lds::UniformSystemList<lds::poisson::System> systems(std::move(systems_vec));
+
+  cout << ".....................................\n";
+  cout << "switched_controller:\n";
+  cout << ".....................................\n";
+  switched_controller.Print();
+  cout << ".....................................\n";
+
+  // Fake measurements
+  Matrix z(n_y, n_t, arma::fill::zeros);
+
+  // Will later contain control.
+  Matrix u(n_u, n_t, arma::fill::zeros);
+
+  // create Matrix to save outputs in...
+  Matrix y_hat(n_y, n_t, arma::fill::zeros);
+  Matrix y_true(n_y, n_t, arma::fill::zeros);
+  Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);
+
+  // modes and gain/disturbance params
+  Matrix x_hat(n_x, n_t, arma::fill::zeros);
+  Matrix x_true(n_x, n_t, arma::fill::zeros);
+  Matrix mode(1, n_t, arma::fill::ones);
+
+  // set initial val
+  y_hat.col(0) = switched_controller.sys().y();
+  y_true.col(0) = controlled_system.y();
+  x_hat.col(0) = switched_controller.sys().x();
+  x_true.col(0) = controlled_system.x();
+
+  cout << "Starting " << n_t * dt << " sec simulation ... \n";
+  auto start = std::chrono::high_resolution_clock::now();
+  for (size_t t = 1; t < n_t; t++) {
+    // Let the controlled system stochastically change gain
+    // Assume another algorithm decodes this mode change and signals the
+    // switched_controller
+    Vector chance(1, arma::fill::randu);
+    if (which_mode == 1)  // mode1
+    {
+      if (chance[0] < pr_21) {
+        which_mode = 2;
+        controlled_system.set_B(b2);
+        if (do_switch_ctrl) {
+          switched_controller.Switch(1);
+        }
+      }
+    } else {  // mode2
+      if (chance[0] < pr_12) {
+        which_mode = 1;
+        controlled_system.set_B(b1);
+        if (do_switch_ctrl) {
+          switched_controller.Switch(0);
+        }
+      }
+    }
+
+    // Simulate the true system.
+    z.col(t) = controlled_system.Simulate(u.col(t - 1));
+
+    // perform control
+    u.col(t) = switched_controller.ControlOutputReference(z.col(t));
+
+    mode.col(t) = which_mode;
+    y_ref.col(t) = y_ref0;
+    y_true.col(t) = controlled_system.y();
+    x_true.col(t) = controlled_system.x();
+    y_hat.col(t) = switched_controller.sys().y();
+    x_hat.col(t) = switched_controller.sys().x();
+  }
+
+  auto finish = std::chrono::high_resolution_clock::now();
+  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
+  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
+  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
+
+  // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,
+  // x_true, m_true saving with hdf5 via armadillo
+  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
+
+  auto dt_vec = Vector(1).fill(dt);
+  dt_vec.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "dt"));
+  y_ref.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "y_ref", replace));
+  u.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "u", replace));
+  z.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "z", replace));
+  x_true.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "x_true", replace));
+  y_true.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "y_true", replace));
+  x_hat.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "x_hat", replace));
+  y_hat.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "y_hat", replace));
+  mode.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "mode", replace));
+
+  return 0;
+}
+```
+
+_Filename: eg_plds_switched_ctrl.cpp_
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/_index.md b/misc/docs-hugo/content/docs/api/Files/_index.md
index 1626ee59..9a1a41bf 100644
--- a/misc/docs-hugo/content/docs/api/Files/_index.md
+++ b/misc/docs-hugo/content/docs/api/Files/_index.md
@@ -1,184 +1,184 @@
----
-title: Files
-
----
-
-# Files
-
-
-
-
-
-
-
-
-
-- **[examples/eg_glds_ctrl.cpp](/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#file-eg-glds-ctrl.cpp)** 
-
-
-
-- **[examples/eg_glds_du_plds_ctrl.cpp](/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#file-eg-glds-du-plds-ctrl.cpp)** 
-
-
-
-- **[examples/eg_plds_ctrl.cpp](/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#file-eg-plds-ctrl.cpp)** 
-
-
-
-- **[examples/eg_plds_est.cpp](/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#file-eg-plds-est.cpp)** 
-
-
-
-- **[examples/eg_plds_switched_ctrl.cpp](/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#file-eg-plds-switched-ctrl.cpp)** 
-
-
-
-
-
-
-
-
-
-
-
-
-- **[ldsCtrlEst_h/lds.h](/lds-ctrl-est/docs/api/files/lds_8h/#file-lds.h)** <br>`lds` namespace 
-
-
-
-- **[ldsCtrlEst_h/lds_ctrl.h](/lds-ctrl-est/docs/api/files/lds__ctrl_8h/#file-lds-ctrl.h)** <br>Controller. 
-
-
-
-- **[ldsCtrlEst_h/lds_fit.h](/lds-ctrl-est/docs/api/files/lds__fit_8h/#file-lds-fit.h)** <br>LDS base fit type. 
-
-
-
-- **[ldsCtrlEst_h/lds_fit_em.h](/lds-ctrl-est/docs/api/files/lds__fit__em_8h/#file-lds-fit-em.h)** <br>subspace identification 
-
-
-
-- **[ldsCtrlEst_h/lds_fit_ssid.h](/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/#file-lds-fit-ssid.h)** <br>subspace identification 
-
-
-
-- **[ldsCtrlEst_h/lds_gaussian.h](/lds-ctrl-est/docs/api/files/lds__gaussian_8h/#file-lds-gaussian.h)** <br>`glds` namespace 
-
-
-
-- **[ldsCtrlEst_h/lds_gaussian_ctrl.h](/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/#file-lds-gaussian-ctrl.h)** <br>GLDS Controller. 
-
-
-
-- **[ldsCtrlEst_h/lds_gaussian_fit.h](/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/#file-lds-gaussian-fit.h)** <br>GLDS fit type. 
-
-
-
-- **[ldsCtrlEst_h/lds_gaussian_fit_em.h](/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/#file-lds-gaussian-fit-em.h)** <br>GLDS E-M fit type. 
-
-
-
-- **[ldsCtrlEst_h/lds_gaussian_fit_ssid.h](/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/#file-lds-gaussian-fit-ssid.h)** <br>GLDS SSID fit type. 
-
-
-
-- **[ldsCtrlEst_h/lds_gaussian_sctrl.h](/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/#file-lds-gaussian-sctrl.h)** <br>GLDS switched controller type. 
-
-
-
-- **[ldsCtrlEst_h/lds_gaussian_sys.h](/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/#file-lds-gaussian-sys.h)** <br>GLDS base type. 
-
-
-
-- **[ldsCtrlEst_h/lds_poisson.h](/lds-ctrl-est/docs/api/files/lds__poisson_8h/#file-lds-poisson.h)** <br>`plds` namespace 
-
-
-
-- **[ldsCtrlEst_h/lds_poisson_ctrl.h](/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/#file-lds-poisson-ctrl.h)** <br>PLDS controller type. 
-
-
-
-- **[ldsCtrlEst_h/lds_poisson_fit.h](/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/#file-lds-poisson-fit.h)** <br>PLDS base fit type. 
-
-
-
-- **[ldsCtrlEst_h/lds_poisson_fit_em.h](/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/#file-lds-poisson-fit-em.h)** <br>PLDS E-M fit type. 
-
-
-
-- **[ldsCtrlEst_h/lds_poisson_fit_ssid.h](/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/#file-lds-poisson-fit-ssid.h)** <br>PLDS SSID fit type. 
-
-
-
-- **[ldsCtrlEst_h/lds_poisson_sctrl.h](/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/#file-lds-poisson-sctrl.h)** <br>PLDS switched controller type. 
-
-
-
-- **[ldsCtrlEst_h/lds_poisson_sys.h](/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/#file-lds-poisson-sys.h)** <br>PLDS base type. 
-
-
-
-- **[ldsCtrlEst_h/lds_sctrl.h](/lds-ctrl-est/docs/api/files/lds__sctrl_8h/#file-lds-sctrl.h)** <br>SwitchedController type. 
-
-
-
-- **[ldsCtrlEst_h/lds_sys.h](/lds-ctrl-est/docs/api/files/lds__sys_8h/#file-lds-sys.h)** <br>LDS base type. 
-
-
-
-- **[ldsCtrlEst_h/lds_uniform_mats.h](/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/#file-lds-uniform-mats.h)** <br>List of uniformly sized matrices. 
-
-
-
-- **[ldsCtrlEst_h/lds_uniform_systems.h](/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/#file-lds-uniform-systems.h)** <br>List of uniformly sized Systems. 
-
-
-
-- **[ldsCtrlEst_h/lds_uniform_vecs.h](/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/#file-lds-uniform-vecs.h)** <br>List of uniformly sized vectors. 
-
-
-
-- **[ldsCtrlEst_h/mex_c_util.h](/lds-ctrl-est/docs/api/files/mex__c__util_8h/#file-mex-c-util.h)** <br>arma <-> mex interoperability utilities (Matlab C API) 
-
-
-
-- **[ldsCtrlEst_h/mex_cpp_util.h](/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/#file-mex-cpp-util.h)** <br>arma <-> mex interoperability utilities (Matlab C++ API) 
-
-
-
-
-
-
-
-
-
-
-
-
-- **[src/lds.cpp](/lds-ctrl-est/docs/api/files/lds_8cpp/#file-lds.cpp)** <br>misc lds namespace functions 
-
-
-
-- **[src/lds_gaussian_sys.cpp](/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/#file-lds-gaussian-sys.cpp)** <br>GLDS base type. 
-
-
-
-- **[src/lds_poisson_sys.cpp](/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/#file-lds-poisson-sys.cpp)** <br>PLDS base type. 
-
-
-
-- **[src/lds_sys.cpp](/lds-ctrl-est/docs/api/files/lds__sys_8cpp/#file-lds-sys.cpp)** <br>LDS base type. 
-
-
-
-- **[src/lds_uniform_vecs.cpp](/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/#file-lds-uniform-vecs.cpp)** <br>Uniformly sized vectors. 
-
-
-
-
-
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:06 Eastern Daylight Time
+---
+title: Files
+
+---
+
+# Files
+
+
+
+
+
+
+
+
+
+- **[examples/eg_glds_ctrl.cpp](/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#file-eg-glds-ctrl.cpp)** 
+
+
+
+- **[examples/eg_glds_du_plds_ctrl.cpp](/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#file-eg-glds-du-plds-ctrl.cpp)** 
+
+
+
+- **[examples/eg_plds_ctrl.cpp](/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#file-eg-plds-ctrl.cpp)** 
+
+
+
+- **[examples/eg_plds_est.cpp](/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#file-eg-plds-est.cpp)** 
+
+
+
+- **[examples/eg_plds_switched_ctrl.cpp](/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#file-eg-plds-switched-ctrl.cpp)** 
+
+
+
+
+
+
+
+
+
+
+
+
+- **[ldsCtrlEst_h/lds.h](/lds-ctrl-est/docs/api/files/lds_8h/#file-lds.h)** <br>`lds` namespace 
+
+
+
+- **[ldsCtrlEst_h/lds_ctrl.h](/lds-ctrl-est/docs/api/files/lds__ctrl_8h/#file-lds-ctrl.h)** <br>Controller. 
+
+
+
+- **[ldsCtrlEst_h/lds_fit.h](/lds-ctrl-est/docs/api/files/lds__fit_8h/#file-lds-fit.h)** <br>LDS base fit type. 
+
+
+
+- **[ldsCtrlEst_h/lds_fit_em.h](/lds-ctrl-est/docs/api/files/lds__fit__em_8h/#file-lds-fit-em.h)** <br>subspace identification 
+
+
+
+- **[ldsCtrlEst_h/lds_fit_ssid.h](/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/#file-lds-fit-ssid.h)** <br>subspace identification 
+
+
+
+- **[ldsCtrlEst_h/lds_gaussian.h](/lds-ctrl-est/docs/api/files/lds__gaussian_8h/#file-lds-gaussian.h)** <br>`glds` namespace 
+
+
+
+- **[ldsCtrlEst_h/lds_gaussian_ctrl.h](/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/#file-lds-gaussian-ctrl.h)** <br>GLDS Controller. 
+
+
+
+- **[ldsCtrlEst_h/lds_gaussian_fit.h](/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/#file-lds-gaussian-fit.h)** <br>GLDS fit type. 
+
+
+
+- **[ldsCtrlEst_h/lds_gaussian_fit_em.h](/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/#file-lds-gaussian-fit-em.h)** <br>GLDS E-M fit type. 
+
+
+
+- **[ldsCtrlEst_h/lds_gaussian_fit_ssid.h](/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/#file-lds-gaussian-fit-ssid.h)** <br>GLDS SSID fit type. 
+
+
+
+- **[ldsCtrlEst_h/lds_gaussian_sctrl.h](/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/#file-lds-gaussian-sctrl.h)** <br>GLDS switched controller type. 
+
+
+
+- **[ldsCtrlEst_h/lds_gaussian_sys.h](/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/#file-lds-gaussian-sys.h)** <br>GLDS base type. 
+
+
+
+- **[ldsCtrlEst_h/lds_poisson.h](/lds-ctrl-est/docs/api/files/lds__poisson_8h/#file-lds-poisson.h)** <br>`plds` namespace 
+
+
+
+- **[ldsCtrlEst_h/lds_poisson_ctrl.h](/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/#file-lds-poisson-ctrl.h)** <br>PLDS controller type. 
+
+
+
+- **[ldsCtrlEst_h/lds_poisson_fit.h](/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/#file-lds-poisson-fit.h)** <br>PLDS base fit type. 
+
+
+
+- **[ldsCtrlEst_h/lds_poisson_fit_em.h](/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/#file-lds-poisson-fit-em.h)** <br>PLDS E-M fit type. 
+
+
+
+- **[ldsCtrlEst_h/lds_poisson_fit_ssid.h](/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/#file-lds-poisson-fit-ssid.h)** <br>PLDS SSID fit type. 
+
+
+
+- **[ldsCtrlEst_h/lds_poisson_sctrl.h](/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/#file-lds-poisson-sctrl.h)** <br>PLDS switched controller type. 
+
+
+
+- **[ldsCtrlEst_h/lds_poisson_sys.h](/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/#file-lds-poisson-sys.h)** <br>PLDS base type. 
+
+
+
+- **[ldsCtrlEst_h/lds_sctrl.h](/lds-ctrl-est/docs/api/files/lds__sctrl_8h/#file-lds-sctrl.h)** <br>SwitchedController type. 
+
+
+
+- **[ldsCtrlEst_h/lds_sys.h](/lds-ctrl-est/docs/api/files/lds__sys_8h/#file-lds-sys.h)** <br>LDS base type. 
+
+
+
+- **[ldsCtrlEst_h/lds_uniform_mats.h](/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/#file-lds-uniform-mats.h)** <br>List of uniformly sized matrices. 
+
+
+
+- **[ldsCtrlEst_h/lds_uniform_systems.h](/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/#file-lds-uniform-systems.h)** <br>List of uniformly sized Systems. 
+
+
+
+- **[ldsCtrlEst_h/lds_uniform_vecs.h](/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/#file-lds-uniform-vecs.h)** <br>List of uniformly sized vectors. 
+
+
+
+- **[ldsCtrlEst_h/mex_c_util.h](/lds-ctrl-est/docs/api/files/mex__c__util_8h/#file-mex-c-util.h)** <br>arma <-> mex interoperability utilities (Matlab C API) 
+
+
+
+- **[ldsCtrlEst_h/mex_cpp_util.h](/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/#file-mex-cpp-util.h)** <br>arma <-> mex interoperability utilities (Matlab C++ API) 
+
+
+
+
+
+
+
+
+
+
+
+
+- **[src/lds.cpp](/lds-ctrl-est/docs/api/files/lds_8cpp/#file-lds.cpp)** <br>misc lds namespace functions 
+
+
+
+- **[src/lds_gaussian_sys.cpp](/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/#file-lds-gaussian-sys.cpp)** <br>GLDS base type. 
+
+
+
+- **[src/lds_poisson_sys.cpp](/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/#file-lds-poisson-sys.cpp)** <br>PLDS base type. 
+
+
+
+- **[src/lds_sys.cpp](/lds-ctrl-est/docs/api/files/lds__sys_8cpp/#file-lds-sys.cpp)** <br>LDS base type. 
+
+
+
+- **[src/lds_uniform_vecs.cpp](/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/#file-lds-uniform-vecs.cpp)** <br>Uniformly sized vectors. 
+
+
+
+
+
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/dir_156a98879751e549d6939ca71a62d61f.md b/misc/docs-hugo/content/docs/api/Files/dir_156a98879751e549d6939ca71a62d61f.md
index f96b896a..b48621f7 100644
--- a/misc/docs-hugo/content/docs/api/Files/dir_156a98879751e549d6939ca71a62d61f.md
+++ b/misc/docs-hugo/content/docs/api/Files/dir_156a98879751e549d6939ca71a62d61f.md
@@ -1,49 +1,49 @@
----
-title: ldsCtrlEst_h
-
----
-
-# ldsCtrlEst_h
-
-
-
-## Files
-
-| Name           |
-| -------------- |
-| **[ldsCtrlEst_h/lds.h](/lds-ctrl-est/docs/api/files/lds_8h/#file-lds.h)** <br>`lds` namespace  |
-| **[ldsCtrlEst_h/lds_ctrl.h](/lds-ctrl-est/docs/api/files/lds__ctrl_8h/#file-lds-ctrl.h)** <br>Controller.  |
-| **[ldsCtrlEst_h/lds_fit.h](/lds-ctrl-est/docs/api/files/lds__fit_8h/#file-lds-fit.h)** <br>LDS base fit type.  |
-| **[ldsCtrlEst_h/lds_fit_em.h](/lds-ctrl-est/docs/api/files/lds__fit__em_8h/#file-lds-fit-em.h)** <br>subspace identification  |
-| **[ldsCtrlEst_h/lds_fit_ssid.h](/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/#file-lds-fit-ssid.h)** <br>subspace identification  |
-| **[ldsCtrlEst_h/lds_gaussian.h](/lds-ctrl-est/docs/api/files/lds__gaussian_8h/#file-lds-gaussian.h)** <br>`glds` namespace  |
-| **[ldsCtrlEst_h/lds_gaussian_ctrl.h](/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/#file-lds-gaussian-ctrl.h)** <br>GLDS Controller.  |
-| **[ldsCtrlEst_h/lds_gaussian_fit.h](/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/#file-lds-gaussian-fit.h)** <br>GLDS fit type.  |
-| **[ldsCtrlEst_h/lds_gaussian_fit_em.h](/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/#file-lds-gaussian-fit-em.h)** <br>GLDS E-M fit type.  |
-| **[ldsCtrlEst_h/lds_gaussian_fit_ssid.h](/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/#file-lds-gaussian-fit-ssid.h)** <br>GLDS SSID fit type.  |
-| **[ldsCtrlEst_h/lds_gaussian_sctrl.h](/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/#file-lds-gaussian-sctrl.h)** <br>GLDS switched controller type.  |
-| **[ldsCtrlEst_h/lds_gaussian_sys.h](/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/#file-lds-gaussian-sys.h)** <br>GLDS base type.  |
-| **[ldsCtrlEst_h/lds_poisson.h](/lds-ctrl-est/docs/api/files/lds__poisson_8h/#file-lds-poisson.h)** <br>`plds` namespace  |
-| **[ldsCtrlEst_h/lds_poisson_ctrl.h](/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/#file-lds-poisson-ctrl.h)** <br>PLDS controller type.  |
-| **[ldsCtrlEst_h/lds_poisson_fit.h](/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/#file-lds-poisson-fit.h)** <br>PLDS base fit type.  |
-| **[ldsCtrlEst_h/lds_poisson_fit_em.h](/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/#file-lds-poisson-fit-em.h)** <br>PLDS E-M fit type.  |
-| **[ldsCtrlEst_h/lds_poisson_fit_ssid.h](/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/#file-lds-poisson-fit-ssid.h)** <br>PLDS SSID fit type.  |
-| **[ldsCtrlEst_h/lds_poisson_sctrl.h](/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/#file-lds-poisson-sctrl.h)** <br>PLDS switched controller type.  |
-| **[ldsCtrlEst_h/lds_poisson_sys.h](/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/#file-lds-poisson-sys.h)** <br>PLDS base type.  |
-| **[ldsCtrlEst_h/lds_sctrl.h](/lds-ctrl-est/docs/api/files/lds__sctrl_8h/#file-lds-sctrl.h)** <br>SwitchedController type.  |
-| **[ldsCtrlEst_h/lds_sys.h](/lds-ctrl-est/docs/api/files/lds__sys_8h/#file-lds-sys.h)** <br>LDS base type.  |
-| **[ldsCtrlEst_h/lds_uniform_mats.h](/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/#file-lds-uniform-mats.h)** <br>List of uniformly sized matrices.  |
-| **[ldsCtrlEst_h/lds_uniform_systems.h](/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/#file-lds-uniform-systems.h)** <br>List of uniformly sized Systems.  |
-| **[ldsCtrlEst_h/lds_uniform_vecs.h](/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/#file-lds-uniform-vecs.h)** <br>List of uniformly sized vectors.  |
-| **[ldsCtrlEst_h/mex_c_util.h](/lds-ctrl-est/docs/api/files/mex__c__util_8h/#file-mex-c-util.h)** <br>arma <-> mex interoperability utilities (Matlab C API)  |
-| **[ldsCtrlEst_h/mex_cpp_util.h](/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/#file-mex-cpp-util.h)** <br>arma <-> mex interoperability utilities (Matlab C++ API)  |
-
-
-
-
-
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h
+
+---
+
+# ldsCtrlEst_h
+
+
+
+## Files
+
+| Name           |
+| -------------- |
+| **[ldsCtrlEst_h/lds.h](/lds-ctrl-est/docs/api/files/lds_8h/#file-lds.h)** <br>`lds` namespace  |
+| **[ldsCtrlEst_h/lds_ctrl.h](/lds-ctrl-est/docs/api/files/lds__ctrl_8h/#file-lds-ctrl.h)** <br>Controller.  |
+| **[ldsCtrlEst_h/lds_fit.h](/lds-ctrl-est/docs/api/files/lds__fit_8h/#file-lds-fit.h)** <br>LDS base fit type.  |
+| **[ldsCtrlEst_h/lds_fit_em.h](/lds-ctrl-est/docs/api/files/lds__fit__em_8h/#file-lds-fit-em.h)** <br>subspace identification  |
+| **[ldsCtrlEst_h/lds_fit_ssid.h](/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/#file-lds-fit-ssid.h)** <br>subspace identification  |
+| **[ldsCtrlEst_h/lds_gaussian.h](/lds-ctrl-est/docs/api/files/lds__gaussian_8h/#file-lds-gaussian.h)** <br>`glds` namespace  |
+| **[ldsCtrlEst_h/lds_gaussian_ctrl.h](/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/#file-lds-gaussian-ctrl.h)** <br>GLDS Controller.  |
+| **[ldsCtrlEst_h/lds_gaussian_fit.h](/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/#file-lds-gaussian-fit.h)** <br>GLDS fit type.  |
+| **[ldsCtrlEst_h/lds_gaussian_fit_em.h](/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/#file-lds-gaussian-fit-em.h)** <br>GLDS E-M fit type.  |
+| **[ldsCtrlEst_h/lds_gaussian_fit_ssid.h](/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/#file-lds-gaussian-fit-ssid.h)** <br>GLDS SSID fit type.  |
+| **[ldsCtrlEst_h/lds_gaussian_sctrl.h](/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/#file-lds-gaussian-sctrl.h)** <br>GLDS switched controller type.  |
+| **[ldsCtrlEst_h/lds_gaussian_sys.h](/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/#file-lds-gaussian-sys.h)** <br>GLDS base type.  |
+| **[ldsCtrlEst_h/lds_poisson.h](/lds-ctrl-est/docs/api/files/lds__poisson_8h/#file-lds-poisson.h)** <br>`plds` namespace  |
+| **[ldsCtrlEst_h/lds_poisson_ctrl.h](/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/#file-lds-poisson-ctrl.h)** <br>PLDS controller type.  |
+| **[ldsCtrlEst_h/lds_poisson_fit.h](/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/#file-lds-poisson-fit.h)** <br>PLDS base fit type.  |
+| **[ldsCtrlEst_h/lds_poisson_fit_em.h](/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/#file-lds-poisson-fit-em.h)** <br>PLDS E-M fit type.  |
+| **[ldsCtrlEst_h/lds_poisson_fit_ssid.h](/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/#file-lds-poisson-fit-ssid.h)** <br>PLDS SSID fit type.  |
+| **[ldsCtrlEst_h/lds_poisson_sctrl.h](/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/#file-lds-poisson-sctrl.h)** <br>PLDS switched controller type.  |
+| **[ldsCtrlEst_h/lds_poisson_sys.h](/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/#file-lds-poisson-sys.h)** <br>PLDS base type.  |
+| **[ldsCtrlEst_h/lds_sctrl.h](/lds-ctrl-est/docs/api/files/lds__sctrl_8h/#file-lds-sctrl.h)** <br>SwitchedController type.  |
+| **[ldsCtrlEst_h/lds_sys.h](/lds-ctrl-est/docs/api/files/lds__sys_8h/#file-lds-sys.h)** <br>LDS base type.  |
+| **[ldsCtrlEst_h/lds_uniform_mats.h](/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/#file-lds-uniform-mats.h)** <br>List of uniformly sized matrices.  |
+| **[ldsCtrlEst_h/lds_uniform_systems.h](/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/#file-lds-uniform-systems.h)** <br>List of uniformly sized Systems.  |
+| **[ldsCtrlEst_h/lds_uniform_vecs.h](/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/#file-lds-uniform-vecs.h)** <br>List of uniformly sized vectors.  |
+| **[ldsCtrlEst_h/mex_c_util.h](/lds-ctrl-est/docs/api/files/mex__c__util_8h/#file-mex-c-util.h)** <br>arma <-> mex interoperability utilities (Matlab C API)  |
+| **[ldsCtrlEst_h/mex_cpp_util.h](/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/#file-mex-cpp-util.h)** <br>arma <-> mex interoperability utilities (Matlab C++ API)  |
+
+
+
+
+
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/dir_68267d1309a1af8e8297ef4c3efbcdba.md b/misc/docs-hugo/content/docs/api/Files/dir_68267d1309a1af8e8297ef4c3efbcdba.md
index c97c28f4..767ff3ed 100644
--- a/misc/docs-hugo/content/docs/api/Files/dir_68267d1309a1af8e8297ef4c3efbcdba.md
+++ b/misc/docs-hugo/content/docs/api/Files/dir_68267d1309a1af8e8297ef4c3efbcdba.md
@@ -1,28 +1,28 @@
----
-title: src
-
----
-
-# src
-
-
-
-## Files
-
-| Name           |
-| -------------- |
-| **[src/lds.cpp](/lds-ctrl-est/docs/api/files/lds_8cpp/#file-lds.cpp)** <br>misc lds namespace functions  |
-| **[src/lds_gaussian_sys.cpp](/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/#file-lds-gaussian-sys.cpp)** <br>GLDS base type.  |
-| **[src/lds_poisson_sys.cpp](/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/#file-lds-poisson-sys.cpp)** <br>PLDS base type.  |
-| **[src/lds_sys.cpp](/lds-ctrl-est/docs/api/files/lds__sys_8cpp/#file-lds-sys.cpp)** <br>LDS base type.  |
-| **[src/lds_uniform_vecs.cpp](/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/#file-lds-uniform-vecs.cpp)** <br>Uniformly sized vectors.  |
-
-
-
-
-
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: src
+
+---
+
+# src
+
+
+
+## Files
+
+| Name           |
+| -------------- |
+| **[src/lds.cpp](/lds-ctrl-est/docs/api/files/lds_8cpp/#file-lds.cpp)** <br>misc lds namespace functions  |
+| **[src/lds_gaussian_sys.cpp](/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/#file-lds-gaussian-sys.cpp)** <br>GLDS base type.  |
+| **[src/lds_poisson_sys.cpp](/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/#file-lds-poisson-sys.cpp)** <br>PLDS base type.  |
+| **[src/lds_sys.cpp](/lds-ctrl-est/docs/api/files/lds__sys_8cpp/#file-lds-sys.cpp)** <br>LDS base type.  |
+| **[src/lds_uniform_vecs.cpp](/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/#file-lds-uniform-vecs.cpp)** <br>Uniformly sized vectors.  |
+
+
+
+
+
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/dir_d28a4824dc47e487b107a5db32ef43c4.md b/misc/docs-hugo/content/docs/api/Files/dir_d28a4824dc47e487b107a5db32ef43c4.md
index 8c4d9d91..3237c746 100644
--- a/misc/docs-hugo/content/docs/api/Files/dir_d28a4824dc47e487b107a5db32ef43c4.md
+++ b/misc/docs-hugo/content/docs/api/Files/dir_d28a4824dc47e487b107a5db32ef43c4.md
@@ -1,28 +1,28 @@
----
-title: examples
-
----
-
-# examples
-
-
-
-## Files
-
-| Name           |
-| -------------- |
-| **[examples/eg_glds_ctrl.cpp](/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#file-eg-glds-ctrl.cpp)**  |
-| **[examples/eg_glds_du_plds_ctrl.cpp](/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#file-eg-glds-du-plds-ctrl.cpp)**  |
-| **[examples/eg_plds_ctrl.cpp](/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#file-eg-plds-ctrl.cpp)**  |
-| **[examples/eg_plds_est.cpp](/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#file-eg-plds-est.cpp)**  |
-| **[examples/eg_plds_switched_ctrl.cpp](/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#file-eg-plds-switched-ctrl.cpp)**  |
-
-
-
-
-
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
+---
+title: examples
+
+---
+
+# examples
+
+
+
+## Files
+
+| Name           |
+| -------------- |
+| **[examples/eg_glds_ctrl.cpp](/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#file-eg-glds-ctrl.cpp)**  |
+| **[examples/eg_glds_du_plds_ctrl.cpp](/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#file-eg-glds-du-plds-ctrl.cpp)**  |
+| **[examples/eg_plds_ctrl.cpp](/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#file-eg-plds-ctrl.cpp)**  |
+| **[examples/eg_plds_est.cpp](/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#file-eg-plds-est.cpp)**  |
+| **[examples/eg_plds_switched_ctrl.cpp](/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#file-eg-plds-switched-ctrl.cpp)**  |
+
+
+
+
+
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/dir_d44c64559bbebec7f509842c48db8b23.md b/misc/docs-hugo/content/docs/api/Files/dir_d44c64559bbebec7f509842c48db8b23.md
index afab6649..7874871d 100644
--- a/misc/docs-hugo/content/docs/api/Files/dir_d44c64559bbebec7f509842c48db8b23.md
+++ b/misc/docs-hugo/content/docs/api/Files/dir_d44c64559bbebec7f509842c48db8b23.md
@@ -1,24 +1,24 @@
----
-title: include
-
----
-
-# include
-
-
-
-## Directories
-
-| Name           |
-| -------------- |
-| **[ldsCtrlEst_h](/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/#dir-ldsctrlest-h)**  |
-
-
-
-
-
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: include
+
+---
+
+# include
+
+
+
+## Directories
+
+| Name           |
+| -------------- |
+| **[ldsCtrlEst_h](/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/#dir-ldsctrlest-h)**  |
+
+
+
+
+
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/eg__glds__ctrl_8cpp.md b/misc/docs-hugo/content/docs/api/Files/eg__glds__ctrl_8cpp.md
index 26bbf313..935a0731 100644
--- a/misc/docs-hugo/content/docs/api/Files/eg__glds__ctrl_8cpp.md
+++ b/misc/docs-hugo/content/docs/api/Files/eg__glds__ctrl_8cpp.md
@@ -1,297 +1,334 @@
----
-title: examples/eg_glds_ctrl.cpp
-
----
-
-# examples/eg_glds_ctrl.cpp
-
-
-
-## Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| auto | **[main](/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#function-main)**() |
-
-
-## Function Details
-
-### main
-
-```cpp
-auto main()
-```
-
-
-
-Going to simulate a switching disturbance (m) acting on system 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst>
-
-using lds::Matrix;
-using lds::Vector;
-using lds::data_t;
-using std::cout;
-
-auto main() -> int {
-  cout << " ********** Example Gaussian LDS Control ********** \n\n";
-
-  // Make 1st-order SISO system, sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;
-  size_t n_x = 1;
-  size_t n_y = 1;
-
-  // no time steps for simulation.
-  auto n_t = static_cast<size_t>(5.0 / dt);
-
-  // construct ground truth system to be controlled...
-  // initializes to random walk model with top-most n_y state observed
-  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);
-
-  // Ground-truth parameters for the controlled system
-  // (stand-in for physical system to be controlled)
-  Matrix a_true(n_x, n_x, arma::fill::eye);
-  a_true[0] = exp(-dt / 0.01);
-  Matrix b_true = Matrix(n_x, n_u).fill(2e-4);
-  // control signal to model input unit conversion e.g., V -> mW/mm2:
-  Vector g_true = Vector(n_y).fill(10.0);
-
-  // output noise covariance
-  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;
-
-  size_t which_m = 0;  // whether low or high disturbance (0, 1)
-  data_t m_low = 5 * dt * (1 - a_true[0]);
-  data_t pr_lo2hi = 1e-3;  // probability of going from low to high disturb.
-  data_t m_high = 20 * dt * (1 - a_true[0]);
-  data_t pr_hi2lo = pr_lo2hi;
-
-  // initially let m be low
-  Vector m0_true = Vector(n_x).fill(m_low);
-
-  // Assign params.
-  controlled_system.set_A(a_true);
-  controlled_system.set_B(b_true);
-  controlled_system.set_m(m0_true);
-  controlled_system.set_g(g_true);
-  controlled_system.set_R(r_true);
-
-  cout << ".....................................\n";
-  cout << "controlled_system:\n";
-  cout << ".....................................\n";
-  controlled_system.Print();
-  cout << ".....................................\n";
-
-  // make a controller
-  lds::gaussian::Controller controller;
-  {
-    // Create **incorrect** model used for control.
-    // (e.g., imperfect model fitting)
-    Matrix b_controller = b_true / 2;
-
-    // let's assume zero process disturbance initially
-    // (will be re-estimating)
-    Vector m_controller = Vector(n_x, arma::fill::zeros);
-
-    // for this demo, just use arbitrary default R
-    Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0;
-
-    lds::gaussian::System controller_system(controlled_system);
-    controller_system.set_B(b_controller);
-    controller_system.set_m(m_controller);
-    controller_system.set_R(r_controller);
-    controller_system.Reset();  // reset to new m
-
-    // going to adaptively re-estimate the disturbance
-    controller_system.do_adapt_m = true;
-
-    // set adaptation rate by changing covariance of assumed process noise
-    // acting on random-walk evolution of m
-    Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6;
-    controller_system.set_Q_m(q_m);
-
-    // create controller
-    // lower and upper bounds on control signal (e.g., in Volts)
-    data_t u_lb = 0.0;  // [=] V
-    data_t u_ub = 5.0;  // [=] V
-    controller = std::move(
-        lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub));
-  }
-
-  // Control variables:
-  // if following enabled, adapts set point with re-estimated process
-  // disturbance n.b., should not need integral action if this is enabled as the
-  // adaptive estimator minimizes DC error
-  bool do_adaptive_set_point = false;
-
-  // Reference/target output, controller gains
-  Vector y_ref0 = Vector(n_y).fill(20.0 * dt);
-  Matrix k_x = Matrix(n_u, n_x).fill(100);     // gains on state error
-  Matrix k_inty = Matrix(n_u, n_y).fill(1e3);  // gains on integrated err
-
-  // setting initial state to target to avoid error at onset:
-  Vector x0 = Vector(n_x).fill(y_ref0[0]);
-
-  // set up controller type bit mask so controller knows how to proceed
-  size_t control_type = 0;
-  if (do_adaptive_set_point) {
-    // adapt set point with estimated disturbance
-    control_type = control_type | lds::kControlTypeAdaptM;
-  } else {
-    // use integral action to minimize DC error
-    control_type = control_type | lds::kControlTypeIntY;
-  }
-
-  // set controller type
-  controller.set_control_type(control_type);
-
-  // Let's say these controller gains were designed assuming g was 9 V/(mW/mm2):
-  Vector g_design = Vector(n_u).fill(9);
-
-  // Set params.
-  // **n.b. using arbitrary defaults for Q, R in this example. Really, these
-  // should be set by users, as they tune characteristics of Kalman filter.
-  // Users can also choose not to recursively calculate the estimator gain and
-  // supply it (setKe) instead of covariances.**
-  controller.set_y_ref(y_ref0);
-  controller.set_Kc(k_x);
-  controller.set_Kc_inty(k_inty);
-  controller.set_g_design(g_design);
-
-  cout << ".....................................\n";
-  cout << "control system:\n";
-  cout << ".....................................\n";
-  controller.Print();
-  cout << ".....................................\n";
-
-  // set up variables for simulation
-  // create Matrix to save outputs in...
-  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];
-
-  // Simulated measurements
-  Matrix z(n_y, n_t, arma::fill::zeros);
-
-  // simulated control signal ([=] V)
-  Matrix u(n_u, n_t, arma::fill::zeros);
-
-  // outputs, states and gain/disturbance params
-  // *_hat indicates online estimates
-  Matrix y_hat(n_y, n_t, arma::fill::zeros);
-  Matrix x_hat(n_x, n_t, arma::fill::zeros);
-  Matrix m_hat(n_x, n_t, arma::fill::zeros);
-
-  // *_true indicates ground truth (system being controlled)
-  Matrix y_true(n_y, n_t, arma::fill::zeros);
-  Matrix x_true(n_x, n_t, arma::fill::zeros);
-  Matrix m_true(n_x, n_t, arma::fill::zeros);
-
-  // set initial val
-  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y();
-  y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();
-
-  x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x();
-  x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();
-
-  m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m();
-  m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();
-
-  cout << "Starting " << n_t * dt << " sec simulation ... \n";
-  auto start = std::chrono::high_resolution_clock::now();
-  for (size_t t = 1; t < n_t; t++) {
-    // simulate a stochastically switched disturbance
-    Vector chance = arma::randu<Vector>(1);
-    if (which_m == 0)  // low disturbance
-    {
-      if (chance[0] < pr_lo2hi) {  // switches low -> high disturbance
-        m0_true = std::vector<data_t>(n_x, m_high);
-        which_m = 1;
-      }
-    } else {                       // high disturbance
-      if (chance[0] < pr_hi2lo) {  // swithces high -> low disturbance
-        m0_true = std::vector<data_t>(n_x, m_low);
-        which_m = 0;
-      }
-    }
-    controlled_system.set_m(m0_true);
-
-    // input
-    Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true);
-
-    // Simulate the true system.
-    z.col(t) = controlled_system.Simulate(u_tm1);
-
-    // This method uses a steady-state solution to control problem to calculate
-    // x_ref, u_ref from reference output y_ref. Therefore, it is only
-    // applicable to regulation problems or cases where reference trajectory
-    // changes slowly compared to system dynamics.
-    u.col(t) = controller.ControlOutputReference(z.col(t));
-
-    // save the signals
-    y_true.col(t) = controlled_system.y();
-    x_true.col(t) = controlled_system.x();
-    m_true.col(t) = controlled_system.m();
-
-    y_hat.col(t) = controller.sys().y();
-    x_hat.col(t) = controller.sys().x();
-    m_hat.col(t) = controller.sys().m();
-  }
-
-  auto finish = std::chrono::high_resolution_clock::now();
-  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
-  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
-  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
-
-  cout << "Saving simulation data to disk.\n";
-
-  // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,
-  // xTrue, mTrue saving with hdf5 via armadillo
-  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
-
-  auto dt_vec = Vector(1).fill(dt);
-  dt_vec.save(arma::hdf5_name("eg_glds_ctrl.h5", "dt"));
-  y_ref.save(arma::hdf5_name("eg_glds_ctrl.h5", "y_ref", replace));
-  u.save(arma::hdf5_name("eg_glds_ctrl.h5", "u", replace));
-  z.save(arma::hdf5_name("eg_glds_ctrl.h5", "z", replace));
-  x_true.save(arma::hdf5_name("eg_glds_ctrl.h5", "x_true", replace));
-  m_true.save(arma::hdf5_name("eg_glds_ctrl.h5", "m_true", replace));
-  y_true.save(arma::hdf5_name("eg_glds_ctrl.h5", "y_true", replace));
-  x_hat.save(arma::hdf5_name("eg_glds_ctrl.h5", "x_hat", replace));
-  m_hat.save(arma::hdf5_name("eg_glds_ctrl.h5", "m_hat", replace));
-  y_hat.save(arma::hdf5_name("eg_glds_ctrl.h5", "y_hat", replace));
-
-  cout << "fin.\n";
-  return 0;
-}
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
+---
+title: examples/eg_glds_ctrl.cpp
+
+---
+
+# examples/eg_glds_ctrl.cpp
+
+
+
+## Types
+
+|                | Name           |
+| -------------- | -------------- |
+| using arma::Mat< data_t > | **[Matrix](/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#using-matrix)**  |
+| using arma::Col< data_t > | **[Vector](/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#using-vector)**  |
+| using double | **[data_t](/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#using-data-t)**  |
+
+## Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| int | **[main](/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#function-main)**() |
+
+## Type Details
+
+### Matrix
+
+```cpp
+using lds::Matrix =  arma::Mat<data_t>;
+```
+
+
+
+### Vector
+
+```cpp
+using lds::Vector =  arma::Col<data_t>;
+```
+
+
+
+### data_t
+
+```cpp
+using lds::data_t =  double;
+```
+
+
+
+Type of all data in library. If need 32b, change `double` to `float`. This could be potentially useful for large scale problems where there are memory constraints. 
+
+
+
+## Function Details
+
+### main
+
+```cpp
+int main()
+```
+
+
+
+Going to simulate a switching disturbance (m) acting on system
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#include <ldsCtrlEst>
+
+using lds::Matrix;
+using lds::Vector;
+using lds::data_t;
+using std::cout;
+
+auto main() -> int {
+  cout << " ********** Example Gaussian LDS Control ********** \n\n";
+
+  // Make 1st-order SISO system, sampled at 1kHz
+  data_t dt = 1e-3;
+  size_t n_u = 1;
+  size_t n_x = 1;
+  size_t n_y = 1;
+
+  // no time steps for simulation.
+  auto n_t = static_cast<size_t>(5.0 / dt);
+
+  // construct ground truth system to be controlled...
+  // initializes to random walk model with top-most n_y state observed
+  lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);
+
+  // Ground-truth parameters for the controlled system
+  // (stand-in for physical system to be controlled)
+  Matrix a_true(n_x, n_x, arma::fill::eye);
+  a_true[0] = exp(-dt / 0.01);
+  Matrix b_true = Matrix(n_x, n_u).fill(2e-4);
+  // control signal to model input unit conversion e.g., V -> mW/mm2:
+  Vector g_true = Vector(n_y).fill(10.0);
+
+  // output noise covariance
+  Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;
+
+  size_t which_m = 0;  // whether low or high disturbance (0, 1)
+  data_t m_low = 5 * dt * (1 - a_true[0]);
+  data_t pr_lo2hi = 1e-3;  // probability of going from low to high disturb.
+  data_t m_high = 20 * dt * (1 - a_true[0]);
+  data_t pr_hi2lo = pr_lo2hi;
+
+  // initially let m be low
+  Vector m0_true = Vector(n_x).fill(m_low);
+
+  // Assign params.
+  controlled_system.set_A(a_true);
+  controlled_system.set_B(b_true);
+  controlled_system.set_m(m0_true);
+  controlled_system.set_g(g_true);
+  controlled_system.set_R(r_true);
+
+  cout << ".....................................\n";
+  cout << "controlled_system:\n";
+  cout << ".....................................\n";
+  controlled_system.Print();
+  cout << ".....................................\n";
+
+  // make a controller
+  lds::gaussian::Controller controller;
+  {
+    // Create **incorrect** model used for control.
+    // (e.g., imperfect model fitting)
+    Matrix b_controller = b_true / 2;
+
+    // let's assume zero process disturbance initially
+    // (will be re-estimating)
+    Vector m_controller = Vector(n_x, arma::fill::zeros);
+
+    // for this demo, just use arbitrary default R
+    Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0;
+
+    lds::gaussian::System controller_system(controlled_system);
+    controller_system.set_B(b_controller);
+    controller_system.set_m(m_controller);
+    controller_system.set_R(r_controller);
+    controller_system.Reset();  // reset to new m
+
+    // going to adaptively re-estimate the disturbance
+    controller_system.do_adapt_m = true;
+
+    // set adaptation rate by changing covariance of assumed process noise
+    // acting on random-walk evolution of m
+    Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6;
+    controller_system.set_Q_m(q_m);
+
+    // create controller
+    // lower and upper bounds on control signal (e.g., in Volts)
+    data_t u_lb = 0.0;  // [=] V
+    data_t u_ub = 5.0;  // [=] V
+    controller = std::move(
+        lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub));
+  }
+
+  // Control variables:
+  // if following enabled, adapts set point with re-estimated process
+  // disturbance n.b., should not need integral action if this is enabled as the
+  // adaptive estimator minimizes DC error
+  bool do_adaptive_set_point = false;
+
+  // Reference/target output, controller gains
+  Vector y_ref0 = Vector(n_y).fill(20.0 * dt);
+  Matrix k_x = Matrix(n_u, n_x).fill(100);     // gains on state error
+  Matrix k_inty = Matrix(n_u, n_y).fill(1e3);  // gains on integrated err
+
+  // setting initial state to target to avoid error at onset:
+  Vector x0 = Vector(n_x).fill(y_ref0[0]);
+
+  // set up controller type bit mask so controller knows how to proceed
+  size_t control_type = 0;
+  if (do_adaptive_set_point) {
+    // adapt set point with estimated disturbance
+    control_type = control_type | lds::kControlTypeAdaptM;
+  } else {
+    // use integral action to minimize DC error
+    control_type = control_type | lds::kControlTypeIntY;
+  }
+
+  // set controller type
+  controller.set_control_type(control_type);
+
+  // Let's say these controller gains were designed assuming g was 9 V/(mW/mm2):
+  Vector g_design = Vector(n_u).fill(9);
+
+  // Set params.
+  // **n.b. using arbitrary defaults for Q, R in this example. Really, these
+  // should be set by users, as they tune characteristics of Kalman filter.
+  // Users can also choose not to recursively calculate the estimator gain and
+  // supply it (setKe) instead of covariances.**
+  controller.set_y_ref(y_ref0);
+  controller.set_Kc(k_x);
+  controller.set_Kc_inty(k_inty);
+  controller.set_g_design(g_design);
+
+  cout << ".....................................\n";
+  cout << "control system:\n";
+  cout << ".....................................\n";
+  controller.Print();
+  cout << ".....................................\n";
+
+  // set up variables for simulation
+  // create Matrix to save outputs in...
+  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];
+
+  // Simulated measurements
+  Matrix z(n_y, n_t, arma::fill::zeros);
+
+  // simulated control signal ([=] V)
+  Matrix u(n_u, n_t, arma::fill::zeros);
+
+  // outputs, states and gain/disturbance params
+  // *_hat indicates online estimates
+  Matrix y_hat(n_y, n_t, arma::fill::zeros);
+  Matrix x_hat(n_x, n_t, arma::fill::zeros);
+  Matrix m_hat(n_x, n_t, arma::fill::zeros);
+
+  // *_true indicates ground truth (system being controlled)
+  Matrix y_true(n_y, n_t, arma::fill::zeros);
+  Matrix x_true(n_x, n_t, arma::fill::zeros);
+  Matrix m_true(n_x, n_t, arma::fill::zeros);
+
+  // set initial val
+  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y();
+  y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();
+
+  x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x();
+  x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();
+
+  m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m();
+  m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();
+
+  cout << "Starting " << n_t * dt << " sec simulation ... \n";
+  auto start = std::chrono::high_resolution_clock::now();
+  for (size_t t = 1; t < n_t; t++) {
+    // simulate a stochastically switched disturbance
+    Vector chance = arma::randu<Vector>(1);
+    if (which_m == 0)  // low disturbance
+    {
+      if (chance[0] < pr_lo2hi) {  // switches low -> high disturbance
+        m0_true = std::vector<data_t>(n_x, m_high);
+        which_m = 1;
+      }
+    } else {                       // high disturbance
+      if (chance[0] < pr_hi2lo) {  // swithces high -> low disturbance
+        m0_true = std::vector<data_t>(n_x, m_low);
+        which_m = 0;
+      }
+    }
+    controlled_system.set_m(m0_true);
+
+    // input
+    Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true);
+
+    // Simulate the true system.
+    z.col(t) = controlled_system.Simulate(u_tm1);
+
+    // This method uses a steady-state solution to control problem to calculate
+    // x_ref, u_ref from reference output y_ref. Therefore, it is only
+    // applicable to regulation problems or cases where reference trajectory
+    // changes slowly compared to system dynamics.
+    u.col(t) = controller.ControlOutputReference(z.col(t));
+
+    // save the signals
+    y_true.col(t) = controlled_system.y();
+    x_true.col(t) = controlled_system.x();
+    m_true.col(t) = controlled_system.m();
+
+    y_hat.col(t) = controller.sys().y();
+    x_hat.col(t) = controller.sys().x();
+    m_hat.col(t) = controller.sys().m();
+  }
+
+  auto finish = std::chrono::high_resolution_clock::now();
+  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
+  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
+  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
+
+  cout << "Saving simulation data to disk.\n";
+
+  // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,
+  // xTrue, mTrue saving with hdf5 via armadillo
+  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
+
+  auto dt_vec = Vector(1).fill(dt);
+  dt_vec.save(arma::hdf5_name("eg_glds_ctrl.h5", "dt"));
+  y_ref.save(arma::hdf5_name("eg_glds_ctrl.h5", "y_ref", replace));
+  u.save(arma::hdf5_name("eg_glds_ctrl.h5", "u", replace));
+  z.save(arma::hdf5_name("eg_glds_ctrl.h5", "z", replace));
+  x_true.save(arma::hdf5_name("eg_glds_ctrl.h5", "x_true", replace));
+  m_true.save(arma::hdf5_name("eg_glds_ctrl.h5", "m_true", replace));
+  y_true.save(arma::hdf5_name("eg_glds_ctrl.h5", "y_true", replace));
+  x_hat.save(arma::hdf5_name("eg_glds_ctrl.h5", "x_hat", replace));
+  m_hat.save(arma::hdf5_name("eg_glds_ctrl.h5", "m_hat", replace));
+  y_hat.save(arma::hdf5_name("eg_glds_ctrl.h5", "y_hat", replace));
+
+  cout << "fin.\n";
+  return 0;
+}
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/eg__glds__du__plds__ctrl_8cpp.md b/misc/docs-hugo/content/docs/api/Files/eg__glds__du__plds__ctrl_8cpp.md
index 71cef5d6..c5958668 100644
--- a/misc/docs-hugo/content/docs/api/Files/eg__glds__du__plds__ctrl_8cpp.md
+++ b/misc/docs-hugo/content/docs/api/Files/eg__glds__du__plds__ctrl_8cpp.md
@@ -1,298 +1,335 @@
----
-title: examples/eg_glds_du_plds_ctrl.cpp
-
----
-
-# examples/eg_glds_du_plds_ctrl.cpp
-
-
-
-## Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| auto | **[main](/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#function-main)**() |
-
-
-## Function Details
-
-### main
-
-```cpp
-auto main()
-```
-
-
-
-Going to simulate a switching disturbance (m) acting on system 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst>
-
-using lds::data_t;
-using lds::Matrix;
-using lds::Vector;
-using std::cout;
-
-auto main() -> int {
-  cout << " ********** Example Gaussian LDS du Control of PLDS ********** \n\n";
-
-  // Make 1st-order SISO system, sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;
-  size_t n_x = 1;
-  size_t n_y = 1;
-
-  // no time steps for simulation.
-  auto n_t = static_cast<size_t>(5.0 / dt);
-
-  // construct ground truth system to be controlled...
-  // initializes to random walk model with top-most n_y state observed
-  lds::poisson::System controlled_system(n_u, n_x, n_y, dt);
-
-  // Ground-truth parameters for the controlled system
-  // (stand-in for physical system to be controlled)
-  Matrix a_true(n_x, n_x, arma::fill::eye);
-  a_true[0] = exp(-dt / 0.01);
-  Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2);
-  // control signal to model input unit conversion e.g., V -> mW/mm2:
-  Vector g_true = Vector(n_y).fill(10.0);
-
-  size_t which_m = 0;  // whether low or high disturbance (0, 1)
-  data_t m_low = log(1 * dt) * (1 - a_true[0]);
-  data_t pr_lo2hi =
-      0;  // 1e-3;  // probability of going from low to high disturb.
-  data_t m_high = log(20 * dt) * (1 - a_true[0]);
-  data_t pr_hi2lo = pr_lo2hi;
-
-  // initially let m be low
-  Vector m0_true = Vector(n_x).fill(m_low);
-  Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);
-
-  // Assign params.
-  controlled_system.set_A(a_true);
-  controlled_system.set_B(b_true);
-  controlled_system.set_m(m0_true);
-  controlled_system.set_g(g_true);
-  controlled_system.set_x0(x0_true);
-  controlled_system.Reset();
-
-  cout << ".....................................\n";
-  cout << "controlled_system:\n";
-  cout << ".....................................\n";
-  controlled_system.Print();
-  cout << ".....................................\n";
-
-  // make a controller
-  lds::gaussian::Controller controller;
-  {
-    // Create **incorrect** model used for control.
-    // (e.g., imperfect model fitting)
-    Matrix b_controller = b_true / 50;
-
-    // let's assume zero process disturbance initially
-    // (will be re-estimating)
-    Vector m_controller = Vector(n_x, arma::fill::zeros);
-
-    // process noise covariance
-    Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8;
-
-    // output noise covariance
-    Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2;
-
-    lds::gaussian::System controller_system(n_u, n_x, n_y, dt);
-    controller_system.set_A(a_true);
-    controller_system.set_B(b_controller);
-    controller_system.set_g(g_true);
-    controller_system.set_m(m_controller);
-    controller_system.set_Q(q_controller);
-    controller_system.set_R(r_controller);
-    controller_system.Reset();  // reset to new m
-
-    // going to adaptively re-estimate the disturbance
-    controller_system.do_adapt_m = true;
-
-    // set adaptation rate by changing covariance of assumed process noise
-    // acting on random-walk evolution of m
-    Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8;
-    controller_system.set_Q_m(q_m);
-
-    // create controller
-    // lower and upper bounds on control signal (e.g., in Volts)
-    data_t u_lb = 0.0;  // [=] V
-    data_t u_ub = 5.0;  // [=] V
-    controller = std::move(
-        lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub));
-  }
-
-  // Control variables:
-  // Reference/target output, controller gains
-  Vector y_ref0 = Vector(n_y).fill(20.0 * dt);
-
-  // to design for this example, augmented state with control and made the input
-  // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du =
-  // 1e-2.
-  Matrix k_x = Matrix(n_u, n_x).fill(2.44);     // gains on state error
-  Matrix k_inty = Matrix(n_u, n_y).fill(97.4);  // gains on integrated err
-  Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2);  // gains on input amp
-
-  // set up controller type bit mask so controller knows how to proceed
-  size_t control_type = 0;
-  // use integral action to minimize DC error
-  control_type = control_type | lds::kControlTypeIntY;
-  // update change in control (LP filters control)
-  control_type = control_type | lds::kControlTypeDeltaU;
-
-  // set controller type
-  controller.set_control_type(control_type);
-
-  // Let's say these controller gains were designed assuming g was 9 V/(mW/mm2):
-  Vector g_design = Vector(n_u).fill(10);
-
-  // Set params.
-  // **n.b. using arbitrary defaults for Q, R in this example. Really, these
-  // should be set by users, as they tune characteristics of Kalman filter.
-  // Users can also choose not to recursively calculate the estimator gain and
-  // supply it (setKe) instead of covariances.**
-  controller.set_y_ref(y_ref0);
-  controller.set_Kc(k_x);
-  controller.set_Kc_inty(k_inty);
-  controller.set_Kc_u(k_u);
-  controller.set_g_design(g_design);
-
-  cout << ".....................................\n";
-  cout << "control system:\n";
-  cout << ".....................................\n";
-  controller.Print();
-  cout << ".....................................\n";
-
-  // set up variables for simulation
-  // create Matrix to save outputs in...
-  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];
-
-  // Simulated measurements
-  Matrix z(n_y, n_t, arma::fill::zeros);
-
-  // simulated control signal ([=] V)
-  Matrix u(n_u, n_t, arma::fill::zeros);
-
-  // outputs, states and gain/disturbance params
-  // *_hat indicates online estimates
-  Matrix y_hat(n_y, n_t, arma::fill::zeros);
-  Matrix x_hat(n_x, n_t, arma::fill::zeros);
-  Matrix m_hat(n_x, n_t, arma::fill::zeros);
-
-  // *_true indicates ground truth (system being controlled)
-  Matrix y_true(n_y, n_t, arma::fill::zeros);
-  Matrix x_true(n_x, n_t, arma::fill::zeros);
-  Matrix m_true(n_x, n_t, arma::fill::zeros);
-
-  // get initial val
-  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y();
-  y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();
-
-  x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x();
-  x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();
-
-  m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m();
-  m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();
-
-  cout << "Starting " << n_t * dt << " sec simulation ... \n";
-  auto start = std::chrono::high_resolution_clock::now();
-  for (size_t t = 1; t < n_t; t++) {
-    // simulate a stochastically switched disturbance
-    Vector chance = arma::randu<Vector>(1);
-    if (which_m == 0)  // low disturbance
-    {
-      if (chance[0] < pr_lo2hi) {  // switches low -> high disturbance
-        m0_true = std::vector<data_t>(n_x, m_high);
-        which_m = 1;
-      }
-    } else {                       // high disturbance
-      if (chance[0] < pr_hi2lo) {  // swithces high -> low disturbance
-        m0_true = std::vector<data_t>(n_x, m_low);
-        which_m = 0;
-      }
-    }
-    controlled_system.set_m(m0_true);
-
-    // input
-    Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true);
-
-    // Simulate the true system.
-    z.col(t) = controlled_system.Simulate(u_tm1);
-
-    // This method uses a steady-state solution to control problem to calculate
-    // x_ref, u_ref from reference output y_ref. Therefore, it is only
-    // applicable to regulation problems or cases where reference trajectory
-    // changes slowly compared to system dynamics.
-    u.col(t) = controller.ControlOutputReference(z.col(t));
-
-    // save the signals
-    y_true.col(t) = controlled_system.y();
-    x_true.col(t) = controlled_system.x();
-    m_true.col(t) = controlled_system.m();
-
-    y_hat.col(t) = controller.sys().y();
-    x_hat.col(t) = controller.sys().x();
-    m_hat.col(t) = controller.sys().m();
-  }
-
-  auto finish = std::chrono::high_resolution_clock::now();
-  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
-  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
-  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
-
-  cout << "Saving simulation data to disk.\n";
-
-  // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,
-  // xTrue, mTrue saving with hdf5 via armadillo
-  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
-
-  auto dt_vec = Vector(1).fill(dt);
-  dt_vec.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "dt"));
-  y_ref.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "y_ref", replace));
-  u.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "u", replace));
-  z.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "z", replace));
-  x_true.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "x_true", replace));
-  m_true.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "m_true", replace));
-  y_true.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "y_true", replace));
-  x_hat.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "x_hat", replace));
-  m_hat.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "m_hat", replace));
-  y_hat.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "y_hat", replace));
-
-  cout << "fin.\n";
-  return 0;
-}
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
+---
+title: examples/eg_glds_du_plds_ctrl.cpp
+
+---
+
+# examples/eg_glds_du_plds_ctrl.cpp
+
+
+
+## Types
+
+|                | Name           |
+| -------------- | -------------- |
+| using double | **[data_t](/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#using-data-t)**  |
+| using arma::Mat< data_t > | **[Matrix](/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#using-matrix)**  |
+| using arma::Col< data_t > | **[Vector](/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#using-vector)**  |
+
+## Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| int | **[main](/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#function-main)**() |
+
+## Type Details
+
+### data_t
+
+```cpp
+using lds::data_t =  double;
+```
+
+
+
+Type of all data in library. If need 32b, change `double` to `float`. This could be potentially useful for large scale problems where there are memory constraints. 
+
+
+### Matrix
+
+```cpp
+using lds::Matrix =  arma::Mat<data_t>;
+```
+
+
+
+### Vector
+
+```cpp
+using lds::Vector =  arma::Col<data_t>;
+```
+
+
+
+
+## Function Details
+
+### main
+
+```cpp
+int main()
+```
+
+
+
+Going to simulate a switching disturbance (m) acting on system
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#include <ldsCtrlEst>
+
+using lds::data_t;
+using lds::Matrix;
+using lds::Vector;
+using std::cout;
+
+auto main() -> int {
+  cout << " ********** Example Gaussian LDS du Control of PLDS ********** \n\n";
+
+  // Make 1st-order SISO system, sampled at 1kHz
+  data_t dt = 1e-3;
+  size_t n_u = 1;
+  size_t n_x = 1;
+  size_t n_y = 1;
+
+  // no time steps for simulation.
+  auto n_t = static_cast<size_t>(5.0 / dt);
+
+  // construct ground truth system to be controlled...
+  // initializes to random walk model with top-most n_y state observed
+  lds::poisson::System controlled_system(n_u, n_x, n_y, dt);
+
+  // Ground-truth parameters for the controlled system
+  // (stand-in for physical system to be controlled)
+  Matrix a_true(n_x, n_x, arma::fill::eye);
+  a_true[0] = exp(-dt / 0.01);
+  Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2);
+  // control signal to model input unit conversion e.g., V -> mW/mm2:
+  Vector g_true = Vector(n_y).fill(10.0);
+
+  size_t which_m = 0;  // whether low or high disturbance (0, 1)
+  data_t m_low = log(1 * dt) * (1 - a_true[0]);
+  data_t pr_lo2hi =
+      0;  // 1e-3;  // probability of going from low to high disturb.
+  data_t m_high = log(20 * dt) * (1 - a_true[0]);
+  data_t pr_hi2lo = pr_lo2hi;
+
+  // initially let m be low
+  Vector m0_true = Vector(n_x).fill(m_low);
+  Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);
+
+  // Assign params.
+  controlled_system.set_A(a_true);
+  controlled_system.set_B(b_true);
+  controlled_system.set_m(m0_true);
+  controlled_system.set_g(g_true);
+  controlled_system.set_x0(x0_true);
+  controlled_system.Reset();
+
+  cout << ".....................................\n";
+  cout << "controlled_system:\n";
+  cout << ".....................................\n";
+  controlled_system.Print();
+  cout << ".....................................\n";
+
+  // make a controller
+  lds::gaussian::Controller controller;
+  {
+    // Create **incorrect** model used for control.
+    // (e.g., imperfect model fitting)
+    Matrix b_controller = b_true / 50;
+
+    // let's assume zero process disturbance initially
+    // (will be re-estimating)
+    Vector m_controller = Vector(n_x, arma::fill::zeros);
+
+    // process noise covariance
+    Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8;
+
+    // output noise covariance
+    Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2;
+
+    lds::gaussian::System controller_system(n_u, n_x, n_y, dt);
+    controller_system.set_A(a_true);
+    controller_system.set_B(b_controller);
+    controller_system.set_g(g_true);
+    controller_system.set_m(m_controller);
+    controller_system.set_Q(q_controller);
+    controller_system.set_R(r_controller);
+    controller_system.Reset();  // reset to new m
+
+    // going to adaptively re-estimate the disturbance
+    controller_system.do_adapt_m = true;
+
+    // set adaptation rate by changing covariance of assumed process noise
+    // acting on random-walk evolution of m
+    Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8;
+    controller_system.set_Q_m(q_m);
+
+    // create controller
+    // lower and upper bounds on control signal (e.g., in Volts)
+    data_t u_lb = 0.0;  // [=] V
+    data_t u_ub = 5.0;  // [=] V
+    controller = std::move(
+        lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub));
+  }
+
+  // Control variables:
+  // Reference/target output, controller gains
+  Vector y_ref0 = Vector(n_y).fill(20.0 * dt);
+
+  // to design for this example, augmented state with control and made the input
+  // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du =
+  // 1e-2.
+  Matrix k_x = Matrix(n_u, n_x).fill(2.44);     // gains on state error
+  Matrix k_inty = Matrix(n_u, n_y).fill(97.4);  // gains on integrated err
+  Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2);  // gains on input amp
+
+  // set up controller type bit mask so controller knows how to proceed
+  size_t control_type = 0;
+  // use integral action to minimize DC error
+  control_type = control_type | lds::kControlTypeIntY;
+  // update change in control (LP filters control)
+  control_type = control_type | lds::kControlTypeDeltaU;
+
+  // set controller type
+  controller.set_control_type(control_type);
+
+  // Let's say these controller gains were designed assuming g was 9 V/(mW/mm2):
+  Vector g_design = Vector(n_u).fill(10);
+
+  // Set params.
+  // **n.b. using arbitrary defaults for Q, R in this example. Really, these
+  // should be set by users, as they tune characteristics of Kalman filter.
+  // Users can also choose not to recursively calculate the estimator gain and
+  // supply it (setKe) instead of covariances.**
+  controller.set_y_ref(y_ref0);
+  controller.set_Kc(k_x);
+  controller.set_Kc_inty(k_inty);
+  controller.set_Kc_u(k_u);
+  controller.set_g_design(g_design);
+
+  cout << ".....................................\n";
+  cout << "control system:\n";
+  cout << ".....................................\n";
+  controller.Print();
+  cout << ".....................................\n";
+
+  // set up variables for simulation
+  // create Matrix to save outputs in...
+  Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];
+
+  // Simulated measurements
+  Matrix z(n_y, n_t, arma::fill::zeros);
+
+  // simulated control signal ([=] V)
+  Matrix u(n_u, n_t, arma::fill::zeros);
+
+  // outputs, states and gain/disturbance params
+  // *_hat indicates online estimates
+  Matrix y_hat(n_y, n_t, arma::fill::zeros);
+  Matrix x_hat(n_x, n_t, arma::fill::zeros);
+  Matrix m_hat(n_x, n_t, arma::fill::zeros);
+
+  // *_true indicates ground truth (system being controlled)
+  Matrix y_true(n_y, n_t, arma::fill::zeros);
+  Matrix x_true(n_x, n_t, arma::fill::zeros);
+  Matrix m_true(n_x, n_t, arma::fill::zeros);
+
+  // get initial val
+  y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y();
+  y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();
+
+  x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x();
+  x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();
+
+  m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m();
+  m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();
+
+  cout << "Starting " << n_t * dt << " sec simulation ... \n";
+  auto start = std::chrono::high_resolution_clock::now();
+  for (size_t t = 1; t < n_t; t++) {
+    // simulate a stochastically switched disturbance
+    Vector chance = arma::randu<Vector>(1);
+    if (which_m == 0)  // low disturbance
+    {
+      if (chance[0] < pr_lo2hi) {  // switches low -> high disturbance
+        m0_true = std::vector<data_t>(n_x, m_high);
+        which_m = 1;
+      }
+    } else {                       // high disturbance
+      if (chance[0] < pr_hi2lo) {  // swithces high -> low disturbance
+        m0_true = std::vector<data_t>(n_x, m_low);
+        which_m = 0;
+      }
+    }
+    controlled_system.set_m(m0_true);
+
+    // input
+    Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true);
+
+    // Simulate the true system.
+    z.col(t) = controlled_system.Simulate(u_tm1);
+
+    // This method uses a steady-state solution to control problem to calculate
+    // x_ref, u_ref from reference output y_ref. Therefore, it is only
+    // applicable to regulation problems or cases where reference trajectory
+    // changes slowly compared to system dynamics.
+    u.col(t) = controller.ControlOutputReference(z.col(t));
+
+    // save the signals
+    y_true.col(t) = controlled_system.y();
+    x_true.col(t) = controlled_system.x();
+    m_true.col(t) = controlled_system.m();
+
+    y_hat.col(t) = controller.sys().y();
+    x_hat.col(t) = controller.sys().x();
+    m_hat.col(t) = controller.sys().m();
+  }
+
+  auto finish = std::chrono::high_resolution_clock::now();
+  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
+  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
+  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
+
+  cout << "Saving simulation data to disk.\n";
+
+  // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,
+  // xTrue, mTrue saving with hdf5 via armadillo
+  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
+
+  auto dt_vec = Vector(1).fill(dt);
+  dt_vec.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "dt"));
+  y_ref.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "y_ref", replace));
+  u.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "u", replace));
+  z.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "z", replace));
+  x_true.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "x_true", replace));
+  m_true.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "m_true", replace));
+  y_true.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "y_true", replace));
+  x_hat.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "x_hat", replace));
+  m_hat.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "m_hat", replace));
+  y_hat.save(arma::hdf5_name("eg_glds_du_plds_ctrl.h5", "y_hat", replace));
+
+  cout << "fin.\n";
+  return 0;
+}
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/eg__plds__ctrl_8cpp.md b/misc/docs-hugo/content/docs/api/Files/eg__plds__ctrl_8cpp.md
index 87aab564..1fc2bc79 100644
--- a/misc/docs-hugo/content/docs/api/Files/eg__plds__ctrl_8cpp.md
+++ b/misc/docs-hugo/content/docs/api/Files/eg__plds__ctrl_8cpp.md
@@ -1,267 +1,304 @@
----
-title: examples/eg_plds_ctrl.cpp
-
----
-
-# examples/eg_plds_ctrl.cpp
-
-
-
-## Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| auto | **[main](/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#function-main)**() |
-
-
-## Function Details
-
-### main
-
-```cpp
-auto main()
-```
-
-
-
-Going to simulate a switching disturbance (m) acting on system 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst>
-
-using lds::Matrix;
-using lds::Vector;
-using lds::data_t;
-using std::cout;
-
-auto main() -> int {
-  cout << " ********** Example Poisson LDS Control ********** \n\n";
-
-  // Make SISO system sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;
-  size_t n_x = 1;
-  size_t n_y = 1;
-
-  // no time steps for simulation.
-  auto n_t = static_cast<size_t>(10.0 / dt);
-
-  // Control variables: _reference/target output, controller gains
-  // n.b., Can either use Vector (arma::Col) or std::vector
-  Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt;
-  Matrix k_x =
-      Matrix(n_u, n_x, arma::fill::zeros) + 1;  // gains on state error
-  Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) +
-                   10;  // gains on integrated output err
-
-  // Set control type bit mask, so controller knows what to do
-  size_t control_type = lds::kControlTypeIntY;  // integral action
-
-  // Ground-truth parameters for the controlled system
-  // (stand-in for physical system to be controlled)
-  Matrix a_true(n_x, n_x, arma::fill::eye);
-  a_true[0] = 0.986;
-  Matrix b_true(n_x, n_u, arma::fill::zeros);
-  b_true[0] = 0.054;
-  Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);
-
-  size_t which_m = 0;
-  data_t m_low = log(1 * dt) * (1 - a_true[0]);
-  data_t pr_lo2hi = 1e-3;
-  data_t m_high = log(20 * dt) * (1 - a_true[0]);
-  data_t pr_hi2lo = pr_lo2hi;
-
-  Vector m0_true = Vector(n_x, arma::fill::ones) * m_low;
-  // construct ground truth system to be controlled...
-  lds::poisson::System controlled_system(n_u, n_x, n_y, dt);
-
-  // Assign params.
-  controlled_system.set_A(a_true);
-  controlled_system.set_B(b_true);
-  controlled_system.set_m(m0_true);
-  controlled_system.set_x0(x0_true);
-  // reset to initial conditions
-  controlled_system.Reset();
-
-  cout << ".....................................\n";
-  cout << "controlled_system:\n";
-  cout << ".....................................\n";
-  controlled_system.Print();
-  cout << ".....................................\n";
-
-  // Create the controller
-  lds::poisson::Controller controller;
-  {
-    // Create model used for control.
-    lds::poisson::System controller_system(controlled_system);
-
-    // for this example, assume model correct, except disturbance
-    Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low;
-    Vector x0_controller = arma::log(y_ref0);
-    controller_system.set_m(m0_controller);
-    controller_system.set_x0(x0_controller);
-    controller_system.Reset(); //reset to new init condition
-
-    // adaptively re-estimate process disturbance (m)
-    controller_system.do_adapt_m = true;
-
-    // set adaptation rate by changing covariance of assumed process noise
-    // acting on random-walk evolution of m
-    Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5;
-    controller_system.set_Q_m(q_m);
-
-    data_t u_lb = 0.0;
-    data_t u_ub = 5.0;
-    controller = std::move(
-        lds::poisson::Controller(std::move(controller_system), u_lb, u_ub));
-  }
-  // set controller type
-  controller.set_control_type(control_type);
-
-  // set controller gains
-  controller.set_Kc(k_x);
-  controller.set_Kc_inty(k_inty);
-
-  // to protect against integral windup when output is consistently above
-  // target:
-  data_t tau_awu(0.1);
-  controller.set_tau_awu(tau_awu);
-
-  cout << ".....................................\n";
-  cout << "controller:\n";
-  cout << ".....................................\n";
-  controller.Print();
-  cout << ".....................................\n";
-
-  // create Matrix to save outputs in...
-  Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros);
-  y_ref.each_col() += y_ref0;
-
-  // Simulated measurements
-  Matrix z(n_y, n_t, arma::fill::zeros);
-
-  // simulated control signal ([=] V)
-  Matrix u(n_u, n_t, arma::fill::zeros);
-
-  // outputs, states and gain/disturbance params
-  // *_hat indicates online estimates
-  Matrix y_hat(n_y, n_t, arma::fill::zeros);
-  Matrix x_hat(n_x, n_t, arma::fill::zeros);
-  Matrix m_hat(n_y, n_t, arma::fill::zeros);
-
-  // *_true indicates ground truth (system being controlled)
-  Matrix y_true(n_y, n_t, arma::fill::zeros);
-  Matrix x_true(n_x, n_t, arma::fill::zeros);
-  Matrix m_true(n_y, n_t, arma::fill::zeros);
-
-  // set initial val
-  y_hat.col(0) = controller.sys().y();
-  y_true.col(0) = controlled_system.y();
-
-  x_hat.col(0) = controller.sys().x();
-  x_true.col(0) = controlled_system.x();
-
-  m_hat.col(0) = controller.sys().m();
-  m_true.col(0) = controlled_system.m();
-
-  cout << "Starting " << n_t * dt << " sec simulation ... \n";
-  auto start = std::chrono::high_resolution_clock::now();
-  for (size_t t = 1; t < n_t; t++) {
-    // simulate a stochastically switched disturbance
-    Vector chance = arma::randu<Vector>(1);
-    if (which_m == 0)  // low disturbance
-    {
-      if (chance[0] < pr_lo2hi) {  // switches low -> high disturbance
-        m0_true = std::vector<data_t>(n_x, m_high);
-        which_m = 1;
-      }
-    } else {                       // high disturbance
-      if (chance[0] < pr_hi2lo) {  // swithces high -> low disturbance
-        m0_true = std::vector<data_t>(n_x, m_low);
-        which_m = 0;
-      }
-    }
-    controlled_system.set_m(m0_true);
-
-    // e.g., use sinusoidal reference
-    data_t f = 0.5;  // freq [=] Hz
-    Vector t_vec = Vector(n_y, arma::fill::ones) * t;
-    y_ref.col(t) +=
-        y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4);
-
-    // Simulate the true system.
-    z.col(t)=controlled_system.Simulate(u.col(t-1));
-
-    // This method uses a steady-state solution to control problem to calculate
-    // x_ref, u_ref from reference output y_ref. Notably, it does this in the
-    // log-linear space (i.e., log(y)).
-    //
-    // Therefore, it is only applicable to regulation problems or cases where
-    // reference trajectory changes slowly compared to system dynamics.
-    controller.set_y_ref(y_ref.col(t));
-    u.col(t)=controller.ControlOutputReference(z.col(t));
-
-    y_true.col(t) = controlled_system.y();
-    x_true.col(t) = controlled_system.x();
-    m_true.col(t) = controlled_system.m();
-
-    y_hat.col(t) = controller.sys().y();
-    x_hat.col(t) = controller.sys().x();
-    m_hat.col(t) = controller.sys().m();
-  }
-
-  auto finish = std::chrono::high_resolution_clock::now();
-  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
-  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
-  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
-
-  // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,
-  // x_true, m_true saving with hdf5 via armadillo
-  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
-
-  auto dt_vec = Vector(1).fill(dt);
-  dt_vec.save(arma::hdf5_name("eg_plds_ctrl.h5", "dt"));
-  y_ref.save(arma::hdf5_name("eg_plds_ctrl.h5", "y_ref", replace));
-  u.save(arma::hdf5_name("eg_plds_ctrl.h5", "u", replace));
-  z.save(arma::hdf5_name("eg_plds_ctrl.h5", "z", replace));
-  x_true.save(arma::hdf5_name("eg_plds_ctrl.h5", "x_true", replace));
-  m_true.save(arma::hdf5_name("eg_plds_ctrl.h5", "m_true", replace));
-  y_true.save(arma::hdf5_name("eg_plds_ctrl.h5", "y_true", replace));
-  x_hat.save(arma::hdf5_name("eg_plds_ctrl.h5", "x_hat", replace));
-  m_hat.save(arma::hdf5_name("eg_plds_ctrl.h5", "m_hat", replace));
-  y_hat.save(arma::hdf5_name("eg_plds_ctrl.h5", "y_hat", replace));
-
-  return 0;
-}
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
+---
+title: examples/eg_plds_ctrl.cpp
+
+---
+
+# examples/eg_plds_ctrl.cpp
+
+
+
+## Types
+
+|                | Name           |
+| -------------- | -------------- |
+| using arma::Mat< data_t > | **[Matrix](/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#using-matrix)**  |
+| using arma::Col< data_t > | **[Vector](/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#using-vector)**  |
+| using double | **[data_t](/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#using-data-t)**  |
+
+## Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| int | **[main](/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#function-main)**() |
+
+## Type Details
+
+### Matrix
+
+```cpp
+using lds::Matrix =  arma::Mat<data_t>;
+```
+
+
+
+### Vector
+
+```cpp
+using lds::Vector =  arma::Col<data_t>;
+```
+
+
+
+### data_t
+
+```cpp
+using lds::data_t =  double;
+```
+
+
+
+Type of all data in library. If need 32b, change `double` to `float`. This could be potentially useful for large scale problems where there are memory constraints. 
+
+
+
+## Function Details
+
+### main
+
+```cpp
+int main()
+```
+
+
+
+Going to simulate a switching disturbance (m) acting on system
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#include <ldsCtrlEst>
+
+using lds::Matrix;
+using lds::Vector;
+using lds::data_t;
+using std::cout;
+
+auto main() -> int {
+  cout << " ********** Example Poisson LDS Control ********** \n\n";
+
+  // Make SISO system sampled at 1kHz
+  data_t dt = 1e-3;
+  size_t n_u = 1;
+  size_t n_x = 1;
+  size_t n_y = 1;
+
+  // no time steps for simulation.
+  auto n_t = static_cast<size_t>(10.0 / dt);
+
+  // Control variables: _reference/target output, controller gains
+  // n.b., Can either use Vector (arma::Col) or std::vector
+  Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt;
+  Matrix k_x =
+      Matrix(n_u, n_x, arma::fill::zeros) + 1;  // gains on state error
+  Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) +
+                   10;  // gains on integrated output err
+
+  // Set control type bit mask, so controller knows what to do
+  size_t control_type = lds::kControlTypeIntY;  // integral action
+
+  // Ground-truth parameters for the controlled system
+  // (stand-in for physical system to be controlled)
+  Matrix a_true(n_x, n_x, arma::fill::eye);
+  a_true[0] = 0.986;
+  Matrix b_true(n_x, n_u, arma::fill::zeros);
+  b_true[0] = 0.054;
+  Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);
+
+  size_t which_m = 0;
+  data_t m_low = log(1 * dt) * (1 - a_true[0]);
+  data_t pr_lo2hi = 1e-3;
+  data_t m_high = log(20 * dt) * (1 - a_true[0]);
+  data_t pr_hi2lo = pr_lo2hi;
+
+  Vector m0_true = Vector(n_x, arma::fill::ones) * m_low;
+  // construct ground truth system to be controlled...
+  lds::poisson::System controlled_system(n_u, n_x, n_y, dt);
+
+  // Assign params.
+  controlled_system.set_A(a_true);
+  controlled_system.set_B(b_true);
+  controlled_system.set_m(m0_true);
+  controlled_system.set_x0(x0_true);
+  // reset to initial conditions
+  controlled_system.Reset();
+
+  cout << ".....................................\n";
+  cout << "controlled_system:\n";
+  cout << ".....................................\n";
+  controlled_system.Print();
+  cout << ".....................................\n";
+
+  // Create the controller
+  lds::poisson::Controller controller;
+  {
+    // Create model used for control.
+    lds::poisson::System controller_system(controlled_system);
+
+    // for this example, assume model correct, except disturbance
+    Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low;
+    Vector x0_controller = arma::log(y_ref0);
+    controller_system.set_m(m0_controller);
+    controller_system.set_x0(x0_controller);
+    controller_system.Reset(); //reset to new init condition
+
+    // adaptively re-estimate process disturbance (m)
+    controller_system.do_adapt_m = true;
+
+    // set adaptation rate by changing covariance of assumed process noise
+    // acting on random-walk evolution of m
+    Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5;
+    controller_system.set_Q_m(q_m);
+
+    data_t u_lb = 0.0;
+    data_t u_ub = 5.0;
+    controller = std::move(
+        lds::poisson::Controller(std::move(controller_system), u_lb, u_ub));
+  }
+  // set controller type
+  controller.set_control_type(control_type);
+
+  // set controller gains
+  controller.set_Kc(k_x);
+  controller.set_Kc_inty(k_inty);
+
+  // to protect against integral windup when output is consistently above
+  // target:
+  data_t tau_awu(0.1);
+  controller.set_tau_awu(tau_awu);
+
+  cout << ".....................................\n";
+  cout << "controller:\n";
+  cout << ".....................................\n";
+  controller.Print();
+  cout << ".....................................\n";
+
+  // create Matrix to save outputs in...
+  Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros);
+  y_ref.each_col() += y_ref0;
+
+  // Simulated measurements
+  Matrix z(n_y, n_t, arma::fill::zeros);
+
+  // simulated control signal ([=] V)
+  Matrix u(n_u, n_t, arma::fill::zeros);
+
+  // outputs, states and gain/disturbance params
+  // *_hat indicates online estimates
+  Matrix y_hat(n_y, n_t, arma::fill::zeros);
+  Matrix x_hat(n_x, n_t, arma::fill::zeros);
+  Matrix m_hat(n_y, n_t, arma::fill::zeros);
+
+  // *_true indicates ground truth (system being controlled)
+  Matrix y_true(n_y, n_t, arma::fill::zeros);
+  Matrix x_true(n_x, n_t, arma::fill::zeros);
+  Matrix m_true(n_y, n_t, arma::fill::zeros);
+
+  // set initial val
+  y_hat.col(0) = controller.sys().y();
+  y_true.col(0) = controlled_system.y();
+
+  x_hat.col(0) = controller.sys().x();
+  x_true.col(0) = controlled_system.x();
+
+  m_hat.col(0) = controller.sys().m();
+  m_true.col(0) = controlled_system.m();
+
+  cout << "Starting " << n_t * dt << " sec simulation ... \n";
+  auto start = std::chrono::high_resolution_clock::now();
+  for (size_t t = 1; t < n_t; t++) {
+    // simulate a stochastically switched disturbance
+    Vector chance = arma::randu<Vector>(1);
+    if (which_m == 0)  // low disturbance
+    {
+      if (chance[0] < pr_lo2hi) {  // switches low -> high disturbance
+        m0_true = std::vector<data_t>(n_x, m_high);
+        which_m = 1;
+      }
+    } else {                       // high disturbance
+      if (chance[0] < pr_hi2lo) {  // swithces high -> low disturbance
+        m0_true = std::vector<data_t>(n_x, m_low);
+        which_m = 0;
+      }
+    }
+    controlled_system.set_m(m0_true);
+
+    // e.g., use sinusoidal reference
+    data_t f = 0.5;  // freq [=] Hz
+    Vector t_vec = Vector(n_y, arma::fill::ones) * t;
+    y_ref.col(t) +=
+        y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4);
+
+    // Simulate the true system.
+    z.col(t)=controlled_system.Simulate(u.col(t-1));
+
+    // This method uses a steady-state solution to control problem to calculate
+    // x_ref, u_ref from reference output y_ref. Notably, it does this in the
+    // log-linear space (i.e., log(y)).
+    //
+    // Therefore, it is only applicable to regulation problems or cases where
+    // reference trajectory changes slowly compared to system dynamics.
+    controller.set_y_ref(y_ref.col(t));
+    u.col(t)=controller.ControlOutputReference(z.col(t));
+
+    y_true.col(t) = controlled_system.y();
+    x_true.col(t) = controlled_system.x();
+    m_true.col(t) = controlled_system.m();
+
+    y_hat.col(t) = controller.sys().y();
+    x_hat.col(t) = controller.sys().x();
+    m_hat.col(t) = controller.sys().m();
+  }
+
+  auto finish = std::chrono::high_resolution_clock::now();
+  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
+  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
+  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
+
+  // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,
+  // x_true, m_true saving with hdf5 via armadillo
+  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
+
+  auto dt_vec = Vector(1).fill(dt);
+  dt_vec.save(arma::hdf5_name("eg_plds_ctrl.h5", "dt"));
+  y_ref.save(arma::hdf5_name("eg_plds_ctrl.h5", "y_ref", replace));
+  u.save(arma::hdf5_name("eg_plds_ctrl.h5", "u", replace));
+  z.save(arma::hdf5_name("eg_plds_ctrl.h5", "z", replace));
+  x_true.save(arma::hdf5_name("eg_plds_ctrl.h5", "x_true", replace));
+  m_true.save(arma::hdf5_name("eg_plds_ctrl.h5", "m_true", replace));
+  y_true.save(arma::hdf5_name("eg_plds_ctrl.h5", "y_true", replace));
+  x_hat.save(arma::hdf5_name("eg_plds_ctrl.h5", "x_hat", replace));
+  m_hat.save(arma::hdf5_name("eg_plds_ctrl.h5", "m_hat", replace));
+  y_hat.save(arma::hdf5_name("eg_plds_ctrl.h5", "y_hat", replace));
+
+  return 0;
+}
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/eg__plds__est_8cpp.md b/misc/docs-hugo/content/docs/api/Files/eg__plds__est_8cpp.md
index fda45792..8114f4e8 100644
--- a/misc/docs-hugo/content/docs/api/Files/eg__plds__est_8cpp.md
+++ b/misc/docs-hugo/content/docs/api/Files/eg__plds__est_8cpp.md
@@ -1,227 +1,264 @@
----
-title: examples/eg_plds_est.cpp
-
----
-
-# examples/eg_plds_est.cpp
-
-
-
-## Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| Matrix | **[random_walk](/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#function-random-walk)**(size_t n_t, const Matrix & Q, const Vector & x0) |
-| int | **[main](/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#function-main)**() |
-
-
-## Function Details
-
-### random_walk
-
-```cpp
-Matrix random_walk(
-    size_t n_t,
-    const Matrix & Q,
-    const Vector & x0
-)
-```
-
-
-
-### main
-
-```cpp
-int main()
-```
-
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst>
-
-using lds::Matrix;
-using lds::Vector;
-using lds::data_t;
-using std::cout;
-
-// for generating random input
-Matrix random_walk(size_t n_t, const Matrix& Q, const Vector& x0);
-
-int main() {
-  cout << " ********** Example Poisson LDS Estimation ********** \n\n";
-
-  // Make SISO system sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;                           // no. inputs
-  size_t n_x = 1;                           // no. states
-  size_t n_y = 1;                           // no. outputs
-  auto n_t = static_cast<size_t>(30 / dt);  // no time steps for simulation.
-
-  // construct ground truth system...
-  lds::poisson::System system_true(n_u, n_x, n_y, dt);
-
-  // Model parameters
-  Matrix a_true(n_x, n_x, arma::fill::eye);
-  a_true[0] = exp(-dt / 0.075);
-  Matrix b_true = Matrix(n_x, n_u).fill(1e-2);
-  Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2);  // disturbance
-  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) -
-                                        a_true);  // initial state
-
-  // Assign params.
-  system_true.set_A(a_true);
-  system_true.set_B(b_true);
-  system_true.set_x0(x0_true);
-  system_true.set_m(m0_true);
-  system_true.Reset();
-
-  // Construct system for estimation
-  // e.g., will create a model with incorrect disturbance
-  lds::poisson::System system_estimator(n_u, n_x, n_y, dt);
-
-  // Can copy parameters from another system object
-  system_estimator = system_true;
-
-  // wrong disturbance
-  Vector m0_est = m0_true * 2;
-  system_estimator.set_m(m0_est);
-
-  // set new initial conditions
-  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) -
-                                      a_true);  // initial state
-  system_estimator.set_x0(x0_est);
-  system_estimator.Reset();  // reset to initial condition.
-
-  // turn on adaptive disturbance estimation
-  system_estimator.do_adapt_m = true;
-
-  // set adaptation rate by changing covariance of assumed process noise acting
-  // on random-walk evolution of m
-  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6;
-  system_estimator.set_Q_m(q_m);
-
-  cout << ".....................................\n";
-  cout << "estimator:\n";
-  cout << ".....................................\n";
-  system_estimator.Print();
-  cout << ".....................................\n";
-
-  // Set up simulation :
-  // Simulated measurements
-  Matrix z(n_y, n_t, arma::fill::zeros);
-
-  // Stimulus (generate random stimulus)
-  Matrix q_u =
-      Matrix(n_u, n_u, arma::fill::eye) * 1e-3;  // cov of random walk
-  Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));
-
-  // create matrix to save outputs in...
-  Matrix y_hat(n_y, n_t, arma::fill::zeros);
-  Matrix y_true(n_y, n_t, arma::fill::zeros);
-
-  // states and disturbance params
-  Matrix x_hat(n_x, n_t, arma::fill::zeros);
-  Matrix m_hat(n_x, n_t, arma::fill::zeros);
-
-  Matrix x_true(n_x, n_t, arma::fill::zeros);
-  Matrix m_true(n_x, n_t, arma::fill::zeros);
-
-  // initial conditions
-  y_hat.col(0) = system_estimator.y();
-  y_true.col(0) = system_true.y();
-  x_hat.col(0) = system_estimator.x();
-  x_true.col(0) = system_true.x();
-  m_hat.col(0) = system_estimator.m();
-  m_true.col(0) = system_true.m();
-
-  cout << "Starting " << n_t * dt << " sec simlation ... \n";
-  auto start = std::chrono::high_resolution_clock::now();
-  for (size_t t = 1; t < n_t; t++) {
-    // Simlate the true system.
-    z.col(t) = system_true.Simulate(u.col(t - 1));
-
-    // Filter (predict -> update)
-    system_estimator.Filter(u.col(t - 1), z.col(t));
-
-    // save signals
-    y_hat.col(t) = system_estimator.y();
-    y_true.col(t) = system_true.y();
-
-    x_true.col(t) = system_true.x();
-    m_true.col(t) = system_true.m();
-
-    x_hat.col(t) = system_estimator.x();
-    m_hat.col(t) = system_estimator.m();
-  }
-
-  auto finish = std::chrono::high_resolution_clock::now();
-  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
-  cout << "Finished simlation in " << sim_time_ms.count() << " ms.\n";
-  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
-
-  // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true,
-  // x_true, m_true saving with hdf5 via armadillo
-  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
-
-  auto dt_vec = Vector(1).fill(dt);
-  dt_vec.save(arma::hdf5_name("eg_plds_est.h5", "dt"));
-  u.save(arma::hdf5_name("eg_plds_est.h5", "u", replace));
-  z.save(arma::hdf5_name("eg_plds_est.h5", "z", replace));
-  x_true.save(arma::hdf5_name("eg_plds_est.h5", "x_true", replace));
-  m_true.save(arma::hdf5_name("eg_plds_est.h5", "m_true", replace));
-  y_true.save(arma::hdf5_name("eg_plds_est.h5", "y_true", replace));
-  x_hat.save(arma::hdf5_name("eg_plds_est.h5", "x_hat", replace));
-  m_hat.save(arma::hdf5_name("eg_plds_est.h5", "m_hat", replace));
-  y_hat.save(arma::hdf5_name("eg_plds_est.h5", "y_hat", replace));
-
-  return 0;
-}
-
-// for generating random input
-Matrix random_walk(size_t n_t, const Matrix& Q, const Vector& x0) {
-  size_t n = Q.n_rows;
-
-  if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) {
-    throw std::logic_error("Q must be `n` x `n`.");
-  }
-
-  Matrix x(n, n_t, arma::fill::zeros);
-  x.col(0) = x0;
-  for (size_t t = 1; t < n_t; t++) {
-    x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q);
-  }
-
-  return x;
-}
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: examples/eg_plds_est.cpp
+
+---
+
+# examples/eg_plds_est.cpp
+
+
+
+## Types
+
+|                | Name           |
+| -------------- | -------------- |
+| using arma::Mat< data_t > | **[Matrix](/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#using-matrix)**  |
+| using arma::Col< data_t > | **[Vector](/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#using-vector)**  |
+| using double | **[data_t](/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#using-data-t)**  |
+
+## Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| Matrix | **[random_walk](/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#function-random-walk)**(size_t n_t, const Matrix & Q, const Vector & x0) |
+| int | **[main](/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#function-main)**() |
+
+## Type Details
+
+### Matrix
+
+```cpp
+using lds::Matrix =  arma::Mat<data_t>;
+```
+
+
+
+### Vector
+
+```cpp
+using lds::Vector =  arma::Col<data_t>;
+```
+
+
+
+### data_t
+
+```cpp
+using lds::data_t =  double;
+```
+
+
+
+Type of all data in library. If need 32b, change `double` to `float`. This could be potentially useful for large scale problems where there are memory constraints. 
+
+
+
+## Function Details
+
+### random_walk
+
+```cpp
+Matrix random_walk(
+    size_t n_t,
+    const Matrix & Q,
+    const Vector & x0
+)
+```
+
+
+
+### main
+
+```cpp
+int main()
+```
+
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#include <ldsCtrlEst>
+
+using lds::Matrix;
+using lds::Vector;
+using lds::data_t;
+using std::cout;
+
+// for generating random input
+Matrix random_walk(size_t n_t, const Matrix& Q, const Vector& x0);
+
+int main() {
+  cout << " ********** Example Poisson LDS Estimation ********** \n\n";
+
+  // Make SISO system sampled at 1kHz
+  data_t dt = 1e-3;
+  size_t n_u = 1;                           // no. inputs
+  size_t n_x = 1;                           // no. states
+  size_t n_y = 1;                           // no. outputs
+  auto n_t = static_cast<size_t>(30 / dt);  // no time steps for simulation.
+
+  // construct ground truth system...
+  lds::poisson::System system_true(n_u, n_x, n_y, dt);
+
+  // Model parameters
+  Matrix a_true(n_x, n_x, arma::fill::eye);
+  a_true[0] = exp(-dt / 0.075);
+  Matrix b_true = Matrix(n_x, n_u).fill(1e-2);
+  Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2);  // disturbance
+  Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) -
+                                        a_true);  // initial state
+
+  // Assign params.
+  system_true.set_A(a_true);
+  system_true.set_B(b_true);
+  system_true.set_x0(x0_true);
+  system_true.set_m(m0_true);
+  system_true.Reset();
+
+  // Construct system for estimation
+  // e.g., will create a model with incorrect disturbance
+  lds::poisson::System system_estimator(n_u, n_x, n_y, dt);
+
+  // Can copy parameters from another system object
+  system_estimator = system_true;
+
+  // wrong disturbance
+  Vector m0_est = m0_true * 2;
+  system_estimator.set_m(m0_est);
+
+  // set new initial conditions
+  Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) -
+                                      a_true);  // initial state
+  system_estimator.set_x0(x0_est);
+  system_estimator.Reset();  // reset to initial condition.
+
+  // turn on adaptive disturbance estimation
+  system_estimator.do_adapt_m = true;
+
+  // set adaptation rate by changing covariance of assumed process noise acting
+  // on random-walk evolution of m
+  Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6;
+  system_estimator.set_Q_m(q_m);
+
+  cout << ".....................................\n";
+  cout << "estimator:\n";
+  cout << ".....................................\n";
+  system_estimator.Print();
+  cout << ".....................................\n";
+
+  // Set up simulation :
+  // Simulated measurements
+  Matrix z(n_y, n_t, arma::fill::zeros);
+
+  // Stimulus (generate random stimulus)
+  Matrix q_u =
+      Matrix(n_u, n_u, arma::fill::eye) * 1e-3;  // cov of random walk
+  Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));
+
+  // create matrix to save outputs in...
+  Matrix y_hat(n_y, n_t, arma::fill::zeros);
+  Matrix y_true(n_y, n_t, arma::fill::zeros);
+
+  // states and disturbance params
+  Matrix x_hat(n_x, n_t, arma::fill::zeros);
+  Matrix m_hat(n_x, n_t, arma::fill::zeros);
+
+  Matrix x_true(n_x, n_t, arma::fill::zeros);
+  Matrix m_true(n_x, n_t, arma::fill::zeros);
+
+  // initial conditions
+  y_hat.col(0) = system_estimator.y();
+  y_true.col(0) = system_true.y();
+  x_hat.col(0) = system_estimator.x();
+  x_true.col(0) = system_true.x();
+  m_hat.col(0) = system_estimator.m();
+  m_true.col(0) = system_true.m();
+
+  cout << "Starting " << n_t * dt << " sec simlation ... \n";
+  auto start = std::chrono::high_resolution_clock::now();
+  for (size_t t = 1; t < n_t; t++) {
+    // Simlate the true system.
+    z.col(t) = system_true.Simulate(u.col(t - 1));
+
+    // Filter (predict -> update)
+    system_estimator.Filter(u.col(t - 1), z.col(t));
+
+    // save signals
+    y_hat.col(t) = system_estimator.y();
+    y_true.col(t) = system_true.y();
+
+    x_true.col(t) = system_true.x();
+    m_true.col(t) = system_true.m();
+
+    x_hat.col(t) = system_estimator.x();
+    m_hat.col(t) = system_estimator.m();
+  }
+
+  auto finish = std::chrono::high_resolution_clock::now();
+  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
+  cout << "Finished simlation in " << sim_time_ms.count() << " ms.\n";
+  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
+
+  // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true,
+  // x_true, m_true saving with hdf5 via armadillo
+  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
+
+  auto dt_vec = Vector(1).fill(dt);
+  dt_vec.save(arma::hdf5_name("eg_plds_est.h5", "dt"));
+  u.save(arma::hdf5_name("eg_plds_est.h5", "u", replace));
+  z.save(arma::hdf5_name("eg_plds_est.h5", "z", replace));
+  x_true.save(arma::hdf5_name("eg_plds_est.h5", "x_true", replace));
+  m_true.save(arma::hdf5_name("eg_plds_est.h5", "m_true", replace));
+  y_true.save(arma::hdf5_name("eg_plds_est.h5", "y_true", replace));
+  x_hat.save(arma::hdf5_name("eg_plds_est.h5", "x_hat", replace));
+  m_hat.save(arma::hdf5_name("eg_plds_est.h5", "m_hat", replace));
+  y_hat.save(arma::hdf5_name("eg_plds_est.h5", "y_hat", replace));
+
+  return 0;
+}
+
+// for generating random input
+Matrix random_walk(size_t n_t, const Matrix& Q, const Vector& x0) {
+  size_t n = Q.n_rows;
+
+  if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) {
+    throw std::logic_error("Q must be `n` x `n`.");
+  }
+
+  Matrix x(n, n_t, arma::fill::zeros);
+  x.col(0) = x0;
+  for (size_t t = 1; t < n_t; t++) {
+    x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q);
+  }
+
+  return x;
+}
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/eg__plds__switched__ctrl_8cpp.md b/misc/docs-hugo/content/docs/api/Files/eg__plds__switched__ctrl_8cpp.md
index 361148d0..540db8ea 100644
--- a/misc/docs-hugo/content/docs/api/Files/eg__plds__switched__ctrl_8cpp.md
+++ b/misc/docs-hugo/content/docs/api/Files/eg__plds__switched__ctrl_8cpp.md
@@ -1,259 +1,296 @@
----
-title: examples/eg_plds_switched_ctrl.cpp
-
----
-
-# examples/eg_plds_switched_ctrl.cpp
-
-
-
-## Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| auto | **[main](/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#function-main)**() |
-
-
-## Function Details
-
-### main
-
-```cpp
-auto main()
-```
-
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst>
-
-using lds::data_t;
-using lds::Matrix;
-using lds::Vector;
-using std::cout;
-
-auto main() -> int {
-  cout << " ********** Example Switched Poisson LDS Control ********** \n\n";
-
-  // whether to do switched control
-  bool do_switch_ctrl = true;
-
-  // Make SISO system sampled at 1kHz
-  data_t dt = 1e-3;
-  size_t n_u = 1;
-  size_t n_x = 1;
-  size_t n_y = 1;
-
-  // no time steps for simulation.
-  auto n_t = static_cast<size_t>(30.0 / dt);
-
-  // for simulating switching
-  size_t which_mode = 1;
-  data_t pr_21 = 1e-3;   // prob mode 1 -> 2
-  data_t pr_12 = pr_21;  // prob mode 2 -> 1
-
-  // simulated system being controlled
-  lds::poisson::System controlled_system(n_u, n_x, n_y, dt);
-
-  // **Assume the system is not well characterized by one LDS, but is well
-  // characterized by two LDS models with different input matrices.**
-  data_t scale_sys_b = 2;
-
-  Matrix a(n_x, n_x, arma::fill::eye);
-  a[0] = 0.985;
-  Matrix b1 = Matrix(n_x, n_u).fill(0.05);
-  Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));
-
-  controlled_system.set_A(a);
-  controlled_system.set_B(b1);
-  controlled_system.set_d(d);
-  controlled_system.Reset();  // reset to initial conditions
-
-  // reference
-  Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);
-
-  // Let underlying system 1 be more sensitive than system 2
-  Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);
-
-  // create switched controller
-  lds::poisson::SwitchedController switched_controller;
-  lds::UniformMatrixList<> k_x;  // feedback controller gains
-  {
-    // create switched controller sub-systems
-    // system 1
-    lds::poisson::System sys1(controlled_system);
-
-    // set process noise covariance
-    Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3;
-    sys1.set_Q(q_controller);
-
-    // adaptively estimate process disturbance (m)
-    // n.b. using arbitrary default value for process noise if enabled.
-    sys1.do_adapt_m = true;
-
-    // setting initial mode to target to avoid large error at onset:
-    Vector x0_controller = arma::log(y_ref0) - d;
-    sys1.set_x0(x0_controller);
-    sys1.Reset();  // reset to initial conditions
-
-    cout << ".....................................\n";
-    cout << "sys1:\n";
-    cout << ".....................................\n";
-    sys1.Print();
-    cout << ".....................................\n";
-
-    // system 2
-    lds::poisson::System sys2 = sys1;
-
-    // set parameters
-    sys2.set_B(b2);
-
-    cout << ".....................................\n";
-    cout << "sys2:\n";
-    cout << ".....................................\n";
-    sys2.Print();
-    cout << ".....................................\n";
-
-    lds::UniformSystemList<lds::poisson::System> systems({sys1, sys2});
-
-    // controller gains for underlying system s:
-    Matrix k_x1(n_u, n_x, arma::fill::ones);
-    Matrix k_x2 = scale_sys_b * k_x1;  // system2 is x-times less sensitive.
-    k_x = lds::UniformMatrixList<>({k_x1, k_x2});
-
-    data_t u_lb = 0.0;
-    data_t u_ub = 5.0;
-    switched_controller = std::move(
-        lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub));
-  }
-  // Control variables
-  size_t control_type = 0;  // no integral action, etc
-  switched_controller.set_control_type(control_type);
-  switched_controller.set_Kc(std::move(k_x));
-
-  switched_controller.set_y_ref(y_ref0);
-
-  std::vector<lds::poisson::System> systems_vec(3, lds::poisson::System());
-  lds::UniformSystemList<lds::poisson::System> systems(std::move(systems_vec));
-
-  cout << ".....................................\n";
-  cout << "switched_controller:\n";
-  cout << ".....................................\n";
-  switched_controller.Print();
-  cout << ".....................................\n";
-
-  // Fake measurements
-  Matrix z(n_y, n_t, arma::fill::zeros);
-
-  // Will later contain control.
-  Matrix u(n_u, n_t, arma::fill::zeros);
-
-  // create Matrix to save outputs in...
-  Matrix y_hat(n_y, n_t, arma::fill::zeros);
-  Matrix y_true(n_y, n_t, arma::fill::zeros);
-  Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);
-
-  // modes and gain/disturbance params
-  Matrix x_hat(n_x, n_t, arma::fill::zeros);
-  Matrix x_true(n_x, n_t, arma::fill::zeros);
-  Matrix mode(1, n_t, arma::fill::ones);
-
-  // set initial val
-  y_hat.col(0) = switched_controller.sys().y();
-  y_true.col(0) = controlled_system.y();
-  x_hat.col(0) = switched_controller.sys().x();
-  x_true.col(0) = controlled_system.x();
-
-  cout << "Starting " << n_t * dt << " sec simulation ... \n";
-  auto start = std::chrono::high_resolution_clock::now();
-  for (size_t t = 1; t < n_t; t++) {
-    // Let the controlled system stochastically change gain
-    // Assume another algorithm decodes this mode change and signals the
-    // switched_controller
-    Vector chance(1, arma::fill::randu);
-    if (which_mode == 1)  // mode1
-    {
-      if (chance[0] < pr_21) {
-        which_mode = 2;
-        controlled_system.set_B(b2);
-        if (do_switch_ctrl) {
-          switched_controller.Switch(1);
-        }
-      }
-    } else {  // mode2
-      if (chance[0] < pr_12) {
-        which_mode = 1;
-        controlled_system.set_B(b1);
-        if (do_switch_ctrl) {
-          switched_controller.Switch(0);
-        }
-      }
-    }
-
-    // Simulate the true system.
-    z.col(t) = controlled_system.Simulate(u.col(t - 1));
-
-    // perform control
-    u.col(t) = switched_controller.ControlOutputReference(z.col(t));
-
-    mode.col(t) = which_mode;
-    y_ref.col(t) = y_ref0;
-    y_true.col(t) = controlled_system.y();
-    x_true.col(t) = controlled_system.x();
-    y_hat.col(t) = switched_controller.sys().y();
-    x_hat.col(t) = switched_controller.sys().x();
-  }
-
-  auto finish = std::chrono::high_resolution_clock::now();
-  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
-  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
-  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
-
-  // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,
-  // x_true, m_true saving with hdf5 via armadillo
-  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
-
-  auto dt_vec = Vector(1).fill(dt);
-  dt_vec.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "dt"));
-  y_ref.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "y_ref", replace));
-  u.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "u", replace));
-  z.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "z", replace));
-  x_true.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "x_true", replace));
-  y_true.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "y_true", replace));
-  x_hat.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "x_hat", replace));
-  y_hat.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "y_hat", replace));
-  mode.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "mode", replace));
-
-  return 0;
-}
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: examples/eg_plds_switched_ctrl.cpp
+
+---
+
+# examples/eg_plds_switched_ctrl.cpp
+
+
+
+## Types
+
+|                | Name           |
+| -------------- | -------------- |
+| using double | **[data_t](/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#using-data-t)**  |
+| using arma::Mat< data_t > | **[Matrix](/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#using-matrix)**  |
+| using arma::Col< data_t > | **[Vector](/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#using-vector)**  |
+
+## Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| int | **[main](/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#function-main)**() |
+
+## Type Details
+
+### data_t
+
+```cpp
+using lds::data_t =  double;
+```
+
+
+
+Type of all data in library. If need 32b, change `double` to `float`. This could be potentially useful for large scale problems where there are memory constraints. 
+
+
+### Matrix
+
+```cpp
+using lds::Matrix =  arma::Mat<data_t>;
+```
+
+
+
+### Vector
+
+```cpp
+using lds::Vector =  arma::Col<data_t>;
+```
+
+
+
+
+## Function Details
+
+### main
+
+```cpp
+int main()
+```
+
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#include <ldsCtrlEst>
+
+using lds::data_t;
+using lds::Matrix;
+using lds::Vector;
+using std::cout;
+
+auto main() -> int {
+  cout << " ********** Example Switched Poisson LDS Control ********** \n\n";
+
+  // whether to do switched control
+  bool do_switch_ctrl = true;
+
+  // Make SISO system sampled at 1kHz
+  data_t dt = 1e-3;
+  size_t n_u = 1;
+  size_t n_x = 1;
+  size_t n_y = 1;
+
+  // no time steps for simulation.
+  auto n_t = static_cast<size_t>(30.0 / dt);
+
+  // for simulating switching
+  size_t which_mode = 1;
+  data_t pr_21 = 1e-3;   // prob mode 1 -> 2
+  data_t pr_12 = pr_21;  // prob mode 2 -> 1
+
+  // simulated system being controlled
+  lds::poisson::System controlled_system(n_u, n_x, n_y, dt);
+
+  // **Assume the system is not well characterized by one LDS, but is well
+  // characterized by two LDS models with different input matrices.**
+  data_t scale_sys_b = 2;
+
+  Matrix a(n_x, n_x, arma::fill::eye);
+  a[0] = 0.985;
+  Matrix b1 = Matrix(n_x, n_u).fill(0.05);
+  Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));
+
+  controlled_system.set_A(a);
+  controlled_system.set_B(b1);
+  controlled_system.set_d(d);
+  controlled_system.Reset();  // reset to initial conditions
+
+  // reference
+  Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);
+
+  // Let underlying system 1 be more sensitive than system 2
+  Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);
+
+  // create switched controller
+  lds::poisson::SwitchedController switched_controller;
+  lds::UniformMatrixList<> k_x;  // feedback controller gains
+  {
+    // create switched controller sub-systems
+    // system 1
+    lds::poisson::System sys1(controlled_system);
+
+    // set process noise covariance
+    Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3;
+    sys1.set_Q(q_controller);
+
+    // adaptively estimate process disturbance (m)
+    // n.b. using arbitrary default value for process noise if enabled.
+    sys1.do_adapt_m = true;
+
+    // setting initial mode to target to avoid large error at onset:
+    Vector x0_controller = arma::log(y_ref0) - d;
+    sys1.set_x0(x0_controller);
+    sys1.Reset();  // reset to initial conditions
+
+    cout << ".....................................\n";
+    cout << "sys1:\n";
+    cout << ".....................................\n";
+    sys1.Print();
+    cout << ".....................................\n";
+
+    // system 2
+    lds::poisson::System sys2 = sys1;
+
+    // set parameters
+    sys2.set_B(b2);
+
+    cout << ".....................................\n";
+    cout << "sys2:\n";
+    cout << ".....................................\n";
+    sys2.Print();
+    cout << ".....................................\n";
+
+    lds::UniformSystemList<lds::poisson::System> systems({sys1, sys2});
+
+    // controller gains for underlying system s:
+    Matrix k_x1(n_u, n_x, arma::fill::ones);
+    Matrix k_x2 = scale_sys_b * k_x1;  // system2 is x-times less sensitive.
+    k_x = lds::UniformMatrixList<>({k_x1, k_x2});
+
+    data_t u_lb = 0.0;
+    data_t u_ub = 5.0;
+    switched_controller = std::move(
+        lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub));
+  }
+  // Control variables
+  size_t control_type = 0;  // no integral action, etc
+  switched_controller.set_control_type(control_type);
+  switched_controller.set_Kc(std::move(k_x));
+
+  switched_controller.set_y_ref(y_ref0);
+
+  std::vector<lds::poisson::System> systems_vec(3, lds::poisson::System());
+  lds::UniformSystemList<lds::poisson::System> systems(std::move(systems_vec));
+
+  cout << ".....................................\n";
+  cout << "switched_controller:\n";
+  cout << ".....................................\n";
+  switched_controller.Print();
+  cout << ".....................................\n";
+
+  // Fake measurements
+  Matrix z(n_y, n_t, arma::fill::zeros);
+
+  // Will later contain control.
+  Matrix u(n_u, n_t, arma::fill::zeros);
+
+  // create Matrix to save outputs in...
+  Matrix y_hat(n_y, n_t, arma::fill::zeros);
+  Matrix y_true(n_y, n_t, arma::fill::zeros);
+  Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);
+
+  // modes and gain/disturbance params
+  Matrix x_hat(n_x, n_t, arma::fill::zeros);
+  Matrix x_true(n_x, n_t, arma::fill::zeros);
+  Matrix mode(1, n_t, arma::fill::ones);
+
+  // set initial val
+  y_hat.col(0) = switched_controller.sys().y();
+  y_true.col(0) = controlled_system.y();
+  x_hat.col(0) = switched_controller.sys().x();
+  x_true.col(0) = controlled_system.x();
+
+  cout << "Starting " << n_t * dt << " sec simulation ... \n";
+  auto start = std::chrono::high_resolution_clock::now();
+  for (size_t t = 1; t < n_t; t++) {
+    // Let the controlled system stochastically change gain
+    // Assume another algorithm decodes this mode change and signals the
+    // switched_controller
+    Vector chance(1, arma::fill::randu);
+    if (which_mode == 1)  // mode1
+    {
+      if (chance[0] < pr_21) {
+        which_mode = 2;
+        controlled_system.set_B(b2);
+        if (do_switch_ctrl) {
+          switched_controller.Switch(1);
+        }
+      }
+    } else {  // mode2
+      if (chance[0] < pr_12) {
+        which_mode = 1;
+        controlled_system.set_B(b1);
+        if (do_switch_ctrl) {
+          switched_controller.Switch(0);
+        }
+      }
+    }
+
+    // Simulate the true system.
+    z.col(t) = controlled_system.Simulate(u.col(t - 1));
+
+    // perform control
+    u.col(t) = switched_controller.ControlOutputReference(z.col(t));
+
+    mode.col(t) = which_mode;
+    y_ref.col(t) = y_ref0;
+    y_true.col(t) = controlled_system.y();
+    x_true.col(t) = controlled_system.x();
+    y_hat.col(t) = switched_controller.sys().y();
+    x_hat.col(t) = switched_controller.sys().x();
+  }
+
+  auto finish = std::chrono::high_resolution_clock::now();
+  std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
+  cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
+  cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
+
+  // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,
+  // x_true, m_true saving with hdf5 via armadillo
+  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
+
+  auto dt_vec = Vector(1).fill(dt);
+  dt_vec.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "dt"));
+  y_ref.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "y_ref", replace));
+  u.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "u", replace));
+  z.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "z", replace));
+  x_true.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "x_true", replace));
+  y_true.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "y_true", replace));
+  x_hat.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "x_hat", replace));
+  y_hat.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "y_hat", replace));
+  mode.save(arma::hdf5_name("eg_plds_switched_ctrl.h5", "mode", replace));
+
+  return 0;
+}
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds_8cpp.md b/misc/docs-hugo/content/docs/api/Files/lds_8cpp.md
index a08e813f..52875193 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds_8cpp.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds_8cpp.md
@@ -1,162 +1,162 @@
----
-title: src/lds.cpp
-summary: misc lds namespace functions 
-
----
-
-# src/lds.cpp
-
-misc lds namespace functions  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-
-## Detailed Description
-
-
-
-This file implements miscellaneous lds namespace functions not bound to a class. 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- lds.cpp - LDS -----------------------------------------------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst_h/lds.h>
-
-// insert any necessary function definitions here.
-namespace lds {
-
-void ForceSymPD(Matrix& X) {
-  if (X.is_sympd() || !X.is_square()) {
-    return;
-  }
-
-  // make symmetric
-  X = (X + X.t()) / 2;
-
-  // for eigenval decomp
-  bool did_succeed(true);
-  Vector d;
-  Matrix u;
-
-  // see first method (which may not be ideal):
-  // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/
-  size_t k(1);
-  bool is_sympd = X.is_sympd();
-  Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye);
-
-  while (!is_sympd) {
-    if (k > 100) {
-      did_succeed = arma::eig_sym(d, u, X, "std");
-      data_t min_eig = arma::min(d);
-      std::cerr << "After multiple iterations, min eigen val = " << min_eig
-                << ".\n";
-      throw std::runtime_error(
-          "Failed to make matrix symmetric positive definite.");
-      return;
-    }
-
-    // Limit(d, arma::eps(0), kInf);  // force to be positive...
-    // Matrix d_diag = arma::diagmat(d);
-    // X = u * d_diag * u.t();
-
-    did_succeed = arma::eig_sym(d, u, X, "std");
-    if (!did_succeed) {
-      throw std::runtime_error("ForceSymPD failed.");
-    }
-
-    data_t min_eig = arma::min(d);
-    X += id * abs(min_eig) + arma::datum::eps;
-
-    // make sure symm:
-    X = (X + X.t()) / 2;
-    // double check eigenvals positive after symmetrizing:
-    arma::eig_sym(d, u, X, "std");
-    min_eig = arma::min(d);
-    is_sympd = min_eig > 0;
-    k++;
-  }
-}
-
-void ForceSymMinEig(Matrix& X, data_t eig_min) {
-  if (!X.is_square()) {
-    return;
-  }
-
-  // make symmetric
-  X = (X + X.t()) / 2;
-
-  bool did_succeed(true);
-  Vector d;
-  Matrix u;
-  did_succeed = arma::eig_sym(d, u, X, "std");
-  if (!did_succeed) {
-    throw std::runtime_error("ForceSymMinEig failed.");
-  }
-  Limit(d, eig_min + arma::eps(eig_min), kInf);  // enforce lower bound
-  Matrix d_diag = arma::diagmat(d);
-  X = u * d_diag * u.t();
-
-  // double check symmetric
-  X = (X + X.t()) / 2;
-}
-
-void lq(Matrix& L, Matrix& Qt, const Matrix& X) {
-  bool did_succeed(true);
-  did_succeed = arma::qr_econ(Qt, L, X.t());
-  if (!did_succeed) {
-    throw std::runtime_error("LQ decomposition failed.");
-  }
-  arma::inplace_trans(L);
-  arma::inplace_trans(Qt);
-}
-
-Matrix calcCov(const Matrix& A, const Matrix& B) {
-  // subtract out mean
-  auto m_a = arma::mean(A, 1);
-  Matrix a0 = A;
-  a0.each_col() -= m_a;
-
-  auto m_b = arma::mean(B, 1);
-  Matrix b0 = B;
-  b0.each_col() -= m_b;
-
-  Matrix cov = a0 * b0.t() / a0.n_cols;
-  return cov;
-}
-
-}  // namespace lds
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: src/lds.cpp
+summary: misc lds namespace functions 
+
+---
+
+# src/lds.cpp
+
+misc lds namespace functions  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+
+## Detailed Description
+
+
+
+This file implements miscellaneous lds namespace functions not bound to a class. 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- lds.cpp - LDS -----------------------------------------------------===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#include <ldsCtrlEst_h/lds.h>
+
+// insert any necessary function definitions here.
+namespace lds {
+
+void ForceSymPD(Matrix& X) {
+  if (X.is_sympd() || !X.is_square()) {
+    return;
+  }
+
+  // make symmetric
+  X = (X + X.t()) / 2;
+
+  // for eigenval decomp
+  bool did_succeed(true);
+  Vector d;
+  Matrix u;
+
+  // see first method (which may not be ideal):
+  // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/
+  size_t k(1);
+  bool is_sympd = X.is_sympd();
+  Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye);
+
+  while (!is_sympd) {
+    if (k > 100) {
+      did_succeed = arma::eig_sym(d, u, X, "std");
+      data_t min_eig = arma::min(d);
+      std::cerr << "After multiple iterations, min eigen val = " << min_eig
+                << ".\n";
+      throw std::runtime_error(
+          "Failed to make matrix symmetric positive definite.");
+      return;
+    }
+
+    // Limit(d, arma::eps(0), kInf);  // force to be positive...
+    // Matrix d_diag = arma::diagmat(d);
+    // X = u * d_diag * u.t();
+
+    did_succeed = arma::eig_sym(d, u, X, "std");
+    if (!did_succeed) {
+      throw std::runtime_error("ForceSymPD failed.");
+    }
+
+    data_t min_eig = arma::min(d);
+    X += id * abs(min_eig) + arma::datum::eps;
+
+    // make sure symm:
+    X = (X + X.t()) / 2;
+    // double check eigenvals positive after symmetrizing:
+    arma::eig_sym(d, u, X, "std");
+    min_eig = arma::min(d);
+    is_sympd = min_eig > 0;
+    k++;
+  }
+}
+
+void ForceSymMinEig(Matrix& X, data_t eig_min) {
+  if (!X.is_square()) {
+    return;
+  }
+
+  // make symmetric
+  X = (X + X.t()) / 2;
+
+  bool did_succeed(true);
+  Vector d;
+  Matrix u;
+  did_succeed = arma::eig_sym(d, u, X, "std");
+  if (!did_succeed) {
+    throw std::runtime_error("ForceSymMinEig failed.");
+  }
+  Limit(d, eig_min + arma::eps(eig_min), kInf);  // enforce lower bound
+  Matrix d_diag = arma::diagmat(d);
+  X = u * d_diag * u.t();
+
+  // double check symmetric
+  X = (X + X.t()) / 2;
+}
+
+void lq(Matrix& L, Matrix& Qt, const Matrix& X) {
+  bool did_succeed(true);
+  did_succeed = arma::qr_econ(Qt, L, X.t());
+  if (!did_succeed) {
+    throw std::runtime_error("LQ decomposition failed.");
+  }
+  arma::inplace_trans(L);
+  arma::inplace_trans(Qt);
+}
+
+Matrix calcCov(const Matrix& A, const Matrix& B) {
+  // subtract out mean
+  auto m_a = arma::mean(A, 1);
+  Matrix a0 = A;
+  a0.each_col() -= m_a;
+
+  auto m_b = arma::mean(B, 1);
+  Matrix b0 = B;
+  b0.each_col() -= m_b;
+
+  Matrix cov = a0 * b0.t() / a0.n_cols;
+  return cov;
+}
+
+}  // namespace lds
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds_8h.md b/misc/docs-hugo/content/docs/api/Files/lds_8h.md
index 87b7121a..6d054aee 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds_8h.md
@@ -1,184 +1,184 @@
----
-title: ldsCtrlEst_h/lds.h
-summary: lds namespace 
-
----
-
-# ldsCtrlEst_h/lds.h
-
-`lds` namespace  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-
-## Detailed Description
-
-
-
-This file defines the `lds` namespace, which will be an umbrella for linear dynamical systems with Gaussian (`[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)`) or Poisson (`[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)`) observations. 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_H
-#define LDSCTRLEST_LDS_H
-
-// #ifndef LDSCTRLEST
-// #include <ldsCtrlEst>
-// #endif
-
-#include <armadillo>
-
-namespace lds {
-using data_t = double;  // may change to float (but breaks mex functions)
-using Vector = arma::Col<data_t>;
-using Matrix = arma::Mat<data_t>;
-using Cube = arma::Cube<data_t>;
-using View = arma::subview<data_t>;
-
-namespace fill = arma::fill;
-
-
-static const std::size_t kControlTypeDeltaU = 0x1;
-
-static const std::size_t kControlTypeIntY = kControlTypeDeltaU << 1;
-
-static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU << 2;
-
-static const data_t kInf = std::numeric_limits<data_t>::infinity();
-static const data_t kPi = arma::datum::pi;
-
-static const data_t kDefaultP0 = 1e-6;  
-static const data_t kDefaultQ0 = 1e-6;  
-static const data_t kDefaultR0 = 1e-2;  
-
-enum SSIDWt {
-  kSSIDNone,   
-  kSSIDMOESP,  
-  kSSIDCVA     
-};
-
-enum MatrixListFreeDim {
-  kMatFreeDimNone,  
-  kMatFreeDim1,     
-  kMatFreeDim2      
-};
-
-// TODO(mfbolus): for SwitchedController, may want systems to have differing
-// numbers of states. Use this enum as template parameter?
-// enum SystemListFreeDim {
-//   kSysFreeDimNone,
-//   kSysFreeDimX  ///< allow state dim (x) of systems in list to be hetero
-// };
-
-// place hard limits on contents of vecors/mats
-void Limit(std::vector<data_t>& x, data_t lb, data_t ub);
-void Limit(Vector& x, data_t lb, data_t ub);
-void Limit(Matrix& x, data_t lb, data_t ub);
-
-// in-place assign that errs if there are dimension mismatches:
-void Reassign(Vector& some, const Vector& other,
-              const std::string& parenthetical = "Reassign");
-void Reassign(Matrix& some, const Matrix& other,
-              const std::string& parenthetical = "Reassign");
-
-// TODO(mfbolus): this is a fudge, but for some reason, cov mats often going
-// numerically asymm.
-
-void ForceSymPD(Matrix& X);
-
-void ForceSymMinEig(Matrix& X, data_t eig_min = 0);
-
-void lq(Matrix& L, Matrix& Qt, const Matrix& X);
-
-Matrix calcCov(const Matrix& A, const Matrix& B);
-
-inline void Limit(std::vector<data_t>& x, data_t lb, data_t ub) {
-  for (data_t& el : x) {
-    el = el < lb ? lb : el;
-    el = el > ub ? ub : el;
-  }
-}
-inline void Limit(Vector& x, data_t lb, data_t ub) {
-  for (data_t& el : x) {
-    el = el < lb ? lb : el;
-    el = el > ub ? ub : el;
-  }
-}
-inline void Limit(Matrix& x, data_t lb, data_t ub) {
-  for (data_t& el : x) {
-    el = el < lb ? lb : el;
-    el = el > ub ? ub : el;
-  }
-}
-
-inline void Reassign(Vector& some, const Vector& other,
-                     const std::string& parenthetical) {
-  // check dimensions
-  if (other.n_elem != some.n_elem) {
-    std::ostringstream ss;
-    ss << "cannot reassign vector of size " << some.n_elem
-       << " with vector of size " << other.n_elem << "(" << parenthetical
-       << ")";
-    throw std::runtime_error(ss.str());
-  }
-
-  for (size_t k = 0; k < some.n_elem; k++) {
-    some[k] = other[k];
-  }
-}
-
-inline void Reassign(Matrix& some, const Matrix& other,
-                     const std::string& parenthetical) {
-  // check dimensions
-  if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) {
-    std::ostringstream ss;
-    ss << "cannot reassign matrix of size " << some.n_rows << "x" << some.n_cols
-       << " with matrix of size " << other.n_rows << "x" << other.n_cols << "("
-       << parenthetical << ")";
-    throw std::runtime_error(ss.str());
-  }
-
-  for (size_t k = 0; k < some.n_elem; k++) {
-    some[k] = other[k];
-  }
-}
-
-}  // namespace lds
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds.h
+summary: lds namespace 
+
+---
+
+# ldsCtrlEst_h/lds.h
+
+`lds` namespace  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+
+## Detailed Description
+
+
+
+This file defines the `lds` namespace, which will be an umbrella for linear dynamical systems with Gaussian (`[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)`) or Poisson (`[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)`) observations. 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_H
+#define LDSCTRLEST_LDS_H
+
+// #ifndef LDSCTRLEST
+// #include <ldsCtrlEst>
+// #endif
+
+#include <armadillo>
+
+namespace lds {
+using data_t = double;  // may change to float (but breaks mex functions)
+using Vector = arma::Col<data_t>;
+using Matrix = arma::Mat<data_t>;
+using Cube = arma::Cube<data_t>;
+using View = arma::subview<data_t>;
+
+namespace fill = arma::fill;
+
+
+static const std::size_t kControlTypeDeltaU = 0x1;
+
+static const std::size_t kControlTypeIntY = kControlTypeDeltaU << 1;
+
+static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU << 2;
+
+static const data_t kInf = std::numeric_limits<data_t>::infinity();
+static const data_t kPi = arma::datum::pi;
+
+static const data_t kDefaultP0 = 1e-6;  
+static const data_t kDefaultQ0 = 1e-6;  
+static const data_t kDefaultR0 = 1e-2;  
+
+enum SSIDWt {
+  kSSIDNone,   
+  kSSIDMOESP,  
+  kSSIDCVA     
+};
+
+enum MatrixListFreeDim {
+  kMatFreeDimNone,  
+  kMatFreeDim1,     
+  kMatFreeDim2      
+};
+
+// TODO(mfbolus): for SwitchedController, may want systems to have differing
+// numbers of states. Use this enum as template parameter?
+// enum SystemListFreeDim {
+//   kSysFreeDimNone,
+//   kSysFreeDimX  ///< allow state dim (x) of systems in list to be hetero
+// };
+
+// place hard limits on contents of vecors/mats
+void Limit(std::vector<data_t>& x, data_t lb, data_t ub);
+void Limit(Vector& x, data_t lb, data_t ub);
+void Limit(Matrix& x, data_t lb, data_t ub);
+
+// in-place assign that errs if there are dimension mismatches:
+void Reassign(Vector& some, const Vector& other,
+              const std::string& parenthetical = "Reassign");
+void Reassign(Matrix& some, const Matrix& other,
+              const std::string& parenthetical = "Reassign");
+
+// TODO(mfbolus): this is a fudge, but for some reason, cov mats often going
+// numerically asymm.
+
+void ForceSymPD(Matrix& X);
+
+void ForceSymMinEig(Matrix& X, data_t eig_min = 0);
+
+void lq(Matrix& L, Matrix& Qt, const Matrix& X);
+
+Matrix calcCov(const Matrix& A, const Matrix& B);
+
+inline void Limit(std::vector<data_t>& x, data_t lb, data_t ub) {
+  for (data_t& el : x) {
+    el = el < lb ? lb : el;
+    el = el > ub ? ub : el;
+  }
+}
+inline void Limit(Vector& x, data_t lb, data_t ub) {
+  for (data_t& el : x) {
+    el = el < lb ? lb : el;
+    el = el > ub ? ub : el;
+  }
+}
+inline void Limit(Matrix& x, data_t lb, data_t ub) {
+  for (data_t& el : x) {
+    el = el < lb ? lb : el;
+    el = el > ub ? ub : el;
+  }
+}
+
+inline void Reassign(Vector& some, const Vector& other,
+                     const std::string& parenthetical) {
+  // check dimensions
+  if (other.n_elem != some.n_elem) {
+    std::ostringstream ss;
+    ss << "cannot reassign vector of size " << some.n_elem
+       << " with vector of size " << other.n_elem << "(" << parenthetical
+       << ")";
+    throw std::runtime_error(ss.str());
+  }
+
+  for (size_t k = 0; k < some.n_elem; k++) {
+    some[k] = other[k];
+  }
+}
+
+inline void Reassign(Matrix& some, const Matrix& other,
+                     const std::string& parenthetical) {
+  // check dimensions
+  if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) {
+    std::ostringstream ss;
+    ss << "cannot reassign matrix of size " << some.n_rows << "x" << some.n_cols
+       << " with matrix of size " << other.n_rows << "x" << other.n_cols << "("
+       << parenthetical << ")";
+    throw std::runtime_error(ss.str());
+  }
+
+  for (size_t k = 0; k < some.n_elem; k++) {
+    some[k] = other[k];
+  }
+}
+
+}  // namespace lds
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__ctrl_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__ctrl_8h.md
index e40d81f0..ac9d3a01 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__ctrl_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__ctrl_8h.md
@@ -1,551 +1,551 @@
----
-title: ldsCtrlEst_h/lds_ctrl.h
-summary: Controller. 
-
----
-
-# ldsCtrlEst_h/lds_ctrl.h
-
-Controller.  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/)**  |
-
-## Detailed Description
-
-
-
-This file declares the type for control of a linear dynamical system ([lds::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/)). 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_CTRL_H
-#define LDSCTRLEST_LDS_CTRL_H
-
-// namespace
-#include "lds.h"
-// system type
-#include "lds_sys.h"
-
-namespace lds {
-
-template <typename System>
-class Controller {
-  static_assert(std::is_base_of<lds::System, System>::value,
-                "System must be derived from lds::System type.");
-
- public:
-  Controller() = default;
-
-  Controller(const System& sys, data_t u_lb, data_t u_ub,
-             size_t control_type = 0);
-
-  Controller(System&& sys, data_t u_lb, data_t u_ub, size_t control_type = 0);
-
-  const Vector& Control(const Vector& z, bool do_control = true,
-                        bool do_lock_control = false,
-                        data_t sigma_soft_start = 0, data_t sigma_u_noise = 0,
-                        bool do_reset_at_control_onset = true);
-
-  const Vector& ControlOutputReference(const Vector& z, bool do_control = true,
-                                       bool do_estimation = true,
-                                       bool do_lock_control = false,
-                                       data_t sigma_soft_start = 0,
-                                       data_t sigma_u_noise = 0,
-                                       bool do_reset_at_control_onset = true);
-
-  // get methods:
-  const System& sys() const { return sys_; };
-  const Matrix& Kc() const { return Kc_; };
-  const Matrix& Kc_inty() const { return Kc_inty_; };
-  const Matrix& Kc_u() const { return Kc_u_; };
-  const Vector& g_design() const { return g_design_; };
-  const Vector& u_ref() const { return u_ref_; };
-  const Vector& x_ref() const { return x_ref_; };
-  const Vector& y_ref() const { return y_ref_; };
-  size_t control_type() const { return control_type_; };
-  data_t tau_awu() const { return tau_awu_; };
-  data_t u_lb() const { return u_lb_; };
-  data_t u_ub() const { return u_ub_; };
-
-  // set methods
-  void set_sys(const System& sys) {
-    bool does_match = sys_.n_u() == sys.n_u();
-    does_match = does_match && (sys_.n_x() == sys.n_x());
-    does_match = does_match && (sys_.n_y() == sys.n_y());
-    if (does_match) {
-      sys_ = sys;
-    } else {
-      throw std::runtime_error(
-          "new system argument to `set_sys` does not match dimensionality of "
-          "existing system");
-    }
-  };
-  void set_g_design(const Vector& g_design) { Reassign(g_design_, g_design); };
-  void set_u_ref(const Vector& u_ref) { Reassign(u_ref_, u_ref); };
-  void set_x_ref(const Vector& x_ref) {
-    Reassign(x_ref_, x_ref);
-    cx_ref_ = sys_.C() * x_ref_;
-  };
-
-  // y_ref needs to be handled differently depending on output fn.
-  // (need to populate cx_ref_ too, which depends on output fn)
-  virtual void set_y_ref(const Vector& y_ref) { Reassign(y_ref_, y_ref); };
-
-  void set_Kc(const Matrix& Kc) { Reassign(Kc_, Kc); };
-  void set_Kc_inty(const Matrix& Kc_inty) { Reassign(Kc_inty_, Kc_inty); };
-  void set_Kc_u(const Matrix& Kc_u) { Reassign(Kc_u_, Kc_u); };
-  void set_tau_awu(data_t tau) {
-    tau_awu_ = tau;
-    k_awu_ = sys_.dt() / tau_awu_;
-  };
-
-  void set_control_type(size_t control_type);
-
-  // There is no reason u_lb/ub should not be public, but making set methods
-  // anyway.
-  void set_u_lb(data_t u_lb) { u_lb_ = u_lb; };
-
-  void set_u_ub(data_t u_ub) { u_ub_ = u_ub; };
-
-  void Reset() {
-    sys_.Reset();
-    u_ref_.zeros();
-    u_ref_prev_.zeros();
-    int_e_.zeros();
-    int_e_awu_adjust_.zeros();
-    u_sat_.zeros();
-    u_saturated_ = false;
-    t_since_control_onset_ = 0.0;
-  };
-
-  void Print() {
-    sys_.Print();
-    std::cout << "g_design : " << g_design_ << "\n";
-    std::cout << "u_lb : " << u_lb_ << "\n";
-    std::cout << "u_ub : " << u_ub_ << "\n";
-  };
-
- protected:
-  System sys_;  
-
-  Vector u_;         
-  Vector u_return_;  
-
-  Vector g_design_;  
-
-  //  reference signals
-  Vector u_ref_;  
-  // create no set method for this:
-  Vector u_ref_prev_;  
-  Vector x_ref_;       
-  Vector y_ref_;       
-  Vector cx_ref_;
-
-  // Controller gains
-  Matrix Kc_;  
-  Matrix
-      Kc_u_;  
-  Matrix Kc_inty_;  
-
-  // control after g inversion
-  // do not need set methods for these.
-  Vector du_ref_;
-  Vector dv_ref_;
-  Vector v_ref_;
-  Vector dv_;
-  Vector v_;  
-
-  // integral error
-  // do not need set method for this
-  Vector int_e_;             
-  Vector int_e_awu_adjust_;  
-  Vector u_sat_;  
-
-  bool do_control_prev_ = false;
-  bool do_lock_control_prev_ = false;
-
-  // whether the g of system has become inverted from what you think it is
-  // (gain_ref)
-  bool u_saturated_ =
-      false;  
-
-  // should be safe to have references here bc nothing needs to be done
-  // (like reset vars) when it changes...
-  data_t u_lb_{};  
-  data_t u_ub_{};  
-
-  data_t tau_awu_{};  
-  data_t k_awu_ = 0;
-
-  data_t t_since_control_onset_ = 0;  
-  size_t control_type_{};             
-
- private:
-  void CalcControl(bool do_control = true, bool do_estimation = true,
-                   bool do_lock_control = false, data_t sigma_soft_start = 0,
-                   data_t sigma_u_noise = 0,
-                   bool do_reset_at_control_onset = true);
-
-  void CalcSteadyStateSetPoint();
-
-  void AntiWindup();
-
-  void InitVars(size_t control_type);
-};
-
-// Implement the above:
-
-template <typename System>
-inline Controller<System>::Controller(const System& sys, data_t u_lb,
-                                      data_t u_ub, size_t control_type)
-    : sys_(sys),
-      u_lb_(u_lb),
-      u_ub_(u_ub),
-      tau_awu_(lds::kInf) {
-  InitVars(control_type);
-}
-
-template <typename System>
-inline Controller<System>::Controller(System&& sys, data_t u_lb, data_t u_ub,
-                                      size_t control_type)
-    : sys_(std::move(sys)),
-      u_lb_(u_lb),
-      u_ub_(u_ub),
-      tau_awu_(lds::kInf) {
-  InitVars(control_type);
-}
-
-template <typename System>
-inline void Controller<System>::set_control_type(size_t control_type) {
-  if (control_type_ == control_type) {
-    return;
-  }
-
-  // creating a blank slate...
-  control_type_ = 0;
-  Kc_inty_.zeros(0, 0);
-  Kc_u_.zeros(0, 0);
-  int_e_.zeros(0, 0);
-  int_e_awu_adjust_.zeros(0, 0);
-
-  // controller was designed to minimize integral error
-  if (control_type & kControlTypeIntY) {
-    Kc_inty_.zeros(sys_.n_u(), sys_.n_y());
-    int_e_.zeros(sys_.n_y());
-    int_e_awu_adjust_.zeros(sys_.n_u());
-    control_type_ = control_type_ | kControlTypeIntY;
-  }
-
-  // controller was designed to minimize deltaU
-  // (i.e. state augmented with u)
-  if (control_type & kControlTypeDeltaU) {
-    Kc_u_.zeros(sys_.n_u(), sys_.n_u());
-    control_type_ = control_type_ | kControlTypeDeltaU;
-  }
-
-  // whether to adapt set point calculate with (re-estimated) process
-  // disturbance (m)
-  if (control_type & kControlTypeAdaptM) {
-    if (sys_.do_adapt_m)  // only if adapting m...
-    {
-      control_type_ = control_type_ | kControlTypeAdaptM;
-    }
-  }
-}  // set_control_type
-
-template <typename System>
-inline const Vector& Controller<System>::Control(
-    const Vector& z, bool do_control, bool do_lock_control,
-    data_t sigma_soft_start, data_t sigma_u_noise,
-    bool do_reset_at_control_onset) {
-  // update state estimates, given latest measurement
-  sys_.Filter(u_, z);
-
-  bool do_estimation = true;  // always have estimator on in this case
-
-  // calculate control signal
-  CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start,
-              sigma_u_noise, do_reset_at_control_onset);
-
-  return u_return_;
-}
-
-template <typename System>
-inline const Vector& Controller<System>::ControlOutputReference(
-    const Vector& z, bool do_control, bool do_estimation, bool do_lock_control,
-    data_t sigma_soft_start, data_t sigma_u_noise,
-    bool do_reset_at_control_onset) {
-  // update state estimates, given latest measurement
-  if (do_estimation) {
-    sys_.Filter(u_, z);
-  } else {
-    sys_.f(u_);
-  }
-
-  // calculate the set point
-  // solves for u_ref and x_ref when output is at y_ref at steady state.
-  if (do_control) {
-    CalcSteadyStateSetPoint();
-  }
-
-  // calculate control signal
-  CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start,
-              sigma_u_noise, do_reset_at_control_onset);
-
-  return u_return_;
-}
-
-template <typename System>
-inline void Controller<System>::CalcControl(bool do_control, bool do_estimation,
-                                            bool do_lock_control,
-                                            data_t sigma_soft_start,
-                                            data_t sigma_u_noise,
-                                            bool do_reset_at_control_onset) {
-  if (do_control && do_estimation) {
-    if (!do_control_prev_) {
-      if (do_reset_at_control_onset) {
-        Reset();
-      }
-      t_since_control_onset_ = 0.0;
-    } else {
-      t_since_control_onset_ += sys_.dt();
-    }
-
-    // enforce softstart on control vars.
-    if (sigma_soft_start > 0) {
-      // half-Gaussian soft-start scaling factor
-      data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) /
-                                     (2 * pow(sigma_soft_start, 2)));
-      u_ref_ *= soft_start_sf;
-      // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too
-      // x_ref_ *= soft_start_sf;
-      // cx_ref_ *= soft_start_sf;
-      // y_ref_ *= soft_start_sf;
-    }
-
-    if (!do_lock_control) {
-      // first do u -> v change of vars. (v = g.*u)
-      // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver
-      // control voltage u[=]V)
-      v_ref_ = g_design_ % u_ref_;
-
-      // Given FB, calc. the change in control
-      if (control_type_ & kControlTypeDeltaU) {
-        // if control designed to minimize not u but deltaU (i.e. state aug with
-        // u):
-
-        // TODO(mfbolus): Commented out for now. See note below.
-        // du_ref_ = u_ref_ - u_ref_prev_;
-        // dv_ref_ = g_design_ % du_ref_;
-
-        // TODO(mfbolus): Assuming users want *smooth* control signals if using
-        // kControlTypeDeltaU, it should be the case that dv_ref_ is --> 0. May
-        // want to revisit, but I am going to force it to be zero unless a
-        // situation arises that argues for keeping the above.
-        dv_ref_.zeros();
-
-        dv_ = dv_ref_;                     // nominally-optimal.
-        dv_ -= Kc_ * (sys_.x() - x_ref_);  // instantaneous state error
-        dv_ -= Kc_u_ * (v_ - v_ref_);      // penalty on amp u (rel to ref)
-
-        if (control_type_ & kControlTypeIntY) {
-          // TODO(mfbolus): one approach to protection against integral windup
-          // would be to not integrate error when control signal saturated:
-
-          // if(!uSaturated)
-          int_e_ += (sys_.cx() - cx_ref_) * sys_.dt();  // integrated error
-          dv_ -= Kc_inty_ * int_e_;  // control for integrated error
-        }
-
-        // update the control
-        v_ += dv_;
-      } else {
-        v_ = v_ref_;                      // nominally-optimal.
-        v_ -= Kc_ * (sys_.x() - x_ref_);  // instantaneous state error
-
-        if (control_type_ & kControlTypeIntY) {
-          // TODO(mfbolus): one approach to protection against integral windup
-          // would be to not integrate error when control signal saturated:
-
-          // if (!uSaturated)
-          int_e_ += (sys_.cx() - cx_ref_) * sys_.dt();  // integrated error
-          v_ -= Kc_inty_ * int_e_;  // control for integrated error
-        }
-      }
-
-      // convert back to control voltage u[=]V
-      u_ = v_ / sys_.g();
-    }       // else do nothing until lock is low
-  } else {  // if not control
-    // feed through u_ref in open loop
-    u_ = u_ref_ % g_design_ / sys_.g();
-    v_ = sys_.g() % u_;
-    u_ref_.zeros();
-    int_e_.zeros();
-    int_e_awu_adjust_.zeros();
-    u_sat_.zeros();
-  }  // ends do_control
-
-  // enforce box constraints (and antiwindup)
-  AntiWindup();
-
-  // add noise to input?
-  // The value for u that is *returned* to user after addition of any noise,
-  // while keeping controller/estimator blind to this addition.
-  u_return_ = u_;
-  if ((sigma_u_noise > 0.0) && (do_control && !do_lock_control)) {
-    u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn);
-    Limit(u_return_, u_lb_, u_ub_);
-  };
-
-  // For next time step:
-  u_ref_prev_ = u_ref_;
-  do_control_prev_ = do_control;
-  do_lock_control_prev_ = do_lock_control;
-}  // CalcControl
-
-template <typename System>
-inline void Controller<System>::CalcSteadyStateSetPoint() {
-  // Linearly-constrained least squares (ls).
-  //
-  // _reference:
-  //  Boyd & Vandenberghe (2018) Introduction to Applied Linear Algebra
-  //
-  Matrix a_ls =
-      join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros));
-  Vector b_ls = cx_ref_;
-  Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye),
-                           sys_.B() * arma::diagmat(sys_.g()));
-  Vector d_ls = -sys_.m0();
-  if (control_type_ & kControlTypeAdaptM) {
-    d_ls = -sys_.m();  // adapt setpoint calc with disturbance?
-  }
-
-  Matrix a_ls_t = a_ls.t();  // TODO(mfbolus): not sure why but causes seg
-                             // fault if I do not do this.
-  Matrix phi_ls =
-      join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()),
-                join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros)));
-  // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse:
-  Matrix inv_phi = pinv(phi_ls);
-  Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls);
-  x_ref_ = xulam.subvec(0, sys_.n_x() - 1);
-  u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1);
-  cx_ref_ = sys_.C() * x_ref_;
-}  // CalcSteadyStateSetPoint
-
-template <typename System>
-void Controller<System>::AntiWindup() {
-  u_saturated_ = false;
-  u_sat_ = u_;
-
-  // limit u and flag whether saturated
-  for (size_t k = 0; k < u_.n_elem; k++) {
-    if (u_[k] < u_lb_) {
-      u_sat_[k] = u_lb_;
-      u_saturated_ = true;
-    }
-
-    if (u_[k] > u_ub_) {
-      u_sat_[k] = u_ub_;
-      u_saturated_ = true;
-    }
-  }
-
-  if ((control_type_ & kControlTypeIntY) && (tau_awu_ < lds::kInf)) {
-    // one-step back-calculation (calculate intE for u=u_sat)
-    // (Astroem, Rundqwist 1989 warn against using this...)
-    // int_e_awu_adjust_ =
-    //     solve(Kc_inty_, (u_ - u_sat_));  // pinv(Kc_inty) * (u-uSat);
-
-    // gradual: see Astroem, Rundqwist 1989
-    // this is a fudge for doing MIMO gradual
-    // n.b., went ahead and multiplied 1/T by dt so don't have to do that here.
-    int_e_awu_adjust_ =
-        k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_);
-    // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_);
-
-    int_e_ += int_e_awu_adjust_;
-  }
-
-  // set u to saturated version
-  u_ = u_sat_;
-}
-
-template <typename System>
-void Controller<System>::InitVars(size_t control_type) {
-  // initialize to default values
-  u_ref_ = Vector(sys_.n_u(), fill::zeros);
-  u_ref_prev_ = Vector(sys_.n_u(), fill::zeros);
-  x_ref_ = Vector(sys_.n_x(), fill::zeros);
-  y_ref_ = Vector(sys_.n_y(), fill::zeros);
-  cx_ref_ = Vector(sys_.n_y(), fill::zeros);
-
-  u_ = Vector(sys_.n_u(), fill::zeros);
-  u_return_ = Vector(sys_.n_u(), fill::zeros);
-  u_sat_ = Vector(sys_.n_u(), fill::zeros);
-
-  // Might not need all these, so zero elements until later.
-  Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros);
-  Kc_u_ = Matrix(0, 0, fill::zeros);
-  Kc_inty_ = Matrix(0, 0, fill::zeros);
-
-  g_design_ = sys_.g();  // by default, same as model
-  dv_ = Vector(sys_.n_u(), fill::zeros);
-  v_ = Vector(sys_.n_u(), fill::zeros);
-  du_ref_ = Vector(sys_.n_u(), fill::zeros);
-  dv_ref_ = Vector(sys_.n_u(), fill::zeros);
-  v_ref_ = Vector(sys_.n_u(), fill::zeros);
-
-  int_e_ = Vector(0, fill::zeros);
-  int_e_awu_adjust_ = Vector(0, fill::zeros);
-
-  set_control_type(control_type);
-}
-
-}  // namespace lds
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_ctrl.h
+summary: Controller. 
+
+---
+
+# ldsCtrlEst_h/lds_ctrl.h
+
+Controller.  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/)**  |
+
+## Detailed Description
+
+
+
+This file declares the type for control of a linear dynamical system ([lds::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/)). 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_CTRL_H
+#define LDSCTRLEST_LDS_CTRL_H
+
+// namespace
+#include "lds.h"
+// system type
+#include "lds_sys.h"
+
+namespace lds {
+
+template <typename System>
+class Controller {
+  static_assert(std::is_base_of<lds::System, System>::value,
+                "System must be derived from lds::System type.");
+
+ public:
+  Controller() = default;
+
+  Controller(const System& sys, data_t u_lb, data_t u_ub,
+             size_t control_type = 0);
+
+  Controller(System&& sys, data_t u_lb, data_t u_ub, size_t control_type = 0);
+
+  const Vector& Control(const Vector& z, bool do_control = true,
+                        bool do_lock_control = false,
+                        data_t sigma_soft_start = 0, data_t sigma_u_noise = 0,
+                        bool do_reset_at_control_onset = true);
+
+  const Vector& ControlOutputReference(const Vector& z, bool do_control = true,
+                                       bool do_estimation = true,
+                                       bool do_lock_control = false,
+                                       data_t sigma_soft_start = 0,
+                                       data_t sigma_u_noise = 0,
+                                       bool do_reset_at_control_onset = true);
+
+  // get methods:
+  const System& sys() const { return sys_; };
+  const Matrix& Kc() const { return Kc_; };
+  const Matrix& Kc_inty() const { return Kc_inty_; };
+  const Matrix& Kc_u() const { return Kc_u_; };
+  const Vector& g_design() const { return g_design_; };
+  const Vector& u_ref() const { return u_ref_; };
+  const Vector& x_ref() const { return x_ref_; };
+  const Vector& y_ref() const { return y_ref_; };
+  size_t control_type() const { return control_type_; };
+  data_t tau_awu() const { return tau_awu_; };
+  data_t u_lb() const { return u_lb_; };
+  data_t u_ub() const { return u_ub_; };
+
+  // set methods
+  void set_sys(const System& sys) {
+    bool does_match = sys_.n_u() == sys.n_u();
+    does_match = does_match && (sys_.n_x() == sys.n_x());
+    does_match = does_match && (sys_.n_y() == sys.n_y());
+    if (does_match) {
+      sys_ = sys;
+    } else {
+      throw std::runtime_error(
+          "new system argument to `set_sys` does not match dimensionality of "
+          "existing system");
+    }
+  };
+  void set_g_design(const Vector& g_design) { Reassign(g_design_, g_design); };
+  void set_u_ref(const Vector& u_ref) { Reassign(u_ref_, u_ref); };
+  void set_x_ref(const Vector& x_ref) {
+    Reassign(x_ref_, x_ref);
+    cx_ref_ = sys_.C() * x_ref_;
+  };
+
+  // y_ref needs to be handled differently depending on output fn.
+  // (need to populate cx_ref_ too, which depends on output fn)
+  virtual void set_y_ref(const Vector& y_ref) { Reassign(y_ref_, y_ref); };
+
+  void set_Kc(const Matrix& Kc) { Reassign(Kc_, Kc); };
+  void set_Kc_inty(const Matrix& Kc_inty) { Reassign(Kc_inty_, Kc_inty); };
+  void set_Kc_u(const Matrix& Kc_u) { Reassign(Kc_u_, Kc_u); };
+  void set_tau_awu(data_t tau) {
+    tau_awu_ = tau;
+    k_awu_ = sys_.dt() / tau_awu_;
+  };
+
+  void set_control_type(size_t control_type);
+
+  // There is no reason u_lb/ub should not be public, but making set methods
+  // anyway.
+  void set_u_lb(data_t u_lb) { u_lb_ = u_lb; };
+
+  void set_u_ub(data_t u_ub) { u_ub_ = u_ub; };
+
+  void Reset() {
+    sys_.Reset();
+    u_ref_.zeros();
+    u_ref_prev_.zeros();
+    int_e_.zeros();
+    int_e_awu_adjust_.zeros();
+    u_sat_.zeros();
+    u_saturated_ = false;
+    t_since_control_onset_ = 0.0;
+  };
+
+  void Print() {
+    sys_.Print();
+    std::cout << "g_design : " << g_design_ << "\n";
+    std::cout << "u_lb : " << u_lb_ << "\n";
+    std::cout << "u_ub : " << u_ub_ << "\n";
+  };
+
+ protected:
+  System sys_;  
+
+  Vector u_;         
+  Vector u_return_;  
+
+  Vector g_design_;  
+
+  //  reference signals
+  Vector u_ref_;  
+  // create no set method for this:
+  Vector u_ref_prev_;  
+  Vector x_ref_;       
+  Vector y_ref_;       
+  Vector cx_ref_;
+
+  // Controller gains
+  Matrix Kc_;  
+  Matrix
+      Kc_u_;  
+  Matrix Kc_inty_;  
+
+  // control after g inversion
+  // do not need set methods for these.
+  Vector du_ref_;
+  Vector dv_ref_;
+  Vector v_ref_;
+  Vector dv_;
+  Vector v_;  
+
+  // integral error
+  // do not need set method for this
+  Vector int_e_;             
+  Vector int_e_awu_adjust_;  
+  Vector u_sat_;  
+
+  bool do_control_prev_ = false;
+  bool do_lock_control_prev_ = false;
+
+  // whether the g of system has become inverted from what you think it is
+  // (gain_ref)
+  bool u_saturated_ =
+      false;  
+
+  // should be safe to have references here bc nothing needs to be done
+  // (like reset vars) when it changes...
+  data_t u_lb_{};  
+  data_t u_ub_{};  
+
+  data_t tau_awu_{};  
+  data_t k_awu_ = 0;
+
+  data_t t_since_control_onset_ = 0;  
+  size_t control_type_{};             
+
+ private:
+  void CalcControl(bool do_control = true, bool do_estimation = true,
+                   bool do_lock_control = false, data_t sigma_soft_start = 0,
+                   data_t sigma_u_noise = 0,
+                   bool do_reset_at_control_onset = true);
+
+  void CalcSteadyStateSetPoint();
+
+  void AntiWindup();
+
+  void InitVars(size_t control_type);
+};
+
+// Implement the above:
+
+template <typename System>
+inline Controller<System>::Controller(const System& sys, data_t u_lb,
+                                      data_t u_ub, size_t control_type)
+    : sys_(sys),
+      u_lb_(u_lb),
+      u_ub_(u_ub),
+      tau_awu_(lds::kInf) {
+  InitVars(control_type);
+}
+
+template <typename System>
+inline Controller<System>::Controller(System&& sys, data_t u_lb, data_t u_ub,
+                                      size_t control_type)
+    : sys_(std::move(sys)),
+      u_lb_(u_lb),
+      u_ub_(u_ub),
+      tau_awu_(lds::kInf) {
+  InitVars(control_type);
+}
+
+template <typename System>
+inline void Controller<System>::set_control_type(size_t control_type) {
+  if (control_type_ == control_type) {
+    return;
+  }
+
+  // creating a blank slate...
+  control_type_ = 0;
+  Kc_inty_.zeros(0, 0);
+  Kc_u_.zeros(0, 0);
+  int_e_.zeros(0, 0);
+  int_e_awu_adjust_.zeros(0, 0);
+
+  // controller was designed to minimize integral error
+  if (control_type & kControlTypeIntY) {
+    Kc_inty_.zeros(sys_.n_u(), sys_.n_y());
+    int_e_.zeros(sys_.n_y());
+    int_e_awu_adjust_.zeros(sys_.n_u());
+    control_type_ = control_type_ | kControlTypeIntY;
+  }
+
+  // controller was designed to minimize deltaU
+  // (i.e. state augmented with u)
+  if (control_type & kControlTypeDeltaU) {
+    Kc_u_.zeros(sys_.n_u(), sys_.n_u());
+    control_type_ = control_type_ | kControlTypeDeltaU;
+  }
+
+  // whether to adapt set point calculate with (re-estimated) process
+  // disturbance (m)
+  if (control_type & kControlTypeAdaptM) {
+    if (sys_.do_adapt_m)  // only if adapting m...
+    {
+      control_type_ = control_type_ | kControlTypeAdaptM;
+    }
+  }
+}  // set_control_type
+
+template <typename System>
+inline const Vector& Controller<System>::Control(
+    const Vector& z, bool do_control, bool do_lock_control,
+    data_t sigma_soft_start, data_t sigma_u_noise,
+    bool do_reset_at_control_onset) {
+  // update state estimates, given latest measurement
+  sys_.Filter(u_, z);
+
+  bool do_estimation = true;  // always have estimator on in this case
+
+  // calculate control signal
+  CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start,
+              sigma_u_noise, do_reset_at_control_onset);
+
+  return u_return_;
+}
+
+template <typename System>
+inline const Vector& Controller<System>::ControlOutputReference(
+    const Vector& z, bool do_control, bool do_estimation, bool do_lock_control,
+    data_t sigma_soft_start, data_t sigma_u_noise,
+    bool do_reset_at_control_onset) {
+  // update state estimates, given latest measurement
+  if (do_estimation) {
+    sys_.Filter(u_, z);
+  } else {
+    sys_.f(u_);
+  }
+
+  // calculate the set point
+  // solves for u_ref and x_ref when output is at y_ref at steady state.
+  if (do_control) {
+    CalcSteadyStateSetPoint();
+  }
+
+  // calculate control signal
+  CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start,
+              sigma_u_noise, do_reset_at_control_onset);
+
+  return u_return_;
+}
+
+template <typename System>
+inline void Controller<System>::CalcControl(bool do_control, bool do_estimation,
+                                            bool do_lock_control,
+                                            data_t sigma_soft_start,
+                                            data_t sigma_u_noise,
+                                            bool do_reset_at_control_onset) {
+  if (do_control && do_estimation) {
+    if (!do_control_prev_) {
+      if (do_reset_at_control_onset) {
+        Reset();
+      }
+      t_since_control_onset_ = 0.0;
+    } else {
+      t_since_control_onset_ += sys_.dt();
+    }
+
+    // enforce softstart on control vars.
+    if (sigma_soft_start > 0) {
+      // half-Gaussian soft-start scaling factor
+      data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) /
+                                     (2 * pow(sigma_soft_start, 2)));
+      u_ref_ *= soft_start_sf;
+      // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too
+      // x_ref_ *= soft_start_sf;
+      // cx_ref_ *= soft_start_sf;
+      // y_ref_ *= soft_start_sf;
+    }
+
+    if (!do_lock_control) {
+      // first do u -> v change of vars. (v = g.*u)
+      // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver
+      // control voltage u[=]V)
+      v_ref_ = g_design_ % u_ref_;
+
+      // Given FB, calc. the change in control
+      if (control_type_ & kControlTypeDeltaU) {
+        // if control designed to minimize not u but deltaU (i.e. state aug with
+        // u):
+
+        // TODO(mfbolus): Commented out for now. See note below.
+        // du_ref_ = u_ref_ - u_ref_prev_;
+        // dv_ref_ = g_design_ % du_ref_;
+
+        // TODO(mfbolus): Assuming users want *smooth* control signals if using
+        // kControlTypeDeltaU, it should be the case that dv_ref_ is --> 0. May
+        // want to revisit, but I am going to force it to be zero unless a
+        // situation arises that argues for keeping the above.
+        dv_ref_.zeros();
+
+        dv_ = dv_ref_;                     // nominally-optimal.
+        dv_ -= Kc_ * (sys_.x() - x_ref_);  // instantaneous state error
+        dv_ -= Kc_u_ * (v_ - v_ref_);      // penalty on amp u (rel to ref)
+
+        if (control_type_ & kControlTypeIntY) {
+          // TODO(mfbolus): one approach to protection against integral windup
+          // would be to not integrate error when control signal saturated:
+
+          // if(!uSaturated)
+          int_e_ += (sys_.cx() - cx_ref_) * sys_.dt();  // integrated error
+          dv_ -= Kc_inty_ * int_e_;  // control for integrated error
+        }
+
+        // update the control
+        v_ += dv_;
+      } else {
+        v_ = v_ref_;                      // nominally-optimal.
+        v_ -= Kc_ * (sys_.x() - x_ref_);  // instantaneous state error
+
+        if (control_type_ & kControlTypeIntY) {
+          // TODO(mfbolus): one approach to protection against integral windup
+          // would be to not integrate error when control signal saturated:
+
+          // if (!uSaturated)
+          int_e_ += (sys_.cx() - cx_ref_) * sys_.dt();  // integrated error
+          v_ -= Kc_inty_ * int_e_;  // control for integrated error
+        }
+      }
+
+      // convert back to control voltage u[=]V
+      u_ = v_ / sys_.g();
+    }       // else do nothing until lock is low
+  } else {  // if not control
+    // feed through u_ref in open loop
+    u_ = u_ref_ % g_design_ / sys_.g();
+    v_ = sys_.g() % u_;
+    u_ref_.zeros();
+    int_e_.zeros();
+    int_e_awu_adjust_.zeros();
+    u_sat_.zeros();
+  }  // ends do_control
+
+  // enforce box constraints (and antiwindup)
+  AntiWindup();
+
+  // add noise to input?
+  // The value for u that is *returned* to user after addition of any noise,
+  // while keeping controller/estimator blind to this addition.
+  u_return_ = u_;
+  if ((sigma_u_noise > 0.0) && (do_control && !do_lock_control)) {
+    u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn);
+    Limit(u_return_, u_lb_, u_ub_);
+  };
+
+  // For next time step:
+  u_ref_prev_ = u_ref_;
+  do_control_prev_ = do_control;
+  do_lock_control_prev_ = do_lock_control;
+}  // CalcControl
+
+template <typename System>
+inline void Controller<System>::CalcSteadyStateSetPoint() {
+  // Linearly-constrained least squares (ls).
+  //
+  // _reference:
+  //  Boyd & Vandenberghe (2018) Introduction to Applied Linear Algebra
+  //
+  Matrix a_ls =
+      join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros));
+  Vector b_ls = cx_ref_;
+  Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye),
+                           sys_.B() * arma::diagmat(sys_.g()));
+  Vector d_ls = -sys_.m0();
+  if (control_type_ & kControlTypeAdaptM) {
+    d_ls = -sys_.m();  // adapt setpoint calc with disturbance?
+  }
+
+  Matrix a_ls_t = a_ls.t();  // TODO(mfbolus): not sure why but causes seg
+                             // fault if I do not do this.
+  Matrix phi_ls =
+      join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()),
+                join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros)));
+  // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse:
+  Matrix inv_phi = pinv(phi_ls);
+  Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls);
+  x_ref_ = xulam.subvec(0, sys_.n_x() - 1);
+  u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1);
+  cx_ref_ = sys_.C() * x_ref_;
+}  // CalcSteadyStateSetPoint
+
+template <typename System>
+void Controller<System>::AntiWindup() {
+  u_saturated_ = false;
+  u_sat_ = u_;
+
+  // limit u and flag whether saturated
+  for (size_t k = 0; k < u_.n_elem; k++) {
+    if (u_[k] < u_lb_) {
+      u_sat_[k] = u_lb_;
+      u_saturated_ = true;
+    }
+
+    if (u_[k] > u_ub_) {
+      u_sat_[k] = u_ub_;
+      u_saturated_ = true;
+    }
+  }
+
+  if ((control_type_ & kControlTypeIntY) && (tau_awu_ < lds::kInf)) {
+    // one-step back-calculation (calculate intE for u=u_sat)
+    // (Astroem, Rundqwist 1989 warn against using this...)
+    // int_e_awu_adjust_ =
+    //     solve(Kc_inty_, (u_ - u_sat_));  // pinv(Kc_inty) * (u-uSat);
+
+    // gradual: see Astroem, Rundqwist 1989
+    // this is a fudge for doing MIMO gradual
+    // n.b., went ahead and multiplied 1/T by dt so don't have to do that here.
+    int_e_awu_adjust_ =
+        k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_);
+    // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_);
+
+    int_e_ += int_e_awu_adjust_;
+  }
+
+  // set u to saturated version
+  u_ = u_sat_;
+}
+
+template <typename System>
+void Controller<System>::InitVars(size_t control_type) {
+  // initialize to default values
+  u_ref_ = Vector(sys_.n_u(), fill::zeros);
+  u_ref_prev_ = Vector(sys_.n_u(), fill::zeros);
+  x_ref_ = Vector(sys_.n_x(), fill::zeros);
+  y_ref_ = Vector(sys_.n_y(), fill::zeros);
+  cx_ref_ = Vector(sys_.n_y(), fill::zeros);
+
+  u_ = Vector(sys_.n_u(), fill::zeros);
+  u_return_ = Vector(sys_.n_u(), fill::zeros);
+  u_sat_ = Vector(sys_.n_u(), fill::zeros);
+
+  // Might not need all these, so zero elements until later.
+  Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros);
+  Kc_u_ = Matrix(0, 0, fill::zeros);
+  Kc_inty_ = Matrix(0, 0, fill::zeros);
+
+  g_design_ = sys_.g();  // by default, same as model
+  dv_ = Vector(sys_.n_u(), fill::zeros);
+  v_ = Vector(sys_.n_u(), fill::zeros);
+  du_ref_ = Vector(sys_.n_u(), fill::zeros);
+  dv_ref_ = Vector(sys_.n_u(), fill::zeros);
+  v_ref_ = Vector(sys_.n_u(), fill::zeros);
+
+  int_e_ = Vector(0, fill::zeros);
+  int_e_awu_adjust_ = Vector(0, fill::zeros);
+
+  set_control_type(control_type);
+}
+
+}  // namespace lds
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__fit_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__fit_8h.md
index f1642701..8a0cbf0d 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__fit_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__fit_8h.md
@@ -1,151 +1,151 @@
----
-title: ldsCtrlEst_h/lds_fit.h
-summary: LDS base fit type. 
-
----
-
-# ldsCtrlEst_h/lds_fit.h
-
-LDS base fit type.  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/)** <br>LDS [Fit]() Type.  |
-
-## Detailed Description
-
-
-
-This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting. 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDS_FIT_HPP
-#define LDS_FIT_HPP
-
-// namespace
-#include "lds.h"
-#include "lds_uniform_mats.h"
-
-namespace lds {
-class Fit {
- public:
-  Fit() = default;
-  Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt);
-
-  virtual ~Fit() = default;
-
-  // get methods
-  size_t n_u() const { return n_u_; };
-  size_t n_x() const { return n_x_; };
-  size_t n_y() const { return n_y_; };
-  data_t dt() const { return dt_; };
-  const Matrix& A() const { return A_; };
-  const Matrix& B() const { return B_; };
-  const Vector& g() const { return g_; };
-  const Vector& m() const { return m_; };
-  const Matrix& Q() const { return Q_; };
-  const Vector& x0() const { return x0_; };
-  const Matrix& P0() const { return P0_; };
-  const Matrix& C() const { return C_; };
-  const Vector& d() const { return d_; };
-  // gets measurement noise
-  virtual const Matrix& R() const = 0;
-
-  // set methods (e.g., seeding initial fit values)
-  void set_A(const Matrix& A) { Reassign(A_, A); };
-  void set_B(const Matrix& B) { Reassign(B_, B); };
-  void set_g(const Vector& g) { Reassign(g_, g); };
-  void set_m(const Vector& m) { Reassign(m_, m); };
-  void set_Q(const Matrix& Q) {
-    Reassign(Q_, Q);
-    ForceSymPD(Q_);
-  };
-  virtual void set_R(const Matrix& R) = 0;
-  void set_x0(const Vector& x0) { Reassign(x0_, x0); };
-  void set_P0(const Matrix& P0) {
-    Reassign(P0_, P0);
-    ForceSymPD(P0_);
-  };
-  void set_C(const Matrix& C) { Reassign(C_, C); };
-  void set_d(const Vector& d) { Reassign(d_, d); };
-
-  View f(Matrix& x, const Matrix& u, size_t t) {
-    x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_;
-    return x.col(t);
-  };
-
-  View f(Matrix& x_pre, const Matrix& x_post, const Matrix& u, size_t t) {
-    x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_;
-    return x_pre.col(t);
-  };
-
-  virtual View h(Matrix& y, const Matrix& x, size_t t) = 0;
-
- protected:
-  data_t dt_{};  
-
-  // Dynamics
-  Matrix A_;  
-  Matrix B_;  
-  Vector g_;  
-  Vector m_;  
-  Matrix Q_;  
-
-  // Output
-  Matrix C_;  
-  Vector d_;  
-  Matrix R_; 
-
-  // initial conditions
-  Vector x0_;  
-  Matrix P0_;  
-
-  size_t n_u_{};  
-  size_t n_x_{};  
-  size_t n_y_{};  
-};
-
-}  // namespace lds
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_fit.h
+summary: LDS base fit type. 
+
+---
+
+# ldsCtrlEst_h/lds_fit.h
+
+LDS base fit type.  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/)** <br>LDS [Fit]() Type.  |
+
+## Detailed Description
+
+
+
+This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting. 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDS_FIT_HPP
+#define LDS_FIT_HPP
+
+// namespace
+#include "lds.h"
+#include "lds_uniform_mats.h"
+
+namespace lds {
+class Fit {
+ public:
+  Fit() = default;
+  Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt);
+
+  virtual ~Fit() = default;
+
+  // get methods
+  size_t n_u() const { return n_u_; };
+  size_t n_x() const { return n_x_; };
+  size_t n_y() const { return n_y_; };
+  data_t dt() const { return dt_; };
+  const Matrix& A() const { return A_; };
+  const Matrix& B() const { return B_; };
+  const Vector& g() const { return g_; };
+  const Vector& m() const { return m_; };
+  const Matrix& Q() const { return Q_; };
+  const Vector& x0() const { return x0_; };
+  const Matrix& P0() const { return P0_; };
+  const Matrix& C() const { return C_; };
+  const Vector& d() const { return d_; };
+  // gets measurement noise
+  virtual const Matrix& R() const = 0;
+
+  // set methods (e.g., seeding initial fit values)
+  void set_A(const Matrix& A) { Reassign(A_, A); };
+  void set_B(const Matrix& B) { Reassign(B_, B); };
+  void set_g(const Vector& g) { Reassign(g_, g); };
+  void set_m(const Vector& m) { Reassign(m_, m); };
+  void set_Q(const Matrix& Q) {
+    Reassign(Q_, Q);
+    ForceSymPD(Q_);
+  };
+  virtual void set_R(const Matrix& R) = 0;
+  void set_x0(const Vector& x0) { Reassign(x0_, x0); };
+  void set_P0(const Matrix& P0) {
+    Reassign(P0_, P0);
+    ForceSymPD(P0_);
+  };
+  void set_C(const Matrix& C) { Reassign(C_, C); };
+  void set_d(const Vector& d) { Reassign(d_, d); };
+
+  View f(Matrix& x, const Matrix& u, size_t t) {
+    x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_;
+    return x.col(t);
+  };
+
+  View f(Matrix& x_pre, const Matrix& x_post, const Matrix& u, size_t t) {
+    x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_;
+    return x_pre.col(t);
+  };
+
+  virtual View h(Matrix& y, const Matrix& x, size_t t) = 0;
+
+ protected:
+  data_t dt_{};  
+
+  // Dynamics
+  Matrix A_;  
+  Matrix B_;  
+  Vector g_;  
+  Vector m_;  
+  Matrix Q_;  
+
+  // Output
+  Matrix C_;  
+  Vector d_;  
+  Matrix R_; 
+
+  // initial conditions
+  Vector x0_;  
+  Matrix P0_;  
+
+  size_t n_u_{};  
+  size_t n_x_{};  
+  size_t n_y_{};  
+};
+
+}  // namespace lds
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__fit__em_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__fit__em_8h.md
index ae2dbecb..4b678e48 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__fit__em_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__fit__em_8h.md
@@ -1,634 +1,634 @@
----
-title: ldsCtrlEst_h/lds_fit_em.h
-summary: subspace identification 
-
----
-
-# ldsCtrlEst_h/lds_fit_em.h
-
-subspace identification  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)**  |
-
-## Detailed Description
-
-
-
-This file declares the type for fitting a linear dynamical system by expectation-maximization ([lds::EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)). 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_EMAX_H
-#define LDSCTRLEST_LDS_EMAX_H
-
-#include "lds_fit.h"
-
-namespace lds {
-
-template <typename Fit>
-class EM {
-  static_assert(std::is_base_of<lds::Fit, Fit>::value,
-                "Fit must be derived from lds::Fit type.");
-
- public:
-  EM() = default;
-
-  EM(size_t n_x, data_t dt, UniformMatrixList<kMatFreeDim2>&& u_train,
-     UniformMatrixList<kMatFreeDim2>&& z_train);
-
-  EM(const Fit& fit0, UniformMatrixList<kMatFreeDim2>&& u_train,
-     UniformMatrixList<kMatFreeDim2>&& z_train);
-
-  virtual ~EM() = default;
-
-  const Fit& Run(bool calc_dynamics = true, bool calc_Q = true,
-                 bool calc_init = true, bool calc_output = true,
-                 bool calc_measurement = true, size_t max_iter = 100,
-                 data_t tol = 1e-2);
-
-  std::tuple<UniformMatrixList<kMatFreeDim2>, UniformMatrixList<kMatFreeDim2>>
-  ReturnData() {
-    auto tuple = std::make_tuple(std::move(u_), std::move(z_));
-    // auto tuple = std::make_tuple(u_, z_);
-    u_ = UniformMatrixList<kMatFreeDim2>();
-    z_ = UniformMatrixList<kMatFreeDim2>();
-    return tuple;
-  }
-
-  const std::vector<Matrix>& x() const { return x_; };
-  const std::vector<Matrix>& y() const { return y_; };
-
-  const Matrix& sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; };
-  const Matrix& sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; };
-  const Matrix& sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; };
-  size_t n_t_tot() { return n_t_tot_; }
-
-  const Vector& theta() const { return theta_; };
-
- protected:
-  void Expectation(bool force_common_initial = false);
-
-  void Maximization(bool calc_dynamics = true, bool calc_Q = true,
-                    bool calc_init = false, bool calc_output = false,
-                    bool calc_measurement = false);
-
-  void MaximizeDynamics();
-  void MaximizeQ();
-  void MaximizeInitial();
-  virtual void MaximizeOutput() = 0;
-  virtual void MaximizeMeasurement() = 0;
-
-  void Smooth(bool force_common_initial);
-
-  virtual void RecurseKe(Matrix& Ke, Cube& P_pre, Cube& P_post, size_t t) = 0;
-
-  void Reset();
-
-  void InitVars();
-
-  Vector UpdateTheta();
-
-  // input/output training data
-  UniformMatrixList<kMatFreeDim2> u_;  
-  UniformMatrixList<kMatFreeDim2> z_;  
-  std::vector<Matrix> x_;              
-  std::vector<Cube> P_;                
-  std::vector<Cube> P_t_tm1_;          
-  std::vector<Matrix> y_;              
-  Matrix diag_y_;
-
-  // expectations calculated in E-step
-  Matrix sum_E_x_t_x_t_;        
-  Matrix sum_E_xu_tm1_xu_tm1_;  
-  Matrix sum_E_xu_t_xu_tm1_;    
-
-  Fit fit_;
-  Vector theta_;
-
-  data_t dt_{};              
-  size_t n_u_{};             
-  size_t n_x_{};             
-  size_t n_y_{};             
-  size_t n_trials_{};        
-  std::vector<size_t> n_t_;  
-  size_t n_t_tot_{};         
-};
-
-template <typename Fit>
-EM<Fit>::EM(size_t n_x, data_t dt, UniformMatrixList<kMatFreeDim2>&& u_train,
-            UniformMatrixList<kMatFreeDim2>&& z_train) {
-  n_u_ = u_train.at(0).n_rows;
-  n_y_ = z_train.at(0).n_rows;
-  fit_ = Fit(n_u_, n_x, n_y_, dt);
-  u_ = std::move(u_train);
-  z_ = std::move(z_train);
-  InitVars();
-}
-
-template <typename Fit>
-EM<Fit>::EM(const Fit& fit0, UniformMatrixList<kMatFreeDim2>&& u_train,
-            UniformMatrixList<kMatFreeDim2>&& z_train) {
-  // make sure fit dims match I/O data
-  if (fit0.n_u() != u_train.at(0).n_rows) {
-    throw std::runtime_error(
-        "Initial fit and input training data have inconsistent dimensions");
-  }
-  if (fit0.n_y() != z_train.at(0).n_rows) {
-    throw std::runtime_error(
-        "Initial fit and output training data have inconsistent dimensions");
-  }
-
-  fit_ = fit0;
-  u_ = std::move(u_train);
-  z_ = std::move(z_train);
-
-  InitVars();
-}
-
-template <typename Fit>
-void EM<Fit>::InitVars() {
-  // check input/output data dimensions are consistent
-  if (z_.size() != u_.size()) {
-    throw std::runtime_error(
-        "I/O training data have different number of trials.");
-  }
-  n_trials_ = u_.size();
-
-  n_t_tot_ = 0;
-  n_t_ = std::vector<size_t>(n_trials_);
-  for (size_t trial = 0; trial < n_trials_; trial++) {
-    if (z_.at(trial).n_cols != u_.at(trial).n_cols) {
-      throw std::runtime_error(
-          "I/O training data have different number of time steps.");
-    }
-    n_t_[trial] = u_.at(trial).n_cols;
-    n_t_tot_ += n_t_[trial];
-  }
-
-  n_u_ = fit_.n_u();
-  n_x_ = fit_.n_x();
-  n_y_ = fit_.n_y();
-  dt_ = fit_.dt();
-
-  x_ = std::vector<Matrix>(n_trials_);
-  P_ = std::vector<Cube>(n_trials_);
-  P_t_tm1_ = std::vector<Cube>(n_trials_);
-  y_ = std::vector<Matrix>(n_trials_);
-  for (size_t trial = 0; trial < n_trials_; trial++) {
-    x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros);
-    P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros);
-    P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros);
-    y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros);
-  }
-
-  diag_y_ = Matrix(n_y_, n_y_, fill::zeros);
-
-  // covariances in expectation step
-  sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros);
-  sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros);
-  sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros);
-}
-
-template <typename Fit>
-const Fit& EM<Fit>::Run(bool calc_dynamics, bool calc_Q, bool calc_init,
-                        bool calc_output, bool calc_measurement,
-                        size_t max_iter, data_t tol) {
-  Reset();  // to initial conditions
-
-  size_t n_params =
-      3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_;
-  Vector theta(n_params);
-  Vector theta_new(n_params);
-  data_t max_dtheta = 1;
-
-  // if solving for initial conditions, allow them be varied.
-  // otherwise, freeze at provided values.
-  bool force_common_initial = !calc_init;
-
-  // go until parameter convergence
-  for (size_t l = 0; l < max_iter; l++) {
-    theta_ = UpdateTheta();
-
-    std::cout << "Iteration " << l + 1 << "/" << max_iter << " ...\n";
-
-    Expectation(force_common_initial);
-    Maximization(calc_dynamics, calc_Q, calc_init, calc_output,
-                 calc_measurement);
-
-    // check convergence
-    theta_new = UpdateTheta();
-
-    Vector dtheta = abs(theta_new - theta_) / abs(theta_);
-    // some parameters could be zero...
-    arma::uvec ubi_finite = find_finite(dtheta);
-
-    max_dtheta = max(dtheta.elem(ubi_finite));
-    std::cout << "max dtheta: " << max_dtheta << "\n";
-    if (max_dtheta < tol) {
-      std::cout << "Converged.\n";
-      break;
-    }
-
-    std::cout << "\n";
-  }
-
-  return fit_;
-}
-
-template <typename Fit>
-void EM<Fit>::Smooth(bool force_common_initial) {
-  Matrix k_e(n_x_, n_y_);  // estimator gain
-  Cube k_backfilt;         // back-filtering gains
-
-  // TODO(mfbolus): this loop could be made parallel
-  for (size_t trial = 0; trial < z_.size(); trial++) {
-    Matrix x_pre(n_x_, n_t_[trial], fill::zeros);
-    Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros);
-    Matrix x_post(n_x_, n_t_[trial], fill::zeros);
-    Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros);
-
-    if (force_common_initial)  // forces all trials to have same initial
-                               // conditions.
-    {
-      x_[trial].col(0) = fit_.x0();
-      P_[trial].slice(0) = fit_.P0();
-    }
-    y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d();
-
-    // This *should not* be necessary but make sure P is symmetric.
-    ForceSymPD(P_[trial].slice(0));
-
-    x_pre.col(0) = x_[trial].col(0);
-    p_pre.slice(0) = P_[trial].slice(0);
-
-    x_post.col(0) = x_[trial].col(0);
-    p_post.slice(0) = P_[trial].slice(0);
-
-    // filter
-    for (size_t t = 1; t < n_t_[trial]; t++) {
-      // predict
-      fit_.f(x_pre, x_post, u_.at(trial), t);
-      fit_.h(y_[trial], x_pre, t);
-      diag_y_.diag() = y_[trial].col(t);  // TODO(mfbolus): change if parallel
-
-      // update --> posterior estimation
-      RecurseKe(k_e, p_pre, p_post, t);
-      x_post.col(t) =
-          x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t));
-      y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d();
-    }
-
-    // backfilter -> Smoothed estimate
-    // Reference:
-    // Shumway et Stoffer (1982)
-    ForceSymPD(p_post.slice(n_t_[trial] - 1));
-    k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros);
-    x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1);
-    P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1);
-    for (size_t t = (n_t_[trial] - 1); t > 0; t--) {
-      // TODO(mfmbolus): should not be necessary to force symm positive def
-      ForceSymPD(p_pre.slice(t));
-      ForceSymPD(p_post.slice(t - 1));
-      ForceSymPD(P_[trial].slice(t));
-      k_backfilt.slice(t - 1) =
-          p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t));
-      x_[trial].col(t - 1) =
-          x_post.col(t - 1) +
-          k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t));
-      P_[trial].slice(t - 1) =
-          p_post.slice(t - 1) + k_backfilt.slice(t - 1) *
-                                    (P_[trial].slice(t) - p_pre.slice(t)) *
-                                    k_backfilt.slice(t - 1).t();
-    }
-
-    // do the same for P_t_tm1
-    Matrix id(n_x_, n_x_, fill::eye);
-    P_t_tm1_[trial].slice(n_t_[trial] - 1) =
-        (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2);
-    for (size_t t = (n_t_[trial] - 1); t > 1; t--) {
-      P_t_tm1_[trial].slice(t - 1) =
-          p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() +
-          k_backfilt.slice(t - 1) *
-              (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) *
-              k_backfilt.slice(t - 2).t();
-    }
-
-    // finally, get smoothed estimate of output
-    for (size_t t = 0; t < n_t_[trial]; t++) {
-      fit_.h(y_[trial], x_[trial], t);
-    }  // samps loop
-  }    // trial loop
-}  // Smooth
-
-// template <typename Fit>
-// void EM<Fit>::RecurseKe(Matrix& Ke, Cube& P_pre, Cube& P_post, size_t t) {
-//   // predict covar
-//   P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q();
-
-//   // update Ke
-//   Ke = P_pre.slice(t) * fit_.C().t() *
-//        inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R());
-
-//   // update cov
-//   // Reference: Ghahramani et Hinton (1996)
-//   P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t);
-
-//   // // n.b. for poisson :
-//   // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() +
-//   fit_.Q();
-//   // // update cov
-//   // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ *
-//   // fit_.C());
-//   // // update Ke
-//   // Ke = P_post.slice(t) * fit_.C();
-// }
-
-template <typename Fit>
-void EM<Fit>::Expectation(bool force_common_initial) {
-  // calculate the mean/cov of state needed for maximizing E[pr(z|theta)]
-  Smooth(force_common_initial);
-
-  // now get the various forms of sum(E[xx']) needed
-  // n.b. Going to start at t=1 rather than 0 bc most max terms need that.
-  // so really "n_t_tot_" is (n_t_tot_-1)
-  n_t_tot_ = 0;
-  sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros);
-  sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros);
-  sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros);
-
-  Vector xu_tm1(n_x_ + n_u_, fill::zeros);
-  Vector xu_t(n_x_ + n_u_, fill::zeros);
-
-  for (size_t trial = 0; trial < z_.size(); trial++) {
-    for (size_t t = 1; t < n_t_[trial]; t++) {
-      // ------------ sum_E_x_t_x_t ------------
-      sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t();
-      sum_E_x_t_x_t_ += P_[trial].slice(t);
-
-      // ------------ sum_E_xu_tm1_xu_tm1 ------------
-      xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1));
-      sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t();
-      sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) +=
-          P_[trial].slice(t - 1);
-
-      // ------------ sum_E_xu_t_xu_tm1 ------------
-      xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t));
-      sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t();
-      sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) +=
-          P_t_tm1_[trial].slice(t);
-
-      n_t_tot_ += 1;
-    }  // time
-  }    // trial
-}  // Expectation
-
-template <typename Fit>
-void EM<Fit>::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init,
-                           bool calc_output, bool calc_measurement) {
-  if (calc_output) {
-    MaximizeOutput();
-  }
-
-  if (calc_measurement) {
-    MaximizeMeasurement();
-  }
-
-  if (calc_dynamics) {
-    MaximizeDynamics();
-  }
-
-  if (calc_Q) {
-    MaximizeQ();
-  }
-
-  if (calc_init) {
-    MaximizeInitial();
-  }
-}  // Maximization
-
-template <typename Fit>
-void EM<Fit>::MaximizeDynamics() {
-  // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996)
-  Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) *
-              inv_sympd(sum_E_xu_tm1_xu_tm1_);
-  fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1));
-  fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1));
-  std::cout << "A_new[0]: " << fit_.A()[0] << "\n";
-  std::cout << "B_new[0]: " << fit_.B()[0] << "\n";
-}
-
-template <typename Fit>
-void EM<Fit>::MaximizeQ() {
-  // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996)
-  // View sum_e_x_t_xu_tm1 =
-  //     sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1);
-  // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 *
-  //                                inv_sympd(sum_E_xu_tm1_xu_tm1_) *
-  //                                sum_e_x_t_xu_tm1.t();
-  // q /= n_t_tot_;
-
-  // this way is same as above iff dynamics were just updated:
-  // View sum_e_x_t_xu_tm1 =
-  //     sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1);
-  // Matrix ab = arma::join_horiz(fit_.A(), fit_.B());
-  // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t();
-  // q /= n_t_tot_;
-
-  // From scratch method:
-  // Q is covariance of the error between state and dynamics-predicted state
-  // (aka process noise)
-  // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})']
-  // t-1 terms:
-  View sum_e_x_tm1_x_tm1 =
-      sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1);
-  View sum_e_u_tm1_u_tm1 =
-      sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1);
-  View sum_e_x_tm1_u_tm1 =
-      sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1);
-
-  // t, t-1 terms:
-  View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1);
-  View sum_e_x_t_u_tm1 =
-      sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1);
-
-  Matrix q = sum_E_x_t_x_t_;
-  q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t();
-  q -= sum_e_x_t_x_tm1 * fit_.A().t();
-  q -= fit_.A() * sum_e_x_t_x_tm1.t();
-  // input-related terms:
-  q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t();
-  q -= sum_e_x_t_u_tm1 * fit_.B().t();
-  q -= fit_.B() * sum_e_x_t_u_tm1.t();
-  q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t();
-  q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t();
-  q /= n_t_tot_;
-
-  fit_.set_Q(q);
-  std::cout << "Q_new[0]: " << fit_.Q()[0] << "\n";
-  // std::cout << "Q_new: \n" << fit_.Q() << "\n";
-}
-
-template <typename Fit>
-void EM<Fit>::MaximizeInitial() {
-  Vector x0 = fit_.x0();
-  x0.zeros();
-  for (size_t trial = 0; trial < z_.size(); trial++) {
-    x0 += x_[trial].col(0);
-  }
-  x0 /= z_.size();
-  std::cout << "x0_new[0]: " << x0[0] << "\n";
-
-  // always recalc P0 even if the initial state is fixed (at zero, for
-  // example)
-  Matrix e_var(n_x_, n_x_, fill::zeros);
-  for (size_t trial = 0; trial < z_.size(); trial++) {
-    e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t();
-  }
-  e_var /= z_.size();
-
-  // go ahead and subtract x0*x0' so don't have to below.
-  e_var -= x0 * x0.t();
-
-  // To get P0, going to get initial P_ per trial and average.
-  // (which might be wrong, but need a single number)
-  Matrix p0 = fit_.P0();
-  p0.zeros();
-  for (size_t trial = 0; trial < z_.size(); trial++) {
-    p0 +=
-        (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var;
-  }
-  p0 /= z_.size();
-
-  fit_.set_P0(p0);
-  std::cout << "P0_new[0]: " << fit_.P0()[0] << "\n";
-}
-
-template <typename Fit>
-void EM<Fit>::MaximizeOutput() {
-  // solve for C+d:
-  Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros);
-  Vector x1(n_x_ + 1, fill::zeros);
-  x1[n_x_] = 1.0;  // augment with one to solve for bias
-  Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros);
-  for (size_t trial = 0; trial < z_.size(); trial++) {
-    for (size_t t = 1; t < n_t_[trial]; t++) {
-      x1.subvec(0, n_x_ - 1) = x_[trial].col(t);
-      sum_zx += z_.at(trial).col(t) * x1.t();
-      sum_e_x1_x1 += x1 * x1.t();
-      sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t);
-    }
-  }
-  Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1);
-  fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1));
-  fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_)));
-  std::cout << "C_new[0]: " << fit_.C()[0] << "\n";
-  std::cout << "d_new[0]: " << fit_.d()[0] << "\n";
-}
-
-template <typename Fit>
-void EM<Fit>::MaximizeMeasurement() {
-  // Solve for measurement noise covar
-  size_t n_t_tot = 0;
-  // Ghahgramani, Hinton 1996:
-  Matrix sum_zz(n_y_, n_y_, fill::zeros);
-  Matrix sum_yz(n_y_, n_y_, fill::zeros);
-  for (size_t trial = 0; trial < z_.size(); trial++) {
-    for (size_t t = 1; t < n_t_[trial]; t++) {
-      sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t();
-      // Use Cnew:
-      sum_yz +=
-          (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t();
-      n_t_tot += 1;
-    }
-  }
-  fit_.set_R((sum_zz - sum_yz) / n_t_tot);
-  std::cout << "R_new[0]: " << fit_.R()[0] << "\n";
-}
-
-template <typename Fit>
-void EM<Fit>::Reset() {
-  // reset to initial conditions
-  for (size_t trial = 0; trial < n_trials_; trial++) {
-    x_[trial].col(0) = fit_.x0();
-    P_[trial].slice(0) = fit_.P0();
-    y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d();
-  }
-}
-
-template <typename Fit>
-Vector EM<Fit>::UpdateTheta() {
-  // TODO(mfbolus): This should include n_y_ more params for d.
-  size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_;
-  if (fit_.R().n_elem > 0) {
-    n_params += n_y_ * n_y_;
-  }
-  Vector theta(n_params);
-
-  size_t idx_start = 0;
-  theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A());
-  idx_start += n_x_ * n_x_;
-  theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B());
-  idx_start += n_x_ * n_u_;
-  theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q());
-  idx_start += n_x_ * n_x_;
-  theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0());
-  idx_start += n_x_;
-  theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0());
-  idx_start += n_x_ * n_x_;
-  theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C());
-  idx_start += n_y_ * n_x_;
-  theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d());
-  idx_start += n_y_;
-  if (fit_.R().n_elem > 0) {
-    theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R());
-  }
-
-  return theta;
-}
-
-}  // namespace lds
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_fit_em.h
+summary: subspace identification 
+
+---
+
+# ldsCtrlEst_h/lds_fit_em.h
+
+subspace identification  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/)**  |
+
+## Detailed Description
+
+
+
+This file declares the type for fitting a linear dynamical system by expectation-maximization ([lds::EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/)). 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_EMAX_H
+#define LDSCTRLEST_LDS_EMAX_H
+
+#include "lds_fit.h"
+
+namespace lds {
+
+template <typename Fit>
+class EM {
+  static_assert(std::is_base_of<lds::Fit, Fit>::value,
+                "Fit must be derived from lds::Fit type.");
+
+ public:
+  EM() = default;
+
+  EM(size_t n_x, data_t dt, UniformMatrixList<kMatFreeDim2>&& u_train,
+     UniformMatrixList<kMatFreeDim2>&& z_train);
+
+  EM(const Fit& fit0, UniformMatrixList<kMatFreeDim2>&& u_train,
+     UniformMatrixList<kMatFreeDim2>&& z_train);
+
+  virtual ~EM() = default;
+
+  const Fit& Run(bool calc_dynamics = true, bool calc_Q = true,
+                 bool calc_init = true, bool calc_output = true,
+                 bool calc_measurement = true, size_t max_iter = 100,
+                 data_t tol = 1e-2);
+
+  std::tuple<UniformMatrixList<kMatFreeDim2>, UniformMatrixList<kMatFreeDim2>>
+  ReturnData() {
+    auto tuple = std::make_tuple(std::move(u_), std::move(z_));
+    // auto tuple = std::make_tuple(u_, z_);
+    u_ = UniformMatrixList<kMatFreeDim2>();
+    z_ = UniformMatrixList<kMatFreeDim2>();
+    return tuple;
+  }
+
+  const std::vector<Matrix>& x() const { return x_; };
+  const std::vector<Matrix>& y() const { return y_; };
+
+  const Matrix& sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; };
+  const Matrix& sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; };
+  const Matrix& sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; };
+  size_t n_t_tot() { return n_t_tot_; }
+
+  const Vector& theta() const { return theta_; };
+
+ protected:
+  void Expectation(bool force_common_initial = false);
+
+  void Maximization(bool calc_dynamics = true, bool calc_Q = true,
+                    bool calc_init = false, bool calc_output = false,
+                    bool calc_measurement = false);
+
+  void MaximizeDynamics();
+  void MaximizeQ();
+  void MaximizeInitial();
+  virtual void MaximizeOutput() = 0;
+  virtual void MaximizeMeasurement() = 0;
+
+  void Smooth(bool force_common_initial);
+
+  virtual void RecurseKe(Matrix& Ke, Cube& P_pre, Cube& P_post, size_t t) = 0;
+
+  void Reset();
+
+  void InitVars();
+
+  Vector UpdateTheta();
+
+  // input/output training data
+  UniformMatrixList<kMatFreeDim2> u_;  
+  UniformMatrixList<kMatFreeDim2> z_;  
+  std::vector<Matrix> x_;              
+  std::vector<Cube> P_;                
+  std::vector<Cube> P_t_tm1_;          
+  std::vector<Matrix> y_;              
+  Matrix diag_y_;
+
+  // expectations calculated in E-step
+  Matrix sum_E_x_t_x_t_;        
+  Matrix sum_E_xu_tm1_xu_tm1_;  
+  Matrix sum_E_xu_t_xu_tm1_;    
+
+  Fit fit_;
+  Vector theta_;
+
+  data_t dt_{};              
+  size_t n_u_{};             
+  size_t n_x_{};             
+  size_t n_y_{};             
+  size_t n_trials_{};        
+  std::vector<size_t> n_t_;  
+  size_t n_t_tot_{};         
+};
+
+template <typename Fit>
+EM<Fit>::EM(size_t n_x, data_t dt, UniformMatrixList<kMatFreeDim2>&& u_train,
+            UniformMatrixList<kMatFreeDim2>&& z_train) {
+  n_u_ = u_train.at(0).n_rows;
+  n_y_ = z_train.at(0).n_rows;
+  fit_ = Fit(n_u_, n_x, n_y_, dt);
+  u_ = std::move(u_train);
+  z_ = std::move(z_train);
+  InitVars();
+}
+
+template <typename Fit>
+EM<Fit>::EM(const Fit& fit0, UniformMatrixList<kMatFreeDim2>&& u_train,
+            UniformMatrixList<kMatFreeDim2>&& z_train) {
+  // make sure fit dims match I/O data
+  if (fit0.n_u() != u_train.at(0).n_rows) {
+    throw std::runtime_error(
+        "Initial fit and input training data have inconsistent dimensions");
+  }
+  if (fit0.n_y() != z_train.at(0).n_rows) {
+    throw std::runtime_error(
+        "Initial fit and output training data have inconsistent dimensions");
+  }
+
+  fit_ = fit0;
+  u_ = std::move(u_train);
+  z_ = std::move(z_train);
+
+  InitVars();
+}
+
+template <typename Fit>
+void EM<Fit>::InitVars() {
+  // check input/output data dimensions are consistent
+  if (z_.size() != u_.size()) {
+    throw std::runtime_error(
+        "I/O training data have different number of trials.");
+  }
+  n_trials_ = u_.size();
+
+  n_t_tot_ = 0;
+  n_t_ = std::vector<size_t>(n_trials_);
+  for (size_t trial = 0; trial < n_trials_; trial++) {
+    if (z_.at(trial).n_cols != u_.at(trial).n_cols) {
+      throw std::runtime_error(
+          "I/O training data have different number of time steps.");
+    }
+    n_t_[trial] = u_.at(trial).n_cols;
+    n_t_tot_ += n_t_[trial];
+  }
+
+  n_u_ = fit_.n_u();
+  n_x_ = fit_.n_x();
+  n_y_ = fit_.n_y();
+  dt_ = fit_.dt();
+
+  x_ = std::vector<Matrix>(n_trials_);
+  P_ = std::vector<Cube>(n_trials_);
+  P_t_tm1_ = std::vector<Cube>(n_trials_);
+  y_ = std::vector<Matrix>(n_trials_);
+  for (size_t trial = 0; trial < n_trials_; trial++) {
+    x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros);
+    P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros);
+    P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros);
+    y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros);
+  }
+
+  diag_y_ = Matrix(n_y_, n_y_, fill::zeros);
+
+  // covariances in expectation step
+  sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros);
+  sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros);
+  sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros);
+}
+
+template <typename Fit>
+const Fit& EM<Fit>::Run(bool calc_dynamics, bool calc_Q, bool calc_init,
+                        bool calc_output, bool calc_measurement,
+                        size_t max_iter, data_t tol) {
+  Reset();  // to initial conditions
+
+  size_t n_params =
+      3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_;
+  Vector theta(n_params);
+  Vector theta_new(n_params);
+  data_t max_dtheta = 1;
+
+  // if solving for initial conditions, allow them be varied.
+  // otherwise, freeze at provided values.
+  bool force_common_initial = !calc_init;
+
+  // go until parameter convergence
+  for (size_t l = 0; l < max_iter; l++) {
+    theta_ = UpdateTheta();
+
+    std::cout << "Iteration " << l + 1 << "/" << max_iter << " ...\n";
+
+    Expectation(force_common_initial);
+    Maximization(calc_dynamics, calc_Q, calc_init, calc_output,
+                 calc_measurement);
+
+    // check convergence
+    theta_new = UpdateTheta();
+
+    Vector dtheta = abs(theta_new - theta_) / abs(theta_);
+    // some parameters could be zero...
+    arma::uvec ubi_finite = find_finite(dtheta);
+
+    max_dtheta = max(dtheta.elem(ubi_finite));
+    std::cout << "max dtheta: " << max_dtheta << "\n";
+    if (max_dtheta < tol) {
+      std::cout << "Converged.\n";
+      break;
+    }
+
+    std::cout << "\n";
+  }
+
+  return fit_;
+}
+
+template <typename Fit>
+void EM<Fit>::Smooth(bool force_common_initial) {
+  Matrix k_e(n_x_, n_y_);  // estimator gain
+  Cube k_backfilt;         // back-filtering gains
+
+  // TODO(mfbolus): this loop could be made parallel
+  for (size_t trial = 0; trial < z_.size(); trial++) {
+    Matrix x_pre(n_x_, n_t_[trial], fill::zeros);
+    Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros);
+    Matrix x_post(n_x_, n_t_[trial], fill::zeros);
+    Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros);
+
+    if (force_common_initial)  // forces all trials to have same initial
+                               // conditions.
+    {
+      x_[trial].col(0) = fit_.x0();
+      P_[trial].slice(0) = fit_.P0();
+    }
+    y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d();
+
+    // This *should not* be necessary but make sure P is symmetric.
+    ForceSymPD(P_[trial].slice(0));
+
+    x_pre.col(0) = x_[trial].col(0);
+    p_pre.slice(0) = P_[trial].slice(0);
+
+    x_post.col(0) = x_[trial].col(0);
+    p_post.slice(0) = P_[trial].slice(0);
+
+    // filter
+    for (size_t t = 1; t < n_t_[trial]; t++) {
+      // predict
+      fit_.f(x_pre, x_post, u_.at(trial), t);
+      fit_.h(y_[trial], x_pre, t);
+      diag_y_.diag() = y_[trial].col(t);  // TODO(mfbolus): change if parallel
+
+      // update --> posterior estimation
+      RecurseKe(k_e, p_pre, p_post, t);
+      x_post.col(t) =
+          x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t));
+      y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d();
+    }
+
+    // backfilter -> Smoothed estimate
+    // Reference:
+    // Shumway et Stoffer (1982)
+    ForceSymPD(p_post.slice(n_t_[trial] - 1));
+    k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros);
+    x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1);
+    P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1);
+    for (size_t t = (n_t_[trial] - 1); t > 0; t--) {
+      // TODO(mfmbolus): should not be necessary to force symm positive def
+      ForceSymPD(p_pre.slice(t));
+      ForceSymPD(p_post.slice(t - 1));
+      ForceSymPD(P_[trial].slice(t));
+      k_backfilt.slice(t - 1) =
+          p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t));
+      x_[trial].col(t - 1) =
+          x_post.col(t - 1) +
+          k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t));
+      P_[trial].slice(t - 1) =
+          p_post.slice(t - 1) + k_backfilt.slice(t - 1) *
+                                    (P_[trial].slice(t) - p_pre.slice(t)) *
+                                    k_backfilt.slice(t - 1).t();
+    }
+
+    // do the same for P_t_tm1
+    Matrix id(n_x_, n_x_, fill::eye);
+    P_t_tm1_[trial].slice(n_t_[trial] - 1) =
+        (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2);
+    for (size_t t = (n_t_[trial] - 1); t > 1; t--) {
+      P_t_tm1_[trial].slice(t - 1) =
+          p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() +
+          k_backfilt.slice(t - 1) *
+              (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) *
+              k_backfilt.slice(t - 2).t();
+    }
+
+    // finally, get smoothed estimate of output
+    for (size_t t = 0; t < n_t_[trial]; t++) {
+      fit_.h(y_[trial], x_[trial], t);
+    }  // samps loop
+  }    // trial loop
+}  // Smooth
+
+// template <typename Fit>
+// void EM<Fit>::RecurseKe(Matrix& Ke, Cube& P_pre, Cube& P_post, size_t t) {
+//   // predict covar
+//   P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q();
+
+//   // update Ke
+//   Ke = P_pre.slice(t) * fit_.C().t() *
+//        inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R());
+
+//   // update cov
+//   // Reference: Ghahramani et Hinton (1996)
+//   P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t);
+
+//   // // n.b. for poisson :
+//   // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() +
+//   fit_.Q();
+//   // // update cov
+//   // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ *
+//   // fit_.C());
+//   // // update Ke
+//   // Ke = P_post.slice(t) * fit_.C();
+// }
+
+template <typename Fit>
+void EM<Fit>::Expectation(bool force_common_initial) {
+  // calculate the mean/cov of state needed for maximizing E[pr(z|theta)]
+  Smooth(force_common_initial);
+
+  // now get the various forms of sum(E[xx']) needed
+  // n.b. Going to start at t=1 rather than 0 bc most max terms need that.
+  // so really "n_t_tot_" is (n_t_tot_-1)
+  n_t_tot_ = 0;
+  sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros);
+  sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros);
+  sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros);
+
+  Vector xu_tm1(n_x_ + n_u_, fill::zeros);
+  Vector xu_t(n_x_ + n_u_, fill::zeros);
+
+  for (size_t trial = 0; trial < z_.size(); trial++) {
+    for (size_t t = 1; t < n_t_[trial]; t++) {
+      // ------------ sum_E_x_t_x_t ------------
+      sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t();
+      sum_E_x_t_x_t_ += P_[trial].slice(t);
+
+      // ------------ sum_E_xu_tm1_xu_tm1 ------------
+      xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1));
+      sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t();
+      sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) +=
+          P_[trial].slice(t - 1);
+
+      // ------------ sum_E_xu_t_xu_tm1 ------------
+      xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t));
+      sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t();
+      sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) +=
+          P_t_tm1_[trial].slice(t);
+
+      n_t_tot_ += 1;
+    }  // time
+  }    // trial
+}  // Expectation
+
+template <typename Fit>
+void EM<Fit>::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init,
+                           bool calc_output, bool calc_measurement) {
+  if (calc_output) {
+    MaximizeOutput();
+  }
+
+  if (calc_measurement) {
+    MaximizeMeasurement();
+  }
+
+  if (calc_dynamics) {
+    MaximizeDynamics();
+  }
+
+  if (calc_Q) {
+    MaximizeQ();
+  }
+
+  if (calc_init) {
+    MaximizeInitial();
+  }
+}  // Maximization
+
+template <typename Fit>
+void EM<Fit>::MaximizeDynamics() {
+  // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996)
+  Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) *
+              inv_sympd(sum_E_xu_tm1_xu_tm1_);
+  fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1));
+  fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1));
+  std::cout << "A_new[0]: " << fit_.A()[0] << "\n";
+  std::cout << "B_new[0]: " << fit_.B()[0] << "\n";
+}
+
+template <typename Fit>
+void EM<Fit>::MaximizeQ() {
+  // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996)
+  // View sum_e_x_t_xu_tm1 =
+  //     sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1);
+  // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 *
+  //                                inv_sympd(sum_E_xu_tm1_xu_tm1_) *
+  //                                sum_e_x_t_xu_tm1.t();
+  // q /= n_t_tot_;
+
+  // this way is same as above iff dynamics were just updated:
+  // View sum_e_x_t_xu_tm1 =
+  //     sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1);
+  // Matrix ab = arma::join_horiz(fit_.A(), fit_.B());
+  // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t();
+  // q /= n_t_tot_;
+
+  // From scratch method:
+  // Q is covariance of the error between state and dynamics-predicted state
+  // (aka process noise)
+  // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})']
+  // t-1 terms:
+  View sum_e_x_tm1_x_tm1 =
+      sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1);
+  View sum_e_u_tm1_u_tm1 =
+      sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1);
+  View sum_e_x_tm1_u_tm1 =
+      sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1);
+
+  // t, t-1 terms:
+  View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1);
+  View sum_e_x_t_u_tm1 =
+      sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1);
+
+  Matrix q = sum_E_x_t_x_t_;
+  q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t();
+  q -= sum_e_x_t_x_tm1 * fit_.A().t();
+  q -= fit_.A() * sum_e_x_t_x_tm1.t();
+  // input-related terms:
+  q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t();
+  q -= sum_e_x_t_u_tm1 * fit_.B().t();
+  q -= fit_.B() * sum_e_x_t_u_tm1.t();
+  q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t();
+  q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t();
+  q /= n_t_tot_;
+
+  fit_.set_Q(q);
+  std::cout << "Q_new[0]: " << fit_.Q()[0] << "\n";
+  // std::cout << "Q_new: \n" << fit_.Q() << "\n";
+}
+
+template <typename Fit>
+void EM<Fit>::MaximizeInitial() {
+  Vector x0 = fit_.x0();
+  x0.zeros();
+  for (size_t trial = 0; trial < z_.size(); trial++) {
+    x0 += x_[trial].col(0);
+  }
+  x0 /= z_.size();
+  std::cout << "x0_new[0]: " << x0[0] << "\n";
+
+  // always recalc P0 even if the initial state is fixed (at zero, for
+  // example)
+  Matrix e_var(n_x_, n_x_, fill::zeros);
+  for (size_t trial = 0; trial < z_.size(); trial++) {
+    e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t();
+  }
+  e_var /= z_.size();
+
+  // go ahead and subtract x0*x0' so don't have to below.
+  e_var -= x0 * x0.t();
+
+  // To get P0, going to get initial P_ per trial and average.
+  // (which might be wrong, but need a single number)
+  Matrix p0 = fit_.P0();
+  p0.zeros();
+  for (size_t trial = 0; trial < z_.size(); trial++) {
+    p0 +=
+        (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var;
+  }
+  p0 /= z_.size();
+
+  fit_.set_P0(p0);
+  std::cout << "P0_new[0]: " << fit_.P0()[0] << "\n";
+}
+
+template <typename Fit>
+void EM<Fit>::MaximizeOutput() {
+  // solve for C+d:
+  Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros);
+  Vector x1(n_x_ + 1, fill::zeros);
+  x1[n_x_] = 1.0;  // augment with one to solve for bias
+  Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros);
+  for (size_t trial = 0; trial < z_.size(); trial++) {
+    for (size_t t = 1; t < n_t_[trial]; t++) {
+      x1.subvec(0, n_x_ - 1) = x_[trial].col(t);
+      sum_zx += z_.at(trial).col(t) * x1.t();
+      sum_e_x1_x1 += x1 * x1.t();
+      sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t);
+    }
+  }
+  Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1);
+  fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1));
+  fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_)));
+  std::cout << "C_new[0]: " << fit_.C()[0] << "\n";
+  std::cout << "d_new[0]: " << fit_.d()[0] << "\n";
+}
+
+template <typename Fit>
+void EM<Fit>::MaximizeMeasurement() {
+  // Solve for measurement noise covar
+  size_t n_t_tot = 0;
+  // Ghahgramani, Hinton 1996:
+  Matrix sum_zz(n_y_, n_y_, fill::zeros);
+  Matrix sum_yz(n_y_, n_y_, fill::zeros);
+  for (size_t trial = 0; trial < z_.size(); trial++) {
+    for (size_t t = 1; t < n_t_[trial]; t++) {
+      sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t();
+      // Use Cnew:
+      sum_yz +=
+          (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t();
+      n_t_tot += 1;
+    }
+  }
+  fit_.set_R((sum_zz - sum_yz) / n_t_tot);
+  std::cout << "R_new[0]: " << fit_.R()[0] << "\n";
+}
+
+template <typename Fit>
+void EM<Fit>::Reset() {
+  // reset to initial conditions
+  for (size_t trial = 0; trial < n_trials_; trial++) {
+    x_[trial].col(0) = fit_.x0();
+    P_[trial].slice(0) = fit_.P0();
+    y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d();
+  }
+}
+
+template <typename Fit>
+Vector EM<Fit>::UpdateTheta() {
+  // TODO(mfbolus): This should include n_y_ more params for d.
+  size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_;
+  if (fit_.R().n_elem > 0) {
+    n_params += n_y_ * n_y_;
+  }
+  Vector theta(n_params);
+
+  size_t idx_start = 0;
+  theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A());
+  idx_start += n_x_ * n_x_;
+  theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B());
+  idx_start += n_x_ * n_u_;
+  theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q());
+  idx_start += n_x_ * n_x_;
+  theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0());
+  idx_start += n_x_;
+  theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0());
+  idx_start += n_x_ * n_x_;
+  theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C());
+  idx_start += n_y_ * n_x_;
+  theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d());
+  idx_start += n_y_;
+  if (fit_.R().n_elem > 0) {
+    theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R());
+  }
+
+  return theta;
+}
+
+}  // namespace lds
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__fit__ssid_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__fit__ssid_8h.md
index c0fd6b5d..05d7bdd1 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__fit__ssid_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__fit__ssid_8h.md
@@ -1,581 +1,581 @@
----
-title: ldsCtrlEst_h/lds_fit_ssid.h
-summary: subspace identification 
-
----
-
-# ldsCtrlEst_h/lds_fit_ssid.h
-
-subspace identification  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)**  |
-
-## Detailed Description
-
-
-
-This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm (`[lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)<Fit>`).
-
-References: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_FIT_SSID_H
-#define LDSCTRLEST_LDS_FIT_SSID_H
-
-#include "lds_fit.h"
-
-namespace lds {
-
-template <typename Fit>
-class SSID {
-  static_assert(std::is_base_of<lds::Fit, Fit>::value,
-                "Fit must be derived from lds::Fit type.");
-
- public:
-  SSID() = default;
-
-  SSID(size_t n_x, size_t n_h, data_t dt,
-       UniformMatrixList<kMatFreeDim2>&& u_train,
-       UniformMatrixList<kMatFreeDim2>&& z_train,
-       const Vector& d = Vector(1).fill(-kInf));
-
-  std::tuple<Fit, Vector> Run(SSIDWt ssid_wt);
-
-  std::tuple<UniformMatrixList<kMatFreeDim2>, UniformMatrixList<kMatFreeDim2>>
-  ReturnData() {
-    auto tuple = std::make_tuple(std::move(u_), std::move(z_));
-    u_ = UniformMatrixList<kMatFreeDim2>();
-    z_ = UniformMatrixList<kMatFreeDim2>();
-    return tuple;
-  }
-
- protected:
-  void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001);
-
-  void CreateHankelDataMat();
-
-  virtual void DecomposeData() = 0;
-
-  void CalcSVD(SSIDWt wt);
-
-  void Solve(data_t wt_dc);
-
-  void RecomputeExtObs();
-
-  // input/output training data
-  UniformMatrixList<kMatFreeDim2> u_;  
-  UniformMatrixList<kMatFreeDim2> z_;  
-  Matrix D_;                           
-
-  Fit fit_;      
-  Matrix g_dc_;  
-
-  data_t dt_{};   
-  size_t n_u_{};  
-  size_t n_x_{};  
-  size_t n_y_{};  
-  size_t n_h_{};
-  size_t n_trials_{};        
-  std::vector<size_t> n_t_;  
-  size_t n_t_tot_{};         
-
-  Matrix L_;          
-  Vector s_;          
-  Matrix ext_obs_t_;  
-};
-
-template <typename Fit>
-SSID<Fit>::SSID(size_t n_x, size_t n_h, data_t dt,
-                UniformMatrixList<kMatFreeDim2>&& u_train,
-                UniformMatrixList<kMatFreeDim2>&& z_train, const Vector& d) {
-  // check input/output data dimensions are consistent
-  if (z_train.size() != u_train.size()) {
-    throw std::runtime_error(
-        "I/O training data have different number of trials.");
-  }
-  n_trials_ = u_train.size();
-
-  n_t_tot_ = 0;
-  n_t_ = std::vector<size_t>(n_trials_);
-  for (size_t trial = 0; trial < n_trials_; trial++) {
-    if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) {
-      throw std::runtime_error(
-          "I/O training data have different number of time steps.");
-    }
-    n_t_[trial] = u_train.at(trial).n_cols;
-    n_t_tot_ += n_t_[trial];
-  }
-
-  dt_ = dt;
-  n_x_ = n_x;
-  n_u_ = u_train.at(0).n_rows;
-  n_y_ = z_train.at(0).n_rows;
-  n_h_ = n_h;
-
-  // dimensionality check for eventual block-hankel data matrix
-  size_t len = n_t_tot_ - 2 * n_h_ + 1;
-  if (len < (2 * n_h_ * (n_u_ + n_y_))) {
-    std::ostringstream ss;
-    ss << "Dataset problem! More rows than columns in block-hankel data "
-          "matrix: 2*(n_u+n_y)*n_h > data-length! Need higher data-length or "
-          "lower n_h.";
-    throw std::runtime_error(ss.str());
-  }
-
-  fit_ = Fit(n_u_, n_x_, n_y_, dt_);
-
-  u_ = std::move(u_train);
-  z_ = std::move(z_train);
-
-  if (!d.is_finite() || (d.n_rows != n_y_)) {
-    // TODO(mfbolus): implement least-square solution for impulse response with
-    // a second input of ones. Data-driven way of accounting for offset *not*
-    // driven by an input.
-    //
-    // For now, calculate output bias (d) as the
-    // output wherever the stimulus has not been on for some amount of time.
-    // convolve u with rectangle and take all samples. This is a reasonable
-    // approach, since often when autonomous systems are fit (i.e., systems with
-    // no input), they will subtract off the mean of the output. This
-    // essentially amounts to setting output bias to the mean of the output when
-    // there is no stimulation.
-    data_t t_silence = 0.1;
-    data_t thresh_silence = 0.001;
-    CalcD(t_silence, thresh_silence);
-  } else {
-    fit_.set_d(d);
-  }
-}
-
-template <typename Fit>
-std::tuple<Fit, Vector> SSID<Fit>::Run(SSIDWt ssid_wt) {
-  // the weight on minimizing dc I/O gain only works for gaussian,
-  // and hopefully not necessary with appropriate dataset.
-  data_t wt_dc = 0;
-  // std::cout << "creating hankel mat\n";
-  CreateHankelDataMat();
-  // std::cout << "decomposing data\n";
-  DecomposeData();
-  // std::cout << "calculating svd\n";
-  CalcSVD(ssid_wt);
-  // std::cout << "solving for params\n";
-  Solve(wt_dc);
-  // std::cout << "fin\n";
-  return std::make_tuple(fit_, s_);
-}
-
-template <typename Fit>
-void SSID<Fit>::CalcD(data_t t_silence, data_t thresh_silence) {
-  Vector d(z_.at(0).n_rows, fill::zeros);
-  Vector win(static_cast<size_t>(t_silence / dt_), fill::ones);
-  Vector sum_z_silence(n_y_, fill::zeros);
-  size_t n_silence(0);
-  for (size_t trial = 0; trial < u_.size(); trial++) {
-    // find silent samples
-    // start by convolving with
-    Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0));
-    Vector u_conv = conv(sum_u, win, "same");
-
-    // get only the samples that are silent...
-    arma::uvec ubi_silence = find(u_conv <= thresh_silence);
-    if (ubi_silence.n_elem > 0) {
-      sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1);
-      n_silence += ubi_silence.n_elem;
-    }
-  }
-  if (n_silence > 0) {
-    d = sum_z_silence / n_silence;
-  }
-  fit_.set_d(d);
-}
-
-template <typename Fit>
-void SSID<Fit>::CreateHankelDataMat() {
-  // temporary copy of data
-  Matrix z(n_y_, n_t_tot_, fill::zeros);
-  Matrix u(n_u_, n_t_tot_, fill::zeros);
-  size_t so_far(0);
-
-  for (size_t trial = 0; trial < z_.size(); trial++) {
-    z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial);
-    u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial);
-    so_far += n_t_.at(trial);
-  }
-
-  // remove output bias
-  z.each_col() -= fit_.d();
-
-  // calculate I/O gain @ DC while data in convenient form
-  g_dc_ = z * pinv(u);
-  // std::cout << "G0_data = " << g_dc_ << "\n";
-
-  // create hankel data matrix
-  size_t len = z.n_cols - 2 * n_h_ + 1;  // data length in hankel mat
-
-  // block-hankel data matrix
-  D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros);
-  // past input
-  auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1);
-  // future input
-  auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1);
-  // past output
-  auto y_p =
-      D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1);
-  // future output
-  auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0,
-                       2 * n_h_ * (n_u_ + n_y_) - 1, len - 1);
-
-  size_t idx = 0;
-  for (size_t k = 0; k < len; k++) {
-    idx = 0;
-    for (size_t kk = k; kk < (n_h_ + k); kk++) {
-      u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk);
-      idx += n_u_;
-    }
-
-    idx = 0;
-    for (size_t kk = (n_h_ + k); kk < (2 * n_h_ + k); kk++) {
-      u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk);
-      idx += n_u_;
-    }
-
-    idx = 0;
-    for (size_t kk = k; kk < (n_h_ + k); kk++) {
-      y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk);
-      idx += n_y_;
-    }
-
-    idx = 0;
-    for (size_t kk = (n_h_ + k); kk < (2 * n_h_ + k); kk++) {
-      y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk);
-      idx += n_y_;
-    }
-  }
-
-  D_ /= sqrt(static_cast<data_t>(len));
-}
-
-// template <typename Fit>
-// void SSID<Fit>::DecomposeData() {
-//   // do LQ decomp instead of calculating covariance expensive way
-//   // Note that "R" in van Overschee is lower-triangular (L), not "R" in QR
-//   // decomp. Very confusing.
-//   Matrix q_t;
-//   lq(L_, q_t, D_);
-//   // van Overschee zeros out the other elements.
-//   L_ = trimatl(L_);
-// }
-
-template <typename Fit>
-void SSID<Fit>::CalcSVD(SSIDWt wt) {
-  // submats that will be needed:
-  auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1,
-                           n_h_ * (2 * n_u_ + n_y_) - 1);
-  auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1);
-  auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1);
-  auto R_23_13 =
-      L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1);
-  auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1,
-                           n_h_ * (2 * n_u_ + n_y_) - 1);
-  auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1,
-                           n_h_ * (2 * n_u_) - 1);
-  auto R_44 =
-      L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1,
-                n_h_ * (2 * n_u_ + n_y_) - 1);
-  auto R_56_14 =
-      L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1,
-                n_h_ * (2 * n_u_ + n_y_) - 1);
-
-  Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14);
-  auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1);
-  auto Luf =
-      Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1);
-  auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1,
-                                n_h_ * (2 * n_u_ + n_y_) - 1);
-
-  // aka: R_f
-  Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0,
-                             2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1);
-  // from van Overschee subid.m:
-  // Rf = R((2*m+l)*i+1:2*(m+l)*i,:);   % Future outputs
-
-  Matrix U;
-  Matrix V;
-  switch (wt) {
-    case kSSIDNone: {
-      // No weighting. (what van Overschee calls "N4SID")
-      Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14;
-      arma::svd(U, s_, V, O_k_sans_Qt, "std");
-    } break;
-    case kSSIDMOESP: {
-      // MOESP weighting
-      // This is what they use in the "robust" algorithm van Overschee, de Moor
-      // 1996
-      Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) -
-                  R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13;
-      Matrix O_k_ortho_Uf_sans_Qt =
-          join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44);
-      svd(U, s_, V, O_k_ortho_Uf_sans_Qt, "std");
-    } break;
-    case kSSIDCVA: {
-      // CVA weighting
-      // See van Overschee's matlab code (subid.m):
-      // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems
-      Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) -
-                  R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13;
-      Matrix O_k_ortho_Uf_sans_Qt =
-          join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44);
-
-      Matrix inv_w1;
-      Matrix qt1;
-      lq(inv_w1, qt1, R_56_16);  // lq decomp of R_f (future output data)
-      inv_w1 = trimatl(inv_w1);
-      inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1);
-      Matrix w_o_w = arma::solve(
-          inv_w1, O_k_ortho_Uf_sans_Qt);  // alternatively
-                                          // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt
-      svd(U, s_, V, w_o_w, "std");
-
-      U = inv_w1 * U;
-      break;
-    }
-  }
-
-  // Truncate to model order (heart of ssid method)
-  auto s_hat = s_.subvec(0, n_x_ - 1);
-  Matrix diag_sqrt_s = diagmat(sqrt(s_hat));
-  auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1);
-
-  // get extended observability and controllability mats
-  ext_obs_t_ = u_hat * diag_sqrt_s;  // extended observability matrix
-}
-
-template <typename Fit>
-void SSID<Fit>::Solve(data_t wt_dc) {
-  // required submats
-  auto R_56_14 =
-      L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1,
-                n_h_ * (2 * n_u_ + n_y_) - 1);
-  auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1,
-                           n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1);
-  auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0,
-                           2 * n_h_ * (n_u_ + n_y_) - 1,
-                           n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1);
-  auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0,
-                           n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1,
-                           n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1);
-  auto R_56_15 =
-      L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1,
-                n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1);
-
-  // Solve for params using appropriate algorithm:
-  // robust deterministic/stochastic algorithm in van Overschee 1996
-  // algorithm that the authors say "works" in practice.
-  auto ext_obs_tm1 = ext_obs_t_.submat(
-      0, 0, ext_obs_t_.n_rows - 1 - n_y_,
-      ext_obs_t_.n_cols - 1);  // extended observability matrix
-
-  // This is what textbook (1996) says:
-  //
-  // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15);
-  //
-  // HOWEVER, do not know why but have to fill the last place with zeros like
-  // authors' matlab implementation (see `subid.m`)
-  // Otherwise, get ridiculous covariances (although A,C estimates are close to
-  // same...)
-  Matrix Tr = join_vert(
-      join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)),
-      R_23_15);
-  Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15);
-  Matrix S = Tl * pinv(Tr);
-
-  // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure
-  // stability.
-  fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1));
-  Matrix ext_obs_t_p1 = join_vert(
-      ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1),
-      Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros));
-  fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1);
-
-  // At this point, van Overschee & de Moor suggest re-calculating ext_obs_t_,
-  // ext_obs_tm1 from (A, C) because it was just an approximation. This is
-  RecomputeExtObs();
-  ext_obs_tm1 = ext_obs_t_.submat(
-      0, 0, ext_obs_t_.n_rows - 1 - n_y_,
-      ext_obs_t_.n_cols - 1);  // extended observability matrix
-  Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15);
-  Tr = join_vert(
-      join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)),
-      R_23_15);
-  S = Tl * pinv(Tr);
-
-  Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_);
-  Matrix Mcurly = pinv(ext_obs_tm1);
-  Matrix Pcurly = Tl - Lcurly * R_56_15;
-  Vector Pvec = vectorise(Pcurly);
-  Matrix Qcurly = R_23_15;
-
-  // Identify [D; B], assuming D=0 and ensuring DC gain is correct
-  Matrix sum_QcurlyT_kron_Ncurly(
-      (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_),
-      fill::zeros);
-
-  Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols,
-                         fill::eye);
-  eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1,
-                         eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1;
-
-  // van Overschee (1996) p. 126
-  Matrix N1_Tl = -Lcurly;
-  N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) +=
-      join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly);
-  N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) +=
-      Matrix(n_y_, n_y_, fill::eye);
-
-  Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros);
-  Matrix N_k;
-  for (size_t k = 0; k < n_h_; k++) {
-    auto Qcurly_k =
-        Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1);
-
-    Nk_Tl.zeros();
-    Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) =
-        N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1);
-    N_k = Nk_Tl * eye_ext_obs_tm1;
-
-    sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k);
-  }
-
-  Matrix err_vec;
-  if (wt_dc > 0) {
-    // Constraints enforced by weighted least squares
-    //
-    // Reference:
-    //
-    // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly
-    // Excited Industrial Systems. Conference in Decision and Control.
-
-    // constraint 1: assume D=0 --> remove the components for Dvec (this is
-    // actually a hard constraint in that it ignores D)
-    Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly;
-    sum_QcurlyT_kron_Ncurly =
-        Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_);
-
-    size_t kkk = 0;
-    for (size_t k = 1; k < (n_u_ + 1); k++) {
-      size_t start_idx = k * (n_y_ + n_x_) - n_x_;
-      for (size_t kk = 0; kk < n_x_; kk++) {
-        sum_QcurlyT_kron_Ncurly.col(kkk) =
-            sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk);
-        kkk++;
-      }
-    }
-
-    // constraint 2: Make sure DC I/O gain is correct
-    Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A());
-    Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_));
-    Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0);
-    Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 =
-        join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0);
-
-    // WEIGHTED LS
-    // Important in practice because I care a lot about at least getting the DC
-    // gain correct. Put x weight on minimizing error at DC, relative to others
-    Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows,
-             sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye);
-    // Make weight on minimizing DC error immense so at least that
-    // should be nailed.
-    size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows;
-    size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows;
-    size_t stop_row = w.n_rows - 1;
-    size_t stop_col = w.n_cols - 1;
-    // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale
-    // weight with data length?
-    w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc;
-    Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w *
-                       sum_QcurlyT_kron_Ncurly_b_to_g0) *
-                   sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec;
-
-    fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_));
-
-    // Calculate residuals and their cov.
-    // Because I've added constraints, I need to re-calculate the right term
-    // with b_vec instead of how van Overschee do in final algorithm.
-    err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec;
-  } else {
-    // default way: *no* constraint on G0 or D=0
-    Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec;
-    // TODO(mfbolus) n.b., this gets thrown away...
-    // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_);
-    fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_));
-    err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec;
-  }
-  // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols);
-
-  // TODO(mfbolus): Something is wrong with the error calculation above.
-  // Use the way van overschee does it in `subid.m`
-  // WARNING: this ignores any above constraints, so Q, R will be approximate...
-  Matrix err = Tl - S * Tr;
-  Matrix cov_err = err * err.t();
-  fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1));
-  fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1));
-}
-
-template <typename Fit>
-void SSID<Fit>::RecomputeExtObs() {
-  ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C();
-  for (size_t k = 2; k < (n_h_ + 1); k++) {
-    ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) =
-        ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1,
-                          ext_obs_t_.n_cols - 1) *
-        fit_.A();
-  }
-}
-
-}  // namespace lds
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_fit_ssid.h
+summary: subspace identification 
+
+---
+
+# ldsCtrlEst_h/lds_fit_ssid.h
+
+subspace identification  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)**  |
+
+## Detailed Description
+
+
+
+This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm (`[lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)<Fit>`).
+
+References: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_FIT_SSID_H
+#define LDSCTRLEST_LDS_FIT_SSID_H
+
+#include "lds_fit.h"
+
+namespace lds {
+
+template <typename Fit>
+class SSID {
+  static_assert(std::is_base_of<lds::Fit, Fit>::value,
+                "Fit must be derived from lds::Fit type.");
+
+ public:
+  SSID() = default;
+
+  SSID(size_t n_x, size_t n_h, data_t dt,
+       UniformMatrixList<kMatFreeDim2>&& u_train,
+       UniformMatrixList<kMatFreeDim2>&& z_train,
+       const Vector& d = Vector(1).fill(-kInf));
+
+  std::tuple<Fit, Vector> Run(SSIDWt ssid_wt);
+
+  std::tuple<UniformMatrixList<kMatFreeDim2>, UniformMatrixList<kMatFreeDim2>>
+  ReturnData() {
+    auto tuple = std::make_tuple(std::move(u_), std::move(z_));
+    u_ = UniformMatrixList<kMatFreeDim2>();
+    z_ = UniformMatrixList<kMatFreeDim2>();
+    return tuple;
+  }
+
+ protected:
+  void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001);
+
+  void CreateHankelDataMat();
+
+  virtual void DecomposeData() = 0;
+
+  void CalcSVD(SSIDWt wt);
+
+  void Solve(data_t wt_dc);
+
+  void RecomputeExtObs();
+
+  // input/output training data
+  UniformMatrixList<kMatFreeDim2> u_;  
+  UniformMatrixList<kMatFreeDim2> z_;  
+  Matrix D_;                           
+
+  Fit fit_;      
+  Matrix g_dc_;  
+
+  data_t dt_{};   
+  size_t n_u_{};  
+  size_t n_x_{};  
+  size_t n_y_{};  
+  size_t n_h_{};
+  size_t n_trials_{};        
+  std::vector<size_t> n_t_;  
+  size_t n_t_tot_{};         
+
+  Matrix L_;          
+  Vector s_;          
+  Matrix ext_obs_t_;  
+};
+
+template <typename Fit>
+SSID<Fit>::SSID(size_t n_x, size_t n_h, data_t dt,
+                UniformMatrixList<kMatFreeDim2>&& u_train,
+                UniformMatrixList<kMatFreeDim2>&& z_train, const Vector& d) {
+  // check input/output data dimensions are consistent
+  if (z_train.size() != u_train.size()) {
+    throw std::runtime_error(
+        "I/O training data have different number of trials.");
+  }
+  n_trials_ = u_train.size();
+
+  n_t_tot_ = 0;
+  n_t_ = std::vector<size_t>(n_trials_);
+  for (size_t trial = 0; trial < n_trials_; trial++) {
+    if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) {
+      throw std::runtime_error(
+          "I/O training data have different number of time steps.");
+    }
+    n_t_[trial] = u_train.at(trial).n_cols;
+    n_t_tot_ += n_t_[trial];
+  }
+
+  dt_ = dt;
+  n_x_ = n_x;
+  n_u_ = u_train.at(0).n_rows;
+  n_y_ = z_train.at(0).n_rows;
+  n_h_ = n_h;
+
+  // dimensionality check for eventual block-hankel data matrix
+  size_t len = n_t_tot_ - 2 * n_h_ + 1;
+  if (len < (2 * n_h_ * (n_u_ + n_y_))) {
+    std::ostringstream ss;
+    ss << "Dataset problem! More rows than columns in block-hankel data "
+          "matrix: 2*(n_u+n_y)*n_h > data-length! Need higher data-length or "
+          "lower n_h.";
+    throw std::runtime_error(ss.str());
+  }
+
+  fit_ = Fit(n_u_, n_x_, n_y_, dt_);
+
+  u_ = std::move(u_train);
+  z_ = std::move(z_train);
+
+  if (!d.is_finite() || (d.n_rows != n_y_)) {
+    // TODO(mfbolus): implement least-square solution for impulse response with
+    // a second input of ones. Data-driven way of accounting for offset *not*
+    // driven by an input.
+    //
+    // For now, calculate output bias (d) as the
+    // output wherever the stimulus has not been on for some amount of time.
+    // convolve u with rectangle and take all samples. This is a reasonable
+    // approach, since often when autonomous systems are fit (i.e., systems with
+    // no input), they will subtract off the mean of the output. This
+    // essentially amounts to setting output bias to the mean of the output when
+    // there is no stimulation.
+    data_t t_silence = 0.1;
+    data_t thresh_silence = 0.001;
+    CalcD(t_silence, thresh_silence);
+  } else {
+    fit_.set_d(d);
+  }
+}
+
+template <typename Fit>
+std::tuple<Fit, Vector> SSID<Fit>::Run(SSIDWt ssid_wt) {
+  // the weight on minimizing dc I/O gain only works for gaussian,
+  // and hopefully not necessary with appropriate dataset.
+  data_t wt_dc = 0;
+  // std::cout << "creating hankel mat\n";
+  CreateHankelDataMat();
+  // std::cout << "decomposing data\n";
+  DecomposeData();
+  // std::cout << "calculating svd\n";
+  CalcSVD(ssid_wt);
+  // std::cout << "solving for params\n";
+  Solve(wt_dc);
+  // std::cout << "fin\n";
+  return std::make_tuple(fit_, s_);
+}
+
+template <typename Fit>
+void SSID<Fit>::CalcD(data_t t_silence, data_t thresh_silence) {
+  Vector d(z_.at(0).n_rows, fill::zeros);
+  Vector win(static_cast<size_t>(t_silence / dt_), fill::ones);
+  Vector sum_z_silence(n_y_, fill::zeros);
+  size_t n_silence(0);
+  for (size_t trial = 0; trial < u_.size(); trial++) {
+    // find silent samples
+    // start by convolving with
+    Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0));
+    Vector u_conv = conv(sum_u, win, "same");
+
+    // get only the samples that are silent...
+    arma::uvec ubi_silence = find(u_conv <= thresh_silence);
+    if (ubi_silence.n_elem > 0) {
+      sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1);
+      n_silence += ubi_silence.n_elem;
+    }
+  }
+  if (n_silence > 0) {
+    d = sum_z_silence / n_silence;
+  }
+  fit_.set_d(d);
+}
+
+template <typename Fit>
+void SSID<Fit>::CreateHankelDataMat() {
+  // temporary copy of data
+  Matrix z(n_y_, n_t_tot_, fill::zeros);
+  Matrix u(n_u_, n_t_tot_, fill::zeros);
+  size_t so_far(0);
+
+  for (size_t trial = 0; trial < z_.size(); trial++) {
+    z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial);
+    u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial);
+    so_far += n_t_.at(trial);
+  }
+
+  // remove output bias
+  z.each_col() -= fit_.d();
+
+  // calculate I/O gain @ DC while data in convenient form
+  g_dc_ = z * pinv(u);
+  // std::cout << "G0_data = " << g_dc_ << "\n";
+
+  // create hankel data matrix
+  size_t len = z.n_cols - 2 * n_h_ + 1;  // data length in hankel mat
+
+  // block-hankel data matrix
+  D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros);
+  // past input
+  auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1);
+  // future input
+  auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1);
+  // past output
+  auto y_p =
+      D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1);
+  // future output
+  auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0,
+                       2 * n_h_ * (n_u_ + n_y_) - 1, len - 1);
+
+  size_t idx = 0;
+  for (size_t k = 0; k < len; k++) {
+    idx = 0;
+    for (size_t kk = k; kk < (n_h_ + k); kk++) {
+      u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk);
+      idx += n_u_;
+    }
+
+    idx = 0;
+    for (size_t kk = (n_h_ + k); kk < (2 * n_h_ + k); kk++) {
+      u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk);
+      idx += n_u_;
+    }
+
+    idx = 0;
+    for (size_t kk = k; kk < (n_h_ + k); kk++) {
+      y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk);
+      idx += n_y_;
+    }
+
+    idx = 0;
+    for (size_t kk = (n_h_ + k); kk < (2 * n_h_ + k); kk++) {
+      y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk);
+      idx += n_y_;
+    }
+  }
+
+  D_ /= sqrt(static_cast<data_t>(len));
+}
+
+// template <typename Fit>
+// void SSID<Fit>::DecomposeData() {
+//   // do LQ decomp instead of calculating covariance expensive way
+//   // Note that "R" in van Overschee is lower-triangular (L), not "R" in QR
+//   // decomp. Very confusing.
+//   Matrix q_t;
+//   lq(L_, q_t, D_);
+//   // van Overschee zeros out the other elements.
+//   L_ = trimatl(L_);
+// }
+
+template <typename Fit>
+void SSID<Fit>::CalcSVD(SSIDWt wt) {
+  // submats that will be needed:
+  auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1,
+                           n_h_ * (2 * n_u_ + n_y_) - 1);
+  auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1);
+  auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1);
+  auto R_23_13 =
+      L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1);
+  auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1,
+                           n_h_ * (2 * n_u_ + n_y_) - 1);
+  auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1,
+                           n_h_ * (2 * n_u_) - 1);
+  auto R_44 =
+      L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1,
+                n_h_ * (2 * n_u_ + n_y_) - 1);
+  auto R_56_14 =
+      L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1,
+                n_h_ * (2 * n_u_ + n_y_) - 1);
+
+  Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14);
+  auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1);
+  auto Luf =
+      Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1);
+  auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1,
+                                n_h_ * (2 * n_u_ + n_y_) - 1);
+
+  // aka: R_f
+  Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0,
+                             2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1);
+  // from van Overschee subid.m:
+  // Rf = R((2*m+l)*i+1:2*(m+l)*i,:);   % Future outputs
+
+  Matrix U;
+  Matrix V;
+  switch (wt) {
+    case kSSIDNone: {
+      // No weighting. (what van Overschee calls "N4SID")
+      Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14;
+      arma::svd(U, s_, V, O_k_sans_Qt, "std");
+    } break;
+    case kSSIDMOESP: {
+      // MOESP weighting
+      // This is what they use in the "robust" algorithm van Overschee, de Moor
+      // 1996
+      Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) -
+                  R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13;
+      Matrix O_k_ortho_Uf_sans_Qt =
+          join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44);
+      svd(U, s_, V, O_k_ortho_Uf_sans_Qt, "std");
+    } break;
+    case kSSIDCVA: {
+      // CVA weighting
+      // See van Overschee's matlab code (subid.m):
+      // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems
+      Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) -
+                  R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13;
+      Matrix O_k_ortho_Uf_sans_Qt =
+          join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44);
+
+      Matrix inv_w1;
+      Matrix qt1;
+      lq(inv_w1, qt1, R_56_16);  // lq decomp of R_f (future output data)
+      inv_w1 = trimatl(inv_w1);
+      inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1);
+      Matrix w_o_w = arma::solve(
+          inv_w1, O_k_ortho_Uf_sans_Qt);  // alternatively
+                                          // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt
+      svd(U, s_, V, w_o_w, "std");
+
+      U = inv_w1 * U;
+      break;
+    }
+  }
+
+  // Truncate to model order (heart of ssid method)
+  auto s_hat = s_.subvec(0, n_x_ - 1);
+  Matrix diag_sqrt_s = diagmat(sqrt(s_hat));
+  auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1);
+
+  // get extended observability and controllability mats
+  ext_obs_t_ = u_hat * diag_sqrt_s;  // extended observability matrix
+}
+
+template <typename Fit>
+void SSID<Fit>::Solve(data_t wt_dc) {
+  // required submats
+  auto R_56_14 =
+      L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1,
+                n_h_ * (2 * n_u_ + n_y_) - 1);
+  auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1,
+                           n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1);
+  auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0,
+                           2 * n_h_ * (n_u_ + n_y_) - 1,
+                           n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1);
+  auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0,
+                           n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1,
+                           n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1);
+  auto R_56_15 =
+      L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1,
+                n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1);
+
+  // Solve for params using appropriate algorithm:
+  // robust deterministic/stochastic algorithm in van Overschee 1996
+  // algorithm that the authors say "works" in practice.
+  auto ext_obs_tm1 = ext_obs_t_.submat(
+      0, 0, ext_obs_t_.n_rows - 1 - n_y_,
+      ext_obs_t_.n_cols - 1);  // extended observability matrix
+
+  // This is what textbook (1996) says:
+  //
+  // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15);
+  //
+  // HOWEVER, do not know why but have to fill the last place with zeros like
+  // authors' matlab implementation (see `subid.m`)
+  // Otherwise, get ridiculous covariances (although A,C estimates are close to
+  // same...)
+  Matrix Tr = join_vert(
+      join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)),
+      R_23_15);
+  Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15);
+  Matrix S = Tl * pinv(Tr);
+
+  // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure
+  // stability.
+  fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1));
+  Matrix ext_obs_t_p1 = join_vert(
+      ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1),
+      Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros));
+  fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1);
+
+  // At this point, van Overschee & de Moor suggest re-calculating ext_obs_t_,
+  // ext_obs_tm1 from (A, C) because it was just an approximation. This is
+  RecomputeExtObs();
+  ext_obs_tm1 = ext_obs_t_.submat(
+      0, 0, ext_obs_t_.n_rows - 1 - n_y_,
+      ext_obs_t_.n_cols - 1);  // extended observability matrix
+  Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15);
+  Tr = join_vert(
+      join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)),
+      R_23_15);
+  S = Tl * pinv(Tr);
+
+  Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_);
+  Matrix Mcurly = pinv(ext_obs_tm1);
+  Matrix Pcurly = Tl - Lcurly * R_56_15;
+  Vector Pvec = vectorise(Pcurly);
+  Matrix Qcurly = R_23_15;
+
+  // Identify [D; B], assuming D=0 and ensuring DC gain is correct
+  Matrix sum_QcurlyT_kron_Ncurly(
+      (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_),
+      fill::zeros);
+
+  Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols,
+                         fill::eye);
+  eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1,
+                         eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1;
+
+  // van Overschee (1996) p. 126
+  Matrix N1_Tl = -Lcurly;
+  N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) +=
+      join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly);
+  N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) +=
+      Matrix(n_y_, n_y_, fill::eye);
+
+  Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros);
+  Matrix N_k;
+  for (size_t k = 0; k < n_h_; k++) {
+    auto Qcurly_k =
+        Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1);
+
+    Nk_Tl.zeros();
+    Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) =
+        N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1);
+    N_k = Nk_Tl * eye_ext_obs_tm1;
+
+    sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k);
+  }
+
+  Matrix err_vec;
+  if (wt_dc > 0) {
+    // Constraints enforced by weighted least squares
+    //
+    // Reference:
+    //
+    // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly
+    // Excited Industrial Systems. Conference in Decision and Control.
+
+    // constraint 1: assume D=0 --> remove the components for Dvec (this is
+    // actually a hard constraint in that it ignores D)
+    Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly;
+    sum_QcurlyT_kron_Ncurly =
+        Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_);
+
+    size_t kkk = 0;
+    for (size_t k = 1; k < (n_u_ + 1); k++) {
+      size_t start_idx = k * (n_y_ + n_x_) - n_x_;
+      for (size_t kk = 0; kk < n_x_; kk++) {
+        sum_QcurlyT_kron_Ncurly.col(kkk) =
+            sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk);
+        kkk++;
+      }
+    }
+
+    // constraint 2: Make sure DC I/O gain is correct
+    Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A());
+    Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_));
+    Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0);
+    Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 =
+        join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0);
+
+    // WEIGHTED LS
+    // Important in practice because I care a lot about at least getting the DC
+    // gain correct. Put x weight on minimizing error at DC, relative to others
+    Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows,
+             sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye);
+    // Make weight on minimizing DC error immense so at least that
+    // should be nailed.
+    size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows;
+    size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows;
+    size_t stop_row = w.n_rows - 1;
+    size_t stop_col = w.n_cols - 1;
+    // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale
+    // weight with data length?
+    w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc;
+    Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w *
+                       sum_QcurlyT_kron_Ncurly_b_to_g0) *
+                   sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec;
+
+    fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_));
+
+    // Calculate residuals and their cov.
+    // Because I've added constraints, I need to re-calculate the right term
+    // with b_vec instead of how van Overschee do in final algorithm.
+    err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec;
+  } else {
+    // default way: *no* constraint on G0 or D=0
+    Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec;
+    // TODO(mfbolus) n.b., this gets thrown away...
+    // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_);
+    fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_));
+    err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec;
+  }
+  // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols);
+
+  // TODO(mfbolus): Something is wrong with the error calculation above.
+  // Use the way van overschee does it in `subid.m`
+  // WARNING: this ignores any above constraints, so Q, R will be approximate...
+  Matrix err = Tl - S * Tr;
+  Matrix cov_err = err * err.t();
+  fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1));
+  fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1));
+}
+
+template <typename Fit>
+void SSID<Fit>::RecomputeExtObs() {
+  ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C();
+  for (size_t k = 2; k < (n_h_ + 1); k++) {
+    ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) =
+        ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1,
+                          ext_obs_t_.n_cols - 1) *
+        fit_.A();
+  }
+}
+
+}  // namespace lds
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__gaussian_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__gaussian_8h.md
index 51a85fdd..2f5b8380 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__gaussian_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__gaussian_8h.md
@@ -1,71 +1,71 @@
----
-title: ldsCtrlEst_h/lds_gaussian.h
-summary: glds namespace 
-
----
-
-# ldsCtrlEst_h/lds_gaussian.h
-
-`glds` namespace  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-| **[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)** <br>Linear Dynamical Systems with Gaussian observations.  |
-
-## Detailed Description
-
-
-
-This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations (`[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)`). 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_GAUSSIAN_H
-#define LDSCTRLEST_LDS_GAUSSIAN_H
-
-// namespace
-#include "lds.h"
-
-namespace lds {
-namespace gaussian {
-// insert any Gaussian-specific things here...
-}  // namespace gaussian
-}  // namespace lds
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_gaussian.h
+summary: glds namespace 
+
+---
+
+# ldsCtrlEst_h/lds_gaussian.h
+
+`glds` namespace  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+| **[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)** <br>Linear Dynamical Systems with Gaussian observations.  |
+
+## Detailed Description
+
+
+
+This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations (`[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)`). 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_GAUSSIAN_H
+#define LDSCTRLEST_LDS_GAUSSIAN_H
+
+// namespace
+#include "lds.h"
+
+namespace lds {
+namespace gaussian {
+// insert any Gaussian-specific things here...
+}  // namespace gaussian
+}  // namespace lds
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__ctrl_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__ctrl_8h.md
index fafc590a..201a48df 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__ctrl_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__ctrl_8h.md
@@ -1,116 +1,116 @@
----
-title: ldsCtrlEst_h/lds_gaussian_ctrl.h
-summary: GLDS Controller. 
-
----
-
-# ldsCtrlEst_h/lds_gaussian_ctrl.h
-
-GLDS Controller.  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-| **[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)** <br>Linear Dynamical Systems with Gaussian observations.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::gaussian::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1controller/)** <br>Gaussian-observation [Controller]() Type.  |
-
-## Detailed Description
-
-
-
-This file declares and partially defines the type for control of a gaussian-observation linear dynamical system ([lds::gaussian::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1controller/)). It inherits functionality from the underlying GLDS model type ([lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/)), including state estimation. 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H
-#define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H
-
-// namespace
-#include "lds_gaussian.h"
-// system
-#include "lds_gaussian_sys.h"
-// controller
-#include "lds_ctrl.h"
-
-namespace lds {
-namespace gaussian {
-class Controller : public lds::Controller<System> {
- public:
-  void set_y_ref(const Vector& y_ref) override {
-    Reassign(y_ref_,y_ref);
-    cx_ref_ = y_ref - sys_.d();
-  };
-
-  // make sure base class template methods available
-  using lds::Controller<System>::Controller;
-  using lds::Controller<System>::Control;
-  using lds::Controller<System>::ControlOutputReference;
-
-  using lds::Controller<System>::sys;
-  using lds::Controller<System>::Kc;
-  using lds::Controller<System>::Kc_inty;
-  using lds::Controller<System>::Kc_u;
-  using lds::Controller<System>::g_design;
-  using lds::Controller<System>::u_ref;
-  using lds::Controller<System>::x_ref;
-  using lds::Controller<System>::y_ref;
-  using lds::Controller<System>::control_type;
-
-  using lds::Controller<System>::set_sys;
-  using lds::Controller<System>::set_g_design;
-  using lds::Controller<System>::set_u_ref;
-  using lds::Controller<System>::set_x_ref;
-  using lds::Controller<System>::set_y_ref;
-  using lds::Controller<System>::set_Kc;
-  using lds::Controller<System>::set_Kc_inty;
-  using lds::Controller<System>::set_Kc_u;
-  using lds::Controller<System>::set_tau_awu;
-  using lds::Controller<System>::set_control_type;
-
-  using lds::Controller<System>::Reset;
-  using lds::Controller<System>::Print;
-};
-}  // namespace gaussian
-}  // namespace lds
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_gaussian_ctrl.h
+summary: GLDS Controller. 
+
+---
+
+# ldsCtrlEst_h/lds_gaussian_ctrl.h
+
+GLDS Controller.  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+| **[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)** <br>Linear Dynamical Systems with Gaussian observations.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::gaussian::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/)** <br>Gaussian-observation [Controller]() Type.  |
+
+## Detailed Description
+
+
+
+This file declares and partially defines the type for control of a gaussian-observation linear dynamical system ([lds::gaussian::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/)). It inherits functionality from the underlying GLDS model type ([lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/)), including state estimation. 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H
+#define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H
+
+// namespace
+#include "lds_gaussian.h"
+// system
+#include "lds_gaussian_sys.h"
+// controller
+#include "lds_ctrl.h"
+
+namespace lds {
+namespace gaussian {
+class Controller : public lds::Controller<System> {
+ public:
+  void set_y_ref(const Vector& y_ref) override {
+    Reassign(y_ref_,y_ref);
+    cx_ref_ = y_ref - sys_.d();
+  };
+
+  // make sure base class template methods available
+  using lds::Controller<System>::Controller;
+  using lds::Controller<System>::Control;
+  using lds::Controller<System>::ControlOutputReference;
+
+  using lds::Controller<System>::sys;
+  using lds::Controller<System>::Kc;
+  using lds::Controller<System>::Kc_inty;
+  using lds::Controller<System>::Kc_u;
+  using lds::Controller<System>::g_design;
+  using lds::Controller<System>::u_ref;
+  using lds::Controller<System>::x_ref;
+  using lds::Controller<System>::y_ref;
+  using lds::Controller<System>::control_type;
+
+  using lds::Controller<System>::set_sys;
+  using lds::Controller<System>::set_g_design;
+  using lds::Controller<System>::set_u_ref;
+  using lds::Controller<System>::set_x_ref;
+  using lds::Controller<System>::set_y_ref;
+  using lds::Controller<System>::set_Kc;
+  using lds::Controller<System>::set_Kc_inty;
+  using lds::Controller<System>::set_Kc_u;
+  using lds::Controller<System>::set_tau_awu;
+  using lds::Controller<System>::set_control_type;
+
+  using lds::Controller<System>::Reset;
+  using lds::Controller<System>::Print;
+};
+}  // namespace gaussian
+}  // namespace lds
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit_8h.md
index cff73443..572148f5 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit_8h.md
@@ -1,94 +1,94 @@
----
-title: ldsCtrlEst_h/lds_gaussian_fit.h
-summary: GLDS fit type. 
-
----
-
-# ldsCtrlEst_h/lds_gaussian_fit.h
-
-GLDS fit type.  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-| **[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)** <br>Linear Dynamical Systems with Gaussian observations.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::gaussian::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/)** <br>GLDS [Fit]() Type.  |
-
-## Detailed Description
-
-
-
-This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM). 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H
-#define LDSCTRLEST_LDS_GAUSSIAN_FIT_H
-
-// namespace
-#include "lds_gaussian.h"
-// fit type
-#include "lds_fit.h"
-
-namespace lds {
-namespace gaussian {
-class Fit : public lds::Fit {
- public:
-  Fit() = default;
-
-  Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt);
-  const Matrix& R() const override { return R_; };
-  void set_R(const Matrix& R) override {
-    Reassign(R_, R);
-    ForceSymPD(R_);
-  };
-
-  View h(Matrix& y, const Matrix& x, size_t t) override {
-    y.col(t) = C_ * x.col(t) + d_;
-    return y.col(t);
-  };
-};
-
-};  // namespace gaussian
-}  // namespace lds
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_gaussian_fit.h
+summary: GLDS fit type. 
+
+---
+
+# ldsCtrlEst_h/lds_gaussian_fit.h
+
+GLDS fit type.  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+| **[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)** <br>Linear Dynamical Systems with Gaussian observations.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::gaussian::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/)** <br>GLDS [Fit]() Type.  |
+
+## Detailed Description
+
+
+
+This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM). 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H
+#define LDSCTRLEST_LDS_GAUSSIAN_FIT_H
+
+// namespace
+#include "lds_gaussian.h"
+// fit type
+#include "lds_fit.h"
+
+namespace lds {
+namespace gaussian {
+class Fit : public lds::Fit {
+ public:
+  Fit() = default;
+
+  Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt);
+  const Matrix& R() const override { return R_; };
+  void set_R(const Matrix& R) override {
+    Reassign(R_, R);
+    ForceSymPD(R_);
+  };
+
+  View h(Matrix& y, const Matrix& x, size_t t) override {
+    y.col(t) = C_ * x.col(t) + d_;
+    return y.col(t);
+  };
+};
+
+};  // namespace gaussian
+}  // namespace lds
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit__em_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit__em_8h.md
index 68ad2306..3537a36e 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit__em_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit__em_8h.md
@@ -1,92 +1,92 @@
----
-title: ldsCtrlEst_h/lds_gaussian_fit_em.h
-summary: GLDS E-M fit type. 
-
----
-
-# ldsCtrlEst_h/lds_gaussian_fit_em.h
-
-GLDS E-M fit type.  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-| **[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)** <br>Linear Dynamical Systems with Gaussian observations.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::gaussian::FitEM](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitem/)** <br>GLDS E-M [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/) Type.  |
-
-## Detailed Description
-
-
-
-This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (`lds::gaussian::emFit_t`).
-
-References: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).
-
-[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2. 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H
-#define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H
-
-#include "lds_fit_em.h"
-#include "lds_gaussian_fit.h"
-
-namespace lds {
-namespace gaussian {
-class FitEM : public EM<Fit> {
- public:
-  using EM<Fit>::EM;
-
- private:
-  void MaximizeOutput() override;
-
-  void MaximizeMeasurement() override;
-
-  void RecurseKe(Matrix& Ke, Cube& P_pre, Cube& P_post, size_t t) override;
-};
-
-}  // namespace gaussian
-}  // namespace lds
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_gaussian_fit_em.h
+summary: GLDS E-M fit type. 
+
+---
+
+# ldsCtrlEst_h/lds_gaussian_fit_em.h
+
+GLDS E-M fit type.  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+| **[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)** <br>Linear Dynamical Systems with Gaussian observations.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::gaussian::FitEM](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/)** <br>GLDS E-M [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/) Type.  |
+
+## Detailed Description
+
+
+
+This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (`lds::gaussian::emFit_t`).
+
+References: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).
+
+[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2. 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H
+#define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H
+
+#include "lds_fit_em.h"
+#include "lds_gaussian_fit.h"
+
+namespace lds {
+namespace gaussian {
+class FitEM : public EM<Fit> {
+ public:
+  using EM<Fit>::EM;
+
+ private:
+  void MaximizeOutput() override;
+
+  void MaximizeMeasurement() override;
+
+  void RecurseKe(Matrix& Ke, Cube& P_pre, Cube& P_post, size_t t) override;
+};
+
+}  // namespace gaussian
+}  // namespace lds
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit__ssid_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit__ssid_8h.md
index 9379f5f2..7dc32c1a 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit__ssid_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit__ssid_8h.md
@@ -1,91 +1,91 @@
----
-title: ldsCtrlEst_h/lds_gaussian_fit_ssid.h
-summary: GLDS SSID fit type. 
-
----
-
-# ldsCtrlEst_h/lds_gaussian_fit_ssid.h
-
-GLDS SSID fit type.  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-| **[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)** <br>Linear Dynamical Systems with Gaussian observations.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitssid/)** <br>Subspace Identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)) for GLDS.  |
-
-## Detailed Description
-
-
-
-This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (`lds::gaussian::ssidFit_t`).
-
-References: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H
-#define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H
-
-#include "lds_fit_ssid.h"
-#include "lds_gaussian_fit.h"
-
-namespace lds {
-namespace gaussian {
-class FitSSID : public SSID<Fit> {
- public:
-  using SSID<Fit>::SSID;
-  using SSID<Fit>::Run;
-
- private:
-  using SSID<Fit>::CreateHankelDataMat;
-  using SSID<Fit>::CalcSVD;
-  using SSID<Fit>::Solve;
-
-  void DecomposeData() override;
-
-  void SolveVanOverschee();
-};
-}  // namespace gaussian
-}  // namespace lds
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_gaussian_fit_ssid.h
+summary: GLDS SSID fit type. 
+
+---
+
+# ldsCtrlEst_h/lds_gaussian_fit_ssid.h
+
+GLDS SSID fit type.  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+| **[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)** <br>Linear Dynamical Systems with Gaussian observations.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/)** <br>Subspace Identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)) for GLDS.  |
+
+## Detailed Description
+
+
+
+This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (`lds::gaussian::ssidFit_t`).
+
+References: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H
+#define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H
+
+#include "lds_fit_ssid.h"
+#include "lds_gaussian_fit.h"
+
+namespace lds {
+namespace gaussian {
+class FitSSID : public SSID<Fit> {
+ public:
+  using SSID<Fit>::SSID;
+  using SSID<Fit>::Run;
+
+ private:
+  using SSID<Fit>::CreateHankelDataMat;
+  using SSID<Fit>::CalcSVD;
+  using SSID<Fit>::Solve;
+
+  void DecomposeData() override;
+
+  void SolveVanOverschee();
+};
+}  // namespace gaussian
+}  // namespace lds
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sctrl_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sctrl_8h.md
index 338d66db..fcc3a305 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sctrl_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sctrl_8h.md
@@ -1,114 +1,114 @@
----
-title: ldsCtrlEst_h/lds_gaussian_sctrl.h
-summary: GLDS switched controller type. 
-
----
-
-# ldsCtrlEst_h/lds_gaussian_sctrl.h
-
-GLDS switched controller type.  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-| **[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)** <br>Linear Dynamical Systems with Gaussian observations.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::gaussian::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1switchedcontroller/)** <br>Gaussian-observation [SwitchedController]() Type.  |
-
-## Detailed Description
-
-
-
-This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems ([lds::gaussian::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1switchedcontroller/)). 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H
-#define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H
-
-// controller type
-#include "lds_gaussian_ctrl.h"
-// switched controller
-#include "lds_sctrl.h"
-
-namespace lds {
-namespace gaussian {
-class SwitchedController : public lds::SwitchedController<System> {
- public:
-  void set_y_ref(const Vector& y_ref) override {
-    Reassign(y_ref_, y_ref);
-    cx_ref_ = y_ref - sys_.d();
-  }
-
-  // make sure base class template methods available
-  using lds::SwitchedController<System>::SwitchedController;
-  using lds::SwitchedController<System>::Switch;
-  using lds::SwitchedController<System>::Control;
-  using lds::SwitchedController<System>::ControlOutputReference;
-
-  using lds::SwitchedController<System>::sys;
-  using lds::SwitchedController<System>::Kc;
-  using lds::SwitchedController<System>::Kc_inty;
-  using lds::SwitchedController<System>::Kc_u;
-  using lds::SwitchedController<System>::g_design;
-  using lds::SwitchedController<System>::u_ref;
-  using lds::SwitchedController<System>::x_ref;
-  using lds::SwitchedController<System>::y_ref;
-  using lds::SwitchedController<System>::control_type;
-
-  using lds::SwitchedController<System>::set_g_design;
-  using lds::SwitchedController<System>::set_u_ref;
-  using lds::SwitchedController<System>::set_x_ref;
-  using lds::SwitchedController<System>::set_y_ref;
-  using lds::SwitchedController<System>::set_Kc;
-  using lds::SwitchedController<System>::set_Kc_inty;
-  using lds::SwitchedController<System>::set_Kc_u;
-  using lds::SwitchedController<System>::set_tau_awu;
-  using lds::SwitchedController<System>::set_control_type;
-
-  using lds::SwitchedController<System>::Reset;
-  using lds::SwitchedController<System>::Print;
-};  // SwitchedController
-}  // namespace gaussian
-}  // namespace lds
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_gaussian_sctrl.h
+summary: GLDS switched controller type. 
+
+---
+
+# ldsCtrlEst_h/lds_gaussian_sctrl.h
+
+GLDS switched controller type.  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+| **[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)** <br>Linear Dynamical Systems with Gaussian observations.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::gaussian::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/)** <br>Gaussian-observation [SwitchedController]() Type.  |
+
+## Detailed Description
+
+
+
+This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems ([lds::gaussian::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/)). 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H
+#define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H
+
+// controller type
+#include "lds_gaussian_ctrl.h"
+// switched controller
+#include "lds_sctrl.h"
+
+namespace lds {
+namespace gaussian {
+class SwitchedController : public lds::SwitchedController<System> {
+ public:
+  void set_y_ref(const Vector& y_ref) override {
+    Reassign(y_ref_, y_ref);
+    cx_ref_ = y_ref - sys_.d();
+  }
+
+  // make sure base class template methods available
+  using lds::SwitchedController<System>::SwitchedController;
+  using lds::SwitchedController<System>::Switch;
+  using lds::SwitchedController<System>::Control;
+  using lds::SwitchedController<System>::ControlOutputReference;
+
+  using lds::SwitchedController<System>::sys;
+  using lds::SwitchedController<System>::Kc;
+  using lds::SwitchedController<System>::Kc_inty;
+  using lds::SwitchedController<System>::Kc_u;
+  using lds::SwitchedController<System>::g_design;
+  using lds::SwitchedController<System>::u_ref;
+  using lds::SwitchedController<System>::x_ref;
+  using lds::SwitchedController<System>::y_ref;
+  using lds::SwitchedController<System>::control_type;
+
+  using lds::SwitchedController<System>::set_g_design;
+  using lds::SwitchedController<System>::set_u_ref;
+  using lds::SwitchedController<System>::set_x_ref;
+  using lds::SwitchedController<System>::set_y_ref;
+  using lds::SwitchedController<System>::set_Kc;
+  using lds::SwitchedController<System>::set_Kc_inty;
+  using lds::SwitchedController<System>::set_Kc_u;
+  using lds::SwitchedController<System>::set_tau_awu;
+  using lds::SwitchedController<System>::set_control_type;
+
+  using lds::SwitchedController<System>::Reset;
+  using lds::SwitchedController<System>::Print;
+};  // SwitchedController
+}  // namespace gaussian
+}  // namespace lds
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sys_8cpp.md b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sys_8cpp.md
index 0cbdc0f3..8843a354 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sys_8cpp.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sys_8cpp.md
@@ -1,98 +1,98 @@
----
-title: src/lds_gaussian_sys.cpp
-summary: GLDS base type. 
-
----
-
-# src/lds_gaussian_sys.cpp
-
-GLDS base type.  [More...](#detailed-description)
-
-
-
-## Detailed Description
-
-
-
-This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (`lds::gaussian::sys_t`). It inherits functionality from the underlying linear dynamical system (`lds::sys_t`). 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst_h/lds_gaussian_sys.h>
-
-lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0,
-                   data_t q0, data_t r0)
-    : lds::System(n_u, n_x, n_y, dt, p0, q0) {
-
-  R_.zeros(n_y, n_y);
-  R_.diag().fill(r0);
-
-  do_recurse_Ke_=true;
-};
-
-// recursively estimate Ke
-void lds::gaussian::System::RecurseKe() {
-  if (!do_recurse_Ke_) {
-    return;
-  }
-
-  // predict covariance
-  P_ = A_ * P_ * A_.t() + Q_;
-
-  // calc Kalman gain
-  Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_);
-
-  // update covariance
-  // Reference: Ghahramani et Hinton (1996)
-  P_ = P_ - Ke_ * C_ * P_;
-
-  if (do_adapt_m) {
-    P_m_ += Q_m_;  // A_m = I (i.e., random walk)
-    Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_);
-    P_m_ = P_m_ - Ke_m_ * C_ * P_m_;
-  }
-}
-
-// Simulate
-const lds::Vector& lds::gaussian::System::Simulate(const Vector& u_tm1){
-  f(u_tm1, true);//simulate dynamics with noise added
-  h();//output
-  z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure
-  return z_;
-}
-
-void lds::gaussian::System::Print() {
-  lds::System::Print();
-  std::cout << "R: \n" << R_ << "\n";
-}
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: src/lds_gaussian_sys.cpp
+summary: GLDS base type. 
+
+---
+
+# src/lds_gaussian_sys.cpp
+
+GLDS base type.  [More...](#detailed-description)
+
+
+
+## Detailed Description
+
+
+
+This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (`lds::gaussian::sys_t`). It inherits functionality from the underlying linear dynamical system (`lds::sys_t`). 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#include <ldsCtrlEst_h/lds_gaussian_sys.h>
+
+lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0,
+                   data_t q0, data_t r0)
+    : lds::System(n_u, n_x, n_y, dt, p0, q0) {
+
+  R_.zeros(n_y, n_y);
+  R_.diag().fill(r0);
+
+  do_recurse_Ke_=true;
+};
+
+// recursively estimate Ke
+void lds::gaussian::System::RecurseKe() {
+  if (!do_recurse_Ke_) {
+    return;
+  }
+
+  // predict covariance
+  P_ = A_ * P_ * A_.t() + Q_;
+
+  // calc Kalman gain
+  Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_);
+
+  // update covariance
+  // Reference: Ghahramani et Hinton (1996)
+  P_ = P_ - Ke_ * C_ * P_;
+
+  if (do_adapt_m) {
+    P_m_ += Q_m_;  // A_m = I (i.e., random walk)
+    Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_);
+    P_m_ = P_m_ - Ke_m_ * C_ * P_m_;
+  }
+}
+
+// Simulate
+const lds::Vector& lds::gaussian::System::Simulate(const Vector& u_tm1){
+  f(u_tm1, true);//simulate dynamics with noise added
+  h();//output
+  z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure
+  return z_;
+}
+
+void lds::gaussian::System::Print() {
+  lds::System::Print();
+  std::cout << "R: \n" << R_ << "\n";
+}
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sys_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sys_8h.md
index 7649b701..bea98c6c 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sys_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sys_8h.md
@@ -1,126 +1,128 @@
----
-title: ldsCtrlEst_h/lds_gaussian_sys.h
-summary: GLDS base type. 
-
----
-
-# ldsCtrlEst_h/lds_gaussian_sys.h
-
-GLDS base type.  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-| **[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)** <br>Linear Dynamical Systems with Gaussian observations.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/)** <br>Gaussian LDS Type.  |
-
-## Detailed Description
-
-
-
-This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (`[lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/)`). It inherits functionality from the underlying linear dynamical system (`[lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)`). 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H
-#define LDSCTRLEST_LDS_GAUSSIAN_SYS_H
-
-// namespace
-#include "lds_gaussian.h"
-// system
-#include "lds_sys.h"
-
-namespace lds {
-namespace gaussian {
-class System : public lds::System {
- public:
-  System() = default;
-
-  System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt,
-         data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0,
-         data_t r0 = kDefaultR0);
-
-  const Vector& Simulate(const Vector& u_tm1) override;
-
-  // get methods
-  const Matrix& R() const { return R_; };
-
-  // set methods
-  void set_Q(const Matrix& Q) {
-    lds::System::set_Q(Q);
-    do_recurse_Ke_ = true;
-  }
-  void set_R(const Matrix& R) {
-    Reassign(R_,R);
-    do_recurse_Ke_ = true;
-  };
-
-  void set_Ke(const Matrix& Ke) {
-    Reassign(Ke_,Ke);
-    // if users have set Ke, they must not want to calculate it online.
-    do_recurse_Ke_ = false;
-  };
-  void set_Ke_m(const Matrix& Ke_m) {
-    Reassign(Ke_m_,Ke_m);
-    // if users have set Ke, they must not want to calculate it online.
-    do_recurse_Ke_ = false;
-  };
-
-  void Print();
-
- protected:
-  void h() override {
-    cx_ = C_ * x_;
-    y_ = cx_ + d_;
-  };
-
-  void RecurseKe() override;
-
-  // Gaussian-output-specific
-  Matrix R_;           
-  bool do_recurse_Ke_{};  
-};                      // System
-}  // namespace gaussian
-}  // namespace lds
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_gaussian_sys.h
+summary: GLDS base type. 
+
+---
+
+# ldsCtrlEst_h/lds_gaussian_sys.h
+
+GLDS base type.  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+| **[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)** <br>Linear Dynamical Systems with Gaussian observations.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/)** <br>Gaussian LDS Type.  |
+
+## Detailed Description
+
+
+
+This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (`[lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/)`). It inherits functionality from the underlying linear dynamical system (`[lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)`). 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H
+#define LDSCTRLEST_LDS_GAUSSIAN_SYS_H
+
+// namespace
+#include "lds_gaussian.h"
+// system
+#include "lds_sys.h"
+
+namespace lds {
+namespace gaussian {
+class System : public lds::System {
+ public:
+  System() = default;
+
+  System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt,
+         data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0,
+         data_t r0 = kDefaultR0);
+
+  const Vector& Simulate(const Vector& u_tm1) override;
+
+  // get methods
+  const Matrix& R() const { return R_; };
+
+  // set methods
+  void set_Q(const Matrix& Q) {
+    lds::System::set_Q(Q);
+    do_recurse_Ke_ = true;
+  }
+  void set_R(const Matrix& R) {
+    Reassign(R_, R);
+    do_recurse_Ke_ = true;
+  };
+
+  void set_Ke(const Matrix& Ke) {
+    Reassign(Ke_, Ke);
+    // if users have set Ke, they must not want to calculate it online.
+    do_recurse_Ke_ = false;
+  };
+  void set_Ke_m(const Matrix& Ke_m) {
+    Reassign(Ke_m_, Ke_m);
+    // if users have set Ke, they must not want to calculate it online.
+    do_recurse_Ke_ = false;
+  };
+
+  void Print();
+
+ protected:
+  void h() override {
+    cx_ = C_ * x_;
+    y_ = cx_ + d_;
+  };
+
+  Vector h_(Vector x) override { return C_ * x + d_; };
+
+  void RecurseKe() override;
+
+  // Gaussian-output-specific
+  Matrix R_;              
+  bool do_recurse_Ke_{};  
+};  // System
+}  // namespace gaussian
+}  // namespace lds
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__poisson_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__poisson_8h.md
index bf41756f..56413ec6 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__poisson_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__poisson_8h.md
@@ -1,75 +1,75 @@
----
-title: ldsCtrlEst_h/lds_poisson.h
-summary: plds namespace 
-
----
-
-# ldsCtrlEst_h/lds_poisson.h
-
-`plds` namespace  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-| **[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)** <br>Linear Dynamical Systems with Poisson observations.  |
-
-## Detailed Description
-
-
-
-This file declares and partially defines the namespace for linear dynamical systems with Poisson observations (`[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)`). 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_POISSON_H
-#define LDSCTRLEST_LDS_POISSON_H
-
-#include "lds.h"
-
-namespace lds {
-namespace poisson {
-// TODO(mfbolus): Not sure if defining these as static here makes the most
-// sense. Is there a downside to letting multiple poisson System objects share a
-// common random number generator?
-static std::random_device rd;  
-static std::mt19937 rng = std::mt19937(
-    rd());  
-}  // namespace poisson
-}  // namespace lds
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_poisson.h
+summary: plds namespace 
+
+---
+
+# ldsCtrlEst_h/lds_poisson.h
+
+`plds` namespace  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+| **[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)** <br>Linear Dynamical Systems with Poisson observations.  |
+
+## Detailed Description
+
+
+
+This file declares and partially defines the namespace for linear dynamical systems with Poisson observations (`[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)`). 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_POISSON_H
+#define LDSCTRLEST_LDS_POISSON_H
+
+#include "lds.h"
+
+namespace lds {
+namespace poisson {
+// TODO(mfbolus): Not sure if defining these as static here makes the most
+// sense. Is there a downside to letting multiple poisson System objects share a
+// common random number generator?
+static std::random_device rd;  
+static std::mt19937 rng = std::mt19937(
+    rd());  
+}  // namespace poisson
+}  // namespace lds
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__poisson__ctrl_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__poisson__ctrl_8h.md
index fa6691c7..47682eb1 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__poisson__ctrl_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__poisson__ctrl_8h.md
@@ -1,121 +1,121 @@
----
-title: ldsCtrlEst_h/lds_poisson_ctrl.h
-summary: PLDS controller type. 
-
----
-
-# ldsCtrlEst_h/lds_poisson_ctrl.h
-
-PLDS controller type.  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-| **[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)** <br>Linear Dynamical Systems with Poisson observations.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/)** <br>PLDS [Controller]() Type.  |
-
-## Detailed Description
-
-
-
-This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system (`[lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/)`). It inherits functionality from the underlying PLDS model type (`[lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/)`), including state estimation. 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_POISSON_CTRL_H
-#define LDSCTRLEST_LDS_POISSON_CTRL_H
-
-// namespace
-#include "lds_poisson.h"
-// system type
-#include "lds_poisson_sys.h"
-// control type
-#include "lds_ctrl.h"
-
-namespace lds {
-namespace poisson {
-class Controller : public lds::Controller<System> {
- public:
-  void set_y_ref(const Vector& y_ref) override {
-    Reassign(y_ref_, y_ref);
-    lds::Limit(y_ref_, kYRefLb, lds::kInf);
-    cx_ref_ = log(y_ref_) - sys_.d();
-  };
-
-  // make sure base class template methods available
-  using lds::Controller<System>::Controller;
-  using lds::Controller<System>::Control;
-  using lds::Controller<System>::ControlOutputReference;
-
-  using lds::Controller<System>::sys;
-  using lds::Controller<System>::Kc;
-  using lds::Controller<System>::Kc_inty;
-  using lds::Controller<System>::Kc_u;
-  using lds::Controller<System>::g_design;
-  using lds::Controller<System>::u_ref;
-  using lds::Controller<System>::x_ref;
-  using lds::Controller<System>::y_ref;
-  using lds::Controller<System>::control_type;
-
-  using lds::Controller<System>::set_sys;
-  using lds::Controller<System>::set_g_design;
-  using lds::Controller<System>::set_u_ref;
-  using lds::Controller<System>::set_x_ref;
-  using lds::Controller<System>::set_y_ref;
-  using lds::Controller<System>::set_Kc;
-  using lds::Controller<System>::set_Kc_inty;
-  using lds::Controller<System>::set_Kc_u;
-  using lds::Controller<System>::set_tau_awu;
-  using lds::Controller<System>::set_control_type;
-
-  using lds::Controller<System>::Reset;
-  using lds::Controller<System>::Print;
-
- private:
-  constexpr static const data_t kYRefLb =
-      1e-4;  
-};
-}  // namespace poisson
-}  // namespace lds
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_poisson_ctrl.h
+summary: PLDS controller type. 
+
+---
+
+# ldsCtrlEst_h/lds_poisson_ctrl.h
+
+PLDS controller type.  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+| **[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)** <br>Linear Dynamical Systems with Poisson observations.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/)** <br>PLDS [Controller]() Type.  |
+
+## Detailed Description
+
+
+
+This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system (`[lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/)`). It inherits functionality from the underlying PLDS model type (`[lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)`), including state estimation. 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_POISSON_CTRL_H
+#define LDSCTRLEST_LDS_POISSON_CTRL_H
+
+// namespace
+#include "lds_poisson.h"
+// system type
+#include "lds_poisson_sys.h"
+// control type
+#include "lds_ctrl.h"
+
+namespace lds {
+namespace poisson {
+class Controller : public lds::Controller<System> {
+ public:
+  void set_y_ref(const Vector& y_ref) override {
+    Reassign(y_ref_, y_ref);
+    lds::Limit(y_ref_, kYRefLb, lds::kInf);
+    cx_ref_ = log(y_ref_) - sys_.d();
+  };
+
+  // make sure base class template methods available
+  using lds::Controller<System>::Controller;
+  using lds::Controller<System>::Control;
+  using lds::Controller<System>::ControlOutputReference;
+
+  using lds::Controller<System>::sys;
+  using lds::Controller<System>::Kc;
+  using lds::Controller<System>::Kc_inty;
+  using lds::Controller<System>::Kc_u;
+  using lds::Controller<System>::g_design;
+  using lds::Controller<System>::u_ref;
+  using lds::Controller<System>::x_ref;
+  using lds::Controller<System>::y_ref;
+  using lds::Controller<System>::control_type;
+
+  using lds::Controller<System>::set_sys;
+  using lds::Controller<System>::set_g_design;
+  using lds::Controller<System>::set_u_ref;
+  using lds::Controller<System>::set_x_ref;
+  using lds::Controller<System>::set_y_ref;
+  using lds::Controller<System>::set_Kc;
+  using lds::Controller<System>::set_Kc_inty;
+  using lds::Controller<System>::set_Kc_u;
+  using lds::Controller<System>::set_tau_awu;
+  using lds::Controller<System>::set_control_type;
+
+  using lds::Controller<System>::Reset;
+  using lds::Controller<System>::Print;
+
+ private:
+  constexpr static const data_t kYRefLb =
+      1e-4;  
+};
+}  // namespace poisson
+}  // namespace lds
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit_8h.md
index 144d1b6b..e0da461b 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit_8h.md
@@ -1,102 +1,102 @@
----
-title: ldsCtrlEst_h/lds_poisson_fit.h
-summary: PLDS base fit type. 
-
----
-
-# ldsCtrlEst_h/lds_poisson_fit.h
-
-PLDS base fit type.  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-| **[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)** <br>Linear Dynamical Systems with Poisson observations.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::poisson::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/)** <br>PLDS [Fit]() Type.  |
-
-## Detailed Description
-
-
-
-This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM). 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_POISSON_FIT_H
-#define LDSCTRLEST_LDS_POISSON_FIT_H
-
-// namespace
-#include "lds_poisson.h"
-// fit
-#include "lds_fit.h"
-
-namespace lds {
-namespace poisson {
-class Fit : public lds::Fit {
- public:
-  Fit() = default;
-
-  Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)
-      : lds::Fit(n_u, n_x, n_y, dt){};
-
-  View h(Matrix& y, const Matrix& x, size_t t) override {
-    y.col(t) = exp(C_ * x.col(t) + d_);
-    return y.col(t);
-  };
-
-  void set_R(const Matrix& R) override {
-    std::cerr
-        << "WARNING: Cannot set R (R[0] = " << R.at(0)
-        << "). No Gaussian measurement noise in Poisson observation model.\n";
-  };
-
-  const Matrix& R() const override {
-    return R_;
-  };
-
-};
-
-};  // namespace poisson
-}  // namespace lds
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_poisson_fit.h
+summary: PLDS base fit type. 
+
+---
+
+# ldsCtrlEst_h/lds_poisson_fit.h
+
+PLDS base fit type.  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+| **[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)** <br>Linear Dynamical Systems with Poisson observations.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::poisson::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/)** <br>PLDS [Fit]() Type.  |
+
+## Detailed Description
+
+
+
+This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM). 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_POISSON_FIT_H
+#define LDSCTRLEST_LDS_POISSON_FIT_H
+
+// namespace
+#include "lds_poisson.h"
+// fit
+#include "lds_fit.h"
+
+namespace lds {
+namespace poisson {
+class Fit : public lds::Fit {
+ public:
+  Fit() = default;
+
+  Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)
+      : lds::Fit(n_u, n_x, n_y, dt){};
+
+  View h(Matrix& y, const Matrix& x, size_t t) override {
+    y.col(t) = exp(C_ * x.col(t) + d_);
+    return y.col(t);
+  };
+
+  void set_R(const Matrix& R) override {
+    std::cerr
+        << "WARNING: Cannot set R (R[0] = " << R.at(0)
+        << "). No Gaussian measurement noise in Poisson observation model.\n";
+  };
+
+  const Matrix& R() const override {
+    return R_;
+  };
+
+};
+
+};  // namespace poisson
+}  // namespace lds
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit__em_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit__em_8h.md
index 86a2ab2c..98906e9e 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit__em_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit__em_8h.md
@@ -1,98 +1,98 @@
----
-title: ldsCtrlEst_h/lds_poisson_fit_em.h
-summary: PLDS E-M fit type. 
-
----
-
-# ldsCtrlEst_h/lds_poisson_fit_em.h
-
-PLDS E-M fit type.  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-| **[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)** <br>Linear Dynamical Systems with Poisson observations.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::poisson::FitEM](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitem/)** <br>PLDS E-M [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/) Type.  |
-
-## Detailed Description
-
-
-
-This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (`lds::gaussian::emFit_t`).
-
-References: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).
-
-[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.
-
-[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation. 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H
-#define LDSCTRLEST_LDS_POISSON_FIT_EM_H
-
-#include "lds_fit_em.h"
-#include "lds_poisson_fit.h"
-
-namespace lds {
-namespace poisson {
-class FitEM : public EM<Fit> {
- public:
-  using EM<Fit>::EM;
-
- private:
-  void MaximizeOutput() override;
-
-  void MaximizeMeasurement() override{};
-
-  void RecurseKe(Matrix& Ke, Cube& P_pre, Cube& P_post, size_t t) override;
-
-  data_t NewtonSolveC();
-
-  void AnalyticalSolveD();
-};
-
-}  // namespace poisson
-}  // namespace lds
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_poisson_fit_em.h
+summary: PLDS E-M fit type. 
+
+---
+
+# ldsCtrlEst_h/lds_poisson_fit_em.h
+
+PLDS E-M fit type.  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+| **[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)** <br>Linear Dynamical Systems with Poisson observations.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::poisson::FitEM](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/)** <br>PLDS E-M [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/) Type.  |
+
+## Detailed Description
+
+
+
+This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (`lds::gaussian::emFit_t`).
+
+References: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).
+
+[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.
+
+[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation. 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H
+#define LDSCTRLEST_LDS_POISSON_FIT_EM_H
+
+#include "lds_fit_em.h"
+#include "lds_poisson_fit.h"
+
+namespace lds {
+namespace poisson {
+class FitEM : public EM<Fit> {
+ public:
+  using EM<Fit>::EM;
+
+ private:
+  void MaximizeOutput() override;
+
+  void MaximizeMeasurement() override{};
+
+  void RecurseKe(Matrix& Ke, Cube& P_pre, Cube& P_post, size_t t) override;
+
+  data_t NewtonSolveC();
+
+  void AnalyticalSolveD();
+};
+
+}  // namespace poisson
+}  // namespace lds
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit__ssid_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit__ssid_8h.md
index dd806216..922747b9 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit__ssid_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit__ssid_8h.md
@@ -1,90 +1,90 @@
----
-title: ldsCtrlEst_h/lds_poisson_fit_ssid.h
-summary: PLDS SSID fit type. 
-
----
-
-# ldsCtrlEst_h/lds_poisson_fit_ssid.h
-
-PLDS SSID fit type.  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-| **[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)** <br>Linear Dynamical Systems with Poisson observations.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::poisson::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitssid/)** <br>Subspace Identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)) for PLDS.  |
-
-## Detailed Description
-
-
-
-This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm (`[lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitssid/)`).
-
-References: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25. 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H
-#define LDSCTRLEST_LDS_POISSON_FIT_SSID_H
-
-#include "lds_fit_ssid.h"
-#include "lds_poisson_fit.h"
-
-namespace lds {
-namespace poisson {
-class FitSSID : public SSID<Fit> {
- public:
-  using SSID<Fit>::SSID;
-
- private:
-  void DecomposeData() override;
-
-  void CalcCov();
-
-  void PoissonToGaussianMoments();
-  Matrix cov_;
-};
-
-}  // namespace poisson
-}  // namespace lds
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_poisson_fit_ssid.h
+summary: PLDS SSID fit type. 
+
+---
+
+# ldsCtrlEst_h/lds_poisson_fit_ssid.h
+
+PLDS SSID fit type.  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+| **[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)** <br>Linear Dynamical Systems with Poisson observations.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::poisson::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/)** <br>Subspace Identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)) for PLDS.  |
+
+## Detailed Description
+
+
+
+This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm (`[lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/)`).
+
+References: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25. 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H
+#define LDSCTRLEST_LDS_POISSON_FIT_SSID_H
+
+#include "lds_fit_ssid.h"
+#include "lds_poisson_fit.h"
+
+namespace lds {
+namespace poisson {
+class FitSSID : public SSID<Fit> {
+ public:
+  using SSID<Fit>::SSID;
+
+ private:
+  void DecomposeData() override;
+
+  void CalcCov();
+
+  void PoissonToGaussianMoments();
+  Matrix cov_;
+};
+
+}  // namespace poisson
+}  // namespace lds
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__poisson__sctrl_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__poisson__sctrl_8h.md
index 885a2585..f9b45231 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__poisson__sctrl_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__poisson__sctrl_8h.md
@@ -1,117 +1,117 @@
----
-title: ldsCtrlEst_h/lds_poisson_sctrl.h
-summary: PLDS switched controller type. 
-
----
-
-# ldsCtrlEst_h/lds_poisson_sctrl.h
-
-PLDS switched controller type.  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-| **[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)** <br>Linear Dynamical Systems with Poisson observations.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::poisson::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1switchedcontroller/)** <br>Poisson-observation [SwitchedController]() Type.  |
-
-## Detailed Description
-
-
-
-This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems ([lds::poisson::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1switchedcontroller/)). 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H
-#define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H
-
-#include "lds_poisson_ctrl.h"
-#include "lds_sctrl.h"
-
-namespace lds {
-namespace poisson {
-class SwitchedController : public lds::SwitchedController<System> {
- public:
-  void set_y_ref(const Vector& y_ref) override {
-    Reassign(y_ref_,y_ref);
-    lds::Limit(y_ref_, kYRefLB, lds::kInf);
-    cx_ref_ = log(y_ref_) - sys_.d();
-  };
-
-  // make sure base class template methods available
-  using lds::SwitchedController<System>::SwitchedController;
-  using lds::SwitchedController<System>::Switch;
-  using lds::SwitchedController<System>::Control;
-  using lds::SwitchedController<System>::ControlOutputReference;
-
-  using lds::SwitchedController<System>::sys;
-  using lds::SwitchedController<System>::Kc;
-  using lds::SwitchedController<System>::Kc_inty;
-  using lds::SwitchedController<System>::Kc_u;
-  using lds::SwitchedController<System>::g_design;
-  using lds::SwitchedController<System>::u_ref;
-  using lds::SwitchedController<System>::x_ref;
-  using lds::SwitchedController<System>::y_ref;
-  using lds::SwitchedController<System>::control_type;
-
-  using lds::SwitchedController<System>::set_g_design;
-  using lds::SwitchedController<System>::set_u_ref;
-  using lds::SwitchedController<System>::set_x_ref;
-  using lds::SwitchedController<System>::set_y_ref;
-  using lds::SwitchedController<System>::set_Kc;
-  using lds::SwitchedController<System>::set_Kc_inty;
-  using lds::SwitchedController<System>::set_Kc_u;
-  using lds::SwitchedController<System>::set_tau_awu;
-  using lds::SwitchedController<System>::set_control_type;
-
-  using lds::SwitchedController<System>::Reset;
-  using lds::SwitchedController<System>::Print;
-
- private:
-  constexpr static data_t kYRefLB =
-      1e-4;  
-};
-}  // namespace poisson
-}  // namespace lds
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_poisson_sctrl.h
+summary: PLDS switched controller type. 
+
+---
+
+# ldsCtrlEst_h/lds_poisson_sctrl.h
+
+PLDS switched controller type.  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+| **[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)** <br>Linear Dynamical Systems with Poisson observations.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::poisson::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/)** <br>Poisson-observation [SwitchedController]() Type.  |
+
+## Detailed Description
+
+
+
+This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems ([lds::poisson::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/)). 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H
+#define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H
+
+#include "lds_poisson_ctrl.h"
+#include "lds_sctrl.h"
+
+namespace lds {
+namespace poisson {
+class SwitchedController : public lds::SwitchedController<System> {
+ public:
+  void set_y_ref(const Vector& y_ref) override {
+    Reassign(y_ref_,y_ref);
+    lds::Limit(y_ref_, kYRefLB, lds::kInf);
+    cx_ref_ = log(y_ref_) - sys_.d();
+  };
+
+  // make sure base class template methods available
+  using lds::SwitchedController<System>::SwitchedController;
+  using lds::SwitchedController<System>::Switch;
+  using lds::SwitchedController<System>::Control;
+  using lds::SwitchedController<System>::ControlOutputReference;
+
+  using lds::SwitchedController<System>::sys;
+  using lds::SwitchedController<System>::Kc;
+  using lds::SwitchedController<System>::Kc_inty;
+  using lds::SwitchedController<System>::Kc_u;
+  using lds::SwitchedController<System>::g_design;
+  using lds::SwitchedController<System>::u_ref;
+  using lds::SwitchedController<System>::x_ref;
+  using lds::SwitchedController<System>::y_ref;
+  using lds::SwitchedController<System>::control_type;
+
+  using lds::SwitchedController<System>::set_g_design;
+  using lds::SwitchedController<System>::set_u_ref;
+  using lds::SwitchedController<System>::set_x_ref;
+  using lds::SwitchedController<System>::set_y_ref;
+  using lds::SwitchedController<System>::set_Kc;
+  using lds::SwitchedController<System>::set_Kc_inty;
+  using lds::SwitchedController<System>::set_Kc_u;
+  using lds::SwitchedController<System>::set_tau_awu;
+  using lds::SwitchedController<System>::set_control_type;
+
+  using lds::SwitchedController<System>::Reset;
+  using lds::SwitchedController<System>::Print;
+
+ private:
+  constexpr static data_t kYRefLB =
+      1e-4;  
+};
+}  // namespace poisson
+}  // namespace lds
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__poisson__sys_8cpp.md b/misc/docs-hugo/content/docs/api/Files/lds__poisson__sys_8cpp.md
index 5981e03e..4b1e16f4 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__poisson__sys_8cpp.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__poisson__sys_8cpp.md
@@ -1,94 +1,94 @@
----
-title: src/lds_poisson_sys.cpp
-summary: PLDS base type. 
-
----
-
-# src/lds_poisson_sys.cpp
-
-PLDS base type.  [More...](#detailed-description)
-
-
-
-## Detailed Description
-
-
-
-This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (`lds::poisson::sys_t`). It inherits functionality from the underlying linear dynamical system (`lds::sys_t`). 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst_h/lds_poisson_sys.h>
-
-lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt,
-                             data_t p0, data_t q0)
-    : lds::System(n_u, n_x, n_y, dt, p0, q0) {
-  diag_y_ = diagmat(y_);
-  pd_ = std::poisson_distribution<size_t>(0);
-};
-
-// Correct: Given measurement (z) and current input (u), update estimate of the
-// state, covar, output.
-//
-// see Eden et al. 2004
-void lds::poisson::System::RecurseKe() {
-  // predict covariance
-  P_ = A_ * P_ * A_.t() + Q_;
-
-  // update cov
-  P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_);
-  Ke_ = P_ * C_.t();
-  if (do_adapt_m) {
-    P_m_ += Q_m_;                                     // predict (A_m = I)
-    P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_);  // update
-    Ke_m_ = P_m_ * C_.t();
-  }
-}
-
-// Simulate Measurement: z ~ Poisson(y)
-const lds::Vector& lds::poisson::System::Simulate(const Vector& u_tm1) {
-  f(u_tm1, true);  // simulate dynamics with noise added
-  h();             // output
-
-  z_.zeros();
-  for (std::size_t k = 0; k < n_y_; k++) {
-    // construct a Poisson distribution object with mean y[k]
-    pd_ = std::poisson_distribution<size_t>(y_[k]);
-    // pull random sample from this distribution
-    z_[k] = pd_(rng);
-  }
-
-  return z_;
-}
-// ******************* SYS_T *******************
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: src/lds_poisson_sys.cpp
+summary: PLDS base type. 
+
+---
+
+# src/lds_poisson_sys.cpp
+
+PLDS base type.  [More...](#detailed-description)
+
+
+
+## Detailed Description
+
+
+
+This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (`lds::poisson::sys_t`). It inherits functionality from the underlying linear dynamical system (`lds::sys_t`). 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#include <ldsCtrlEst_h/lds_poisson_sys.h>
+
+lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt,
+                             data_t p0, data_t q0)
+    : lds::System(n_u, n_x, n_y, dt, p0, q0) {
+  diag_y_ = diagmat(y_);
+  pd_ = std::poisson_distribution<size_t>(0);
+};
+
+// Correct: Given measurement (z) and current input (u), update estimate of the
+// state, covar, output.
+//
+// see Eden et al. 2004
+void lds::poisson::System::RecurseKe() {
+  // predict covariance
+  P_ = A_ * P_ * A_.t() + Q_;
+
+  // update cov
+  P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_);
+  Ke_ = P_ * C_.t();
+  if (do_adapt_m) {
+    P_m_ += Q_m_;                                     // predict (A_m = I)
+    P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_);  // update
+    Ke_m_ = P_m_ * C_.t();
+  }
+}
+
+// Simulate Measurement: z ~ Poisson(y)
+const lds::Vector& lds::poisson::System::Simulate(const Vector& u_tm1) {
+  f(u_tm1, true);  // simulate dynamics with noise added
+  h();             // output
+
+  z_.zeros();
+  for (std::size_t k = 0; k < n_y_; k++) {
+    // construct a Poisson distribution object with mean y[k]
+    pd_ = std::poisson_distribution<size_t>(y_[k]);
+    // pull random sample from this distribution
+    z_[k] = pd_(rng);
+  }
+
+  return z_;
+}
+// ******************* SYS_T *******************
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__poisson__sys_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__poisson__sys_8h.md
index 4dbed5d6..d508364e 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__poisson__sys_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__poisson__sys_8h.md
@@ -1,106 +1,108 @@
----
-title: ldsCtrlEst_h/lds_poisson_sys.h
-summary: PLDS base type. 
-
----
-
-# ldsCtrlEst_h/lds_poisson_sys.h
-
-PLDS base type.  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-| **[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)** <br>Linear Dynamical Systems with Poisson observations.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/)** <br>Poisson [System]() type.  |
-
-## Detailed Description
-
-
-
-This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (`[lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/)`). It inherits functionality from the underlying linear dynamical system (`[lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)`). 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_POISSON_SYS_H
-#define LDSCTRLEST_LDS_POISSON_SYS_H
-
-// namespace
-#include "lds_poisson.h"
-// system
-#include "lds_sys.h"
-
-// needed for Poisson random number generation
-#include <random>
-
-namespace lds {
-namespace poisson {
-
-class System : public lds::System {
- public:
-  System() = default;
-
-  System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt,
-         data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0);
-
-  const Vector& Simulate(const Vector& u_tm1) override;
-
- protected:
-  void h() override {
-    cx_ = C_ * x_;
-    y_ = exp(cx_ + d_);
-    diag_y_.diag() = y_;
-  };
-
-  void RecurseKe() override;
-
- private:
-  // Poisson-output-specific
-  Matrix diag_y_;                 
-  std::poisson_distribution<size_t>
-      pd_;  
-};          // System
-}  // namespace poisson
-}  // namespace lds
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_poisson_sys.h
+summary: PLDS base type. 
+
+---
+
+# ldsCtrlEst_h/lds_poisson_sys.h
+
+PLDS base type.  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+| **[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)** <br>Linear Dynamical Systems with Poisson observations.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)** <br>Poisson [System]() type.  |
+
+## Detailed Description
+
+
+
+This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (`[lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)`). It inherits functionality from the underlying linear dynamical system (`[lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)`). 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_POISSON_SYS_H
+#define LDSCTRLEST_LDS_POISSON_SYS_H
+
+// namespace
+#include "lds_poisson.h"
+// system
+#include "lds_sys.h"
+
+// needed for Poisson random number generation
+#include <random>
+
+namespace lds {
+namespace poisson {
+
+class System : public lds::System {
+ public:
+  System() = default;
+
+  System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt,
+         data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0);
+
+  const Vector& Simulate(const Vector& u_tm1) override;
+
+ protected:
+  void h() override {
+    cx_ = C_ * x_;
+    y_ = exp(cx_ + d_);
+    diag_y_.diag() = y_;
+  };
+
+  Vector h_(Vector x) override { return exp(C_ * x + d_); };
+
+  void RecurseKe() override;
+
+ private:
+  // Poisson-output-specific
+  Matrix diag_y_;  
+  std::poisson_distribution<size_t>
+      pd_;  
+};  // System
+}  // namespace poisson
+}  // namespace lds
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__sctrl_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__sctrl_8h.md
index c59f532d..54127497 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__sctrl_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__sctrl_8h.md
@@ -1,275 +1,275 @@
----
-title: ldsCtrlEst_h/lds_sctrl.h
-summary: SwitchedController type. 
-
----
-
-# ldsCtrlEst_h/lds_sctrl.h
-
-SwitchedController type.  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/)** <br>[SwitchedController]() Type.  |
-
-## Detailed Description
-
-
-
-This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems ([lds::gaussian::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1switchedcontroller/)). 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_SCTRL_H
-#define LDSCTRLEST_LDS_SCTRL_H
-
-#include "lds_ctrl.h"
-#include "lds_uniform_mats.h"
-#include "lds_uniform_vecs.h"
-
-namespace lds {
-template <typename System>
-class SwitchedController : public Controller<System> {
- public:
-  SwitchedController() = default;
-
-  SwitchedController(const std::vector<System>& systems, data_t u_lb,
-                     data_t u_ub, size_t control_type = 0);
-
-  SwitchedController(std::vector<System>&& systems, data_t u_lb, data_t u_ub,
-                     size_t control_type = 0);
-
-  void Switch(size_t idx, bool do_force_switch = false);
-
-  void set_Kc(const UniformMatrixList<>& Kc) {
-    Kc_list_ = Kc;
-    Kc_ = Kc_list_.at(0);  // set to first
-    if (idx_ != 0) {
-      Switch(idx_, true);
-    }
-  };
-  void set_Kc(UniformMatrixList<>&& Kc) {
-    Kc_list_ = std::move(Kc);
-    Kc_ = Kc_list_.at(0);  // set to first
-    if (idx_ != 0) {
-      Switch(idx_, true);
-    }
-  };
-
-  void set_Kc_inty(const UniformMatrixList<>& Kc_inty) {
-    Kc_inty_list_ = Kc_inty;
-    Kc_inty_ = Kc_inty_list_.at(0);  // set to first
-    if (idx_ != 0) {
-      Switch(idx_, true);
-    }
-  };
-  void set_Kc_inty(UniformMatrixList<>&& Kc_inty) {
-    Kc_inty_list_ = std::move(Kc_inty);
-    Kc_inty_ = Kc_inty_list_.at(0);  // set to first
-    if (idx_ != 0) {
-      Switch(idx_, true);
-    }
-  };
-
-  void set_Kc_u(const UniformMatrixList<>& Kc_u) {
-    Kc_u_list_ = Kc_u;
-    Kc_u_ = Kc_u_list_.at(0);  // set to first
-    if (idx_ != 0) {
-      Switch(idx_, true);
-    }
-  };
-  void set_Kc_u(UniformMatrixList<>&& Kc_u) {
-    Kc_u_list_ = std::move(Kc_u);
-    Kc_u_ = Kc_u_list_.at(0);  // set to first
-    if (idx_ != 0) {
-      Switch(idx_, true);
-    }
-  };
-
-  void set_g_design(const UniformVectorList& g) {
-    g_design_list_ = g;
-    g_design_ = g_design_list_.at(0);  // set to first
-    if (idx_ != 0) {
-      Switch(idx_, true);
-    }
-  };
-  void set_g_design(UniformVectorList&& g) {
-    g_design_list_ = std::move(g);
-    g_design_ = g_design_list_.at(0);  // set to first
-    if (idx_ != 0) {
-      Switch(idx_, true);
-    }
-  };
-
-  // make sure base class template methods available
-  using lds::Controller<System>::Controller;
-  using lds::Controller<System>::Control;
-  using lds::Controller<System>::ControlOutputReference;
-
-  using lds::Controller<System>::sys;
-  using lds::Controller<System>::Kc;
-  using lds::Controller<System>::Kc_inty;
-  using lds::Controller<System>::Kc_u;
-  using lds::Controller<System>::g_design;
-  using lds::Controller<System>::u_ref;
-  using lds::Controller<System>::x_ref;
-  using lds::Controller<System>::y_ref;
-  using lds::Controller<System>::control_type;
-
-  using lds::Controller<System>::set_u_ref;
-  using lds::Controller<System>::set_x_ref;
-  using lds::Controller<System>::set_y_ref;
-  using lds::Controller<System>::set_tau_awu;
-  using lds::Controller<System>::set_control_type;
-
-  using lds::Controller<System>::Reset;
-  using lds::Controller<System>::Print;
-
- protected:
-  std::vector<System>
-      systems_;     
-  size_t n_sys_{};  
-  size_t idx_{};    
-
-  // controller gains could be different for each
-  UniformMatrixList<> Kc_list_;
-  UniformMatrixList<> Kc_inty_list_;
-  UniformMatrixList<> Kc_u_list_;
-
-  // design-phase input gain could also be different
-  UniformVectorList g_design_list_;
-
-  // TODO(mfbolus): not sure why I need to do this.
-  using Controller<System>::Kc_;
-  using Controller<System>::Kc_inty_;
-  using Controller<System>::Kc_u_;
-  using Controller<System>::g_design_;
-  using Controller<System>::sys_;
-  // using Controller<System>::u_ref_;
-  // using Controller<System>::x_ref_;
-  // using Controller<System>::y_ref_;
-  //
-  using Controller<System>::control_type_;
-
- private:
-  void InitVars();
-
-  using lds::Controller<System>::set_sys;
-  // using Controller<System>::set_Kc;
-  // using Controller<System>::set_Kc_inty;
-  // using Controller<System>::set_Kc_u;
-  // using Controller<System>::set_g_design;
-};
-
-template <typename System>
-inline SwitchedController<System>::SwitchedController(
-    const std::vector<System>& systems, data_t u_lb, data_t u_ub,
-    size_t control_type)
-    : Controller<System>(systems.at(0), u_lb, u_ub, control_type),
-      systems_(systems) {
-  InitVars();
-}
-
-template <typename System>
-inline SwitchedController<System>::SwitchedController(
-    std::vector<System>&& systems, data_t u_lb, data_t u_ub,
-    size_t control_type)
-    : Controller<System>(System(systems.at(0).n_u(), systems.at(0).n_x(),
-                                systems.at(0).n_y(), systems.at(0).dt()),
-                         u_lb, u_ub, control_type),
-      systems_(std::move(systems)) {
-  InitVars();
-}
-
-template <typename System>
-inline void SwitchedController<System>::InitVars() {
-  n_sys_ = systems_.size();
-  sys_ = systems_.at(0);
-
-  Kc_list_ = UniformMatrixList<>(std::vector<Matrix>(n_sys_, Kc_));
-  Kc_inty_list_ = UniformMatrixList<>(std::vector<Matrix>(n_sys_, Kc_inty_));
-  Kc_u_list_ = UniformMatrixList<>(std::vector<Matrix>(n_sys_, Kc_inty_));
-  g_design_list_ = UniformVectorList(std::vector<Vector>(n_sys_, g_design_));
-}
-
-template <typename System>
-inline void SwitchedController<System>::Switch(size_t idx,
-                                               bool do_force_switch) {
-  if ((idx == idx_) && !do_force_switch) {
-    return;  // already there.
-  }
-
-  // put old up and get new one out
-  systems_.at(idx_) = std::move(sys_);
-  sys_ = std::move(systems_.at(idx));
-
-  // set the state of this system to that of the previous system
-  // TODO(mfbolus): This will only work as intended if state matrix is the same.
-  // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp.
-  sys_.set_m(systems_.at(idx_).m(), true);
-  sys_.set_x(systems_.at(idx_).x());
-
-  // swap controller gains
-  Kc_list_.Swap(Kc_, idx_);
-  Kc_list_.Swap(Kc_, idx);
-
-  if (control_type_ & kControlTypeIntY) {
-    Kc_inty_list_.Swap(Kc_inty_, idx_);
-    Kc_inty_list_.Swap(Kc_inty_, idx);
-  }
-
-  if (control_type_ & kControlTypeDeltaU) {
-    Kc_u_list_.Swap(Kc_u_, idx_);
-    Kc_u_list_.Swap(Kc_u_, idx);
-  }
-
-  g_design_list_.Swap(g_design_, idx_);
-  g_design_list_.Swap(g_design_, idx);
-
-  idx_ = idx;
-}  // Switch
-
-}  // namespace lds
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_sctrl.h
+summary: SwitchedController type. 
+
+---
+
+# ldsCtrlEst_h/lds_sctrl.h
+
+SwitchedController type.  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/)** <br>[SwitchedController]() Type.  |
+
+## Detailed Description
+
+
+
+This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems ([lds::gaussian::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/)). 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_SCTRL_H
+#define LDSCTRLEST_LDS_SCTRL_H
+
+#include "lds_ctrl.h"
+#include "lds_uniform_mats.h"
+#include "lds_uniform_vecs.h"
+
+namespace lds {
+template <typename System>
+class SwitchedController : public Controller<System> {
+ public:
+  SwitchedController() = default;
+
+  SwitchedController(const std::vector<System>& systems, data_t u_lb,
+                     data_t u_ub, size_t control_type = 0);
+
+  SwitchedController(std::vector<System>&& systems, data_t u_lb, data_t u_ub,
+                     size_t control_type = 0);
+
+  void Switch(size_t idx, bool do_force_switch = false);
+
+  void set_Kc(const UniformMatrixList<>& Kc) {
+    Kc_list_ = Kc;
+    Kc_ = Kc_list_.at(0);  // set to first
+    if (idx_ != 0) {
+      Switch(idx_, true);
+    }
+  };
+  void set_Kc(UniformMatrixList<>&& Kc) {
+    Kc_list_ = std::move(Kc);
+    Kc_ = Kc_list_.at(0);  // set to first
+    if (idx_ != 0) {
+      Switch(idx_, true);
+    }
+  };
+
+  void set_Kc_inty(const UniformMatrixList<>& Kc_inty) {
+    Kc_inty_list_ = Kc_inty;
+    Kc_inty_ = Kc_inty_list_.at(0);  // set to first
+    if (idx_ != 0) {
+      Switch(idx_, true);
+    }
+  };
+  void set_Kc_inty(UniformMatrixList<>&& Kc_inty) {
+    Kc_inty_list_ = std::move(Kc_inty);
+    Kc_inty_ = Kc_inty_list_.at(0);  // set to first
+    if (idx_ != 0) {
+      Switch(idx_, true);
+    }
+  };
+
+  void set_Kc_u(const UniformMatrixList<>& Kc_u) {
+    Kc_u_list_ = Kc_u;
+    Kc_u_ = Kc_u_list_.at(0);  // set to first
+    if (idx_ != 0) {
+      Switch(idx_, true);
+    }
+  };
+  void set_Kc_u(UniformMatrixList<>&& Kc_u) {
+    Kc_u_list_ = std::move(Kc_u);
+    Kc_u_ = Kc_u_list_.at(0);  // set to first
+    if (idx_ != 0) {
+      Switch(idx_, true);
+    }
+  };
+
+  void set_g_design(const UniformVectorList& g) {
+    g_design_list_ = g;
+    g_design_ = g_design_list_.at(0);  // set to first
+    if (idx_ != 0) {
+      Switch(idx_, true);
+    }
+  };
+  void set_g_design(UniformVectorList&& g) {
+    g_design_list_ = std::move(g);
+    g_design_ = g_design_list_.at(0);  // set to first
+    if (idx_ != 0) {
+      Switch(idx_, true);
+    }
+  };
+
+  // make sure base class template methods available
+  using lds::Controller<System>::Controller;
+  using lds::Controller<System>::Control;
+  using lds::Controller<System>::ControlOutputReference;
+
+  using lds::Controller<System>::sys;
+  using lds::Controller<System>::Kc;
+  using lds::Controller<System>::Kc_inty;
+  using lds::Controller<System>::Kc_u;
+  using lds::Controller<System>::g_design;
+  using lds::Controller<System>::u_ref;
+  using lds::Controller<System>::x_ref;
+  using lds::Controller<System>::y_ref;
+  using lds::Controller<System>::control_type;
+
+  using lds::Controller<System>::set_u_ref;
+  using lds::Controller<System>::set_x_ref;
+  using lds::Controller<System>::set_y_ref;
+  using lds::Controller<System>::set_tau_awu;
+  using lds::Controller<System>::set_control_type;
+
+  using lds::Controller<System>::Reset;
+  using lds::Controller<System>::Print;
+
+ protected:
+  std::vector<System>
+      systems_;     
+  size_t n_sys_{};  
+  size_t idx_{};    
+
+  // controller gains could be different for each
+  UniformMatrixList<> Kc_list_;
+  UniformMatrixList<> Kc_inty_list_;
+  UniformMatrixList<> Kc_u_list_;
+
+  // design-phase input gain could also be different
+  UniformVectorList g_design_list_;
+
+  // TODO(mfbolus): not sure why I need to do this.
+  using Controller<System>::Kc_;
+  using Controller<System>::Kc_inty_;
+  using Controller<System>::Kc_u_;
+  using Controller<System>::g_design_;
+  using Controller<System>::sys_;
+  // using Controller<System>::u_ref_;
+  // using Controller<System>::x_ref_;
+  // using Controller<System>::y_ref_;
+  //
+  using Controller<System>::control_type_;
+
+ private:
+  void InitVars();
+
+  using lds::Controller<System>::set_sys;
+  // using Controller<System>::set_Kc;
+  // using Controller<System>::set_Kc_inty;
+  // using Controller<System>::set_Kc_u;
+  // using Controller<System>::set_g_design;
+};
+
+template <typename System>
+inline SwitchedController<System>::SwitchedController(
+    const std::vector<System>& systems, data_t u_lb, data_t u_ub,
+    size_t control_type)
+    : Controller<System>(systems.at(0), u_lb, u_ub, control_type),
+      systems_(systems) {
+  InitVars();
+}
+
+template <typename System>
+inline SwitchedController<System>::SwitchedController(
+    std::vector<System>&& systems, data_t u_lb, data_t u_ub,
+    size_t control_type)
+    : Controller<System>(System(systems.at(0).n_u(), systems.at(0).n_x(),
+                                systems.at(0).n_y(), systems.at(0).dt()),
+                         u_lb, u_ub, control_type),
+      systems_(std::move(systems)) {
+  InitVars();
+}
+
+template <typename System>
+inline void SwitchedController<System>::InitVars() {
+  n_sys_ = systems_.size();
+  sys_ = systems_.at(0);
+
+  Kc_list_ = UniformMatrixList<>(std::vector<Matrix>(n_sys_, Kc_));
+  Kc_inty_list_ = UniformMatrixList<>(std::vector<Matrix>(n_sys_, Kc_inty_));
+  Kc_u_list_ = UniformMatrixList<>(std::vector<Matrix>(n_sys_, Kc_inty_));
+  g_design_list_ = UniformVectorList(std::vector<Vector>(n_sys_, g_design_));
+}
+
+template <typename System>
+inline void SwitchedController<System>::Switch(size_t idx,
+                                               bool do_force_switch) {
+  if ((idx == idx_) && !do_force_switch) {
+    return;  // already there.
+  }
+
+  // put old up and get new one out
+  systems_.at(idx_) = std::move(sys_);
+  sys_ = std::move(systems_.at(idx));
+
+  // set the state of this system to that of the previous system
+  // TODO(mfbolus): This will only work as intended if state matrix is the same.
+  // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp.
+  sys_.set_m(systems_.at(idx_).m(), true);
+  sys_.set_x(systems_.at(idx_).x());
+
+  // swap controller gains
+  Kc_list_.Swap(Kc_, idx_);
+  Kc_list_.Swap(Kc_, idx);
+
+  if (control_type_ & kControlTypeIntY) {
+    Kc_inty_list_.Swap(Kc_inty_, idx_);
+    Kc_inty_list_.Swap(Kc_inty_, idx);
+  }
+
+  if (control_type_ & kControlTypeDeltaU) {
+    Kc_u_list_.Swap(Kc_u_, idx_);
+    Kc_u_list_.Swap(Kc_u_, idx);
+  }
+
+  g_design_list_.Swap(g_design_, idx_);
+  g_design_list_.Swap(g_design_, idx);
+
+  idx_ = idx;
+}  // Switch
+
+}  // namespace lds
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__sys_8cpp.md b/misc/docs-hugo/content/docs/api/Files/lds__sys_8cpp.md
index 8e313360..3c311f36 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__sys_8cpp.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__sys_8cpp.md
@@ -1,140 +1,184 @@
----
-title: src/lds_sys.cpp
-summary: LDS base type. 
-
----
-
-# src/lds_sys.cpp
-
-LDS base type.  [More...](#detailed-description)
-
-
-
-## Detailed Description
-
-
-
-This file implements the base type for linear dynamical systems ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class. 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- lds_sys.cpp - LDS -------------------------------------------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst_h/lds_sys.h>
-
-lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0,
-                    data_t q0)
-    : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) {
-  InitVars(p0, q0);
-}
-
-void lds::System::InitVars(data_t p0, data_t q0) {
-  // initial conditions.
-  x0_ = Vector(n_x_, fill::zeros);  // includes bias (nY) and g (nU)
-  P0_ = p0 * Matrix(n_x_, n_x_, fill::eye);
-
-  m0_ = x0_;
-  P0_m_ = P0_;
-
-  // signals
-  x_ = x0_;
-  P_ = P0_;
-  m_ = m0_;
-  P_m_ = P0_m_;
-  y_ = Vector(n_y_, fill::zeros);
-  cx_ = Vector(n_y_, fill::zeros);
-  z_ = Vector(n_y_, fill::zeros);
-
-  // By default, random walk where each state is independent
-  // In this way, provides independent estimates of rate per channel of output.
-  A_ = Matrix(n_x_, n_x_, fill::eye);
-  B_ = Matrix(n_x_, n_u_, fill::zeros);
-  g_ = Vector(n_u_, fill::ones);
-  Q_ = q0 * Matrix(n_x_, n_x_, fill::eye);
-  Q_m_ = Q_;
-
-  C_ = Matrix(n_y_, n_x_, fill::eye);  // each state will map to an output by
-  d_ = Vector(n_y_, fill::zeros);
-
-  Ke_ = Matrix(n_x_, n_y_, fill::zeros);    // estimator gain.
-  Ke_m_ = Matrix(n_x_, n_y_, fill::zeros);  // estimator gain for m adaptation.
-
-  do_adapt_m = false;
-}
-
-// Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state
-// and update estimate of the state, covar, output using Kalman filter
-void lds::System::Filter(const Vector& u_tm1, const Vector& z_t) {
-  // predict mean
-  f(u_tm1);  // dynamics
-
-  h();  // output
-
-  // recursively calculate esimator gains (or just keep existing values)
-  // (also predicts+updates estimate covariance)
-  RecurseKe();
-
-  // update
-  x_ += Ke_ * (z_t - y_);
-  if (do_adapt_m) {
-    m_ += Ke_m_ * (z_t - y_);  // adaptively estimating disturbance
-  }
-
-  // With new state, estimate output.
-  h();  // --> posterior
-}
-
-void lds::System::Reset() {
-  // reset to initial conditions
-  x_ = x0_;      // mean
-  P_ = P0_;      // cov of state estimate
-  m_ = m0_;      // process disturbance
-  P_m_ = P0_m_;  // cov of disturbance estimate
-  h();
-}
-
-void lds::System::Print() {
-  std::cout << "\n ********** SYSTEM ********** \n";
-  std::cout << "x: \n" << x_ << "\n";
-  std::cout << "P: \n" << P_ << "\n";
-  std::cout << "A: \n" << A_ << "\n";
-  std::cout << "B: \n" << B_ << "\n";
-  std::cout << "g: \n" << g_ << "\n";
-  std::cout << "m: \n" << m_ << "\n";
-  std::cout << "Q: \n" << Q_ << "\n";
-  std::cout << "Q_m: \n" << Q_m_ << "\n";
-  std::cout << "d: \n" << d_ << "\n";
-  std::cout << "C: \n" << C_ << "\n";
-  std::cout << "y: \n" << y_ << "\n";
-}
-
-//******************* SYS_T *******************
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: src/lds_sys.cpp
+summary: LDS base type. 
+
+---
+
+# src/lds_sys.cpp
+
+LDS base type.  [More...](#detailed-description)
+
+
+
+## Detailed Description
+
+
+
+This file implements the base type for linear dynamical systems ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class. 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- lds_sys.cpp - LDS -------------------------------------------------===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#include <ldsCtrlEst_h/lds_sys.h>
+
+#include <vector>
+
+lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0,
+                    data_t q0)
+    : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) {
+  InitVars(p0, q0);
+}
+
+void lds::System::InitVars(data_t p0, data_t q0) {
+  // initial conditions.
+  x0_ = Vector(n_x_, fill::zeros);  // includes bias (nY) and g (nU)
+  P0_ = p0 * Matrix(n_x_, n_x_, fill::eye);
+
+  m0_ = x0_;
+  P0_m_ = P0_;
+
+  // signals
+  x_ = x0_;
+  P_ = P0_;
+  m_ = m0_;
+  P_m_ = P0_m_;
+  y_ = Vector(n_y_, fill::zeros);
+  cx_ = Vector(n_y_, fill::zeros);
+  z_ = Vector(n_y_, fill::zeros);
+
+  // By default, random walk where each state is independent
+  // In this way, provides independent estimates of rate per channel of output.
+  A_ = Matrix(n_x_, n_x_, fill::eye);
+  B_ = Matrix(n_x_, n_u_, fill::zeros);
+  g_ = Vector(n_u_, fill::ones);
+  Q_ = q0 * Matrix(n_x_, n_x_, fill::eye);
+  Q_m_ = Q_;
+
+  C_ = Matrix(n_y_, n_x_, fill::eye);  // each state will map to an output by
+  d_ = Vector(n_y_, fill::zeros);
+
+  Ke_ = Matrix(n_x_, n_y_, fill::zeros);    // estimator gain.
+  Ke_m_ = Matrix(n_x_, n_y_, fill::zeros);  // estimator gain for m adaptation.
+
+  do_adapt_m = false;
+}
+
+// Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state
+// and update estimate of the state, covar, output using Kalman filter
+void lds::System::Filter(const Vector& u_tm1, const Vector& z_t) {
+  // predict mean
+  f(u_tm1);  // dynamics
+
+  h();  // output
+
+  // recursively calculate esimator gains (or just keep existing values)
+  // (also predicts+updates estimate covariance)
+  RecurseKe();
+
+  // update
+  x_ += Ke_ * (z_t - y_);
+  if (do_adapt_m) {
+    m_ += Ke_m_ * (z_t - y_);  // adaptively estimating disturbance
+  }
+
+  // With new state, estimate output.
+  h();  // --> posterior
+}
+
+void lds::System::Reset() {
+  // reset to initial conditions
+  x_ = x0_;      // mean
+  P_ = P0_;      // cov of state estimate
+  m_ = m0_;      // process disturbance
+  P_m_ = P0_m_;  // cov of disturbance estimate
+  h();
+}
+
+std::vector<lds::UniformMatrixList<lds::kMatFreeDim2>>
+lds::System::nstep_pred_block(lds::UniformMatrixList<lds::kMatFreeDim2> u,
+                              lds::UniformMatrixList<lds::kMatFreeDim2> z,
+                              size_t n_pred) {
+  lds::UniformMatrixList<lds::kMatFreeDim2> x_filt;
+  lds::UniformMatrixList<lds::kMatFreeDim2> x_pred;
+  lds::UniformMatrixList<lds::kMatFreeDim2> y_pred;
+
+  for (size_t k = 0; k < u.size(); k++) {
+    Reset();
+    size_t n_t = arma::size(u[k])[1];
+    Matrix x_filt_k(n_x_, n_t, fill::zeros);
+    Matrix x_pred_k(n_x_, n_t - n_pred, fill::zeros);
+    Matrix y_pred_k(n_y_, n_t - n_pred, fill::zeros);
+
+    for (size_t t = 0; t < n_t - n_pred; t++) {
+      Vector x_pred_ahead = x_;
+      for (size_t t_u = t; t_u < t + n_pred; t_u++) {
+        x_pred_ahead = A_ * x_pred_ahead + B_ * u[k].col(t_u);
+      }
+      x_pred_k.col(t) = x_pred_ahead;
+      y_pred_k.col(t) = h_(x_pred_ahead);
+      if (t > 0) {
+        Filter(u[k].col(t - 1), z[k].col(t));
+      }
+      x_filt_k.col(t) = x_;  // given previous measurment
+    }
+
+    for (size_t t = n_t - n_pred; t < n_t; t++) {
+      if (t > 0) {
+        Filter(u[k].col(t - 1), z[k].col(t));
+      }
+      x_filt_k.col(t) = x_;
+    }
+
+    x_filt.append(x_filt_k);
+    x_pred.append(x_pred_k);
+    y_pred.append(y_pred_k);
+  }
+  return {x_filt, x_pred, y_pred};
+}
+
+void lds::System::Print() {
+  std::cout << "\n ********** SYSTEM ********** \n";
+  std::cout << "x: \n" << x_ << "\n";
+  std::cout << "P: \n" << P_ << "\n";
+  std::cout << "A: \n" << A_ << "\n";
+  std::cout << "B: \n" << B_ << "\n";
+  std::cout << "g: \n" << g_ << "\n";
+  std::cout << "m: \n" << m_ << "\n";
+  std::cout << "Q: \n" << Q_ << "\n";
+  std::cout << "Q_m: \n" << Q_m_ << "\n";
+  std::cout << "d: \n" << d_ << "\n";
+  std::cout << "C: \n" << C_ << "\n";
+  std::cout << "y: \n" << y_ << "\n";
+}
+
+//******************* SYS_T *******************
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__sys_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__sys_8h.md
index 9e9688d3..244b68e1 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__sys_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__sys_8h.md
@@ -1,184 +1,191 @@
----
-title: ldsCtrlEst_h/lds_sys.h
-summary: LDS base type. 
-
----
-
-# ldsCtrlEst_h/lds_sys.h
-
-LDS base type.  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)** <br>Linear Dynamical [System]() Type.  |
-
-## Detailed Description
-
-
-
-This file declares and partially defines the base type for linear dynamical systems (`[lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)`). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class. 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// Limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_SYS_H
-#define LDSCTRLEST_LDS_SYS_H
-
-#include "lds.h"
-
-namespace lds {
-class System {
- public:
-  System() = default;
-
-  System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0,
-         data_t q0 = kDefaultQ0);
-
-  virtual ~System() {}
-
-  void Filter(const Vector& u_tm1, const Vector& z);
-
-  virtual const Vector& Simulate(const Vector& u_tm1) = 0;
-
-  void f(const Vector& u, bool do_add_noise = false) {
-    x_ = A_ * x_ + B_ * (g_ % u) + m_;
-    if (do_add_noise) {
-      x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_);
-    }
-  };
-
-  virtual void h() = 0;
-
-  size_t n_u() const { return n_u_; };
-  size_t n_x() const { return n_x_; };
-  size_t n_y() const { return n_y_; };
-  data_t dt() const { return dt_; };
-
-  const Vector& x() const { return x_; };
-  const Matrix& P() const { return P_; };
-  const Vector& m() const { return m_; };
-  const Matrix& P_m() const { return P_m_; };
-  const Vector& cx() const { return cx_; };
-  const Vector& y() const { return y_; };
-
-  const Vector& x0() const { return x0_; };
-  const Vector& m0() const { return m0_; };
-
-  const Matrix& A() const { return A_; };
-  const Matrix& B() const { return B_; };
-  const Vector& g() const { return g_; };
-  const Matrix& C() const { return C_; };
-  const Vector& d() const { return d_; };
-  const Matrix& Ke() const { return Ke_; };
-  const Matrix& Ke_m() const { return Ke_m_; };
-  const Matrix& Q() { return Q_; };
-  const Matrix& Q_m() { return Q_m_; };
-  const Matrix& P0() { return P0_; };
-  const Matrix& P0_m() { return P0_m_; };
-
-  void set_A(const Matrix& A) { Reassign(A_, A); };
-  void set_B(const Matrix& B) { Reassign(B_, B); };
-  void set_m(const Vector& m, bool do_force_assign=false) {
-    Reassign(m0_, m);
-    if ((!do_adapt_m) || do_force_assign) {
-      Reassign(m_, m);
-    }
-  };
-  void set_g(const Vector& g) { Reassign(g_, g); };
-  void set_Q(const Matrix& Q) { Reassign(Q_, Q); };
-  void set_Q_m(const Matrix& Q_m) { Reassign(Q_m_, Q_m); };
-  void set_x0(const Vector& x0) { Reassign(x0_, x0); };
-  void set_P0(const Matrix& P0) { Reassign(P0_, P0); };
-  void set_P0_m(const Matrix& P0_m) { Reassign(P0_m_, P0_m); };
-  void set_C(const Matrix& C) { Reassign(C_, C); };
-  void set_d(const Vector& d) { Reassign(d_, d); };
-  void set_x(const Vector& x) {
-    Reassign(x_, x);
-    h();
-  };
-
-  void Reset();
-
-  void Print();
-
-  // safe to leave this public and non-const
-  bool do_adapt_m{};  
-
- protected:
-  virtual void RecurseKe() = 0;
-  void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0);
-
-  std::size_t n_x_{};  
-  std::size_t n_u_{};  
-  std::size_t n_y_{};  
-  data_t dt_{};        
-
-  // Signals:
-  Vector x_;    
-  Matrix P_;    
-  Vector m_;    
-  Matrix P_m_;  
-  Vector cx_;   
-  Vector y_;    
-  Vector z_;    
-
-  // Parameters:
-  Vector x0_;    
-  Matrix P0_;    
-  Vector m0_;    
-  Matrix P0_m_;  
-  Matrix A_;     
-  Matrix B_;     
-  Vector g_;     
-  Matrix Q_;     
-  Matrix Q_m_;   
-  Matrix C_;     
-  Vector d_;     
-
-  Matrix Ke_;    
-  Matrix Ke_m_;  
-};               // System
-
-}  // namespace lds
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_sys.h
+summary: LDS base type. 
+
+---
+
+# ldsCtrlEst_h/lds_sys.h
+
+LDS base type.  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)** <br>Linear Dynamical [System]() Type.  |
+
+## Detailed Description
+
+
+
+This file declares and partially defines the base type for linear dynamical systems (`[lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)`). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class. 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// Limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_SYS_H
+#define LDSCTRLEST_LDS_SYS_H
+
+#include "lds.h"
+#include "lds_uniform_mats.h"
+
+namespace lds {
+class System {
+ public:
+  System() = default;
+
+  System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0,
+         data_t q0 = kDefaultQ0);
+
+  virtual ~System() {}
+
+  void Filter(const Vector& u_tm1, const Vector& z);
+
+  virtual const Vector& Simulate(const Vector& u_tm1) = 0;
+
+  void f(const Vector& u, bool do_add_noise = false) {
+    x_ = A_ * x_ + B_ * (g_ % u) + m_;
+    if (do_add_noise) {
+      x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_);
+    }
+  };
+
+  virtual void h() = 0;
+
+  virtual Vector h_(Vector x) = 0;
+
+  size_t n_u() const { return n_u_; };
+  size_t n_x() const { return n_x_; };
+  size_t n_y() const { return n_y_; };
+  data_t dt() const { return dt_; };
+
+  const Vector& x() const { return x_; };
+  const Matrix& P() const { return P_; };
+  const Vector& m() const { return m_; };
+  const Matrix& P_m() const { return P_m_; };
+  const Vector& cx() const { return cx_; };
+  const Vector& y() const { return y_; };
+
+  const Vector& x0() const { return x0_; };
+  const Vector& m0() const { return m0_; };
+
+  const Matrix& A() const { return A_; };
+  const Matrix& B() const { return B_; };
+  const Vector& g() const { return g_; };
+  const Matrix& C() const { return C_; };
+  const Vector& d() const { return d_; };
+  const Matrix& Ke() const { return Ke_; };
+  const Matrix& Ke_m() const { return Ke_m_; };
+  const Matrix& Q() { return Q_; };
+  const Matrix& Q_m() { return Q_m_; };
+  const Matrix& P0() { return P0_; };
+  const Matrix& P0_m() { return P0_m_; };
+
+  void set_A(const Matrix& A) { Reassign(A_, A); };
+  void set_B(const Matrix& B) { Reassign(B_, B); };
+  void set_m(const Vector& m, bool do_force_assign = false) {
+    Reassign(m0_, m);
+    if ((!do_adapt_m) || do_force_assign) {
+      Reassign(m_, m);
+    }
+  };
+  void set_g(const Vector& g) { Reassign(g_, g); };
+  void set_Q(const Matrix& Q) { Reassign(Q_, Q); };
+  void set_Q_m(const Matrix& Q_m) { Reassign(Q_m_, Q_m); };
+  void set_x0(const Vector& x0) { Reassign(x0_, x0); };
+  void set_P0(const Matrix& P0) { Reassign(P0_, P0); };
+  void set_P0_m(const Matrix& P0_m) { Reassign(P0_m_, P0_m); };
+  void set_C(const Matrix& C) { Reassign(C_, C); };
+  void set_d(const Vector& d) { Reassign(d_, d); };
+  void set_x(const Vector& x) {
+    Reassign(x_, x);
+    h();
+  };
+
+  void Reset();
+
+  std::vector<UniformMatrixList<kMatFreeDim2>> nstep_pred_block(
+      UniformMatrixList<kMatFreeDim2> u, UniformMatrixList<kMatFreeDim2> z,
+      size_t n_pred = 1);
+
+  void Print();
+
+  // safe to leave this public and non-const
+  bool do_adapt_m{};  
+
+ protected:
+  virtual void RecurseKe() = 0;
+  void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0);
+
+  std::size_t n_x_{};  
+  std::size_t n_u_{};  
+  std::size_t n_y_{};  
+  data_t dt_{};        
+
+  // Signals:
+  Vector x_;    
+  Matrix P_;    
+  Vector m_;    
+  Matrix P_m_;  
+  Vector cx_;   
+  Vector y_;    
+  Vector z_;    
+
+  // Parameters:
+  Vector x0_;    
+  Matrix P0_;    
+  Vector m0_;    
+  Matrix P0_m_;  
+  Matrix A_;     
+  Matrix B_;     
+  Vector g_;     
+  Matrix Q_;     
+  Matrix Q_m_;   
+  Matrix C_;     
+  Vector d_;     
+
+  Matrix Ke_;    
+  Matrix Ke_m_;  
+};  // System
+
+}  // namespace lds
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__uniform__mats_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__uniform__mats_8h.md
index fa447ebb..3f735c63 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__uniform__mats_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__uniform__mats_8h.md
@@ -1,330 +1,343 @@
----
-title: ldsCtrlEst_h/lds_uniform_mats.h
-summary: List of uniformly sized matrices. 
-
----
-
-# ldsCtrlEst_h/lds_uniform_mats.h
-
-List of uniformly sized matrices.  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)**  |
-
-## Detailed Description
-
-
-
-This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list. 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// Limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H
-#define LDSCTRLEST_LDS_UNIFORM_MATS_H
-
-#include <array>   // std::array
-#include <vector>  // std::vector
-
-#include "lds.h"
-
-namespace lds {
-template <MatrixListFreeDim D = kMatFreeDimNone>
-class UniformMatrixList : public std::vector<Matrix> {
- private:
-  // TODO(mfbolus): would rather *uncomment* the below for sake of conversion
-  // using std::vector<Matrix>::vector;
-  using std::vector<Matrix>::operator=;
-  using std::vector<Matrix>::operator[];
-  using std::vector<Matrix>::begin;
-  using std::vector<Matrix>::end;
-  using std::vector<Matrix>::size;
-
- public:
-  using std::vector<Matrix>::at;
-  UniformMatrixList() = default;
-
-  explicit UniformMatrixList(const std::vector<Matrix>& mats,
-                             std::array<size_t, 2> dim = {0, 0});
-
-  explicit UniformMatrixList(std::vector<Matrix>&& mats,
-                             std::array<size_t, 2> dim = {0, 0});
-
-  UniformMatrixList(std::initializer_list<Matrix> mats,
-                    std::array<size_t, 2> dim = {0, 0});
-
-  UniformMatrixList(const UniformMatrixList<D>& that);
-
-  UniformMatrixList(UniformMatrixList<D>&& that) noexcept;
-
-  ~UniformMatrixList() = default;
-
-  const std::array<size_t, 2>& dim(size_t n = 0) const { return dim_.at(n); }
-
-  size_t size() { return std::vector<Matrix>::size(); };
-
-  const Matrix& at(size_t n) { return std::vector<Matrix>::at(n); };
-
-  void Swap(Matrix& that, size_t n);
-
-  UniformMatrixList<D>& operator=(const UniformMatrixList<D>& that);
-  UniformMatrixList<D>& operator=(UniformMatrixList<D>&& that) noexcept;
-
- private:
-  void CheckDimensions(std::array<size_t, 2> dim);
-  std::vector<std::array<size_t, 2>> dim_;
-};
-
-template <MatrixListFreeDim D>
-inline void UniformMatrixList<D>::Swap(Matrix& that, size_t n) {
-  // make sure request in range
-  if (n >= this->size()) {
-    std::cerr
-        << "Requested UniformMatrixList element out of bounds. Skipping.\n";
-    return;
-  }
-  // check dim
-  bool does_match = true;
-  if (!(D == kMatFreeDim1)) {
-    does_match = does_match && (dim_[0][0] == that.n_rows);
-  }
-  if (!(D == kMatFreeDim2)) {
-    does_match = does_match && (dim_[0][1] == that.n_cols);
-  }
-  if (!does_match) {
-    std::cerr << "Cannot swap a UniformMatrixList element for an element of "
-                 "different size. Skipping.\n";
-    return;
-  }
-  // if checks pass, perform swap
-  Matrix tmp = std::move((*this)[n]);
-  (*this)[n] = std::move(that);
-  that = std::move(tmp);
-
-  if (D == kMatFreeDim1) {
-    this->dim_[n][0] = (*this)[n].n_rows;
-  }
-  if (D == kMatFreeDim2) {
-    this->dim_[n][1] = (*this)[n].n_cols;
-  }
-}
-
-template <MatrixListFreeDim D>
-inline UniformMatrixList<D>& UniformMatrixList<D>::operator=(
-    const UniformMatrixList<D>& that) {
-  // make sure dim_ vector is initialized
-  if (dim_.empty()) {
-    dim_ = std::vector<std::array<size_t, 2>>(that.size(), {0, 0});
-  }
-  // check dimensions
-  if (!this->empty()) {
-    if (this->size() != that.size()) {
-      std::ostringstream ss;
-      ss << "cannot reassign " << this->size() << " matrices with "
-         << that.size() << " matrices";
-      throw std::runtime_error(ss.str());
-    }
-
-    // if dimensions a not zero and do not match, skip move with error message.
-    bool dims_nonzero = true;
-    for (auto d : dim_) {
-      if (!(D == kMatFreeDim1) && d[0] < 1) {
-        dims_nonzero = false;
-        break;
-      }
-      if (!(D == kMatFreeDim2) && d[1] < 1) {
-        dims_nonzero = false;
-        break;
-      }
-    }
-    if (dims_nonzero) {
-      bool does_match = true;
-      if (!(D == kMatFreeDim1)) {
-        does_match = does_match && (dim_[0][0] == that.at(0).n_rows);
-      }
-      if (!(D == kMatFreeDim2)) {
-        does_match = does_match && (dim_[0][1] == that.at(0).n_cols);
-      }
-      if (!does_match) {
-        std::ostringstream ss;
-        ss << "cannot reassign matrices of size " << dim_[0][0] << "x"
-           << dim_[0][1] << " with matrices of size " << that.at(0).n_rows
-           << "x" << that.at(0).n_cols;
-        throw std::runtime_error(ss.str());
-      }
-    }
-  }
-
-  for (size_t k = 0; k < this->size(); k++) {
-    (*this)[k] = that[k];
-    dim_[k] = that.dim(k);
-  }
-
-  return (*this);
-}
-
-template <MatrixListFreeDim D>
-inline UniformMatrixList<D>& UniformMatrixList<D>::operator=(
-    UniformMatrixList<D>&& that) noexcept {
-  // // check dimensions
-  // // if empty, assume a default constructed object and safe to move
-  // if (!this->empty()) {
-  //   if (this->size() != that.size()) {
-  //     std::cerr << "Cannot reassign " << this->size() << " matrices with "
-  //               << that.size() << " matrices. Skipping.\n";
-  //     return (*this);
-  //   }
-  //
-  //   // if dimensions a not zero and do not match, skip move with error
-  //   message. bool dims_nonzero = true; for (auto d : dim_) {
-  //     if (!(D == kMatFreeDim1) && (d[0] < 1)) {
-  //       dims_nonzero = false;
-  //       break;
-  //     }
-  //     if (!(D == kMatFreeDim2) && (d[1] < 1)) {
-  //       dims_nonzero = false;
-  //       break;
-  //     }
-  //   }
-  //
-  //   if (dims_nonzero) {
-  //     bool does_match = true;
-  //     if (!(D == kMatFreeDim1)) {
-  //       does_match = does_match && (dim_[0][0] == that.at(0).n_rows);
-  //     }
-  //
-  //     if (!(D == kMatFreeDim2)) {
-  //       does_match = does_match && (dim_[0][1] == that.at(0).n_cols);
-  //     }
-  //
-  //     if (!does_match) {
-  //       this->at(0).print("this[0] = ");
-  //       that.at(0).print("that[0] = ");
-  //       std::cerr
-  //           << "Cannot move a UniformMatrixList element of size (" <<
-  //           that.at(0).n_rows << "," << that.at(0).n_cols << ") for an
-  //           element of size (" << dim_[0][0] << "," << dim_[0][1] << ").
-  //           Skipping.\n";
-  //       return (*this);
-  //     }
-  //   }
-  // }
-
-  dim_ = that.dim_;
-  std::vector<Matrix>::operator=(std::move(that));
-
-  return (*this);
-}
-
-template <MatrixListFreeDim D>
-UniformMatrixList<D>::UniformMatrixList(const std::vector<Matrix>& mats,
-                                        std::array<size_t, 2> dim)
-    : vector(mats) {
-  CheckDimensions(dim);
-}
-
-template <MatrixListFreeDim D>
-UniformMatrixList<D>::UniformMatrixList(std::vector<Matrix>&& mats,
-                                        std::array<size_t, 2> dim)
-    : vector(std::move(mats)) {
-  CheckDimensions(dim);
-};
-
-template <MatrixListFreeDim D>
-UniformMatrixList<D>::UniformMatrixList(std::initializer_list<Matrix> mats,
-                                        std::array<size_t, 2> dim)
-    : vector(mats) {
-  CheckDimensions(dim);
-};
-
-template <MatrixListFreeDim D>
-UniformMatrixList<D>::UniformMatrixList(const UniformMatrixList<D>& that)
-    : vector(that) {
-  (*this) = that;
-}
-
-template <MatrixListFreeDim D>
-UniformMatrixList<D>::UniformMatrixList(UniformMatrixList<D>&& that) noexcept
-    : vector(std::move(that)) {
-  for (size_t k = 0; k < this->size(); k++) {
-    std::array<size_t, 2> dim_k({this->at(k).n_rows, this->at(k).n_cols});
-    dim_.push_back(dim_k);
-  }
-}
-
-template <MatrixListFreeDim D>
-void UniformMatrixList<D>::CheckDimensions(std::array<size_t, 2> dim) {
-  // change behavior based on free dim D
-  if ((dim[0] == 0) && !(D == kMatFreeDim1)) {
-    dim[0] = this->at(0).n_rows;
-  }
-  if ((dim[1] == 0) && !(D == kMatFreeDim2)) {
-    dim[1] = this->at(0).n_cols;
-  }
-
-  // make sure dimensiolaties are all uniform
-  bool does_match(true);
-  for (const Matrix& mat : *this) {
-    if (!(D == kMatFreeDim1)) {
-      does_match = does_match && (mat.n_rows == dim[0]);
-    }
-    if (!(D == kMatFreeDim2)) {
-      does_match = does_match && (mat.n_cols == dim[1]);
-    }
-    if (!does_match) {
-      throw std::runtime_error(
-          "Dimensionality of one or more input matrices are not uniform.");
-    }
-  }
-
-  dim_ = std::vector<std::array<size_t, 2>>(this->size(), dim);
-  for (size_t k = 0; k < this->size(); k++) {
-    dim_[k][0] = (*this)[k].n_rows;
-    dim_[k][1] = (*this)[k].n_cols;
-  }
-}
-
-}  // namespace lds
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_uniform_mats.h
+summary: List of uniformly sized matrices. 
+
+---
+
+# ldsCtrlEst_h/lds_uniform_mats.h
+
+List of uniformly sized matrices.  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)**  |
+
+## Detailed Description
+
+
+
+This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list. 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// Limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H
+#define LDSCTRLEST_LDS_UNIFORM_MATS_H
+
+#include <array>   // std::array
+#include <vector>  // std::vector
+
+#include "lds.h"
+
+namespace lds {
+template <MatrixListFreeDim D = kMatFreeDimNone>
+class UniformMatrixList : public std::vector<Matrix> {
+ private:
+  // TODO(mfbolus): would rather *uncomment* the below for sake of conversion
+  // using std::vector<Matrix>::vector;
+  // don't allow push_back to be used since it doesn't check dims
+  using std::vector<Matrix>::push_back;
+
+ public:
+  using std::vector<Matrix>::operator=;
+  using std::vector<Matrix>::operator[];
+  using std::vector<Matrix>::begin;
+  using std::vector<Matrix>::end;
+  using std::vector<Matrix>::size;
+  using std::vector<Matrix>::at;
+  UniformMatrixList() = default;
+
+  explicit UniformMatrixList(const std::vector<Matrix>& mats,
+                             std::array<size_t, 2> dim = {0, 0});
+
+  explicit UniformMatrixList(std::vector<Matrix>&& mats,
+                             std::array<size_t, 2> dim = {0, 0});
+
+  UniformMatrixList(std::initializer_list<Matrix> mats,
+                    std::array<size_t, 2> dim = {0, 0});
+
+  UniformMatrixList(const UniformMatrixList<D>& that);
+
+  UniformMatrixList(UniformMatrixList<D>&& that) noexcept;
+
+  ~UniformMatrixList() = default;
+
+  const std::array<size_t, 2>& dim(size_t n = 0) const { return dim_.at(n); }
+
+  size_t size() { return std::vector<Matrix>::size(); };
+
+  const Matrix& at(size_t n) { return std::vector<Matrix>::at(n); };
+
+  void Swap(Matrix& that, size_t n);
+
+  UniformMatrixList<D>& operator=(const UniformMatrixList<D>& that);
+  UniformMatrixList<D>& operator=(UniformMatrixList<D>&& that) noexcept;
+  void append(const Matrix& mat);
+
+ private:
+  void CheckDimensions(std::array<size_t, 2> dim);
+  std::vector<std::array<size_t, 2>> dim_;
+};
+
+template <MatrixListFreeDim D>
+inline void UniformMatrixList<D>::Swap(Matrix& that, size_t n) {
+  // make sure request in range
+  if (n >= this->size()) {
+    std::cerr
+        << "Requested UniformMatrixList element out of bounds. Skipping.\n";
+    return;
+  }
+  // check dim
+  bool does_match = true;
+  if (!(D == kMatFreeDim1)) {
+    does_match = does_match && (dim_[0][0] == that.n_rows);
+  }
+  if (!(D == kMatFreeDim2)) {
+    does_match = does_match && (dim_[0][1] == that.n_cols);
+  }
+  if (!does_match) {
+    std::cerr << "Cannot swap a UniformMatrixList element for an element of "
+                 "different size. Skipping.\n";
+    return;
+  }
+  // if checks pass, perform swap
+  // not moving, since it causes memory issues.
+  // so this method isn't a memory-saver as designed for now
+  Matrix tmp = (*this)[n];
+  (*this)[n] = that;
+  that = tmp;
+
+  if (D == kMatFreeDim1) {
+    this->dim_[n][0] = (*this)[n].n_rows;
+  }
+  if (D == kMatFreeDim2) {
+    this->dim_[n][1] = (*this)[n].n_cols;
+  }
+}
+
+template <MatrixListFreeDim D>
+void UniformMatrixList<D>::append(const Matrix& mat) {
+  std::array<size_t, 2> dim({mat.n_rows, mat.n_cols});
+  CheckDimensions(dim);
+  std::vector<Matrix>::push_back(mat);
+  dim_.push_back(dim);
+}
+
+template <MatrixListFreeDim D>
+inline UniformMatrixList<D>& UniformMatrixList<D>::operator=(
+    const UniformMatrixList<D>& that) {
+  // make sure dim_ vector is initialized
+  if (dim_.empty()) {
+    dim_ = std::vector<std::array<size_t, 2>>(that.size(), {0, 0});
+  }
+  // check dimensions
+  if (!this->empty()) {
+    if (this->size() != that.size()) {
+      std::ostringstream ss;
+      ss << "cannot reassign " << this->size() << " matrices with "
+         << that.size() << " matrices";
+      throw std::runtime_error(ss.str());
+    }
+
+    // if dimensions a not zero and do not match, skip move with error message.
+    bool dims_nonzero = true;
+    for (auto d : dim_) {
+      if (!(D == kMatFreeDim1) && d[0] < 1) {
+        dims_nonzero = false;
+        break;
+      }
+      if (!(D == kMatFreeDim2) && d[1] < 1) {
+        dims_nonzero = false;
+        break;
+      }
+    }
+    if (dims_nonzero) {
+      bool does_match = true;
+      if (!(D == kMatFreeDim1)) {
+        does_match = does_match && (dim_[0][0] == that.at(0).n_rows);
+      }
+      if (!(D == kMatFreeDim2)) {
+        does_match = does_match && (dim_[0][1] == that.at(0).n_cols);
+      }
+      if (!does_match) {
+        std::ostringstream ss;
+        ss << "cannot reassign matrices of size " << dim_[0][0] << "x"
+           << dim_[0][1] << " with matrices of size " << that.at(0).n_rows
+           << "x" << that.at(0).n_cols;
+        throw std::runtime_error(ss.str());
+      }
+    }
+  }
+
+  for (size_t k = 0; k < this->size(); k++) {
+    (*this)[k] = that[k];
+    dim_[k] = that.dim(k);
+  }
+
+  return (*this);
+}
+
+template <MatrixListFreeDim D>
+inline UniformMatrixList<D>& UniformMatrixList<D>::operator=(
+    UniformMatrixList<D>&& that) noexcept {
+  // // check dimensions
+  // // if empty, assume a default constructed object and safe to move
+  // if (!this->empty()) {
+  //   if (this->size() != that.size()) {
+  //     std::cerr << "Cannot reassign " << this->size() << " matrices with "
+  //               << that.size() << " matrices. Skipping.\n";
+  //     return (*this);
+  //   }
+  //
+  //   // if dimensions a not zero and do not match, skip move with error
+  //   message. bool dims_nonzero = true; for (auto d : dim_) {
+  //     if (!(D == kMatFreeDim1) && (d[0] < 1)) {
+  //       dims_nonzero = false;
+  //       break;
+  //     }
+  //     if (!(D == kMatFreeDim2) && (d[1] < 1)) {
+  //       dims_nonzero = false;
+  //       break;
+  //     }
+  //   }
+  //
+  //   if (dims_nonzero) {
+  //     bool does_match = true;
+  //     if (!(D == kMatFreeDim1)) {
+  //       does_match = does_match && (dim_[0][0] == that.at(0).n_rows);
+  //     }
+  //
+  //     if (!(D == kMatFreeDim2)) {
+  //       does_match = does_match && (dim_[0][1] == that.at(0).n_cols);
+  //     }
+  //
+  //     if (!does_match) {
+  //       this->at(0).print("this[0] = ");
+  //       that.at(0).print("that[0] = ");
+  //       std::cerr
+  //           << "Cannot move a UniformMatrixList element of size (" <<
+  //           that.at(0).n_rows << "," << that.at(0).n_cols << ") for an
+  //           element of size (" << dim_[0][0] << "," << dim_[0][1] << ").
+  //           Skipping.\n";
+  //       return (*this);
+  //     }
+  //   }
+  // }
+
+  dim_ = that.dim_;
+  std::vector<Matrix>::operator=(std::move(that));
+
+  return (*this);
+}
+
+template <MatrixListFreeDim D>
+UniformMatrixList<D>::UniformMatrixList(const std::vector<Matrix>& mats,
+                                        std::array<size_t, 2> dim)
+    : vector(mats) {
+  CheckDimensions(dim);
+}
+
+template <MatrixListFreeDim D>
+UniformMatrixList<D>::UniformMatrixList(std::vector<Matrix>&& mats,
+                                        std::array<size_t, 2> dim)
+    : vector(std::move(mats)) {
+  CheckDimensions(dim);
+};
+
+template <MatrixListFreeDim D>
+UniformMatrixList<D>::UniformMatrixList(std::initializer_list<Matrix> mats,
+                                        std::array<size_t, 2> dim)
+    : vector(mats) {
+  CheckDimensions(dim);
+};
+
+template <MatrixListFreeDim D>
+UniformMatrixList<D>::UniformMatrixList(const UniformMatrixList<D>& that)
+    : vector(that) {
+  (*this) = that;
+}
+
+template <MatrixListFreeDim D>
+UniformMatrixList<D>::UniformMatrixList(UniformMatrixList<D>&& that) noexcept
+    : vector(std::move(that)) {
+  for (size_t k = 0; k < this->size(); k++) {
+    std::array<size_t, 2> dim_k({this->at(k).n_rows, this->at(k).n_cols});
+    dim_.push_back(dim_k);
+  }
+}
+
+template <MatrixListFreeDim D>
+void UniformMatrixList<D>::CheckDimensions(std::array<size_t, 2> dim) {
+  // change behavior based on free dim D
+  if ((dim[0] == 0) && !(D == kMatFreeDim1)) {
+    dim[0] = this->at(0).n_rows;
+  }
+  if ((dim[1] == 0) && !(D == kMatFreeDim2)) {
+    dim[1] = this->at(0).n_cols;
+  }
+
+  // make sure dimensiolaties are all uniform
+  bool does_match(true);
+  for (const Matrix& mat : *this) {
+    if (!(D == kMatFreeDim1)) {
+      does_match = does_match && (mat.n_rows == dim[0]);
+    }
+    if (!(D == kMatFreeDim2)) {
+      does_match = does_match && (mat.n_cols == dim[1]);
+    }
+    if (!does_match) {
+      throw std::runtime_error(
+          "Dimensionality of one or more input matrices are not uniform.");
+    }
+  }
+
+  dim_ = std::vector<std::array<size_t, 2>>(this->size(), dim);
+  for (size_t k = 0; k < this->size(); k++) {
+    dim_[k][0] = (*this)[k].n_rows;
+    dim_[k][1] = (*this)[k].n_cols;
+  }
+}
+
+}  // namespace lds
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__uniform__systems_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__uniform__systems_8h.md
index 33c86e2f..7b49aa65 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__uniform__systems_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__uniform__systems_8h.md
@@ -1,268 +1,268 @@
----
-title: ldsCtrlEst_h/lds_uniform_systems.h
-summary: List of uniformly sized Systems. 
-
----
-
-# ldsCtrlEst_h/lds_uniform_systems.h
-
-List of uniformly sized Systems.  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/)**  |
-
-## Detailed Description
-
-
-
-This file provides a container for uniformly sized Systems. 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// Limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H
-#define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H
-
-#include <array>   // std::array
-#include <vector>  // std::vector
-
-// namespace
-#include "lds.h"
-// System type
-#include "lds_sys.h"
-
-namespace lds {
-template <typename System>
-class UniformSystemList : public std::vector<System> {
-  static_assert(std::is_base_of<lds::System, System>::value,
-                "System must be derived from lds::System type.");
-
- private:
-  // TODO(mfbolus): would rather *uncomment* the below for sake of conversion
-  // using std::vector<System>::vector;
-  using std::vector<System>::operator=;
-  using std::vector<System>::operator[];
-  using std::vector<System>::at;
-  using std::vector<System>::begin;
-  using std::vector<System>::end;
-  using std::vector<System>::size;
-
- public:
-  UniformSystemList() = default;
-
-  explicit UniformSystemList(const std::vector<System>& systems,
-                             std::array<size_t, 3> dim = {0, 0, 0});
-
-  explicit UniformSystemList(std::vector<System>&& systems,
-                             std::array<size_t, 3> dim = {0, 0, 0});
-
-  UniformSystemList(std::initializer_list<System> systems,
-                    std::array<size_t, 3> dim = {0, 0, 0});
-
-  UniformSystemList(const UniformSystemList& that);
-  UniformSystemList(UniformSystemList&& that) noexcept;
-
-  ~UniformSystemList() = default;
-
-  const std::array<size_t, 3>& dim() const { return dim_; }
-
-  size_t size() { return std::vector<System>::size(); };
-
-  const System& at(size_t n) { return std::vector<System>::at(n); };
-
-  void Swap(System& that, size_t n);
-
-  UniformSystemList& operator=(const UniformSystemList& that);
-  UniformSystemList& operator=(UniformSystemList&& that) noexcept;
-
- private:
-  void CheckDimensions(std::array<size_t, 3> dim);
-  std::array<size_t, 3> dim_{};
-};
-
-template <typename System>
-inline void UniformSystemList<System>::Swap(System& that, size_t n) {
-  // make sure request in range
-  if (n >= this->size()) {
-    std::cerr
-        << "Requested UniformSystemList element out of bounds. Skipping.\n";
-    return;
-  }
-  // check dim
-  bool does_match = (dim_[0] == that.n_u()) && (dim_[1] == that.n_x()) &&
-                    (dim_[2] == that.n_y());
-  if (!does_match) {
-    std::cerr << "Cannot swap a UniformSystemList element for an element of "
-                 "different size. Skipping.\n";
-    return;
-  }
-  // if checks pass, perform swap
-  System tmp = std::move((*this)[n]);
-  (*this)[n] = std::move(that);
-  that = std::move(tmp);
-}
-
-template <typename System>
-inline UniformSystemList<System>& UniformSystemList<System>::operator=(
-    const UniformSystemList& that) {
-  // check dimensions
-  if (!this->empty()) {
-    if (this->size() != that.size()) {
-      std::ostringstream ss;
-      ss << "cannot reassign " << this->size() << " systems with "
-         << that.size() << " systems";
-      throw std::runtime_error(ss.str());
-    }
-
-    if (dim_[0] + dim_[1] + dim_[2]) {
-      std::array<size_t, 3> other_dim(that.dim());
-      if (dim_ != other_dim) {
-        std::ostringstream ss;
-        ss << "cannot reassign systems of size " << dim_[0] << "x" << dim_[1]
-           << "x" << dim_[2] << " with systems of size " << other_dim[0] << "x"
-           << other_dim[1] << "x" << dim_[2];
-        throw std::runtime_error(ss.str());
-      }
-    }
-  }
-
-  for (size_t k = 0; k < this->size(); k++) {
-    (*this)[k] = that[k];
-  }
-  return (*this);
-}
-
-template <typename System>
-inline UniformSystemList<System>& UniformSystemList<System>::operator=(
-    UniformSystemList&& that) noexcept {
-  // // check dimensions
-  // // if empty, assume a default constructed object and safe to move
-  // if (!this->empty()) {
-  //   if (this->size() != that.size()) {
-  //     std::cerr << "Cannot reassign " << this->size() << " systems with "
-  //               << that.size() << " systems. Skipping.\n";
-  //     return (*this);
-  //   }
-  //
-  //   // if dimensions a not zero and do not match, skip move with error
-  //   message. if (dim_[0] + dim_[1] + dim_[2]) {
-  //     bool does_match = (dim_[0] == that.at(0).n_u()) &&
-  //                       (dim_[1] == that.at(0).n_x()) &&
-  //                       (dim_[2] == that.at(0).n_y());
-  //     if (!does_match) {
-  //       std::cerr
-  //           << "Cannot move a UniformSystemList element for an element of "
-  //              "different size. Skipping.\n";
-  //       return (*this);
-  //     }
-  //   }
-  // }
-
-  dim_ = that.dim_;
-  std::vector<System>::operator=(std::move(that));
-
-  return (*this);
-}
-
-template <typename System>
-UniformSystemList<System>::UniformSystemList(const std::vector<System>& systems,
-                                             std::array<size_t, 3> dim)
-    : std::vector<System>(systems) {
-  CheckDimensions(dim);
-}
-
-template <typename System>
-UniformSystemList<System>::UniformSystemList(std::vector<System>&& systems,
-                                             std::array<size_t, 3> dim)
-    : std::vector<System>(std::move(systems)) {
-  CheckDimensions(dim);
-};
-
-template <typename System>
-UniformSystemList<System>::UniformSystemList(
-    std::initializer_list<System> systems, std::array<size_t, 3> dim)
-    : std::vector<System>(systems) {
-  CheckDimensions(dim);
-};
-
-template <typename System>
-UniformSystemList<System>::UniformSystemList(const UniformSystemList& that)
-    : std::vector<System>(that) {
-  (*this) = that;
-}
-
-template <typename System>
-UniformSystemList<System>::UniformSystemList(UniformSystemList&& that) noexcept
-    : std::vector<System>(std::move(that)) {
-  this->dim_[0] = this->at(0).n_u();
-  this->dim_[1] = this->at(0).n_x();
-  this->dim_[2] = this->at(0).n_y();
-}
-
-template <typename System>
-void UniformSystemList<System>::CheckDimensions(std::array<size_t, 3> dim) {
-  if (dim[0] + dim[1] + dim[2]) {
-    dim_ = dim;
-  } else {
-    dim_[0] = this->at(0).n_u();
-    dim_[1] = this->at(0).n_x();
-    dim_[2] = this->at(0).n_y();
-  }
-
-  // make sure dimensiolaties are all uniform
-  bool does_match(true);
-  for (const System& sys : *this) {
-    does_match = does_match && (sys.n_u() == dim_[0]);
-    does_match = does_match && (sys.n_x() == dim_[1]);
-    does_match = does_match && (sys.n_y() == dim_[2]);
-    if (!does_match) {
-      throw std::runtime_error(
-          "Dimensionality of one or more input systems are not uniform.");
-    }
-  }
-}
-
-}  // namespace lds
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_uniform_systems.h
+summary: List of uniformly sized Systems. 
+
+---
+
+# ldsCtrlEst_h/lds_uniform_systems.h
+
+List of uniformly sized Systems.  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/)**  |
+
+## Detailed Description
+
+
+
+This file provides a container for uniformly sized Systems. 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// Limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H
+#define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H
+
+#include <array>   // std::array
+#include <vector>  // std::vector
+
+// namespace
+#include "lds.h"
+// System type
+#include "lds_sys.h"
+
+namespace lds {
+template <typename System>
+class UniformSystemList : public std::vector<System> {
+  static_assert(std::is_base_of<lds::System, System>::value,
+                "System must be derived from lds::System type.");
+
+ private:
+  // TODO(mfbolus): would rather *uncomment* the below for sake of conversion
+  // using std::vector<System>::vector;
+  using std::vector<System>::operator=;
+  using std::vector<System>::operator[];
+  using std::vector<System>::at;
+  using std::vector<System>::begin;
+  using std::vector<System>::end;
+  using std::vector<System>::size;
+
+ public:
+  UniformSystemList() = default;
+
+  explicit UniformSystemList(const std::vector<System>& systems,
+                             std::array<size_t, 3> dim = {0, 0, 0});
+
+  explicit UniformSystemList(std::vector<System>&& systems,
+                             std::array<size_t, 3> dim = {0, 0, 0});
+
+  UniformSystemList(std::initializer_list<System> systems,
+                    std::array<size_t, 3> dim = {0, 0, 0});
+
+  UniformSystemList(const UniformSystemList& that);
+  UniformSystemList(UniformSystemList&& that) noexcept;
+
+  ~UniformSystemList() = default;
+
+  const std::array<size_t, 3>& dim() const { return dim_; }
+
+  size_t size() { return std::vector<System>::size(); };
+
+  const System& at(size_t n) { return std::vector<System>::at(n); };
+
+  void Swap(System& that, size_t n);
+
+  UniformSystemList& operator=(const UniformSystemList& that);
+  UniformSystemList& operator=(UniformSystemList&& that) noexcept;
+
+ private:
+  void CheckDimensions(std::array<size_t, 3> dim);
+  std::array<size_t, 3> dim_{};
+};
+
+template <typename System>
+inline void UniformSystemList<System>::Swap(System& that, size_t n) {
+  // make sure request in range
+  if (n >= this->size()) {
+    std::cerr
+        << "Requested UniformSystemList element out of bounds. Skipping.\n";
+    return;
+  }
+  // check dim
+  bool does_match = (dim_[0] == that.n_u()) && (dim_[1] == that.n_x()) &&
+                    (dim_[2] == that.n_y());
+  if (!does_match) {
+    std::cerr << "Cannot swap a UniformSystemList element for an element of "
+                 "different size. Skipping.\n";
+    return;
+  }
+  // if checks pass, perform swap
+  System tmp = std::move((*this)[n]);
+  (*this)[n] = std::move(that);
+  that = std::move(tmp);
+}
+
+template <typename System>
+inline UniformSystemList<System>& UniformSystemList<System>::operator=(
+    const UniformSystemList& that) {
+  // check dimensions
+  if (!this->empty()) {
+    if (this->size() != that.size()) {
+      std::ostringstream ss;
+      ss << "cannot reassign " << this->size() << " systems with "
+         << that.size() << " systems";
+      throw std::runtime_error(ss.str());
+    }
+
+    if (dim_[0] + dim_[1] + dim_[2]) {
+      std::array<size_t, 3> other_dim(that.dim());
+      if (dim_ != other_dim) {
+        std::ostringstream ss;
+        ss << "cannot reassign systems of size " << dim_[0] << "x" << dim_[1]
+           << "x" << dim_[2] << " with systems of size " << other_dim[0] << "x"
+           << other_dim[1] << "x" << dim_[2];
+        throw std::runtime_error(ss.str());
+      }
+    }
+  }
+
+  for (size_t k = 0; k < this->size(); k++) {
+    (*this)[k] = that[k];
+  }
+  return (*this);
+}
+
+template <typename System>
+inline UniformSystemList<System>& UniformSystemList<System>::operator=(
+    UniformSystemList&& that) noexcept {
+  // // check dimensions
+  // // if empty, assume a default constructed object and safe to move
+  // if (!this->empty()) {
+  //   if (this->size() != that.size()) {
+  //     std::cerr << "Cannot reassign " << this->size() << " systems with "
+  //               << that.size() << " systems. Skipping.\n";
+  //     return (*this);
+  //   }
+  //
+  //   // if dimensions a not zero and do not match, skip move with error
+  //   message. if (dim_[0] + dim_[1] + dim_[2]) {
+  //     bool does_match = (dim_[0] == that.at(0).n_u()) &&
+  //                       (dim_[1] == that.at(0).n_x()) &&
+  //                       (dim_[2] == that.at(0).n_y());
+  //     if (!does_match) {
+  //       std::cerr
+  //           << "Cannot move a UniformSystemList element for an element of "
+  //              "different size. Skipping.\n";
+  //       return (*this);
+  //     }
+  //   }
+  // }
+
+  dim_ = that.dim_;
+  std::vector<System>::operator=(std::move(that));
+
+  return (*this);
+}
+
+template <typename System>
+UniformSystemList<System>::UniformSystemList(const std::vector<System>& systems,
+                                             std::array<size_t, 3> dim)
+    : std::vector<System>(systems) {
+  CheckDimensions(dim);
+}
+
+template <typename System>
+UniformSystemList<System>::UniformSystemList(std::vector<System>&& systems,
+                                             std::array<size_t, 3> dim)
+    : std::vector<System>(std::move(systems)) {
+  CheckDimensions(dim);
+};
+
+template <typename System>
+UniformSystemList<System>::UniformSystemList(
+    std::initializer_list<System> systems, std::array<size_t, 3> dim)
+    : std::vector<System>(systems) {
+  CheckDimensions(dim);
+};
+
+template <typename System>
+UniformSystemList<System>::UniformSystemList(const UniformSystemList& that)
+    : std::vector<System>(that) {
+  (*this) = that;
+}
+
+template <typename System>
+UniformSystemList<System>::UniformSystemList(UniformSystemList&& that) noexcept
+    : std::vector<System>(std::move(that)) {
+  this->dim_[0] = this->at(0).n_u();
+  this->dim_[1] = this->at(0).n_x();
+  this->dim_[2] = this->at(0).n_y();
+}
+
+template <typename System>
+void UniformSystemList<System>::CheckDimensions(std::array<size_t, 3> dim) {
+  if (dim[0] + dim[1] + dim[2]) {
+    dim_ = dim;
+  } else {
+    dim_[0] = this->at(0).n_u();
+    dim_[1] = this->at(0).n_x();
+    dim_[2] = this->at(0).n_y();
+  }
+
+  // make sure dimensiolaties are all uniform
+  bool does_match(true);
+  for (const System& sys : *this) {
+    does_match = does_match && (sys.n_u() == dim_[0]);
+    does_match = does_match && (sys.n_x() == dim_[1]);
+    does_match = does_match && (sys.n_y() == dim_[2]);
+    if (!does_match) {
+      throw std::runtime_error(
+          "Dimensionality of one or more input systems are not uniform.");
+    }
+  }
+}
+
+}  // namespace lds
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__uniform__vecs_8cpp.md b/misc/docs-hugo/content/docs/api/Files/lds__uniform__vecs_8cpp.md
index 1d6a78af..2e6e9187 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__uniform__vecs_8cpp.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__uniform__vecs_8cpp.md
@@ -1,107 +1,107 @@
----
-title: src/lds_uniform_vecs.cpp
-summary: Uniformly sized vectors. 
-
----
-
-# src/lds_uniform_vecs.cpp
-
-Uniformly sized vectors.  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-
-## Detailed Description
-
-
-
-This file provides a container for uniformly sized vectors. 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// Limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#include <ldsCtrlEst_h/lds_uniform_vecs.h>
-
-namespace lds {
-
-UniformVectorList::UniformVectorList(const std::vector<Vector>& vecs,
-                                     size_t dim)
-    : vector(vecs) {
-  CheckDimensions(dim);
-}
-
-UniformVectorList::UniformVectorList(std::vector<Vector>&& vecs, size_t dim)
-    : vector(std::move(vecs)) {
-  CheckDimensions(dim);
-};
-
-UniformVectorList::UniformVectorList(std::initializer_list<Vector> vecs,
-                                     size_t dim)
-    : vector(vecs) {
-  CheckDimensions(dim);
-};
-
-UniformVectorList::UniformVectorList(const UniformVectorList& that)
-    : vector(that) {
-  (*this) = that;
-}
-
-UniformVectorList::UniformVectorList(UniformVectorList&& that) noexcept
-    : vector(std::move(that)) {
-  this->dim_ = this->at(0).n_elem;
-}
-
-void UniformVectorList::CheckDimensions(size_t dim) {
-  if (dim) {
-    dim_ = dim;
-  } else {
-    dim_ = this->at(0).n_elem;
-  }
-
-  // make sure dimensiolaties are all uniform
-  bool does_match(true);
-  for (const Vector& vec : *this) {
-    does_match = does_match && (vec.n_elem == dim_);
-    if (!does_match) {
-      throw std::runtime_error(
-          "Dimensionality of one or more input matrices are not uniform.");
-    }
-  }
-}
-
-}  // namespace lds
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: src/lds_uniform_vecs.cpp
+summary: Uniformly sized vectors. 
+
+---
+
+# src/lds_uniform_vecs.cpp
+
+Uniformly sized vectors.  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+
+## Detailed Description
+
+
+
+This file provides a container for uniformly sized vectors. 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// Limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#include <ldsCtrlEst_h/lds_uniform_vecs.h>
+
+namespace lds {
+
+UniformVectorList::UniformVectorList(const std::vector<Vector>& vecs,
+                                     size_t dim)
+    : vector(vecs) {
+  CheckDimensions(dim);
+}
+
+UniformVectorList::UniformVectorList(std::vector<Vector>&& vecs, size_t dim)
+    : vector(std::move(vecs)) {
+  CheckDimensions(dim);
+};
+
+UniformVectorList::UniformVectorList(std::initializer_list<Vector> vecs,
+                                     size_t dim)
+    : vector(vecs) {
+  CheckDimensions(dim);
+};
+
+UniformVectorList::UniformVectorList(const UniformVectorList& that)
+    : vector(that) {
+  (*this) = that;
+}
+
+UniformVectorList::UniformVectorList(UniformVectorList&& that) noexcept
+    : vector(std::move(that)) {
+  this->dim_ = this->at(0).n_elem;
+}
+
+void UniformVectorList::CheckDimensions(size_t dim) {
+  if (dim) {
+    dim_ = dim;
+  } else {
+    dim_ = this->at(0).n_elem;
+  }
+
+  // make sure dimensiolaties are all uniform
+  bool does_match(true);
+  for (const Vector& vec : *this) {
+    does_match = does_match && (vec.n_elem == dim_);
+    if (!does_match) {
+      throw std::runtime_error(
+          "Dimensionality of one or more input matrices are not uniform.");
+    }
+  }
+}
+
+}  // namespace lds
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__uniform__vecs_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__uniform__vecs_8h.md
index 620ae01e..338d1489 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__uniform__vecs_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__uniform__vecs_8h.md
@@ -1,192 +1,192 @@
----
-title: ldsCtrlEst_h/lds_uniform_vecs.h
-summary: List of uniformly sized vectors. 
-
----
-
-# ldsCtrlEst_h/lds_uniform_vecs.h
-
-List of uniformly sized vectors.  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/)**  |
-
-## Detailed Description
-
-
-
-This file provides a container for uniformly sized vectors. 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// Limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H
-#define LDSCTRLEST_LDS_UNIFORM_VECS_H
-
-#include <array>   // std::array
-#include <vector>  // std::vector
-
-#include "lds.h"
-
-namespace lds {
-
-class UniformVectorList : public std::vector<Vector> {
- private:
-  // TODO(mfbolus): would rather *uncomment* the below for sake of conversion
-  // using std::vector<Vector>::vector;
-  using std::vector<Vector>::operator=;
-  using std::vector<Vector>::operator[];
-  using std::vector<Vector>::at;
-  using std::vector<Vector>::begin;
-  using std::vector<Vector>::end;
-  using std::vector<Vector>::size;
-
- public:
-  UniformVectorList() = default;
-
-  explicit UniformVectorList(const std::vector<Vector>& vecs, size_t dim = 0);
-
-  explicit UniformVectorList(std::vector<Vector>&& vecs, size_t dim = 0);
-
-  UniformVectorList(std::initializer_list<Vector> vecs, size_t dim = 0);
-
-  UniformVectorList(const UniformVectorList& that);
-  UniformVectorList(UniformVectorList&& that) noexcept;
-  ~UniformVectorList() = default;
-
-  size_t dim() const { return dim_; }
-
-  size_t size() { return std::vector<Vector>::size(); };
-
-  const Vector& at(size_t n) { return std::vector<Vector>::at(n); };
-
-  void Swap(Vector& that, size_t n);
-
-  UniformVectorList& operator=(const UniformVectorList& that);
-  UniformVectorList& operator=(UniformVectorList&& that) noexcept;
-
- private:
-  void CheckDimensions(size_t dim);
-  size_t dim_{};
-};
-
-inline void UniformVectorList::Swap(Vector& that, size_t n) {
-  // make sure request in range
-  if (n >= this->size()) {
-    std::cerr
-        << "Requested UniformMatrixList element out of bounds. Skipping.\n";
-    return;
-  }
-  // check dim
-  bool does_match = dim_ == that.n_elem;
-  if (!does_match) {
-    std::cerr << "Cannot swap a UniformMatrixList element for an element of "
-                 "different size. Skipping.\n";
-    return;
-  }
-  // if checks pass, perform swap
-  Vector tmp = std::move((*this)[n]);
-  (*this)[n] = std::move(that);
-  that = std::move(tmp);
-}
-
-inline UniformVectorList& UniformVectorList::operator=(
-    const UniformVectorList& that) {
-  // check dimensions
-  if (!this->empty()) {
-    if (this->size() != that.size()) {
-      std::ostringstream ss;
-      ss << "cannot reassign " << this->size() << " vectors with "
-         << that.size() << " vectors";
-      throw std::runtime_error(ss.str());
-    }
-
-    if (dim_) {
-      size_t other_dim(that.dim());
-      if (dim_ != other_dim) {
-        std::ostringstream ss;
-        ss << "cannot reassign vectors of size " << dim_
-           << " with vectors of size " << other_dim;
-        throw std::runtime_error(ss.str());
-      }
-    }
-  }
-
-  for (size_t k = 0; k < this->size(); k++) {
-    (*this)[k] = that[k];
-  }
-
-  return (*this);
-}
-
-inline UniformVectorList& UniformVectorList::operator=(
-    UniformVectorList&& that) noexcept {
-  // // check dimensions
-  // if (!this->empty()) {
-  //   if (this->size() != that.size()) {
-  //     std::cerr << "Cannot reassign " << this->size() << " vectors with "
-  //               << that.size() << " vectors. Skipping.\n";
-  //     return (*this);
-  //   }
-  //
-  //   if (dim_) {
-  //     size_t other_dim(that.dim());
-  //     if (dim_ != other_dim) {
-  //       std::cerr << "Cannot reassign vectors of size " << dim_
-  //                 << " with matrices of size " << other_dim << ".
-  //                 Skipping.\n";
-  //       return (*this);
-  //     }
-  //   }
-  // }
-
-  dim_ = that.dim_;
-  std::vector<Vector>::operator=(std::move(that));
-
-  return (*this);
-}
-
-}  // namespace lds
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/lds_uniform_vecs.h
+summary: List of uniformly sized vectors. 
+
+---
+
+# ldsCtrlEst_h/lds_uniform_vecs.h
+
+List of uniformly sized vectors.  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds](/lds-ctrl-est/docs/api/namespaces/namespacelds/)** <br>Linear Dynamical Systems (LDS) namespace.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/)**  |
+
+## Detailed Description
+
+
+
+This file provides a container for uniformly sized vectors. 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// Limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H
+#define LDSCTRLEST_LDS_UNIFORM_VECS_H
+
+#include <array>   // std::array
+#include <vector>  // std::vector
+
+#include "lds.h"
+
+namespace lds {
+
+class UniformVectorList : public std::vector<Vector> {
+ private:
+  // TODO(mfbolus): would rather *uncomment* the below for sake of conversion
+  // using std::vector<Vector>::vector;
+  using std::vector<Vector>::operator=;
+  using std::vector<Vector>::operator[];
+  using std::vector<Vector>::at;
+  using std::vector<Vector>::begin;
+  using std::vector<Vector>::end;
+  using std::vector<Vector>::size;
+
+ public:
+  UniformVectorList() = default;
+
+  explicit UniformVectorList(const std::vector<Vector>& vecs, size_t dim = 0);
+
+  explicit UniformVectorList(std::vector<Vector>&& vecs, size_t dim = 0);
+
+  UniformVectorList(std::initializer_list<Vector> vecs, size_t dim = 0);
+
+  UniformVectorList(const UniformVectorList& that);
+  UniformVectorList(UniformVectorList&& that) noexcept;
+  ~UniformVectorList() = default;
+
+  size_t dim() const { return dim_; }
+
+  size_t size() { return std::vector<Vector>::size(); };
+
+  const Vector& at(size_t n) { return std::vector<Vector>::at(n); };
+
+  void Swap(Vector& that, size_t n);
+
+  UniformVectorList& operator=(const UniformVectorList& that);
+  UniformVectorList& operator=(UniformVectorList&& that) noexcept;
+
+ private:
+  void CheckDimensions(size_t dim);
+  size_t dim_{};
+};
+
+inline void UniformVectorList::Swap(Vector& that, size_t n) {
+  // make sure request in range
+  if (n >= this->size()) {
+    std::cerr
+        << "Requested UniformMatrixList element out of bounds. Skipping.\n";
+    return;
+  }
+  // check dim
+  bool does_match = dim_ == that.n_elem;
+  if (!does_match) {
+    std::cerr << "Cannot swap a UniformMatrixList element for an element of "
+                 "different size. Skipping.\n";
+    return;
+  }
+  // if checks pass, perform swap
+  Vector tmp = std::move((*this)[n]);
+  (*this)[n] = std::move(that);
+  that = std::move(tmp);
+}
+
+inline UniformVectorList& UniformVectorList::operator=(
+    const UniformVectorList& that) {
+  // check dimensions
+  if (!this->empty()) {
+    if (this->size() != that.size()) {
+      std::ostringstream ss;
+      ss << "cannot reassign " << this->size() << " vectors with "
+         << that.size() << " vectors";
+      throw std::runtime_error(ss.str());
+    }
+
+    if (dim_) {
+      size_t other_dim(that.dim());
+      if (dim_ != other_dim) {
+        std::ostringstream ss;
+        ss << "cannot reassign vectors of size " << dim_
+           << " with vectors of size " << other_dim;
+        throw std::runtime_error(ss.str());
+      }
+    }
+  }
+
+  for (size_t k = 0; k < this->size(); k++) {
+    (*this)[k] = that[k];
+  }
+
+  return (*this);
+}
+
+inline UniformVectorList& UniformVectorList::operator=(
+    UniformVectorList&& that) noexcept {
+  // // check dimensions
+  // if (!this->empty()) {
+  //   if (this->size() != that.size()) {
+  //     std::cerr << "Cannot reassign " << this->size() << " vectors with "
+  //               << that.size() << " vectors. Skipping.\n";
+  //     return (*this);
+  //   }
+  //
+  //   if (dim_) {
+  //     size_t other_dim(that.dim());
+  //     if (dim_ != other_dim) {
+  //       std::cerr << "Cannot reassign vectors of size " << dim_
+  //                 << " with matrices of size " << other_dim << ".
+  //                 Skipping.\n";
+  //       return (*this);
+  //     }
+  //   }
+  // }
+
+  dim_ = that.dim_;
+  std::vector<Vector>::operator=(std::move(that));
+
+  return (*this);
+}
+
+}  // namespace lds
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/mex__c__util_8h.md b/misc/docs-hugo/content/docs/api/Files/mex__c__util_8h.md
index c64256a1..e6da187b 100644
--- a/misc/docs-hugo/content/docs/api/Files/mex__c__util_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/mex__c__util_8h.md
@@ -1,132 +1,132 @@
----
-title: ldsCtrlEst_h/mex_c_util.h
-summary: arma <-> mex interoperability utilities (Matlab C API) 
-
----
-
-# ldsCtrlEst_h/mex_c_util.h
-
-arma <-> mex interoperability utilities (Matlab C API)  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[armamexc](/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/)** <br>arma/mex interface using Matlab C API  |
-
-## Detailed Description
-
-
-
-This file defines utility functions for interoperability between armadillo and Matlab/Octave's C mex API. 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_MEXC_UTIL_H
-#define LDSCTRLEST_MEXC_UTIL_H
-
-#include <ldsCtrlEst>
-
-#include "mex.h"
-
-// // If Matlab_FOUND, include matrix.h.
-// // (Octave does not need/have it.)
-// #ifdef Matlab_FOUND
-// #include "matrix.h"
-// #endif
-
-namespace armamexc {
-template <class T>
-inline auto m2T_scalar(const mxArray *matlab_scalar) -> T {
-  if (mxGetData(matlab_scalar)) {
-    return static_cast<T>(mxGetScalar(matlab_scalar));
-  }
-  mexErrMsgTxt("No data available.");
-  return 0;
-}
-
-template <class T>
-inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false,
-                    bool strict = true) -> arma::Mat<T> {
-  if (mxGetData(matlab_mat)) {
-    const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat);
-    if (n_dim == 2) {
-      return arma::Mat<T>(static_cast<T *>(mxGetData(matlab_mat)),
-                          mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem,
-                          strict);
-    }
-    mexErrMsgTxt("Number of dimensions must be 2.");
-    return arma::Mat<T>();
-  }
-  mexErrMsgTxt("No data available.");
-  return arma::Mat<T>();
-}
-
-// TODO(mfbolus): make these templated.
-
-template <typename T>
-inline auto a2m_mat(arma::Mat<T> const &arma_mat) -> mxArray * {
-  mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols,
-                                              mxDOUBLE_CLASS, mxREAL);
-  if (matlab_mat) {
-    auto *dst_pointer = static_cast<T *>(mxGetData(matlab_mat));
-    const auto *src_pointer = const_cast<T *>(arma_mat.memptr());
-    // TODO(mfbolus): I just want to MOVE the data, not copy.
-    std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem);
-    return matlab_mat;
-  }
-  mexErrMsgTxt("Failed to create matlab mat from arma::Mat.");
-  return nullptr;
-}
-
-template <typename T>
-inline auto a2m_vec(arma::Col<T> const &arma_vec) -> mxArray * {
-  mxArray *matlab_mat =
-      mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL);
-  if (matlab_mat) {
-    auto *dst_pointer = static_cast<T *>(mxGetData(matlab_mat));
-    const auto *src_pointer = const_cast<T *>(arma_vec.memptr());
-    // TODO(mfbolus): I just want to MOVE the data, not copy.
-    std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem);
-    return matlab_mat;
-  }
-  mexErrMsgTxt("Failed to create matlab mat from arma::Col.");
-  return nullptr;
-}
-
-}  // namespace armamexc
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/mex_c_util.h
+summary: arma <-> mex interoperability utilities (Matlab C API) 
+
+---
+
+# ldsCtrlEst_h/mex_c_util.h
+
+arma <-> mex interoperability utilities (Matlab C API)  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[armamexc](/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/)** <br>arma/mex interface using Matlab C API  |
+
+## Detailed Description
+
+
+
+This file defines utility functions for interoperability between armadillo and Matlab/Octave's C mex API. 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_MEXC_UTIL_H
+#define LDSCTRLEST_MEXC_UTIL_H
+
+#include <ldsCtrlEst>
+
+#include "mex.h"
+
+// // If Matlab_FOUND, include matrix.h.
+// // (Octave does not need/have it.)
+// #ifdef Matlab_FOUND
+// #include "matrix.h"
+// #endif
+
+namespace armamexc {
+template <class T>
+inline auto m2T_scalar(const mxArray *matlab_scalar) -> T {
+  if (mxGetData(matlab_scalar)) {
+    return static_cast<T>(mxGetScalar(matlab_scalar));
+  }
+  mexErrMsgTxt("No data available.");
+  return 0;
+}
+
+template <class T>
+inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false,
+                    bool strict = true) -> arma::Mat<T> {
+  if (mxGetData(matlab_mat)) {
+    const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat);
+    if (n_dim == 2) {
+      return arma::Mat<T>(static_cast<T *>(mxGetData(matlab_mat)),
+                          mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem,
+                          strict);
+    }
+    mexErrMsgTxt("Number of dimensions must be 2.");
+    return arma::Mat<T>();
+  }
+  mexErrMsgTxt("No data available.");
+  return arma::Mat<T>();
+}
+
+// TODO(mfbolus): make these templated.
+
+template <typename T>
+inline auto a2m_mat(arma::Mat<T> const &arma_mat) -> mxArray * {
+  mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols,
+                                              mxDOUBLE_CLASS, mxREAL);
+  if (matlab_mat) {
+    auto *dst_pointer = static_cast<T *>(mxGetData(matlab_mat));
+    const auto *src_pointer = const_cast<T *>(arma_mat.memptr());
+    // TODO(mfbolus): I just want to MOVE the data, not copy.
+    std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem);
+    return matlab_mat;
+  }
+  mexErrMsgTxt("Failed to create matlab mat from arma::Mat.");
+  return nullptr;
+}
+
+template <typename T>
+inline auto a2m_vec(arma::Col<T> const &arma_vec) -> mxArray * {
+  mxArray *matlab_mat =
+      mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL);
+  if (matlab_mat) {
+    auto *dst_pointer = static_cast<T *>(mxGetData(matlab_mat));
+    const auto *src_pointer = const_cast<T *>(arma_vec.memptr());
+    // TODO(mfbolus): I just want to MOVE the data, not copy.
+    std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem);
+    return matlab_mat;
+  }
+  mexErrMsgTxt("Failed to create matlab mat from arma::Col.");
+  return nullptr;
+}
+
+}  // namespace armamexc
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Files/mex__cpp__util_8h.md b/misc/docs-hugo/content/docs/api/Files/mex__cpp__util_8h.md
index b8dec155..efc0b13c 100644
--- a/misc/docs-hugo/content/docs/api/Files/mex__cpp__util_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/mex__cpp__util_8h.md
@@ -1,153 +1,153 @@
----
-title: ldsCtrlEst_h/mex_cpp_util.h
-summary: arma <-> mex interoperability utilities (Matlab C++ API) 
-
----
-
-# ldsCtrlEst_h/mex_cpp_util.h
-
-arma <-> mex interoperability utilities (Matlab C++ API)  [More...](#detailed-description)
-
-
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[armamexcpp](/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/)** <br>arma/mex interface using Matlab C++ API  |
-
-## Detailed Description
-
-
-
-This file defines utility functions for interoperability between armadillo and Matlab's C++ mex API. 
-
-
-
-
-
-## Source code
-
-```cpp
-//===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===//
-//
-// Copyright 2021 Michael Bolus
-// Copyright 2021 Georgia Institute of Technology
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-
-#ifndef LDSCTRLEST_MEXCPP_UTIL_H
-#define LDSCTRLEST_MEXCPP_UTIL_H
-
-#include <ldsCtrlEst>
-
-#include "mex.hpp"
-#include "mexAdapter.hpp"
-
-namespace armamexcpp {
-template <class T>
-std::vector<arma::Mat<T>> m2a_cellmat(matlab::data::CellArray& matlab_cell) {
-  size_t n_cells = matlab_cell.getNumberOfElements();
-  std::vector<arma::Mat<T>> arma_mat(n_cells,
-                                     arma::Mat<T>(1, 1, arma::fill::zeros));
-  for (size_t k = 0; k < n_cells; k++) {
-    matlab::data::TypedArray<T> matlab_mat = matlab_cell[k];
-    auto dims = matlab_mat.getDimensions();
-    arma_mat[k] = arma::Mat<T>(matlab_mat.release().get(), dims[0], dims[1]);
-  }
-  return arma_mat;
-};
-
-template <class T>
-std::vector<T> m2s_vec(matlab::data::TypedArray<T>& matlab_array) {
-  size_t n_elem = matlab_array.getNumberOfElements();
-  T* ptr = matlab_array.release().get();
-  std::vector<T> vec(ptr, ptr + n_elem);
-  return vec;
-};
-
-template <class T>
-arma::Col<T> m2a_vec(matlab::data::TypedArray<T> matlab_array) {
-  size_t n_elem = matlab_array.getNumberOfElements();
-  // T* ptr = matlab_array.release().get();
-  // arma::Col<T> vec(ptr, n_elem);  //, false);
-  // TODO(mfbolus): for some reason, using the above pointer at times leads to
-  // getting garbage values. matlab array values may be stored in non-contiguous
-  // memory?
-  arma::Col<T> vec(n_elem, arma::fill::zeros);
-  for (size_t k = 0; k < n_elem; k++) {
-    vec[k] = matlab_array[k];
-  }
-  return vec;
-};
-
-template <class T>
-arma::Mat<T> m2a_mat(matlab::data::TypedArray<T> matlab_array) {
-  // ArrayDimensions == std::vector<size_t>
-  auto dims = matlab_array.getDimensions();
-  // T* ptr = matlab_array.release().get();
-  // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false)
-  // arma::Mat<T> mat(ptr, dims[0], dims[1]);  //, false);
-
-  // TODO(mfbolus): for some reason, using the above pointer at times leads to
-  // getting garbage values. matlab array values may be stored in non-contiguous
-  // memory?
-  //
-  // armadillo and matlab both use column-major ordering, so this should work:
-  size_t n_elem = dims[0] * dims[1];
-  arma::Mat<T> mat(dims[0], dims[1], arma::fill::zeros);
-  size_t k(0);
-  for (auto m: matlab_array) {
-    mat[k] = m;
-    k++;
-  }
-  return mat;
-};
-
-template <class T>
-matlab::data::TypedArray<T> a2m_mat(const arma::Mat<T>& arma_mat,
-                                    matlab::data::ArrayFactory& factory) {
-  const matlab::data::TypedArray<T> matlab_mat = factory.createArray<T>(
-      {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(),
-      arma_mat.memptr() + arma_mat.n_elem);
-  return matlab_mat;
-};
-
-template <class T>
-matlab::data::TypedArray<T> a2m_vec(const arma::Col<T>& arma_vec,
-                                    matlab::data::ArrayFactory& factory) {
-  const matlab::data::TypedArray<T> matlab_mat =
-      factory.createArray<T>({arma_vec.n_elem, 1}, arma_vec.memptr(),
-                             arma_vec.memptr() + arma_vec.n_elem);
-  return matlab_mat;
-};
-
-template <class T>
-matlab::data::TypedArray<T> s2m_vec(const std::vector<T>& std_vec,
-                                    matlab::data::ArrayFactory& factory) {
-  const matlab::data::TypedArray<T> matlab_mat = factory.createArray<T>(
-      {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size());
-  return matlab_mat;
-};
-}  // namespace armamexcpp
-
-#endif
-```
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:05 Eastern Daylight Time
+---
+title: ldsCtrlEst_h/mex_cpp_util.h
+summary: arma <-> mex interoperability utilities (Matlab C++ API) 
+
+---
+
+# ldsCtrlEst_h/mex_cpp_util.h
+
+arma <-> mex interoperability utilities (Matlab C++ API)  [More...](#detailed-description)
+
+
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[armamexcpp](/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/)** <br>arma/mex interface using Matlab C++ API  |
+
+## Detailed Description
+
+
+
+This file defines utility functions for interoperability between armadillo and Matlab's C++ mex API. 
+
+
+
+
+
+## Source code
+
+```cpp
+//===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#ifndef LDSCTRLEST_MEXCPP_UTIL_H
+#define LDSCTRLEST_MEXCPP_UTIL_H
+
+#include <ldsCtrlEst>
+
+#include "mex.hpp"
+#include "mexAdapter.hpp"
+
+namespace armamexcpp {
+template <class T>
+std::vector<arma::Mat<T>> m2a_cellmat(matlab::data::CellArray& matlab_cell) {
+  size_t n_cells = matlab_cell.getNumberOfElements();
+  std::vector<arma::Mat<T>> arma_mat(n_cells,
+                                     arma::Mat<T>(1, 1, arma::fill::zeros));
+  for (size_t k = 0; k < n_cells; k++) {
+    matlab::data::TypedArray<T> matlab_mat = matlab_cell[k];
+    auto dims = matlab_mat.getDimensions();
+    arma_mat[k] = arma::Mat<T>(matlab_mat.release().get(), dims[0], dims[1]);
+  }
+  return arma_mat;
+};
+
+template <class T>
+std::vector<T> m2s_vec(matlab::data::TypedArray<T>& matlab_array) {
+  size_t n_elem = matlab_array.getNumberOfElements();
+  T* ptr = matlab_array.release().get();
+  std::vector<T> vec(ptr, ptr + n_elem);
+  return vec;
+};
+
+template <class T>
+arma::Col<T> m2a_vec(matlab::data::TypedArray<T> matlab_array) {
+  size_t n_elem = matlab_array.getNumberOfElements();
+  // T* ptr = matlab_array.release().get();
+  // arma::Col<T> vec(ptr, n_elem);  //, false);
+  // TODO(mfbolus): for some reason, using the above pointer at times leads to
+  // getting garbage values. matlab array values may be stored in non-contiguous
+  // memory?
+  arma::Col<T> vec(n_elem, arma::fill::zeros);
+  for (size_t k = 0; k < n_elem; k++) {
+    vec[k] = matlab_array[k];
+  }
+  return vec;
+};
+
+template <class T>
+arma::Mat<T> m2a_mat(matlab::data::TypedArray<T> matlab_array) {
+  // ArrayDimensions == std::vector<size_t>
+  auto dims = matlab_array.getDimensions();
+  // T* ptr = matlab_array.release().get();
+  // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false)
+  // arma::Mat<T> mat(ptr, dims[0], dims[1]);  //, false);
+
+  // TODO(mfbolus): for some reason, using the above pointer at times leads to
+  // getting garbage values. matlab array values may be stored in non-contiguous
+  // memory?
+  //
+  // armadillo and matlab both use column-major ordering, so this should work:
+  size_t n_elem = dims[0] * dims[1];
+  arma::Mat<T> mat(dims[0], dims[1], arma::fill::zeros);
+  size_t k(0);
+  for (auto m: matlab_array) {
+    mat[k] = m;
+    k++;
+  }
+  return mat;
+};
+
+template <class T>
+matlab::data::TypedArray<T> a2m_mat(const arma::Mat<T>& arma_mat,
+                                    matlab::data::ArrayFactory& factory) {
+  const matlab::data::TypedArray<T> matlab_mat = factory.createArray<T>(
+      {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(),
+      arma_mat.memptr() + arma_mat.n_elem);
+  return matlab_mat;
+};
+
+template <class T>
+matlab::data::TypedArray<T> a2m_vec(const arma::Col<T>& arma_vec,
+                                    matlab::data::ArrayFactory& factory) {
+  const matlab::data::TypedArray<T> matlab_mat =
+      factory.createArray<T>({arma_vec.n_elem, 1}, arma_vec.memptr(),
+                             arma_vec.memptr() + arma_vec.n_elem);
+  return matlab_mat;
+};
+
+template <class T>
+matlab::data::TypedArray<T> s2m_vec(const std::vector<T>& std_vec,
+                                    matlab::data::ArrayFactory& factory) {
+  const matlab::data::TypedArray<T> matlab_mat = factory.createArray<T>(
+      {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size());
+  return matlab_mat;
+};
+}  // namespace armamexcpp
+
+#endif
+```
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Modules/_index.md b/misc/docs-hugo/content/docs/api/Modules/_index.md
index a4bc6ccf..01cdf812 100644
--- a/misc/docs-hugo/content/docs/api/Modules/_index.md
+++ b/misc/docs-hugo/content/docs/api/Modules/_index.md
@@ -1,28 +1,28 @@
----
-title: Modules
-
----
-
-# Modules
-
-
-
-
-
-
-
-- **[Control Mode Bit Masks](/lds-ctrl-est/docs/api/modules/group__control__masks/)** <br>provides fill types for constructing new armadillo vectors, matrices 
-
-
-
-
-
-- **[Defaults](/lds-ctrl-est/docs/api/modules/group__defaults/)** 
-
-
-
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:06 Eastern Daylight Time
+---
+title: Modules
+
+---
+
+# Modules
+
+
+
+
+
+
+
+- **[Control Mode Bit Masks](/lds-ctrl-est/docs/api/modules/group__control__masks/)** <br>provides fill types for constructing new armadillo vectors, matrices 
+
+
+
+
+
+- **[Defaults](/lds-ctrl-est/docs/api/modules/group__defaults/)** 
+
+
+
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Modules/group__control__masks.md b/misc/docs-hugo/content/docs/api/Modules/group__control__masks.md
index 972b05de..a846662f 100644
--- a/misc/docs-hugo/content/docs/api/Modules/group__control__masks.md
+++ b/misc/docs-hugo/content/docs/api/Modules/group__control__masks.md
@@ -1,70 +1,70 @@
----
-title: Control Mode Bit Masks
-summary: provides fill types for constructing new armadillo vectors, matrices 
-
----
-
-# Control Mode Bit Masks
-
-provides fill types for constructing new armadillo vectors, matrices <br> <br>[More...](#detailed-description)
-<br>
-
-
-## Attributes
-
-|                | Name           |
-| -------------- | -------------- |
-| const std::size_t | **[kControlTypeDeltaU](/lds-ctrl-est/docs/api/modules/group__control__masks/#variable-kcontroltypedeltau)** <br>control designed to penalize change in input  |
-| const std::size_t | **[kControlTypeIntY](/lds-ctrl-est/docs/api/modules/group__control__masks/#variable-kcontroltypeinty)** <br>control using integral action  |
-| const std::size_t | **[kControlTypeAdaptM](/lds-ctrl-est/docs/api/modules/group__control__masks/#variable-kcontroltypeadaptm)** <br>adapt control setpoint with re-estimated disturbance `m` |
-
-## Detailed Description
-
-
-
-Control mode bit masks. These can be bit-wise OR'd to use in combination. 
-
-
-
-## Attribute Details
-
-### kControlTypeDeltaU
-
-```cpp
-static const std::size_t kControlTypeDeltaU = 0x1;
-```
-
-
-
-Control was designed to penalize change in input (i.e., the state was augmented with input `u`) 
-
-
-### kControlTypeIntY
-
-```cpp
-static const std::size_t kControlTypeIntY = kControlTypeDeltaU << 1;
-```
-
-
-
-Control using integral action (i.e., the state was augmented with output `y` during design) 
-
-
-### kControlTypeAdaptM
-
-```cpp
-static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU << 2;
-```
-
-
-
-Adapt control setpoint adapted with re-estimated process disturbance `m`. 
-
-
-
-
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
+---
+title: Control Mode Bit Masks
+summary: provides fill types for constructing new armadillo vectors, matrices 
+
+---
+
+# Control Mode Bit Masks
+
+provides fill types for constructing new armadillo vectors, matrices <br> <br>[More...](#detailed-description)
+<br>
+
+
+## Attributes
+
+|                | Name           |
+| -------------- | -------------- |
+| const std::size_t | **[kControlTypeDeltaU](/lds-ctrl-est/docs/api/modules/group__control__masks/#variable-kcontroltypedeltau)** <br>control designed to penalize change in input  |
+| const std::size_t | **[kControlTypeIntY](/lds-ctrl-est/docs/api/modules/group__control__masks/#variable-kcontroltypeinty)** <br>control using integral action  |
+| const std::size_t | **[kControlTypeAdaptM](/lds-ctrl-est/docs/api/modules/group__control__masks/#variable-kcontroltypeadaptm)** <br>adapt control setpoint with re-estimated disturbance `m` |
+
+## Detailed Description
+
+
+
+Control mode bit masks. These can be bit-wise OR'd to use in combination. 
+
+
+
+## Attribute Details
+
+### kControlTypeDeltaU
+
+```cpp
+static const std::size_t kControlTypeDeltaU = 0x1;
+```
+
+
+
+Control was designed to penalize change in input (i.e., the state was augmented with input `u`) 
+
+
+### kControlTypeIntY
+
+```cpp
+static const std::size_t kControlTypeIntY = kControlTypeDeltaU << 1;
+```
+
+
+
+Control using integral action (i.e., the state was augmented with output `y` during design) 
+
+
+### kControlTypeAdaptM
+
+```cpp
+static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU << 2;
+```
+
+
+
+Adapt control setpoint adapted with re-estimated process disturbance `m`. 
+
+
+
+
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Modules/group__defaults.md b/misc/docs-hugo/content/docs/api/Modules/group__defaults.md
index 4f744568..f72c3c16 100644
--- a/misc/docs-hugo/content/docs/api/Modules/group__defaults.md
+++ b/misc/docs-hugo/content/docs/api/Modules/group__defaults.md
@@ -1,60 +1,60 @@
----
-title: Defaults
-
----
-
-# Defaults
-
-<br> <br>[More...](#detailed-description)
-<br>
-
-
-## Attributes
-
-|                | Name           |
-| -------------- | -------------- |
-| const data_t | **[kDefaultP0](/lds-ctrl-est/docs/api/modules/group__defaults/#variable-kdefaultp0)** <br>default state estimate covar  |
-| const data_t | **[kDefaultQ0](/lds-ctrl-est/docs/api/modules/group__defaults/#variable-kdefaultq0)** <br>default process noise covar  |
-| const data_t | **[kDefaultR0](/lds-ctrl-est/docs/api/modules/group__defaults/#variable-kdefaultr0)** <br>default output noise covar  |
-
-## Detailed Description
-
-
-
-Default values for common variables (e.g., default diagonal elements of covariances) 
-
-
-
-## Attribute Details
-
-### kDefaultP0
-
-```cpp
-static const data_t kDefaultP0 = 1e-6;
-```
-
-
-
-### kDefaultQ0
-
-```cpp
-static const data_t kDefaultQ0 = 1e-6;
-```
-
-
-
-### kDefaultR0
-
-```cpp
-static const data_t kDefaultR0 = 1e-2;
-```
-
-
-
-
-
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
+---
+title: Defaults
+
+---
+
+# Defaults
+
+<br> <br>[More...](#detailed-description)
+<br>
+
+
+## Attributes
+
+|                | Name           |
+| -------------- | -------------- |
+| const data_t | **[kDefaultP0](/lds-ctrl-est/docs/api/modules/group__defaults/#variable-kdefaultp0)** <br>default state estimate covar  |
+| const data_t | **[kDefaultQ0](/lds-ctrl-est/docs/api/modules/group__defaults/#variable-kdefaultq0)** <br>default process noise covar  |
+| const data_t | **[kDefaultR0](/lds-ctrl-est/docs/api/modules/group__defaults/#variable-kdefaultr0)** <br>default output noise covar  |
+
+## Detailed Description
+
+
+
+Default values for common variables (e.g., default diagonal elements of covariances) 
+
+
+
+## Attribute Details
+
+### kDefaultP0
+
+```cpp
+static const data_t kDefaultP0 = 1e-6;
+```
+
+
+
+### kDefaultQ0
+
+```cpp
+static const data_t kDefaultQ0 = 1e-6;
+```
+
+
+
+### kDefaultR0
+
+```cpp
+static const data_t kDefaultR0 = 1e-2;
+```
+
+
+
+
+
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Namespaces/_index.md b/misc/docs-hugo/content/docs/api/Namespaces/_index.md
index 0ff4e7f6..41d6e411 100644
--- a/misc/docs-hugo/content/docs/api/Namespaces/_index.md
+++ b/misc/docs-hugo/content/docs/api/Namespaces/_index.md
@@ -1,38 +1,42 @@
----
-title: Namespaces
-
----
-
-# Namespaces
-
-
-
-
-
-
-- **[armamexc](/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/)** <br>arma/mex interface using Matlab C API 
-
-
-
-- **[armamexcpp](/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/)** <br>arma/mex interface using Matlab C++ API 
-
-
-
-
-
-
-- **[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)** <br>Linear Dynamical Systems with Gaussian observations. 
-
-
-
-- **[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)** <br>Linear Dynamical Systems with Poisson observations. 
-
-
-
-
-
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:06 Eastern Daylight Time
+---
+title: Namespaces
+
+---
+
+# Namespaces
+
+
+
+
+
+
+- **[armamexc](/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/)** <br>arma/mex interface using Matlab C API 
+
+
+
+- **[armamexcpp](/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/)** <br>arma/mex interface using Matlab C++ API 
+
+
+
+
+
+
+- **[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)** <br>Linear Dynamical Systems with Gaussian observations. 
+
+
+
+- **[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)** <br>Linear Dynamical Systems with Poisson observations. 
+
+
+
+
+
+
+- **[std](/lds-ctrl-est/docs/api/namespaces/namespacestd/)** 
+
+
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Namespaces/namespacearmamexc.md b/misc/docs-hugo/content/docs/api/Namespaces/namespacearmamexc.md
index fb69bd3e..924e1250 100644
--- a/misc/docs-hugo/content/docs/api/Namespaces/namespacearmamexc.md
+++ b/misc/docs-hugo/content/docs/api/Namespaces/namespacearmamexc.md
@@ -1,124 +1,124 @@
----
-title: armamexc
-summary: arma/mex interface using Matlab C API 
-
----
-
-# armamexc
-
-arma/mex interface using Matlab C API <br> <br>[More...](#detailed-description)
-<br>
-
-
-## Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| template <class T \> <br>auto | **[m2T_scalar](/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/#function-m2t-scalar)**(const mxArray * matlab_scalar)<br>Convert Matlab mxArray to scalar of type T.  |
-| template <class T \> <br>auto | **[m2a_mat](/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/#function-m2a-mat)**(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)<br>Convert matlab matrix to armadillo.  |
-| template <typename T \> <br>auto | **[a2m_mat](/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/#function-a2m-mat)**(arma::Mat< T > const & arma_mat)<br>Convert armadillo to matlab matrix.  |
-| template <typename T \> <br>auto | **[a2m_vec](/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/#function-a2m-vec)**(arma::Col< T > const & arma_vec)<br>Convert armadillo to matlab vector.  |
-
-## Detailed Description
-
-
-
-Utilities for arma/mex interface _using Matlab C API_
-
-
-## Function Details
-
-### m2T_scalar
-
-```cpp
-template <class T >
-inline auto m2T_scalar(
-    const mxArray * matlab_scalar
-)
-```
-
-
-
-**Parameters**:
-
-  * **matlab_scalar** matlab scalar
-
-
-**Template Parameters**:
-
-  * **T** type
-
-
-**Return**: scalar of type T 
-
-### m2a_mat
-
-```cpp
-template <class T >
-inline auto m2a_mat(
-    const mxArray * matlab_mat,
-    bool copy_aux_mem =false,
-    bool strict =true
-)
-```
-
-
-
-**Parameters**:
-
-  * **matlab_mat** matlab matrix 
-  * **copy_aux_mem** [optional] whether to copy auxiliary memory 
-  * **strict** [optional] strictly enforce the above
-
-
-**Template Parameters**:
-
-  * **T** type
-
-
-**Return**: armadillo matrix of type T 
-
-### a2m_mat
-
-```cpp
-template <typename T >
-inline auto a2m_mat(
-    arma::Mat< T > const & arma_mat
-)
-```
-
-
-
-**Parameters**:
-
-  * **arma_mat** armadillo matrix
-
-
-**Return**: matlab matrix 
-
-### a2m_vec
-
-```cpp
-template <typename T >
-inline auto a2m_vec(
-    arma::Col< T > const & arma_vec
-)
-```
-
-
-
-**Parameters**:
-
-  * **arma_vec** armadillo vector
-
-
-**Return**: matlab vector 
-
-
-
-
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:03 Eastern Daylight Time
+---
+title: armamexc
+summary: arma/mex interface using Matlab C API 
+
+---
+
+# armamexc
+
+arma/mex interface using Matlab C API <br> <br>[More...](#detailed-description)
+<br>
+
+
+## Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| template <class T \> <br>T | **[m2T_scalar](/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/#function-m2t-scalar)**(const mxArray * matlab_scalar)<br>Convert Matlab mxArray to scalar of type T.  |
+| template <class T \> <br>arma::Mat< T > | **[m2a_mat](/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/#function-m2a-mat)**(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)<br>Convert matlab matrix to armadillo.  |
+| template <typename T \> <br>mxArray * | **[a2m_mat](/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/#function-a2m-mat)**(arma::Mat< T > const & arma_mat)<br>Convert armadillo to matlab matrix.  |
+| template <typename T \> <br>mxArray * | **[a2m_vec](/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/#function-a2m-vec)**(arma::Col< T > const & arma_vec)<br>Convert armadillo to matlab vector.  |
+
+## Detailed Description
+
+
+
+Utilities for arma/mex interface _using Matlab C API_
+
+
+## Function Details
+
+### m2T_scalar
+
+```cpp
+template <class T >
+inline T m2T_scalar(
+    const mxArray * matlab_scalar
+)
+```
+
+
+
+**Parameters**:
+
+  * **matlab_scalar** matlab scalar
+
+
+**Template Parameters**:
+
+  * **T** type
+
+
+**Return**: scalar of type T 
+
+### m2a_mat
+
+```cpp
+template <class T >
+inline arma::Mat< T > m2a_mat(
+    const mxArray * matlab_mat,
+    bool copy_aux_mem =false,
+    bool strict =true
+)
+```
+
+
+
+**Parameters**:
+
+  * **matlab_mat** matlab matrix 
+  * **copy_aux_mem** [optional] whether to copy auxiliary memory 
+  * **strict** [optional] strictly enforce the above
+
+
+**Template Parameters**:
+
+  * **T** type
+
+
+**Return**: armadillo matrix of type T 
+
+### a2m_mat
+
+```cpp
+template <typename T >
+inline mxArray * a2m_mat(
+    arma::Mat< T > const & arma_mat
+)
+```
+
+
+
+**Parameters**:
+
+  * **arma_mat** armadillo matrix
+
+
+**Return**: matlab matrix 
+
+### a2m_vec
+
+```cpp
+template <typename T >
+inline mxArray * a2m_vec(
+    arma::Col< T > const & arma_vec
+)
+```
+
+
+
+**Parameters**:
+
+  * **arma_vec** armadillo vector
+
+
+**Return**: matlab vector 
+
+
+
+
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:26 EST
diff --git a/misc/docs-hugo/content/docs/api/Namespaces/namespacearmamexcpp.md b/misc/docs-hugo/content/docs/api/Namespaces/namespacearmamexcpp.md
index e160ecf4..777df93f 100644
--- a/misc/docs-hugo/content/docs/api/Namespaces/namespacearmamexcpp.md
+++ b/misc/docs-hugo/content/docs/api/Namespaces/namespacearmamexcpp.md
@@ -1,208 +1,208 @@
----
-title: armamexcpp
-summary: arma/mex interface using Matlab C++ API 
-
----
-
-# armamexcpp
-
-arma/mex interface using Matlab C++ API <br> <br>[More...](#detailed-description)
-<br>
-
-
-## Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| template <class T \> <br>std::vector< arma::Mat< T > > | **[m2a_cellmat](/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/#function-m2a-cellmat)**(matlab::data::CellArray & matlab_cell)<br>Convert matlab cell array to vector of armadillo matrices.  |
-| template <class T \> <br>std::vector< T > | **[m2s_vec](/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/#function-m2s-vec)**(matlab::data::TypedArray< T > & matlab_array)<br>Convert matlab matrix to a vector of scalars.  |
-| template <class T \> <br>arma::Col< T > | **[m2a_vec](/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/#function-m2a-vec)**(matlab::data::TypedArray< T > matlab_array)<br>Convert matlab to armadillo vector.  |
-| template <class T \> <br>arma::Mat< T > | **[m2a_mat](/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/#function-m2a-mat)**(matlab::data::TypedArray< T > matlab_array)<br>Convert matlab to armadillo matrix.  |
-| template <class T \> <br>matlab::data::TypedArray< T > | **[a2m_mat](/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/#function-a2m-mat)**(const arma::Mat< T > & arma_mat, matlab::data::ArrayFactory & factory)<br>Convert armadillo to matlab matrix.  |
-| template <class T \> <br>matlab::data::TypedArray< T > | **[a2m_vec](/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/#function-a2m-vec)**(const arma::Col< T > & arma_vec, matlab::data::ArrayFactory & factory)<br>Convert armadillo to matlab vector.  |
-| template <class T \> <br>matlab::data::TypedArray< T > | **[s2m_vec](/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/#function-s2m-vec)**(const std::vector< T > & std_vec, matlab::data::ArrayFactory & factory)<br>Convert vector of scalar T to matlab matrix.  |
-
-## Detailed Description
-
-
-
-utilities for arma/mex interface _using Matlab C++ API_
-
-
-## Function Details
-
-### m2a_cellmat
-
-```cpp
-template <class T >
-std::vector< arma::Mat< T > > m2a_cellmat(
-    matlab::data::CellArray & matlab_cell
-)
-```
-
-
-
-**Parameters**:
-
-  * **matlab_cell** matlab cell
-
-
-**Template Parameters**:
-
-  * **T** type
-
-
-**Return**: vector of armadillo matrices of type T 
-
-### m2s_vec
-
-```cpp
-template <class T >
-std::vector< T > m2s_vec(
-    matlab::data::TypedArray< T > & matlab_array
-)
-```
-
-
-
-**Parameters**:
-
-  * **matlab_array** matlab array
-
-
-**Template Parameters**:
-
-  * **T** type
-
-
-**Return**: vector of type T 
-
-### m2a_vec
-
-```cpp
-template <class T >
-arma::Col< T > m2a_vec(
-    matlab::data::TypedArray< T > matlab_array
-)
-```
-
-
-
-**Parameters**:
-
-  * **matlab_array** matlab array
-
-
-**Template Parameters**:
-
-  * **T** type
-
-
-**Return**: armadillo vector of type T 
-
-### m2a_mat
-
-```cpp
-template <class T >
-arma::Mat< T > m2a_mat(
-    matlab::data::TypedArray< T > matlab_array
-)
-```
-
-
-
-**Parameters**:
-
-  * **matlab_array** matlab matrix
-
-
-**Template Parameters**:
-
-  * **T** type
-
-
-**Return**: armadillo matrix of type T 
-
-### a2m_mat
-
-```cpp
-template <class T >
-matlab::data::TypedArray< T > a2m_mat(
-    const arma::Mat< T > & arma_mat,
-    matlab::data::ArrayFactory & factory
-)
-```
-
-
-
-**Parameters**:
-
-  * **arma_mat** arma matrix 
-  * **factory** matlab "array factory"
-
-
-**Template Parameters**:
-
-  * **T** type
-
-
-**Return**: matlab matrix 
-
-### a2m_vec
-
-```cpp
-template <class T >
-matlab::data::TypedArray< T > a2m_vec(
-    const arma::Col< T > & arma_vec,
-    matlab::data::ArrayFactory & factory
-)
-```
-
-
-
-**Parameters**:
-
-  * **arma_vec** armadillo vector 
-  * **factory** matlab "array factory"
-
-
-**Template Parameters**:
-
-  * **T** type
-
-
-**Return**: matlab matrix 
-
-### s2m_vec
-
-```cpp
-template <class T >
-matlab::data::TypedArray< T > s2m_vec(
-    const std::vector< T > & std_vec,
-    matlab::data::ArrayFactory & factory
-)
-```
-
-
-
-**Parameters**:
-
-  * **std_vec** standard vector 
-  * **factory** matlab "array factory"
-
-
-**Template Parameters**:
-
-  * **T** type
-
-
-**Return**: matlab matrix 
-
-
-
-
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:03 Eastern Daylight Time
+---
+title: armamexcpp
+summary: arma/mex interface using Matlab C++ API 
+
+---
+
+# armamexcpp
+
+arma/mex interface using Matlab C++ API <br> <br>[More...](#detailed-description)
+<br>
+
+
+## Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| template <class T \> <br>std::vector< arma::Mat< T > > | **[m2a_cellmat](/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/#function-m2a-cellmat)**(matlab::data::CellArray & matlab_cell)<br>Convert matlab cell array to vector of armadillo matrices.  |
+| template <class T \> <br>std::vector< T > | **[m2s_vec](/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/#function-m2s-vec)**(matlab::data::TypedArray< T > & matlab_array)<br>Convert matlab matrix to a vector of scalars.  |
+| template <class T \> <br>arma::Col< T > | **[m2a_vec](/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/#function-m2a-vec)**(matlab::data::TypedArray< T > matlab_array)<br>Convert matlab to armadillo vector.  |
+| template <class T \> <br>arma::Mat< T > | **[m2a_mat](/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/#function-m2a-mat)**(matlab::data::TypedArray< T > matlab_array)<br>Convert matlab to armadillo matrix.  |
+| template <class T \> <br>matlab::data::TypedArray< T > | **[a2m_mat](/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/#function-a2m-mat)**(const arma::Mat< T > & arma_mat, matlab::data::ArrayFactory & factory)<br>Convert armadillo to matlab matrix.  |
+| template <class T \> <br>matlab::data::TypedArray< T > | **[a2m_vec](/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/#function-a2m-vec)**(const arma::Col< T > & arma_vec, matlab::data::ArrayFactory & factory)<br>Convert armadillo to matlab vector.  |
+| template <class T \> <br>matlab::data::TypedArray< T > | **[s2m_vec](/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/#function-s2m-vec)**(const std::vector< T > & std_vec, matlab::data::ArrayFactory & factory)<br>Convert vector of scalar T to matlab matrix.  |
+
+## Detailed Description
+
+
+
+utilities for arma/mex interface _using Matlab C++ API_
+
+
+## Function Details
+
+### m2a_cellmat
+
+```cpp
+template <class T >
+std::vector< arma::Mat< T > > m2a_cellmat(
+    matlab::data::CellArray & matlab_cell
+)
+```
+
+
+
+**Parameters**:
+
+  * **matlab_cell** matlab cell
+
+
+**Template Parameters**:
+
+  * **T** type
+
+
+**Return**: vector of armadillo matrices of type T 
+
+### m2s_vec
+
+```cpp
+template <class T >
+std::vector< T > m2s_vec(
+    matlab::data::TypedArray< T > & matlab_array
+)
+```
+
+
+
+**Parameters**:
+
+  * **matlab_array** matlab array
+
+
+**Template Parameters**:
+
+  * **T** type
+
+
+**Return**: vector of type T 
+
+### m2a_vec
+
+```cpp
+template <class T >
+arma::Col< T > m2a_vec(
+    matlab::data::TypedArray< T > matlab_array
+)
+```
+
+
+
+**Parameters**:
+
+  * **matlab_array** matlab array
+
+
+**Template Parameters**:
+
+  * **T** type
+
+
+**Return**: armadillo vector of type T 
+
+### m2a_mat
+
+```cpp
+template <class T >
+arma::Mat< T > m2a_mat(
+    matlab::data::TypedArray< T > matlab_array
+)
+```
+
+
+
+**Parameters**:
+
+  * **matlab_array** matlab matrix
+
+
+**Template Parameters**:
+
+  * **T** type
+
+
+**Return**: armadillo matrix of type T 
+
+### a2m_mat
+
+```cpp
+template <class T >
+matlab::data::TypedArray< T > a2m_mat(
+    const arma::Mat< T > & arma_mat,
+    matlab::data::ArrayFactory & factory
+)
+```
+
+
+
+**Parameters**:
+
+  * **arma_mat** arma matrix 
+  * **factory** matlab "array factory"
+
+
+**Template Parameters**:
+
+  * **T** type
+
+
+**Return**: matlab matrix 
+
+### a2m_vec
+
+```cpp
+template <class T >
+matlab::data::TypedArray< T > a2m_vec(
+    const arma::Col< T > & arma_vec,
+    matlab::data::ArrayFactory & factory
+)
+```
+
+
+
+**Parameters**:
+
+  * **arma_vec** armadillo vector 
+  * **factory** matlab "array factory"
+
+
+**Template Parameters**:
+
+  * **T** type
+
+
+**Return**: matlab matrix 
+
+### s2m_vec
+
+```cpp
+template <class T >
+matlab::data::TypedArray< T > s2m_vec(
+    const std::vector< T > & std_vec,
+    matlab::data::ArrayFactory & factory
+)
+```
+
+
+
+**Parameters**:
+
+  * **std_vec** standard vector 
+  * **factory** matlab "array factory"
+
+
+**Template Parameters**:
+
+  * **T** type
+
+
+**Return**: matlab matrix 
+
+
+
+
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:26 EST
diff --git a/misc/docs-hugo/content/docs/api/Namespaces/namespacelds.md b/misc/docs-hugo/content/docs/api/Namespaces/namespacelds.md
index f10de3ef..221f358a 100644
--- a/misc/docs-hugo/content/docs/api/Namespaces/namespacelds.md
+++ b/misc/docs-hugo/content/docs/api/Namespaces/namespacelds.md
@@ -1,376 +1,313 @@
----
-title: lds
-summary: Linear Dynamical Systems (LDS) namespace. 
-
----
-
-# lds
-
-Linear Dynamical Systems (LDS) namespace. <br>
-
-## Namespaces
-
-| Name           |
-| -------------- |
-| **[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)** <br>Linear Dynamical Systems with Gaussian observations.  |
-| **[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)** <br>Linear Dynamical Systems with Poisson observations.  |
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1controller/)**  |
-| class | **[lds::EM](/lds-ctrl-est/docs/api/classes/classlds_1_1em/)**  |
-| class | **[lds::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1fit/)** <br>LDS [Fit]() Type.  |
-| class | **[lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)**  |
-| class | **[lds::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1switchedcontroller/)** <br>[SwitchedController]() Type.  |
-| class | **[lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1system/)** <br>Linear Dynamical [System]() Type.  |
-| class | **[lds::UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformmatrixlist/)**  |
-| class | **[lds::UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformsystemlist/)**  |
-| class | **[lds::UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1uniformvectorlist/)**  |
-
-## Types
-
-|                | Name           |
-| -------------- | -------------- |
-| enum| **[SSIDWt](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt)** { kSSIDNone, kSSIDMOESP, kSSIDCVA}<br>weighting options for [SSID]() |
-| enum| **[MatrixListFreeDim](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-matrixlistfreedim)** { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2} |
-| using double | **[data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t)**  |
-| using arma::Col< [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) > | **[Vector](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-vector)**  |
-| using arma::Mat< [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) > | **[Matrix](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-matrix)**  |
-| using arma::Cube< [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) > | **[Cube](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-cube)**  |
-| using arma::subview< [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) > | **[View](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-view)**  |
-
-## Functions
-
-|                | Name           |
-| -------------- | -------------- |
-| void | **[Limit](/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-limit)**(std::vector< [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) > & x, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) ub) |
-| void | **[Limit](/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-limit)**(Vector & x, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) ub) |
-| void | **[Limit](/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-limit)**(Matrix & x, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) ub) |
-| void | **[Reassign](/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-reassign)**(Vector & some, const Vector & other, const std::string & parenthetical ="Reassign")<br>reassigns contents of some Vector in place  |
-| void | **[Reassign](/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-reassign)**(Matrix & some, const Matrix & other, const std::string & parenthetical ="Reassign")<br>reassigns contents of some Matrix in place  |
-| void | **[ForceSymPD](/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-forcesympd)**(Matrix & X)<br>forces matrix to be symmetric positive-definite  |
-| void | **[ForceSymMinEig](/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-forcesymmineig)**(Matrix & X, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) eig_min =0)<br>forces matrix to be symmetric and have a minimum eigenvalue  |
-| void | **[lq](/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-lq)**(Matrix & L, Matrix & Qt, const Matrix & X)<br>LQ decomposition.  |
-| Matrix | **[calcCov](/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-calccov)**(const Matrix & A, const Matrix & B)<br>Calculate covariance matrix.  |
-
-## Attributes
-
-|                | Name           |
-| -------------- | -------------- |
-| const std::size_t | **[kControlTypeDeltaU](/lds-ctrl-est/docs/api/modules/group__control__masks/#variable-kcontroltypedeltau)** <br>control designed to penalize change in input  |
-| const std::size_t | **[kControlTypeIntY](/lds-ctrl-est/docs/api/modules/group__control__masks/#variable-kcontroltypeinty)** <br>control using integral action  |
-| const std::size_t | **[kControlTypeAdaptM](/lds-ctrl-est/docs/api/modules/group__control__masks/#variable-kcontroltypeadaptm)** <br>adapt control setpoint with re-estimated disturbance `m` |
-| const [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[kInf](/lds-ctrl-est/docs/api/namespaces/namespacelds/#variable-kinf)** <br>Some useful numbers.  |
-| const [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[kPi](/lds-ctrl-est/docs/api/namespaces/namespacelds/#variable-kpi)**  |
-| const [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[kDefaultP0](/lds-ctrl-est/docs/api/modules/group__defaults/#variable-kdefaultp0)** <br>default state estimate covar  |
-| const [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[kDefaultQ0](/lds-ctrl-est/docs/api/modules/group__defaults/#variable-kdefaultq0)** <br>default process noise covar  |
-| const [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[kDefaultR0](/lds-ctrl-est/docs/api/modules/group__defaults/#variable-kdefaultr0)** <br>default output noise covar  |
-
-## Type Details
-
-### SSIDWt
-
-| Enumerator | Value | Description |
-| ---------- | ----- | ----------- |
-| kSSIDNone | | None.   |
-| kSSIDMOESP | | MOESP (AKA "robust method" in van Overschee 1996)   |
-| kSSIDCVA | | CVA "Canonical Variate Analysis".   |
-
-
-
-
-
-Weighting options for singular value decomposition performed during subspace identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/))
-
-Reference:
-
-van Overschee, de Moor. 1996. Subspace Identification for Linear Systems. 
-
-
-### MatrixListFreeDim
-
-| Enumerator | Value | Description |
-| ---------- | ----- | ----------- |
-| kMatFreeDimNone | | neither dim free to be hetero in mat list   |
-| kMatFreeDim1 | | allow 1st dim of mats in list to be hetero   |
-| kMatFreeDim2 | | allow 2nd dim of mats in list to be hetero   |
-
-
-
-
-
-### data_t
-
-```cpp
-using lds::data_t = typedef double;
-```
-
-
-
-Type of all data in library. If need 32b, change `double` to `float`. This could be potentially useful for large scale problems where there are memory constraints. 
-
-
-### Vector
-
-```cpp
-using lds::Vector = typedef arma::Col<data_t>;
-```
-
-
-
-### Matrix
-
-```cpp
-using lds::Matrix = typedef arma::Mat<data_t>;
-```
-
-
-
-### Cube
-
-```cpp
-using lds::Cube = typedef arma::Cube<data_t>;
-```
-
-
-
-### View
-
-```cpp
-using lds::View = typedef arma::subview<data_t>;
-```
-
-
-
-
-## Function Details
-
-### Limit
-
-```cpp
-inline void Limit(
-    std::vector< data_t > & x,
-    data_t lb,
-    data_t ub
-)
-```
-
-
-
-### Limit
-
-```cpp
-inline void Limit(
-    Vector & x,
-    data_t lb,
-    data_t ub
-)
-```
-
-
-
-### Limit
-
-```cpp
-inline void Limit(
-    Matrix & x,
-    data_t lb,
-    data_t ub
-)
-```
-
-
-
-### Reassign
-
-```cpp
-inline void Reassign(
-    Vector & some,
-    const Vector & other,
-    const std::string & parenthetical ="Reassign"
-)
-```
-
-
-
-**Parameters**:
-
-  * **some** some Vector 
-  * **other** other Vector 
-  * **parenthetical** optional description provided by caller to ease debugging 
-
-
-### Reassign
-
-```cpp
-inline void Reassign(
-    Matrix & some,
-    const Matrix & other,
-    const std::string & parenthetical ="Reassign"
-)
-```
-
-
-
-**Parameters**:
-
-  * **some** some Matrix 
-  * **other** other Matrix 
-  * **parenthetical** optional description provided by caller to ease debugging 
-
-
-### ForceSymPD
-
-```cpp
-void ForceSymPD(
-    Matrix & X
-)
-```
-
-
-
-**Parameters**:
-
-  * **X** mutated matrix 
-
-
-### ForceSymMinEig
-
-```cpp
-void ForceSymMinEig(
-    Matrix & X,
-    data_t eig_min =0
-)
-```
-
-
-
-**Parameters**:
-
-  * **X** mutated matrix 
-  * **eig_min** [optional] minimum eigen value 
-
-
-### lq
-
-```cpp
-void lq(
-    Matrix & L,
-    Matrix & Qt,
-    const Matrix & X
-)
-```
-
-
-
-**Parameters**:
-
-  * **L** lower triangle matrix 
-  * **Qt** orthonormal matrix (transposed cf QR decomp) 
-  * **X** matrix being decomposed 
-
-
-### calcCov
-
-```cpp
-Matrix calcCov(
-    const Matrix & A,
-    const Matrix & B
-)
-```
-
-
-
-**Parameters**:
-
-  * **A** some matrix 
-  * **B** some other matrix
-
-
-**Return**: covariance 
-
-
-## Attribute Details
-
-### kControlTypeDeltaU
-
-```cpp
-static const std::size_t kControlTypeDeltaU = 0x1;
-```
-
-
-
-Control was designed to penalize change in input (i.e., the state was augmented with input `u`) 
-
-
-### kControlTypeIntY
-
-```cpp
-static const std::size_t kControlTypeIntY = kControlTypeDeltaU << 1;
-```
-
-
-
-Control using integral action (i.e., the state was augmented with output `y` during design) 
-
-
-### kControlTypeAdaptM
-
-```cpp
-static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU << 2;
-```
-
-
-
-Adapt control setpoint adapted with re-estimated process disturbance `m`. 
-
-
-### kInf
-
-```cpp
-static const data_t kInf = std::numeric_limits<data_t>::infinity();
-```
-
-
-
-### kPi
-
-```cpp
-static const data_t kPi = arma::datum::pi;
-```
-
-
-
-### kDefaultP0
-
-```cpp
-static const data_t kDefaultP0 = 1e-6;
-```
-
-
-
-### kDefaultQ0
-
-```cpp
-static const data_t kDefaultQ0 = 1e-6;
-```
-
-
-
-### kDefaultR0
-
-```cpp
-static const data_t kDefaultR0 = 1e-2;
-```
-
-
-
-
-
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:03 Eastern Daylight Time
+---
+title: lds
+summary: Linear Dynamical Systems (LDS) namespace. 
+
+---
+
+# lds
+
+Linear Dynamical Systems (LDS) namespace. <br>
+
+## Namespaces
+
+| Name           |
+| -------------- |
+| **[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)** <br>Linear Dynamical Systems with Gaussian observations.  |
+| **[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)** <br>Linear Dynamical Systems with Poisson observations.  |
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/)**  |
+| class | **[lds::EM](/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/)**  |
+| class | **[lds::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/)** <br>LDS [Fit]() Type.  |
+| class | **[lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)**  |
+| class | **[lds::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/)** <br>[SwitchedController]() Type.  |
+| class | **[lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)** <br>Linear Dynamical [System]() Type.  |
+| class | **[lds::UniformMatrixList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/)**  |
+| class | **[lds::UniformSystemList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/)**  |
+| class | **[lds::UniformVectorList](/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/)**  |
+
+## Types
+
+|                | Name           |
+| -------------- | -------------- |
+| enum| **[SSIDWt](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt)** { kSSIDNone, kSSIDMOESP, kSSIDCVA}<br>weighting options for [SSID]() |
+| enum| **[MatrixListFreeDim](/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-matrixlistfreedim)** { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2} |
+| using double | **[data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t)**  |
+| using arma::Col< [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) > | **[Vector](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-vector)**  |
+| using arma::Mat< [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) > | **[Matrix](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-matrix)**  |
+| using arma::Cube< [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) > | **[Cube](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-cube)**  |
+| using arma::subview< [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) > | **[View](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-view)**  |
+
+## Functions
+
+|                | Name           |
+| -------------- | -------------- |
+| void | **[Limit](/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-limit)**(std::vector< [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) > & x, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) ub) |
+| void | **[Limit](/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-limit)**(Vector & x, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) ub) |
+| void | **[Limit](/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-limit)**(Matrix & x, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) lb, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) ub) |
+| void | **[Reassign](/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-reassign)**(Vector & some, const Vector & other, const std::string & parenthetical ="Reassign")<br>reassigns contents of some Vector in place  |
+| void | **[Reassign](/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-reassign)**(Matrix & some, const Matrix & other, const std::string & parenthetical ="Reassign")<br>reassigns contents of some Matrix in place  |
+| void | **[ForceSymPD](/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-forcesympd)**(Matrix & X)<br>forces matrix to be symmetric positive-definite  |
+| void | **[ForceSymMinEig](/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-forcesymmineig)**(Matrix & X, [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) eig_min =0)<br>forces matrix to be symmetric and have a minimum eigenvalue  |
+| void | **[lq](/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-lq)**(Matrix & L, Matrix & Qt, const Matrix & X)<br>LQ decomposition.  |
+| Matrix | **[calcCov](/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-calccov)**(const Matrix & A, const Matrix & B)<br>Calculate covariance matrix.  |
+
+## Attributes
+
+|                | Name           |
+| -------------- | -------------- |
+| const [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[kInf](/lds-ctrl-est/docs/api/namespaces/namespacelds/#variable-kinf)** <br>Some useful numbers.  |
+| const [data_t](/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t) | **[kPi](/lds-ctrl-est/docs/api/namespaces/namespacelds/#variable-kpi)**  |
+
+## Type Details
+
+### SSIDWt
+
+| Enumerator | Value | Description |
+| ---------- | ----- | ----------- |
+| kSSIDNone | | None.   |
+| kSSIDMOESP | | MOESP (AKA "robust method" in van Overschee 1996)   |
+| kSSIDCVA | | CVA "Canonical Variate Analysis".   |
+
+
+
+
+
+Weighting options for singular value decomposition performed during subspace identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/))
+
+Reference:
+
+van Overschee, de Moor. 1996. Subspace Identification for Linear Systems. 
+
+
+### MatrixListFreeDim
+
+| Enumerator | Value | Description |
+| ---------- | ----- | ----------- |
+| kMatFreeDimNone | | neither dim free to be hetero in mat list   |
+| kMatFreeDim1 | | allow 1st dim of mats in list to be hetero   |
+| kMatFreeDim2 | | allow 2nd dim of mats in list to be hetero   |
+
+
+
+
+
+### data_t
+
+```cpp
+using lds::data_t =  double;
+```
+
+
+
+Type of all data in library. If need 32b, change `double` to `float`. This could be potentially useful for large scale problems where there are memory constraints. 
+
+
+### Vector
+
+```cpp
+using lds::Vector =  arma::Col<data_t>;
+```
+
+
+
+### Matrix
+
+```cpp
+using lds::Matrix =  arma::Mat<data_t>;
+```
+
+
+
+### Cube
+
+```cpp
+using lds::Cube =  arma::Cube<data_t>;
+```
+
+
+
+### View
+
+```cpp
+using lds::View =  arma::subview<data_t>;
+```
+
+
+
+
+## Function Details
+
+### Limit
+
+```cpp
+inline void Limit(
+    std::vector< data_t > & x,
+    data_t lb,
+    data_t ub
+)
+```
+
+
+
+### Limit
+
+```cpp
+inline void Limit(
+    Vector & x,
+    data_t lb,
+    data_t ub
+)
+```
+
+
+
+### Limit
+
+```cpp
+inline void Limit(
+    Matrix & x,
+    data_t lb,
+    data_t ub
+)
+```
+
+
+
+### Reassign
+
+```cpp
+inline void Reassign(
+    Vector & some,
+    const Vector & other,
+    const std::string & parenthetical ="Reassign"
+)
+```
+
+
+
+**Parameters**:
+
+  * **some** some Vector 
+  * **other** other Vector 
+  * **parenthetical** optional description provided by caller to ease debugging 
+
+
+### Reassign
+
+```cpp
+inline void Reassign(
+    Matrix & some,
+    const Matrix & other,
+    const std::string & parenthetical ="Reassign"
+)
+```
+
+
+
+**Parameters**:
+
+  * **some** some Matrix 
+  * **other** other Matrix 
+  * **parenthetical** optional description provided by caller to ease debugging 
+
+
+### ForceSymPD
+
+```cpp
+void ForceSymPD(
+    Matrix & X
+)
+```
+
+
+
+**Parameters**:
+
+  * **X** mutated matrix 
+
+
+### ForceSymMinEig
+
+```cpp
+void ForceSymMinEig(
+    Matrix & X,
+    data_t eig_min =0
+)
+```
+
+
+
+**Parameters**:
+
+  * **X** mutated matrix 
+  * **eig_min** [optional] minimum eigen value 
+
+
+### lq
+
+```cpp
+void lq(
+    Matrix & L,
+    Matrix & Qt,
+    const Matrix & X
+)
+```
+
+
+
+**Parameters**:
+
+  * **L** lower triangle matrix 
+  * **Qt** orthonormal matrix (transposed cf QR decomp) 
+  * **X** matrix being decomposed 
+
+
+### calcCov
+
+```cpp
+Matrix calcCov(
+    const Matrix & A,
+    const Matrix & B
+)
+```
+
+
+
+**Parameters**:
+
+  * **A** some matrix 
+  * **B** some other matrix
+
+
+**Return**: covariance 
+
+
+## Attribute Details
+
+### kInf
+
+```cpp
+static const data_t kInf = std::numeric_limits<data_t>::infinity();
+```
+
+
+
+### kPi
+
+```cpp
+static const data_t kPi = arma::datum::pi;
+```
+
+
+
+
+
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:26 EST
diff --git a/misc/docs-hugo/content/docs/api/Namespaces/namespacelds_1_1gaussian.md b/misc/docs-hugo/content/docs/api/Namespaces/namespacelds_1_1gaussian.md
index 7f4734fa..e4dfbbe7 100644
--- a/misc/docs-hugo/content/docs/api/Namespaces/namespacelds_1_1gaussian.md
+++ b/misc/docs-hugo/content/docs/api/Namespaces/namespacelds_1_1gaussian.md
@@ -1,30 +1,30 @@
----
-title: lds::gaussian
-summary: Linear Dynamical Systems with Gaussian observations. 
-
----
-
-# lds::gaussian
-
-Linear Dynamical Systems with Gaussian observations. <br>
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::gaussian::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1controller/)** <br>Gaussian-observation [Controller]() Type.  |
-| class | **[lds::gaussian::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/)** <br>GLDS [Fit]() Type.  |
-| class | **[lds::gaussian::FitEM](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitem/)** <br>GLDS E-M [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fit/) Type.  |
-| class | **[lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1fitssid/)** <br>Subspace Identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)) for GLDS.  |
-| class | **[lds::gaussian::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1switchedcontroller/)** <br>Gaussian-observation [SwitchedController]() Type.  |
-| class | **[lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1system/)** <br>Gaussian LDS Type.  |
-
-
-
-
-
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
+---
+title: lds::gaussian
+summary: Linear Dynamical Systems with Gaussian observations. 
+
+---
+
+# lds::gaussian
+
+Linear Dynamical Systems with Gaussian observations. <br>
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::gaussian::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/)** <br>Gaussian-observation [Controller]() Type.  |
+| class | **[lds::gaussian::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/)** <br>GLDS [Fit]() Type.  |
+| class | **[lds::gaussian::FitEM](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/)** <br>GLDS E-M [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/) Type.  |
+| class | **[lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/)** <br>Subspace Identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)) for GLDS.  |
+| class | **[lds::gaussian::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/)** <br>Gaussian-observation [SwitchedController]() Type.  |
+| class | **[lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/)** <br>Gaussian LDS Type.  |
+
+
+
+
+
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Namespaces/namespacelds_1_1poisson.md b/misc/docs-hugo/content/docs/api/Namespaces/namespacelds_1_1poisson.md
index d3ee26e8..7620c8d7 100644
--- a/misc/docs-hugo/content/docs/api/Namespaces/namespacelds_1_1poisson.md
+++ b/misc/docs-hugo/content/docs/api/Namespaces/namespacelds_1_1poisson.md
@@ -1,56 +1,56 @@
----
-title: lds::poisson
-summary: Linear Dynamical Systems with Poisson observations. 
-
----
-
-# lds::poisson
-
-Linear Dynamical Systems with Poisson observations. <br>
-
-## Classes
-
-|                | Name           |
-| -------------- | -------------- |
-| class | **[lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1controller/)** <br>PLDS [Controller]() Type.  |
-| class | **[lds::poisson::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/)** <br>PLDS [Fit]() Type.  |
-| class | **[lds::poisson::FitEM](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitem/)** <br>PLDS E-M [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fit/) Type.  |
-| class | **[lds::poisson::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1fitssid/)** <br>Subspace Identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1ssid/)) for PLDS.  |
-| class | **[lds::poisson::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1switchedcontroller/)** <br>Poisson-observation [SwitchedController]() Type.  |
-| class | **[lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1system/)** <br>Poisson [System]() type.  |
-
-## Attributes
-
-|                | Name           |
-| -------------- | -------------- |
-| std::random_device | **[rd](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/#variable-rd)** <br>random device for simulating poisson data  |
-| std::mt19937 | **[rng](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/#variable-rng)** <br>random number generator for simulating poisson data  |
-
-
-
-## Attribute Details
-
-### rd
-
-```cpp
-static std::random_device rd;
-```
-
-
-
-### rng
-
-```cpp
-static std::mt19937 rng = std::mt19937(
-    rd());
-```
-
-
-
-
-
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time
+---
+title: lds::poisson
+summary: Linear Dynamical Systems with Poisson observations. 
+
+---
+
+# lds::poisson
+
+Linear Dynamical Systems with Poisson observations. <br>
+
+## Classes
+
+|                | Name           |
+| -------------- | -------------- |
+| class | **[lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/)** <br>PLDS [Controller]() Type.  |
+| class | **[lds::poisson::Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/)** <br>PLDS [Fit]() Type.  |
+| class | **[lds::poisson::FitEM](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/)** <br>PLDS E-M [Fit](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/) Type.  |
+| class | **[lds::poisson::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/)** <br>Subspace Identification ([SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)) for PLDS.  |
+| class | **[lds::poisson::SwitchedController](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/)** <br>Poisson-observation [SwitchedController]() Type.  |
+| class | **[lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)** <br>Poisson [System]() type.  |
+
+## Attributes
+
+|                | Name           |
+| -------------- | -------------- |
+| std::random_device | **[rd](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/#variable-rd)** <br>random device for simulating poisson data  |
+| std::mt19937 | **[rng](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/#variable-rng)** <br>random number generator for simulating poisson data  |
+
+
+
+## Attribute Details
+
+### rd
+
+```cpp
+static std::random_device rd;
+```
+
+
+
+### rng
+
+```cpp
+static std::mt19937 rng = std::mt19937(
+    rd());
+```
+
+
+
+
+
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Namespaces/namespacestd.md b/misc/docs-hugo/content/docs/api/Namespaces/namespacestd.md
new file mode 100644
index 00000000..e7e779a1
--- /dev/null
+++ b/misc/docs-hugo/content/docs/api/Namespaces/namespacestd.md
@@ -0,0 +1,18 @@
+---
+title: std
+
+---
+
+# std
+
+<br>
+
+
+
+
+
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/api/Pages/_index.md b/misc/docs-hugo/content/docs/api/Pages/_index.md
index 75a00e31..0c91eafb 100644
--- a/misc/docs-hugo/content/docs/api/Pages/_index.md
+++ b/misc/docs-hugo/content/docs/api/Pages/_index.md
@@ -1,16 +1,16 @@
----
-title: Pages
-
----
-
-# Pages
-
-
-
-
-
-
-
--------------------------------
-
-Updated on 19 May 2022 at 17:16:06 Eastern Daylight Time
+---
+title: Pages
+
+---
+
+# Pages
+
+
+
+
+
+
+
+-------------------------------
+
+Updated on  5 March 2025 at 21:41:27 EST
diff --git a/misc/docs-hugo/content/docs/getting-started/getting-started.md b/misc/docs-hugo/content/docs/getting-started/getting-started.md
index c68271df..fe624740 100644
--- a/misc/docs-hugo/content/docs/getting-started/getting-started.md
+++ b/misc/docs-hugo/content/docs/getting-started/getting-started.md
@@ -26,16 +26,16 @@ Homebrew is "The Missing Package Manager for macOS" which will make installing l
 /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
 ```
 
-You can then use it to install CMake and gfortran:
+You can then use it to install CMake, gfortran, and pkg-config:
 ```shell
-brew install cmake gfortran
+brew install cmake gfortran pkg-config
 ```
 
 ## Linux Pre-requisites
 
 You'll need Git, CMake, GCC, gfortran, etc.
 ```shell
-sudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential
+sudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential ninja-build
 ```
 
 ## Windows Installation
diff --git a/misc/docs-hugo/content/docs/terminology/control-estimation.md b/misc/docs-hugo/content/docs/terminology/control-estimation.md
index 6f951be9..56a9b8eb 100644
--- a/misc/docs-hugo/content/docs/terminology/control-estimation.md
+++ b/misc/docs-hugo/content/docs/terminology/control-estimation.md
@@ -63,3 +63,8 @@ Integral action and the {{<katex>}} \Delta \mathbf{u} {{</katex>}} control law c
 ## Calculating reference state-control from output
 In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce {{<katex>}} \mathbf{x}^{\rm ref} {{</katex>}} and {{<katex>}} \mathbf{u}^{\rm ref} {{</katex>}}. Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (`lds::Controller<System>::ControlOutputReference`). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.
 
+## Model Predictive Control
+Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.
+
+In the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time. 
+
diff --git a/misc/docs-hugo/content/docs/terminology/model.md b/misc/docs-hugo/content/docs/terminology/model.md
index fc5c7e2d..18d4d537 100644
--- a/misc/docs-hugo/content/docs/terminology/model.md
+++ b/misc/docs-hugo/content/docs/terminology/model.md
@@ -63,3 +63,9 @@ z_{t}^i \sim \rm{Poisson} \left(y_{t}^i \right)
 
     c           : i^th row of output matrix (C)
     d           : output bias
+
+
+## Model Predictive Control (MPC)
+Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.
+
+In the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.
diff --git a/misc/docs-hugo/content/issues-contributing.md b/misc/docs-hugo/content/issues-contributing.md
index 1930e065..3c0b4b2e 100644
--- a/misc/docs-hugo/content/issues-contributing.md
+++ b/misc/docs-hugo/content/issues-contributing.md
@@ -3,3 +3,12 @@ If you encounter bugs when using this library or have specific feature requests
 
 # Contributing
 We welcome any community contributions to this project. Please fork the repository and if possible use `clang-format` and `clang-tidy` to conform to the coding format/style of this repository.
+
+When editing any documentation/guides, please use the markdown docs in `misc/docs-hugo` instead of directly editing the HTML docs. 
+This may require having `hugo`, `graphviz` and `doxygen` installed through `brew`, as well as `doxybook2` installed through a git clone.
+
+Clone the `doxybook2` repository online and place the executable in your `usr/local/bin` folder or another `bin` folder. 
+Run `sudo chmod +x /usr/local/bin/doxybook2` to give doxybook2 permissions. Run `doxybook2 --help` to ensure that this works properly. If permission is still denied, navigate to System Preferences > Security & Privacy > General.
+You should see a message at the bottom that says: "doxybook2 was blocked from use because it is not from an identified developer." Click Allow Anyway.
+
+Updated docs can be built by running `cd scripts` and `./update-docs.sh`. After a successful build, navigate to `/misc/docs-hugo/` and run `hugo server` to create a local host of the website. Access the website using local host to ensure the correct results.
diff --git a/misc/docs-hugo/layouts/partials/docs/menu-filetree.html b/misc/docs-hugo/layouts/partials/docs/menu-filetree.html
new file mode 100644
index 00000000..f8290df3
--- /dev/null
+++ b/misc/docs-hugo/layouts/partials/docs/menu-filetree.html
@@ -0,0 +1,45 @@
+{{ $bookSection := default "docs" .Site.Params.BookSection  }}
+{{ if eq $bookSection "*" }}
+  {{ $bookSection = "/" }}{{/* Backward compatibility */}}
+{{ end }}
+
+{{ with .Site.GetPage $bookSection }}
+  {{ template "book-section-children" (dict "Section" . "CurrentPage" $) }}
+{{ end }}
+
+{{ define "book-section-children" }}{{/* (dict "Section" .Section "CurrentPage" .CurrentPage) */}}
+  <ul>
+    {{ range (where .Section.Pages "Params.bookhidden" "ne" true) }}
+      {{ if .IsSection }}
+        <li {{- if .Params.BookFlatSection }} class="book-section-flat" {{ end -}}>
+          {{ template "book-page-link" (dict "Page" . "CurrentPage" $.CurrentPage) }}
+          {{ template "book-section-children" (dict "Section" . "CurrentPage" $.CurrentPage) }}
+        </li>
+      {{ else if and .IsPage .Content }}
+        <li>
+          {{ template "book-page-link" (dict "Page" . "CurrentPage" $.CurrentPage) }}
+        </li>
+      {{ end }}
+    {{ end }}
+  </ul>
+{{ end }}
+
+{{ define "book-page-link" }}{{/* (dict "Page" .Page "CurrentPage" .CurrentPage) */}}
+  {{ $current := eq .CurrentPage .Page }}
+  {{ $ancestor := .Page.IsAncestor .CurrentPage }}
+
+  {{ if .Page.Params.bookCollapseSection }}
+    <input type="checkbox" id="section-{{ md5 .Page }}" class="toggle" {{ if or $current $ancestor }}checked{{ end }} />
+    <label for="section-{{ md5 .Page }}" class="flex justify-between">
+      <a {{ if .Page.Content }}href="{{ .Page.Permalink }}"{{ else }}role="button"{{ end }} class="{{ if $current }}active{{ end }}">
+        {{- partial "docs/title" .Page -}}
+      </a>
+    </label>
+  {{ else if .Page.Content }}
+    <a href="{{ .Page.Permalink }}" class="{{ if $current }} active{{ end }}">
+      {{- partial "docs/title" .Page -}}
+    </a>
+  {{ else }}
+    <span>{{- partial "docs/title" .Page -}}</span>
+  {{ end }}
+{{ end }}
diff --git a/docs/book.min.57f7f660871517a5bfcfb5e2de853d806f7e34d94ebd5f3f3bad62e9ddbae209.css b/misc/docs-hugo/resources/_gen/assets/book.scss_b807c86e8030af4cdc30edccea379f5f.content
similarity index 100%
rename from docs/book.min.57f7f660871517a5bfcfb5e2de853d806f7e34d94ebd5f3f3bad62e9ddbae209.css
rename to misc/docs-hugo/resources/_gen/assets/book.scss_b807c86e8030af4cdc30edccea379f5f.content
diff --git a/misc/docs-hugo/resources/_gen/assets/book.scss_b807c86e8030af4cdc30edccea379f5f.json b/misc/docs-hugo/resources/_gen/assets/book.scss_b807c86e8030af4cdc30edccea379f5f.json
new file mode 100644
index 00000000..1790550f
--- /dev/null
+++ b/misc/docs-hugo/resources/_gen/assets/book.scss_b807c86e8030af4cdc30edccea379f5f.json
@@ -0,0 +1 @@
+{"Target":"book.min.57f7f660871517a5bfcfb5e2de853d806f7e34d94ebd5f3f3bad62e9ddbae209.css","MediaType":"text/css","Data":{"Integrity":"sha256-V/f2YIcVF6W/z7Xi3oU9gG9+NNlOvV8/O61i6d264gk="}}
\ No newline at end of file
diff --git a/scripts/convert-doxygen-to-md.sh b/scripts/convert-doxygen-to-md.sh
index a45a9b97..88d3308a 100755
--- a/scripts/convert-doxygen-to-md.sh
+++ b/scripts/convert-doxygen-to-md.sh
@@ -1,5 +1,8 @@
 #!/bin/sh
 
+# Create directories if they don't exist
+mkdir -p ../misc/docs-hugo/content/docs/api
+
 # remove existing api docs
 rm -rf ../misc/docs-hugo/content/docs/api/*
 
diff --git a/scripts/update-docs.sh b/scripts/update-docs.sh
index 5c65947a..05d240d0 100755
--- a/scripts/update-docs.sh
+++ b/scripts/update-docs.sh
@@ -1,11 +1,11 @@
 #!/bin/sh
 
 # convert doxygen xml to markdown for usage with hugo static site gen
-./convert-doxygen-to-md.sh
+./convert-doxygen-to-md.sh && cd ..
 
 # generate site
-cd ../misc/docs-hugo && hugo && cd ../../scripts
+cd misc/docs-hugo && hugo && cd ../..
 
 # remove existing docs/ and add new in its place
-rm -rf ../docs/* && cp -r ../misc/docs-hugo/public/* ../docs/
+rm -rf ../docs/* && cp -r misc/docs-hugo/public/* ../docs/
 

From 9f8e030ee979a124b1f7686ae084e0bc3fbe09ff Mon Sep 17 00:00:00 2001
From: Muhammad Yousuf <muhammad.y.2305@gmail.com>
Date: Mon, 31 Mar 2025 16:05:41 -0400
Subject: [PATCH 3/4] Fixed docs folder

---
 scripts/update-docs.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/update-docs.sh b/scripts/update-docs.sh
index 05d240d0..b556044f 100755
--- a/scripts/update-docs.sh
+++ b/scripts/update-docs.sh
@@ -7,5 +7,5 @@
 cd misc/docs-hugo && hugo && cd ../..
 
 # remove existing docs/ and add new in its place
-rm -rf ../docs/* && cp -r misc/docs-hugo/public/* ../docs/
+rm -rf docs/* && cp -r misc/docs-hugo/public/* docs/
 

From d5102f415754287c684913f72a7e6a4aca3181e5 Mon Sep 17 00:00:00 2001
From: Muhammad Yousuf <muhammad.y.2305@gmail.com>
Date: Mon, 31 Mar 2025 16:07:10 -0400
Subject: [PATCH 4/4] Fixing the docs folder

---
 docs/404.html                                 |   51 +
 docs/acknowledgements/index.html              |  244 +++
 ...853d806f7e34d94ebd5f3f3bad62e9ddbae209.css |    1 +
 docs/categories/index.html                    |  269 +++
 docs/categories/index.xml                     |   11 +
 docs/categories/page/1/index.html             |   10 +
 ...lasslds_1_1_controller__inherit__graph.png |  Bin 0 -> 28819 bytes
 docs/classlds_1_1_system__inherit__graph.png  |  Bin 0 -> 7184 bytes
 .../classlds_1_1_controller/index.html        | 1482 +++++++++++++++++
 .../api/classes/classlds_1_1_e_m/index.html   | 1229 ++++++++++++++
 .../api/classes/classlds_1_1_fit/index.html   |  907 ++++++++++
 .../classes/classlds_1_1_s_s_i_d/index.html   |  896 ++++++++++
 .../index.html                                |  846 ++++++++++
 .../classes/classlds_1_1_system/index.html    | 1324 +++++++++++++++
 .../index.html                                |  647 +++++++
 .../index.html                                |  617 +++++++
 .../index.html                                |  600 +++++++
 .../index.html                                |  560 +++++++
 .../classlds_1_1gaussian_1_1_fit/index.html   |  553 ++++++
 .../index.html                                |  465 ++++++
 .../index.html                                |  392 +++++
 .../index.html                                |  658 ++++++++
 .../index.html                                |  855 ++++++++++
 .../index.html                                |  560 +++++++
 .../classlds_1_1poisson_1_1_fit/index.html    |  553 ++++++
 .../index.html                                |  465 ++++++
 .../index.html                                |  392 +++++
 .../index.html                                |  658 ++++++++
 .../classlds_1_1poisson_1_1_system/index.html |  733 ++++++++
 docs/docs/api/classes/index.html              |  398 +++++
 docs/docs/api/classes/index.xml               |  158 ++
 .../eg_glds_ctrl_8cpp-example/index.html      |  468 ++++++
 .../index.html                                |  471 ++++++
 .../eg_plds_ctrl_8cpp-example/index.html      |  444 +++++
 .../eg_plds_est_8cpp-example/index.html       |  393 +++++
 .../index.html                                |  441 +++++
 docs/docs/api/examples/index.html             |  286 ++++
 docs/docs/api/examples/index.xml              |   46 +
 .../index.html                                |  437 +++++
 .../index.html                                |  311 ++++
 .../index.html                                |  311 ++++
 .../index.html                                |  287 ++++
 .../api/files/eg__glds__ctrl_8cpp/index.html  |  679 ++++++++
 .../eg__glds__du__plds__ctrl_8cpp/index.html  |  691 ++++++++
 .../api/files/eg__plds__ctrl_8cpp/index.html  |  649 ++++++++
 .../api/files/eg__plds__est_8cpp/index.html   |  617 +++++++
 .../eg__plds__switched__ctrl_8cpp/index.html  |  880 ++++++++++
 docs/docs/api/files/index.html                |  438 +++++
 docs/docs/api/files/index.xml                 |  291 ++++
 docs/docs/api/files/lds_8cpp/index.html       |  405 +++++
 docs/docs/api/files/lds_8h/index.html         |  427 +++++
 docs/docs/api/files/lds__ctrl_8h/index.html   |  808 +++++++++
 docs/docs/api/files/lds__fit_8h/index.html    |  408 +++++
 .../docs/api/files/lds__fit__em_8h/index.html |  891 ++++++++++
 .../api/files/lds__fit__ssid_8h/index.html    |  837 ++++++++++
 .../api/files/lds__gaussian_8h/index.html     |  316 ++++
 .../files/lds__gaussian__ctrl_8h/index.html   |  375 +++++
 .../files/lds__gaussian__fit_8h/index.html    |  353 ++++
 .../lds__gaussian__fit__em_8h/index.html      |  349 ++++
 .../lds__gaussian__fit__ssid_8h/index.html    |  349 ++++
 .../files/lds__gaussian__sctrl_8h/index.html  |  373 +++++
 .../files/lds__gaussian__sys_8cpp/index.html  |  329 ++++
 .../files/lds__gaussian__sys_8h/index.html    |  387 +++++
 .../docs/api/files/lds__poisson_8h/index.html |  320 ++++
 .../files/lds__poisson__ctrl_8h/index.html    |  380 +++++
 .../api/files/lds__poisson__fit_8h/index.html |  361 ++++
 .../files/lds__poisson__fit__em_8h/index.html |  354 ++++
 .../lds__poisson__fit__ssid_8h/index.html     |  348 ++++
 .../files/lds__poisson__sctrl_8h/index.html   |  376 +++++
 .../files/lds__poisson__sys_8cpp/index.html   |  325 ++++
 .../api/files/lds__poisson__sys_8h/index.html |  367 ++++
 docs/docs/api/files/lds__sctrl_8h/index.html  |  532 ++++++
 docs/docs/api/files/lds__sys_8cpp/index.html  |  415 +++++
 docs/docs/api/files/lds__sys_8h/index.html    |  448 +++++
 .../files/lds__uniform__mats_8h/index.html    |  600 +++++++
 .../files/lds__uniform__systems_8h/index.html |  525 ++++++
 .../files/lds__uniform__vecs_8cpp/index.html  |  350 ++++
 .../files/lds__uniform__vecs_8h/index.html    |  449 +++++
 .../docs/api/files/mex__c__util_8h/index.html |  375 +++++
 .../api/files/mex__cpp__util_8h/index.html    |  396 +++++
 docs/docs/api/index.html                      |  404 +++++
 docs/docs/api/index.xml                       |   11 +
 .../modules/group__control__masks/index.html  |  321 ++++
 .../api/modules/group__defaults/index.html    |  376 +++++
 docs/docs/api/modules/index.html              |  265 +++
 docs/docs/api/modules/index.xml               |   25 +
 docs/docs/api/namespaces/index.html           |  286 ++++
 docs/docs/api/namespaces/index.xml            |   53 +
 .../namespaces/namespacearmamexc/index.html   |  373 +++++
 .../namespaces/namespacearmamexcpp/index.html |  452 +++++
 .../api/namespaces/namespacelds/index.html    |  723 ++++++++
 .../namespacelds_1_1gaussian/index.html       |  289 ++++
 .../namespacelds_1_1poisson/index.html        |  340 ++++
 .../api/namespaces/namespacestd/index.html    |  249 +++
 docs/docs/api/pages/index.html                |  247 +++
 docs/docs/api/pages/index.xml                 |   11 +
 .../getting-started/index.html                |  405 +++++
 docs/docs/getting-started/windows/index.html  |  320 ++++
 docs/docs/index.html                          |  243 +++
 docs/docs/index.xml                           |   39 +
 .../terminology/control-estimation/index.html |  368 ++++
 docs/docs/terminology/model/index.html        |  361 ++++
 .../docs/tutorials/eg_glds_control/index.html |  439 +++++
 .../eg_plds_state_estimation/index.html       |  368 ++++
 .../eg_switched_plds_control/index.html       |  412 +++++
 docs/docs/tutorials/index.html                |  243 +++
 docs/docs/tutorials/index.xml                 |   32 +
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 docs/eg_plds_ctrl_output.png                  |  Bin 0 -> 217595 bytes
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 docs/eg_plds_switched_ctrl_output.png         |  Bin 0 -> 156078 bytes
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 docs/favicon.png                              |  Bin 0 -> 109 bytes
 docs/favicon.svg                              |    1 +
 docs/flexsearch.min.js                        |   42 +
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 docs/index.html                               |  292 ++++
 docs/index.xml                                |  592 +++++++
 docs/issues-contributing/index.html           |  263 +++
 docs/katex/auto-render.min.js                 |    1 +
 docs/katex/fonts/KaTeX_AMS-Regular.ttf        |  Bin 0 -> 70972 bytes
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diff --git a/docs/404.html b/docs/404.html
new file mode 100644
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+<!DOCTYPE html>
+<html lang="en">
+
+  <head>
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diff --git a/docs/acknowledgements/index.html b/docs/acknowledgements/index.html
new file mode 100644
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--- /dev/null
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+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="
+  Acknowledgements
+  #
+
+Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/acknowledgements/">
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+  <meta property="og:description" content="Acknowledgements # Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.">
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+  <input type="checkbox" class="hidden toggle" id="toc-control" />
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+    <aside class="book-menu">
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+        
+  <nav>
+<h2 class="book-brand">
+  <a href="/lds-ctrl-est/"><img src="/lds-ctrl-est/ldsctrlest-logo.png" alt="Logo" /><span>LDS C&amp;E</span>
+  </a>
+</h2>
+
+
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+  <div class="book-search-spinner hidden"></div>
+  <ul id="book-search-results"></ul>
+</div>
+
+
+
+
+
+
+
+
+
+
+
+  <ul>
+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
+<li><a href="/lds-ctrl-est/docs/api/classes/">Classes</a></li>
+<li><a href="/lds-ctrl-est/docs/api/modules/">Modules</a></li>
+<li><a href="/lds-ctrl-est/docs/api/files/">Files</a></li>
+<li><a href="/lds-ctrl-est/docs/api/examples/">Examples</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/issues-contributing/"><strong>Reporting Issues &amp; Contributing</strong></a></li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"class=active><strong>Acknowledgements</strong></a></li>
+</ul>
+<br />
+
+
+
+
+
+
+  
+<ul>
+  
+  <li>
+    <a href="https://github.com/stanley-rozell/lds-ctrl-est" target="_blank" rel="noopener">
+        Github
+      </a>
+  </li>
+  
+</ul>
+
+
+
+
+
+
+</nav>
+
+
+
+
+  <script>(function(){var e=document.querySelector("aside .book-menu-content");addEventListener("beforeunload",function(){localStorage.setItem("menu.scrollTop",e.scrollTop)}),e.scrollTop=localStorage.getItem("menu.scrollTop")})()</script>
+
+
+ 
+      </div>
+    </aside>
+
+    <div class="book-page">
+      <header class="book-header">
+        
+  <div class="flex align-center justify-between">
+  <label for="menu-control">
+    <img src="/lds-ctrl-est/svg/menu.svg" class="book-icon" alt="Menu" />
+  </label>
+
+  <strong>Acknowledgements</strong>
+
+  <label for="toc-control">
+    
+    <img src="/lds-ctrl-est/svg/toc.svg" class="book-icon" alt="Table of Contents" />
+    
+  </label>
+</div>
+
+
+  
+  <aside class="hidden clearfix">
+    
+  
+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#acknowledgements">Acknowledgements</a></li>
+  </ul>
+</nav>
+
+
+
+  </aside>
+  
+ 
+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="acknowledgements">
+  Acknowledgements
+  <a class="anchor" href="#acknowledgements">#</a>
+</h1>
+<p>Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.</p>
+</article>
+ 
+      
+
+      <footer class="book-footer">
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+      <div class="book-toc-content">
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+  <ul>
+    <li><a href="#acknowledgements">Acknowledgements</a></li>
+  </ul>
+</nav>
+
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+      </div>
+    </aside>
+    
+  </main>
+
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+</body>
+</html>
+
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diff --git a/docs/categories/index.html b/docs/categories/index.html
new file mode 100644
index 00000000..742e621f
--- /dev/null
+++ b/docs/categories/index.html
@@ -0,0 +1,269 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/categories/">
+  <meta property="og:site_name" content="LDS C&E">
+  <meta property="og:title" content="Categories">
+  <meta property="og:locale" content="en">
+  <meta property="og:type" content="website">
+<title>Categories | LDS C&amp;E</title>
+<link rel="manifest" href="/lds-ctrl-est/manifest.json">
+<link rel="icon" href="/lds-ctrl-est/favicon.png" type="image/x-icon">
+<link rel="stylesheet" href="/lds-ctrl-est/book.min.57f7f660871517a5bfcfb5e2de853d806f7e34d94ebd5f3f3bad62e9ddbae209.css" integrity="sha256-V/f2YIcVF6W/z7Xi3oU9gG9&#43;NNlOvV8/O61i6d264gk=" crossorigin="anonymous">
+  <script defer src="/lds-ctrl-est/flexsearch.min.js"></script>
+  <script defer src="/lds-ctrl-est/en.search.min.4f6b617641b714cbf70266302fdeb12a9567eb52ebfb017533f9c9af1b260392.js" integrity="sha256-T2thdkG3FMv3AmYwL96xKpVn61Lr&#43;wF1M/nJrxsmA5I=" crossorigin="anonymous"></script>
+<link rel="alternate" type="application/rss+xml" href="https://stanley-rozell.github.io/lds-ctrl-est/categories/index.xml" title="LDS C&E" />
+<!--
+Made with Book Theme
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+  
+</head>
+<body dir="ltr">
+  <input type="checkbox" class="hidden toggle" id="menu-control" />
+  <input type="checkbox" class="hidden toggle" id="toc-control" />
+  <main class="container flex">
+    <aside class="book-menu">
+      <div class="book-menu-content">
+        
+  <nav>
+<h2 class="book-brand">
+  <a href="/lds-ctrl-est/"><img src="/lds-ctrl-est/ldsctrlest-logo.png" alt="Logo" /><span>LDS C&amp;E</span>
+  </a>
+</h2>
+
+
+<div class="book-search">
+  <input type="text" id="book-search-input" placeholder="Search" aria-label="Search" maxlength="64" data-hotkeys="s/" />
+  <div class="book-search-spinner hidden"></div>
+  <ul id="book-search-results"></ul>
+</div>
+
+
+
+
+
+
+
+
+
+
+
+  <ul>
+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
+<li><a href="/lds-ctrl-est/docs/api/classes/">Classes</a></li>
+<li><a href="/lds-ctrl-est/docs/api/modules/">Modules</a></li>
+<li><a href="/lds-ctrl-est/docs/api/files/">Files</a></li>
+<li><a href="/lds-ctrl-est/docs/api/examples/">Examples</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/issues-contributing/"><strong>Reporting Issues &amp; Contributing</strong></a></li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
+</ul>
+<br />
+
+
+
+
+
+
+  
+<ul>
+  
+  <li>
+    <a href="https://github.com/stanley-rozell/lds-ctrl-est" target="_blank" rel="noopener">
+        Github
+      </a>
+  </li>
+  
+</ul>
+
+
+
+
+
+
+</nav>
+
+
+
+
+  <script>(function(){var e=document.querySelector("aside .book-menu-content");addEventListener("beforeunload",function(){localStorage.setItem("menu.scrollTop",e.scrollTop)}),e.scrollTop=localStorage.getItem("menu.scrollTop")})()</script>
+
+
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+
+    <div class="book-page">
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+        
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+  <label for="menu-control">
+    <img src="/lds-ctrl-est/svg/menu.svg" class="book-icon" alt="Menu" />
+  </label>
+
+  <strong>Categories</strong>
+
+  <label for="toc-control">
+    
+    <img src="/lds-ctrl-est/svg/toc.svg" class="book-icon" alt="Table of Contents" />
+    
+  </label>
+</div>
+
+
+  
+  <aside class="hidden clearfix">
+    
+  <nav>
+  <ul>
+  
+    
+    <li class="book-section-flat">
+      <strong>Categories</strong>
+      <ul>
+      
+      </ul>
+    </li>
+    
+  
+    
+    <li class="book-section-flat">
+      <strong>Tags</strong>
+      <ul>
+      
+      </ul>
+    </li>
+    
+  
+  </ul>
+</nav>
+
+
+  </aside>
+  
+ 
+      </header>
+
+      
+      
+  
+
+  
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+      
+
+      <footer class="book-footer">
+        
+  <div class="flex flex-wrap justify-between">
+
+
+
+
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+</div>
+
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+
+  <script>(function(){function e(e){const t=window.getSelection(),n=document.createRange();n.selectNodeContents(e),t.removeAllRanges(),t.addRange(n)}document.querySelectorAll("pre code").forEach(t=>{t.addEventListener("click",function(){e(t.parentElement),navigator.clipboard&&navigator.clipboard.writeText(t.parentElement.textContent)})})})()</script>
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+      <div class="book-toc-content">
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+  <nav>
+  <ul>
+  
+    
+    <li class="book-section-flat">
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+      <ul>
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+    
+  
+    
+    <li class="book-section-flat">
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+    
+  </main>
+
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+</body>
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+
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+
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+    <title>Categories on LDS C&amp;E</title>
+    <link>https://stanley-rozell.github.io/lds-ctrl-est/categories/</link>
+    <description>Recent content in Categories on LDS C&amp;E</description>
+    <generator>Hugo</generator>
+    <language>en</language>
+    <atom:link href="https://stanley-rozell.github.io/lds-ctrl-est/categories/index.xml" rel="self" type="application/rss+xml" />
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+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <title>https://stanley-rozell.github.io/lds-ctrl-est/categories/</title>
+    <link rel="canonical" href="https://stanley-rozell.github.io/lds-ctrl-est/categories/">
+    <meta name="robots" content="noindex">
+    <meta charset="utf-8">
+    <meta http-equiv="refresh" content="0; url=https://stanley-rozell.github.io/lds-ctrl-est/categories/">
+  </head>
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diff --git a/docs/docs/api/classes/classlds_1_1_controller/index.html b/docs/docs/api/classes/classlds_1_1_controller/index.html
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+++ b/docs/docs/api/classes/classlds_1_1_controller/index.html
@@ -0,0 +1,1482 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="
+  lds::Controller
+  #
+
+More&hellip;
+Inherited by lds::SwitchedController&lt; System &gt;, lds::gaussian::Controller, lds::poisson::Controller
+
+  Public Functions
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          
+          Controller() =defaultConstructs a new Controller.
+      
+      
+          
+          Controller(const System &amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)Constructs a new Controller.
+      
+      
+          
+          Controller(System &amp;&amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)Constructs a new Controller by moving the system object.
+      
+      
+          const Vector &amp;
+          Control(const Vector &amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)updates control signal (single-step)
+      
+      
+          const Vector &amp;
+          ControlOutputReference(const Vector &amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)updates control signal, given previously-set (single-step)
+      
+      
+          const System &amp;
+          sys() const
+      
+      
+          const Matrix &amp;
+          Kc() constGet state feedback controller gain.
+      
+      
+          const Matrix &amp;
+          Kc_inty() constGet integral controller gain.
+      
+      
+          const Matrix &amp;
+          Kc_u() constGet input feedback controller gain.
+      
+      
+          const Vector &amp;
+          g_design() constGet input gain used in controller design.
+      
+      
+          const Vector &amp;
+          u_ref() constGet reference input.
+      
+      
+          const Vector &amp;
+          x_ref() constGet reference state.
+      
+      
+          const Vector &amp;
+          y_ref() constGet reference output.
+      
+      
+          size_t
+          control_type() constGet controller type.
+      
+      
+          data_t
+          tau_awu() constGet time constant of anti-integral-windup.
+      
+      
+          data_t
+          u_lb() constGet control lower bound.
+      
+      
+          data_t
+          u_ub() constGet control upper bound.
+      
+      
+          void
+          set_sys(const System &amp; sys)Set system.
+      
+      
+          void
+          set_g_design(const Vector &amp; g_design)Set input gain used in controller design (g_design)
+      
+      
+          void
+          set_u_ref(const Vector &amp; u_ref)Set reference input (u_ref)
+      
+      
+          void
+          set_x_ref(const Vector &amp; x_ref)Set reference state (x_ref)
+      
+      
+          virtual void
+          set_y_ref(const Vector &amp; y_ref)Set reference output (y_ref)
+      
+      
+          void
+          set_Kc(const Matrix &amp; Kc)Set state controller gain.
+      
+      
+          void
+          set_Kc_inty(const Matrix &amp; Kc_inty)Set integral controller gain.
+      
+      
+          void
+          set_Kc_u(const Matrix &amp; Kc_u)Set input controller gain.
+      
+      
+          void
+          set_tau_awu(data_t tau)Set time constant of anti-integral-windup.
+      
+      
+          void
+          set_control_type(size_t control_type)Sets the control type.
+      
+      
+          void
+          set_u_lb(data_t u_lb)sets control lower bound
+      
+      
+          void
+          set_u_ub(data_t u_ub)Sets control upper bound.
+      
+      
+          void
+          Reset()reset system and control variables.
+      
+      
+          void
+          Print()prints variables to stdout
+      
+  
+
+
+  Protected Attributes
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          System
+          sys_ underlying LDS
+      
+      
+          Vector
+          u_ control signal
+      
+      
+          Vector
+          u_return_ control signal that is returned to user
+      
+      
+          Vector
+          g_design_ input gain of the system used for controller design
+      
+      
+          Vector
+          u_ref_ reference input
+      
+      
+          Vector
+          u_ref_prev_ reference input at previous time step
+      
+      
+          Vector
+          x_ref_ reference state
+      
+      
+          Vector
+          y_ref_ reference output
+      
+      
+          Vector
+          cx_ref_
+      
+      
+          Matrix
+          Kc_ state controller gain
+      
+      
+          Matrix
+          Kc_u_ input controller gain (optional when control updates \deltaU)
+      
+      
+          Matrix
+          Kc_inty_ integral controller gain
+      
+      
+          Vector
+          du_ref_
+      
+      
+          Vector
+          dv_ref_
+      
+      
+          Vector
+          v_ref_
+      
+      
+          Vector
+          dv_
+      
+      
+          Vector
+          v_ Control after g inversion (e.g., control in physical units)
+      
+      
+          Vector
+          int_e_ integrated error
+      
+      
+          Vector
+          int_e_awu_adjust_ anti-windup adjustment to intE
+      
+      
+          Vector
+          u_sat_ control signal after saturation (for antiWindup)
+      
+      
+          bool
+          do_control_prev_
+      
+      
+          bool
+          do_lock_control_prev_
+      
+      
+          bool
+          u_saturated_ whether control signal has reached saturation limits
+      
+      
+          data_t
+          u_lb_ lower bound on control
+      
+      
+          data_t
+          u_ub_ upper bound on control
+      
+      
+          data_t
+          tau_awu_ antiwindup time constant
+      
+      
+          data_t
+          k_awu_
+      
+      
+          data_t
+          t_since_control_onset_ time since control epoch onset
+      
+      
+          size_t
+          control_type_ controller type
+      
+  
+
+
+  Detailed Description
+  #
+
+template &lt;typename System &gt;
+class lds::Controller;
+
+
+
+  Public Function Details
+  #
+
+
+  Controller
+  #
+
+Controller() =default
+
+
+  Controller
+  #
+
+inline Controller(
+    const System &amp; sys,
+    data_t u_lb,
+    data_t u_ub,
+    size_t control_type =0
+)
+Parameters:">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">
+  <meta property="og:site_name" content="LDS C&E">
+  <meta property="og:title" content="lds::Controller">
+  <meta property="og:description" content="lds::Controller # More…
+Inherited by lds::SwitchedController&lt; System &gt;, lds::gaussian::Controller, lds::poisson::Controller
+Public Functions # Name Controller() =default
+Constructs a new Controller. Controller(const System &amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)
+Constructs a new Controller. Controller(System &amp;&amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)
+Constructs a new Controller by moving the system object. const Vector &amp; Control(const Vector &amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)
+updates control signal (single-step) const Vector &amp; ControlOutputReference(const Vector &amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)
+updates control signal, given previously-set (single-step) const System &amp; sys() const const Matrix &amp; Kc() const
+Get state feedback controller gain. const Matrix &amp; Kc_inty() const
+Get integral controller gain. const Matrix &amp; Kc_u() const
+Get input feedback controller gain. const Vector &amp; g_design() const
+Get input gain used in controller design. const Vector &amp; u_ref() const
+Get reference input. const Vector &amp; x_ref() const
+Get reference state. const Vector &amp; y_ref() const
+Get reference output. size_t control_type() const
+Get controller type. data_t tau_awu() const
+Get time constant of anti-integral-windup. data_t u_lb() const
+Get control lower bound. data_t u_ub() const
+Get control upper bound. void set_sys(const System &amp; sys)
+Set system. void set_g_design(const Vector &amp; g_design)
+Set input gain used in controller design (g_design) void set_u_ref(const Vector &amp; u_ref)
+Set reference input (u_ref) void set_x_ref(const Vector &amp; x_ref)
+Set reference state (x_ref) virtual void set_y_ref(const Vector &amp; y_ref)
+Set reference output (y_ref) void set_Kc(const Matrix &amp; Kc)
+Set state controller gain. void set_Kc_inty(const Matrix &amp; Kc_inty)
+Set integral controller gain. void set_Kc_u(const Matrix &amp; Kc_u)
+Set input controller gain. void set_tau_awu(data_t tau)
+Set time constant of anti-integral-windup. void set_control_type(size_t control_type)
+Sets the control type. void set_u_lb(data_t u_lb)
+sets control lower bound void set_u_ub(data_t u_ub)
+Sets control upper bound. void Reset()
+reset system and control variables. void Print()
+prints variables to stdout Protected Attributes # Name System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template &lt;typename System &gt; class lds::Controller; Public Function Details # Controller # Controller() =default Controller # inline Controller( const System &amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:">
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+  <ul>
+    <li><a href="#ldscontroller">lds::Controller</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#protected-attributes">Protected Attributes</a></li>
+        <li><a href="#detailed-description">Detailed Description</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#controller"><strong>Controller</strong></a></li>
+            <li><a href="#controller-1"><strong>Controller</strong></a></li>
+            <li><a href="#controller-2"><strong>Controller</strong></a></li>
+            <li><a href="#control"><strong>Control</strong></a></li>
+            <li><a href="#controloutputreference"><strong>ControlOutputReference</strong></a></li>
+            <li><a href="#sys"><strong>sys</strong></a></li>
+            <li><a href="#kc"><strong>Kc</strong></a></li>
+            <li><a href="#kc_"><strong>Kc_inty</strong></a></li>
+            <li><a href="#kc_-1"><strong>Kc_u</strong></a></li>
+            <li><a href="#g_"><strong>g_design</strong></a></li>
+            <li><a href="#u_"><strong>u_ref</strong></a></li>
+            <li><a href="#x_"><strong>x_ref</strong></a></li>
+            <li><a href="#y_"><strong>y_ref</strong></a></li>
+            <li><a href="#control_"><strong>control_type</strong></a></li>
+            <li><a href="#tau_"><strong>tau_awu</strong></a></li>
+            <li><a href="#u_-1"><strong>u_lb</strong></a></li>
+            <li><a href="#u_-2"><strong>u_ub</strong></a></li>
+            <li><a href="#set_"><strong>set_sys</strong></a></li>
+            <li><a href="#set_-1"><strong>set_g_design</strong></a></li>
+            <li><a href="#set_-2"><strong>set_u_ref</strong></a></li>
+            <li><a href="#set_-3"><strong>set_x_ref</strong></a></li>
+            <li><a href="#set_-4"><strong>set_y_ref</strong></a></li>
+            <li><a href="#set_-5"><strong>set_Kc</strong></a></li>
+            <li><a href="#set_-6"><strong>set_Kc_inty</strong></a></li>
+            <li><a href="#set_-7"><strong>set_Kc_u</strong></a></li>
+            <li><a href="#set_-8"><strong>set_tau_awu</strong></a></li>
+            <li><a href="#set_-9"><strong>set_control_type</strong></a></li>
+            <li><a href="#set_-10"><strong>set_u_lb</strong></a></li>
+            <li><a href="#set_-11"><strong>set_u_ub</strong></a></li>
+            <li><a href="#reset"><strong>Reset</strong></a></li>
+            <li><a href="#print"><strong>Print</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-attribute-details">Protected Attribute Details</a>
+          <ul>
+            <li><a href="#sys_"><strong>sys_</strong></a></li>
+            <li><a href="#u_-3"><strong>u_</strong></a></li>
+            <li><a href="#u_-4"><strong>u_return_</strong></a></li>
+            <li><a href="#g_-1"><strong>g_design_</strong></a></li>
+            <li><a href="#u_-5"><strong>u_ref_</strong></a></li>
+            <li><a href="#u_-6"><strong>u_ref_prev_</strong></a></li>
+            <li><a href="#x_-1"><strong>x_ref_</strong></a></li>
+            <li><a href="#y_-1"><strong>y_ref_</strong></a></li>
+            <li><a href="#cx_"><strong>cx_ref_</strong></a></li>
+            <li><a href="#kc_-2"><strong>Kc_</strong></a></li>
+            <li><a href="#kc_-3"><strong>Kc_u_</strong></a></li>
+            <li><a href="#kc_-4"><strong>Kc_inty_</strong></a></li>
+            <li><a href="#du_"><strong>du_ref_</strong></a></li>
+            <li><a href="#dv_"><strong>dv_ref_</strong></a></li>
+            <li><a href="#v_"><strong>v_ref_</strong></a></li>
+            <li><a href="#dv_-1"><strong>dv_</strong></a></li>
+            <li><a href="#v_-1"><strong>v_</strong></a></li>
+            <li><a href="#int_"><strong>int_e_</strong></a></li>
+            <li><a href="#int_-1"><strong>int_e_awu_adjust_</strong></a></li>
+            <li><a href="#u_-7"><strong>u_sat_</strong></a></li>
+            <li><a href="#do_"><strong>do_control_prev_</strong></a></li>
+            <li><a href="#do_-1"><strong>do_lock_control_prev_</strong></a></li>
+            <li><a href="#u_-8"><strong>u_saturated_</strong></a></li>
+            <li><a href="#u_-9"><strong>u_lb_</strong></a></li>
+            <li><a href="#u_-10"><strong>u_ub_</strong></a></li>
+            <li><a href="#tau_-1"><strong>tau_awu_</strong></a></li>
+            <li><a href="#k_"><strong>k_awu_</strong></a></li>
+            <li><a href="#t_"><strong>t_since_control_onset_</strong></a></li>
+            <li><a href="#control_-1"><strong>control_type_</strong></a></li>
+          </ul>
+        </li>
+      </ul>
+    </li>
+  </ul>
+</nav>
+
+
+
+  </aside>
+  
+ 
+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="ldscontroller">
+  lds::Controller
+  <a class="anchor" href="#ldscontroller">#</a>
+</h1>
+<p><a href="#detailed-description">More&hellip;</a></p>
+<p>Inherited by <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/">lds::SwitchedController&lt; System &gt;</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/">lds::gaussian::Controller</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/">lds::poisson::Controller</a></p>
+<h2 id="public-functions">
+  Public Functions
+  <a class="anchor" href="#public-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller">Controller</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">Controller</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller">Controller</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp; sys, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub, size_t control_type =0)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">Controller</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller">Controller</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp;&amp; sys, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub, size_t control_type =0)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">Controller</a> by moving the system object.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control">Control</a></strong>(const Vector &amp; z, bool do_control =true, bool do_lock_control =false, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_soft_start =0, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal (single-step)</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controloutputreference">ControlOutputReference</a></strong>(const Vector &amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_soft_start =0, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal, given previously-set (single-step)</td>
+      </tr>
+      <tr>
+          <td>const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-sys">sys</a></strong>() const</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc">Kc</a></strong>() const<br>Get state feedback controller gain.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-inty">Kc_inty</a></strong>() const<br>Get integral controller gain.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-u">Kc_u</a></strong>() const<br>Get input feedback controller gain.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-g-design">g_design</a></strong>() const<br>Get input gain used in controller design.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ref">u_ref</a></strong>() const<br>Get reference input.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-x-ref">x_ref</a></strong>() const<br>Get reference state.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-y-ref">y_ref</a></strong>() const<br>Get reference output.</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control-type">control_type</a></strong>() const<br>Get controller type.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-tau-awu">tau_awu</a></strong>() const<br>Get time constant of anti-integral-windup.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-lb">u_lb</a></strong>() const<br>Get control lower bound.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ub">u_ub</a></strong>() const<br>Get control upper bound.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-sys">set_sys</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp; sys)<br>Set system.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-g-design">set_g_design</a></strong>(const Vector &amp; g_design)<br>Set input gain used in controller design (g_design)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ref">set_u_ref</a></strong>(const Vector &amp; u_ref)<br>Set reference input (u_ref)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-x-ref">set_x_ref</a></strong>(const Vector &amp; x_ref)<br>Set reference state (x_ref)</td>
+      </tr>
+      <tr>
+          <td>virtual void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-y-ref">set_y_ref</a></strong>(const Vector &amp; y_ref)<br>Set reference output (y_ref)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc">set_Kc</a></strong>(const Matrix &amp; Kc)<br>Set state controller gain.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-inty">set_Kc_inty</a></strong>(const Matrix &amp; Kc_inty)<br>Set integral controller gain.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-u">set_Kc_u</a></strong>(const Matrix &amp; Kc_u)<br>Set input controller gain.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-tau-awu">set_tau_awu</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> tau)<br>Set time constant of anti-integral-windup.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-control-type">set_control_type</a></strong>(size_t control_type)<br>Sets the control type.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-lb">set_u_lb</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb)<br>sets control lower bound</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ub">set_u_ub</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub)<br>Sets control upper bound.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-reset">Reset</a></strong>()<br>reset system and control variables.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-print">Print</a></strong>()<br>prints variables to stdout</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="protected-attributes">
+  Protected Attributes
+  <a class="anchor" href="#protected-attributes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-sys-">sys_</a></strong> <br>underlying LDS</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-">u_</a></strong> <br>control signal</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-return-">u_return_</a></strong> <br>control signal that is <em>returned</em> to user</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-g-design-">g_design_</a></strong> <br>input gain of the system used for controller design</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-">u_ref_</a></strong> <br>reference input</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-prev-">u_ref_prev_</a></strong> <br>reference input at previous time step</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-x-ref-">x_ref_</a></strong> <br>reference state</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-y-ref-">y_ref_</a></strong> <br>reference output</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-cx-ref-">cx_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-">Kc_</a></strong> <br>state controller gain</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-u-">Kc_u_</a></strong> <br>input controller gain (optional when control updates \deltaU)</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-inty-">Kc_inty_</a></strong> <br>integral controller gain</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-du-ref-">du_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-ref-">dv_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-ref-">v_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-">dv_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-">v_</a></strong> <br>Control after g inversion (e.g., control in physical units)</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-">int_e_</a></strong> <br>integrated error</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-awu-adjust-">int_e_awu_adjust_</a></strong> <br>anti-windup adjustment to intE</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-sat-">u_sat_</a></strong> <br>control signal after saturation (for antiWindup)</td>
+      </tr>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-control-prev-">do_control_prev_</a></strong></td>
+      </tr>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-lock-control-prev-">do_lock_control_prev_</a></strong></td>
+      </tr>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-saturated-">u_saturated_</a></strong> <br>whether control signal has reached saturation limits</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-lb-">u_lb_</a></strong> <br>lower bound on control</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ub-">u_ub_</a></strong> <br>upper bound on control</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-tau-awu-">tau_awu_</a></strong> <br>antiwindup time constant</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-k-awu-">k_awu_</a></strong></td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-t-since-control-onset-">t_since_control_onset_</a></strong> <br>time since control epoch onset</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-control-type-">control_type_</a></strong> <br>controller type</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System <span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">lds</span><span style="color:#f92672">::</span>Controller;
+</span></span></code></pre></div><hr>
+<hr>
+<h2 id="public-function-details">
+  Public Function Details
+  <a class="anchor" href="#public-function-details">#</a>
+</h2>
+<h3 id="controller">
+  <strong>Controller</strong>
+  <a class="anchor" href="#controller">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Controller() <span style="color:#f92672">=</span><span style="color:#66d9ef">default</span>
+</span></span></code></pre></div><hr>
+<h3 id="controller-1">
+  <strong>Controller</strong>
+  <a class="anchor" href="#controller-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> Controller(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> System <span style="color:#f92672">&amp;</span> sys,
+</span></span><span style="display:flex;"><span>    data_t u_lb,
+</span></span><span style="display:flex;"><span>    data_t u_ub,
+</span></span><span style="display:flex;"><span>    size_t control_type <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>sys</strong> <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> (derived from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">lds::System</a>)</li>
+<li><strong>u_lb</strong> lower bound on control (u)</li>
+<li><strong>u_ub</strong> upper bound on control (u)</li>
+<li><strong>control_type</strong> [optional] control type bit mask</li>
+</ul>
+<p><strong>Template Parameters</strong>:</p>
+<ul>
+<li><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a></strong> type derived from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">lds::System</a></li>
+</ul>
+<hr>
+<h3 id="controller-2">
+  <strong>Controller</strong>
+  <a class="anchor" href="#controller-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> Controller(
+</span></span><span style="display:flex;"><span>    System <span style="color:#f92672">&amp;&amp;</span> sys,
+</span></span><span style="display:flex;"><span>    data_t u_lb,
+</span></span><span style="display:flex;"><span>    data_t u_ub,
+</span></span><span style="display:flex;"><span>    size_t control_type <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>sys</strong> <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> (derived from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">lds::System</a>)</li>
+<li><strong>u_lb</strong> lower bound on control (u)</li>
+<li><strong>u_ub</strong> upper bound on control (u)</li>
+<li><strong>control_type</strong> [optional] control type bit mask</li>
+</ul>
+<p><strong>Template Parameters</strong>:</p>
+<ul>
+<li><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a></strong> type derived from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">lds::System</a></li>
+</ul>
+<hr>
+<h3 id="control">
+  <strong>Control</strong>
+  <a class="anchor" href="#control">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> Control(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> z,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> do_control <span style="color:#f92672">=</span>true,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> do_lock_control <span style="color:#f92672">=</span>false,
+</span></span><span style="display:flex;"><span>    data_t sigma_soft_start <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>,
+</span></span><span style="display:flex;"><span>    data_t sigma_u_noise <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> do_reset_at_control_onset <span style="color:#f92672">=</span>true
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>z</strong> measurement</li>
+<li><strong>do_control</strong> [optional] whether to update control (true) or simply feed through u_ref (false)</li>
+<li><strong>do_lock_control</strong> [optional] whether to lock control at its current value</li>
+<li><strong>sigma_soft_start</strong> [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true)</li>
+<li><strong>sigma_u_noise</strong> [optional] standard deviation (sigma) of Gaussian noise added on top of control signal</li>
+<li><strong>do_reset_at_control_onset</strong> [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true)</li>
+</ul>
+<p><strong>Return</strong>: updated control signal</p>
+<p>Updates the control signal (single-step). This is the most flexible option, but requires user to have set the controller&rsquo;s y_ref, x_ref, and u_ref variables.</p>
+<hr>
+<h3 id="controloutputreference">
+  <strong>ControlOutputReference</strong>
+  <a class="anchor" href="#controloutputreference">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> ControlOutputReference(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> z,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> do_control <span style="color:#f92672">=</span>true,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> do_estimation <span style="color:#f92672">=</span>true,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> do_lock_control <span style="color:#f92672">=</span>false,
+</span></span><span style="display:flex;"><span>    data_t sigma_soft_start <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>,
+</span></span><span style="display:flex;"><span>    data_t sigma_u_noise <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> do_reset_at_control_onset <span style="color:#f92672">=</span>true
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>z</strong> measurement</li>
+<li><strong>do_control</strong> [optional] whether to update control (true) or simply feed through u_ref (false)</li>
+<li><strong>do_estimation</strong> [optional] whether to update state estimate (if false, effectively open-loop control)</li>
+<li><strong>do_lock_control</strong> [optional] whether to lock control at its current value</li>
+<li><strong>sigma_soft_start</strong> [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true)</li>
+<li><strong>sigma_u_noise</strong> [optional] standard deviation (sigma) of Gaussian noise added on top of control signal</li>
+<li><strong>do_reset_at_control_onset</strong> [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true)</li>
+</ul>
+<p><strong>Return</strong>: updated control signal</p>
+<p>Updates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.</p>
+<hr>
+<h3 id="sys">
+  <strong>sys</strong>
+  <a class="anchor" href="#sys">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> System <span style="color:#f92672">&amp;</span> sys() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="kc">
+  <strong>Kc</strong>
+  <a class="anchor" href="#kc">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> Kc() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="kc_">
+  <strong>Kc_inty</strong>
+  <a class="anchor" href="#kc_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> Kc_inty() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="kc_-1">
+  <strong>Kc_u</strong>
+  <a class="anchor" href="#kc_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> Kc_u() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="g_">
+  <strong>g_design</strong>
+  <a class="anchor" href="#g_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> g_design() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="u_">
+  <strong>u_ref</strong>
+  <a class="anchor" href="#u_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> u_ref() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="x_">
+  <strong>x_ref</strong>
+  <a class="anchor" href="#x_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> x_ref() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="y_">
+  <strong>y_ref</strong>
+  <a class="anchor" href="#y_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> y_ref() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="control_">
+  <strong>control_type</strong>
+  <a class="anchor" href="#control_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> size_t control_type() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="tau_">
+  <strong>tau_awu</strong>
+  <a class="anchor" href="#tau_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> data_t tau_awu() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="u_-1">
+  <strong>u_lb</strong>
+  <a class="anchor" href="#u_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> data_t u_lb() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="u_-2">
+  <strong>u_ub</strong>
+  <a class="anchor" href="#u_-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> data_t u_ub() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="set_">
+  <strong>set_sys</strong>
+  <a class="anchor" href="#set_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_sys(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> System <span style="color:#f92672">&amp;</span> sys
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-1">
+  <strong>set_g_design</strong>
+  <a class="anchor" href="#set_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_g_design(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> g_design
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-2">
+  <strong>set_u_ref</strong>
+  <a class="anchor" href="#set_-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_u_ref(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> u_ref
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-3">
+  <strong>set_x_ref</strong>
+  <a class="anchor" href="#set_-3">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_x_ref(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> x_ref
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-4">
+  <strong>set_y_ref</strong>
+  <a class="anchor" href="#set_-4">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> set_y_ref(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> y_ref
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Reimplemented by</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/#function-set-y-ref">lds::gaussian::Controller::set_y_ref</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/#function-set-y-ref">lds::gaussian::SwitchedController::set_y_ref</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/#function-set-y-ref">lds::poisson::Controller::set_y_ref</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/#function-set-y-ref">lds::poisson::SwitchedController::set_y_ref</a></p>
+<hr>
+<h3 id="set_-5">
+  <strong>set_Kc</strong>
+  <a class="anchor" href="#set_-5">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_Kc(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> Kc
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-6">
+  <strong>set_Kc_inty</strong>
+  <a class="anchor" href="#set_-6">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_Kc_inty(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> Kc_inty
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-7">
+  <strong>set_Kc_u</strong>
+  <a class="anchor" href="#set_-7">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_Kc_u(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> Kc_u
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-8">
+  <strong>set_tau_awu</strong>
+  <a class="anchor" href="#set_-8">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_tau_awu(
+</span></span><span style="display:flex;"><span>    data_t tau
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-9">
+  <strong>set_control_type</strong>
+  <a class="anchor" href="#set_-9">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_control_type(
+</span></span><span style="display:flex;"><span>    size_t control_type
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>control_type</strong> control type bit mask</li>
+</ul>
+<p><strong>Template Parameters</strong>:</p>
+<ul>
+<li><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a></strong> type derived from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">lds::System</a></li>
+</ul>
+<hr>
+<h3 id="set_-10">
+  <strong>set_u_lb</strong>
+  <a class="anchor" href="#set_-10">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_u_lb(
+</span></span><span style="display:flex;"><span>    data_t u_lb
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>u_lb</strong> control lower bound</li>
+</ul>
+<hr>
+<h3 id="set_-11">
+  <strong>set_u_ub</strong>
+  <a class="anchor" href="#set_-11">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_u_ub(
+</span></span><span style="display:flex;"><span>    data_t u_ub
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>u_ub</strong> control upper bound</li>
+</ul>
+<hr>
+<h3 id="reset">
+  <strong>Reset</strong>
+  <a class="anchor" href="#reset">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> Reset()
+</span></span></code></pre></div><hr>
+<h3 id="print">
+  <strong>Print</strong>
+  <a class="anchor" href="#print">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> Print()
+</span></span></code></pre></div><hr>
+<h2 id="protected-attribute-details">
+  Protected Attribute Details
+  <a class="anchor" href="#protected-attribute-details">#</a>
+</h2>
+<h3 id="sys_">
+  <strong>sys_</strong>
+  <a class="anchor" href="#sys_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>System sys_;
+</span></span></code></pre></div><hr>
+<h3 id="u_-3">
+  <strong>u_</strong>
+  <a class="anchor" href="#u_-3">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector u_;
+</span></span></code></pre></div><hr>
+<h3 id="u_-4">
+  <strong>u_return_</strong>
+  <a class="anchor" href="#u_-4">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector u_return_;
+</span></span></code></pre></div><hr>
+<h3 id="g_-1">
+  <strong>g_design_</strong>
+  <a class="anchor" href="#g_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector g_design_;
+</span></span></code></pre></div><hr>
+<h3 id="u_-5">
+  <strong>u_ref_</strong>
+  <a class="anchor" href="#u_-5">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector u_ref_;
+</span></span></code></pre></div><hr>
+<h3 id="u_-6">
+  <strong>u_ref_prev_</strong>
+  <a class="anchor" href="#u_-6">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector u_ref_prev_;
+</span></span></code></pre></div><hr>
+<h3 id="x_-1">
+  <strong>x_ref_</strong>
+  <a class="anchor" href="#x_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector x_ref_;
+</span></span></code></pre></div><hr>
+<h3 id="y_-1">
+  <strong>y_ref_</strong>
+  <a class="anchor" href="#y_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector y_ref_;
+</span></span></code></pre></div><hr>
+<h3 id="cx_">
+  <strong>cx_ref_</strong>
+  <a class="anchor" href="#cx_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector cx_ref_;
+</span></span></code></pre></div><hr>
+<h3 id="kc_-2">
+  <strong>Kc_</strong>
+  <a class="anchor" href="#kc_-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix Kc_;
+</span></span></code></pre></div><hr>
+<h3 id="kc_-3">
+  <strong>Kc_u_</strong>
+  <a class="anchor" href="#kc_-3">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix Kc_u_;
+</span></span></code></pre></div><hr>
+<h3 id="kc_-4">
+  <strong>Kc_inty_</strong>
+  <a class="anchor" href="#kc_-4">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix Kc_inty_;
+</span></span></code></pre></div><hr>
+<h3 id="du_">
+  <strong>du_ref_</strong>
+  <a class="anchor" href="#du_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector du_ref_;
+</span></span></code></pre></div><hr>
+<h3 id="dv_">
+  <strong>dv_ref_</strong>
+  <a class="anchor" href="#dv_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector dv_ref_;
+</span></span></code></pre></div><hr>
+<h3 id="v_">
+  <strong>v_ref_</strong>
+  <a class="anchor" href="#v_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector v_ref_;
+</span></span></code></pre></div><hr>
+<h3 id="dv_-1">
+  <strong>dv_</strong>
+  <a class="anchor" href="#dv_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector dv_;
+</span></span></code></pre></div><hr>
+<h3 id="v_-1">
+  <strong>v_</strong>
+  <a class="anchor" href="#v_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector v_;
+</span></span></code></pre></div><hr>
+<h3 id="int_">
+  <strong>int_e_</strong>
+  <a class="anchor" href="#int_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector int_e_;
+</span></span></code></pre></div><hr>
+<h3 id="int_-1">
+  <strong>int_e_awu_adjust_</strong>
+  <a class="anchor" href="#int_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector int_e_awu_adjust_;
+</span></span></code></pre></div><hr>
+<h3 id="u_-7">
+  <strong>u_sat_</strong>
+  <a class="anchor" href="#u_-7">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector u_sat_;
+</span></span></code></pre></div><hr>
+<h3 id="do_">
+  <strong>do_control_prev_</strong>
+  <a class="anchor" href="#do_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">bool</span> do_control_prev_ <span style="color:#f92672">=</span> false;
+</span></span></code></pre></div><hr>
+<h3 id="do_-1">
+  <strong>do_lock_control_prev_</strong>
+  <a class="anchor" href="#do_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">bool</span> do_lock_control_prev_ <span style="color:#f92672">=</span> false;
+</span></span></code></pre></div><hr>
+<h3 id="u_-8">
+  <strong>u_saturated_</strong>
+  <a class="anchor" href="#u_-8">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">bool</span> u_saturated_ <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      false;
+</span></span></code></pre></div><hr>
+<h3 id="u_-9">
+  <strong>u_lb_</strong>
+  <a class="anchor" href="#u_-9">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>data_t u_lb_ {};
+</span></span></code></pre></div><hr>
+<h3 id="u_-10">
+  <strong>u_ub_</strong>
+  <a class="anchor" href="#u_-10">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>data_t u_ub_ {};
+</span></span></code></pre></div><hr>
+<h3 id="tau_-1">
+  <strong>tau_awu_</strong>
+  <a class="anchor" href="#tau_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>data_t tau_awu_ {};
+</span></span></code></pre></div><hr>
+<h3 id="k_">
+  <strong>k_awu_</strong>
+  <a class="anchor" href="#k_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>data_t k_awu_ <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span></code></pre></div><hr>
+<h3 id="t_">
+  <strong>t_since_control_onset_</strong>
+  <a class="anchor" href="#t_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>data_t t_since_control_onset_ <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span></code></pre></div><hr>
+<h3 id="control_-1">
+  <strong>control_type_</strong>
+  <a class="anchor" href="#control_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>size_t control_type_ {};
+</span></span></code></pre></div><hr>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
+      
+
+      <footer class="book-footer">
+        
+  <div class="flex flex-wrap justify-between">
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+      <label for="menu-control" class="hidden book-menu-overlay"></label>
+    </div>
+
+    
+    <aside class="book-toc">
+      <div class="book-toc-content">
+        
+  
+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#ldscontroller">lds::Controller</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#protected-attributes">Protected Attributes</a></li>
+        <li><a href="#detailed-description">Detailed Description</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#controller"><strong>Controller</strong></a></li>
+            <li><a href="#controller-1"><strong>Controller</strong></a></li>
+            <li><a href="#controller-2"><strong>Controller</strong></a></li>
+            <li><a href="#control"><strong>Control</strong></a></li>
+            <li><a href="#controloutputreference"><strong>ControlOutputReference</strong></a></li>
+            <li><a href="#sys"><strong>sys</strong></a></li>
+            <li><a href="#kc"><strong>Kc</strong></a></li>
+            <li><a href="#kc_"><strong>Kc_inty</strong></a></li>
+            <li><a href="#kc_-1"><strong>Kc_u</strong></a></li>
+            <li><a href="#g_"><strong>g_design</strong></a></li>
+            <li><a href="#u_"><strong>u_ref</strong></a></li>
+            <li><a href="#x_"><strong>x_ref</strong></a></li>
+            <li><a href="#y_"><strong>y_ref</strong></a></li>
+            <li><a href="#control_"><strong>control_type</strong></a></li>
+            <li><a href="#tau_"><strong>tau_awu</strong></a></li>
+            <li><a href="#u_-1"><strong>u_lb</strong></a></li>
+            <li><a href="#u_-2"><strong>u_ub</strong></a></li>
+            <li><a href="#set_"><strong>set_sys</strong></a></li>
+            <li><a href="#set_-1"><strong>set_g_design</strong></a></li>
+            <li><a href="#set_-2"><strong>set_u_ref</strong></a></li>
+            <li><a href="#set_-3"><strong>set_x_ref</strong></a></li>
+            <li><a href="#set_-4"><strong>set_y_ref</strong></a></li>
+            <li><a href="#set_-5"><strong>set_Kc</strong></a></li>
+            <li><a href="#set_-6"><strong>set_Kc_inty</strong></a></li>
+            <li><a href="#set_-7"><strong>set_Kc_u</strong></a></li>
+            <li><a href="#set_-8"><strong>set_tau_awu</strong></a></li>
+            <li><a href="#set_-9"><strong>set_control_type</strong></a></li>
+            <li><a href="#set_-10"><strong>set_u_lb</strong></a></li>
+            <li><a href="#set_-11"><strong>set_u_ub</strong></a></li>
+            <li><a href="#reset"><strong>Reset</strong></a></li>
+            <li><a href="#print"><strong>Print</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-attribute-details">Protected Attribute Details</a>
+          <ul>
+            <li><a href="#sys_"><strong>sys_</strong></a></li>
+            <li><a href="#u_-3"><strong>u_</strong></a></li>
+            <li><a href="#u_-4"><strong>u_return_</strong></a></li>
+            <li><a href="#g_-1"><strong>g_design_</strong></a></li>
+            <li><a href="#u_-5"><strong>u_ref_</strong></a></li>
+            <li><a href="#u_-6"><strong>u_ref_prev_</strong></a></li>
+            <li><a href="#x_-1"><strong>x_ref_</strong></a></li>
+            <li><a href="#y_-1"><strong>y_ref_</strong></a></li>
+            <li><a href="#cx_"><strong>cx_ref_</strong></a></li>
+            <li><a href="#kc_-2"><strong>Kc_</strong></a></li>
+            <li><a href="#kc_-3"><strong>Kc_u_</strong></a></li>
+            <li><a href="#kc_-4"><strong>Kc_inty_</strong></a></li>
+            <li><a href="#du_"><strong>du_ref_</strong></a></li>
+            <li><a href="#dv_"><strong>dv_ref_</strong></a></li>
+            <li><a href="#v_"><strong>v_ref_</strong></a></li>
+            <li><a href="#dv_-1"><strong>dv_</strong></a></li>
+            <li><a href="#v_-1"><strong>v_</strong></a></li>
+            <li><a href="#int_"><strong>int_e_</strong></a></li>
+            <li><a href="#int_-1"><strong>int_e_awu_adjust_</strong></a></li>
+            <li><a href="#u_-7"><strong>u_sat_</strong></a></li>
+            <li><a href="#do_"><strong>do_control_prev_</strong></a></li>
+            <li><a href="#do_-1"><strong>do_lock_control_prev_</strong></a></li>
+            <li><a href="#u_-8"><strong>u_saturated_</strong></a></li>
+            <li><a href="#u_-9"><strong>u_lb_</strong></a></li>
+            <li><a href="#u_-10"><strong>u_ub_</strong></a></li>
+            <li><a href="#tau_-1"><strong>tau_awu_</strong></a></li>
+            <li><a href="#k_"><strong>k_awu_</strong></a></li>
+            <li><a href="#t_"><strong>t_since_control_onset_</strong></a></li>
+            <li><a href="#control_-1"><strong>control_type_</strong></a></li>
+          </ul>
+        </li>
+      </ul>
+    </li>
+  </ul>
+</nav>
+
+
+ 
+      </div>
+    </aside>
+    
+  </main>
+
+  
+</body>
+</html>
+
+
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+
+
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+
+
+
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+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="
+  lds::EM
+  #
+
+More&hellip;
+Inherited by lds::gaussian::FitEM, lds::poisson::FitEM
+
+  Public Functions
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          
+          EM() =defaultConstructs a new EMFit type.
+      
+      
+          
+          EM(size_t n_x, data_t dt, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; u_train, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; z_train)Constructs a new EMFit type.
+      
+      
+          
+          EM(const Fit &amp; fit0, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; u_train, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; z_train)Constructs a new EMFit type.
+      
+      
+          virtual
+          ~EM() =default
+      
+      
+          const Fit &amp;
+          Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)Runs fitting by Expectation(E)-Maximization(M)
+      
+      
+          std::tuple&lt; UniformMatrixList&lt; kMatFreeDim2 &gt;, UniformMatrixList&lt; kMatFreeDim2 &gt; &gt;
+          ReturnData()Returns the input/output data to caller.
+      
+      
+          const std::vector&lt; Matrix &gt; &amp;
+          x() constgets estimated state (over time)
+      
+      
+          const std::vector&lt; Matrix &gt; &amp;
+          y() constgets estimated output (over time)
+      
+      
+          const Matrix &amp;
+          sum_E_x_t_x_t() constgets state-input covariance
+      
+      
+          const Matrix &amp;
+          sum_E_xu_tm1_xu_tm1() constgets state-input covariance (t-minus-1)
+      
+      
+          const Matrix &amp;
+          sum_E_xu_t_xu_tm1() constgets single lag state-input covariance
+      
+      
+          size_t
+          n_t_tot()total number of time samples
+      
+      
+          const Vector &amp;
+          theta() constgets parameters updated in M step
+      
+  
+
+
+  Protected Functions
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          void
+          Expectation(bool force_common_initial =false)Expectation step.
+      
+      
+          void
+          Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)Maximization step.
+      
+      
+          void
+          MaximizeDynamics()
+      
+      
+          void
+          MaximizeQ()
+      
+      
+          void
+          MaximizeInitial()
+      
+      
+          virtual void
+          MaximizeOutput() =0
+      
+      
+          virtual void
+          MaximizeMeasurement() =0
+      
+      
+          void
+          Smooth(bool force_common_initial)get smoothed estimates
+      
+      
+          virtual void
+          RecurseKe(Matrix &amp; Ke, Cube &amp; P_pre, Cube &amp; P_post, size_t t) =0recursively update estimator gain Ke
+      
+      
+          void
+          Reset()reset to initial conditions
+      
+      
+          void
+          InitVars()Initializes the variables.
+      
+      
+          Vector
+          UpdateTheta()updates parameter list, theta
+      
+  
+
+
+  Protected Attributes
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          UniformMatrixList&lt; kMatFreeDim2 &gt;
+          u_ input training data
+      
+      
+          UniformMatrixList&lt; kMatFreeDim2 &gt;
+          z_ measurement training data
+      
+      
+          std::vector&lt; Matrix &gt;
+          x_ state estimate
+      
+      
+          std::vector&lt; Cube &gt;
+          P_ state estimate cov
+      
+      
+          std::vector&lt; Cube &gt;
+          P_t_tm1_ single-lag state covariance
+      
+      
+          std::vector&lt; Matrix &gt;
+          y_ output estimate
+      
+      
+          Matrix
+          diag_y_
+      
+      
+          Matrix
+          sum_E_x_t_x_t_ state covariance (current time)
+      
+      
+          Matrix
+          sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1)
+      
+      
+          Matrix
+          sum_E_xu_t_xu_tm1_ single lag state-input covariance
+      
+      
+          Fit
+          fit_
+      
+      
+          Vector
+          theta_
+      
+      
+          data_t
+          dt_ sample period
+      
+      
+          size_t
+          n_u_ number of inputs
+      
+      
+          size_t
+          n_x_ number of states
+      
+      
+          size_t
+          n_y_ number of outputs
+      
+      
+          size_t
+          n_trials_ number of input/output data sequences
+      
+      
+          std::vector&lt; size_t &gt;
+          n_t_ number of time steps
+      
+      
+          size_t
+          n_t_tot_ total number of time steps across trials
+      
+  
+
+
+  Detailed Description
+  #
+
+template &lt;typename Fit &gt;
+class lds::EM;
+
+
+
+  Public Function Details
+  #
+
+
+  EM
+  #
+
+EM() =default
+
+
+  EM
+  #
+
+EM(
+    size_t n_x,
+    data_t dt,
+    UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; u_train,
+    UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; z_train
+)
+Parameters:">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">
+  <meta property="og:site_name" content="LDS C&E">
+  <meta property="og:title" content="lds::EM">
+  <meta property="og:description" content="lds::EM # More…
+Inherited by lds::gaussian::FitEM, lds::poisson::FitEM
+Public Functions # Name EM() =default
+Constructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; u_train, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; z_train)
+Constructs a new EMFit type. EM(const Fit &amp; fit0, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; u_train, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; z_train)
+Constructs a new EMFit type. virtual ~EM() =default const Fit &amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)
+Runs fitting by Expectation(E)-Maximization(M) std::tuple&lt; UniformMatrixList&lt; kMatFreeDim2 &gt;, UniformMatrixList&lt; kMatFreeDim2 &gt; &gt; ReturnData()
+Returns the input/output data to caller. const std::vector&lt; Matrix &gt; &amp; x() const
+gets estimated state (over time) const std::vector&lt; Matrix &gt; &amp; y() const
+gets estimated output (over time) const Matrix &amp; sum_E_x_t_x_t() const
+gets state-input covariance const Matrix &amp; sum_E_xu_tm1_xu_tm1() const
+gets state-input covariance (t-minus-1) const Matrix &amp; sum_E_xu_t_xu_tm1() const
+gets single lag state-input covariance size_t n_t_tot()
+total number of time samples const Vector &amp; theta() const
+gets parameters updated in M step Protected Functions # Name void Expectation(bool force_common_initial =false)
+Expectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)
+Maximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() virtual void MaximizeOutput() =0 virtual void MaximizeMeasurement() =0 void Smooth(bool force_common_initial)
+get smoothed estimates virtual void RecurseKe(Matrix &amp; Ke, Cube &amp; P_pre, Cube &amp; P_post, size_t t) =0
+recursively update estimator gain Ke void Reset()
+reset to initial conditions void InitVars()
+Initializes the variables. Vector UpdateTheta()
+updates parameter list, theta Protected Attributes # Name UniformMatrixList&lt; kMatFreeDim2 &gt; u_ input training data UniformMatrixList&lt; kMatFreeDim2 &gt; z_ measurement training data std::vector&lt; Matrix &gt; x_ state estimate std::vector&lt; Cube &gt; P_ state estimate cov std::vector&lt; Cube &gt; P_t_tm1_ single-lag state covariance std::vector&lt; Matrix &gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector&lt; size_t &gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # template &lt;typename Fit &gt; class lds::EM; Public Function Details # EM # EM() =default EM # EM( size_t n_x, data_t dt, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; u_train, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; z_train ) Parameters:">
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+  <strong>lds::EM</strong>
+
+  <label for="toc-control">
+    
+    <img src="/lds-ctrl-est/svg/toc.svg" class="book-icon" alt="Table of Contents" />
+    
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+    
+  
+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#ldsem">lds::EM</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#protected-functions">Protected Functions</a></li>
+        <li><a href="#protected-attributes">Protected Attributes</a></li>
+        <li><a href="#detailed-description">Detailed Description</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#em"><strong>EM</strong></a></li>
+            <li><a href="#em-1"><strong>EM</strong></a></li>
+            <li><a href="#em-2"><strong>EM</strong></a></li>
+            <li><a href="#heading"><strong>~EM</strong></a></li>
+            <li><a href="#run"><strong>Run</strong></a></li>
+            <li><a href="#returndata"><strong>ReturnData</strong></a></li>
+            <li><a href="#x"><strong>x</strong></a></li>
+            <li><a href="#y"><strong>y</strong></a></li>
+            <li><a href="#sum_"><strong>sum_E_x_t_x_t</strong></a></li>
+            <li><a href="#sum_-1"><strong>sum_E_xu_tm1_xu_tm1</strong></a></li>
+            <li><a href="#sum_-2"><strong>sum_E_xu_t_xu_tm1</strong></a></li>
+            <li><a href="#n_"><strong>n_t_tot</strong></a></li>
+            <li><a href="#theta"><strong>theta</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-function-details">Protected Function Details</a>
+          <ul>
+            <li><a href="#expectation"><strong>Expectation</strong></a></li>
+            <li><a href="#maximization"><strong>Maximization</strong></a></li>
+            <li><a href="#maximizedynamics"><strong>MaximizeDynamics</strong></a></li>
+            <li><a href="#maximizeq"><strong>MaximizeQ</strong></a></li>
+            <li><a href="#maximizeinitial"><strong>MaximizeInitial</strong></a></li>
+            <li><a href="#maximizeoutput"><strong>MaximizeOutput</strong></a></li>
+            <li><a href="#maximizemeasurement"><strong>MaximizeMeasurement</strong></a></li>
+            <li><a href="#smooth"><strong>Smooth</strong></a></li>
+            <li><a href="#recurseke"><strong>RecurseKe</strong></a></li>
+            <li><a href="#reset"><strong>Reset</strong></a></li>
+            <li><a href="#initvars"><strong>InitVars</strong></a></li>
+            <li><a href="#updatetheta"><strong>UpdateTheta</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-attribute-details">Protected Attribute Details</a>
+          <ul>
+            <li><a href="#u_"><strong>u_</strong></a></li>
+            <li><a href="#z_"><strong>z_</strong></a></li>
+            <li><a href="#x_"><strong>x_</strong></a></li>
+            <li><a href="#p_"><strong>P_</strong></a></li>
+            <li><a href="#p_-1"><strong>P_t_tm1_</strong></a></li>
+            <li><a href="#y_"><strong>y_</strong></a></li>
+            <li><a href="#diag_"><strong>diag_y_</strong></a></li>
+            <li><a href="#sum_-3"><strong>sum_E_x_t_x_t_</strong></a></li>
+            <li><a href="#sum_-4"><strong>sum_E_xu_tm1_xu_tm1_</strong></a></li>
+            <li><a href="#sum_-5"><strong>sum_E_xu_t_xu_tm1_</strong></a></li>
+            <li><a href="#fit_"><strong>fit_</strong></a></li>
+            <li><a href="#theta_"><strong>theta_</strong></a></li>
+            <li><a href="#dt_"><strong>dt_</strong></a></li>
+            <li><a href="#n_-1"><strong>n_u_</strong></a></li>
+            <li><a href="#n_-2"><strong>n_x_</strong></a></li>
+            <li><a href="#n_-3"><strong>n_y_</strong></a></li>
+            <li><a href="#n_-4"><strong>n_trials_</strong></a></li>
+            <li><a href="#n_-5"><strong>n_t_</strong></a></li>
+            <li><a href="#n_-6"><strong>n_t_tot_</strong></a></li>
+          </ul>
+        </li>
+      </ul>
+    </li>
+  </ul>
+</nav>
+
+
+
+  </aside>
+  
+ 
+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="ldsem">
+  lds::EM
+  <a class="anchor" href="#ldsem">#</a>
+</h1>
+<p><a href="#detailed-description">More&hellip;</a></p>
+<p>Inherited by <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/">lds::gaussian::FitEM</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/">lds::poisson::FitEM</a></p>
+<h2 id="public-functions">
+  Public Functions
+  <a class="anchor" href="#public-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em">EM</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">EM</a><a href="">Fit</a> type.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em">EM</a></strong>(size_t n_x, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> dt, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &amp;&amp; u_train, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &amp;&amp; z_train)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">EM</a><a href="">Fit</a> type.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em">EM</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a> &amp; fit0, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &amp;&amp; u_train, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &amp;&amp; z_train)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">EM</a><a href="">Fit</a> type.</td>
+      </tr>
+      <tr>
+          <td>virtual</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-~em">~EM</a></strong>() =default</td>
+      </tr>
+      <tr>
+          <td>const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a> &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-run">Run</a></strong>(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> tol =1e-2)<br>Runs fitting by Expectation(E)-Maximization(M)</td>
+      </tr>
+      <tr>
+          <td>std::tuple&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt;, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-returndata">ReturnData</a></strong>()<br>Returns the input/output data to caller.</td>
+      </tr>
+      <tr>
+          <td>const std::vector&lt; Matrix &gt; &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-x">x</a></strong>() const<br>gets estimated state (over time)</td>
+      </tr>
+      <tr>
+          <td>const std::vector&lt; Matrix &gt; &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-y">y</a></strong>() const<br>gets estimated output (over time)</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-x-t-x-t">sum_E_x_t_x_t</a></strong>() const<br>gets state-input covariance</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-xu-tm1-xu-tm1">sum_E_xu_tm1_xu_tm1</a></strong>() const<br>gets state-input covariance (t-minus-1)</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-xu-t-xu-tm1">sum_E_xu_t_xu_tm1</a></strong>() const<br>gets single lag state-input covariance</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-n-t-tot">n_t_tot</a></strong>()<br>total number of time samples</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-theta">theta</a></strong>() const<br>gets parameters updated in M step</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="protected-functions">
+  Protected Functions
+  <a class="anchor" href="#protected-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-expectation">Expectation</a></strong>(bool force_common_initial =false)<br>Expectation step.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximization">Maximization</a></strong>(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)<br>Maximization step.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizedynamics">MaximizeDynamics</a></strong>()</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizeq">MaximizeQ</a></strong>()</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizeinitial">MaximizeInitial</a></strong>()</td>
+      </tr>
+      <tr>
+          <td>virtual void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizeoutput">MaximizeOutput</a></strong>() =0</td>
+      </tr>
+      <tr>
+          <td>virtual void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizemeasurement">MaximizeMeasurement</a></strong>() =0</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-smooth">Smooth</a></strong>(bool force_common_initial)<br>get smoothed estimates</td>
+      </tr>
+      <tr>
+          <td>virtual void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-recurseke">RecurseKe</a></strong>(Matrix &amp; Ke, Cube &amp; P_pre, Cube &amp; P_post, size_t t) =0<br>recursively update estimator gain Ke</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-reset">Reset</a></strong>()<br>reset to initial conditions</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-initvars">InitVars</a></strong>()<br>Initializes the variables.</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-updatetheta">UpdateTheta</a></strong>()<br>updates parameter list, theta</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="protected-attributes">
+  Protected Attributes
+  <a class="anchor" href="#protected-attributes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-u-">u_</a></strong> <br>input training data</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-z-">z_</a></strong> <br>measurement training data</td>
+      </tr>
+      <tr>
+          <td>std::vector&lt; Matrix &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-x-">x_</a></strong> <br>state estimate</td>
+      </tr>
+      <tr>
+          <td>std::vector&lt; Cube &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-p-">P_</a></strong> <br>state estimate cov</td>
+      </tr>
+      <tr>
+          <td>std::vector&lt; Cube &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-p-t-tm1-">P_t_tm1_</a></strong> <br>single-lag state covariance</td>
+      </tr>
+      <tr>
+          <td>std::vector&lt; Matrix &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-y-">y_</a></strong> <br>output estimate</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-diag-y-">diag_y_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-x-t-x-t-">sum_E_x_t_x_t_</a></strong> <br>state covariance (current time)</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-xu-tm1-xu-tm1-">sum_E_xu_tm1_xu_tm1_</a></strong> <br>state-input covariance (t-minus-1)</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-xu-t-xu-tm1-">sum_E_xu_t_xu_tm1_</a></strong> <br>single lag state-input covariance</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-fit-">fit_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-theta-">theta_</a></strong></td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-dt-">dt_</a></strong> <br>sample period</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-u-">n_u_</a></strong> <br>number of inputs</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-x-">n_x_</a></strong> <br>number of states</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-y-">n_y_</a></strong> <br>number of outputs</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-trials-">n_trials_</a></strong> <br>number of input/output data sequences</td>
+      </tr>
+      <tr>
+          <td>std::vector&lt; size_t &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-t-">n_t_</a></strong> <br>number of time steps</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-t-tot-">n_t_tot_</a></strong> <br>total number of time steps across trials</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit <span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">lds</span><span style="color:#f92672">::</span>EM;
+</span></span></code></pre></div><hr>
+<hr>
+<h2 id="public-function-details">
+  Public Function Details
+  <a class="anchor" href="#public-function-details">#</a>
+</h2>
+<h3 id="em">
+  <strong>EM</strong>
+  <a class="anchor" href="#em">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>EM() <span style="color:#f92672">=</span><span style="color:#66d9ef">default</span>
+</span></span></code></pre></div><hr>
+<h3 id="em-1">
+  <strong>EM</strong>
+  <a class="anchor" href="#em-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>EM(
+</span></span><span style="display:flex;"><span>    size_t n_x,
+</span></span><span style="display:flex;"><span>    data_t dt,
+</span></span><span style="display:flex;"><span>    UniformMatrixList<span style="color:#f92672">&lt;</span> kMatFreeDim2 <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;&amp;</span> u_train,
+</span></span><span style="display:flex;"><span>    UniformMatrixList<span style="color:#f92672">&lt;</span> kMatFreeDim2 <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;&amp;</span> z_train
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>n_x</strong> number of states</li>
+<li><strong>dt</strong> sample period</li>
+<li><strong>u_train</strong> input training data</li>
+<li><strong>z_train</strong> measurement training data</li>
+</ul>
+<hr>
+<h3 id="em-2">
+  <strong>EM</strong>
+  <a class="anchor" href="#em-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>EM(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Fit <span style="color:#f92672">&amp;</span> fit0,
+</span></span><span style="display:flex;"><span>    UniformMatrixList<span style="color:#f92672">&lt;</span> kMatFreeDim2 <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;&amp;</span> u_train,
+</span></span><span style="display:flex;"><span>    UniformMatrixList<span style="color:#f92672">&lt;</span> kMatFreeDim2 <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;&amp;</span> z_train
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>fit0</strong> initial fit</li>
+<li><strong>u_train</strong> input training data</li>
+<li><strong>z_train</strong> measurement training data</li>
+</ul>
+<hr>
+<h3 id="heading">
+  <strong>~EM</strong>
+  <a class="anchor" href="#heading">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">virtual</span> <span style="color:#f92672">~</span>EM() <span style="color:#f92672">=</span><span style="color:#66d9ef">default</span>
+</span></span></code></pre></div><hr>
+<h3 id="run">
+  <strong>Run</strong>
+  <a class="anchor" href="#run">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">const</span> Fit <span style="color:#f92672">&amp;</span> Run(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> calc_dynamics <span style="color:#f92672">=</span>true,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> calc_Q <span style="color:#f92672">=</span>true,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> calc_init <span style="color:#f92672">=</span>true,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> calc_output <span style="color:#f92672">=</span>true,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> calc_measurement <span style="color:#f92672">=</span>true,
+</span></span><span style="display:flex;"><span>    size_t max_iter <span style="color:#f92672">=</span><span style="color:#ae81ff">100</span>,
+</span></span><span style="display:flex;"><span>    data_t tol <span style="color:#f92672">=</span><span style="color:#ae81ff">1e-2</span>
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>calc_dynamics</strong> [optional] whether to calculate dynamics (A, B)</li>
+<li><strong>calc_Q</strong> [optional] whether to calculate process noise covariance</li>
+<li><strong>calc_init</strong> [optional] whether to calculate initial conditions</li>
+<li><strong>calc_output</strong> [optional] whether to calculate output function</li>
+<li><strong>calc_measurement</strong> [optional] whether to calculate parameters for measurement/observation law</li>
+<li><strong>max_iter</strong> max number of iterations</li>
+<li><strong>tol</strong> convergence tolerance (max fractional abs change)</li>
+</ul>
+<p><strong>Return</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a></p>
+<hr>
+<h3 id="returndata">
+  <strong>ReturnData</strong>
+  <a class="anchor" href="#returndata">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> std<span style="color:#f92672">::</span>tuple<span style="color:#f92672">&lt;</span> UniformMatrixList<span style="color:#f92672">&lt;</span> kMatFreeDim2 <span style="color:#f92672">&gt;</span>, UniformMatrixList<span style="color:#f92672">&lt;</span> kMatFreeDim2 <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&gt;</span> ReturnData()
+</span></span></code></pre></div><p><strong>Return</strong>: tuple(input data, output data)</p>
+<hr>
+<h3 id="x">
+  <strong>x</strong>
+  <a class="anchor" href="#x">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> Matrix <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;</span> x() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="y">
+  <strong>y</strong>
+  <a class="anchor" href="#y">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> Matrix <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;</span> y() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="sum_">
+  <strong>sum_E_x_t_x_t</strong>
+  <a class="anchor" href="#sum_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> sum_E_x_t_x_t() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="sum_-1">
+  <strong>sum_E_xu_tm1_xu_tm1</strong>
+  <a class="anchor" href="#sum_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> sum_E_xu_tm1_xu_tm1() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="sum_-2">
+  <strong>sum_E_xu_t_xu_tm1</strong>
+  <a class="anchor" href="#sum_-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> sum_E_xu_t_xu_tm1() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="n_">
+  <strong>n_t_tot</strong>
+  <a class="anchor" href="#n_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> size_t n_t_tot()
+</span></span></code></pre></div><hr>
+<h3 id="theta">
+  <strong>theta</strong>
+  <a class="anchor" href="#theta">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> theta() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h2 id="protected-function-details">
+  Protected Function Details
+  <a class="anchor" href="#protected-function-details">#</a>
+</h2>
+<h3 id="expectation">
+  <strong>Expectation</strong>
+  <a class="anchor" href="#expectation">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> Expectation(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> force_common_initial <span style="color:#f92672">=</span>false
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>force_common_initial</strong> whether to force common initial condition for all trials</li>
+</ul>
+<hr>
+<h3 id="maximization">
+  <strong>Maximization</strong>
+  <a class="anchor" href="#maximization">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> Maximization(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> calc_dynamics <span style="color:#f92672">=</span>true,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> calc_Q <span style="color:#f92672">=</span>true,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> calc_init <span style="color:#f92672">=</span>false,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> calc_output <span style="color:#f92672">=</span>false,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> calc_measurement <span style="color:#f92672">=</span>false
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>calc_dynamics</strong> [optional] whether to caclulate dynamics (A, B)</li>
+<li><strong>calc_Q</strong> [optional] whether to calculate process noise covariance</li>
+<li><strong>calc_init</strong> [optional] whether to calculate initial conditions</li>
+<li><strong>calc_output</strong> [optional] whether to calculate output function</li>
+<li><strong>calc_measurement</strong> [optional] whether to calculate parameters for measurement/observation law</li>
+</ul>
+<hr>
+<h3 id="maximizedynamics">
+  <strong>MaximizeDynamics</strong>
+  <a class="anchor" href="#maximizedynamics">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> MaximizeDynamics()
+</span></span></code></pre></div><hr>
+<h3 id="maximizeq">
+  <strong>MaximizeQ</strong>
+  <a class="anchor" href="#maximizeq">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> MaximizeQ()
+</span></span></code></pre></div><hr>
+<h3 id="maximizeinitial">
+  <strong>MaximizeInitial</strong>
+  <a class="anchor" href="#maximizeinitial">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> MaximizeInitial()
+</span></span></code></pre></div><hr>
+<h3 id="maximizeoutput">
+  <strong>MaximizeOutput</strong>
+  <a class="anchor" href="#maximizeoutput">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> MaximizeOutput() <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>
+</span></span></code></pre></div><p><strong>Reimplemented by</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/#function-maximizeoutput">lds::gaussian::FitEM::MaximizeOutput</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/#function-maximizeoutput">lds::poisson::FitEM::MaximizeOutput</a></p>
+<hr>
+<h3 id="maximizemeasurement">
+  <strong>MaximizeMeasurement</strong>
+  <a class="anchor" href="#maximizemeasurement">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> MaximizeMeasurement() <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>
+</span></span></code></pre></div><p><strong>Reimplemented by</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/#function-maximizemeasurement">lds::gaussian::FitEM::MaximizeMeasurement</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/#function-maximizemeasurement">lds::poisson::FitEM::MaximizeMeasurement</a></p>
+<hr>
+<h3 id="smooth">
+  <strong>Smooth</strong>
+  <a class="anchor" href="#smooth">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> Smooth(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> force_common_initial
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>force_common_initial</strong> whether to force common initial conditions</li>
+</ul>
+<hr>
+<h3 id="recurseke">
+  <strong>RecurseKe</strong>
+  <a class="anchor" href="#recurseke">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> RecurseKe(
+</span></span><span style="display:flex;"><span>    Matrix <span style="color:#f92672">&amp;</span> Ke,
+</span></span><span style="display:flex;"><span>    Cube <span style="color:#f92672">&amp;</span> P_pre,
+</span></span><span style="display:flex;"><span>    Cube <span style="color:#f92672">&amp;</span> P_post,
+</span></span><span style="display:flex;"><span>    size_t t
+</span></span><span style="display:flex;"><span>) <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>Ke</strong> estimator gain</li>
+<li><strong>P_pre</strong> cov of predicted state est.</li>
+<li><strong>P_post</strong> cov of postior sate est.</li>
+<li><strong>t</strong> time</li>
+</ul>
+<p><strong>Reimplemented by</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/#function-recurseke">lds::gaussian::FitEM::RecurseKe</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/#function-recurseke">lds::poisson::FitEM::RecurseKe</a></p>
+<hr>
+<h3 id="reset">
+  <strong>Reset</strong>
+  <a class="anchor" href="#reset">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> Reset()
+</span></span></code></pre></div><hr>
+<h3 id="initvars">
+  <strong>InitVars</strong>
+  <a class="anchor" href="#initvars">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> InitVars()
+</span></span></code></pre></div><hr>
+<h3 id="updatetheta">
+  <strong>UpdateTheta</strong>
+  <a class="anchor" href="#updatetheta">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector UpdateTheta()
+</span></span></code></pre></div><p><strong>Return</strong>: parameter list</p>
+<hr>
+<h2 id="protected-attribute-details">
+  Protected Attribute Details
+  <a class="anchor" href="#protected-attribute-details">#</a>
+</h2>
+<h3 id="u_">
+  <strong>u_</strong>
+  <a class="anchor" href="#u_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>UniformMatrixList<span style="color:#f92672">&lt;</span> kMatFreeDim2 <span style="color:#f92672">&gt;</span> u_;
+</span></span></code></pre></div><hr>
+<h3 id="z_">
+  <strong>z_</strong>
+  <a class="anchor" href="#z_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>UniformMatrixList<span style="color:#f92672">&lt;</span> kMatFreeDim2 <span style="color:#f92672">&gt;</span> z_;
+</span></span></code></pre></div><hr>
+<h3 id="x_">
+  <strong>x_</strong>
+  <a class="anchor" href="#x_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> Matrix <span style="color:#f92672">&gt;</span> x_;
+</span></span></code></pre></div><hr>
+<h3 id="p_">
+  <strong>P_</strong>
+  <a class="anchor" href="#p_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> Cube <span style="color:#f92672">&gt;</span> P_;
+</span></span></code></pre></div><hr>
+<h3 id="p_-1">
+  <strong>P_t_tm1_</strong>
+  <a class="anchor" href="#p_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> Cube <span style="color:#f92672">&gt;</span> P_t_tm1_;
+</span></span></code></pre></div><hr>
+<h3 id="y_">
+  <strong>y_</strong>
+  <a class="anchor" href="#y_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> Matrix <span style="color:#f92672">&gt;</span> y_;
+</span></span></code></pre></div><hr>
+<h3 id="diag_">
+  <strong>diag_y_</strong>
+  <a class="anchor" href="#diag_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix diag_y_;
+</span></span></code></pre></div><hr>
+<h3 id="sum_-3">
+  <strong>sum_E_x_t_x_t_</strong>
+  <a class="anchor" href="#sum_-3">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix sum_E_x_t_x_t_;
+</span></span></code></pre></div><hr>
+<h3 id="sum_-4">
+  <strong>sum_E_xu_tm1_xu_tm1_</strong>
+  <a class="anchor" href="#sum_-4">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix sum_E_xu_tm1_xu_tm1_;
+</span></span></code></pre></div><hr>
+<h3 id="sum_-5">
+  <strong>sum_E_xu_t_xu_tm1_</strong>
+  <a class="anchor" href="#sum_-5">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix sum_E_xu_t_xu_tm1_;
+</span></span></code></pre></div><hr>
+<h3 id="fit_">
+  <strong>fit_</strong>
+  <a class="anchor" href="#fit_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Fit fit_;
+</span></span></code></pre></div><hr>
+<h3 id="theta_">
+  <strong>theta_</strong>
+  <a class="anchor" href="#theta_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector theta_;
+</span></span></code></pre></div><hr>
+<h3 id="dt_">
+  <strong>dt_</strong>
+  <a class="anchor" href="#dt_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>data_t dt_ {};
+</span></span></code></pre></div><hr>
+<h3 id="n_-1">
+  <strong>n_u_</strong>
+  <a class="anchor" href="#n_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>size_t n_u_ {};
+</span></span></code></pre></div><hr>
+<h3 id="n_-2">
+  <strong>n_x_</strong>
+  <a class="anchor" href="#n_-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>size_t n_x_ {};
+</span></span></code></pre></div><hr>
+<h3 id="n_-3">
+  <strong>n_y_</strong>
+  <a class="anchor" href="#n_-3">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>size_t n_y_ {};
+</span></span></code></pre></div><hr>
+<h3 id="n_-4">
+  <strong>n_trials_</strong>
+  <a class="anchor" href="#n_-4">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>size_t n_trials_ {};
+</span></span></code></pre></div><hr>
+<h3 id="n_-5">
+  <strong>n_t_</strong>
+  <a class="anchor" href="#n_-5">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> size_t <span style="color:#f92672">&gt;</span> n_t_;
+</span></span></code></pre></div><hr>
+<h3 id="n_-6">
+  <strong>n_t_tot_</strong>
+  <a class="anchor" href="#n_-6">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>size_t n_t_tot_ {};
+</span></span></code></pre></div><hr>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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+  
+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#ldsem">lds::EM</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#protected-functions">Protected Functions</a></li>
+        <li><a href="#protected-attributes">Protected Attributes</a></li>
+        <li><a href="#detailed-description">Detailed Description</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#em"><strong>EM</strong></a></li>
+            <li><a href="#em-1"><strong>EM</strong></a></li>
+            <li><a href="#em-2"><strong>EM</strong></a></li>
+            <li><a href="#heading"><strong>~EM</strong></a></li>
+            <li><a href="#run"><strong>Run</strong></a></li>
+            <li><a href="#returndata"><strong>ReturnData</strong></a></li>
+            <li><a href="#x"><strong>x</strong></a></li>
+            <li><a href="#y"><strong>y</strong></a></li>
+            <li><a href="#sum_"><strong>sum_E_x_t_x_t</strong></a></li>
+            <li><a href="#sum_-1"><strong>sum_E_xu_tm1_xu_tm1</strong></a></li>
+            <li><a href="#sum_-2"><strong>sum_E_xu_t_xu_tm1</strong></a></li>
+            <li><a href="#n_"><strong>n_t_tot</strong></a></li>
+            <li><a href="#theta"><strong>theta</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-function-details">Protected Function Details</a>
+          <ul>
+            <li><a href="#expectation"><strong>Expectation</strong></a></li>
+            <li><a href="#maximization"><strong>Maximization</strong></a></li>
+            <li><a href="#maximizedynamics"><strong>MaximizeDynamics</strong></a></li>
+            <li><a href="#maximizeq"><strong>MaximizeQ</strong></a></li>
+            <li><a href="#maximizeinitial"><strong>MaximizeInitial</strong></a></li>
+            <li><a href="#maximizeoutput"><strong>MaximizeOutput</strong></a></li>
+            <li><a href="#maximizemeasurement"><strong>MaximizeMeasurement</strong></a></li>
+            <li><a href="#smooth"><strong>Smooth</strong></a></li>
+            <li><a href="#recurseke"><strong>RecurseKe</strong></a></li>
+            <li><a href="#reset"><strong>Reset</strong></a></li>
+            <li><a href="#initvars"><strong>InitVars</strong></a></li>
+            <li><a href="#updatetheta"><strong>UpdateTheta</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-attribute-details">Protected Attribute Details</a>
+          <ul>
+            <li><a href="#u_"><strong>u_</strong></a></li>
+            <li><a href="#z_"><strong>z_</strong></a></li>
+            <li><a href="#x_"><strong>x_</strong></a></li>
+            <li><a href="#p_"><strong>P_</strong></a></li>
+            <li><a href="#p_-1"><strong>P_t_tm1_</strong></a></li>
+            <li><a href="#y_"><strong>y_</strong></a></li>
+            <li><a href="#diag_"><strong>diag_y_</strong></a></li>
+            <li><a href="#sum_-3"><strong>sum_E_x_t_x_t_</strong></a></li>
+            <li><a href="#sum_-4"><strong>sum_E_xu_tm1_xu_tm1_</strong></a></li>
+            <li><a href="#sum_-5"><strong>sum_E_xu_t_xu_tm1_</strong></a></li>
+            <li><a href="#fit_"><strong>fit_</strong></a></li>
+            <li><a href="#theta_"><strong>theta_</strong></a></li>
+            <li><a href="#dt_"><strong>dt_</strong></a></li>
+            <li><a href="#n_-1"><strong>n_u_</strong></a></li>
+            <li><a href="#n_-2"><strong>n_x_</strong></a></li>
+            <li><a href="#n_-3"><strong>n_y_</strong></a></li>
+            <li><a href="#n_-4"><strong>n_trials_</strong></a></li>
+            <li><a href="#n_-5"><strong>n_t_</strong></a></li>
+            <li><a href="#n_-6"><strong>n_t_tot_</strong></a></li>
+          </ul>
+        </li>
+      </ul>
+    </li>
+  </ul>
+</nav>
+
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diff --git a/docs/docs/api/classes/classlds_1_1_fit/index.html b/docs/docs/api/classes/classlds_1_1_fit/index.html
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+
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+  <ul>
+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
+<li><a href="/lds-ctrl-est/docs/api/classes/">Classes</a></li>
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+<li><a href="/lds-ctrl-est/issues-contributing/"><strong>Reporting Issues &amp; Contributing</strong></a></li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
+</ul>
+<br />
+
+
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+
+
+
+  
+<ul>
+  
+  <li>
+    <a href="https://github.com/stanley-rozell/lds-ctrl-est" target="_blank" rel="noopener">
+        Github
+      </a>
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+  <strong>lds::Fit</strong>
+
+  <label for="toc-control">
+    
+    <img src="/lds-ctrl-est/svg/toc.svg" class="book-icon" alt="Table of Contents" />
+    
+  </label>
+</div>
+
+
+  
+  <aside class="hidden clearfix">
+    
+  
+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#ldsfit">lds::Fit</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#protected-attributes">Protected Attributes</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#fit"><strong>Fit</strong></a></li>
+            <li><a href="#fit-1"><strong>Fit</strong></a></li>
+            <li><a href="#heading"><strong>~Fit</strong></a></li>
+            <li><a href="#n_"><strong>n_u</strong></a></li>
+            <li><a href="#n_-1"><strong>n_x</strong></a></li>
+            <li><a href="#n_-2"><strong>n_y</strong></a></li>
+            <li><a href="#dt"><strong>dt</strong></a></li>
+            <li><a href="#a"><strong>A</strong></a></li>
+            <li><a href="#b"><strong>B</strong></a></li>
+            <li><a href="#g"><strong>g</strong></a></li>
+            <li><a href="#m"><strong>m</strong></a></li>
+            <li><a href="#q"><strong>Q</strong></a></li>
+            <li><a href="#x0"><strong>x0</strong></a></li>
+            <li><a href="#p0"><strong>P0</strong></a></li>
+            <li><a href="#c"><strong>C</strong></a></li>
+            <li><a href="#d"><strong>d</strong></a></li>
+            <li><a href="#r"><strong>R</strong></a></li>
+            <li><a href="#set_"><strong>set_A</strong></a></li>
+            <li><a href="#set_-1"><strong>set_B</strong></a></li>
+            <li><a href="#set_-2"><strong>set_g</strong></a></li>
+            <li><a href="#set_-3"><strong>set_m</strong></a></li>
+            <li><a href="#set_-4"><strong>set_Q</strong></a></li>
+            <li><a href="#set_-5"><strong>set_R</strong></a></li>
+            <li><a href="#set_-6"><strong>set_x0</strong></a></li>
+            <li><a href="#set_-7"><strong>set_P0</strong></a></li>
+            <li><a href="#set_-8"><strong>set_C</strong></a></li>
+            <li><a href="#set_-9"><strong>set_d</strong></a></li>
+            <li><a href="#f"><strong>f</strong></a></li>
+            <li><a href="#f-1"><strong>f</strong></a></li>
+            <li><a href="#h"><strong>h</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-attribute-details">Protected Attribute Details</a>
+          <ul>
+            <li><a href="#dt_"><strong>dt_</strong></a></li>
+            <li><a href="#a_"><strong>A_</strong></a></li>
+            <li><a href="#b_"><strong>B_</strong></a></li>
+            <li><a href="#g_"><strong>g_</strong></a></li>
+            <li><a href="#m_"><strong>m_</strong></a></li>
+            <li><a href="#q_"><strong>Q_</strong></a></li>
+            <li><a href="#c_"><strong>C_</strong></a></li>
+            <li><a href="#d_"><strong>d_</strong></a></li>
+            <li><a href="#r_"><strong>R_</strong></a></li>
+            <li><a href="#x0_"><strong>x0_</strong></a></li>
+            <li><a href="#p0_"><strong>P0_</strong></a></li>
+            <li><a href="#n_-3"><strong>n_u_</strong></a></li>
+            <li><a href="#n_-4"><strong>n_x_</strong></a></li>
+            <li><a href="#n_-5"><strong>n_y_</strong></a></li>
+          </ul>
+        </li>
+      </ul>
+    </li>
+  </ul>
+</nav>
+
+
+
+  </aside>
+  
+ 
+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="ldsfit">
+  lds::Fit
+  <a class="anchor" href="#ldsfit">#</a>
+</h1>
+<p>LDS <a href="">Fit</a> Type.
+<br /> <code>#include &lt;lds_fit.h&gt;</code></p>
+<p>Inherited by <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/">lds::gaussian::Fit</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/">lds::poisson::Fit</a></p>
+<h2 id="public-functions">
+  Public Functions
+  <a class="anchor" href="#public-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-fit">Fit</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-fit">Fit</a></strong>(size_t n_u, size_t n_x, size_t n_y, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> dt)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a>.</td>
+      </tr>
+      <tr>
+          <td>virtual</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-~fit">~Fit</a></strong>() =default</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-n-u">n_u</a></strong>() const<br>gets number of inputs</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-n-x">n_x</a></strong>() const<br>gets number of states</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-n-y">n_y</a></strong>() const<br>gets number of outputs</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-dt">dt</a></strong>() const<br>gets sample period</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-a">A</a></strong>() const<br>gets state matrix</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-b">B</a></strong>() const<br>gets input matrix</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-g">g</a></strong>() const<br>gets input gain</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-m">m</a></strong>() const<br>gets process disturbance</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-q">Q</a></strong>() const<br>gets process noise covariance</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-x0">x0</a></strong>() const<br>gets initial state estimate</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-p0">P0</a></strong>() const<br>gets covariance of initial state estimate</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-c">C</a></strong>() const<br>gets output matrix</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-d">d</a></strong>() const<br>gets output bias</td>
+      </tr>
+      <tr>
+          <td>virtual const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-r">R</a></strong>() const =0</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-a">set_A</a></strong>(const Matrix &amp; A)<br>sets state matrix</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-b">set_B</a></strong>(const Matrix &amp; B)<br>sets input matrix</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-g">set_g</a></strong>(const Vector &amp; g)<br>sets input gain/conversion factor</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-m">set_m</a></strong>(const Vector &amp; m)<br>sets process disturbance</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-q">set_Q</a></strong>(const Matrix &amp; Q)<br>sets process noise covariance</td>
+      </tr>
+      <tr>
+          <td>virtual void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-r">set_R</a></strong>(const Matrix &amp; R) =0<br>sets output noise covariance (if any)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-x0">set_x0</a></strong>(const Vector &amp; x0)<br>sets initial state estimate</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-p0">set_P0</a></strong>(const Matrix &amp; P0)<br>sets initial state estimate covariance</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-c">set_C</a></strong>(const Matrix &amp; C)<br>sets output matrix</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-d">set_d</a></strong>(const Vector &amp; d)<br>sets output bias</td>
+      </tr>
+      <tr>
+          <td>View</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-f">f</a></strong>(Matrix &amp; x, const Matrix &amp; u, size_t t)<br>system dynamics function</td>
+      </tr>
+      <tr>
+          <td>View</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-f">f</a></strong>(Matrix &amp; x_pre, const Matrix &amp; x_post, const Matrix &amp; u, size_t t)<br>system dynamics function</td>
+      </tr>
+      <tr>
+          <td>virtual View</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-h">h</a></strong>(Matrix &amp; y, const Matrix &amp; x, size_t t) =0<br>output function</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="protected-attributes">
+  Protected Attributes
+  <a class="anchor" href="#protected-attributes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-dt-">dt_</a></strong> <br>sample period</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-a-">A_</a></strong> <br>state matrix</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-b-">B_</a></strong> <br>input matrix</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-g-">g_</a></strong> <br>input gain</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-m-">m_</a></strong> <br>process noise mean</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-q-">Q_</a></strong> <br>process noise cov</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-c-">C_</a></strong> <br>output matrix</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-d-">d_</a></strong> <br>output bias</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-r-">R_</a></strong> <br>measurement noise</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-x0-">x0_</a></strong> <br>initial state</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-p0-">P0_</a></strong> <br>initial covar</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-n-u-">n_u_</a></strong> <br>number of inputs</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-n-x-">n_x_</a></strong> <br>number of states</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-n-y-">n_y_</a></strong> <br>number of outputs</td>
+      </tr>
+  </tbody>
+</table>
+<hr>
+<hr>
+<h2 id="public-function-details">
+  Public Function Details
+  <a class="anchor" href="#public-function-details">#</a>
+</h2>
+<h3 id="fit">
+  <strong>Fit</strong>
+  <a class="anchor" href="#fit">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Fit() <span style="color:#f92672">=</span><span style="color:#66d9ef">default</span>
+</span></span></code></pre></div><hr>
+<h3 id="fit-1">
+  <strong>Fit</strong>
+  <a class="anchor" href="#fit-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Fit(
+</span></span><span style="display:flex;"><span>    size_t n_u,
+</span></span><span style="display:flex;"><span>    size_t n_x,
+</span></span><span style="display:flex;"><span>    size_t n_y,
+</span></span><span style="display:flex;"><span>    data_t dt
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>n_u</strong> number of inputs</li>
+<li><strong>n_x</strong> number of states</li>
+<li><strong>n_y</strong> number of outputs</li>
+<li><strong>dt</strong> sample period</li>
+</ul>
+<hr>
+<h3 id="heading">
+  <strong>~Fit</strong>
+  <a class="anchor" href="#heading">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">virtual</span> <span style="color:#f92672">~</span>Fit() <span style="color:#f92672">=</span><span style="color:#66d9ef">default</span>
+</span></span></code></pre></div><hr>
+<h3 id="n_">
+  <strong>n_u</strong>
+  <a class="anchor" href="#n_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> size_t n_u() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="n_-1">
+  <strong>n_x</strong>
+  <a class="anchor" href="#n_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> size_t n_x() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="n_-2">
+  <strong>n_y</strong>
+  <a class="anchor" href="#n_-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> size_t n_y() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="dt">
+  <strong>dt</strong>
+  <a class="anchor" href="#dt">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> data_t dt() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="a">
+  <strong>A</strong>
+  <a class="anchor" href="#a">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> A() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="b">
+  <strong>B</strong>
+  <a class="anchor" href="#b">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> B() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="g">
+  <strong>g</strong>
+  <a class="anchor" href="#g">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> g() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="m">
+  <strong>m</strong>
+  <a class="anchor" href="#m">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> m() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="q">
+  <strong>Q</strong>
+  <a class="anchor" href="#q">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> Q() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="x0">
+  <strong>x0</strong>
+  <a class="anchor" href="#x0">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> x0() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="p0">
+  <strong>P0</strong>
+  <a class="anchor" href="#p0">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> P0() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="c">
+  <strong>C</strong>
+  <a class="anchor" href="#c">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> C() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="d">
+  <strong>d</strong>
+  <a class="anchor" href="#d">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> d() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="r">
+  <strong>R</strong>
+  <a class="anchor" href="#r">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> R() <span style="color:#66d9ef">const</span> <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>
+</span></span></code></pre></div><p><strong>Reimplemented by</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/#function-r">lds::gaussian::Fit::R</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/#function-r">lds::poisson::Fit::R</a></p>
+<hr>
+<h3 id="set_">
+  <strong>set_A</strong>
+  <a class="anchor" href="#set_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_A(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> A
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-1">
+  <strong>set_B</strong>
+  <a class="anchor" href="#set_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_B(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> B
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-2">
+  <strong>set_g</strong>
+  <a class="anchor" href="#set_-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_g(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> g
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-3">
+  <strong>set_m</strong>
+  <a class="anchor" href="#set_-3">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_m(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> m
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-4">
+  <strong>set_Q</strong>
+  <a class="anchor" href="#set_-4">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_Q(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> Q
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-5">
+  <strong>set_R</strong>
+  <a class="anchor" href="#set_-5">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> set_R(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> R
+</span></span><span style="display:flex;"><span>) <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>
+</span></span></code></pre></div><p><strong>Reimplemented by</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/#function-set-r">lds::gaussian::Fit::set_R</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/#function-set-r">lds::poisson::Fit::set_R</a></p>
+<hr>
+<h3 id="set_-6">
+  <strong>set_x0</strong>
+  <a class="anchor" href="#set_-6">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_x0(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> x0
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-7">
+  <strong>set_P0</strong>
+  <a class="anchor" href="#set_-7">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_P0(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> P0
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-8">
+  <strong>set_C</strong>
+  <a class="anchor" href="#set_-8">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_C(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> C
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-9">
+  <strong>set_d</strong>
+  <a class="anchor" href="#set_-9">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_d(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> d
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="f">
+  <strong>f</strong>
+  <a class="anchor" href="#f">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> View f(
+</span></span><span style="display:flex;"><span>    Matrix <span style="color:#f92672">&amp;</span> x,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> u,
+</span></span><span style="display:flex;"><span>    size_t t
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>x</strong> state estimate (over time)</li>
+<li><strong>u</strong> input (over time)</li>
+<li><strong>t</strong> time index</li>
+</ul>
+<p><strong>Return</strong>: view of updated state</p>
+<hr>
+<h3 id="f-1">
+  <strong>f</strong>
+  <a class="anchor" href="#f-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> View f(
+</span></span><span style="display:flex;"><span>    Matrix <span style="color:#f92672">&amp;</span> x_pre,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> x_post,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> u,
+</span></span><span style="display:flex;"><span>    size_t t
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>x_pre</strong> predicted state est.</li>
+<li><strong>x_post</strong> posterior state est.</li>
+<li><strong>u</strong> input (over time)</li>
+<li><strong>t</strong> time index</li>
+</ul>
+<p><strong>Return</strong>: view of predicted state</p>
+<hr>
+<h3 id="h">
+  <strong>h</strong>
+  <a class="anchor" href="#h">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">virtual</span> View h(
+</span></span><span style="display:flex;"><span>    Matrix <span style="color:#f92672">&amp;</span> y,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> x,
+</span></span><span style="display:flex;"><span>    size_t t
+</span></span><span style="display:flex;"><span>) <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>y</strong> output estimate (over time)</li>
+<li><strong>x</strong> state estimate (over time)</li>
+<li><strong>t</strong> time index</li>
+</ul>
+<p><strong>Return</strong>: output</p>
+<p><strong>Reimplemented by</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/#function-h">lds::gaussian::Fit::h</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/#function-h">lds::poisson::Fit::h</a></p>
+<hr>
+<h2 id="protected-attribute-details">
+  Protected Attribute Details
+  <a class="anchor" href="#protected-attribute-details">#</a>
+</h2>
+<h3 id="dt_">
+  <strong>dt_</strong>
+  <a class="anchor" href="#dt_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>data_t dt_ {};
+</span></span></code></pre></div><hr>
+<h3 id="a_">
+  <strong>A_</strong>
+  <a class="anchor" href="#a_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix A_;
+</span></span></code></pre></div><hr>
+<h3 id="b_">
+  <strong>B_</strong>
+  <a class="anchor" href="#b_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix B_;
+</span></span></code></pre></div><hr>
+<h3 id="g_">
+  <strong>g_</strong>
+  <a class="anchor" href="#g_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector g_;
+</span></span></code></pre></div><hr>
+<h3 id="m_">
+  <strong>m_</strong>
+  <a class="anchor" href="#m_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector m_;
+</span></span></code></pre></div><hr>
+<h3 id="q_">
+  <strong>Q_</strong>
+  <a class="anchor" href="#q_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix Q_;
+</span></span></code></pre></div><hr>
+<h3 id="c_">
+  <strong>C_</strong>
+  <a class="anchor" href="#c_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix C_;
+</span></span></code></pre></div><hr>
+<h3 id="d_">
+  <strong>d_</strong>
+  <a class="anchor" href="#d_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector d_;
+</span></span></code></pre></div><hr>
+<h3 id="r_">
+  <strong>R_</strong>
+  <a class="anchor" href="#r_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix R_;
+</span></span></code></pre></div><hr>
+<h3 id="x0_">
+  <strong>x0_</strong>
+  <a class="anchor" href="#x0_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector x0_;
+</span></span></code></pre></div><hr>
+<h3 id="p0_">
+  <strong>P0_</strong>
+  <a class="anchor" href="#p0_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix P0_;
+</span></span></code></pre></div><hr>
+<h3 id="n_-3">
+  <strong>n_u_</strong>
+  <a class="anchor" href="#n_-3">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>size_t n_u_ {};
+</span></span></code></pre></div><hr>
+<h3 id="n_-4">
+  <strong>n_x_</strong>
+  <a class="anchor" href="#n_-4">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>size_t n_x_ {};
+</span></span></code></pre></div><hr>
+<h3 id="n_-5">
+  <strong>n_y_</strong>
+  <a class="anchor" href="#n_-5">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>size_t n_y_ {};
+</span></span></code></pre></div><hr>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
+      
+
+      <footer class="book-footer">
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+  
+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#ldsfit">lds::Fit</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#protected-attributes">Protected Attributes</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#fit"><strong>Fit</strong></a></li>
+            <li><a href="#fit-1"><strong>Fit</strong></a></li>
+            <li><a href="#heading"><strong>~Fit</strong></a></li>
+            <li><a href="#n_"><strong>n_u</strong></a></li>
+            <li><a href="#n_-1"><strong>n_x</strong></a></li>
+            <li><a href="#n_-2"><strong>n_y</strong></a></li>
+            <li><a href="#dt"><strong>dt</strong></a></li>
+            <li><a href="#a"><strong>A</strong></a></li>
+            <li><a href="#b"><strong>B</strong></a></li>
+            <li><a href="#g"><strong>g</strong></a></li>
+            <li><a href="#m"><strong>m</strong></a></li>
+            <li><a href="#q"><strong>Q</strong></a></li>
+            <li><a href="#x0"><strong>x0</strong></a></li>
+            <li><a href="#p0"><strong>P0</strong></a></li>
+            <li><a href="#c"><strong>C</strong></a></li>
+            <li><a href="#d"><strong>d</strong></a></li>
+            <li><a href="#r"><strong>R</strong></a></li>
+            <li><a href="#set_"><strong>set_A</strong></a></li>
+            <li><a href="#set_-1"><strong>set_B</strong></a></li>
+            <li><a href="#set_-2"><strong>set_g</strong></a></li>
+            <li><a href="#set_-3"><strong>set_m</strong></a></li>
+            <li><a href="#set_-4"><strong>set_Q</strong></a></li>
+            <li><a href="#set_-5"><strong>set_R</strong></a></li>
+            <li><a href="#set_-6"><strong>set_x0</strong></a></li>
+            <li><a href="#set_-7"><strong>set_P0</strong></a></li>
+            <li><a href="#set_-8"><strong>set_C</strong></a></li>
+            <li><a href="#set_-9"><strong>set_d</strong></a></li>
+            <li><a href="#f"><strong>f</strong></a></li>
+            <li><a href="#f-1"><strong>f</strong></a></li>
+            <li><a href="#h"><strong>h</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-attribute-details">Protected Attribute Details</a>
+          <ul>
+            <li><a href="#dt_"><strong>dt_</strong></a></li>
+            <li><a href="#a_"><strong>A_</strong></a></li>
+            <li><a href="#b_"><strong>B_</strong></a></li>
+            <li><a href="#g_"><strong>g_</strong></a></li>
+            <li><a href="#m_"><strong>m_</strong></a></li>
+            <li><a href="#q_"><strong>Q_</strong></a></li>
+            <li><a href="#c_"><strong>C_</strong></a></li>
+            <li><a href="#d_"><strong>d_</strong></a></li>
+            <li><a href="#r_"><strong>R_</strong></a></li>
+            <li><a href="#x0_"><strong>x0_</strong></a></li>
+            <li><a href="#p0_"><strong>P0_</strong></a></li>
+            <li><a href="#n_-3"><strong>n_u_</strong></a></li>
+            <li><a href="#n_-4"><strong>n_x_</strong></a></li>
+            <li><a href="#n_-5"><strong>n_y_</strong></a></li>
+          </ul>
+        </li>
+      </ul>
+    </li>
+  </ul>
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diff --git a/docs/docs/api/classes/classlds_1_1_s_s_i_d/index.html b/docs/docs/api/classes/classlds_1_1_s_s_i_d/index.html
new file mode 100644
index 00000000..ba0faa6e
--- /dev/null
+++ b/docs/docs/api/classes/classlds_1_1_s_s_i_d/index.html
@@ -0,0 +1,896 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="
+  lds::SSID
+  #
+
+More&hellip;
+Inherited by lds::gaussian::FitSSID, lds::poisson::FitSSID
+
+  Public Functions
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          
+          SSID() =defaultConstructs a new SSIDFit type.
+      
+      
+          
+          SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; u_train, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; z_train, const Vector &amp; d =Vector(1).fill(-kInf))Constructs a new SSIDFit type.
+      
+      
+          std::tuple&lt; Fit, Vector &gt;
+          Run(SSIDWt ssid_wt)Runs fitting by subspace identification (SSID)
+      
+      
+          std::tuple&lt; UniformMatrixList&lt; kMatFreeDim2 &gt;, UniformMatrixList&lt; kMatFreeDim2 &gt; &gt;
+          ReturnData()Returns the I/O data to caller.
+      
+  
+
+
+  Protected Functions
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          void
+          CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)Using periods of silence in inputs (u), calculates the output \ bias (d)
+      
+      
+          void
+          CreateHankelDataMat()Creates the block-hankel I/O data matrix.
+      
+      
+          virtual void
+          DecomposeData() =0Decompose data to lower-triangular matrix (used in Solve)
+      
+      
+          void
+          CalcSVD(SSIDWt wt)performs the singular value decomposition (SVD)
+      
+      
+          void
+          Solve(data_t wt_dc)solves for LDS parameters
+      
+      
+          void
+          RecomputeExtObs()recompute extended observability matrix from estimates of A, C
+      
+  
+
+
+  Protected Attributes
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          UniformMatrixList&lt; kMatFreeDim2 &gt;
+          u_ input training data
+      
+      
+          UniformMatrixList&lt; kMatFreeDim2 &gt;
+          z_ measurement training data
+      
+      
+          Matrix
+          D_ block-Hankel I/O data matrix
+      
+      
+          Fit
+          fit_ fit
+      
+      
+          Matrix
+          g_dc_ I/O gain @ DC.
+      
+      
+          data_t
+          dt_ sample period
+      
+      
+          size_t
+          n_u_ number of inputs
+      
+      
+          size_t
+          n_x_ number of states
+      
+      
+          size_t
+          n_y_ number of outputs
+      
+      
+          size_t
+          n_h_
+      
+      
+          size_t
+          n_trials_ number of input/output data sequences
+      
+      
+          std::vector&lt; size_t &gt;
+          n_t_ number of time steps
+      
+      
+          size_t
+          n_t_tot_ total number of time steps across trials
+      
+      
+          Matrix
+          L_ lower triangle decomp of covariance matrix
+      
+      
+          Vector
+          s_ singular values
+      
+      
+          Matrix
+          ext_obs_t_ extended observability matrix
+      
+  
+
+
+  Detailed Description
+  #
+
+template &lt;typename Fit &gt;
+class lds::SSID;
+
+
+
+  Public Function Details
+  #
+
+
+  SSID
+  #
+
+SSID() =default
+
+
+  SSID
+  #
+
+SSID(
+    size_t n_x,
+    size_t n_h,
+    data_t dt,
+    UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; u_train,
+    UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; z_train,
+    const Vector &amp; d =Vector(1).fill(-kInf)
+)
+Parameters:">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">
+  <meta property="og:site_name" content="LDS C&E">
+  <meta property="og:title" content="lds::SSID">
+  <meta property="og:description" content="lds::SSID # More…
+Inherited by lds::gaussian::FitSSID, lds::poisson::FitSSID
+Public Functions # Name SSID() =default
+Constructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; u_train, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; z_train, const Vector &amp; d =Vector(1).fill(-kInf))
+Constructs a new SSIDFit type. std::tuple&lt; Fit, Vector &gt; Run(SSIDWt ssid_wt)
+Runs fitting by subspace identification (SSID) std::tuple&lt; UniformMatrixList&lt; kMatFreeDim2 &gt;, UniformMatrixList&lt; kMatFreeDim2 &gt; &gt; ReturnData()
+Returns the I/O data to caller. Protected Functions # Name void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)
+Using periods of silence in inputs (u), calculates the output \ bias (d) void CreateHankelDataMat()
+Creates the block-hankel I/O data matrix. virtual void DecomposeData() =0
+Decompose data to lower-triangular matrix (used in Solve) void CalcSVD(SSIDWt wt)
+performs the singular value decomposition (SVD) void Solve(data_t wt_dc)
+solves for LDS parameters void RecomputeExtObs()
+recompute extended observability matrix from estimates of A, C Protected Attributes # Name UniformMatrixList&lt; kMatFreeDim2 &gt; u_ input training data UniformMatrixList&lt; kMatFreeDim2 &gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector&lt; size_t &gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Detailed Description # template &lt;typename Fit &gt; class lds::SSID; Public Function Details # SSID # SSID() =default SSID # SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; u_train, UniformMatrixList&lt; kMatFreeDim2 &gt; &amp;&amp; z_train, const Vector &amp; d =Vector(1).fill(-kInf) ) Parameters:">
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+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
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+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/issues-contributing/"><strong>Reporting Issues &amp; Contributing</strong></a></li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
+</ul>
+<br />
+
+
+
+
+
+
+  
+<ul>
+  
+  <li>
+    <a href="https://github.com/stanley-rozell/lds-ctrl-est" target="_blank" rel="noopener">
+        Github
+      </a>
+  </li>
+  
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+
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+
+</nav>
+
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+  <script>(function(){var e=document.querySelector("aside .book-menu-content");addEventListener("beforeunload",function(){localStorage.setItem("menu.scrollTop",e.scrollTop)}),e.scrollTop=localStorage.getItem("menu.scrollTop")})()</script>
+
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+      </div>
+    </aside>
+
+    <div class="book-page">
+      <header class="book-header">
+        
+  <div class="flex align-center justify-between">
+  <label for="menu-control">
+    <img src="/lds-ctrl-est/svg/menu.svg" class="book-icon" alt="Menu" />
+  </label>
+
+  <strong>lds::SSID</strong>
+
+  <label for="toc-control">
+    
+    <img src="/lds-ctrl-est/svg/toc.svg" class="book-icon" alt="Table of Contents" />
+    
+  </label>
+</div>
+
+
+  
+  <aside class="hidden clearfix">
+    
+  
+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#ldsssid">lds::SSID</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#protected-functions">Protected Functions</a></li>
+        <li><a href="#protected-attributes">Protected Attributes</a></li>
+        <li><a href="#detailed-description">Detailed Description</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#ssid"><strong>SSID</strong></a></li>
+            <li><a href="#ssid-1"><strong>SSID</strong></a></li>
+            <li><a href="#run"><strong>Run</strong></a></li>
+            <li><a href="#returndata"><strong>ReturnData</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-function-details">Protected Function Details</a>
+          <ul>
+            <li><a href="#calcd"><strong>CalcD</strong></a></li>
+            <li><a href="#createhankeldatamat"><strong>CreateHankelDataMat</strong></a></li>
+            <li><a href="#decomposedata"><strong>DecomposeData</strong></a></li>
+            <li><a href="#calcsvd"><strong>CalcSVD</strong></a></li>
+            <li><a href="#solve"><strong>Solve</strong></a></li>
+            <li><a href="#recomputeextobs"><strong>RecomputeExtObs</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-attribute-details">Protected Attribute Details</a>
+          <ul>
+            <li><a href="#u_"><strong>u_</strong></a></li>
+            <li><a href="#z_"><strong>z_</strong></a></li>
+            <li><a href="#d_"><strong>D_</strong></a></li>
+            <li><a href="#fit_"><strong>fit_</strong></a></li>
+            <li><a href="#g_"><strong>g_dc_</strong></a></li>
+            <li><a href="#dt_"><strong>dt_</strong></a></li>
+            <li><a href="#n_"><strong>n_u_</strong></a></li>
+            <li><a href="#n_-1"><strong>n_x_</strong></a></li>
+            <li><a href="#n_-2"><strong>n_y_</strong></a></li>
+            <li><a href="#n_-3"><strong>n_h_</strong></a></li>
+            <li><a href="#n_-4"><strong>n_trials_</strong></a></li>
+            <li><a href="#n_-5"><strong>n_t_</strong></a></li>
+            <li><a href="#n_-6"><strong>n_t_tot_</strong></a></li>
+            <li><a href="#l_"><strong>L_</strong></a></li>
+            <li><a href="#s_"><strong>s_</strong></a></li>
+            <li><a href="#ext_"><strong>ext_obs_t_</strong></a></li>
+          </ul>
+        </li>
+      </ul>
+    </li>
+  </ul>
+</nav>
+
+
+
+  </aside>
+  
+ 
+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="ldsssid">
+  lds::SSID
+  <a class="anchor" href="#ldsssid">#</a>
+</h1>
+<p><a href="#detailed-description">More&hellip;</a></p>
+<p>Inherited by <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/">lds::gaussian::FitSSID</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/">lds::poisson::FitSSID</a></p>
+<h2 id="public-functions">
+  Public Functions
+  <a class="anchor" href="#public-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-ssid">SSID</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">SSID</a><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a> type.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-ssid">SSID</a></strong>(size_t n_x, size_t n_h, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> dt, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &amp;&amp; u_train, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &amp;&amp; z_train, const Vector &amp; d =Vector(1).fill(-kInf))<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">SSID</a><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a> type.</td>
+      </tr>
+      <tr>
+          <td>std::tuple&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a>, Vector &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-run">Run</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt">SSIDWt</a> ssid_wt)<br>Runs fitting by subspace identification (<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">SSID</a>)</td>
+      </tr>
+      <tr>
+          <td>std::tuple&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt;, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-returndata">ReturnData</a></strong>()<br>Returns the I/O data to caller.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="protected-functions">
+  Protected Functions
+  <a class="anchor" href="#protected-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-calcd">CalcD</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> t_silence =0.1, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> thresh_silence =0.001)<br>Using periods of silence in inputs (u), calculates the output \ bias (d)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-createhankeldatamat">CreateHankelDataMat</a></strong>()<br>Creates the block-hankel I/O data matrix.</td>
+      </tr>
+      <tr>
+          <td>virtual void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-decomposedata">DecomposeData</a></strong>() =0<br>Decompose data to lower-triangular matrix (used in Solve)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-calcsvd">CalcSVD</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt">SSIDWt</a> wt)<br>performs the singular value decomposition (SVD)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-solve">Solve</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> wt_dc)<br>solves for LDS parameters</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-recomputeextobs">RecomputeExtObs</a></strong>()<br>recompute extended observability matrix from estimates of A, C</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="protected-attributes">
+  Protected Attributes
+  <a class="anchor" href="#protected-attributes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-u-">u_</a></strong> <br>input training data</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-z-">z_</a></strong> <br>measurement training data</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-d-">D_</a></strong> <br>block-Hankel I/O data matrix</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-fit-">fit_</a></strong> <br>fit</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-g-dc-">g_dc_</a></strong> <br>I/O gain @ DC.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-dt-">dt_</a></strong> <br>sample period</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-u-">n_u_</a></strong> <br>number of inputs</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-x-">n_x_</a></strong> <br>number of states</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-y-">n_y_</a></strong> <br>number of outputs</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-h-">n_h_</a></strong></td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-trials-">n_trials_</a></strong> <br>number of input/output data sequences</td>
+      </tr>
+      <tr>
+          <td>std::vector&lt; size_t &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-t-">n_t_</a></strong> <br>number of time steps</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-t-tot-">n_t_tot_</a></strong> <br>total number of time steps across trials</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-l-">L_</a></strong> <br>lower triangle decomp of covariance matrix</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-s-">s_</a></strong> <br>singular values</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-ext-obs-t-">ext_obs_t_</a></strong> <br>extended observability matrix</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit <span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">lds</span><span style="color:#f92672">::</span>SSID;
+</span></span></code></pre></div><hr>
+<hr>
+<h2 id="public-function-details">
+  Public Function Details
+  <a class="anchor" href="#public-function-details">#</a>
+</h2>
+<h3 id="ssid">
+  <strong>SSID</strong>
+  <a class="anchor" href="#ssid">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>SSID() <span style="color:#f92672">=</span><span style="color:#66d9ef">default</span>
+</span></span></code></pre></div><hr>
+<h3 id="ssid-1">
+  <strong>SSID</strong>
+  <a class="anchor" href="#ssid-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>SSID(
+</span></span><span style="display:flex;"><span>    size_t n_x,
+</span></span><span style="display:flex;"><span>    size_t n_h,
+</span></span><span style="display:flex;"><span>    data_t dt,
+</span></span><span style="display:flex;"><span>    UniformMatrixList<span style="color:#f92672">&lt;</span> kMatFreeDim2 <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;&amp;</span> u_train,
+</span></span><span style="display:flex;"><span>    UniformMatrixList<span style="color:#f92672">&lt;</span> kMatFreeDim2 <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;&amp;</span> z_train,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> d <span style="color:#f92672">=</span>Vector(<span style="color:#ae81ff">1</span>).fill(<span style="color:#f92672">-</span>kInf)
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>n_x</strong> number of states</li>
+<li><strong>n_h</strong> size of block-hankel data matrix</li>
+<li><strong>dt</strong> sample period</li>
+<li><strong>u_train</strong> input training data</li>
+<li><strong>z_train</strong> measurement training data</li>
+<li><strong>d</strong> output bias</li>
+</ul>
+<hr>
+<h3 id="run">
+  <strong>Run</strong>
+  <a class="anchor" href="#run">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>std<span style="color:#f92672">::</span>tuple<span style="color:#f92672">&lt;</span> Fit, Vector <span style="color:#f92672">&gt;</span> Run(
+</span></span><span style="display:flex;"><span>    SSIDWt ssid_wt
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>ssid_wt</strong> weight for singular value decomp</li>
+</ul>
+<p><strong>Return</strong>: tuple (<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a>, singular values)</p>
+<hr>
+<h3 id="returndata">
+  <strong>ReturnData</strong>
+  <a class="anchor" href="#returndata">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> std<span style="color:#f92672">::</span>tuple<span style="color:#f92672">&lt;</span> UniformMatrixList<span style="color:#f92672">&lt;</span> kMatFreeDim2 <span style="color:#f92672">&gt;</span>, UniformMatrixList<span style="color:#f92672">&lt;</span> kMatFreeDim2 <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&gt;</span> ReturnData()
+</span></span></code></pre></div><p><strong>Return</strong>: tuple(input data, output data)</p>
+<hr>
+<h2 id="protected-function-details">
+  Protected Function Details
+  <a class="anchor" href="#protected-function-details">#</a>
+</h2>
+<h3 id="calcd">
+  <strong>CalcD</strong>
+  <a class="anchor" href="#calcd">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> CalcD(
+</span></span><span style="display:flex;"><span>    data_t t_silence <span style="color:#f92672">=</span><span style="color:#ae81ff">0.1</span>,
+</span></span><span style="display:flex;"><span>    data_t thresh_silence <span style="color:#f92672">=</span><span style="color:#ae81ff">0.001</span>
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>t_silence</strong> threshold on period of time that qualifies as &ldquo;silence&rdquo;</li>
+<li><strong>thresh_silence</strong> threshold on input amplitude u that qualifies as &ldquo;silence&rdquo;</li>
+</ul>
+<hr>
+<h3 id="createhankeldatamat">
+  <strong>CreateHankelDataMat</strong>
+  <a class="anchor" href="#createhankeldatamat">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> CreateHankelDataMat()
+</span></span></code></pre></div><p>Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.</p>
+<hr>
+<h3 id="decomposedata">
+  <strong>DecomposeData</strong>
+  <a class="anchor" href="#decomposedata">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> DecomposeData() <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>
+</span></span></code></pre></div><p><strong>Reimplemented by</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/#function-decomposedata">lds::gaussian::FitSSID::DecomposeData</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/#function-decomposedata">lds::poisson::FitSSID::DecomposeData</a></p>
+<hr>
+<h3 id="calcsvd">
+  <strong>CalcSVD</strong>
+  <a class="anchor" href="#calcsvd">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> CalcSVD(
+</span></span><span style="display:flex;"><span>    SSIDWt wt
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>ssid_wt</strong> weight for SVD</li>
+</ul>
+<hr>
+<h3 id="solve">
+  <strong>Solve</strong>
+  <a class="anchor" href="#solve">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> Solve(
+</span></span><span style="display:flex;"><span>    data_t wt_dc
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>wt_dc</strong> weight placed on getting correct DC I/O gain</li>
+</ul>
+<hr>
+<h3 id="recomputeextobs">
+  <strong>RecomputeExtObs</strong>
+  <a class="anchor" href="#recomputeextobs">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> RecomputeExtObs()
+</span></span></code></pre></div><hr>
+<h2 id="protected-attribute-details">
+  Protected Attribute Details
+  <a class="anchor" href="#protected-attribute-details">#</a>
+</h2>
+<h3 id="u_">
+  <strong>u_</strong>
+  <a class="anchor" href="#u_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>UniformMatrixList<span style="color:#f92672">&lt;</span> kMatFreeDim2 <span style="color:#f92672">&gt;</span> u_;
+</span></span></code></pre></div><hr>
+<h3 id="z_">
+  <strong>z_</strong>
+  <a class="anchor" href="#z_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>UniformMatrixList<span style="color:#f92672">&lt;</span> kMatFreeDim2 <span style="color:#f92672">&gt;</span> z_;
+</span></span></code></pre></div><hr>
+<h3 id="d_">
+  <strong>D_</strong>
+  <a class="anchor" href="#d_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix D_;
+</span></span></code></pre></div><hr>
+<h3 id="fit_">
+  <strong>fit_</strong>
+  <a class="anchor" href="#fit_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Fit fit_;
+</span></span></code></pre></div><hr>
+<h3 id="g_">
+  <strong>g_dc_</strong>
+  <a class="anchor" href="#g_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix g_dc_;
+</span></span></code></pre></div><hr>
+<h3 id="dt_">
+  <strong>dt_</strong>
+  <a class="anchor" href="#dt_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>data_t dt_ {};
+</span></span></code></pre></div><hr>
+<h3 id="n_">
+  <strong>n_u_</strong>
+  <a class="anchor" href="#n_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>size_t n_u_ {};
+</span></span></code></pre></div><hr>
+<h3 id="n_-1">
+  <strong>n_x_</strong>
+  <a class="anchor" href="#n_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>size_t n_x_ {};
+</span></span></code></pre></div><hr>
+<h3 id="n_-2">
+  <strong>n_y_</strong>
+  <a class="anchor" href="#n_-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>size_t n_y_ {};
+</span></span></code></pre></div><hr>
+<h3 id="n_-3">
+  <strong>n_h_</strong>
+  <a class="anchor" href="#n_-3">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>size_t n_h_ {};
+</span></span></code></pre></div><hr>
+<h3 id="n_-4">
+  <strong>n_trials_</strong>
+  <a class="anchor" href="#n_-4">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>size_t n_trials_ {};
+</span></span></code></pre></div><hr>
+<h3 id="n_-5">
+  <strong>n_t_</strong>
+  <a class="anchor" href="#n_-5">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> size_t <span style="color:#f92672">&gt;</span> n_t_;
+</span></span></code></pre></div><hr>
+<h3 id="n_-6">
+  <strong>n_t_tot_</strong>
+  <a class="anchor" href="#n_-6">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>size_t n_t_tot_ {};
+</span></span></code></pre></div><hr>
+<h3 id="l_">
+  <strong>L_</strong>
+  <a class="anchor" href="#l_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix L_;
+</span></span></code></pre></div><hr>
+<h3 id="s_">
+  <strong>s_</strong>
+  <a class="anchor" href="#s_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector s_;
+</span></span></code></pre></div><hr>
+<h3 id="ext_">
+  <strong>ext_obs_t_</strong>
+  <a class="anchor" href="#ext_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix ext_obs_t_;
+</span></span></code></pre></div><hr>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
+      
+
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+    <li><a href="#ldsssid">lds::SSID</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#protected-functions">Protected Functions</a></li>
+        <li><a href="#protected-attributes">Protected Attributes</a></li>
+        <li><a href="#detailed-description">Detailed Description</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#ssid"><strong>SSID</strong></a></li>
+            <li><a href="#ssid-1"><strong>SSID</strong></a></li>
+            <li><a href="#run"><strong>Run</strong></a></li>
+            <li><a href="#returndata"><strong>ReturnData</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-function-details">Protected Function Details</a>
+          <ul>
+            <li><a href="#calcd"><strong>CalcD</strong></a></li>
+            <li><a href="#createhankeldatamat"><strong>CreateHankelDataMat</strong></a></li>
+            <li><a href="#decomposedata"><strong>DecomposeData</strong></a></li>
+            <li><a href="#calcsvd"><strong>CalcSVD</strong></a></li>
+            <li><a href="#solve"><strong>Solve</strong></a></li>
+            <li><a href="#recomputeextobs"><strong>RecomputeExtObs</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-attribute-details">Protected Attribute Details</a>
+          <ul>
+            <li><a href="#u_"><strong>u_</strong></a></li>
+            <li><a href="#z_"><strong>z_</strong></a></li>
+            <li><a href="#d_"><strong>D_</strong></a></li>
+            <li><a href="#fit_"><strong>fit_</strong></a></li>
+            <li><a href="#g_"><strong>g_dc_</strong></a></li>
+            <li><a href="#dt_"><strong>dt_</strong></a></li>
+            <li><a href="#n_"><strong>n_u_</strong></a></li>
+            <li><a href="#n_-1"><strong>n_x_</strong></a></li>
+            <li><a href="#n_-2"><strong>n_y_</strong></a></li>
+            <li><a href="#n_-3"><strong>n_h_</strong></a></li>
+            <li><a href="#n_-4"><strong>n_trials_</strong></a></li>
+            <li><a href="#n_-5"><strong>n_t_</strong></a></li>
+            <li><a href="#n_-6"><strong>n_t_tot_</strong></a></li>
+            <li><a href="#l_"><strong>L_</strong></a></li>
+            <li><a href="#s_"><strong>s_</strong></a></li>
+            <li><a href="#ext_"><strong>ext_obs_t_</strong></a></li>
+          </ul>
+        </li>
+      </ul>
+    </li>
+  </ul>
+</nav>
+
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+ 
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+  </main>
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+</html>
+
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+
diff --git a/docs/docs/api/classes/classlds_1_1_switched_controller/index.html b/docs/docs/api/classes/classlds_1_1_switched_controller/index.html
new file mode 100644
index 00000000..135042e1
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@@ -0,0 +1,846 @@
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+
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+    
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+    
+  
+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#ldsswitchedcontroller">lds::SwitchedController</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#protected-attributes">Protected Attributes</a></li>
+        <li><a href="#additional-inherited-members">Additional inherited members</a></li>
+        <li><a href="#detailed-description">Detailed Description</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#switchedcontroller"><strong>SwitchedController</strong></a></li>
+            <li><a href="#switchedcontroller-1"><strong>SwitchedController</strong></a></li>
+            <li><a href="#switchedcontroller-2"><strong>SwitchedController</strong></a></li>
+            <li><a href="#switch"><strong>Switch</strong></a></li>
+            <li><a href="#set_"><strong>set_Kc</strong></a></li>
+            <li><a href="#set_-1"><strong>set_Kc</strong></a></li>
+            <li><a href="#set_-2"><strong>set_Kc_inty</strong></a></li>
+            <li><a href="#set_-3"><strong>set_Kc_inty</strong></a></li>
+            <li><a href="#set_-4"><strong>set_Kc_u</strong></a></li>
+            <li><a href="#set_-5"><strong>set_Kc_u</strong></a></li>
+            <li><a href="#set_-6"><strong>set_g_design</strong></a></li>
+            <li><a href="#set_-7"><strong>set_g_design</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-attribute-details">Protected Attribute Details</a>
+          <ul>
+            <li><a href="#systems_"><strong>systems_</strong></a></li>
+            <li><a href="#n_"><strong>n_sys_</strong></a></li>
+            <li><a href="#idx_"><strong>idx_</strong></a></li>
+            <li><a href="#kc_"><strong>Kc_list_</strong></a></li>
+            <li><a href="#kc_-1"><strong>Kc_inty_list_</strong></a></li>
+            <li><a href="#kc_-2"><strong>Kc_u_list_</strong></a></li>
+            <li><a href="#g_"><strong>g_design_list_</strong></a></li>
+          </ul>
+        </li>
+      </ul>
+    </li>
+  </ul>
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+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="ldsswitchedcontroller">
+  lds::SwitchedController
+  <a class="anchor" href="#ldsswitchedcontroller">#</a>
+</h1>
+<p><a href="">SwitchedController</a> Type.  <a href="#detailed-description">More&hellip;</a></p>
+<p><br /> <code>#include &lt;lds_sctrl.h&gt;</code></p>
+<p>Inherits from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">lds::Controller&lt; System &gt;</a></p>
+<p>Inherited by <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/">lds::gaussian::SwitchedController</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/">lds::poisson::SwitchedController</a></p>
+<h2 id="public-functions">
+  Public Functions
+  <a class="anchor" href="#public-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switchedcontroller">SwitchedController</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/">SwitchedController</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switchedcontroller">SwitchedController</a></strong>(const std::vector&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &gt; &amp; systems, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub, size_t control_type =0)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/">SwitchedController</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switchedcontroller">SwitchedController</a></strong>(std::vector&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &gt; &amp;&amp; systems, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub, size_t control_type =0)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/">SwitchedController</a> (moves systems).</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switch">Switch</a></strong>(size_t idx, bool do_force_switch =false)<br>Switch to a different sub-system/controller.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc">set_Kc</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt;&gt; &amp; Kc)<br>sets state feedback gains</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc">set_Kc</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt;&gt; &amp;&amp; Kc)<br>sets state feedback gains (moving)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-inty">set_Kc_inty</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt;&gt; &amp; Kc_inty)<br>sets integral feedback gains</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-inty">set_Kc_inty</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt;&gt; &amp;&amp; Kc_inty)<br>sets integral feedback gains (moving)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-u">set_Kc_u</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt;&gt; &amp; Kc_u)<br>sets input feedback gains</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-u">set_Kc_u</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt;&gt; &amp;&amp; Kc_u)<br>sets input feedback gains (moving)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-g-design">set_g_design</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a> &amp; g)<br>sets input gain used during controller design</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-g-design">set_g_design</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a> &amp;&amp; g)<br>sets input gain used during controller design (moving)</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="protected-attributes">
+  Protected Attributes
+  <a class="anchor" href="#protected-attributes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>std::vector&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-systems-">systems_</a></strong> <br>underlying sub-systems which are switched between</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-n-sys-">n_sys_</a></strong> <br>number of systems</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-idx-">idx_</a></strong> <br>current system/controller index.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-kc-list-">Kc_list_</a></strong></td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-kc-inty-list-">Kc_inty_list_</a></strong></td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-kc-u-list-">Kc_u_list_</a></strong></td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-g-design-list-">g_design_list_</a></strong></td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="additional-inherited-members">
+  Additional inherited members
+  <a class="anchor" href="#additional-inherited-members">#</a>
+</h2>
+<p><strong>Public Functions inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">lds::Controller&lt; System &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller">Controller</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">Controller</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller">Controller</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp; sys, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub, size_t control_type =0)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">Controller</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller">Controller</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp;&amp; sys, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub, size_t control_type =0)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">Controller</a> by moving the system object.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control">Control</a></strong>(const Vector &amp; z, bool do_control =true, bool do_lock_control =false, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_soft_start =0, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal (single-step)</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controloutputreference">ControlOutputReference</a></strong>(const Vector &amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_soft_start =0, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal, given previously-set (single-step)</td>
+      </tr>
+      <tr>
+          <td>const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-sys">sys</a></strong>() const</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc">Kc</a></strong>() const<br>Get state feedback controller gain.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-inty">Kc_inty</a></strong>() const<br>Get integral controller gain.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-u">Kc_u</a></strong>() const<br>Get input feedback controller gain.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-g-design">g_design</a></strong>() const<br>Get input gain used in controller design.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ref">u_ref</a></strong>() const<br>Get reference input.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-x-ref">x_ref</a></strong>() const<br>Get reference state.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-y-ref">y_ref</a></strong>() const<br>Get reference output.</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control-type">control_type</a></strong>() const<br>Get controller type.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-tau-awu">tau_awu</a></strong>() const<br>Get time constant of anti-integral-windup.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-lb">u_lb</a></strong>() const<br>Get control lower bound.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ub">u_ub</a></strong>() const<br>Get control upper bound.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-sys">set_sys</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp; sys)<br>Set system.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ref">set_u_ref</a></strong>(const Vector &amp; u_ref)<br>Set reference input (u_ref)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-x-ref">set_x_ref</a></strong>(const Vector &amp; x_ref)<br>Set reference state (x_ref)</td>
+      </tr>
+      <tr>
+          <td>virtual void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-y-ref">set_y_ref</a></strong>(const Vector &amp; y_ref)<br>Set reference output (y_ref)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-tau-awu">set_tau_awu</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> tau)<br>Set time constant of anti-integral-windup.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-control-type">set_control_type</a></strong>(size_t control_type)<br>Sets the control type.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-lb">set_u_lb</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb)<br>sets control lower bound</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ub">set_u_ub</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub)<br>Sets control upper bound.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-reset">Reset</a></strong>()<br>reset system and control variables.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-print">Print</a></strong>()<br>prints variables to stdout</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Protected Attributes inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">lds::Controller&lt; System &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-sys-">sys_</a></strong> <br>underlying LDS</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-">u_</a></strong> <br>control signal</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-return-">u_return_</a></strong> <br>control signal that is <em>returned</em> to user</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-g-design-">g_design_</a></strong> <br>input gain of the system used for controller design</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-">u_ref_</a></strong> <br>reference input</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-prev-">u_ref_prev_</a></strong> <br>reference input at previous time step</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-x-ref-">x_ref_</a></strong> <br>reference state</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-y-ref-">y_ref_</a></strong> <br>reference output</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-cx-ref-">cx_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-">Kc_</a></strong> <br>state controller gain</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-u-">Kc_u_</a></strong> <br>input controller gain (optional when control updates \deltaU)</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-inty-">Kc_inty_</a></strong> <br>integral controller gain</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-du-ref-">du_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-ref-">dv_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-ref-">v_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-">dv_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-">v_</a></strong> <br>Control after g inversion (e.g., control in physical units)</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-">int_e_</a></strong> <br>integrated error</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-awu-adjust-">int_e_awu_adjust_</a></strong> <br>anti-windup adjustment to intE</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-sat-">u_sat_</a></strong> <br>control signal after saturation (for antiWindup)</td>
+      </tr>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-control-prev-">do_control_prev_</a></strong></td>
+      </tr>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-lock-control-prev-">do_lock_control_prev_</a></strong></td>
+      </tr>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-saturated-">u_saturated_</a></strong> <br>whether control signal has reached saturation limits</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-lb-">u_lb_</a></strong> <br>lower bound on control</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ub-">u_ub_</a></strong> <br>upper bound on control</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-tau-awu-">tau_awu_</a></strong> <br>antiwindup time constant</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-k-awu-">k_awu_</a></strong></td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-t-since-control-onset-">t_since_control_onset_</a></strong> <br>time since control epoch onset</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-control-type-">control_type_</a></strong> <br>controller type</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System <span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">lds</span><span style="color:#f92672">::</span>SwitchedController;
+</span></span></code></pre></div><hr>
+<hr>
+<h2 id="public-function-details">
+  Public Function Details
+  <a class="anchor" href="#public-function-details">#</a>
+</h2>
+<h3 id="switchedcontroller">
+  <strong>SwitchedController</strong>
+  <a class="anchor" href="#switchedcontroller">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>SwitchedController() <span style="color:#f92672">=</span><span style="color:#66d9ef">default</span>
+</span></span></code></pre></div><hr>
+<h3 id="switchedcontroller-1">
+  <strong>SwitchedController</strong>
+  <a class="anchor" href="#switchedcontroller-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> SwitchedController(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> System <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;</span> systems,
+</span></span><span style="display:flex;"><span>    data_t u_lb,
+</span></span><span style="display:flex;"><span>    data_t u_ub,
+</span></span><span style="display:flex;"><span>    size_t control_type <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>systems</strong> vector of sub-systems</li>
+<li><strong>u_lb</strong> lower bound on control (u)</li>
+<li><strong>u_ub</strong> upper bound on control (u)</li>
+<li><strong>control_type</strong> [optional] control type bit mask</li>
+</ul>
+<hr>
+<h3 id="switchedcontroller-2">
+  <strong>SwitchedController</strong>
+  <a class="anchor" href="#switchedcontroller-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> SwitchedController(
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> System <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;&amp;</span> systems,
+</span></span><span style="display:flex;"><span>    data_t u_lb,
+</span></span><span style="display:flex;"><span>    data_t u_ub,
+</span></span><span style="display:flex;"><span>    size_t control_type <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>systems</strong> vector of sub-systems</li>
+<li><strong>u_lb</strong> lower bound on control (u)</li>
+<li><strong>u_ub</strong> upper bound on control (u)</li>
+<li><strong>control_type</strong> [optional] control type bit mask</li>
+</ul>
+<hr>
+<h3 id="switch">
+  <strong>Switch</strong>
+  <a class="anchor" href="#switch">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> Switch(
+</span></span><span style="display:flex;"><span>    size_t idx,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> do_force_switch <span style="color:#f92672">=</span>false
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>idx</strong> index</li>
+<li><strong>do_force_switch</strong> whether to force a system switch even if already there.</li>
+</ul>
+<hr>
+<h3 id="set_">
+  <strong>set_Kc</strong>
+  <a class="anchor" href="#set_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_Kc(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> UniformMatrixList<span style="color:#f92672">&lt;&gt;</span> <span style="color:#f92672">&amp;</span> Kc
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-1">
+  <strong>set_Kc</strong>
+  <a class="anchor" href="#set_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_Kc(
+</span></span><span style="display:flex;"><span>    UniformMatrixList<span style="color:#f92672">&lt;&gt;</span> <span style="color:#f92672">&amp;&amp;</span> Kc
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-2">
+  <strong>set_Kc_inty</strong>
+  <a class="anchor" href="#set_-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_Kc_inty(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> UniformMatrixList<span style="color:#f92672">&lt;&gt;</span> <span style="color:#f92672">&amp;</span> Kc_inty
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-3">
+  <strong>set_Kc_inty</strong>
+  <a class="anchor" href="#set_-3">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_Kc_inty(
+</span></span><span style="display:flex;"><span>    UniformMatrixList<span style="color:#f92672">&lt;&gt;</span> <span style="color:#f92672">&amp;&amp;</span> Kc_inty
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-4">
+  <strong>set_Kc_u</strong>
+  <a class="anchor" href="#set_-4">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_Kc_u(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> UniformMatrixList<span style="color:#f92672">&lt;&gt;</span> <span style="color:#f92672">&amp;</span> Kc_u
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-5">
+  <strong>set_Kc_u</strong>
+  <a class="anchor" href="#set_-5">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_Kc_u(
+</span></span><span style="display:flex;"><span>    UniformMatrixList<span style="color:#f92672">&lt;&gt;</span> <span style="color:#f92672">&amp;&amp;</span> Kc_u
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-6">
+  <strong>set_g_design</strong>
+  <a class="anchor" href="#set_-6">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_g_design(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> UniformVectorList <span style="color:#f92672">&amp;</span> g
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-7">
+  <strong>set_g_design</strong>
+  <a class="anchor" href="#set_-7">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_g_design(
+</span></span><span style="display:flex;"><span>    UniformVectorList <span style="color:#f92672">&amp;&amp;</span> g
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h2 id="protected-attribute-details">
+  Protected Attribute Details
+  <a class="anchor" href="#protected-attribute-details">#</a>
+</h2>
+<h3 id="systems_">
+  <strong>systems_</strong>
+  <a class="anchor" href="#systems_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> System <span style="color:#f92672">&gt;</span> systems_;
+</span></span></code></pre></div><hr>
+<h3 id="n_">
+  <strong>n_sys_</strong>
+  <a class="anchor" href="#n_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>size_t n_sys_ {};
+</span></span></code></pre></div><hr>
+<h3 id="idx_">
+  <strong>idx_</strong>
+  <a class="anchor" href="#idx_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>size_t idx_ {};
+</span></span></code></pre></div><hr>
+<h3 id="kc_">
+  <strong>Kc_list_</strong>
+  <a class="anchor" href="#kc_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>UniformMatrixList Kc_list_;
+</span></span></code></pre></div><hr>
+<h3 id="kc_-1">
+  <strong>Kc_inty_list_</strong>
+  <a class="anchor" href="#kc_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>UniformMatrixList Kc_inty_list_;
+</span></span></code></pre></div><hr>
+<h3 id="kc_-2">
+  <strong>Kc_u_list_</strong>
+  <a class="anchor" href="#kc_-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>UniformMatrixList Kc_u_list_;
+</span></span></code></pre></div><hr>
+<h3 id="g_">
+  <strong>g_design_list_</strong>
+  <a class="anchor" href="#g_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>UniformVectorList g_design_list_;
+</span></span></code></pre></div><hr>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
+      
+
+      <footer class="book-footer">
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+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#ldsswitchedcontroller">lds::SwitchedController</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#protected-attributes">Protected Attributes</a></li>
+        <li><a href="#additional-inherited-members">Additional inherited members</a></li>
+        <li><a href="#detailed-description">Detailed Description</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#switchedcontroller"><strong>SwitchedController</strong></a></li>
+            <li><a href="#switchedcontroller-1"><strong>SwitchedController</strong></a></li>
+            <li><a href="#switchedcontroller-2"><strong>SwitchedController</strong></a></li>
+            <li><a href="#switch"><strong>Switch</strong></a></li>
+            <li><a href="#set_"><strong>set_Kc</strong></a></li>
+            <li><a href="#set_-1"><strong>set_Kc</strong></a></li>
+            <li><a href="#set_-2"><strong>set_Kc_inty</strong></a></li>
+            <li><a href="#set_-3"><strong>set_Kc_inty</strong></a></li>
+            <li><a href="#set_-4"><strong>set_Kc_u</strong></a></li>
+            <li><a href="#set_-5"><strong>set_Kc_u</strong></a></li>
+            <li><a href="#set_-6"><strong>set_g_design</strong></a></li>
+            <li><a href="#set_-7"><strong>set_g_design</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-attribute-details">Protected Attribute Details</a>
+          <ul>
+            <li><a href="#systems_"><strong>systems_</strong></a></li>
+            <li><a href="#n_"><strong>n_sys_</strong></a></li>
+            <li><a href="#idx_"><strong>idx_</strong></a></li>
+            <li><a href="#kc_"><strong>Kc_list_</strong></a></li>
+            <li><a href="#kc_-1"><strong>Kc_inty_list_</strong></a></li>
+            <li><a href="#kc_-2"><strong>Kc_u_list_</strong></a></li>
+            <li><a href="#g_"><strong>g_design_list_</strong></a></li>
+          </ul>
+        </li>
+      </ul>
+    </li>
+  </ul>
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+    <a href="https://github.com/stanley-rozell/lds-ctrl-est" target="_blank" rel="noopener">
+        Github
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+  <strong>lds::System</strong>
+
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+    
+    <img src="/lds-ctrl-est/svg/toc.svg" class="book-icon" alt="Table of Contents" />
+    
+  </label>
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+
+
+  
+  <aside class="hidden clearfix">
+    
+  
+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#ldssystem">lds::System</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#protected-functions">Protected Functions</a></li>
+        <li><a href="#public-attributes">Public Attributes</a></li>
+        <li><a href="#protected-attributes">Protected Attributes</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#system"><strong>System</strong></a></li>
+            <li><a href="#system-1"><strong>System</strong></a></li>
+            <li><a href="#heading"><strong>~System</strong></a></li>
+            <li><a href="#filter"><strong>Filter</strong></a></li>
+            <li><a href="#simulate"><strong>Simulate</strong></a></li>
+            <li><a href="#f"><strong>f</strong></a></li>
+            <li><a href="#h"><strong>h</strong></a></li>
+            <li><a href="#h_"><strong>h_</strong></a></li>
+            <li><a href="#n_"><strong>n_u</strong></a></li>
+            <li><a href="#n_-1"><strong>n_x</strong></a></li>
+            <li><a href="#n_-2"><strong>n_y</strong></a></li>
+            <li><a href="#dt"><strong>dt</strong></a></li>
+            <li><a href="#x"><strong>x</strong></a></li>
+            <li><a href="#p"><strong>P</strong></a></li>
+            <li><a href="#m"><strong>m</strong></a></li>
+            <li><a href="#p_"><strong>P_m</strong></a></li>
+            <li><a href="#cx"><strong>cx</strong></a></li>
+            <li><a href="#y"><strong>y</strong></a></li>
+            <li><a href="#x0"><strong>x0</strong></a></li>
+            <li><a href="#m0"><strong>m0</strong></a></li>
+            <li><a href="#a"><strong>A</strong></a></li>
+            <li><a href="#b"><strong>B</strong></a></li>
+            <li><a href="#g"><strong>g</strong></a></li>
+            <li><a href="#c"><strong>C</strong></a></li>
+            <li><a href="#d"><strong>d</strong></a></li>
+            <li><a href="#ke"><strong>Ke</strong></a></li>
+            <li><a href="#ke_"><strong>Ke_m</strong></a></li>
+            <li><a href="#q"><strong>Q</strong></a></li>
+            <li><a href="#q_"><strong>Q_m</strong></a></li>
+            <li><a href="#p0"><strong>P0</strong></a></li>
+            <li><a href="#p0_"><strong>P0_m</strong></a></li>
+            <li><a href="#set_"><strong>set_A</strong></a></li>
+            <li><a href="#set_-1"><strong>set_B</strong></a></li>
+            <li><a href="#set_-2"><strong>set_m</strong></a></li>
+            <li><a href="#set_-3"><strong>set_g</strong></a></li>
+            <li><a href="#set_-4"><strong>set_Q</strong></a></li>
+            <li><a href="#set_-5"><strong>set_Q_m</strong></a></li>
+            <li><a href="#set_-6"><strong>set_x0</strong></a></li>
+            <li><a href="#set_-7"><strong>set_P0</strong></a></li>
+            <li><a href="#set_-8"><strong>set_P0_m</strong></a></li>
+            <li><a href="#set_-9"><strong>set_C</strong></a></li>
+            <li><a href="#set_-10"><strong>set_d</strong></a></li>
+            <li><a href="#set_-11"><strong>set_x</strong></a></li>
+            <li><a href="#reset"><strong>Reset</strong></a></li>
+            <li><a href="#nstep_"><strong>nstep_pred_block</strong></a></li>
+            <li><a href="#print"><strong>Print</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-function-details">Protected Function Details</a>
+          <ul>
+            <li><a href="#recurseke"><strong>RecurseKe</strong></a></li>
+            <li><a href="#initvars"><strong>InitVars</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#public-attribute-details">Public Attribute Details</a>
+          <ul>
+            <li><a href="#do_"><strong>do_adapt_m</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-attribute-details">Protected Attribute Details</a>
+          <ul>
+            <li><a href="#n_-3"><strong>n_x_</strong></a></li>
+            <li><a href="#n_-4"><strong>n_u_</strong></a></li>
+            <li><a href="#n_-5"><strong>n_y_</strong></a></li>
+            <li><a href="#dt_"><strong>dt_</strong></a></li>
+            <li><a href="#x_"><strong>x_</strong></a></li>
+            <li><a href="#p_-1"><strong>P_</strong></a></li>
+            <li><a href="#m_"><strong>m_</strong></a></li>
+            <li><a href="#p_-2"><strong>P_m_</strong></a></li>
+            <li><a href="#cx_"><strong>cx_</strong></a></li>
+            <li><a href="#y_"><strong>y_</strong></a></li>
+            <li><a href="#z_"><strong>z_</strong></a></li>
+            <li><a href="#x0_"><strong>x0_</strong></a></li>
+            <li><a href="#p0_-1"><strong>P0_</strong></a></li>
+            <li><a href="#m0_"><strong>m0_</strong></a></li>
+            <li><a href="#p0_-2"><strong>P0_m_</strong></a></li>
+            <li><a href="#a_"><strong>A_</strong></a></li>
+            <li><a href="#b_"><strong>B_</strong></a></li>
+            <li><a href="#g_"><strong>g_</strong></a></li>
+            <li><a href="#q_-1"><strong>Q_</strong></a></li>
+            <li><a href="#q_-2"><strong>Q_m_</strong></a></li>
+            <li><a href="#c_"><strong>C_</strong></a></li>
+            <li><a href="#d_"><strong>d_</strong></a></li>
+            <li><a href="#ke_-1"><strong>Ke_</strong></a></li>
+            <li><a href="#ke_-2"><strong>Ke_m_</strong></a></li>
+          </ul>
+        </li>
+      </ul>
+    </li>
+  </ul>
+</nav>
+
+
+
+  </aside>
+  
+ 
+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="ldssystem">
+  lds::System
+  <a class="anchor" href="#ldssystem">#</a>
+</h1>
+<p>Linear Dynamical <a href="">System</a> Type.
+<br /> <code>#include &lt;lds_sys.h&gt;</code></p>
+<p>Inherited by <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/">lds::gaussian::System</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/">lds::poisson::System</a></p>
+<h2 id="public-functions">
+  Public Functions
+  <a class="anchor" href="#public-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-system">System</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-system">System</a></strong>(size_t n_u, size_t n_x, size_t n_y, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> dt, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> p0 =kDefaultP0, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> q0 =kDefaultQ0)<br>constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a></td>
+      </tr>
+      <tr>
+          <td>virtual</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-~system">~System</a></strong>()</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-filter">Filter</a></strong>(const Vector &amp; u_tm1, const Vector &amp; z)<br>Filter data to produce causal state estimates.</td>
+      </tr>
+      <tr>
+          <td>virtual const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-simulate">Simulate</a></strong>(const Vector &amp; u_tm1) =0<br>simulates system (single time step)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-f">f</a></strong>(const Vector &amp; u, bool do_add_noise =false)<br>system dynamics function</td>
+      </tr>
+      <tr>
+          <td>virtual void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-h">h</a></strong>() =0<br>system output function</td>
+      </tr>
+      <tr>
+          <td>virtual Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-h-">h_</a></strong>(Vector x) =0<br>system output function (stateless)</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-n-u">n_u</a></strong>() const<br>Get number of inputs.</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-n-x">n_x</a></strong>() const<br>Get number of states.</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-n-y">n_y</a></strong>() const<br>Get number of outputs.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-dt">dt</a></strong>() const<br>Get sample period.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-x">x</a></strong>() const<br>Get current state.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p">P</a></strong>() const<br>Get covariance of state estimate.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-m">m</a></strong>() const<br>Get current process disturbance/bias.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p-m">P_m</a></strong>() const<br>Get covariance of process disturbance estimate.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-cx">cx</a></strong>() const<br>Get C*x.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-y">y</a></strong>() const<br>Get output.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-x0">x0</a></strong>() const<br>Get initial state.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-m0">m0</a></strong>() const<br>Get initial disturbance.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-a">A</a></strong>() const<br>Get state matrix.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-b">B</a></strong>() const<br>Get input matrix.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-g">g</a></strong>() const<br>Get input gain/conversion factor.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-c">C</a></strong>() const<br>Get output matrix.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-d">d</a></strong>() const<br>Get output bias.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-ke">Ke</a></strong>() const<br>Get estimator gain.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-ke-m">Ke_m</a></strong>() const<br>Get estimator gain for process disturbance (m)</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-q">Q</a></strong>()<br>Get process noise covariance.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-q-m">Q_m</a></strong>()<br>Get process noise covariance of disturbance evoluation.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p0">P0</a></strong>()<br>Get covariance of initial state.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p0-m">P0_m</a></strong>()<br>Get covariance of initial process disturbance.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-a">set_A</a></strong>(const Matrix &amp; A)<br>Set state matrix.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-b">set_B</a></strong>(const Matrix &amp; B)<br>Set input matrix.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-m">set_m</a></strong>(const Vector &amp; m, bool do_force_assign =false)<br>Set process disturbance.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-g">set_g</a></strong>(const Vector &amp; g)<br>Set input gain.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-q">set_Q</a></strong>(const Matrix &amp; Q)<br>Set process noise covariance.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-q-m">set_Q_m</a></strong>(const Matrix &amp; Q_m)<br>Set process noise covariance of disturbance evoluation.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-x0">set_x0</a></strong>(const Vector &amp; x0)<br>Set initial state.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-p0">set_P0</a></strong>(const Matrix &amp; P0)<br>Set covariance of initial state.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-p0-m">set_P0_m</a></strong>(const Matrix &amp; P0_m)<br>Set covariance of initial process disturbance.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-c">set_C</a></strong>(const Matrix &amp; C)<br>Set output matrix.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-d">set_d</a></strong>(const Vector &amp; d)<br>Set output bias.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-x">set_x</a></strong>(const Vector &amp; x)<br>Set state of system.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-reset">Reset</a></strong>()<br>Reset system variables.</td>
+      </tr>
+      <tr>
+          <td>std::vector&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-nstep-pred-block">nstep_pred_block</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; u, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; z, size_t n_pred =1)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-print">Print</a></strong>()<br>Print system variables to stdout.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="protected-functions">
+  Protected Functions
+  <a class="anchor" href="#protected-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>virtual void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-recurseke">RecurseKe</a></strong>() =0<br>Recursively recalculate estimator gain (Ke)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-initvars">InitVars</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> p0 =kDefaultP0, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> q0 =kDefaultQ0)</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="public-attributes">
+  Public Attributes
+  <a class="anchor" href="#public-attributes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-do-adapt-m">do_adapt_m</a></strong> <br>whether to adaptively estimate disturbance m</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="protected-attributes">
+  Protected Attributes
+  <a class="anchor" href="#protected-attributes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>std::size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-n-x-">n_x_</a></strong> <br>number of states</td>
+      </tr>
+      <tr>
+          <td>std::size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-n-u-">n_u_</a></strong> <br>number of inputs</td>
+      </tr>
+      <tr>
+          <td>std::size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-n-y-">n_y_</a></strong> <br>number of outputs</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-dt-">dt_</a></strong> <br>sample period</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-x-">x_</a></strong> <br>state</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p-">P_</a></strong> <br>covariance of state estimate</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-m-">m_</a></strong> <br>process disturbance</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p-m-">P_m_</a></strong> <br>covariance of disturbance estimate</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-cx-">cx_</a></strong> <br>C*x.</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-y-">y_</a></strong> <br>output</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-z-">z_</a></strong> <br>measurement</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-x0-">x0_</a></strong> <br>initial state</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p0-">P0_</a></strong> <br>covariance of initial state estimate</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-m0-">m0_</a></strong> <br>initial process disturbance</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p0-m-">P0_m_</a></strong> <br>covariance of initial disturbance est.</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-a-">A_</a></strong> <br>state matrix</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-b-">B_</a></strong> <br>input matrix</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-g-">g_</a></strong> <br>input gain</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-q-">Q_</a></strong> <br>covariance of process noise</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-q-m-">Q_m_</a></strong> <br>covariance of disturbance random walk</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-c-">C_</a></strong> <br>output matrix</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-d-">d_</a></strong> <br>output bias</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-ke-">Ke_</a></strong> <br>estimator gain</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-ke-m-">Ke_m_</a></strong> <br>estimator gain for process disturbance</td>
+      </tr>
+  </tbody>
+</table>
+<hr>
+<hr>
+<h2 id="public-function-details">
+  Public Function Details
+  <a class="anchor" href="#public-function-details">#</a>
+</h2>
+<h3 id="system">
+  <strong>System</strong>
+  <a class="anchor" href="#system">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>System() <span style="color:#f92672">=</span><span style="color:#66d9ef">default</span>
+</span></span></code></pre></div><hr>
+<h3 id="system-1">
+  <strong>System</strong>
+  <a class="anchor" href="#system-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>System(
+</span></span><span style="display:flex;"><span>    size_t n_u,
+</span></span><span style="display:flex;"><span>    size_t n_x,
+</span></span><span style="display:flex;"><span>    size_t n_y,
+</span></span><span style="display:flex;"><span>    data_t dt,
+</span></span><span style="display:flex;"><span>    data_t p0 <span style="color:#f92672">=</span>kDefaultP0,
+</span></span><span style="display:flex;"><span>    data_t q0 <span style="color:#f92672">=</span>kDefaultQ0
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>n_u</strong> number of inputs</li>
+<li><strong>n_x</strong> number of states</li>
+<li><strong>n_y</strong> number of outputs</li>
+<li><strong>dt</strong> sample period</li>
+<li><strong>p0</strong> diagonal elements for state estimate covariance</li>
+<li><strong>q0</strong> diagonal elements for process noise covariance</li>
+</ul>
+<hr>
+<h3 id="heading">
+  <strong>~System</strong>
+  <a class="anchor" href="#heading">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">virtual</span> <span style="color:#f92672">~</span>System()
+</span></span></code></pre></div><hr>
+<h3 id="filter">
+  <strong>Filter</strong>
+  <a class="anchor" href="#filter">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> Filter(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> u_tm1,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> z
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>u_tm1</strong> input at t-minus-1</li>
+<li><strong>z_t</strong> current measurement</li>
+</ul>
+<p>Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.</p>
+<hr>
+<h3 id="simulate">
+  <strong>Simulate</strong>
+  <a class="anchor" href="#simulate">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> Simulate(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> u_tm1
+</span></span><span style="display:flex;"><span>) <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>u_tm1</strong> input at time t-1</li>
+</ul>
+<p><strong>Return</strong>: simulated measurement at time t</p>
+<p><strong>Reimplemented by</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-simulate">lds::gaussian::System::Simulate</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/#function-simulate">lds::poisson::System::Simulate</a></p>
+<hr>
+<h3 id="f">
+  <strong>f</strong>
+  <a class="anchor" href="#f">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> f(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> u,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> do_add_noise <span style="color:#f92672">=</span>false
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>u</strong> input</li>
+<li><strong>do_add_noise</strong> whether to add simulated process noise</li>
+</ul>
+<hr>
+<h3 id="h">
+  <strong>h</strong>
+  <a class="anchor" href="#h">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> h() <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>
+</span></span></code></pre></div><p><strong>Reimplemented by</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-h">lds::gaussian::System::h</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/#function-h">lds::poisson::System::h</a></p>
+<hr>
+<h3 id="h_">
+  <strong>h_</strong>
+  <a class="anchor" href="#h_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">virtual</span> Vector h_(
+</span></span><span style="display:flex;"><span>    Vector x
+</span></span><span style="display:flex;"><span>) <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>x_t</strong> state at time t</li>
+</ul>
+<p><strong>Return</strong>: predicted state at time t + 1</p>
+<p><strong>Reimplemented by</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-h-">lds::gaussian::System::h_</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/#function-h-">lds::poisson::System::h_</a></p>
+<hr>
+<h3 id="n_">
+  <strong>n_u</strong>
+  <a class="anchor" href="#n_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> size_t n_u() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="n_-1">
+  <strong>n_x</strong>
+  <a class="anchor" href="#n_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> size_t n_x() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="n_-2">
+  <strong>n_y</strong>
+  <a class="anchor" href="#n_-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> size_t n_y() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="dt">
+  <strong>dt</strong>
+  <a class="anchor" href="#dt">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> data_t dt() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="x">
+  <strong>x</strong>
+  <a class="anchor" href="#x">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> x() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="p">
+  <strong>P</strong>
+  <a class="anchor" href="#p">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> P() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="m">
+  <strong>m</strong>
+  <a class="anchor" href="#m">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> m() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="p_">
+  <strong>P_m</strong>
+  <a class="anchor" href="#p_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> P_m() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="cx">
+  <strong>cx</strong>
+  <a class="anchor" href="#cx">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> cx() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="y">
+  <strong>y</strong>
+  <a class="anchor" href="#y">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> y() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="x0">
+  <strong>x0</strong>
+  <a class="anchor" href="#x0">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> x0() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="m0">
+  <strong>m0</strong>
+  <a class="anchor" href="#m0">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> m0() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="a">
+  <strong>A</strong>
+  <a class="anchor" href="#a">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> A() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="b">
+  <strong>B</strong>
+  <a class="anchor" href="#b">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> B() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="g">
+  <strong>g</strong>
+  <a class="anchor" href="#g">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> g() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="c">
+  <strong>C</strong>
+  <a class="anchor" href="#c">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> C() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="d">
+  <strong>d</strong>
+  <a class="anchor" href="#d">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> d() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="ke">
+  <strong>Ke</strong>
+  <a class="anchor" href="#ke">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> Ke() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="ke_">
+  <strong>Ke_m</strong>
+  <a class="anchor" href="#ke_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> Ke_m() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="q">
+  <strong>Q</strong>
+  <a class="anchor" href="#q">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> Q()
+</span></span></code></pre></div><hr>
+<h3 id="q_">
+  <strong>Q_m</strong>
+  <a class="anchor" href="#q_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> Q_m()
+</span></span></code></pre></div><hr>
+<h3 id="p0">
+  <strong>P0</strong>
+  <a class="anchor" href="#p0">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> P0()
+</span></span></code></pre></div><hr>
+<h3 id="p0_">
+  <strong>P0_m</strong>
+  <a class="anchor" href="#p0_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> P0_m()
+</span></span></code></pre></div><hr>
+<h3 id="set_">
+  <strong>set_A</strong>
+  <a class="anchor" href="#set_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_A(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> A
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-1">
+  <strong>set_B</strong>
+  <a class="anchor" href="#set_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_B(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> B
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-2">
+  <strong>set_m</strong>
+  <a class="anchor" href="#set_-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_m(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> m,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> do_force_assign <span style="color:#f92672">=</span>false
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-3">
+  <strong>set_g</strong>
+  <a class="anchor" href="#set_-3">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_g(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> g
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-4">
+  <strong>set_Q</strong>
+  <a class="anchor" href="#set_-4">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_Q(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> Q
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-5">
+  <strong>set_Q_m</strong>
+  <a class="anchor" href="#set_-5">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_Q_m(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> Q_m
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-6">
+  <strong>set_x0</strong>
+  <a class="anchor" href="#set_-6">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_x0(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> x0
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-7">
+  <strong>set_P0</strong>
+  <a class="anchor" href="#set_-7">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_P0(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> P0
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-8">
+  <strong>set_P0_m</strong>
+  <a class="anchor" href="#set_-8">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_P0_m(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> P0_m
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-9">
+  <strong>set_C</strong>
+  <a class="anchor" href="#set_-9">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_C(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> C
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-10">
+  <strong>set_d</strong>
+  <a class="anchor" href="#set_-10">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_d(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> d
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-11">
+  <strong>set_x</strong>
+  <a class="anchor" href="#set_-11">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_x(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> x
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="reset">
+  <strong>Reset</strong>
+  <a class="anchor" href="#reset">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> Reset()
+</span></span></code></pre></div><hr>
+<h3 id="nstep_">
+  <strong>nstep_pred_block</strong>
+  <a class="anchor" href="#nstep_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> UniformMatrixList<span style="color:#f92672">&lt;</span> kMatFreeDim2 <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&gt;</span> nstep_pred_block(
+</span></span><span style="display:flex;"><span>    UniformMatrixList<span style="color:#f92672">&lt;</span> kMatFreeDim2 <span style="color:#f92672">&gt;</span> u,
+</span></span><span style="display:flex;"><span>    UniformMatrixList<span style="color:#f92672">&lt;</span> kMatFreeDim2 <span style="color:#f92672">&gt;</span> z,
+</span></span><span style="display:flex;"><span>    size_t n_pred <span style="color:#f92672">=</span><span style="color:#ae81ff">1</span>
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="print">
+  <strong>Print</strong>
+  <a class="anchor" href="#print">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> Print()
+</span></span></code></pre></div><hr>
+<h2 id="protected-function-details">
+  Protected Function Details
+  <a class="anchor" href="#protected-function-details">#</a>
+</h2>
+<h3 id="recurseke">
+  <strong>RecurseKe</strong>
+  <a class="anchor" href="#recurseke">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> RecurseKe() <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>
+</span></span></code></pre></div><p><strong>Reimplemented by</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-recurseke">lds::gaussian::System::RecurseKe</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/#function-recurseke">lds::poisson::System::RecurseKe</a></p>
+<hr>
+<h3 id="initvars">
+  <strong>InitVars</strong>
+  <a class="anchor" href="#initvars">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> InitVars(
+</span></span><span style="display:flex;"><span>    data_t p0 <span style="color:#f92672">=</span>kDefaultP0,
+</span></span><span style="display:flex;"><span>    data_t q0 <span style="color:#f92672">=</span>kDefaultQ0
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h2 id="public-attribute-details">
+  Public Attribute Details
+  <a class="anchor" href="#public-attribute-details">#</a>
+</h2>
+<h3 id="do_">
+  <strong>do_adapt_m</strong>
+  <a class="anchor" href="#do_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">bool</span> do_adapt_m {};
+</span></span></code></pre></div><hr>
+<h2 id="protected-attribute-details">
+  Protected Attribute Details
+  <a class="anchor" href="#protected-attribute-details">#</a>
+</h2>
+<h3 id="n_-3">
+  <strong>n_x_</strong>
+  <a class="anchor" href="#n_-3">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>std<span style="color:#f92672">::</span>size_t n_x_ {};
+</span></span></code></pre></div><hr>
+<h3 id="n_-4">
+  <strong>n_u_</strong>
+  <a class="anchor" href="#n_-4">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>std<span style="color:#f92672">::</span>size_t n_u_ {};
+</span></span></code></pre></div><hr>
+<h3 id="n_-5">
+  <strong>n_y_</strong>
+  <a class="anchor" href="#n_-5">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>std<span style="color:#f92672">::</span>size_t n_y_ {};
+</span></span></code></pre></div><hr>
+<h3 id="dt_">
+  <strong>dt_</strong>
+  <a class="anchor" href="#dt_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>data_t dt_ {};
+</span></span></code></pre></div><hr>
+<h3 id="x_">
+  <strong>x_</strong>
+  <a class="anchor" href="#x_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector x_;
+</span></span></code></pre></div><hr>
+<h3 id="p_-1">
+  <strong>P_</strong>
+  <a class="anchor" href="#p_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix P_;
+</span></span></code></pre></div><hr>
+<h3 id="m_">
+  <strong>m_</strong>
+  <a class="anchor" href="#m_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector m_;
+</span></span></code></pre></div><hr>
+<h3 id="p_-2">
+  <strong>P_m_</strong>
+  <a class="anchor" href="#p_-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix P_m_;
+</span></span></code></pre></div><hr>
+<h3 id="cx_">
+  <strong>cx_</strong>
+  <a class="anchor" href="#cx_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector cx_;
+</span></span></code></pre></div><hr>
+<h3 id="y_">
+  <strong>y_</strong>
+  <a class="anchor" href="#y_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector y_;
+</span></span></code></pre></div><hr>
+<h3 id="z_">
+  <strong>z_</strong>
+  <a class="anchor" href="#z_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector z_;
+</span></span></code></pre></div><hr>
+<h3 id="x0_">
+  <strong>x0_</strong>
+  <a class="anchor" href="#x0_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector x0_;
+</span></span></code></pre></div><hr>
+<h3 id="p0_-1">
+  <strong>P0_</strong>
+  <a class="anchor" href="#p0_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix P0_;
+</span></span></code></pre></div><hr>
+<h3 id="m0_">
+  <strong>m0_</strong>
+  <a class="anchor" href="#m0_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector m0_;
+</span></span></code></pre></div><hr>
+<h3 id="p0_-2">
+  <strong>P0_m_</strong>
+  <a class="anchor" href="#p0_-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix P0_m_;
+</span></span></code></pre></div><hr>
+<h3 id="a_">
+  <strong>A_</strong>
+  <a class="anchor" href="#a_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix A_;
+</span></span></code></pre></div><hr>
+<h3 id="b_">
+  <strong>B_</strong>
+  <a class="anchor" href="#b_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix B_;
+</span></span></code></pre></div><hr>
+<h3 id="g_">
+  <strong>g_</strong>
+  <a class="anchor" href="#g_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector g_;
+</span></span></code></pre></div><hr>
+<h3 id="q_-1">
+  <strong>Q_</strong>
+  <a class="anchor" href="#q_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix Q_;
+</span></span></code></pre></div><hr>
+<h3 id="q_-2">
+  <strong>Q_m_</strong>
+  <a class="anchor" href="#q_-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix Q_m_;
+</span></span></code></pre></div><hr>
+<h3 id="c_">
+  <strong>C_</strong>
+  <a class="anchor" href="#c_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix C_;
+</span></span></code></pre></div><hr>
+<h3 id="d_">
+  <strong>d_</strong>
+  <a class="anchor" href="#d_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Vector d_;
+</span></span></code></pre></div><hr>
+<h3 id="ke_-1">
+  <strong>Ke_</strong>
+  <a class="anchor" href="#ke_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix Ke_;
+</span></span></code></pre></div><hr>
+<h3 id="ke_-2">
+  <strong>Ke_m_</strong>
+  <a class="anchor" href="#ke_-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix Ke_m_;
+</span></span></code></pre></div><hr>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
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+  <ul>
+    <li><a href="#ldssystem">lds::System</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#protected-functions">Protected Functions</a></li>
+        <li><a href="#public-attributes">Public Attributes</a></li>
+        <li><a href="#protected-attributes">Protected Attributes</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#system"><strong>System</strong></a></li>
+            <li><a href="#system-1"><strong>System</strong></a></li>
+            <li><a href="#heading"><strong>~System</strong></a></li>
+            <li><a href="#filter"><strong>Filter</strong></a></li>
+            <li><a href="#simulate"><strong>Simulate</strong></a></li>
+            <li><a href="#f"><strong>f</strong></a></li>
+            <li><a href="#h"><strong>h</strong></a></li>
+            <li><a href="#h_"><strong>h_</strong></a></li>
+            <li><a href="#n_"><strong>n_u</strong></a></li>
+            <li><a href="#n_-1"><strong>n_x</strong></a></li>
+            <li><a href="#n_-2"><strong>n_y</strong></a></li>
+            <li><a href="#dt"><strong>dt</strong></a></li>
+            <li><a href="#x"><strong>x</strong></a></li>
+            <li><a href="#p"><strong>P</strong></a></li>
+            <li><a href="#m"><strong>m</strong></a></li>
+            <li><a href="#p_"><strong>P_m</strong></a></li>
+            <li><a href="#cx"><strong>cx</strong></a></li>
+            <li><a href="#y"><strong>y</strong></a></li>
+            <li><a href="#x0"><strong>x0</strong></a></li>
+            <li><a href="#m0"><strong>m0</strong></a></li>
+            <li><a href="#a"><strong>A</strong></a></li>
+            <li><a href="#b"><strong>B</strong></a></li>
+            <li><a href="#g"><strong>g</strong></a></li>
+            <li><a href="#c"><strong>C</strong></a></li>
+            <li><a href="#d"><strong>d</strong></a></li>
+            <li><a href="#ke"><strong>Ke</strong></a></li>
+            <li><a href="#ke_"><strong>Ke_m</strong></a></li>
+            <li><a href="#q"><strong>Q</strong></a></li>
+            <li><a href="#q_"><strong>Q_m</strong></a></li>
+            <li><a href="#p0"><strong>P0</strong></a></li>
+            <li><a href="#p0_"><strong>P0_m</strong></a></li>
+            <li><a href="#set_"><strong>set_A</strong></a></li>
+            <li><a href="#set_-1"><strong>set_B</strong></a></li>
+            <li><a href="#set_-2"><strong>set_m</strong></a></li>
+            <li><a href="#set_-3"><strong>set_g</strong></a></li>
+            <li><a href="#set_-4"><strong>set_Q</strong></a></li>
+            <li><a href="#set_-5"><strong>set_Q_m</strong></a></li>
+            <li><a href="#set_-6"><strong>set_x0</strong></a></li>
+            <li><a href="#set_-7"><strong>set_P0</strong></a></li>
+            <li><a href="#set_-8"><strong>set_P0_m</strong></a></li>
+            <li><a href="#set_-9"><strong>set_C</strong></a></li>
+            <li><a href="#set_-10"><strong>set_d</strong></a></li>
+            <li><a href="#set_-11"><strong>set_x</strong></a></li>
+            <li><a href="#reset"><strong>Reset</strong></a></li>
+            <li><a href="#nstep_"><strong>nstep_pred_block</strong></a></li>
+            <li><a href="#print"><strong>Print</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-function-details">Protected Function Details</a>
+          <ul>
+            <li><a href="#recurseke"><strong>RecurseKe</strong></a></li>
+            <li><a href="#initvars"><strong>InitVars</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#public-attribute-details">Public Attribute Details</a>
+          <ul>
+            <li><a href="#do_"><strong>do_adapt_m</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-attribute-details">Protected Attribute Details</a>
+          <ul>
+            <li><a href="#n_-3"><strong>n_x_</strong></a></li>
+            <li><a href="#n_-4"><strong>n_u_</strong></a></li>
+            <li><a href="#n_-5"><strong>n_y_</strong></a></li>
+            <li><a href="#dt_"><strong>dt_</strong></a></li>
+            <li><a href="#x_"><strong>x_</strong></a></li>
+            <li><a href="#p_-1"><strong>P_</strong></a></li>
+            <li><a href="#m_"><strong>m_</strong></a></li>
+            <li><a href="#p_-2"><strong>P_m_</strong></a></li>
+            <li><a href="#cx_"><strong>cx_</strong></a></li>
+            <li><a href="#y_"><strong>y_</strong></a></li>
+            <li><a href="#z_"><strong>z_</strong></a></li>
+            <li><a href="#x0_"><strong>x0_</strong></a></li>
+            <li><a href="#p0_-1"><strong>P0_</strong></a></li>
+            <li><a href="#m0_"><strong>m0_</strong></a></li>
+            <li><a href="#p0_-2"><strong>P0_m_</strong></a></li>
+            <li><a href="#a_"><strong>A_</strong></a></li>
+            <li><a href="#b_"><strong>B_</strong></a></li>
+            <li><a href="#g_"><strong>g_</strong></a></li>
+            <li><a href="#q_-1"><strong>Q_</strong></a></li>
+            <li><a href="#q_-2"><strong>Q_m_</strong></a></li>
+            <li><a href="#c_"><strong>C_</strong></a></li>
+            <li><a href="#d_"><strong>d_</strong></a></li>
+            <li><a href="#ke_-1"><strong>Ke_</strong></a></li>
+            <li><a href="#ke_-2"><strong>Ke_m_</strong></a></li>
+          </ul>
+        </li>
+      </ul>
+    </li>
+  </ul>
+</nav>
+
+
+ 
+      </div>
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+    
+  </main>
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diff --git a/docs/docs/api/classes/classlds_1_1_uniform_matrix_list/index.html b/docs/docs/api/classes/classlds_1_1_uniform_matrix_list/index.html
new file mode 100644
index 00000000..24431c6d
--- /dev/null
+++ b/docs/docs/api/classes/classlds_1_1_uniform_matrix_list/index.html
@@ -0,0 +1,647 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="
+  lds::UniformMatrixList
+  #
+
+More&hellip;
+Inherits from std::vector&lt; Matrix &gt;
+
+  Public Functions
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          
+          UniformMatrixList() =defaultConstructs a new UniformMatrixList.
+      
+      
+          
+          UniformMatrixList(const std::vector&lt; Matrix &gt; &amp; mats, std::array&lt; size_t, 2 &gt; dim ={0, 0})Constructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent.
+      
+      
+          
+          UniformMatrixList(std::vector&lt; Matrix &gt; &amp;&amp; mats, std::array&lt; size_t, 2 &gt; dim ={0, 0})Constructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent.
+      
+      
+          
+          UniformMatrixList(std::initializer_list&lt; Matrix &gt; mats, std::array&lt; size_t, 2 &gt; dim ={0, 0})Constructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent.
+      
+      
+          
+          UniformMatrixList(const UniformMatrixList&lt; D &gt; &amp; that)Constructs a new UniformMatrixList (copy).
+      
+      
+          
+          UniformMatrixList(UniformMatrixList&lt; D &gt; &amp;&amp; that)Constructs a new UniformMatrixList (move).
+      
+      
+          
+          ~UniformMatrixList() =defaultDestroys the object.
+      
+      
+          const std::array&lt; size_t, 2 &gt; &amp;
+          dim(size_t n =0) constgets dimensions of uniformly sized matrices
+      
+      
+          size_t
+          size()size of container
+      
+      
+          const Matrix &amp;
+          at(size_t n)gets reference to n^th element
+      
+      
+          void
+          Swap(Matrix &amp; that, size_t n)swaps input matrix with n^th matrix of list
+      
+      
+          UniformMatrixList&lt; D &gt; &amp;
+          operator=(const UniformMatrixList&lt; D &gt; &amp; that)assigns the contents (copy)
+      
+      
+          UniformMatrixList&lt; D &gt; &amp;
+          operator=(UniformMatrixList&lt; D &gt; &amp;&amp; that)assigns the contents (move)
+      
+      
+          void
+          append(const Matrix &amp; mat)appends a matrix to the list
+      
+  
+
+
+  Detailed Description
+  #
+
+template &lt;MatrixListFreeDim D =kMatFreeDimNone&gt;
+class lds::UniformMatrixList;
+
+
+
+  Public Function Details
+  #
+
+
+  UniformMatrixList
+  #
+
+UniformMatrixList() =default
+
+
+  UniformMatrixList
+  #
+
+explicit UniformMatrixList(
+    const std::vector&lt; Matrix &gt; &amp; mats,
+    std::array&lt; size_t, 2 &gt; dim ={0, 0}
+)
+Parameters:">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">
+  <meta property="og:site_name" content="LDS C&E">
+  <meta property="og:title" content="lds::UniformMatrixList">
+  <meta property="og:description" content="lds::UniformMatrixList # More…
+Inherits from std::vector&lt; Matrix &gt;
+Public Functions # Name UniformMatrixList() =default
+Constructs a new UniformMatrixList. UniformMatrixList(const std::vector&lt; Matrix &gt; &amp; mats, std::array&lt; size_t, 2 &gt; dim ={0, 0})
+Constructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent. UniformMatrixList(std::vector&lt; Matrix &gt; &amp;&amp; mats, std::array&lt; size_t, 2 &gt; dim ={0, 0})
+Constructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent. UniformMatrixList(std::initializer_list&lt; Matrix &gt; mats, std::array&lt; size_t, 2 &gt; dim ={0, 0})
+Constructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent. UniformMatrixList(const UniformMatrixList&lt; D &gt; &amp; that)
+Constructs a new UniformMatrixList (copy). UniformMatrixList(UniformMatrixList&lt; D &gt; &amp;&amp; that)
+Constructs a new UniformMatrixList (move). ~UniformMatrixList() =default
+Destroys the object. const std::array&lt; size_t, 2 &gt; &amp; dim(size_t n =0) const
+gets dimensions of uniformly sized matrices size_t size()
+size of container const Matrix &amp; at(size_t n)
+gets reference to n^th element void Swap(Matrix &amp; that, size_t n)
+swaps input matrix with n^th matrix of list UniformMatrixList&lt; D &gt; &amp; operator=(const UniformMatrixList&lt; D &gt; &amp; that)
+assigns the contents (copy) UniformMatrixList&lt; D &gt; &amp; operator=(UniformMatrixList&lt; D &gt; &amp;&amp; that)
+assigns the contents (move) void append(const Matrix &amp; mat)
+appends a matrix to the list Detailed Description # template &lt;MatrixListFreeDim D =kMatFreeDimNone&gt; class lds::UniformMatrixList; Public Function Details # UniformMatrixList # UniformMatrixList() =default UniformMatrixList # explicit UniformMatrixList( const std::vector&lt; Matrix &gt; &amp; mats, std::array&lt; size_t, 2 &gt; dim ={0, 0} ) Parameters:">
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+  <strong>lds::UniformMatrixList</strong>
+
+  <label for="toc-control">
+    
+    <img src="/lds-ctrl-est/svg/toc.svg" class="book-icon" alt="Table of Contents" />
+    
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+    
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+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#ldsuniformmatrixlist">lds::UniformMatrixList</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#detailed-description">Detailed Description</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#uniformmatrixlist"><strong>UniformMatrixList</strong></a></li>
+            <li><a href="#uniformmatrixlist-1"><strong>UniformMatrixList</strong></a></li>
+            <li><a href="#uniformmatrixlist-2"><strong>UniformMatrixList</strong></a></li>
+            <li><a href="#uniformmatrixlist-3"><strong>UniformMatrixList</strong></a></li>
+            <li><a href="#uniformmatrixlist-4"><strong>UniformMatrixList</strong></a></li>
+            <li><a href="#uniformmatrixlist-5"><strong>UniformMatrixList</strong></a></li>
+            <li><a href="#heading"><strong>~UniformMatrixList</strong></a></li>
+            <li><a href="#dim"><strong>dim</strong></a></li>
+            <li><a href="#size"><strong>size</strong></a></li>
+            <li><a href="#at"><strong>at</strong></a></li>
+            <li><a href="#swap"><strong>Swap</strong></a></li>
+            <li><a href="#operator"><strong>operator=</strong></a></li>
+            <li><a href="#operator-1"><strong>operator=</strong></a></li>
+            <li><a href="#append"><strong>append</strong></a></li>
+          </ul>
+        </li>
+      </ul>
+    </li>
+  </ul>
+</nav>
+
+
+
+  </aside>
+  
+ 
+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="ldsuniformmatrixlist">
+  lds::UniformMatrixList
+  <a class="anchor" href="#ldsuniformmatrixlist">#</a>
+</h1>
+<p><a href="#detailed-description">More&hellip;</a></p>
+<p>Inherits from std::vector&lt; Matrix &gt;</p>
+<h2 id="public-functions">
+  Public Functions
+  <a class="anchor" href="#public-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist">UniformMatrixList</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist">UniformMatrixList</a></strong>(const std::vector&lt; Matrix &gt; &amp; mats, std::array&lt; size_t, 2 &gt; dim ={0, 0})<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a> by copying existing vector of Matrix if dimensions consistent.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist">UniformMatrixList</a></strong>(std::vector&lt; Matrix &gt; &amp;&amp; mats, std::array&lt; size_t, 2 &gt; dim ={0, 0})<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a> by moving existing vector of Matrix if dimensions consistent.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist">UniformMatrixList</a></strong>(std::initializer_list&lt; Matrix &gt; mats, std::array&lt; size_t, 2 &gt; dim ={0, 0})<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a> from initializer_list of Matrix if dimensions consistent.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist">UniformMatrixList</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; D &gt; &amp; that)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a> (copy).</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist">UniformMatrixList</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; D &gt; &amp;&amp; that)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a> (move).</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-~uniformmatrixlist">~UniformMatrixList</a></strong>() =default<br>Destroys the object.</td>
+      </tr>
+      <tr>
+          <td>const std::array&lt; size_t, 2 &gt; &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-dim">dim</a></strong>(size_t n =0) const<br>gets dimensions of uniformly sized matrices</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-size">size</a></strong>()<br>size of container</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-at">at</a></strong>(size_t n)<br>gets reference to n^th element</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-swap">Swap</a></strong>(Matrix &amp; that, size_t n)<br>swaps input matrix with n^th matrix of list</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; D &gt; &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-operator=">operator=</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; D &gt; &amp; that)<br>assigns the contents (copy)</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; D &gt; &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-operator=">operator=</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; D &gt; &amp;&amp; that)<br>assigns the contents (move)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-append">append</a></strong>(const Matrix &amp; mat)<br>appends a matrix to the list</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span>MatrixListFreeDim D <span style="color:#f92672">=</span>kMatFreeDimNone<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">lds</span><span style="color:#f92672">::</span>UniformMatrixList;
+</span></span></code></pre></div><hr>
+<hr>
+<h2 id="public-function-details">
+  Public Function Details
+  <a class="anchor" href="#public-function-details">#</a>
+</h2>
+<h3 id="uniformmatrixlist">
+  <strong>UniformMatrixList</strong>
+  <a class="anchor" href="#uniformmatrixlist">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>UniformMatrixList() <span style="color:#f92672">=</span><span style="color:#66d9ef">default</span>
+</span></span></code></pre></div><hr>
+<h3 id="uniformmatrixlist-1">
+  <strong>UniformMatrixList</strong>
+  <a class="anchor" href="#uniformmatrixlist-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">explicit</span> UniformMatrixList(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> Matrix <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;</span> mats,
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span> size_t, <span style="color:#ae81ff">2</span> <span style="color:#f92672">&gt;</span> dim <span style="color:#f92672">=</span>{<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>}
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>mats</strong> input matrices</li>
+<li><strong>dim</strong> dimensions</li>
+</ul>
+<hr>
+<h3 id="uniformmatrixlist-2">
+  <strong>UniformMatrixList</strong>
+  <a class="anchor" href="#uniformmatrixlist-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">explicit</span> UniformMatrixList(
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> Matrix <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;&amp;</span> mats,
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span> size_t, <span style="color:#ae81ff">2</span> <span style="color:#f92672">&gt;</span> dim <span style="color:#f92672">=</span>{<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>}
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>mats</strong> input matrices</li>
+<li><strong>dim</strong> dimensions</li>
+</ul>
+<hr>
+<h3 id="uniformmatrixlist-3">
+  <strong>UniformMatrixList</strong>
+  <a class="anchor" href="#uniformmatrixlist-3">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>UniformMatrixList(
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>initializer_list<span style="color:#f92672">&lt;</span> Matrix <span style="color:#f92672">&gt;</span> mats,
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span> size_t, <span style="color:#ae81ff">2</span> <span style="color:#f92672">&gt;</span> dim <span style="color:#f92672">=</span>{<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>}
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>mats</strong> input matrices</li>
+<li><strong>dim</strong> dimensions</li>
+</ul>
+<hr>
+<h3 id="uniformmatrixlist-4">
+  <strong>UniformMatrixList</strong>
+  <a class="anchor" href="#uniformmatrixlist-4">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>UniformMatrixList(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> UniformMatrixList<span style="color:#f92672">&lt;</span> D <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;</span> that
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>that</strong> another <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a></li>
+</ul>
+<hr>
+<h3 id="uniformmatrixlist-5">
+  <strong>UniformMatrixList</strong>
+  <a class="anchor" href="#uniformmatrixlist-5">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>UniformMatrixList(
+</span></span><span style="display:flex;"><span>    UniformMatrixList<span style="color:#f92672">&lt;</span> D <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;&amp;</span> that
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>that</strong> another <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a></li>
+</ul>
+<hr>
+<h3 id="heading">
+  <strong>~UniformMatrixList</strong>
+  <a class="anchor" href="#heading">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#f92672">~</span>UniformMatrixList() <span style="color:#f92672">=</span><span style="color:#66d9ef">default</span>
+</span></span></code></pre></div><hr>
+<h3 id="dim">
+  <strong>dim</strong>
+  <a class="anchor" href="#dim">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span> size_t, <span style="color:#ae81ff">2</span> <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;</span> dim(
+</span></span><span style="display:flex;"><span>    size_t n <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>
+</span></span><span style="display:flex;"><span>) <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>n</strong> [optional] index in list of matrices</li>
+</ul>
+<p><strong>Return</strong>: dimensions</p>
+<hr>
+<h3 id="size">
+  <strong>size</strong>
+  <a class="anchor" href="#size">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> size_t size()
+</span></span></code></pre></div><hr>
+<h3 id="at">
+  <strong>at</strong>
+  <a class="anchor" href="#at">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> at(
+</span></span><span style="display:flex;"><span>    size_t n
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="swap">
+  <strong>Swap</strong>
+  <a class="anchor" href="#swap">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> Swap(
+</span></span><span style="display:flex;"><span>    Matrix <span style="color:#f92672">&amp;</span> that,
+</span></span><span style="display:flex;"><span>    size_t n
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>that</strong> input matrix</li>
+<li><strong>n</strong> index where the matrix is moved</li>
+</ul>
+<hr>
+<h3 id="operator">
+  <strong>operator=</strong>
+  <a class="anchor" href="#operator">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> UniformMatrixList<span style="color:#f92672">&lt;</span> D <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;</span> <span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> UniformMatrixList<span style="color:#f92672">&lt;</span> D <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;</span> that
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>that</strong> another <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a></li>
+</ul>
+<p><strong>Return</strong>: reference to object</p>
+<hr>
+<h3 id="operator-1">
+  <strong>operator=</strong>
+  <a class="anchor" href="#operator-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> UniformMatrixList<span style="color:#f92672">&lt;</span> D <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;</span> <span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>(
+</span></span><span style="display:flex;"><span>    UniformMatrixList<span style="color:#f92672">&lt;</span> D <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;&amp;</span> that
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>that</strong> another <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a></li>
+</ul>
+<p><strong>Return</strong>: reference to object</p>
+<hr>
+<h3 id="append">
+  <strong>append</strong>
+  <a class="anchor" href="#append">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> append(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> mat
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>mat</strong> input matrix</li>
+</ul>
+<hr>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
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+      <ul>
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+        <li><a href="#detailed-description">Detailed Description</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#uniformmatrixlist"><strong>UniformMatrixList</strong></a></li>
+            <li><a href="#uniformmatrixlist-1"><strong>UniformMatrixList</strong></a></li>
+            <li><a href="#uniformmatrixlist-2"><strong>UniformMatrixList</strong></a></li>
+            <li><a href="#uniformmatrixlist-3"><strong>UniformMatrixList</strong></a></li>
+            <li><a href="#uniformmatrixlist-4"><strong>UniformMatrixList</strong></a></li>
+            <li><a href="#uniformmatrixlist-5"><strong>UniformMatrixList</strong></a></li>
+            <li><a href="#heading"><strong>~UniformMatrixList</strong></a></li>
+            <li><a href="#dim"><strong>dim</strong></a></li>
+            <li><a href="#size"><strong>size</strong></a></li>
+            <li><a href="#at"><strong>at</strong></a></li>
+            <li><a href="#swap"><strong>Swap</strong></a></li>
+            <li><a href="#operator"><strong>operator=</strong></a></li>
+            <li><a href="#operator-1"><strong>operator=</strong></a></li>
+            <li><a href="#append"><strong>append</strong></a></li>
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diff --git a/docs/docs/api/classes/classlds_1_1_uniform_system_list/index.html b/docs/docs/api/classes/classlds_1_1_uniform_system_list/index.html
new file mode 100644
index 00000000..9f4efc52
--- /dev/null
+++ b/docs/docs/api/classes/classlds_1_1_uniform_system_list/index.html
@@ -0,0 +1,617 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="
+  lds::UniformSystemList
+  #
+
+More&hellip;
+Inherits from std::vector&lt; System &gt;
+
+  Public Functions
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          
+          UniformSystemList() =defaultConstructs a new UniformSystemList.
+      
+      
+          
+          UniformSystemList(const std::vector&lt; System &gt; &amp; systems, std::array&lt; size_t, 3 &gt; dim ={0, 0, 0})Constructs a new UniformSystemList by copying existing vector of System if dimensions consistent.
+      
+      
+          
+          UniformSystemList(std::vector&lt; System &gt; &amp;&amp; systems, std::array&lt; size_t, 3 &gt; dim ={0, 0, 0})Constructs a new UniformSystemList by moving existing vector of System if dimensions consistent.
+      
+      
+          
+          UniformSystemList(std::initializer_list&lt; System &gt; systems, std::array&lt; size_t, 3 &gt; dim ={0, 0, 0})Constructs a new UniformSystemList from initializer_list of System if dimensions consistent.
+      
+      
+          
+          UniformSystemList(const UniformSystemList &amp; that)Constructs a new UniformSystemList (copy).
+      
+      
+          
+          UniformSystemList(UniformSystemList &amp;&amp; that)Constructs a new UniformSystemList (move).
+      
+      
+          
+          ~UniformSystemList() =defaultDestroys the object.
+      
+      
+          const std::array&lt; size_t, 3 &gt; &amp;
+          dim() constgets dimensions of the uniformly sized systems
+      
+      
+          size_t
+          size()size of container
+      
+      
+          const System &amp;
+          at(size_t n)gets reference to n^th element
+      
+      
+          void
+          Swap(System &amp; that, size_t n)swaps input system with n^th system of list
+      
+      
+          UniformSystemList &amp;
+          operator=(const UniformSystemList &amp; that)assigns the contents (copy)
+      
+      
+          UniformSystemList &amp;
+          operator=(UniformSystemList &amp;&amp; that)assigns the contents (move)
+      
+  
+
+
+  Detailed Description
+  #
+
+template &lt;typename System &gt;
+class lds::UniformSystemList;
+
+
+
+  Public Function Details
+  #
+
+
+  UniformSystemList
+  #
+
+UniformSystemList() =default
+
+
+  UniformSystemList
+  #
+
+explicit UniformSystemList(
+    const std::vector&lt; System &gt; &amp; systems,
+    std::array&lt; size_t, 3 &gt; dim ={0, 0, 0}
+)
+Parameters:">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/">
+  <meta property="og:site_name" content="LDS C&E">
+  <meta property="og:title" content="lds::UniformSystemList">
+  <meta property="og:description" content="lds::UniformSystemList # More…
+Inherits from std::vector&lt; System &gt;
+Public Functions # Name UniformSystemList() =default
+Constructs a new UniformSystemList. UniformSystemList(const std::vector&lt; System &gt; &amp; systems, std::array&lt; size_t, 3 &gt; dim ={0, 0, 0})
+Constructs a new UniformSystemList by copying existing vector of System if dimensions consistent. UniformSystemList(std::vector&lt; System &gt; &amp;&amp; systems, std::array&lt; size_t, 3 &gt; dim ={0, 0, 0})
+Constructs a new UniformSystemList by moving existing vector of System if dimensions consistent. UniformSystemList(std::initializer_list&lt; System &gt; systems, std::array&lt; size_t, 3 &gt; dim ={0, 0, 0})
+Constructs a new UniformSystemList from initializer_list of System if dimensions consistent. UniformSystemList(const UniformSystemList &amp; that)
+Constructs a new UniformSystemList (copy). UniformSystemList(UniformSystemList &amp;&amp; that)
+Constructs a new UniformSystemList (move). ~UniformSystemList() =default
+Destroys the object. const std::array&lt; size_t, 3 &gt; &amp; dim() const
+gets dimensions of the uniformly sized systems size_t size()
+size of container const System &amp; at(size_t n)
+gets reference to n^th element void Swap(System &amp; that, size_t n)
+swaps input system with n^th system of list UniformSystemList &amp; operator=(const UniformSystemList &amp; that)
+assigns the contents (copy) UniformSystemList &amp; operator=(UniformSystemList &amp;&amp; that)
+assigns the contents (move) Detailed Description # template &lt;typename System &gt; class lds::UniformSystemList; Public Function Details # UniformSystemList # UniformSystemList() =default UniformSystemList # explicit UniformSystemList( const std::vector&lt; System &gt; &amp; systems, std::array&lt; size_t, 3 &gt; dim ={0, 0, 0} ) Parameters:">
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+  <ul>
+    <li><a href="#ldsuniformsystemlist">lds::UniformSystemList</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#detailed-description">Detailed Description</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#uniformsystemlist"><strong>UniformSystemList</strong></a></li>
+            <li><a href="#uniformsystemlist-1"><strong>UniformSystemList</strong></a></li>
+            <li><a href="#uniformsystemlist-2"><strong>UniformSystemList</strong></a></li>
+            <li><a href="#uniformsystemlist-3"><strong>UniformSystemList</strong></a></li>
+            <li><a href="#uniformsystemlist-4"><strong>UniformSystemList</strong></a></li>
+            <li><a href="#uniformsystemlist-5"><strong>UniformSystemList</strong></a></li>
+            <li><a href="#heading"><strong>~UniformSystemList</strong></a></li>
+            <li><a href="#dim"><strong>dim</strong></a></li>
+            <li><a href="#size"><strong>size</strong></a></li>
+            <li><a href="#at"><strong>at</strong></a></li>
+            <li><a href="#swap"><strong>Swap</strong></a></li>
+            <li><a href="#operator"><strong>operator=</strong></a></li>
+            <li><a href="#operator-1"><strong>operator=</strong></a></li>
+          </ul>
+        </li>
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+  <article class="markdown"><h1 id="ldsuniformsystemlist">
+  lds::UniformSystemList
+  <a class="anchor" href="#ldsuniformsystemlist">#</a>
+</h1>
+<p><a href="#detailed-description">More&hellip;</a></p>
+<p>Inherits from std::vector&lt; System &gt;</p>
+<h2 id="public-functions">
+  Public Functions
+  <a class="anchor" href="#public-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist">UniformSystemList</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/">UniformSystemList</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist">UniformSystemList</a></strong>(const std::vector&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &gt; &amp; systems, std::array&lt; size_t, 3 &gt; dim ={0, 0, 0})<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/">UniformSystemList</a> by copying existing vector of <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> if dimensions consistent.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist">UniformSystemList</a></strong>(std::vector&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &gt; &amp;&amp; systems, std::array&lt; size_t, 3 &gt; dim ={0, 0, 0})<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/">UniformSystemList</a> by moving existing vector of <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> if dimensions consistent.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist">UniformSystemList</a></strong>(std::initializer_list&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &gt; systems, std::array&lt; size_t, 3 &gt; dim ={0, 0, 0})<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/">UniformSystemList</a> from initializer_list of <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> if dimensions consistent.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist">UniformSystemList</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/">UniformSystemList</a> &amp; that)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/">UniformSystemList</a> (copy).</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist">UniformSystemList</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/">UniformSystemList</a> &amp;&amp; that)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/">UniformSystemList</a> (move).</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-~uniformsystemlist">~UniformSystemList</a></strong>() =default<br>Destroys the object.</td>
+      </tr>
+      <tr>
+          <td>const std::array&lt; size_t, 3 &gt; &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-dim">dim</a></strong>() const<br>gets dimensions of the uniformly sized systems</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-size">size</a></strong>()<br>size of container</td>
+      </tr>
+      <tr>
+          <td>const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-at">at</a></strong>(size_t n)<br>gets reference to n^th element</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-swap">Swap</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp; that, size_t n)<br>swaps input system with n^th system of list</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/">UniformSystemList</a> &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-operator=">operator=</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/">UniformSystemList</a> &amp; that)<br>assigns the contents (copy)</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/">UniformSystemList</a> &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-operator=">operator=</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/">UniformSystemList</a> &amp;&amp; that)<br>assigns the contents (move)</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System <span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">lds</span><span style="color:#f92672">::</span>UniformSystemList;
+</span></span></code></pre></div><hr>
+<hr>
+<h2 id="public-function-details">
+  Public Function Details
+  <a class="anchor" href="#public-function-details">#</a>
+</h2>
+<h3 id="uniformsystemlist">
+  <strong>UniformSystemList</strong>
+  <a class="anchor" href="#uniformsystemlist">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>UniformSystemList() <span style="color:#f92672">=</span><span style="color:#66d9ef">default</span>
+</span></span></code></pre></div><hr>
+<h3 id="uniformsystemlist-1">
+  <strong>UniformSystemList</strong>
+  <a class="anchor" href="#uniformsystemlist-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">explicit</span> UniformSystemList(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> System <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;</span> systems,
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span> size_t, <span style="color:#ae81ff">3</span> <span style="color:#f92672">&gt;</span> dim <span style="color:#f92672">=</span>{<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>}
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>systems</strong> input systems</li>
+<li><strong>dim</strong> dimensions (n_u, n_x, n_y)</li>
+</ul>
+<hr>
+<h3 id="uniformsystemlist-2">
+  <strong>UniformSystemList</strong>
+  <a class="anchor" href="#uniformsystemlist-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">explicit</span> UniformSystemList(
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> System <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;&amp;</span> systems,
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span> size_t, <span style="color:#ae81ff">3</span> <span style="color:#f92672">&gt;</span> dim <span style="color:#f92672">=</span>{<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>}
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>systems</strong> input systems</li>
+<li><strong>dim</strong> dimensions (n_u, n_x, n_y)</li>
+</ul>
+<hr>
+<h3 id="uniformsystemlist-3">
+  <strong>UniformSystemList</strong>
+  <a class="anchor" href="#uniformsystemlist-3">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>UniformSystemList(
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>initializer_list<span style="color:#f92672">&lt;</span> System <span style="color:#f92672">&gt;</span> systems,
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span> size_t, <span style="color:#ae81ff">3</span> <span style="color:#f92672">&gt;</span> dim <span style="color:#f92672">=</span>{<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>}
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>systems</strong> input systems</li>
+<li><strong>dim</strong> dimensions (n_u, n_x, n_y)</li>
+</ul>
+<hr>
+<h3 id="uniformsystemlist-4">
+  <strong>UniformSystemList</strong>
+  <a class="anchor" href="#uniformsystemlist-4">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>UniformSystemList(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> UniformSystemList <span style="color:#f92672">&amp;</span> that
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>that</strong> another <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/">UniformSystemList</a></li>
+</ul>
+<hr>
+<h3 id="uniformsystemlist-5">
+  <strong>UniformSystemList</strong>
+  <a class="anchor" href="#uniformsystemlist-5">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>UniformSystemList(
+</span></span><span style="display:flex;"><span>    UniformSystemList <span style="color:#f92672">&amp;&amp;</span> that
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>that</strong> another <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/">UniformSystemList</a></li>
+</ul>
+<hr>
+<h3 id="heading">
+  <strong>~UniformSystemList</strong>
+  <a class="anchor" href="#heading">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#f92672">~</span>UniformSystemList() <span style="color:#f92672">=</span><span style="color:#66d9ef">default</span>
+</span></span></code></pre></div><hr>
+<h3 id="dim">
+  <strong>dim</strong>
+  <a class="anchor" href="#dim">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span> size_t, <span style="color:#ae81ff">3</span> <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;</span> dim() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="size">
+  <strong>size</strong>
+  <a class="anchor" href="#size">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> size_t size()
+</span></span></code></pre></div><hr>
+<h3 id="at">
+  <strong>at</strong>
+  <a class="anchor" href="#at">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> System <span style="color:#f92672">&amp;</span> at(
+</span></span><span style="display:flex;"><span>    size_t n
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="swap">
+  <strong>Swap</strong>
+  <a class="anchor" href="#swap">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> Swap(
+</span></span><span style="display:flex;"><span>    System <span style="color:#f92672">&amp;</span> that,
+</span></span><span style="display:flex;"><span>    size_t n
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>that</strong> input system</li>
+<li><strong>n</strong> index where the system is moved</li>
+</ul>
+<hr>
+<h3 id="operator">
+  <strong>operator=</strong>
+  <a class="anchor" href="#operator">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> UniformSystemList <span style="color:#f92672">&amp;</span> <span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> UniformSystemList <span style="color:#f92672">&amp;</span> that
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>that</strong> another <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/">UniformSystemList</a></li>
+</ul>
+<p><strong>Return</strong>: reference to object</p>
+<hr>
+<h3 id="operator-1">
+  <strong>operator=</strong>
+  <a class="anchor" href="#operator-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> UniformSystemList <span style="color:#f92672">&amp;</span> <span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>(
+</span></span><span style="display:flex;"><span>    UniformSystemList <span style="color:#f92672">&amp;&amp;</span> that
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>that</strong> another <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/">UniformSystemList</a></li>
+</ul>
+<p><strong>Return</strong>: reference to object</p>
+<hr>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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+    <li><a href="#ldsuniformsystemlist">lds::UniformSystemList</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#detailed-description">Detailed Description</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#uniformsystemlist"><strong>UniformSystemList</strong></a></li>
+            <li><a href="#uniformsystemlist-1"><strong>UniformSystemList</strong></a></li>
+            <li><a href="#uniformsystemlist-2"><strong>UniformSystemList</strong></a></li>
+            <li><a href="#uniformsystemlist-3"><strong>UniformSystemList</strong></a></li>
+            <li><a href="#uniformsystemlist-4"><strong>UniformSystemList</strong></a></li>
+            <li><a href="#uniformsystemlist-5"><strong>UniformSystemList</strong></a></li>
+            <li><a href="#heading"><strong>~UniformSystemList</strong></a></li>
+            <li><a href="#dim"><strong>dim</strong></a></li>
+            <li><a href="#size"><strong>size</strong></a></li>
+            <li><a href="#at"><strong>at</strong></a></li>
+            <li><a href="#swap"><strong>Swap</strong></a></li>
+            <li><a href="#operator"><strong>operator=</strong></a></li>
+            <li><a href="#operator-1"><strong>operator=</strong></a></li>
+          </ul>
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diff --git a/docs/docs/api/classes/classlds_1_1_uniform_vector_list/index.html b/docs/docs/api/classes/classlds_1_1_uniform_vector_list/index.html
new file mode 100644
index 00000000..37d10876
--- /dev/null
+++ b/docs/docs/api/classes/classlds_1_1_uniform_vector_list/index.html
@@ -0,0 +1,600 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="
+  lds::UniformVectorList
+  #
+
+Inherits from std::vector&lt; Vector &gt;
+
+  Public Functions
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          
+          UniformVectorList() =defaultConstructs a new UniformVectorList.
+      
+      
+          
+          UniformVectorList(const std::vector&lt; Vector &gt; &amp; vecs, size_t dim =0)Constructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent.
+      
+      
+          
+          UniformVectorList(std::vector&lt; Vector &gt; &amp;&amp; vecs, size_t dim =0)Constructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent.
+      
+      
+          
+          UniformVectorList(std::initializer_list&lt; Vector &gt; vecs, size_t dim =0)Constructs a new UniformVectorList from initializer_list of Vector if dimensions consistent.
+      
+      
+          
+          UniformVectorList(const UniformVectorList &amp; that)Constructs a new UniformVectorList (copy)
+      
+      
+          
+          UniformVectorList(UniformVectorList &amp;&amp; that)Constructs a new UniformVectorList (move)
+      
+      
+          
+          ~UniformVectorList() =defaultDestroys the object.
+      
+      
+          size_t
+          dim() constgets dimensions of the uniformly sized matrices
+      
+      
+          size_t
+          size()size of container
+      
+      
+          const Vector &amp;
+          at(size_t n)gets reference to n^th element
+      
+      
+          void
+          Swap(Vector &amp; that, size_t n)swaps input matrix with n^th vector of list
+      
+      
+          UniformVectorList &amp;
+          operator=(const UniformVectorList &amp; that)assigns the contents (copy)
+      
+      
+          UniformVectorList &amp;
+          operator=(UniformVectorList &amp;&amp; that)assigns the contents (move)
+      
+  
+
+
+
+
+  Public Function Details
+  #
+
+
+  UniformVectorList
+  #
+
+UniformVectorList() =default
+
+
+  UniformVectorList
+  #
+
+explicit UniformVectorList(
+    const std::vector&lt; Vector &gt; &amp; vecs,
+    size_t dim =0
+)
+Parameters:">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">
+  <meta property="og:site_name" content="LDS C&E">
+  <meta property="og:title" content="lds::UniformVectorList">
+  <meta property="og:description" content="lds::UniformVectorList # Inherits from std::vector&lt; Vector &gt;
+Public Functions # Name UniformVectorList() =default
+Constructs a new UniformVectorList. UniformVectorList(const std::vector&lt; Vector &gt; &amp; vecs, size_t dim =0)
+Constructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent. UniformVectorList(std::vector&lt; Vector &gt; &amp;&amp; vecs, size_t dim =0)
+Constructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent. UniformVectorList(std::initializer_list&lt; Vector &gt; vecs, size_t dim =0)
+Constructs a new UniformVectorList from initializer_list of Vector if dimensions consistent. UniformVectorList(const UniformVectorList &amp; that)
+Constructs a new UniformVectorList (copy) UniformVectorList(UniformVectorList &amp;&amp; that)
+Constructs a new UniformVectorList (move) ~UniformVectorList() =default
+Destroys the object. size_t dim() const
+gets dimensions of the uniformly sized matrices size_t size()
+size of container const Vector &amp; at(size_t n)
+gets reference to n^th element void Swap(Vector &amp; that, size_t n)
+swaps input matrix with n^th vector of list UniformVectorList &amp; operator=(const UniformVectorList &amp; that)
+assigns the contents (copy) UniformVectorList &amp; operator=(UniformVectorList &amp;&amp; that)
+assigns the contents (move) Public Function Details # UniformVectorList # UniformVectorList() =default UniformVectorList # explicit UniformVectorList( const std::vector&lt; Vector &gt; &amp; vecs, size_t dim =0 ) Parameters:">
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+  <strong>lds::UniformVectorList</strong>
+
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+    
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+    <li><a href="#ldsuniformvectorlist">lds::UniformVectorList</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#uniformvectorlist"><strong>UniformVectorList</strong></a></li>
+            <li><a href="#uniformvectorlist-1"><strong>UniformVectorList</strong></a></li>
+            <li><a href="#uniformvectorlist-2"><strong>UniformVectorList</strong></a></li>
+            <li><a href="#uniformvectorlist-3"><strong>UniformVectorList</strong></a></li>
+            <li><a href="#uniformvectorlist-4"><strong>UniformVectorList</strong></a></li>
+            <li><a href="#uniformvectorlist-5"><strong>UniformVectorList</strong></a></li>
+            <li><a href="#heading"><strong>~UniformVectorList</strong></a></li>
+            <li><a href="#dim"><strong>dim</strong></a></li>
+            <li><a href="#size"><strong>size</strong></a></li>
+            <li><a href="#at"><strong>at</strong></a></li>
+            <li><a href="#swap"><strong>Swap</strong></a></li>
+            <li><a href="#operator"><strong>operator=</strong></a></li>
+            <li><a href="#operator-1"><strong>operator=</strong></a></li>
+          </ul>
+        </li>
+      </ul>
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+  
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+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="ldsuniformvectorlist">
+  lds::UniformVectorList
+  <a class="anchor" href="#ldsuniformvectorlist">#</a>
+</h1>
+<p>Inherits from std::vector&lt; Vector &gt;</p>
+<h2 id="public-functions">
+  Public Functions
+  <a class="anchor" href="#public-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist">UniformVectorList</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist">UniformVectorList</a></strong>(const std::vector&lt; Vector &gt; &amp; vecs, size_t dim =0)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a> by copying existing vector of Vector if dimensions consistent.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist">UniformVectorList</a></strong>(std::vector&lt; Vector &gt; &amp;&amp; vecs, size_t dim =0)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a> by moving existing vector of Vector if dimensions consistent.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist">UniformVectorList</a></strong>(std::initializer_list&lt; Vector &gt; vecs, size_t dim =0)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a> from initializer_list of Vector if dimensions consistent.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist">UniformVectorList</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a> &amp; that)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a> (copy)</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist">UniformVectorList</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a> &amp;&amp; that)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a> (move)</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-~uniformvectorlist">~UniformVectorList</a></strong>() =default<br>Destroys the object.</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-dim">dim</a></strong>() const<br>gets dimensions of the uniformly sized matrices</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-size">size</a></strong>()<br>size of container</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-at">at</a></strong>(size_t n)<br>gets reference to n^th element</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-swap">Swap</a></strong>(Vector &amp; that, size_t n)<br>swaps input matrix with n^th vector of list</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a> &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-operator=">operator=</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a> &amp; that)<br>assigns the contents (copy)</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a> &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-operator=">operator=</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a> &amp;&amp; that)<br>assigns the contents (move)</td>
+      </tr>
+  </tbody>
+</table>
+<hr>
+<hr>
+<h2 id="public-function-details">
+  Public Function Details
+  <a class="anchor" href="#public-function-details">#</a>
+</h2>
+<h3 id="uniformvectorlist">
+  <strong>UniformVectorList</strong>
+  <a class="anchor" href="#uniformvectorlist">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>UniformVectorList() <span style="color:#f92672">=</span><span style="color:#66d9ef">default</span>
+</span></span></code></pre></div><hr>
+<h3 id="uniformvectorlist-1">
+  <strong>UniformVectorList</strong>
+  <a class="anchor" href="#uniformvectorlist-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">explicit</span> UniformVectorList(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> Vector <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;</span> vecs,
+</span></span><span style="display:flex;"><span>    size_t dim <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>vecs</strong> input vectors</li>
+<li><strong>dims</strong> dimension</li>
+</ul>
+<hr>
+<h3 id="uniformvectorlist-2">
+  <strong>UniformVectorList</strong>
+  <a class="anchor" href="#uniformvectorlist-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">explicit</span> UniformVectorList(
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> Vector <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;&amp;</span> vecs,
+</span></span><span style="display:flex;"><span>    size_t dim <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>vecs</strong> input vectors</li>
+<li><strong>dim</strong> dimension</li>
+</ul>
+<hr>
+<h3 id="uniformvectorlist-3">
+  <strong>UniformVectorList</strong>
+  <a class="anchor" href="#uniformvectorlist-3">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>UniformVectorList(
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>initializer_list<span style="color:#f92672">&lt;</span> Vector <span style="color:#f92672">&gt;</span> vecs,
+</span></span><span style="display:flex;"><span>    size_t dim <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>vecs</strong> input vectors</li>
+<li><strong>dim</strong> dimension</li>
+</ul>
+<hr>
+<h3 id="uniformvectorlist-4">
+  <strong>UniformVectorList</strong>
+  <a class="anchor" href="#uniformvectorlist-4">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>UniformVectorList(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> UniformVectorList <span style="color:#f92672">&amp;</span> that
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>that</strong> another <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a></li>
+</ul>
+<hr>
+<h3 id="uniformvectorlist-5">
+  <strong>UniformVectorList</strong>
+  <a class="anchor" href="#uniformvectorlist-5">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>UniformVectorList(
+</span></span><span style="display:flex;"><span>    UniformVectorList <span style="color:#f92672">&amp;&amp;</span> that
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>that</strong> another <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a></li>
+</ul>
+<hr>
+<h3 id="heading">
+  <strong>~UniformVectorList</strong>
+  <a class="anchor" href="#heading">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#f92672">~</span>UniformVectorList() <span style="color:#f92672">=</span><span style="color:#66d9ef">default</span>
+</span></span></code></pre></div><hr>
+<h3 id="dim">
+  <strong>dim</strong>
+  <a class="anchor" href="#dim">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> size_t dim() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="size">
+  <strong>size</strong>
+  <a class="anchor" href="#size">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> size_t size()
+</span></span></code></pre></div><hr>
+<h3 id="at">
+  <strong>at</strong>
+  <a class="anchor" href="#at">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> at(
+</span></span><span style="display:flex;"><span>    size_t n
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="swap">
+  <strong>Swap</strong>
+  <a class="anchor" href="#swap">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> Swap(
+</span></span><span style="display:flex;"><span>    Vector <span style="color:#f92672">&amp;</span> that,
+</span></span><span style="display:flex;"><span>    size_t n
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>that</strong> input vector</li>
+<li><strong>n</strong> index where the vector is moved</li>
+</ul>
+<hr>
+<h3 id="operator">
+  <strong>operator=</strong>
+  <a class="anchor" href="#operator">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> UniformVectorList <span style="color:#f92672">&amp;</span> <span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> UniformVectorList <span style="color:#f92672">&amp;</span> that
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>that</strong> another <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a></li>
+</ul>
+<p><strong>Return</strong>: reference to object</p>
+<hr>
+<h3 id="operator-1">
+  <strong>operator=</strong>
+  <a class="anchor" href="#operator-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> UniformVectorList <span style="color:#f92672">&amp;</span> <span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>(
+</span></span><span style="display:flex;"><span>    UniformVectorList <span style="color:#f92672">&amp;&amp;</span> that
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>that</strong> another <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a></li>
+</ul>
+<p><strong>Return</strong>: reference to object</p>
+<hr>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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+    <li><a href="#ldsuniformvectorlist">lds::UniformVectorList</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#uniformvectorlist"><strong>UniformVectorList</strong></a></li>
+            <li><a href="#uniformvectorlist-1"><strong>UniformVectorList</strong></a></li>
+            <li><a href="#uniformvectorlist-2"><strong>UniformVectorList</strong></a></li>
+            <li><a href="#uniformvectorlist-3"><strong>UniformVectorList</strong></a></li>
+            <li><a href="#uniformvectorlist-4"><strong>UniformVectorList</strong></a></li>
+            <li><a href="#uniformvectorlist-5"><strong>UniformVectorList</strong></a></li>
+            <li><a href="#heading"><strong>~UniformVectorList</strong></a></li>
+            <li><a href="#dim"><strong>dim</strong></a></li>
+            <li><a href="#size"><strong>size</strong></a></li>
+            <li><a href="#at"><strong>at</strong></a></li>
+            <li><a href="#swap"><strong>Swap</strong></a></li>
+            <li><a href="#operator"><strong>operator=</strong></a></li>
+            <li><a href="#operator-1"><strong>operator=</strong></a></li>
+          </ul>
+        </li>
+      </ul>
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+  </ul>
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diff --git a/docs/docs/api/classes/classlds_1_1gaussian_1_1_controller/index.html b/docs/docs/api/classes/classlds_1_1gaussian_1_1_controller/index.html
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+++ b/docs/docs/api/classes/classlds_1_1gaussian_1_1_controller/index.html
@@ -0,0 +1,560 @@
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+        Github
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+  <strong>lds::gaussian::Controller</strong>
+
+  <label for="toc-control">
+    
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+  <ul>
+    <li><a href="#ldsgaussiancontroller">lds::gaussian::Controller</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#additional-inherited-members">Additional inherited members</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#set_"><strong>set_y_ref</strong></a></li>
+          </ul>
+        </li>
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+      </header>
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+      
+      
+  <article class="markdown"><h1 id="ldsgaussiancontroller">
+  lds::gaussian::Controller
+  <a class="anchor" href="#ldsgaussiancontroller">#</a>
+</h1>
+<p>Gaussian-observation <a href="">Controller</a> Type.
+<br /> <code>#include &lt;lds_gaussian_ctrl.h&gt;</code></p>
+<p>Inherits from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">lds::Controller&lt; System &gt;</a></p>
+<h2 id="public-functions">
+  Public Functions
+  <a class="anchor" href="#public-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>virtual void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/#function-set-y-ref">set_y_ref</a></strong>(const Vector &amp; y_ref) override<br>sets reference output</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="additional-inherited-members">
+  Additional inherited members
+  <a class="anchor" href="#additional-inherited-members">#</a>
+</h2>
+<p><strong>Public Functions inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">lds::Controller&lt; System &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller">Controller</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">Controller</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller">Controller</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp; sys, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub, size_t control_type =0)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">Controller</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller">Controller</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp;&amp; sys, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub, size_t control_type =0)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">Controller</a> by moving the system object.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control">Control</a></strong>(const Vector &amp; z, bool do_control =true, bool do_lock_control =false, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_soft_start =0, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal (single-step)</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controloutputreference">ControlOutputReference</a></strong>(const Vector &amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_soft_start =0, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal, given previously-set (single-step)</td>
+      </tr>
+      <tr>
+          <td>const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-sys">sys</a></strong>() const</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc">Kc</a></strong>() const<br>Get state feedback controller gain.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-inty">Kc_inty</a></strong>() const<br>Get integral controller gain.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-u">Kc_u</a></strong>() const<br>Get input feedback controller gain.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-g-design">g_design</a></strong>() const<br>Get input gain used in controller design.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ref">u_ref</a></strong>() const<br>Get reference input.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-x-ref">x_ref</a></strong>() const<br>Get reference state.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-y-ref">y_ref</a></strong>() const<br>Get reference output.</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control-type">control_type</a></strong>() const<br>Get controller type.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-tau-awu">tau_awu</a></strong>() const<br>Get time constant of anti-integral-windup.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-lb">u_lb</a></strong>() const<br>Get control lower bound.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ub">u_ub</a></strong>() const<br>Get control upper bound.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-sys">set_sys</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp; sys)<br>Set system.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-g-design">set_g_design</a></strong>(const Vector &amp; g_design)<br>Set input gain used in controller design (g_design)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ref">set_u_ref</a></strong>(const Vector &amp; u_ref)<br>Set reference input (u_ref)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-x-ref">set_x_ref</a></strong>(const Vector &amp; x_ref)<br>Set reference state (x_ref)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc">set_Kc</a></strong>(const Matrix &amp; Kc)<br>Set state controller gain.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-inty">set_Kc_inty</a></strong>(const Matrix &amp; Kc_inty)<br>Set integral controller gain.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-u">set_Kc_u</a></strong>(const Matrix &amp; Kc_u)<br>Set input controller gain.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-tau-awu">set_tau_awu</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> tau)<br>Set time constant of anti-integral-windup.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-control-type">set_control_type</a></strong>(size_t control_type)<br>Sets the control type.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-lb">set_u_lb</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb)<br>sets control lower bound</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ub">set_u_ub</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub)<br>Sets control upper bound.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-reset">Reset</a></strong>()<br>reset system and control variables.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-print">Print</a></strong>()<br>prints variables to stdout</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Protected Attributes inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">lds::Controller&lt; System &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-sys-">sys_</a></strong> <br>underlying LDS</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-">u_</a></strong> <br>control signal</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-return-">u_return_</a></strong> <br>control signal that is <em>returned</em> to user</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-g-design-">g_design_</a></strong> <br>input gain of the system used for controller design</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-">u_ref_</a></strong> <br>reference input</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-prev-">u_ref_prev_</a></strong> <br>reference input at previous time step</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-x-ref-">x_ref_</a></strong> <br>reference state</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-y-ref-">y_ref_</a></strong> <br>reference output</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-cx-ref-">cx_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-">Kc_</a></strong> <br>state controller gain</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-u-">Kc_u_</a></strong> <br>input controller gain (optional when control updates \deltaU)</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-inty-">Kc_inty_</a></strong> <br>integral controller gain</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-du-ref-">du_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-ref-">dv_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-ref-">v_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-">dv_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-">v_</a></strong> <br>Control after g inversion (e.g., control in physical units)</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-">int_e_</a></strong> <br>integrated error</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-awu-adjust-">int_e_awu_adjust_</a></strong> <br>anti-windup adjustment to intE</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-sat-">u_sat_</a></strong> <br>control signal after saturation (for antiWindup)</td>
+      </tr>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-control-prev-">do_control_prev_</a></strong></td>
+      </tr>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-lock-control-prev-">do_lock_control_prev_</a></strong></td>
+      </tr>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-saturated-">u_saturated_</a></strong> <br>whether control signal has reached saturation limits</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-lb-">u_lb_</a></strong> <br>lower bound on control</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ub-">u_ub_</a></strong> <br>upper bound on control</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-tau-awu-">tau_awu_</a></strong> <br>antiwindup time constant</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-k-awu-">k_awu_</a></strong></td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-t-since-control-onset-">t_since_control_onset_</a></strong> <br>time since control epoch onset</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-control-type-">control_type_</a></strong> <br>controller type</td>
+      </tr>
+  </tbody>
+</table>
+<hr>
+<hr>
+<h2 id="public-function-details">
+  Public Function Details
+  <a class="anchor" href="#public-function-details">#</a>
+</h2>
+<h3 id="set_">
+  <strong>set_y_ref</strong>
+  <a class="anchor" href="#set_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> set_y_ref(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> y_ref
+</span></span><span style="display:flex;"><span>) <span style="color:#66d9ef">override</span>
+</span></span></code></pre></div><p><strong>Reimplements</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-y-ref">lds::Controller::set_y_ref</a></p>
+<hr>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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+<li><strong>Tutorials</strong>
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+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
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+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
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+<ul>
+<li><strong>API Reference</strong>
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+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
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+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
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+  <ul>
+    <li><a href="#ldsgaussianfit">lds::gaussian::Fit</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#additional-inherited-members">Additional inherited members</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#fit"><strong>Fit</strong></a></li>
+            <li><a href="#fit-1"><strong>Fit</strong></a></li>
+            <li><a href="#r"><strong>R</strong></a></li>
+            <li><a href="#set_"><strong>set_R</strong></a></li>
+            <li><a href="#h"><strong>h</strong></a></li>
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+  <article class="markdown"><h1 id="ldsgaussianfit">
+  lds::gaussian::Fit
+  <a class="anchor" href="#ldsgaussianfit">#</a>
+</h1>
+<p>GLDS <a href="">Fit</a> Type.
+<br /> <code>#include &lt;lds_gaussian_fit.h&gt;</code></p>
+<p>Inherits from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">lds::Fit</a></p>
+<h2 id="public-functions">
+  Public Functions
+  <a class="anchor" href="#public-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/#function-fit">Fit</a></strong>() =default</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/#function-fit">Fit</a></strong>(size_t n_u, size_t n_x, size_t n_y, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> dt)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/">Fit</a>.</td>
+      </tr>
+      <tr>
+          <td>virtual const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/#function-r">R</a></strong>() const override<br>gets measurement noise covariance</td>
+      </tr>
+      <tr>
+          <td>virtual void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/#function-set-r">set_R</a></strong>(const Matrix &amp; R) override<br>sets measurement noise covariance</td>
+      </tr>
+      <tr>
+          <td>virtual View</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/#function-h">h</a></strong>(Matrix &amp; y, const Matrix &amp; x, size_t t) override<br>output function</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="additional-inherited-members">
+  Additional inherited members
+  <a class="anchor" href="#additional-inherited-members">#</a>
+</h2>
+<p><strong>Public Functions inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">lds::Fit</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>virtual</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-~fit">~Fit</a></strong>() =default</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-n-u">n_u</a></strong>() const<br>gets number of inputs</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-n-x">n_x</a></strong>() const<br>gets number of states</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-n-y">n_y</a></strong>() const<br>gets number of outputs</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-dt">dt</a></strong>() const<br>gets sample period</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-a">A</a></strong>() const<br>gets state matrix</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-b">B</a></strong>() const<br>gets input matrix</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-g">g</a></strong>() const<br>gets input gain</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-m">m</a></strong>() const<br>gets process disturbance</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-q">Q</a></strong>() const<br>gets process noise covariance</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-x0">x0</a></strong>() const<br>gets initial state estimate</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-p0">P0</a></strong>() const<br>gets covariance of initial state estimate</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-c">C</a></strong>() const<br>gets output matrix</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-d">d</a></strong>() const<br>gets output bias</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-a">set_A</a></strong>(const Matrix &amp; A)<br>sets state matrix</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-b">set_B</a></strong>(const Matrix &amp; B)<br>sets input matrix</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-g">set_g</a></strong>(const Vector &amp; g)<br>sets input gain/conversion factor</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-m">set_m</a></strong>(const Vector &amp; m)<br>sets process disturbance</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-q">set_Q</a></strong>(const Matrix &amp; Q)<br>sets process noise covariance</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-x0">set_x0</a></strong>(const Vector &amp; x0)<br>sets initial state estimate</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-p0">set_P0</a></strong>(const Matrix &amp; P0)<br>sets initial state estimate covariance</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-c">set_C</a></strong>(const Matrix &amp; C)<br>sets output matrix</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-d">set_d</a></strong>(const Vector &amp; d)<br>sets output bias</td>
+      </tr>
+      <tr>
+          <td>View</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-f">f</a></strong>(Matrix &amp; x, const Matrix &amp; u, size_t t)<br>system dynamics function</td>
+      </tr>
+      <tr>
+          <td>View</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-f">f</a></strong>(Matrix &amp; x_pre, const Matrix &amp; x_post, const Matrix &amp; u, size_t t)<br>system dynamics function</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Protected Attributes inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">lds::Fit</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-dt-">dt_</a></strong> <br>sample period</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-a-">A_</a></strong> <br>state matrix</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-b-">B_</a></strong> <br>input matrix</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-g-">g_</a></strong> <br>input gain</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-m-">m_</a></strong> <br>process noise mean</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-q-">Q_</a></strong> <br>process noise cov</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-c-">C_</a></strong> <br>output matrix</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-d-">d_</a></strong> <br>output bias</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-r-">R_</a></strong> <br>measurement noise</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-x0-">x0_</a></strong> <br>initial state</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-p0-">P0_</a></strong> <br>initial covar</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-n-u-">n_u_</a></strong> <br>number of inputs</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-n-x-">n_x_</a></strong> <br>number of states</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-n-y-">n_y_</a></strong> <br>number of outputs</td>
+      </tr>
+  </tbody>
+</table>
+<hr>
+<hr>
+<h2 id="public-function-details">
+  Public Function Details
+  <a class="anchor" href="#public-function-details">#</a>
+</h2>
+<h3 id="fit">
+  <strong>Fit</strong>
+  <a class="anchor" href="#fit">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Fit() <span style="color:#f92672">=</span><span style="color:#66d9ef">default</span>
+</span></span></code></pre></div><hr>
+<h3 id="fit-1">
+  <strong>Fit</strong>
+  <a class="anchor" href="#fit-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Fit(
+</span></span><span style="display:flex;"><span>    size_t n_u,
+</span></span><span style="display:flex;"><span>    size_t n_x,
+</span></span><span style="display:flex;"><span>    size_t n_y,
+</span></span><span style="display:flex;"><span>    data_t dt
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>n_u</strong> number of inputs</li>
+<li><strong>n_x</strong> number of states</li>
+<li><strong>n_y</strong> number of outputs</li>
+<li><strong>dt</strong> sample period</li>
+</ul>
+<hr>
+<h3 id="r">
+  <strong>R</strong>
+  <a class="anchor" href="#r">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> R() <span style="color:#66d9ef">const</span> <span style="color:#66d9ef">override</span>
+</span></span></code></pre></div><p><strong>Reimplements</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-r">lds::Fit::R</a></p>
+<hr>
+<h3 id="set_">
+  <strong>set_R</strong>
+  <a class="anchor" href="#set_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> set_R(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> R
+</span></span><span style="display:flex;"><span>) <span style="color:#66d9ef">override</span>
+</span></span></code></pre></div><p><strong>Reimplements</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-r">lds::Fit::set_R</a></p>
+<hr>
+<h3 id="h">
+  <strong>h</strong>
+  <a class="anchor" href="#h">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">virtual</span> View h(
+</span></span><span style="display:flex;"><span>    Matrix <span style="color:#f92672">&amp;</span> y,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> x,
+</span></span><span style="display:flex;"><span>    size_t t
+</span></span><span style="display:flex;"><span>) <span style="color:#66d9ef">override</span>
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>y</strong> output estimate (over time)</li>
+<li><strong>x</strong> state estimate (over time)</li>
+<li><strong>t</strong> time index</li>
+</ul>
+<p><strong>Return</strong>: output</p>
+<p><strong>Reimplements</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-h">lds::Fit::h</a></p>
+<hr>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
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+            <li><a href="#fit-1"><strong>Fit</strong></a></li>
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+  lds::gaussian::FitEM
+  <a class="anchor" href="#ldsgaussianfitem">#</a>
+</h1>
+<p>GLDS E-M <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/">Fit</a> Type.  <a href="#detailed-description">More&hellip;</a></p>
+<p><br /> <code>#include &lt;lds_gaussian_fit_em.h&gt;</code></p>
+<p>Inherits from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">lds::EM&lt; Fit &gt;</a></p>
+<h2 id="additional-inherited-members">
+  Additional inherited members
+  <a class="anchor" href="#additional-inherited-members">#</a>
+</h2>
+<p><strong>Public Functions inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">lds::EM&lt; Fit &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em">EM</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">EM</a><a href="">Fit</a> type.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em">EM</a></strong>(size_t n_x, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> dt, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &amp;&amp; u_train, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &amp;&amp; z_train)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">EM</a><a href="">Fit</a> type.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em">EM</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a> &amp; fit0, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &amp;&amp; u_train, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &amp;&amp; z_train)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">EM</a><a href="">Fit</a> type.</td>
+      </tr>
+      <tr>
+          <td>virtual</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-~em">~EM</a></strong>() =default</td>
+      </tr>
+      <tr>
+          <td>const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a> &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-run">Run</a></strong>(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> tol =1e-2)<br>Runs fitting by Expectation(E)-Maximization(M)</td>
+      </tr>
+      <tr>
+          <td>std::tuple&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt;, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-returndata">ReturnData</a></strong>()<br>Returns the input/output data to caller.</td>
+      </tr>
+      <tr>
+          <td>const std::vector&lt; Matrix &gt; &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-x">x</a></strong>() const<br>gets estimated state (over time)</td>
+      </tr>
+      <tr>
+          <td>const std::vector&lt; Matrix &gt; &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-y">y</a></strong>() const<br>gets estimated output (over time)</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-x-t-x-t">sum_E_x_t_x_t</a></strong>() const<br>gets state-input covariance</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-xu-tm1-xu-tm1">sum_E_xu_tm1_xu_tm1</a></strong>() const<br>gets state-input covariance (t-minus-1)</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-xu-t-xu-tm1">sum_E_xu_t_xu_tm1</a></strong>() const<br>gets single lag state-input covariance</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-n-t-tot">n_t_tot</a></strong>()<br>total number of time samples</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-theta">theta</a></strong>() const<br>gets parameters updated in M step</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Protected Functions inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">lds::EM&lt; Fit &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-expectation">Expectation</a></strong>(bool force_common_initial =false)<br>Expectation step.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximization">Maximization</a></strong>(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)<br>Maximization step.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizedynamics">MaximizeDynamics</a></strong>()</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizeq">MaximizeQ</a></strong>()</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizeinitial">MaximizeInitial</a></strong>()</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-smooth">Smooth</a></strong>(bool force_common_initial)<br>get smoothed estimates</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-reset">Reset</a></strong>()<br>reset to initial conditions</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-initvars">InitVars</a></strong>()<br>Initializes the variables.</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-updatetheta">UpdateTheta</a></strong>()<br>updates parameter list, theta</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Protected Attributes inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">lds::EM&lt; Fit &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-u-">u_</a></strong> <br>input training data</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-z-">z_</a></strong> <br>measurement training data</td>
+      </tr>
+      <tr>
+          <td>std::vector&lt; Matrix &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-x-">x_</a></strong> <br>state estimate</td>
+      </tr>
+      <tr>
+          <td>std::vector&lt; Cube &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-p-">P_</a></strong> <br>state estimate cov</td>
+      </tr>
+      <tr>
+          <td>std::vector&lt; Cube &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-p-t-tm1-">P_t_tm1_</a></strong> <br>single-lag state covariance</td>
+      </tr>
+      <tr>
+          <td>std::vector&lt; Matrix &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-y-">y_</a></strong> <br>output estimate</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-diag-y-">diag_y_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-x-t-x-t-">sum_E_x_t_x_t_</a></strong> <br>state covariance (current time)</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-xu-tm1-xu-tm1-">sum_E_xu_tm1_xu_tm1_</a></strong> <br>state-input covariance (t-minus-1)</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-xu-t-xu-tm1-">sum_E_xu_t_xu_tm1_</a></strong> <br>single lag state-input covariance</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-fit-">fit_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-theta-">theta_</a></strong></td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-dt-">dt_</a></strong> <br>sample period</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-u-">n_u_</a></strong> <br>number of inputs</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-x-">n_x_</a></strong> <br>number of states</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-y-">n_y_</a></strong> <br>number of outputs</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-trials-">n_trials_</a></strong> <br>number of input/output data sequences</td>
+      </tr>
+      <tr>
+          <td>std::vector&lt; size_t &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-t-">n_t_</a></strong> <br>number of time steps</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-t-tot-">n_t_tot_</a></strong> <br>total number of time steps across trials</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">lds</span><span style="color:#f92672">::</span>gaussian<span style="color:#f92672">::</span>FitEM;
+</span></span></code></pre></div><pre tabindex="0"><code>        This type is used in the process of fitting GLDS models by
+        expectation-maximization (EM).
+</code></pre><hr>
+<hr>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
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+  <article class="markdown"><h1 id="ldsgaussianfitssid">
+  lds::gaussian::FitSSID
+  <a class="anchor" href="#ldsgaussianfitssid">#</a>
+</h1>
+<p>Subspace Identification (<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">SSID</a>) for GLDS.
+<br /> <code>#include &lt;lds_gaussian_fit_ssid.h&gt;</code></p>
+<p>Inherits from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">lds::SSID&lt; Fit &gt;</a></p>
+<h2 id="additional-inherited-members">
+  Additional inherited members
+  <a class="anchor" href="#additional-inherited-members">#</a>
+</h2>
+<p><strong>Public Functions inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">lds::SSID&lt; Fit &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-ssid">SSID</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">SSID</a><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a> type.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-ssid">SSID</a></strong>(size_t n_x, size_t n_h, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> dt, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &amp;&amp; u_train, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &amp;&amp; z_train, const Vector &amp; d =Vector(1).fill(-kInf))<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">SSID</a><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a> type.</td>
+      </tr>
+      <tr>
+          <td>std::tuple&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a>, Vector &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-run">Run</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt">SSIDWt</a> ssid_wt)<br>Runs fitting by subspace identification (<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">SSID</a>)</td>
+      </tr>
+      <tr>
+          <td>std::tuple&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt;, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-returndata">ReturnData</a></strong>()<br>Returns the I/O data to caller.</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Protected Functions inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">lds::SSID&lt; Fit &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-calcd">CalcD</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> t_silence =0.1, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> thresh_silence =0.001)<br>Using periods of silence in inputs (u), calculates the output \ bias (d)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-createhankeldatamat">CreateHankelDataMat</a></strong>()<br>Creates the block-hankel I/O data matrix.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-calcsvd">CalcSVD</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt">SSIDWt</a> wt)<br>performs the singular value decomposition (SVD)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-solve">Solve</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> wt_dc)<br>solves for LDS parameters</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-recomputeextobs">RecomputeExtObs</a></strong>()<br>recompute extended observability matrix from estimates of A, C</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Protected Attributes inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">lds::SSID&lt; Fit &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-u-">u_</a></strong> <br>input training data</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-z-">z_</a></strong> <br>measurement training data</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-d-">D_</a></strong> <br>block-Hankel I/O data matrix</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-fit-">fit_</a></strong> <br>fit</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-g-dc-">g_dc_</a></strong> <br>I/O gain @ DC.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-dt-">dt_</a></strong> <br>sample period</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-u-">n_u_</a></strong> <br>number of inputs</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-x-">n_x_</a></strong> <br>number of states</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-y-">n_y_</a></strong> <br>number of outputs</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-h-">n_h_</a></strong></td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-trials-">n_trials_</a></strong> <br>number of input/output data sequences</td>
+      </tr>
+      <tr>
+          <td>std::vector&lt; size_t &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-t-">n_t_</a></strong> <br>number of time steps</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-t-tot-">n_t_tot_</a></strong> <br>total number of time steps across trials</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-l-">L_</a></strong> <br>lower triangle decomp of covariance matrix</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-s-">s_</a></strong> <br>singular values</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-ext-obs-t-">ext_obs_t_</a></strong> <br>extended observability matrix</td>
+      </tr>
+  </tbody>
+</table>
+<hr>
+<hr>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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diff --git a/docs/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/index.html b/docs/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/index.html
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+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
+<li><a href="/lds-ctrl-est/docs/api/classes/">Classes</a></li>
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+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
+</ul>
+<br />
+
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+  
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+    <a href="https://github.com/stanley-rozell/lds-ctrl-est" target="_blank" rel="noopener">
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+
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+    
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+  <ul>
+    <li><a href="#ldsgaussianswitchedcontroller">lds::gaussian::SwitchedController</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#additional-inherited-members">Additional inherited members</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#set_"><strong>set_y_ref</strong></a></li>
+          </ul>
+        </li>
+      </ul>
+    </li>
+  </ul>
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+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="ldsgaussianswitchedcontroller">
+  lds::gaussian::SwitchedController
+  <a class="anchor" href="#ldsgaussianswitchedcontroller">#</a>
+</h1>
+<p>Gaussian-observation <a href="">SwitchedController</a> Type.
+<br /> <code>#include &lt;lds_gaussian_sctrl.h&gt;</code></p>
+<p>Inherits from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/">lds::SwitchedController&lt; System &gt;</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">lds::Controller&lt; System &gt;</a></p>
+<h2 id="public-functions">
+  Public Functions
+  <a class="anchor" href="#public-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>virtual void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/#function-set-y-ref">set_y_ref</a></strong>(const Vector &amp; y_ref) override<br>sets reference output</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="additional-inherited-members">
+  Additional inherited members
+  <a class="anchor" href="#additional-inherited-members">#</a>
+</h2>
+<p><strong>Public Functions inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/">lds::SwitchedController&lt; System &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switchedcontroller">SwitchedController</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/">SwitchedController</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switchedcontroller">SwitchedController</a></strong>(const std::vector&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &gt; &amp; systems, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub, size_t control_type =0)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/">SwitchedController</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switchedcontroller">SwitchedController</a></strong>(std::vector&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &gt; &amp;&amp; systems, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub, size_t control_type =0)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/">SwitchedController</a> (moves systems).</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switch">Switch</a></strong>(size_t idx, bool do_force_switch =false)<br>Switch to a different sub-system/controller.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc">set_Kc</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt;&gt; &amp; Kc)<br>sets state feedback gains</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc">set_Kc</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt;&gt; &amp;&amp; Kc)<br>sets state feedback gains (moving)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-inty">set_Kc_inty</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt;&gt; &amp; Kc_inty)<br>sets integral feedback gains</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-inty">set_Kc_inty</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt;&gt; &amp;&amp; Kc_inty)<br>sets integral feedback gains (moving)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-u">set_Kc_u</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt;&gt; &amp; Kc_u)<br>sets input feedback gains</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-u">set_Kc_u</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt;&gt; &amp;&amp; Kc_u)<br>sets input feedback gains (moving)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-g-design">set_g_design</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a> &amp; g)<br>sets input gain used during controller design</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-g-design">set_g_design</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a> &amp;&amp; g)<br>sets input gain used during controller design (moving)</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Protected Attributes inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/">lds::SwitchedController&lt; System &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>std::vector&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-systems-">systems_</a></strong> <br>underlying sub-systems which are switched between</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-n-sys-">n_sys_</a></strong> <br>number of systems</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-idx-">idx_</a></strong> <br>current system/controller index.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-kc-list-">Kc_list_</a></strong></td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-kc-inty-list-">Kc_inty_list_</a></strong></td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-kc-u-list-">Kc_u_list_</a></strong></td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-g-design-list-">g_design_list_</a></strong></td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Public Functions inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">lds::Controller&lt; System &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller">Controller</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">Controller</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller">Controller</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp; sys, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub, size_t control_type =0)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">Controller</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller">Controller</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp;&amp; sys, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub, size_t control_type =0)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">Controller</a> by moving the system object.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control">Control</a></strong>(const Vector &amp; z, bool do_control =true, bool do_lock_control =false, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_soft_start =0, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal (single-step)</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controloutputreference">ControlOutputReference</a></strong>(const Vector &amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_soft_start =0, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal, given previously-set (single-step)</td>
+      </tr>
+      <tr>
+          <td>const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-sys">sys</a></strong>() const</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc">Kc</a></strong>() const<br>Get state feedback controller gain.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-inty">Kc_inty</a></strong>() const<br>Get integral controller gain.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-u">Kc_u</a></strong>() const<br>Get input feedback controller gain.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-g-design">g_design</a></strong>() const<br>Get input gain used in controller design.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ref">u_ref</a></strong>() const<br>Get reference input.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-x-ref">x_ref</a></strong>() const<br>Get reference state.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-y-ref">y_ref</a></strong>() const<br>Get reference output.</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control-type">control_type</a></strong>() const<br>Get controller type.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-tau-awu">tau_awu</a></strong>() const<br>Get time constant of anti-integral-windup.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-lb">u_lb</a></strong>() const<br>Get control lower bound.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ub">u_ub</a></strong>() const<br>Get control upper bound.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-sys">set_sys</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp; sys)<br>Set system.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-g-design">set_g_design</a></strong>(const Vector &amp; g_design)<br>Set input gain used in controller design (g_design)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ref">set_u_ref</a></strong>(const Vector &amp; u_ref)<br>Set reference input (u_ref)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-x-ref">set_x_ref</a></strong>(const Vector &amp; x_ref)<br>Set reference state (x_ref)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc">set_Kc</a></strong>(const Matrix &amp; Kc)<br>Set state controller gain.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-inty">set_Kc_inty</a></strong>(const Matrix &amp; Kc_inty)<br>Set integral controller gain.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-u">set_Kc_u</a></strong>(const Matrix &amp; Kc_u)<br>Set input controller gain.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-tau-awu">set_tau_awu</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> tau)<br>Set time constant of anti-integral-windup.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-control-type">set_control_type</a></strong>(size_t control_type)<br>Sets the control type.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-lb">set_u_lb</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb)<br>sets control lower bound</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ub">set_u_ub</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub)<br>Sets control upper bound.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-reset">Reset</a></strong>()<br>reset system and control variables.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-print">Print</a></strong>()<br>prints variables to stdout</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Protected Attributes inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">lds::Controller&lt; System &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-sys-">sys_</a></strong> <br>underlying LDS</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-">u_</a></strong> <br>control signal</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-return-">u_return_</a></strong> <br>control signal that is <em>returned</em> to user</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-g-design-">g_design_</a></strong> <br>input gain of the system used for controller design</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-">u_ref_</a></strong> <br>reference input</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-prev-">u_ref_prev_</a></strong> <br>reference input at previous time step</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-x-ref-">x_ref_</a></strong> <br>reference state</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-y-ref-">y_ref_</a></strong> <br>reference output</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-cx-ref-">cx_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-">Kc_</a></strong> <br>state controller gain</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-u-">Kc_u_</a></strong> <br>input controller gain (optional when control updates \deltaU)</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-inty-">Kc_inty_</a></strong> <br>integral controller gain</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-du-ref-">du_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-ref-">dv_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-ref-">v_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-">dv_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-">v_</a></strong> <br>Control after g inversion (e.g., control in physical units)</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-">int_e_</a></strong> <br>integrated error</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-awu-adjust-">int_e_awu_adjust_</a></strong> <br>anti-windup adjustment to intE</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-sat-">u_sat_</a></strong> <br>control signal after saturation (for antiWindup)</td>
+      </tr>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-control-prev-">do_control_prev_</a></strong></td>
+      </tr>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-lock-control-prev-">do_lock_control_prev_</a></strong></td>
+      </tr>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-saturated-">u_saturated_</a></strong> <br>whether control signal has reached saturation limits</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-lb-">u_lb_</a></strong> <br>lower bound on control</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ub-">u_ub_</a></strong> <br>upper bound on control</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-tau-awu-">tau_awu_</a></strong> <br>antiwindup time constant</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-k-awu-">k_awu_</a></strong></td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-t-since-control-onset-">t_since_control_onset_</a></strong> <br>time since control epoch onset</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-control-type-">control_type_</a></strong> <br>controller type</td>
+      </tr>
+  </tbody>
+</table>
+<hr>
+<hr>
+<h2 id="public-function-details">
+  Public Function Details
+  <a class="anchor" href="#public-function-details">#</a>
+</h2>
+<h3 id="set_">
+  <strong>set_y_ref</strong>
+  <a class="anchor" href="#set_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> set_y_ref(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> y_ref
+</span></span><span style="display:flex;"><span>) <span style="color:#66d9ef">override</span>
+</span></span></code></pre></div><p><strong>Reimplements</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-y-ref">lds::Controller::set_y_ref</a></p>
+<hr>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
+      
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+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#ldsgaussianswitchedcontroller">lds::gaussian::SwitchedController</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#additional-inherited-members">Additional inherited members</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#set_"><strong>set_y_ref</strong></a></li>
+          </ul>
+        </li>
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diff --git a/docs/docs/api/classes/classlds_1_1gaussian_1_1_system/index.html b/docs/docs/api/classes/classlds_1_1gaussian_1_1_system/index.html
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@@ -0,0 +1,855 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="Gaussian LDS Type.">
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+  <ul>
+    <li><a href="#ldsgaussiansystem">lds::gaussian::System</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#protected-functions">Protected Functions</a></li>
+        <li><a href="#protected-attributes">Protected Attributes</a></li>
+        <li><a href="#additional-inherited-members">Additional inherited members</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#system"><strong>System</strong></a></li>
+            <li><a href="#system-1"><strong>System</strong></a></li>
+            <li><a href="#simulate"><strong>Simulate</strong></a></li>
+            <li><a href="#r"><strong>R</strong></a></li>
+            <li><a href="#set_"><strong>set_Q</strong></a></li>
+            <li><a href="#set_-1"><strong>set_R</strong></a></li>
+            <li><a href="#set_-2"><strong>set_Ke</strong></a></li>
+            <li><a href="#set_-3"><strong>set_Ke_m</strong></a></li>
+            <li><a href="#print"><strong>Print</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-function-details">Protected Function Details</a>
+          <ul>
+            <li><a href="#h"><strong>h</strong></a></li>
+            <li><a href="#h_"><strong>h_</strong></a></li>
+            <li><a href="#recurseke"><strong>RecurseKe</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-attribute-details">Protected Attribute Details</a>
+          <ul>
+            <li><a href="#r_"><strong>R_</strong></a></li>
+            <li><a href="#do_"><strong>do_recurse_Ke_</strong></a></li>
+          </ul>
+        </li>
+      </ul>
+    </li>
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+      </header>
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+      
+      
+  <article class="markdown"><h1 id="ldsgaussiansystem">
+  lds::gaussian::System
+  <a class="anchor" href="#ldsgaussiansystem">#</a>
+</h1>
+<p>Gaussian LDS Type.
+<br /> <code>#include &lt;lds_gaussian_sys.h&gt;</code></p>
+<p>Inherits from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">lds::System</a></p>
+<h2 id="public-functions">
+  Public Functions
+  <a class="anchor" href="#public-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-system">System</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/">System</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-system">System</a></strong>(std::size_t n_u, std::size_t n_x, std::size_t n_y, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> dt, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> p0 =kDefaultP0, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> q0 =kDefaultQ0, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> r0 =kDefaultR0)<br>Constructs a new Gaussian <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/">System</a>.</td>
+      </tr>
+      <tr>
+          <td>virtual const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-simulate">Simulate</a></strong>(const Vector &amp; u_tm1) override<br>Simulate system measurement.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-r">R</a></strong>() const<br>Get output noise covariance.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-set-q">set_Q</a></strong>(const Matrix &amp; Q)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-set-r">set_R</a></strong>(const Matrix &amp; R)<br>Set output noise covariance.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-set-ke">set_Ke</a></strong>(const Matrix &amp; Ke)<br>Set estimator gain.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-set-ke-m">set_Ke_m</a></strong>(const Matrix &amp; Ke_m)<br>Set disturbance estimator gain.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-print">Print</a></strong>()<br>Print system variables to stdout.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="protected-functions">
+  Protected Functions
+  <a class="anchor" href="#protected-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>virtual void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-h">h</a></strong>() override<br><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/">System</a> output function.</td>
+      </tr>
+      <tr>
+          <td>virtual Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-h-">h_</a></strong>(Vector x) override<br><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/">System</a> output function: stateless.</td>
+      </tr>
+      <tr>
+          <td>virtual void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#function-recurseke">RecurseKe</a></strong>() override<br>Recursively update estimator gain.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="protected-attributes">
+  Protected Attributes
+  <a class="anchor" href="#protected-attributes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#variable-r-">R_</a></strong> <br>covariance of output noise</td>
+      </tr>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/#variable-do-recurse-ke-">do_recurse_Ke_</a></strong> <br>whether to recursively calculate estimator gain</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="additional-inherited-members">
+  Additional inherited members
+  <a class="anchor" href="#additional-inherited-members">#</a>
+</h2>
+<p><strong>Public Functions inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">lds::System</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>virtual</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-~system">~System</a></strong>()</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-filter">Filter</a></strong>(const Vector &amp; u_tm1, const Vector &amp; z)<br>Filter data to produce causal state estimates.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-f">f</a></strong>(const Vector &amp; u, bool do_add_noise =false)<br>system dynamics function</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-n-u">n_u</a></strong>() const<br>Get number of inputs.</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-n-x">n_x</a></strong>() const<br>Get number of states.</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-n-y">n_y</a></strong>() const<br>Get number of outputs.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-dt">dt</a></strong>() const<br>Get sample period.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-x">x</a></strong>() const<br>Get current state.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p">P</a></strong>() const<br>Get covariance of state estimate.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-m">m</a></strong>() const<br>Get current process disturbance/bias.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p-m">P_m</a></strong>() const<br>Get covariance of process disturbance estimate.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-cx">cx</a></strong>() const<br>Get C*x.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-y">y</a></strong>() const<br>Get output.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-x0">x0</a></strong>() const<br>Get initial state.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-m0">m0</a></strong>() const<br>Get initial disturbance.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-a">A</a></strong>() const<br>Get state matrix.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-b">B</a></strong>() const<br>Get input matrix.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-g">g</a></strong>() const<br>Get input gain/conversion factor.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-c">C</a></strong>() const<br>Get output matrix.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-d">d</a></strong>() const<br>Get output bias.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-ke">Ke</a></strong>() const<br>Get estimator gain.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-ke-m">Ke_m</a></strong>() const<br>Get estimator gain for process disturbance (m)</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-q">Q</a></strong>()<br>Get process noise covariance.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-q-m">Q_m</a></strong>()<br>Get process noise covariance of disturbance evoluation.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p0">P0</a></strong>()<br>Get covariance of initial state.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p0-m">P0_m</a></strong>()<br>Get covariance of initial process disturbance.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-a">set_A</a></strong>(const Matrix &amp; A)<br>Set state matrix.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-b">set_B</a></strong>(const Matrix &amp; B)<br>Set input matrix.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-m">set_m</a></strong>(const Vector &amp; m, bool do_force_assign =false)<br>Set process disturbance.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-g">set_g</a></strong>(const Vector &amp; g)<br>Set input gain.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-q-m">set_Q_m</a></strong>(const Matrix &amp; Q_m)<br>Set process noise covariance of disturbance evoluation.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-x0">set_x0</a></strong>(const Vector &amp; x0)<br>Set initial state.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-p0">set_P0</a></strong>(const Matrix &amp; P0)<br>Set covariance of initial state.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-p0-m">set_P0_m</a></strong>(const Matrix &amp; P0_m)<br>Set covariance of initial process disturbance.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-c">set_C</a></strong>(const Matrix &amp; C)<br>Set output matrix.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-d">set_d</a></strong>(const Vector &amp; d)<br>Set output bias.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-x">set_x</a></strong>(const Vector &amp; x)<br>Set state of system.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-reset">Reset</a></strong>()<br>Reset system variables.</td>
+      </tr>
+      <tr>
+          <td>std::vector&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-nstep-pred-block">nstep_pred_block</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; u, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; z, size_t n_pred =1)</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Protected Functions inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">lds::System</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-initvars">InitVars</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> p0 =kDefaultP0, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> q0 =kDefaultQ0)</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Public Attributes inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">lds::System</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-do-adapt-m">do_adapt_m</a></strong> <br>whether to adaptively estimate disturbance m</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Protected Attributes inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">lds::System</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>std::size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-n-x-">n_x_</a></strong> <br>number of states</td>
+      </tr>
+      <tr>
+          <td>std::size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-n-u-">n_u_</a></strong> <br>number of inputs</td>
+      </tr>
+      <tr>
+          <td>std::size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-n-y-">n_y_</a></strong> <br>number of outputs</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-dt-">dt_</a></strong> <br>sample period</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-x-">x_</a></strong> <br>state</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p-">P_</a></strong> <br>covariance of state estimate</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-m-">m_</a></strong> <br>process disturbance</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p-m-">P_m_</a></strong> <br>covariance of disturbance estimate</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-cx-">cx_</a></strong> <br>C*x.</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-y-">y_</a></strong> <br>output</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-z-">z_</a></strong> <br>measurement</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-x0-">x0_</a></strong> <br>initial state</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p0-">P0_</a></strong> <br>covariance of initial state estimate</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-m0-">m0_</a></strong> <br>initial process disturbance</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p0-m-">P0_m_</a></strong> <br>covariance of initial disturbance est.</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-a-">A_</a></strong> <br>state matrix</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-b-">B_</a></strong> <br>input matrix</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-g-">g_</a></strong> <br>input gain</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-q-">Q_</a></strong> <br>covariance of process noise</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-q-m-">Q_m_</a></strong> <br>covariance of disturbance random walk</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-c-">C_</a></strong> <br>output matrix</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-d-">d_</a></strong> <br>output bias</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-ke-">Ke_</a></strong> <br>estimator gain</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-ke-m-">Ke_m_</a></strong> <br>estimator gain for process disturbance</td>
+      </tr>
+  </tbody>
+</table>
+<hr>
+<hr>
+<h2 id="public-function-details">
+  Public Function Details
+  <a class="anchor" href="#public-function-details">#</a>
+</h2>
+<h3 id="system">
+  <strong>System</strong>
+  <a class="anchor" href="#system">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>System() <span style="color:#f92672">=</span><span style="color:#66d9ef">default</span>
+</span></span></code></pre></div><hr>
+<h3 id="system-1">
+  <strong>System</strong>
+  <a class="anchor" href="#system-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>System(
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>size_t n_u,
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>size_t n_x,
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>size_t n_y,
+</span></span><span style="display:flex;"><span>    data_t dt,
+</span></span><span style="display:flex;"><span>    data_t p0 <span style="color:#f92672">=</span>kDefaultP0,
+</span></span><span style="display:flex;"><span>    data_t q0 <span style="color:#f92672">=</span>kDefaultQ0,
+</span></span><span style="display:flex;"><span>    data_t r0 <span style="color:#f92672">=</span>kDefaultR0
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>n_u</strong> number of inputs (u)</li>
+<li><strong>n_x</strong> number of states (x)</li>
+<li><strong>n_y</strong> number of outputs (y)</li>
+<li><strong>dt</strong> sample period</li>
+<li><strong>p0</strong> [optional] initial diagonal elements of state estimate covariance (P)</li>
+<li><strong>q0</strong> [optional] initial diagonal elements of process noise covariance (Q)</li>
+<li><strong>r0</strong> [optional] initial diagonal elements of output noise covariance (R)</li>
+</ul>
+<hr>
+<h3 id="simulate">
+  <strong>Simulate</strong>
+  <a class="anchor" href="#simulate">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> Simulate(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> u_tm1
+</span></span><span style="display:flex;"><span>) <span style="color:#66d9ef">override</span>
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>u_tm1</strong> input at t-1</li>
+</ul>
+<p><strong>Return</strong>: z measurement</p>
+<p><strong>Reimplements</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-simulate">lds::System::Simulate</a></p>
+<p>Simulate system and produce measurement</p>
+<hr>
+<h3 id="r">
+  <strong>R</strong>
+  <a class="anchor" href="#r">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> R() <span style="color:#66d9ef">const</span>
+</span></span></code></pre></div><hr>
+<h3 id="set_">
+  <strong>set_Q</strong>
+  <a class="anchor" href="#set_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_Q(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> Q
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-1">
+  <strong>set_R</strong>
+  <a class="anchor" href="#set_-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_R(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> R
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-2">
+  <strong>set_Ke</strong>
+  <a class="anchor" href="#set_-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_Ke(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> Ke
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="set_-3">
+  <strong>set_Ke_m</strong>
+  <a class="anchor" href="#set_-3">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> set_Ke_m(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> Ke_m
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><hr>
+<h3 id="print">
+  <strong>Print</strong>
+  <a class="anchor" href="#print">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> Print()
+</span></span></code></pre></div><hr>
+<h2 id="protected-function-details">
+  Protected Function Details
+  <a class="anchor" href="#protected-function-details">#</a>
+</h2>
+<h3 id="h">
+  <strong>h</strong>
+  <a class="anchor" href="#h">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> h() <span style="color:#66d9ef">override</span>
+</span></span></code></pre></div><p><strong>Reimplements</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-h">lds::System::h</a></p>
+<hr>
+<h3 id="h_">
+  <strong>h_</strong>
+  <a class="anchor" href="#h_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">virtual</span> Vector h_(
+</span></span><span style="display:flex;"><span>    Vector x
+</span></span><span style="display:flex;"><span>) <span style="color:#66d9ef">override</span>
+</span></span></code></pre></div><p><strong>Reimplements</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-h-">lds::System::h_</a></p>
+<hr>
+<h3 id="recurseke">
+  <strong>RecurseKe</strong>
+  <a class="anchor" href="#recurseke">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> RecurseKe() <span style="color:#66d9ef">override</span>
+</span></span></code></pre></div><p><strong>Reimplements</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-recurseke">lds::System::RecurseKe</a></p>
+<hr>
+<h2 id="protected-attribute-details">
+  Protected Attribute Details
+  <a class="anchor" href="#protected-attribute-details">#</a>
+</h2>
+<h3 id="r_">
+  <strong>R_</strong>
+  <a class="anchor" href="#r_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix R_;
+</span></span></code></pre></div><hr>
+<h3 id="do_">
+  <strong>do_recurse_Ke_</strong>
+  <a class="anchor" href="#do_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">bool</span> do_recurse_Ke_ {};
+</span></span></code></pre></div><hr>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
+      
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+  
+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#ldsgaussiansystem">lds::gaussian::System</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#protected-functions">Protected Functions</a></li>
+        <li><a href="#protected-attributes">Protected Attributes</a></li>
+        <li><a href="#additional-inherited-members">Additional inherited members</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#system"><strong>System</strong></a></li>
+            <li><a href="#system-1"><strong>System</strong></a></li>
+            <li><a href="#simulate"><strong>Simulate</strong></a></li>
+            <li><a href="#r"><strong>R</strong></a></li>
+            <li><a href="#set_"><strong>set_Q</strong></a></li>
+            <li><a href="#set_-1"><strong>set_R</strong></a></li>
+            <li><a href="#set_-2"><strong>set_Ke</strong></a></li>
+            <li><a href="#set_-3"><strong>set_Ke_m</strong></a></li>
+            <li><a href="#print"><strong>Print</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-function-details">Protected Function Details</a>
+          <ul>
+            <li><a href="#h"><strong>h</strong></a></li>
+            <li><a href="#h_"><strong>h_</strong></a></li>
+            <li><a href="#recurseke"><strong>RecurseKe</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-attribute-details">Protected Attribute Details</a>
+          <ul>
+            <li><a href="#r_"><strong>R_</strong></a></li>
+            <li><a href="#do_"><strong>do_recurse_Ke_</strong></a></li>
+          </ul>
+        </li>
+      </ul>
+    </li>
+  </ul>
+</nav>
+
+
+ 
+      </div>
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+  <strong>lds::poisson::Controller</strong>
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+  <ul>
+    <li><a href="#ldspoissoncontroller">lds::poisson::Controller</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#additional-inherited-members">Additional inherited members</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#set_"><strong>set_y_ref</strong></a></li>
+          </ul>
+        </li>
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+  <article class="markdown"><h1 id="ldspoissoncontroller">
+  lds::poisson::Controller
+  <a class="anchor" href="#ldspoissoncontroller">#</a>
+</h1>
+<p>PLDS <a href="">Controller</a> Type.
+<br /> <code>#include &lt;lds_poisson_ctrl.h&gt;</code></p>
+<p>Inherits from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">lds::Controller&lt; System &gt;</a></p>
+<h2 id="public-functions">
+  Public Functions
+  <a class="anchor" href="#public-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>virtual void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/#function-set-y-ref">set_y_ref</a></strong>(const Vector &amp; y_ref) override<br>Set reference output.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="additional-inherited-members">
+  Additional inherited members
+  <a class="anchor" href="#additional-inherited-members">#</a>
+</h2>
+<p><strong>Public Functions inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">lds::Controller&lt; System &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller">Controller</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">Controller</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller">Controller</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp; sys, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub, size_t control_type =0)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">Controller</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller">Controller</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp;&amp; sys, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub, size_t control_type =0)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">Controller</a> by moving the system object.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control">Control</a></strong>(const Vector &amp; z, bool do_control =true, bool do_lock_control =false, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_soft_start =0, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal (single-step)</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controloutputreference">ControlOutputReference</a></strong>(const Vector &amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_soft_start =0, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal, given previously-set (single-step)</td>
+      </tr>
+      <tr>
+          <td>const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-sys">sys</a></strong>() const</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc">Kc</a></strong>() const<br>Get state feedback controller gain.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-inty">Kc_inty</a></strong>() const<br>Get integral controller gain.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-u">Kc_u</a></strong>() const<br>Get input feedback controller gain.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-g-design">g_design</a></strong>() const<br>Get input gain used in controller design.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ref">u_ref</a></strong>() const<br>Get reference input.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-x-ref">x_ref</a></strong>() const<br>Get reference state.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-y-ref">y_ref</a></strong>() const<br>Get reference output.</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control-type">control_type</a></strong>() const<br>Get controller type.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-tau-awu">tau_awu</a></strong>() const<br>Get time constant of anti-integral-windup.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-lb">u_lb</a></strong>() const<br>Get control lower bound.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ub">u_ub</a></strong>() const<br>Get control upper bound.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-sys">set_sys</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp; sys)<br>Set system.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-g-design">set_g_design</a></strong>(const Vector &amp; g_design)<br>Set input gain used in controller design (g_design)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ref">set_u_ref</a></strong>(const Vector &amp; u_ref)<br>Set reference input (u_ref)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-x-ref">set_x_ref</a></strong>(const Vector &amp; x_ref)<br>Set reference state (x_ref)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc">set_Kc</a></strong>(const Matrix &amp; Kc)<br>Set state controller gain.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-inty">set_Kc_inty</a></strong>(const Matrix &amp; Kc_inty)<br>Set integral controller gain.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-u">set_Kc_u</a></strong>(const Matrix &amp; Kc_u)<br>Set input controller gain.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-tau-awu">set_tau_awu</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> tau)<br>Set time constant of anti-integral-windup.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-control-type">set_control_type</a></strong>(size_t control_type)<br>Sets the control type.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-lb">set_u_lb</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb)<br>sets control lower bound</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ub">set_u_ub</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub)<br>Sets control upper bound.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-reset">Reset</a></strong>()<br>reset system and control variables.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-print">Print</a></strong>()<br>prints variables to stdout</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Protected Attributes inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">lds::Controller&lt; System &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-sys-">sys_</a></strong> <br>underlying LDS</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-">u_</a></strong> <br>control signal</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-return-">u_return_</a></strong> <br>control signal that is <em>returned</em> to user</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-g-design-">g_design_</a></strong> <br>input gain of the system used for controller design</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-">u_ref_</a></strong> <br>reference input</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-prev-">u_ref_prev_</a></strong> <br>reference input at previous time step</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-x-ref-">x_ref_</a></strong> <br>reference state</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-y-ref-">y_ref_</a></strong> <br>reference output</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-cx-ref-">cx_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-">Kc_</a></strong> <br>state controller gain</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-u-">Kc_u_</a></strong> <br>input controller gain (optional when control updates \deltaU)</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-inty-">Kc_inty_</a></strong> <br>integral controller gain</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-du-ref-">du_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-ref-">dv_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-ref-">v_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-">dv_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-">v_</a></strong> <br>Control after g inversion (e.g., control in physical units)</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-">int_e_</a></strong> <br>integrated error</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-awu-adjust-">int_e_awu_adjust_</a></strong> <br>anti-windup adjustment to intE</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-sat-">u_sat_</a></strong> <br>control signal after saturation (for antiWindup)</td>
+      </tr>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-control-prev-">do_control_prev_</a></strong></td>
+      </tr>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-lock-control-prev-">do_lock_control_prev_</a></strong></td>
+      </tr>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-saturated-">u_saturated_</a></strong> <br>whether control signal has reached saturation limits</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-lb-">u_lb_</a></strong> <br>lower bound on control</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ub-">u_ub_</a></strong> <br>upper bound on control</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-tau-awu-">tau_awu_</a></strong> <br>antiwindup time constant</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-k-awu-">k_awu_</a></strong></td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-t-since-control-onset-">t_since_control_onset_</a></strong> <br>time since control epoch onset</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-control-type-">control_type_</a></strong> <br>controller type</td>
+      </tr>
+  </tbody>
+</table>
+<hr>
+<hr>
+<h2 id="public-function-details">
+  Public Function Details
+  <a class="anchor" href="#public-function-details">#</a>
+</h2>
+<h3 id="set_">
+  <strong>set_y_ref</strong>
+  <a class="anchor" href="#set_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> set_y_ref(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> y_ref
+</span></span><span style="display:flex;"><span>) <span style="color:#66d9ef">override</span>
+</span></span></code></pre></div><p><strong>Reimplements</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-y-ref">lds::Controller::set_y_ref</a></p>
+<hr>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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+        <li><a href="#additional-inherited-members">Additional inherited members</a></li>
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diff --git a/docs/docs/api/classes/classlds_1_1poisson_1_1_fit/index.html b/docs/docs/api/classes/classlds_1_1poisson_1_1_fit/index.html
new file mode 100644
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+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
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+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
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+<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
+</ul>
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+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
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+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
+</ul>
+<br />
+
+
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+<ul>
+  
+  <li>
+    <a href="https://github.com/stanley-rozell/lds-ctrl-est" target="_blank" rel="noopener">
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+  <strong>lds::poisson::Fit</strong>
+
+  <label for="toc-control">
+    
+    <img src="/lds-ctrl-est/svg/toc.svg" class="book-icon" alt="Table of Contents" />
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+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#ldspoissonfit">lds::poisson::Fit</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#additional-inherited-members">Additional inherited members</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#fit"><strong>Fit</strong></a></li>
+            <li><a href="#fit-1"><strong>Fit</strong></a></li>
+            <li><a href="#h"><strong>h</strong></a></li>
+            <li><a href="#set_"><strong>set_R</strong></a></li>
+            <li><a href="#r"><strong>R</strong></a></li>
+          </ul>
+        </li>
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+  <article class="markdown"><h1 id="ldspoissonfit">
+  lds::poisson::Fit
+  <a class="anchor" href="#ldspoissonfit">#</a>
+</h1>
+<p>PLDS <a href="">Fit</a> Type.
+<br /> <code>#include &lt;lds_poisson_fit.h&gt;</code></p>
+<p>Inherits from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">lds::Fit</a></p>
+<h2 id="public-functions">
+  Public Functions
+  <a class="anchor" href="#public-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/#function-fit">Fit</a></strong>() =default</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/#function-fit">Fit</a></strong>(size_t n_u, size_t n_x, size_t n_y, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> dt)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/">Fit</a>.</td>
+      </tr>
+      <tr>
+          <td>virtual View</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/#function-h">h</a></strong>(Matrix &amp; y, const Matrix &amp; x, size_t t) override<br>output function</td>
+      </tr>
+      <tr>
+          <td>virtual void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/#function-set-r">set_R</a></strong>(const Matrix &amp; R) override<br>sets output noise covariance (if any)</td>
+      </tr>
+      <tr>
+          <td>virtual const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/#function-r">R</a></strong>() const override</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="additional-inherited-members">
+  Additional inherited members
+  <a class="anchor" href="#additional-inherited-members">#</a>
+</h2>
+<p><strong>Public Functions inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">lds::Fit</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>virtual</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-~fit">~Fit</a></strong>() =default</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-n-u">n_u</a></strong>() const<br>gets number of inputs</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-n-x">n_x</a></strong>() const<br>gets number of states</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-n-y">n_y</a></strong>() const<br>gets number of outputs</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-dt">dt</a></strong>() const<br>gets sample period</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-a">A</a></strong>() const<br>gets state matrix</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-b">B</a></strong>() const<br>gets input matrix</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-g">g</a></strong>() const<br>gets input gain</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-m">m</a></strong>() const<br>gets process disturbance</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-q">Q</a></strong>() const<br>gets process noise covariance</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-x0">x0</a></strong>() const<br>gets initial state estimate</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-p0">P0</a></strong>() const<br>gets covariance of initial state estimate</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-c">C</a></strong>() const<br>gets output matrix</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-d">d</a></strong>() const<br>gets output bias</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-a">set_A</a></strong>(const Matrix &amp; A)<br>sets state matrix</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-b">set_B</a></strong>(const Matrix &amp; B)<br>sets input matrix</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-g">set_g</a></strong>(const Vector &amp; g)<br>sets input gain/conversion factor</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-m">set_m</a></strong>(const Vector &amp; m)<br>sets process disturbance</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-q">set_Q</a></strong>(const Matrix &amp; Q)<br>sets process noise covariance</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-x0">set_x0</a></strong>(const Vector &amp; x0)<br>sets initial state estimate</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-p0">set_P0</a></strong>(const Matrix &amp; P0)<br>sets initial state estimate covariance</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-c">set_C</a></strong>(const Matrix &amp; C)<br>sets output matrix</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-d">set_d</a></strong>(const Vector &amp; d)<br>sets output bias</td>
+      </tr>
+      <tr>
+          <td>View</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-f">f</a></strong>(Matrix &amp; x, const Matrix &amp; u, size_t t)<br>system dynamics function</td>
+      </tr>
+      <tr>
+          <td>View</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-f">f</a></strong>(Matrix &amp; x_pre, const Matrix &amp; x_post, const Matrix &amp; u, size_t t)<br>system dynamics function</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Protected Attributes inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">lds::Fit</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-dt-">dt_</a></strong> <br>sample period</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-a-">A_</a></strong> <br>state matrix</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-b-">B_</a></strong> <br>input matrix</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-g-">g_</a></strong> <br>input gain</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-m-">m_</a></strong> <br>process noise mean</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-q-">Q_</a></strong> <br>process noise cov</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-c-">C_</a></strong> <br>output matrix</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-d-">d_</a></strong> <br>output bias</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-r-">R_</a></strong> <br>measurement noise</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-x0-">x0_</a></strong> <br>initial state</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-p0-">P0_</a></strong> <br>initial covar</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-n-u-">n_u_</a></strong> <br>number of inputs</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-n-x-">n_x_</a></strong> <br>number of states</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#variable-n-y-">n_y_</a></strong> <br>number of outputs</td>
+      </tr>
+  </tbody>
+</table>
+<hr>
+<hr>
+<h2 id="public-function-details">
+  Public Function Details
+  <a class="anchor" href="#public-function-details">#</a>
+</h2>
+<h3 id="fit">
+  <strong>Fit</strong>
+  <a class="anchor" href="#fit">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Fit() <span style="color:#f92672">=</span><span style="color:#66d9ef">default</span>
+</span></span></code></pre></div><hr>
+<h3 id="fit-1">
+  <strong>Fit</strong>
+  <a class="anchor" href="#fit-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> Fit(
+</span></span><span style="display:flex;"><span>    size_t n_u,
+</span></span><span style="display:flex;"><span>    size_t n_x,
+</span></span><span style="display:flex;"><span>    size_t n_y,
+</span></span><span style="display:flex;"><span>    data_t dt
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>n_u</strong> number of inputs</li>
+<li><strong>n_x</strong> number of states</li>
+<li><strong>n_y</strong> number of outputs</li>
+<li><strong>dt</strong> sample period</li>
+</ul>
+<hr>
+<h3 id="h">
+  <strong>h</strong>
+  <a class="anchor" href="#h">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">virtual</span> View h(
+</span></span><span style="display:flex;"><span>    Matrix <span style="color:#f92672">&amp;</span> y,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> x,
+</span></span><span style="display:flex;"><span>    size_t t
+</span></span><span style="display:flex;"><span>) <span style="color:#66d9ef">override</span>
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>y</strong> output estimate (over time)</li>
+<li><strong>x</strong> state estimate (over time)</li>
+<li><strong>t</strong> time index</li>
+</ul>
+<p><strong>Return</strong>: output</p>
+<p><strong>Reimplements</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-h">lds::Fit::h</a></p>
+<hr>
+<h3 id="set_">
+  <strong>set_R</strong>
+  <a class="anchor" href="#set_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> set_R(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> R
+</span></span><span style="display:flex;"><span>) <span style="color:#66d9ef">override</span>
+</span></span></code></pre></div><p><strong>Reimplements</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-set-r">lds::Fit::set_R</a></p>
+<hr>
+<h3 id="r">
+  <strong>R</strong>
+  <a class="anchor" href="#r">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> R() <span style="color:#66d9ef">const</span> <span style="color:#66d9ef">override</span>
+</span></span></code></pre></div><p><strong>Reimplements</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/#function-r">lds::Fit::R</a></p>
+<hr>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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+      <ul>
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+          <ul>
+            <li><a href="#fit"><strong>Fit</strong></a></li>
+            <li><a href="#fit-1"><strong>Fit</strong></a></li>
+            <li><a href="#h"><strong>h</strong></a></li>
+            <li><a href="#set_"><strong>set_R</strong></a></li>
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new file mode 100644
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+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
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+</ul>
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+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
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+<li><a href="/lds-ctrl-est/issues-contributing/"><strong>Reporting Issues &amp; Contributing</strong></a></li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
+</ul>
+<br />
+
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+      <ul>
+        <li><a href="#additional-inherited-members">Additional inherited members</a></li>
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+  <article class="markdown"><h1 id="ldspoissonfitem">
+  lds::poisson::FitEM
+  <a class="anchor" href="#ldspoissonfitem">#</a>
+</h1>
+<p>PLDS E-M <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/">Fit</a> Type.  <a href="#detailed-description">More&hellip;</a></p>
+<p><br /> <code>#include &lt;lds_poisson_fit_em.h&gt;</code></p>
+<p>Inherits from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">lds::EM&lt; Fit &gt;</a></p>
+<h2 id="additional-inherited-members">
+  Additional inherited members
+  <a class="anchor" href="#additional-inherited-members">#</a>
+</h2>
+<p><strong>Public Functions inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">lds::EM&lt; Fit &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em">EM</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">EM</a><a href="">Fit</a> type.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em">EM</a></strong>(size_t n_x, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> dt, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &amp;&amp; u_train, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &amp;&amp; z_train)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">EM</a><a href="">Fit</a> type.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em">EM</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a> &amp; fit0, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &amp;&amp; u_train, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &amp;&amp; z_train)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">EM</a><a href="">Fit</a> type.</td>
+      </tr>
+      <tr>
+          <td>virtual</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-~em">~EM</a></strong>() =default</td>
+      </tr>
+      <tr>
+          <td>const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a> &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-run">Run</a></strong>(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> tol =1e-2)<br>Runs fitting by Expectation(E)-Maximization(M)</td>
+      </tr>
+      <tr>
+          <td>std::tuple&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt;, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-returndata">ReturnData</a></strong>()<br>Returns the input/output data to caller.</td>
+      </tr>
+      <tr>
+          <td>const std::vector&lt; Matrix &gt; &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-x">x</a></strong>() const<br>gets estimated state (over time)</td>
+      </tr>
+      <tr>
+          <td>const std::vector&lt; Matrix &gt; &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-y">y</a></strong>() const<br>gets estimated output (over time)</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-x-t-x-t">sum_E_x_t_x_t</a></strong>() const<br>gets state-input covariance</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-xu-tm1-xu-tm1">sum_E_xu_tm1_xu_tm1</a></strong>() const<br>gets state-input covariance (t-minus-1)</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-xu-t-xu-tm1">sum_E_xu_t_xu_tm1</a></strong>() const<br>gets single lag state-input covariance</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-n-t-tot">n_t_tot</a></strong>()<br>total number of time samples</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-theta">theta</a></strong>() const<br>gets parameters updated in M step</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Protected Functions inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">lds::EM&lt; Fit &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-expectation">Expectation</a></strong>(bool force_common_initial =false)<br>Expectation step.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximization">Maximization</a></strong>(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)<br>Maximization step.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizedynamics">MaximizeDynamics</a></strong>()</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizeq">MaximizeQ</a></strong>()</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizeinitial">MaximizeInitial</a></strong>()</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-smooth">Smooth</a></strong>(bool force_common_initial)<br>get smoothed estimates</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-reset">Reset</a></strong>()<br>reset to initial conditions</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-initvars">InitVars</a></strong>()<br>Initializes the variables.</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-updatetheta">UpdateTheta</a></strong>()<br>updates parameter list, theta</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Protected Attributes inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">lds::EM&lt; Fit &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-u-">u_</a></strong> <br>input training data</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-z-">z_</a></strong> <br>measurement training data</td>
+      </tr>
+      <tr>
+          <td>std::vector&lt; Matrix &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-x-">x_</a></strong> <br>state estimate</td>
+      </tr>
+      <tr>
+          <td>std::vector&lt; Cube &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-p-">P_</a></strong> <br>state estimate cov</td>
+      </tr>
+      <tr>
+          <td>std::vector&lt; Cube &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-p-t-tm1-">P_t_tm1_</a></strong> <br>single-lag state covariance</td>
+      </tr>
+      <tr>
+          <td>std::vector&lt; Matrix &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-y-">y_</a></strong> <br>output estimate</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-diag-y-">diag_y_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-x-t-x-t-">sum_E_x_t_x_t_</a></strong> <br>state covariance (current time)</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-xu-tm1-xu-tm1-">sum_E_xu_tm1_xu_tm1_</a></strong> <br>state-input covariance (t-minus-1)</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-xu-t-xu-tm1-">sum_E_xu_t_xu_tm1_</a></strong> <br>single lag state-input covariance</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-fit-">fit_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-theta-">theta_</a></strong></td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-dt-">dt_</a></strong> <br>sample period</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-u-">n_u_</a></strong> <br>number of inputs</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-x-">n_x_</a></strong> <br>number of states</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-y-">n_y_</a></strong> <br>number of outputs</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-trials-">n_trials_</a></strong> <br>number of input/output data sequences</td>
+      </tr>
+      <tr>
+          <td>std::vector&lt; size_t &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-t-">n_t_</a></strong> <br>number of time steps</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-t-tot-">n_t_tot_</a></strong> <br>total number of time steps across trials</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">lds</span><span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>FitEM;
+</span></span></code></pre></div><pre tabindex="0"><code>        This type is used in the process of fitting PLDS models by
+        expectation-maximization (EM).
+</code></pre><hr>
+<hr>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
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+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
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+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
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+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
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+<ul>
+<li><strong>API Reference</strong>
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+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
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+  <article class="markdown"><h1 id="ldspoissonfitssid">
+  lds::poisson::FitSSID
+  <a class="anchor" href="#ldspoissonfitssid">#</a>
+</h1>
+<p>Subspace Identification (<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">SSID</a>) for PLDS.
+<br /> <code>#include &lt;lds_poisson_fit_ssid.h&gt;</code></p>
+<p>Inherits from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">lds::SSID&lt; Fit &gt;</a></p>
+<h2 id="additional-inherited-members">
+  Additional inherited members
+  <a class="anchor" href="#additional-inherited-members">#</a>
+</h2>
+<p><strong>Public Functions inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">lds::SSID&lt; Fit &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-ssid">SSID</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">SSID</a><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a> type.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-ssid">SSID</a></strong>(size_t n_x, size_t n_h, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> dt, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &amp;&amp; u_train, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &amp;&amp; z_train, const Vector &amp; d =Vector(1).fill(-kInf))<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">SSID</a><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a> type.</td>
+      </tr>
+      <tr>
+          <td>std::tuple&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a>, Vector &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-run">Run</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt">SSIDWt</a> ssid_wt)<br>Runs fitting by subspace identification (<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">SSID</a>)</td>
+      </tr>
+      <tr>
+          <td>std::tuple&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt;, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-returndata">ReturnData</a></strong>()<br>Returns the I/O data to caller.</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Protected Functions inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">lds::SSID&lt; Fit &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-calcd">CalcD</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> t_silence =0.1, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> thresh_silence =0.001)<br>Using periods of silence in inputs (u), calculates the output \ bias (d)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-createhankeldatamat">CreateHankelDataMat</a></strong>()<br>Creates the block-hankel I/O data matrix.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-calcsvd">CalcSVD</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt">SSIDWt</a> wt)<br>performs the singular value decomposition (SVD)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-solve">Solve</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> wt_dc)<br>solves for LDS parameters</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-recomputeextobs">RecomputeExtObs</a></strong>()<br>recompute extended observability matrix from estimates of A, C</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Protected Attributes inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">lds::SSID&lt; Fit &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-u-">u_</a></strong> <br>input training data</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-z-">z_</a></strong> <br>measurement training data</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-d-">D_</a></strong> <br>block-Hankel I/O data matrix</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">Fit</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-fit-">fit_</a></strong> <br>fit</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-g-dc-">g_dc_</a></strong> <br>I/O gain @ DC.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-dt-">dt_</a></strong> <br>sample period</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-u-">n_u_</a></strong> <br>number of inputs</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-x-">n_x_</a></strong> <br>number of states</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-y-">n_y_</a></strong> <br>number of outputs</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-h-">n_h_</a></strong></td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-trials-">n_trials_</a></strong> <br>number of input/output data sequences</td>
+      </tr>
+      <tr>
+          <td>std::vector&lt; size_t &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-t-">n_t_</a></strong> <br>number of time steps</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-t-tot-">n_t_tot_</a></strong> <br>total number of time steps across trials</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-l-">L_</a></strong> <br>lower triangle decomp of covariance matrix</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-s-">s_</a></strong> <br>singular values</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-ext-obs-t-">ext_obs_t_</a></strong> <br>extended observability matrix</td>
+      </tr>
+  </tbody>
+</table>
+<hr>
+<hr>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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diff --git a/docs/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/index.html b/docs/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/index.html
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+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
+<li><a href="/lds-ctrl-est/docs/api/classes/">Classes</a></li>
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+<li><a href="/lds-ctrl-est/issues-contributing/"><strong>Reporting Issues &amp; Contributing</strong></a></li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
+</ul>
+<br />
+
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+  
+  <li>
+    <a href="https://github.com/stanley-rozell/lds-ctrl-est" target="_blank" rel="noopener">
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+
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+    
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+  <ul>
+    <li><a href="#ldspoissonswitchedcontroller">lds::poisson::SwitchedController</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#additional-inherited-members">Additional inherited members</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#set_"><strong>set_y_ref</strong></a></li>
+          </ul>
+        </li>
+      </ul>
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+      </header>
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+      
+  <article class="markdown"><h1 id="ldspoissonswitchedcontroller">
+  lds::poisson::SwitchedController
+  <a class="anchor" href="#ldspoissonswitchedcontroller">#</a>
+</h1>
+<p>Poisson-observation <a href="">SwitchedController</a> Type.
+<br /> <code>#include &lt;lds_poisson_sctrl.h&gt;</code></p>
+<p>Inherits from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/">lds::SwitchedController&lt; System &gt;</a>, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">lds::Controller&lt; System &gt;</a></p>
+<h2 id="public-functions">
+  Public Functions
+  <a class="anchor" href="#public-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>virtual void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/#function-set-y-ref">set_y_ref</a></strong>(const Vector &amp; y_ref) override<br>Set reference output.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="additional-inherited-members">
+  Additional inherited members
+  <a class="anchor" href="#additional-inherited-members">#</a>
+</h2>
+<p><strong>Public Functions inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/">lds::SwitchedController&lt; System &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switchedcontroller">SwitchedController</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/">SwitchedController</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switchedcontroller">SwitchedController</a></strong>(const std::vector&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &gt; &amp; systems, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub, size_t control_type =0)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/">SwitchedController</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switchedcontroller">SwitchedController</a></strong>(std::vector&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &gt; &amp;&amp; systems, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub, size_t control_type =0)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/">SwitchedController</a> (moves systems).</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-switch">Switch</a></strong>(size_t idx, bool do_force_switch =false)<br>Switch to a different sub-system/controller.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc">set_Kc</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt;&gt; &amp; Kc)<br>sets state feedback gains</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc">set_Kc</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt;&gt; &amp;&amp; Kc)<br>sets state feedback gains (moving)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-inty">set_Kc_inty</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt;&gt; &amp; Kc_inty)<br>sets integral feedback gains</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-inty">set_Kc_inty</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt;&gt; &amp;&amp; Kc_inty)<br>sets integral feedback gains (moving)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-u">set_Kc_u</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt;&gt; &amp; Kc_u)<br>sets input feedback gains</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-kc-u">set_Kc_u</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt;&gt; &amp;&amp; Kc_u)<br>sets input feedback gains (moving)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-g-design">set_g_design</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a> &amp; g)<br>sets input gain used during controller design</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#function-set-g-design">set_g_design</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a> &amp;&amp; g)<br>sets input gain used during controller design (moving)</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Protected Attributes inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/">lds::SwitchedController&lt; System &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>std::vector&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-systems-">systems_</a></strong> <br>underlying sub-systems which are switched between</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-n-sys-">n_sys_</a></strong> <br>number of systems</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-idx-">idx_</a></strong> <br>current system/controller index.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-kc-list-">Kc_list_</a></strong></td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-kc-inty-list-">Kc_inty_list_</a></strong></td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-kc-u-list-">Kc_u_list_</a></strong></td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">UniformVectorList</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/#variable-g-design-list-">g_design_list_</a></strong></td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Public Functions inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">lds::Controller&lt; System &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller">Controller</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">Controller</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller">Controller</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp; sys, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub, size_t control_type =0)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">Controller</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller">Controller</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp;&amp; sys, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub, size_t control_type =0)<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">Controller</a> by moving the system object.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control">Control</a></strong>(const Vector &amp; z, bool do_control =true, bool do_lock_control =false, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_soft_start =0, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal (single-step)</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controloutputreference">ControlOutputReference</a></strong>(const Vector &amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_soft_start =0, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> sigma_u_noise =0, bool do_reset_at_control_onset =true)<br>updates control signal, given previously-set (single-step)</td>
+      </tr>
+      <tr>
+          <td>const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-sys">sys</a></strong>() const</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc">Kc</a></strong>() const<br>Get state feedback controller gain.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-inty">Kc_inty</a></strong>() const<br>Get integral controller gain.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-u">Kc_u</a></strong>() const<br>Get input feedback controller gain.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-g-design">g_design</a></strong>() const<br>Get input gain used in controller design.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ref">u_ref</a></strong>() const<br>Get reference input.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-x-ref">x_ref</a></strong>() const<br>Get reference state.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-y-ref">y_ref</a></strong>() const<br>Get reference output.</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control-type">control_type</a></strong>() const<br>Get controller type.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-tau-awu">tau_awu</a></strong>() const<br>Get time constant of anti-integral-windup.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-lb">u_lb</a></strong>() const<br>Get control lower bound.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ub">u_ub</a></strong>() const<br>Get control upper bound.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-sys">set_sys</a></strong>(const <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a> &amp; sys)<br>Set system.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-g-design">set_g_design</a></strong>(const Vector &amp; g_design)<br>Set input gain used in controller design (g_design)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ref">set_u_ref</a></strong>(const Vector &amp; u_ref)<br>Set reference input (u_ref)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-x-ref">set_x_ref</a></strong>(const Vector &amp; x_ref)<br>Set reference state (x_ref)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc">set_Kc</a></strong>(const Matrix &amp; Kc)<br>Set state controller gain.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-inty">set_Kc_inty</a></strong>(const Matrix &amp; Kc_inty)<br>Set integral controller gain.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-u">set_Kc_u</a></strong>(const Matrix &amp; Kc_u)<br>Set input controller gain.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-tau-awu">set_tau_awu</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> tau)<br>Set time constant of anti-integral-windup.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-control-type">set_control_type</a></strong>(size_t control_type)<br>Sets the control type.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-lb">set_u_lb</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_lb)<br>sets control lower bound</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ub">set_u_ub</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> u_ub)<br>Sets control upper bound.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-reset">Reset</a></strong>()<br>reset system and control variables.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-print">Print</a></strong>()<br>prints variables to stdout</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Protected Attributes inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">lds::Controller&lt; System &gt;</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">System</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-sys-">sys_</a></strong> <br>underlying LDS</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-">u_</a></strong> <br>control signal</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-return-">u_return_</a></strong> <br>control signal that is <em>returned</em> to user</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-g-design-">g_design_</a></strong> <br>input gain of the system used for controller design</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-">u_ref_</a></strong> <br>reference input</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-prev-">u_ref_prev_</a></strong> <br>reference input at previous time step</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-x-ref-">x_ref_</a></strong> <br>reference state</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-y-ref-">y_ref_</a></strong> <br>reference output</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-cx-ref-">cx_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-">Kc_</a></strong> <br>state controller gain</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-u-">Kc_u_</a></strong> <br>input controller gain (optional when control updates \deltaU)</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-inty-">Kc_inty_</a></strong> <br>integral controller gain</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-du-ref-">du_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-ref-">dv_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-ref-">v_ref_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-">dv_</a></strong></td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-">v_</a></strong> <br>Control after g inversion (e.g., control in physical units)</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-">int_e_</a></strong> <br>integrated error</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-awu-adjust-">int_e_awu_adjust_</a></strong> <br>anti-windup adjustment to intE</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-sat-">u_sat_</a></strong> <br>control signal after saturation (for antiWindup)</td>
+      </tr>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-control-prev-">do_control_prev_</a></strong></td>
+      </tr>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-lock-control-prev-">do_lock_control_prev_</a></strong></td>
+      </tr>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-saturated-">u_saturated_</a></strong> <br>whether control signal has reached saturation limits</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-lb-">u_lb_</a></strong> <br>lower bound on control</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ub-">u_ub_</a></strong> <br>upper bound on control</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-tau-awu-">tau_awu_</a></strong> <br>antiwindup time constant</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-k-awu-">k_awu_</a></strong></td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-t-since-control-onset-">t_since_control_onset_</a></strong> <br>time since control epoch onset</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-control-type-">control_type_</a></strong> <br>controller type</td>
+      </tr>
+  </tbody>
+</table>
+<hr>
+<hr>
+<h2 id="public-function-details">
+  Public Function Details
+  <a class="anchor" href="#public-function-details">#</a>
+</h2>
+<h3 id="set_">
+  <strong>set_y_ref</strong>
+  <a class="anchor" href="#set_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> set_y_ref(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> y_ref
+</span></span><span style="display:flex;"><span>) <span style="color:#66d9ef">override</span>
+</span></span></code></pre></div><p><strong>Reimplements</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-y-ref">lds::Controller::set_y_ref</a></p>
+<hr>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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+  <ul>
+    <li><a href="#ldspoissonswitchedcontroller">lds::poisson::SwitchedController</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#additional-inherited-members">Additional inherited members</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#set_"><strong>set_y_ref</strong></a></li>
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+  <strong>lds::poisson::System</strong>
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+    
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+  <ul>
+    <li><a href="#ldspoissonsystem">lds::poisson::System</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#protected-functions">Protected Functions</a></li>
+        <li><a href="#additional-inherited-members">Additional inherited members</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#system"><strong>System</strong></a></li>
+            <li><a href="#system-1"><strong>System</strong></a></li>
+            <li><a href="#simulate"><strong>Simulate</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-function-details">Protected Function Details</a>
+          <ul>
+            <li><a href="#h"><strong>h</strong></a></li>
+            <li><a href="#h_"><strong>h_</strong></a></li>
+            <li><a href="#recurseke"><strong>RecurseKe</strong></a></li>
+          </ul>
+        </li>
+      </ul>
+    </li>
+  </ul>
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+
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+  </aside>
+  
+ 
+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="ldspoissonsystem">
+  lds::poisson::System
+  <a class="anchor" href="#ldspoissonsystem">#</a>
+</h1>
+<p>Poisson <a href="">System</a> type.
+<br /> <code>#include &lt;lds_poisson_sys.h&gt;</code></p>
+<p>Inherits from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">lds::System</a></p>
+<h2 id="public-functions">
+  Public Functions
+  <a class="anchor" href="#public-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/#function-system">System</a></strong>() =default<br>Constructs a new <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/">System</a>.</td>
+      </tr>
+      <tr>
+          <td></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/#function-system">System</a></strong>(std::size_t n_u, std::size_t n_x, std::size_t n_y, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> dt, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> p0 =kDefaultP0, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> q0 =kDefaultQ0)<br>Constructs a new Poisson <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/">System</a>.</td>
+      </tr>
+      <tr>
+          <td>virtual const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/#function-simulate">Simulate</a></strong>(const Vector &amp; u_tm1) override<br>Simulate system measurement.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="protected-functions">
+  Protected Functions
+  <a class="anchor" href="#protected-functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>virtual void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/#function-h">h</a></strong>() override<br><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/">System</a> output function.</td>
+      </tr>
+      <tr>
+          <td>virtual Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/#function-h-">h_</a></strong>(Vector x) override<br><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/">System</a> output function: stateless.</td>
+      </tr>
+      <tr>
+          <td>virtual void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/#function-recurseke">RecurseKe</a></strong>() override<br>Recursively recalculate estimator gain (Ke)</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="additional-inherited-members">
+  Additional inherited members
+  <a class="anchor" href="#additional-inherited-members">#</a>
+</h2>
+<p><strong>Public Functions inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">lds::System</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>virtual</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-~system">~System</a></strong>()</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-filter">Filter</a></strong>(const Vector &amp; u_tm1, const Vector &amp; z)<br>Filter data to produce causal state estimates.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-f">f</a></strong>(const Vector &amp; u, bool do_add_noise =false)<br>system dynamics function</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-n-u">n_u</a></strong>() const<br>Get number of inputs.</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-n-x">n_x</a></strong>() const<br>Get number of states.</td>
+      </tr>
+      <tr>
+          <td>size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-n-y">n_y</a></strong>() const<br>Get number of outputs.</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-dt">dt</a></strong>() const<br>Get sample period.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-x">x</a></strong>() const<br>Get current state.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p">P</a></strong>() const<br>Get covariance of state estimate.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-m">m</a></strong>() const<br>Get current process disturbance/bias.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p-m">P_m</a></strong>() const<br>Get covariance of process disturbance estimate.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-cx">cx</a></strong>() const<br>Get C*x.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-y">y</a></strong>() const<br>Get output.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-x0">x0</a></strong>() const<br>Get initial state.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-m0">m0</a></strong>() const<br>Get initial disturbance.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-a">A</a></strong>() const<br>Get state matrix.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-b">B</a></strong>() const<br>Get input matrix.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-g">g</a></strong>() const<br>Get input gain/conversion factor.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-c">C</a></strong>() const<br>Get output matrix.</td>
+      </tr>
+      <tr>
+          <td>const Vector &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-d">d</a></strong>() const<br>Get output bias.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-ke">Ke</a></strong>() const<br>Get estimator gain.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-ke-m">Ke_m</a></strong>() const<br>Get estimator gain for process disturbance (m)</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-q">Q</a></strong>()<br>Get process noise covariance.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-q-m">Q_m</a></strong>()<br>Get process noise covariance of disturbance evoluation.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p0">P0</a></strong>()<br>Get covariance of initial state.</td>
+      </tr>
+      <tr>
+          <td>const Matrix &amp;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-p0-m">P0_m</a></strong>()<br>Get covariance of initial process disturbance.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-a">set_A</a></strong>(const Matrix &amp; A)<br>Set state matrix.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-b">set_B</a></strong>(const Matrix &amp; B)<br>Set input matrix.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-m">set_m</a></strong>(const Vector &amp; m, bool do_force_assign =false)<br>Set process disturbance.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-g">set_g</a></strong>(const Vector &amp; g)<br>Set input gain.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-q">set_Q</a></strong>(const Matrix &amp; Q)<br>Set process noise covariance.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-q-m">set_Q_m</a></strong>(const Matrix &amp; Q_m)<br>Set process noise covariance of disturbance evoluation.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-x0">set_x0</a></strong>(const Vector &amp; x0)<br>Set initial state.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-p0">set_P0</a></strong>(const Matrix &amp; P0)<br>Set covariance of initial state.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-p0-m">set_P0_m</a></strong>(const Matrix &amp; P0_m)<br>Set covariance of initial process disturbance.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-c">set_C</a></strong>(const Matrix &amp; C)<br>Set output matrix.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-d">set_d</a></strong>(const Vector &amp; d)<br>Set output bias.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-set-x">set_x</a></strong>(const Vector &amp; x)<br>Set state of system.</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-reset">Reset</a></strong>()<br>Reset system variables.</td>
+      </tr>
+      <tr>
+          <td>std::vector&lt; <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-nstep-pred-block">nstep_pred_block</a></strong>(<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; u, <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">UniformMatrixList</a>&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2">kMatFreeDim2</a> &gt; z, size_t n_pred =1)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-print">Print</a></strong>()<br>Print system variables to stdout.</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Protected Functions inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">lds::System</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-initvars">InitVars</a></strong>(<a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> p0 =kDefaultP0, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> q0 =kDefaultQ0)</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Public Attributes inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">lds::System</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>bool</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-do-adapt-m">do_adapt_m</a></strong> <br>whether to adaptively estimate disturbance m</td>
+      </tr>
+  </tbody>
+</table>
+<p><strong>Protected Attributes inherited from <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">lds::System</a></strong></p>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>std::size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-n-x-">n_x_</a></strong> <br>number of states</td>
+      </tr>
+      <tr>
+          <td>std::size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-n-u-">n_u_</a></strong> <br>number of inputs</td>
+      </tr>
+      <tr>
+          <td>std::size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-n-y-">n_y_</a></strong> <br>number of outputs</td>
+      </tr>
+      <tr>
+          <td><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-dt-">dt_</a></strong> <br>sample period</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-x-">x_</a></strong> <br>state</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p-">P_</a></strong> <br>covariance of state estimate</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-m-">m_</a></strong> <br>process disturbance</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p-m-">P_m_</a></strong> <br>covariance of disturbance estimate</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-cx-">cx_</a></strong> <br>C*x.</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-y-">y_</a></strong> <br>output</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-z-">z_</a></strong> <br>measurement</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-x0-">x0_</a></strong> <br>initial state</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p0-">P0_</a></strong> <br>covariance of initial state estimate</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-m0-">m0_</a></strong> <br>initial process disturbance</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-p0-m-">P0_m_</a></strong> <br>covariance of initial disturbance est.</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-a-">A_</a></strong> <br>state matrix</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-b-">B_</a></strong> <br>input matrix</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-g-">g_</a></strong> <br>input gain</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-q-">Q_</a></strong> <br>covariance of process noise</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-q-m-">Q_m_</a></strong> <br>covariance of disturbance random walk</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-c-">C_</a></strong> <br>output matrix</td>
+      </tr>
+      <tr>
+          <td>Vector</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-d-">d_</a></strong> <br>output bias</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-ke-">Ke_</a></strong> <br>estimator gain</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#variable-ke-m-">Ke_m_</a></strong> <br>estimator gain for process disturbance</td>
+      </tr>
+  </tbody>
+</table>
+<hr>
+<hr>
+<h2 id="public-function-details">
+  Public Function Details
+  <a class="anchor" href="#public-function-details">#</a>
+</h2>
+<h3 id="system">
+  <strong>System</strong>
+  <a class="anchor" href="#system">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>System() <span style="color:#f92672">=</span><span style="color:#66d9ef">default</span>
+</span></span></code></pre></div><hr>
+<h3 id="system-1">
+  <strong>System</strong>
+  <a class="anchor" href="#system-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>System(
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>size_t n_u,
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>size_t n_x,
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>size_t n_y,
+</span></span><span style="display:flex;"><span>    data_t dt,
+</span></span><span style="display:flex;"><span>    data_t p0 <span style="color:#f92672">=</span>kDefaultP0,
+</span></span><span style="display:flex;"><span>    data_t q0 <span style="color:#f92672">=</span>kDefaultQ0
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>n_u</strong> number of inputs</li>
+<li><strong>n_x</strong> number of states</li>
+<li><strong>n_y</strong> number of outputs</li>
+<li><strong>dt</strong> sample period</li>
+<li><strong>p0</strong> [optional] initial diagonal elements of state estimate covariance (P)</li>
+<li><strong>q0</strong> [optional] initial diagonal elements of process noise covariance (Q)</li>
+</ul>
+<hr>
+<h3 id="simulate">
+  <strong>Simulate</strong>
+  <a class="anchor" href="#simulate">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> Simulate(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> u_tm1
+</span></span><span style="display:flex;"><span>) <span style="color:#66d9ef">override</span>
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>u_tm1</strong> input at t-1</li>
+</ul>
+<p><strong>Return</strong>: z measurement</p>
+<p><strong>Reimplements</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-simulate">lds::System::Simulate</a></p>
+<p>Simulate system and produce measurement</p>
+<hr>
+<h2 id="protected-function-details">
+  Protected Function Details
+  <a class="anchor" href="#protected-function-details">#</a>
+</h2>
+<h3 id="h">
+  <strong>h</strong>
+  <a class="anchor" href="#h">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> h() <span style="color:#66d9ef">override</span>
+</span></span></code></pre></div><p><strong>Reimplements</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-h">lds::System::h</a></p>
+<hr>
+<h3 id="h_">
+  <strong>h_</strong>
+  <a class="anchor" href="#h_">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">virtual</span> Vector h_(
+</span></span><span style="display:flex;"><span>    Vector x
+</span></span><span style="display:flex;"><span>) <span style="color:#66d9ef">override</span>
+</span></span></code></pre></div><p><strong>Reimplements</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-h-">lds::System::h_</a></p>
+<hr>
+<h3 id="recurseke">
+  <strong>RecurseKe</strong>
+  <a class="anchor" href="#recurseke">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> RecurseKe() <span style="color:#66d9ef">override</span>
+</span></span></code></pre></div><p><strong>Reimplements</strong>: <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/#function-recurseke">lds::System::RecurseKe</a></p>
+<p>Recursively recalculate estimator gain (Ke).</p>
+<p>References:</p>
+<p>Smith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.</p>
+<p>Eden UT, &hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.</p>
+<hr>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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+  <ul>
+    <li><a href="#ldspoissonsystem">lds::poisson::System</a>
+      <ul>
+        <li><a href="#public-functions">Public Functions</a></li>
+        <li><a href="#protected-functions">Protected Functions</a></li>
+        <li><a href="#additional-inherited-members">Additional inherited members</a></li>
+        <li><a href="#public-function-details">Public Function Details</a>
+          <ul>
+            <li><a href="#system"><strong>System</strong></a></li>
+            <li><a href="#system-1"><strong>System</strong></a></li>
+            <li><a href="#simulate"><strong>Simulate</strong></a></li>
+          </ul>
+        </li>
+        <li><a href="#protected-function-details">Protected Function Details</a>
+          <ul>
+            <li><a href="#h"><strong>h</strong></a></li>
+            <li><a href="#h_"><strong>h_</strong></a></li>
+            <li><a href="#recurseke"><strong>RecurseKe</strong></a></li>
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diff --git a/docs/docs/api/classes/index.html b/docs/docs/api/classes/index.html
new file mode 100644
index 00000000..93ce3684
--- /dev/null
+++ b/docs/docs/api/classes/index.html
@@ -0,0 +1,398 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="
+  Classes
+  #
+
+
+
+lds::Controller
+
+
+lds::EM
+
+
+lds::Fit LDS Fit Type.
+
+
+lds::SSID
+
+
+lds::SwitchedController SwitchedController Type.
+
+
+lds::System Linear Dynamical System Type.
+
+
+lds::UniformMatrixList
+
+
+lds::UniformSystemList
+
+
+lds::UniformVectorList
+
+
+lds::gaussian::Controller Gaussian-observation Controller Type.
+
+
+lds::gaussian::Fit GLDS Fit Type.
+
+
+lds::gaussian::FitEM GLDS E-M Fit Type.
+
+
+lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.
+
+
+lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.
+
+
+lds::gaussian::System Gaussian LDS Type.
+
+
+lds::poisson::Controller PLDS Controller Type.
+
+
+lds::poisson::Fit PLDS Fit Type.
+
+
+lds::poisson::FitEM PLDS E-M Fit Type.
+
+
+lds::poisson::FitSSID Subspace Identification (SSID) for PLDS.
+
+
+lds::poisson::SwitchedController Poisson-observation SwitchedController Type.
+
+
+lds::poisson::System Poisson System type.
+
+
+
+Updated on 31 March 2025 at 16:04:30 EDT">
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+lds::EM
+lds::Fit LDS Fit Type.
+lds::SSID
+lds::SwitchedController SwitchedController Type.
+lds::System Linear Dynamical System Type.
+lds::UniformMatrixList
+lds::UniformSystemList
+lds::UniformVectorList
+lds::gaussian::Controller Gaussian-observation Controller Type.
+lds::gaussian::Fit GLDS Fit Type.
+lds::gaussian::FitEM GLDS E-M Fit Type.
+lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.
+lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.
+lds::gaussian::System Gaussian LDS Type.
+lds::poisson::Controller PLDS Controller Type.
+lds::poisson::Fit PLDS Fit Type.
+lds::poisson::FitEM PLDS E-M Fit Type.
+lds::poisson::FitSSID Subspace Identification (SSID) for PLDS.
+lds::poisson::SwitchedController Poisson-observation SwitchedController Type.
+lds::poisson::System Poisson System type.
+Updated on 31 March 2025 at 16:04:30 EDT">
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+
+  <ul>
+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
+<li><a href="/lds-ctrl-est/docs/api/classes/"class=active>Classes</a></li>
+<li><a href="/lds-ctrl-est/docs/api/modules/">Modules</a></li>
+<li><a href="/lds-ctrl-est/docs/api/files/">Files</a></li>
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+</ul>
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+</ul>
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+<ul>
+<li><a href="/lds-ctrl-est/issues-contributing/"><strong>Reporting Issues &amp; Contributing</strong></a></li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
+</ul>
+<br />
+
+
+
+
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+  <article class="markdown"><h1 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h1>
+<ul>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">lds::Controller</a></strong></p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">lds::EM</a></strong></p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">lds::Fit</a></strong> <br>LDS <a href="">Fit</a> Type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">lds::SSID</a></strong></p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/">lds::SwitchedController</a></strong> <br><a href="">SwitchedController</a> Type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">lds::System</a></strong> <br>Linear Dynamical <a href="">System</a> Type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">lds::UniformMatrixList</a></strong></p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/">lds::UniformSystemList</a></strong></p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">lds::UniformVectorList</a></strong></p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/">lds::gaussian::Controller</a></strong> <br>Gaussian-observation <a href="">Controller</a> Type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/">lds::gaussian::Fit</a></strong> <br>GLDS <a href="">Fit</a> Type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/">lds::gaussian::FitEM</a></strong> <br>GLDS E-M <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/">Fit</a> Type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/">lds::gaussian::FitSSID</a></strong> <br>Subspace Identification (<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">SSID</a>) for GLDS.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/">lds::gaussian::SwitchedController</a></strong> <br>Gaussian-observation <a href="">SwitchedController</a> Type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/">lds::gaussian::System</a></strong> <br>Gaussian LDS Type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/">lds::poisson::Controller</a></strong> <br>PLDS <a href="">Controller</a> Type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/">lds::poisson::Fit</a></strong> <br>PLDS <a href="">Fit</a> Type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/">lds::poisson::FitEM</a></strong> <br>PLDS E-M <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/">Fit</a> Type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/">lds::poisson::FitSSID</a></strong> <br>Subspace Identification (<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">SSID</a>) for PLDS.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/">lds::poisson::SwitchedController</a></strong> <br>Poisson-observation <a href="">SwitchedController</a> Type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/">lds::poisson::System</a></strong> <br>Poisson <a href="">System</a> type.</p>
+</li>
+</ul>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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diff --git a/docs/docs/api/classes/index.xml b/docs/docs/api/classes/index.xml
new file mode 100644
index 00000000..79e538d2
--- /dev/null
+++ b/docs/docs/api/classes/index.xml
@@ -0,0 +1,158 @@
+<?xml version="1.0" encoding="utf-8" standalone="yes"?>
+<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
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+    <title>Classes on LDS C&amp;E</title>
+    <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/</link>
+    <description>Recent content in Classes on LDS C&amp;E</description>
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+    <item>
+      <title>lds::Controller</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/</guid>
+      <description>&lt;h1 id=&#34;ldscontroller&#34;&gt;&#xA;  lds::Controller&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#ldscontroller&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;&lt;a href=&#34;#detailed-description&#34;&gt;More&amp;hellip;&lt;/a&gt;&lt;/p&gt;&#xA;&lt;p&gt;Inherited by &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/&#34;&gt;lds::SwitchedController&amp;lt; System &amp;gt;&lt;/a&gt;, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/&#34;&gt;lds::gaussian::Controller&lt;/a&gt;, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/&#34;&gt;lds::poisson::Controller&lt;/a&gt;&lt;/p&gt;&#xA;&lt;h2 id=&#34;public-functions&#34;&gt;&#xA;  Public Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller&#34;&gt;Controller&lt;/a&gt;&lt;/strong&gt;() =default&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/&#34;&gt;Controller&lt;/a&gt;.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller&#34;&gt;Controller&lt;/a&gt;&lt;/strong&gt;(const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; &amp;amp; sys, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; u_lb, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; u_ub, size_t control_type =0)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/&#34;&gt;Controller&lt;/a&gt;.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller&#34;&gt;Controller&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; &amp;amp;&amp;amp; sys, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; u_lb, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; u_ub, size_t control_type =0)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/&#34;&gt;Controller&lt;/a&gt; by moving the system object.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Vector &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control&#34;&gt;Control&lt;/a&gt;&lt;/strong&gt;(const Vector &amp;amp; z, bool do_control =true, bool do_lock_control =false, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; sigma_soft_start =0, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; sigma_u_noise =0, bool do_reset_at_control_onset =true)&lt;br&gt;updates control signal (single-step)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Vector &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controloutputreference&#34;&gt;ControlOutputReference&lt;/a&gt;&lt;/strong&gt;(const Vector &amp;amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; sigma_soft_start =0, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; sigma_u_noise =0, bool do_reset_at_control_onset =true)&lt;br&gt;updates control signal, given previously-set (single-step)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-sys&#34;&gt;sys&lt;/a&gt;&lt;/strong&gt;() const&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Matrix &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc&#34;&gt;Kc&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get state feedback controller gain.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Matrix &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-inty&#34;&gt;Kc_inty&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get integral controller gain.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Matrix &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-u&#34;&gt;Kc_u&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get input feedback controller gain.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Vector &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-g-design&#34;&gt;g_design&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get input gain used in controller design.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Vector &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ref&#34;&gt;u_ref&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get reference input.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Vector &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-x-ref&#34;&gt;x_ref&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get reference state.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Vector &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-y-ref&#34;&gt;y_ref&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get reference output.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control-type&#34;&gt;control_type&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get controller type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-tau-awu&#34;&gt;tau_awu&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get time constant of anti-integral-windup.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-lb&#34;&gt;u_lb&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get control lower bound.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ub&#34;&gt;u_ub&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get control upper bound.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-sys&#34;&gt;set_sys&lt;/a&gt;&lt;/strong&gt;(const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; &amp;amp; sys)&lt;br&gt;Set system.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-g-design&#34;&gt;set_g_design&lt;/a&gt;&lt;/strong&gt;(const Vector &amp;amp; g_design)&lt;br&gt;Set input gain used in controller design (g_design)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ref&#34;&gt;set_u_ref&lt;/a&gt;&lt;/strong&gt;(const Vector &amp;amp; u_ref)&lt;br&gt;Set reference input (u_ref)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-x-ref&#34;&gt;set_x_ref&lt;/a&gt;&lt;/strong&gt;(const Vector &amp;amp; x_ref)&lt;br&gt;Set reference state (x_ref)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;virtual void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-y-ref&#34;&gt;set_y_ref&lt;/a&gt;&lt;/strong&gt;(const Vector &amp;amp; y_ref)&lt;br&gt;Set reference output (y_ref)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc&#34;&gt;set_Kc&lt;/a&gt;&lt;/strong&gt;(const Matrix &amp;amp; Kc)&lt;br&gt;Set state controller gain.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-inty&#34;&gt;set_Kc_inty&lt;/a&gt;&lt;/strong&gt;(const Matrix &amp;amp; Kc_inty)&lt;br&gt;Set integral controller gain.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-u&#34;&gt;set_Kc_u&lt;/a&gt;&lt;/strong&gt;(const Matrix &amp;amp; Kc_u)&lt;br&gt;Set input controller gain.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-tau-awu&#34;&gt;set_tau_awu&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; tau)&lt;br&gt;Set time constant of anti-integral-windup.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-control-type&#34;&gt;set_control_type&lt;/a&gt;&lt;/strong&gt;(size_t control_type)&lt;br&gt;Sets the control type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-lb&#34;&gt;set_u_lb&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; u_lb)&lt;br&gt;sets control lower bound&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ub&#34;&gt;set_u_ub&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; u_ub)&lt;br&gt;Sets control upper bound.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-reset&#34;&gt;Reset&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;reset system and control variables.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-print&#34;&gt;Print&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;prints variables to stdout&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;protected-attributes&#34;&gt;&#xA;  Protected Attributes&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#protected-attributes&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-sys-&#34;&gt;sys_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;underlying LDS&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-&#34;&gt;u_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;control signal&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-return-&#34;&gt;u_return_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;control signal that is &lt;em&gt;returned&lt;/em&gt; to user&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-g-design-&#34;&gt;g_design_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;input gain of the system used for controller design&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-&#34;&gt;u_ref_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;reference input&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-prev-&#34;&gt;u_ref_prev_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;reference input at previous time step&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-x-ref-&#34;&gt;x_ref_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;reference state&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-y-ref-&#34;&gt;y_ref_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;reference output&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-cx-ref-&#34;&gt;cx_ref_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-&#34;&gt;Kc_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;state controller gain&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-u-&#34;&gt;Kc_u_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;input controller gain (optional when control updates \deltaU)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-inty-&#34;&gt;Kc_inty_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;integral controller gain&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-du-ref-&#34;&gt;du_ref_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-ref-&#34;&gt;dv_ref_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-ref-&#34;&gt;v_ref_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-&#34;&gt;dv_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-&#34;&gt;v_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;Control after g inversion (e.g., control in physical units)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-&#34;&gt;int_e_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;integrated error&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-awu-adjust-&#34;&gt;int_e_awu_adjust_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;anti-windup adjustment to intE&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-sat-&#34;&gt;u_sat_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;control signal after saturation (for antiWindup)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;bool&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-control-prev-&#34;&gt;do_control_prev_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;bool&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-lock-control-prev-&#34;&gt;do_lock_control_prev_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;bool&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-saturated-&#34;&gt;u_saturated_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;whether control signal has reached saturation limits&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-lb-&#34;&gt;u_lb_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;lower bound on control&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ub-&#34;&gt;u_ub_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;upper bound on control&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-tau-awu-&#34;&gt;tau_awu_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;antiwindup time constant&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-k-awu-&#34;&gt;k_awu_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-t-since-control-onset-&#34;&gt;t_since_control_onset_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;time since control epoch onset&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-control-type-&#34;&gt;control_type_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;controller type&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;detailed-description&#34;&gt;&#xA;  Detailed Description&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#detailed-description&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;template&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;typename&lt;/span&gt; System &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;class&lt;/span&gt; &lt;span style=&#34;color:#a6e22e&#34;&gt;lds&lt;/span&gt;&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Controller;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;hr&gt;&#xA;&lt;h2 id=&#34;public-function-details&#34;&gt;&#xA;  Public Function Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-function-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;controller&#34;&gt;&#xA;  &lt;strong&gt;Controller&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#controller&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;Controller() &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;default&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;h3 id=&#34;controller-1&#34;&gt;&#xA;  &lt;strong&gt;Controller&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#controller-1&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;inline&lt;/span&gt; Controller(&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#66d9ef&#34;&gt;const&lt;/span&gt; System &lt;span style=&#34;color:#f92672&#34;&gt;&amp;amp;&lt;/span&gt; sys,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    data_t u_lb,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    data_t u_ub,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    size_t control_type &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;)&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;&lt;strong&gt;Parameters&lt;/strong&gt;:&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>lds::EM</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/</guid>
+      <description>&lt;h1 id=&#34;ldsem&#34;&gt;&#xA;  lds::EM&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#ldsem&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;&lt;a href=&#34;#detailed-description&#34;&gt;More&amp;hellip;&lt;/a&gt;&lt;/p&gt;&#xA;&lt;p&gt;Inherited by &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/&#34;&gt;lds::gaussian::FitEM&lt;/a&gt;, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/&#34;&gt;lds::poisson::FitEM&lt;/a&gt;&lt;/p&gt;&#xA;&lt;h2 id=&#34;public-functions&#34;&gt;&#xA;  Public Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em&#34;&gt;EM&lt;/a&gt;&lt;/strong&gt;() =default&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/&#34;&gt;EM&lt;/a&gt;&lt;a href=&#34;&#34;&gt;Fit&lt;/a&gt; type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em&#34;&gt;EM&lt;/a&gt;&lt;/strong&gt;(size_t n_x, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; dt, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt; &amp;amp;&amp;amp; u_train, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt; &amp;amp;&amp;amp; z_train)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/&#34;&gt;EM&lt;/a&gt;&lt;a href=&#34;&#34;&gt;Fit&lt;/a&gt; type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em&#34;&gt;EM&lt;/a&gt;&lt;/strong&gt;(const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/&#34;&gt;Fit&lt;/a&gt; &amp;amp; fit0, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt; &amp;amp;&amp;amp; u_train, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt; &amp;amp;&amp;amp; z_train)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/&#34;&gt;EM&lt;/a&gt;&lt;a href=&#34;&#34;&gt;Fit&lt;/a&gt; type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;virtual&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-~em&#34;&gt;~EM&lt;/a&gt;&lt;/strong&gt;() =default&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/&#34;&gt;Fit&lt;/a&gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-run&#34;&gt;Run&lt;/a&gt;&lt;/strong&gt;(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; tol =1e-2)&lt;br&gt;Runs fitting by Expectation(E)-Maximization(M)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;std::tuple&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt;, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt; &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-returndata&#34;&gt;ReturnData&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;Returns the input/output data to caller.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const std::vector&amp;lt; Matrix &amp;gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-x&#34;&gt;x&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;gets estimated state (over time)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const std::vector&amp;lt; Matrix &amp;gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-y&#34;&gt;y&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;gets estimated output (over time)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Matrix &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-x-t-x-t&#34;&gt;sum_E_x_t_x_t&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;gets state-input covariance&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Matrix &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-xu-tm1-xu-tm1&#34;&gt;sum_E_xu_tm1_xu_tm1&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;gets state-input covariance (t-minus-1)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Matrix &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-xu-t-xu-tm1&#34;&gt;sum_E_xu_t_xu_tm1&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;gets single lag state-input covariance&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-n-t-tot&#34;&gt;n_t_tot&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;total number of time samples&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Vector &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-theta&#34;&gt;theta&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;gets parameters updated in M step&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;protected-functions&#34;&gt;&#xA;  Protected Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#protected-functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-expectation&#34;&gt;Expectation&lt;/a&gt;&lt;/strong&gt;(bool force_common_initial =false)&lt;br&gt;Expectation step.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximization&#34;&gt;Maximization&lt;/a&gt;&lt;/strong&gt;(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)&lt;br&gt;Maximization step.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizedynamics&#34;&gt;MaximizeDynamics&lt;/a&gt;&lt;/strong&gt;()&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizeq&#34;&gt;MaximizeQ&lt;/a&gt;&lt;/strong&gt;()&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizeinitial&#34;&gt;MaximizeInitial&lt;/a&gt;&lt;/strong&gt;()&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;virtual void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizeoutput&#34;&gt;MaximizeOutput&lt;/a&gt;&lt;/strong&gt;() =0&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;virtual void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizemeasurement&#34;&gt;MaximizeMeasurement&lt;/a&gt;&lt;/strong&gt;() =0&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-smooth&#34;&gt;Smooth&lt;/a&gt;&lt;/strong&gt;(bool force_common_initial)&lt;br&gt;get smoothed estimates&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;virtual void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-recurseke&#34;&gt;RecurseKe&lt;/a&gt;&lt;/strong&gt;(Matrix &amp;amp; Ke, Cube &amp;amp; P_pre, Cube &amp;amp; P_post, size_t t) =0&lt;br&gt;recursively update estimator gain Ke&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-reset&#34;&gt;Reset&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;reset to initial conditions&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-initvars&#34;&gt;InitVars&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;Initializes the variables.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-updatetheta&#34;&gt;UpdateTheta&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;updates parameter list, theta&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;protected-attributes&#34;&gt;&#xA;  Protected Attributes&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#protected-attributes&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-u-&#34;&gt;u_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;input training data&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-z-&#34;&gt;z_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;measurement training data&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;std::vector&amp;lt; Matrix &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-x-&#34;&gt;x_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;state estimate&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;std::vector&amp;lt; Cube &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-p-&#34;&gt;P_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;state estimate cov&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;std::vector&amp;lt; Cube &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-p-t-tm1-&#34;&gt;P_t_tm1_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;single-lag state covariance&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;std::vector&amp;lt; Matrix &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-y-&#34;&gt;y_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;output estimate&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-diag-y-&#34;&gt;diag_y_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-x-t-x-t-&#34;&gt;sum_E_x_t_x_t_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;state covariance (current time)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-xu-tm1-xu-tm1-&#34;&gt;sum_E_xu_tm1_xu_tm1_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;state-input covariance (t-minus-1)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-xu-t-xu-tm1-&#34;&gt;sum_E_xu_t_xu_tm1_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;single lag state-input covariance&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/&#34;&gt;Fit&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-fit-&#34;&gt;fit_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-theta-&#34;&gt;theta_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-dt-&#34;&gt;dt_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;sample period&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-u-&#34;&gt;n_u_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;number of inputs&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-x-&#34;&gt;n_x_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;number of states&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-y-&#34;&gt;n_y_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;number of outputs&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-trials-&#34;&gt;n_trials_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;number of input/output data sequences&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;std::vector&amp;lt; size_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-t-&#34;&gt;n_t_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;number of time steps&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-t-tot-&#34;&gt;n_t_tot_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;total number of time steps across trials&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;detailed-description&#34;&gt;&#xA;  Detailed Description&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#detailed-description&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;template&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;typename&lt;/span&gt; Fit &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;class&lt;/span&gt; &lt;span style=&#34;color:#a6e22e&#34;&gt;lds&lt;/span&gt;&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;EM;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;hr&gt;&#xA;&lt;h2 id=&#34;public-function-details&#34;&gt;&#xA;  Public Function Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-function-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;em&#34;&gt;&#xA;  &lt;strong&gt;EM&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#em&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;EM() &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;default&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;h3 id=&#34;em-1&#34;&gt;&#xA;  &lt;strong&gt;EM&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#em-1&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;EM(&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    size_t n_x,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    data_t dt,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    UniformMatrixList&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; kMatFreeDim2 &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;amp;&amp;amp;&lt;/span&gt; u_train,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    UniformMatrixList&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; kMatFreeDim2 &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;amp;&amp;amp;&lt;/span&gt; z_train&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;)&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;&lt;strong&gt;Parameters&lt;/strong&gt;:&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>lds::Fit</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/</guid>
+      <description>LDS Fit Type.</description>
+    </item>
+    <item>
+      <title>lds::gaussian::Controller</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/</guid>
+      <description>Gaussian-observation Controller Type.</description>
+    </item>
+    <item>
+      <title>lds::gaussian::Fit</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/</guid>
+      <description>GLDS Fit Type.</description>
+    </item>
+    <item>
+      <title>lds::gaussian::FitEM</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/</guid>
+      <description>GLDS E-M Fit Type.</description>
+    </item>
+    <item>
+      <title>lds::gaussian::FitSSID</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/</guid>
+      <description>Subspace Identification (SSID) for GLDS.</description>
+    </item>
+    <item>
+      <title>lds::gaussian::SwitchedController</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/</guid>
+      <description>Gaussian-observation SwitchedController Type.</description>
+    </item>
+    <item>
+      <title>lds::gaussian::System</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/</guid>
+      <description>Gaussian LDS Type.</description>
+    </item>
+    <item>
+      <title>lds::poisson::Controller</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/</guid>
+      <description>PLDS Controller Type.</description>
+    </item>
+    <item>
+      <title>lds::poisson::Fit</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/</guid>
+      <description>PLDS Fit Type.</description>
+    </item>
+    <item>
+      <title>lds::poisson::FitEM</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/</guid>
+      <description>PLDS E-M Fit Type.</description>
+    </item>
+    <item>
+      <title>lds::poisson::FitSSID</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/</guid>
+      <description>Subspace Identification (SSID) for PLDS.</description>
+    </item>
+    <item>
+      <title>lds::poisson::SwitchedController</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/</guid>
+      <description>Poisson-observation SwitchedController Type.</description>
+    </item>
+    <item>
+      <title>lds::poisson::System</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/</guid>
+      <description>Poisson System type.</description>
+    </item>
+    <item>
+      <title>lds::SSID</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/</guid>
+      <description>&lt;h1 id=&#34;ldsssid&#34;&gt;&#xA;  lds::SSID&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#ldsssid&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;&lt;a href=&#34;#detailed-description&#34;&gt;More&amp;hellip;&lt;/a&gt;&lt;/p&gt;&#xA;&lt;p&gt;Inherited by &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/&#34;&gt;lds::gaussian::FitSSID&lt;/a&gt;, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/&#34;&gt;lds::poisson::FitSSID&lt;/a&gt;&lt;/p&gt;&#xA;&lt;h2 id=&#34;public-functions&#34;&gt;&#xA;  Public Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-ssid&#34;&gt;SSID&lt;/a&gt;&lt;/strong&gt;() =default&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/&#34;&gt;SSID&lt;/a&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/&#34;&gt;Fit&lt;/a&gt; type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-ssid&#34;&gt;SSID&lt;/a&gt;&lt;/strong&gt;(size_t n_x, size_t n_h, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; dt, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt; &amp;amp;&amp;amp; u_train, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt; &amp;amp;&amp;amp; z_train, const Vector &amp;amp; d =Vector(1).fill(-kInf))&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/&#34;&gt;SSID&lt;/a&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/&#34;&gt;Fit&lt;/a&gt; type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;std::tuple&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/&#34;&gt;Fit&lt;/a&gt;, Vector &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-run&#34;&gt;Run&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt&#34;&gt;SSIDWt&lt;/a&gt; ssid_wt)&lt;br&gt;Runs fitting by subspace identification (&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/&#34;&gt;SSID&lt;/a&gt;)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;std::tuple&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt;, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt; &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-returndata&#34;&gt;ReturnData&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;Returns the I/O data to caller.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;protected-functions&#34;&gt;&#xA;  Protected Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#protected-functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-calcd&#34;&gt;CalcD&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; t_silence =0.1, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; thresh_silence =0.001)&lt;br&gt;Using periods of silence in inputs (u), calculates the output \ bias (d)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-createhankeldatamat&#34;&gt;CreateHankelDataMat&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;Creates the block-hankel I/O data matrix.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;virtual void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-decomposedata&#34;&gt;DecomposeData&lt;/a&gt;&lt;/strong&gt;() =0&lt;br&gt;Decompose data to lower-triangular matrix (used in Solve)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-calcsvd&#34;&gt;CalcSVD&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt&#34;&gt;SSIDWt&lt;/a&gt; wt)&lt;br&gt;performs the singular value decomposition (SVD)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-solve&#34;&gt;Solve&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; wt_dc)&lt;br&gt;solves for LDS parameters&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-recomputeextobs&#34;&gt;RecomputeExtObs&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;recompute extended observability matrix from estimates of A, C&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;protected-attributes&#34;&gt;&#xA;  Protected Attributes&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#protected-attributes&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-u-&#34;&gt;u_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;input training data&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-z-&#34;&gt;z_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;measurement training data&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-d-&#34;&gt;D_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;block-Hankel I/O data matrix&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/&#34;&gt;Fit&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-fit-&#34;&gt;fit_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;fit&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-g-dc-&#34;&gt;g_dc_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;I/O gain @ DC.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-dt-&#34;&gt;dt_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;sample period&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-u-&#34;&gt;n_u_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;number of inputs&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-x-&#34;&gt;n_x_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;number of states&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-y-&#34;&gt;n_y_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;number of outputs&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-h-&#34;&gt;n_h_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-trials-&#34;&gt;n_trials_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;number of input/output data sequences&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;std::vector&amp;lt; size_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-t-&#34;&gt;n_t_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;number of time steps&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-t-tot-&#34;&gt;n_t_tot_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;total number of time steps across trials&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-l-&#34;&gt;L_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;lower triangle decomp of covariance matrix&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-s-&#34;&gt;s_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;singular values&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-ext-obs-t-&#34;&gt;ext_obs_t_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;extended observability matrix&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;detailed-description&#34;&gt;&#xA;  Detailed Description&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#detailed-description&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;template&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;typename&lt;/span&gt; Fit &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;class&lt;/span&gt; &lt;span style=&#34;color:#a6e22e&#34;&gt;lds&lt;/span&gt;&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;SSID;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;hr&gt;&#xA;&lt;h2 id=&#34;public-function-details&#34;&gt;&#xA;  Public Function Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-function-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;ssid&#34;&gt;&#xA;  &lt;strong&gt;SSID&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#ssid&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;SSID() &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;default&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;h3 id=&#34;ssid-1&#34;&gt;&#xA;  &lt;strong&gt;SSID&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#ssid-1&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;SSID(&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    size_t n_x,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    size_t n_h,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    data_t dt,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    UniformMatrixList&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; kMatFreeDim2 &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;amp;&amp;amp;&lt;/span&gt; u_train,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    UniformMatrixList&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; kMatFreeDim2 &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;amp;&amp;amp;&lt;/span&gt; z_train,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#66d9ef&#34;&gt;const&lt;/span&gt; Vector &lt;span style=&#34;color:#f92672&#34;&gt;&amp;amp;&lt;/span&gt; d &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;Vector(&lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;).fill(&lt;span style=&#34;color:#f92672&#34;&gt;-&lt;/span&gt;kInf)&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;)&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;&lt;strong&gt;Parameters&lt;/strong&gt;:&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>lds::SwitchedController</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/</guid>
+      <description>SwitchedController Type.</description>
+    </item>
+    <item>
+      <title>lds::System</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/</guid>
+      <description>Linear Dynamical System Type.</description>
+    </item>
+    <item>
+      <title>lds::UniformMatrixList</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/</guid>
+      <description>&lt;h1 id=&#34;ldsuniformmatrixlist&#34;&gt;&#xA;  lds::UniformMatrixList&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#ldsuniformmatrixlist&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;&lt;a href=&#34;#detailed-description&#34;&gt;More&amp;hellip;&lt;/a&gt;&lt;/p&gt;&#xA;&lt;p&gt;Inherits from std::vector&amp;lt; Matrix &amp;gt;&lt;/p&gt;&#xA;&lt;h2 id=&#34;public-functions&#34;&gt;&#xA;  Public Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist&#34;&gt;UniformMatrixList&lt;/a&gt;&lt;/strong&gt;() =default&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist&#34;&gt;UniformMatrixList&lt;/a&gt;&lt;/strong&gt;(const std::vector&amp;lt; Matrix &amp;gt; &amp;amp; mats, std::array&amp;lt; size_t, 2 &amp;gt; dim ={0, 0})&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt; by copying existing vector of Matrix if dimensions consistent.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist&#34;&gt;UniformMatrixList&lt;/a&gt;&lt;/strong&gt;(std::vector&amp;lt; Matrix &amp;gt; &amp;amp;&amp;amp; mats, std::array&amp;lt; size_t, 2 &amp;gt; dim ={0, 0})&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt; by moving existing vector of Matrix if dimensions consistent.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist&#34;&gt;UniformMatrixList&lt;/a&gt;&lt;/strong&gt;(std::initializer_list&amp;lt; Matrix &amp;gt; mats, std::array&amp;lt; size_t, 2 &amp;gt; dim ={0, 0})&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt; from initializer_list of Matrix if dimensions consistent.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist&#34;&gt;UniformMatrixList&lt;/a&gt;&lt;/strong&gt;(const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; D &amp;gt; &amp;amp; that)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt; (copy).&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist&#34;&gt;UniformMatrixList&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; D &amp;gt; &amp;amp;&amp;amp; that)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt; (move).&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-~uniformmatrixlist&#34;&gt;~UniformMatrixList&lt;/a&gt;&lt;/strong&gt;() =default&lt;br&gt;Destroys the object.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const std::array&amp;lt; size_t, 2 &amp;gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-dim&#34;&gt;dim&lt;/a&gt;&lt;/strong&gt;(size_t n =0) const&lt;br&gt;gets dimensions of uniformly sized matrices&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-size&#34;&gt;size&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;size of container&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Matrix &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-at&#34;&gt;at&lt;/a&gt;&lt;/strong&gt;(size_t n)&lt;br&gt;gets reference to n^th element&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-swap&#34;&gt;Swap&lt;/a&gt;&lt;/strong&gt;(Matrix &amp;amp; that, size_t n)&lt;br&gt;swaps input matrix with n^th matrix of list&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; D &amp;gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-operator=&#34;&gt;operator=&lt;/a&gt;&lt;/strong&gt;(const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; D &amp;gt; &amp;amp; that)&lt;br&gt;assigns the contents (copy)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; D &amp;gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-operator=&#34;&gt;operator=&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; D &amp;gt; &amp;amp;&amp;amp; that)&lt;br&gt;assigns the contents (move)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-append&#34;&gt;append&lt;/a&gt;&lt;/strong&gt;(const Matrix &amp;amp; mat)&lt;br&gt;appends a matrix to the list&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;detailed-description&#34;&gt;&#xA;  Detailed Description&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#detailed-description&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;template&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;MatrixListFreeDim D &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;kMatFreeDimNone&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;class&lt;/span&gt; &lt;span style=&#34;color:#a6e22e&#34;&gt;lds&lt;/span&gt;&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;UniformMatrixList;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;hr&gt;&#xA;&lt;h2 id=&#34;public-function-details&#34;&gt;&#xA;  Public Function Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-function-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;uniformmatrixlist&#34;&gt;&#xA;  &lt;strong&gt;UniformMatrixList&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#uniformmatrixlist&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;UniformMatrixList() &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;default&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;h3 id=&#34;uniformmatrixlist-1&#34;&gt;&#xA;  &lt;strong&gt;UniformMatrixList&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#uniformmatrixlist-1&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;explicit&lt;/span&gt; UniformMatrixList(&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#66d9ef&#34;&gt;const&lt;/span&gt; std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;vector&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; Matrix &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;amp;&lt;/span&gt; mats,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;array&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; size_t, &lt;span style=&#34;color:#ae81ff&#34;&gt;2&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; dim &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;{&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;, &lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;}&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;)&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;&lt;strong&gt;Parameters&lt;/strong&gt;:&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>lds::UniformSystemList</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/</guid>
+      <description>&lt;h1 id=&#34;ldsuniformsystemlist&#34;&gt;&#xA;  lds::UniformSystemList&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#ldsuniformsystemlist&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;&lt;a href=&#34;#detailed-description&#34;&gt;More&amp;hellip;&lt;/a&gt;&lt;/p&gt;&#xA;&lt;p&gt;Inherits from std::vector&amp;lt; System &amp;gt;&lt;/p&gt;&#xA;&lt;h2 id=&#34;public-functions&#34;&gt;&#xA;  Public Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist&#34;&gt;UniformSystemList&lt;/a&gt;&lt;/strong&gt;() =default&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt;.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist&#34;&gt;UniformSystemList&lt;/a&gt;&lt;/strong&gt;(const std::vector&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; &amp;gt; &amp;amp; systems, std::array&amp;lt; size_t, 3 &amp;gt; dim ={0, 0, 0})&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; by copying existing vector of &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; if dimensions consistent.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist&#34;&gt;UniformSystemList&lt;/a&gt;&lt;/strong&gt;(std::vector&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; &amp;gt; &amp;amp;&amp;amp; systems, std::array&amp;lt; size_t, 3 &amp;gt; dim ={0, 0, 0})&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; by moving existing vector of &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; if dimensions consistent.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist&#34;&gt;UniformSystemList&lt;/a&gt;&lt;/strong&gt;(std::initializer_list&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; &amp;gt; systems, std::array&amp;lt; size_t, 3 &amp;gt; dim ={0, 0, 0})&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; from initializer_list of &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; if dimensions consistent.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist&#34;&gt;UniformSystemList&lt;/a&gt;&lt;/strong&gt;(const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; &amp;amp; that)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; (copy).&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist&#34;&gt;UniformSystemList&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; &amp;amp;&amp;amp; that)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; (move).&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-~uniformsystemlist&#34;&gt;~UniformSystemList&lt;/a&gt;&lt;/strong&gt;() =default&lt;br&gt;Destroys the object.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const std::array&amp;lt; size_t, 3 &amp;gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-dim&#34;&gt;dim&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;gets dimensions of the uniformly sized systems&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-size&#34;&gt;size&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;size of container&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-at&#34;&gt;at&lt;/a&gt;&lt;/strong&gt;(size_t n)&lt;br&gt;gets reference to n^th element&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-swap&#34;&gt;Swap&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; &amp;amp; that, size_t n)&lt;br&gt;swaps input system with n^th system of list&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-operator=&#34;&gt;operator=&lt;/a&gt;&lt;/strong&gt;(const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; &amp;amp; that)&lt;br&gt;assigns the contents (copy)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-operator=&#34;&gt;operator=&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; &amp;amp;&amp;amp; that)&lt;br&gt;assigns the contents (move)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;detailed-description&#34;&gt;&#xA;  Detailed Description&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#detailed-description&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;template&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;typename&lt;/span&gt; System &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;class&lt;/span&gt; &lt;span style=&#34;color:#a6e22e&#34;&gt;lds&lt;/span&gt;&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;UniformSystemList;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;hr&gt;&#xA;&lt;h2 id=&#34;public-function-details&#34;&gt;&#xA;  Public Function Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-function-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;uniformsystemlist&#34;&gt;&#xA;  &lt;strong&gt;UniformSystemList&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#uniformsystemlist&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;UniformSystemList() &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;default&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;h3 id=&#34;uniformsystemlist-1&#34;&gt;&#xA;  &lt;strong&gt;UniformSystemList&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#uniformsystemlist-1&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;explicit&lt;/span&gt; UniformSystemList(&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#66d9ef&#34;&gt;const&lt;/span&gt; std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;vector&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; System &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;amp;&lt;/span&gt; systems,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;array&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; size_t, &lt;span style=&#34;color:#ae81ff&#34;&gt;3&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; dim &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;{&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;, &lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;, &lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;}&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;)&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;&lt;strong&gt;Parameters&lt;/strong&gt;:&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>lds::UniformVectorList</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/</guid>
+      <description>&lt;h1 id=&#34;ldsuniformvectorlist&#34;&gt;&#xA;  lds::UniformVectorList&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#ldsuniformvectorlist&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;Inherits from std::vector&amp;lt; Vector &amp;gt;&lt;/p&gt;&#xA;&lt;h2 id=&#34;public-functions&#34;&gt;&#xA;  Public Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist&#34;&gt;UniformVectorList&lt;/a&gt;&lt;/strong&gt;() =default&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt;.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist&#34;&gt;UniformVectorList&lt;/a&gt;&lt;/strong&gt;(const std::vector&amp;lt; Vector &amp;gt; &amp;amp; vecs, size_t dim =0)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; by copying existing vector of Vector if dimensions consistent.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist&#34;&gt;UniformVectorList&lt;/a&gt;&lt;/strong&gt;(std::vector&amp;lt; Vector &amp;gt; &amp;amp;&amp;amp; vecs, size_t dim =0)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; by moving existing vector of Vector if dimensions consistent.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist&#34;&gt;UniformVectorList&lt;/a&gt;&lt;/strong&gt;(std::initializer_list&amp;lt; Vector &amp;gt; vecs, size_t dim =0)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; from initializer_list of Vector if dimensions consistent.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist&#34;&gt;UniformVectorList&lt;/a&gt;&lt;/strong&gt;(const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; &amp;amp; that)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; (copy)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist&#34;&gt;UniformVectorList&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; &amp;amp;&amp;amp; that)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; (move)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-~uniformvectorlist&#34;&gt;~UniformVectorList&lt;/a&gt;&lt;/strong&gt;() =default&lt;br&gt;Destroys the object.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-dim&#34;&gt;dim&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;gets dimensions of the uniformly sized matrices&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-size&#34;&gt;size&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;size of container&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Vector &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-at&#34;&gt;at&lt;/a&gt;&lt;/strong&gt;(size_t n)&lt;br&gt;gets reference to n^th element&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-swap&#34;&gt;Swap&lt;/a&gt;&lt;/strong&gt;(Vector &amp;amp; that, size_t n)&lt;br&gt;swaps input matrix with n^th vector of list&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-operator=&#34;&gt;operator=&lt;/a&gt;&lt;/strong&gt;(const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; &amp;amp; that)&lt;br&gt;assigns the contents (copy)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-operator=&#34;&gt;operator=&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; &amp;amp;&amp;amp; that)&lt;br&gt;assigns the contents (move)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;hr&gt;&#xA;&lt;hr&gt;&#xA;&lt;h2 id=&#34;public-function-details&#34;&gt;&#xA;  Public Function Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-function-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;uniformvectorlist&#34;&gt;&#xA;  &lt;strong&gt;UniformVectorList&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#uniformvectorlist&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;UniformVectorList() &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;default&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;h3 id=&#34;uniformvectorlist-1&#34;&gt;&#xA;  &lt;strong&gt;UniformVectorList&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#uniformvectorlist-1&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;explicit&lt;/span&gt; UniformVectorList(&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#66d9ef&#34;&gt;const&lt;/span&gt; std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;vector&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; Vector &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;amp;&lt;/span&gt; vecs,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    size_t dim &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;)&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;&lt;strong&gt;Parameters&lt;/strong&gt;:&lt;/p&gt;</description>
+    </item>
+  </channel>
+</rss>
diff --git a/docs/docs/api/examples/eg_glds_ctrl_8cpp-example/index.html b/docs/docs/api/examples/eg_glds_ctrl_8cpp-example/index.html
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+  <article class="markdown"><h1 id="eg_glds_ctrlcpp">
+  eg_glds_ctrl.cpp
+  <a class="anchor" href="#eg_glds_ctrlcpp">#</a>
+</h1>
+<p>Example GLDS Control ```cpp</p>
+<p>//===&ndash; eg_glds_ctrl.cpp - Example GLDS Control &mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the &ldquo;License&rdquo;);
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     <a href="http://www.apache.org/licenses/LICENSE-2.0">http://www.apache.org/licenses/LICENSE-2.0</a>
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an &ldquo;AS IS&rdquo; BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;-===//
+//===&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;-===//</p>
+<p>#include <ldsCtrlEst></p>
+<p>using lds::Matrix;
+using lds::Vector;
+using lds::data_t;
+using std::cout;</p>
+<p>auto main() -&gt; int {
+cout &laquo; &quot; ********** Example Gaussian LDS Control ********** \n\n&quot;;</p>
+<p>// Make 1st-order SISO system, sampled at 1kHz
+data_t dt = 1e-3;
+size_t n_u = 1;
+size_t n_x = 1;
+size_t n_y = 1;</p>
+<p>// no time steps for simulation.
+auto n_t = static_cast&lt;size_t&gt;(5.0 / dt);</p>
+<p>// construct ground truth system to be controlled&hellip;
+// initializes to random walk model with top-most n_y state observed
+lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);</p>
+<p>// Ground-truth parameters for the controlled system
+// (stand-in for physical system to be controlled)
+Matrix a_true(n_x, n_x, arma::fill::eye);
+a_true[0] = exp(-dt / 0.01);
+Matrix b_true = Matrix(n_x, n_u).fill(2e-4);
+// control signal to model input unit conversion e.g., V -&gt; mW/mm2:
+Vector g_true = Vector(n_y).fill(10.0);</p>
+<p>// output noise covariance
+Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;</p>
+<p>size_t which_m = 0;  // whether low or high disturbance (0, 1)
+data_t m_low = 5 * dt * (1 - a_true[0]);
+data_t pr_lo2hi = 1e-3;  // probability of going from low to high disturb.
+data_t m_high = 20 * dt * (1 - a_true[0]);
+data_t pr_hi2lo = pr_lo2hi;</p>
+<p>// initially let m be low
+Vector m0_true = Vector(n_x).fill(m_low);</p>
+<p>// Assign params.
+controlled_system.set_A(a_true);
+controlled_system.set_B(b_true);
+controlled_system.set_m(m0_true);
+controlled_system.set_g(g_true);
+controlled_system.set_R(r_true);</p>
+<p>cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;
+cout &laquo; &ldquo;controlled_system:\n&rdquo;;
+cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;
+controlled_system.Print();
+cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;</p>
+<p>// make a controller
+lds::gaussian::Controller controller;
+{
+// Create <strong>incorrect</strong> model used for control.
+// (e.g., imperfect model fitting)
+Matrix b_controller = b_true / 2;</p>
+<pre><code>// let's assume zero process disturbance initially
+// (will be re-estimating)
+Vector m_controller = Vector(n_x, arma::fill::zeros);
+
+// for this demo, just use arbitrary default R
+Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0;
+
+lds::gaussian::System controller_system(controlled_system);
+controller_system.set_B(b_controller);
+controller_system.set_m(m_controller);
+controller_system.set_R(r_controller);
+controller_system.Reset();  // reset to new m
+
+// going to adaptively re-estimate the disturbance
+controller_system.do_adapt_m = true;
+
+// set adaptation rate by changing covariance of assumed process noise
+// acting on random-walk evolution of m
+Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6;
+controller_system.set_Q_m(q_m);
+
+// create controller
+// lower and upper bounds on control signal (e.g., in Volts)
+data_t u_lb = 0.0;  // [=] V
+data_t u_ub = 5.0;  // [=] V
+controller = std::move(
+    lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub));
+</code></pre>
+<p>}</p>
+<p>// Control variables:
+// if following enabled, adapts set point with re-estimated process
+// disturbance n.b., should not need integral action if this is enabled as the
+// adaptive estimator minimizes DC error
+bool do_adaptive_set_point = false;</p>
+<p>// Reference/target output, controller gains
+Vector y_ref0 = Vector(n_y).fill(20.0 * dt);
+Matrix k_x = Matrix(n_u, n_x).fill(100);     // gains on state error
+Matrix k_inty = Matrix(n_u, n_y).fill(1e3);  // gains on integrated err</p>
+<p>// setting initial state to target to avoid error at onset:
+Vector x0 = Vector(n_x).fill(y_ref0[0]);</p>
+<p>// set up controller type bit mask so controller knows how to proceed
+size_t control_type = 0;
+if (do_adaptive_set_point) {
+// adapt set point with estimated disturbance
+control_type = control_type | lds::kControlTypeAdaptM;
+} else {
+// use integral action to minimize DC error
+control_type = control_type | lds::kControlTypeIntY;
+}</p>
+<p>// set controller type
+controller.set_control_type(control_type);</p>
+<p>// Let&rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2):
+Vector g_design = Vector(n_u).fill(9);</p>
+<p>// Set params.
+// <strong>n.b. using arbitrary defaults for Q, R in this example. Really, these
+// should be set by users, as they tune characteristics of Kalman filter.
+// Users can also choose not to recursively calculate the estimator gain and
+// supply it (setKe) instead of covariances.</strong>
+controller.set_y_ref(y_ref0);
+controller.set_Kc(k_x);
+controller.set_Kc_inty(k_inty);
+controller.set_g_design(g_design);</p>
+<p>cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;
+cout &laquo; &ldquo;control system:\n&rdquo;;
+cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;
+controller.Print();
+cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;</p>
+<p>// set up variables for simulation
+// create Matrix to save outputs in&hellip;
+Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];</p>
+<p>// Simulated measurements
+Matrix z(n_y, n_t, arma::fill::zeros);</p>
+<p>// simulated control signal ([=] V)
+Matrix u(n_u, n_t, arma::fill::zeros);</p>
+<p>// outputs, states and gain/disturbance params
+// *_hat indicates online estimates
+Matrix y_hat(n_y, n_t, arma::fill::zeros);
+Matrix x_hat(n_x, n_t, arma::fill::zeros);
+Matrix m_hat(n_x, n_t, arma::fill::zeros);</p>
+<p>// *_true indicates ground truth (system being controlled)
+Matrix y_true(n_y, n_t, arma::fill::zeros);
+Matrix x_true(n_x, n_t, arma::fill::zeros);
+Matrix m_true(n_x, n_t, arma::fill::zeros);</p>
+<p>// set initial val
+y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y();
+y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();</p>
+<p>x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x();
+x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();</p>
+<p>m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m();
+m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();</p>
+<p>cout &laquo; &ldquo;Starting &quot; &laquo; n_t * dt &laquo; &quot; sec simulation &hellip; \n&rdquo;;
+auto start = std::chrono::high_resolution_clock::now();
+for (size_t t = 1; t &lt; n_t; t++) {
+// simulate a stochastically switched disturbance
+Vector chance = arma::randu<Vector>(1);
+if (which_m == 0)  // low disturbance
+{
+if (chance[0] &lt; pr_lo2hi) {  // switches low -&gt; high disturbance
+m0_true = std::vector&lt;data_t&gt;(n_x, m_high);
+which_m = 1;
+}
+} else {                       // high disturbance
+if (chance[0] &lt; pr_hi2lo) {  // swithces high -&gt; low disturbance
+m0_true = std::vector&lt;data_t&gt;(n_x, m_low);
+which_m = 0;
+}
+}
+controlled_system.set_m(m0_true);</p>
+<pre><code>// input
+Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true);
+
+// Simulate the true system.
+z.col(t) = controlled_system.Simulate(u_tm1);
+
+// This method uses a steady-state solution to control problem to calculate
+// x_ref, u_ref from reference output y_ref. Therefore, it is only
+// applicable to regulation problems or cases where reference trajectory
+// changes slowly compared to system dynamics.
+u.col(t) = controller.ControlOutputReference(z.col(t));
+
+// save the signals
+y_true.col(t) = controlled_system.y();
+x_true.col(t) = controlled_system.x();
+m_true.col(t) = controlled_system.m();
+
+y_hat.col(t) = controller.sys().y();
+x_hat.col(t) = controller.sys().x();
+m_hat.col(t) = controller.sys().m();
+</code></pre>
+<p>}</p>
+<p>auto finish = std::chrono::high_resolution_clock::now();
+std::chrono::duration&lt;data_t, std::milli&gt; sim_time_ms = finish - start;
+cout &laquo; &ldquo;Finished simulation in &quot; &laquo; sim_time_ms.count() &laquo; &quot; ms.\n&rdquo;;
+cout &laquo; &ldquo;(app. &quot; &laquo; (sim_time_ms.count() / n_t) * 1e3 &laquo; &quot; us/time-step)\n&rdquo;;</p>
+<p>cout &laquo; &ldquo;Saving simulation data to disk.\n&rdquo;;</p>
+<p>// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,
+// xTrue, mTrue saving with hdf5 via armadillo
+arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;</p>
+<p>auto dt_vec = Vector(1).fill(dt);
+dt_vec.save(arma::hdf5_name(&ldquo;eg_glds_ctrl.h5&rdquo;, &ldquo;dt&rdquo;));
+y_ref.save(arma::hdf5_name(&ldquo;eg_glds_ctrl.h5&rdquo;, &ldquo;y_ref&rdquo;, replace));
+u.save(arma::hdf5_name(&ldquo;eg_glds_ctrl.h5&rdquo;, &ldquo;u&rdquo;, replace));
+z.save(arma::hdf5_name(&ldquo;eg_glds_ctrl.h5&rdquo;, &ldquo;z&rdquo;, replace));
+x_true.save(arma::hdf5_name(&ldquo;eg_glds_ctrl.h5&rdquo;, &ldquo;x_true&rdquo;, replace));
+m_true.save(arma::hdf5_name(&ldquo;eg_glds_ctrl.h5&rdquo;, &ldquo;m_true&rdquo;, replace));
+y_true.save(arma::hdf5_name(&ldquo;eg_glds_ctrl.h5&rdquo;, &ldquo;y_true&rdquo;, replace));
+x_hat.save(arma::hdf5_name(&ldquo;eg_glds_ctrl.h5&rdquo;, &ldquo;x_hat&rdquo;, replace));
+m_hat.save(arma::hdf5_name(&ldquo;eg_glds_ctrl.h5&rdquo;, &ldquo;m_hat&rdquo;, replace));
+y_hat.save(arma::hdf5_name(&ldquo;eg_glds_ctrl.h5&rdquo;, &ldquo;y_hat&rdquo;, replace));</p>
+<p>cout &laquo; &ldquo;fin.\n&rdquo;;
+return 0;
+}</p>
+<pre tabindex="0"><code>
+_Filename: eg_glds_ctrl.cpp_
+
+-------------------------------
+
+Updated on 31 March 2025 at 16:04:30 EDT
+</code></pre></article>
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+  eg_glds_du_plds_ctrl.cpp
+  <a class="anchor" href="#eg_glds_du_plds_ctrlcpp">#</a>
+</h1>
+<p>Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u). ```cpp</p>
+<p>//===&ndash; eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS &mdash;===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the &ldquo;License&rdquo;);
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     <a href="http://www.apache.org/licenses/LICENSE-2.0">http://www.apache.org/licenses/LICENSE-2.0</a>
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an &ldquo;AS IS&rdquo; BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;-===//
+//===&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;-===//</p>
+<p>#include <ldsCtrlEst></p>
+<p>using lds::data_t;
+using lds::Matrix;
+using lds::Vector;
+using std::cout;</p>
+<p>auto main() -&gt; int {
+cout &laquo; &quot; ********** Example Gaussian LDS du Control of PLDS ********** \n\n&quot;;</p>
+<p>// Make 1st-order SISO system, sampled at 1kHz
+data_t dt = 1e-3;
+size_t n_u = 1;
+size_t n_x = 1;
+size_t n_y = 1;</p>
+<p>// no time steps for simulation.
+auto n_t = static_cast&lt;size_t&gt;(5.0 / dt);</p>
+<p>// construct ground truth system to be controlled&hellip;
+// initializes to random walk model with top-most n_y state observed
+lds::poisson::System controlled_system(n_u, n_x, n_y, dt);</p>
+<p>// Ground-truth parameters for the controlled system
+// (stand-in for physical system to be controlled)
+Matrix a_true(n_x, n_x, arma::fill::eye);
+a_true[0] = exp(-dt / 0.01);
+Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2);
+// control signal to model input unit conversion e.g., V -&gt; mW/mm2:
+Vector g_true = Vector(n_y).fill(10.0);</p>
+<p>size_t which_m = 0;  // whether low or high disturbance (0, 1)
+data_t m_low = log(1 * dt) * (1 - a_true[0]);
+data_t pr_lo2hi =
+0;  // 1e-3;  // probability of going from low to high disturb.
+data_t m_high = log(20 * dt) * (1 - a_true[0]);
+data_t pr_hi2lo = pr_lo2hi;</p>
+<p>// initially let m be low
+Vector m0_true = Vector(n_x).fill(m_low);
+Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);</p>
+<p>// Assign params.
+controlled_system.set_A(a_true);
+controlled_system.set_B(b_true);
+controlled_system.set_m(m0_true);
+controlled_system.set_g(g_true);
+controlled_system.set_x0(x0_true);
+controlled_system.Reset();</p>
+<p>cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;
+cout &laquo; &ldquo;controlled_system:\n&rdquo;;
+cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;
+controlled_system.Print();
+cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;</p>
+<p>// make a controller
+lds::gaussian::Controller controller;
+{
+// Create <strong>incorrect</strong> model used for control.
+// (e.g., imperfect model fitting)
+Matrix b_controller = b_true / 50;</p>
+<pre><code>// let's assume zero process disturbance initially
+// (will be re-estimating)
+Vector m_controller = Vector(n_x, arma::fill::zeros);
+
+// process noise covariance
+Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8;
+
+// output noise covariance
+Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2;
+
+lds::gaussian::System controller_system(n_u, n_x, n_y, dt);
+controller_system.set_A(a_true);
+controller_system.set_B(b_controller);
+controller_system.set_g(g_true);
+controller_system.set_m(m_controller);
+controller_system.set_Q(q_controller);
+controller_system.set_R(r_controller);
+controller_system.Reset();  // reset to new m
+
+// going to adaptively re-estimate the disturbance
+controller_system.do_adapt_m = true;
+
+// set adaptation rate by changing covariance of assumed process noise
+// acting on random-walk evolution of m
+Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8;
+controller_system.set_Q_m(q_m);
+
+// create controller
+// lower and upper bounds on control signal (e.g., in Volts)
+data_t u_lb = 0.0;  // [=] V
+data_t u_ub = 5.0;  // [=] V
+controller = std::move(
+    lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub));
+</code></pre>
+<p>}</p>
+<p>// Control variables:
+// Reference/target output, controller gains
+Vector y_ref0 = Vector(n_y).fill(20.0 * dt);</p>
+<p>// to design for this example, augmented state with control and made the input
+// du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du =
+// 1e-2.
+Matrix k_x = Matrix(n_u, n_x).fill(2.44);     // gains on state error
+Matrix k_inty = Matrix(n_u, n_y).fill(97.4);  // gains on integrated err
+Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2);  // gains on input amp</p>
+<p>// set up controller type bit mask so controller knows how to proceed
+size_t control_type = 0;
+// use integral action to minimize DC error
+control_type = control_type | lds::kControlTypeIntY;
+// update change in control (LP filters control)
+control_type = control_type | lds::kControlTypeDeltaU;</p>
+<p>// set controller type
+controller.set_control_type(control_type);</p>
+<p>// Let&rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2):
+Vector g_design = Vector(n_u).fill(10);</p>
+<p>// Set params.
+// <strong>n.b. using arbitrary defaults for Q, R in this example. Really, these
+// should be set by users, as they tune characteristics of Kalman filter.
+// Users can also choose not to recursively calculate the estimator gain and
+// supply it (setKe) instead of covariances.</strong>
+controller.set_y_ref(y_ref0);
+controller.set_Kc(k_x);
+controller.set_Kc_inty(k_inty);
+controller.set_Kc_u(k_u);
+controller.set_g_design(g_design);</p>
+<p>cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;
+cout &laquo; &ldquo;control system:\n&rdquo;;
+cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;
+controller.Print();
+cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;</p>
+<p>// set up variables for simulation
+// create Matrix to save outputs in&hellip;
+Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];</p>
+<p>// Simulated measurements
+Matrix z(n_y, n_t, arma::fill::zeros);</p>
+<p>// simulated control signal ([=] V)
+Matrix u(n_u, n_t, arma::fill::zeros);</p>
+<p>// outputs, states and gain/disturbance params
+// *_hat indicates online estimates
+Matrix y_hat(n_y, n_t, arma::fill::zeros);
+Matrix x_hat(n_x, n_t, arma::fill::zeros);
+Matrix m_hat(n_x, n_t, arma::fill::zeros);</p>
+<p>// *_true indicates ground truth (system being controlled)
+Matrix y_true(n_y, n_t, arma::fill::zeros);
+Matrix x_true(n_x, n_t, arma::fill::zeros);
+Matrix m_true(n_x, n_t, arma::fill::zeros);</p>
+<p>// get initial val
+y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y();
+y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();</p>
+<p>x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x();
+x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();</p>
+<p>m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m();
+m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();</p>
+<p>cout &laquo; &ldquo;Starting &quot; &laquo; n_t * dt &laquo; &quot; sec simulation &hellip; \n&rdquo;;
+auto start = std::chrono::high_resolution_clock::now();
+for (size_t t = 1; t &lt; n_t; t++) {
+// simulate a stochastically switched disturbance
+Vector chance = arma::randu<Vector>(1);
+if (which_m == 0)  // low disturbance
+{
+if (chance[0] &lt; pr_lo2hi) {  // switches low -&gt; high disturbance
+m0_true = std::vector&lt;data_t&gt;(n_x, m_high);
+which_m = 1;
+}
+} else {                       // high disturbance
+if (chance[0] &lt; pr_hi2lo) {  // swithces high -&gt; low disturbance
+m0_true = std::vector&lt;data_t&gt;(n_x, m_low);
+which_m = 0;
+}
+}
+controlled_system.set_m(m0_true);</p>
+<pre><code>// input
+Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true);
+
+// Simulate the true system.
+z.col(t) = controlled_system.Simulate(u_tm1);
+
+// This method uses a steady-state solution to control problem to calculate
+// x_ref, u_ref from reference output y_ref. Therefore, it is only
+// applicable to regulation problems or cases where reference trajectory
+// changes slowly compared to system dynamics.
+u.col(t) = controller.ControlOutputReference(z.col(t));
+
+// save the signals
+y_true.col(t) = controlled_system.y();
+x_true.col(t) = controlled_system.x();
+m_true.col(t) = controlled_system.m();
+
+y_hat.col(t) = controller.sys().y();
+x_hat.col(t) = controller.sys().x();
+m_hat.col(t) = controller.sys().m();
+</code></pre>
+<p>}</p>
+<p>auto finish = std::chrono::high_resolution_clock::now();
+std::chrono::duration&lt;data_t, std::milli&gt; sim_time_ms = finish - start;
+cout &laquo; &ldquo;Finished simulation in &quot; &laquo; sim_time_ms.count() &laquo; &quot; ms.\n&rdquo;;
+cout &laquo; &ldquo;(app. &quot; &laquo; (sim_time_ms.count() / n_t) * 1e3 &laquo; &quot; us/time-step)\n&rdquo;;</p>
+<p>cout &laquo; &ldquo;Saving simulation data to disk.\n&rdquo;;</p>
+<p>// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,
+// xTrue, mTrue saving with hdf5 via armadillo
+arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;</p>
+<p>auto dt_vec = Vector(1).fill(dt);
+dt_vec.save(arma::hdf5_name(&ldquo;eg_glds_du_plds_ctrl.h5&rdquo;, &ldquo;dt&rdquo;));
+y_ref.save(arma::hdf5_name(&ldquo;eg_glds_du_plds_ctrl.h5&rdquo;, &ldquo;y_ref&rdquo;, replace));
+u.save(arma::hdf5_name(&ldquo;eg_glds_du_plds_ctrl.h5&rdquo;, &ldquo;u&rdquo;, replace));
+z.save(arma::hdf5_name(&ldquo;eg_glds_du_plds_ctrl.h5&rdquo;, &ldquo;z&rdquo;, replace));
+x_true.save(arma::hdf5_name(&ldquo;eg_glds_du_plds_ctrl.h5&rdquo;, &ldquo;x_true&rdquo;, replace));
+m_true.save(arma::hdf5_name(&ldquo;eg_glds_du_plds_ctrl.h5&rdquo;, &ldquo;m_true&rdquo;, replace));
+y_true.save(arma::hdf5_name(&ldquo;eg_glds_du_plds_ctrl.h5&rdquo;, &ldquo;y_true&rdquo;, replace));
+x_hat.save(arma::hdf5_name(&ldquo;eg_glds_du_plds_ctrl.h5&rdquo;, &ldquo;x_hat&rdquo;, replace));
+m_hat.save(arma::hdf5_name(&ldquo;eg_glds_du_plds_ctrl.h5&rdquo;, &ldquo;m_hat&rdquo;, replace));
+y_hat.save(arma::hdf5_name(&ldquo;eg_glds_du_plds_ctrl.h5&rdquo;, &ldquo;y_hat&rdquo;, replace));</p>
+<p>cout &laquo; &ldquo;fin.\n&rdquo;;
+return 0;
+}</p>
+<pre tabindex="0"><code>
+_Filename: eg_glds_du_plds_ctrl.cpp_
+
+-------------------------------
+
+Updated on 31 March 2025 at 16:04:30 EDT
+</code></pre></article>
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+  <article class="markdown"><h1 id="eg_plds_ctrlcpp">
+  eg_plds_ctrl.cpp
+  <a class="anchor" href="#eg_plds_ctrlcpp">#</a>
+</h1>
+<p>Example PLDS Control ```cpp</p>
+<p>//===&ndash; eg_plds_ctrl.cpp - Example PLDS Control &mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the &ldquo;License&rdquo;);
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     <a href="http://www.apache.org/licenses/LICENSE-2.0">http://www.apache.org/licenses/LICENSE-2.0</a>
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an &ldquo;AS IS&rdquo; BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;-===//
+//===&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;-===//</p>
+<p>#include <ldsCtrlEst></p>
+<p>using lds::Matrix;
+using lds::Vector;
+using lds::data_t;
+using std::cout;</p>
+<p>auto main() -&gt; int {
+cout &laquo; &quot; ********** Example Poisson LDS Control ********** \n\n&quot;;</p>
+<p>// Make SISO system sampled at 1kHz
+data_t dt = 1e-3;
+size_t n_u = 1;
+size_t n_x = 1;
+size_t n_y = 1;</p>
+<p>// no time steps for simulation.
+auto n_t = static_cast&lt;size_t&gt;(10.0 / dt);</p>
+<p>// Control variables: _reference/target output, controller gains
+// n.b., Can either use Vector (arma::Col) or std::vector
+Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt;
+Matrix k_x =
+Matrix(n_u, n_x, arma::fill::zeros) + 1;  // gains on state error
+Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) +
+10;  // gains on integrated output err</p>
+<p>// Set control type bit mask, so controller knows what to do
+size_t control_type = lds::kControlTypeIntY;  // integral action</p>
+<p>// Ground-truth parameters for the controlled system
+// (stand-in for physical system to be controlled)
+Matrix a_true(n_x, n_x, arma::fill::eye);
+a_true[0] = 0.986;
+Matrix b_true(n_x, n_u, arma::fill::zeros);
+b_true[0] = 0.054;
+Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);</p>
+<p>size_t which_m = 0;
+data_t m_low = log(1 * dt) * (1 - a_true[0]);
+data_t pr_lo2hi = 1e-3;
+data_t m_high = log(20 * dt) * (1 - a_true[0]);
+data_t pr_hi2lo = pr_lo2hi;</p>
+<p>Vector m0_true = Vector(n_x, arma::fill::ones) * m_low;
+// construct ground truth system to be controlled&hellip;
+lds::poisson::System controlled_system(n_u, n_x, n_y, dt);</p>
+<p>// Assign params.
+controlled_system.set_A(a_true);
+controlled_system.set_B(b_true);
+controlled_system.set_m(m0_true);
+controlled_system.set_x0(x0_true);
+// reset to initial conditions
+controlled_system.Reset();</p>
+<p>cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;
+cout &laquo; &ldquo;controlled_system:\n&rdquo;;
+cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;
+controlled_system.Print();
+cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;</p>
+<p>// Create the controller
+lds::poisson::Controller controller;
+{
+// Create model used for control.
+lds::poisson::System controller_system(controlled_system);</p>
+<pre><code>// for this example, assume model correct, except disturbance
+Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low;
+Vector x0_controller = arma::log(y_ref0);
+controller_system.set_m(m0_controller);
+controller_system.set_x0(x0_controller);
+controller_system.Reset(); //reset to new init condition
+
+// adaptively re-estimate process disturbance (m)
+controller_system.do_adapt_m = true;
+
+// set adaptation rate by changing covariance of assumed process noise
+// acting on random-walk evolution of m
+Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5;
+controller_system.set_Q_m(q_m);
+
+data_t u_lb = 0.0;
+data_t u_ub = 5.0;
+controller = std::move(
+    lds::poisson::Controller(std::move(controller_system), u_lb, u_ub));
+</code></pre>
+<p>}
+// set controller type
+controller.set_control_type(control_type);</p>
+<p>// set controller gains
+controller.set_Kc(k_x);
+controller.set_Kc_inty(k_inty);</p>
+<p>// to protect against integral windup when output is consistently above
+// target:
+data_t tau_awu(0.1);
+controller.set_tau_awu(tau_awu);</p>
+<p>cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;
+cout &laquo; &ldquo;controller:\n&rdquo;;
+cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;
+controller.Print();
+cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;</p>
+<p>// create Matrix to save outputs in&hellip;
+Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros);
+y_ref.each_col() += y_ref0;</p>
+<p>// Simulated measurements
+Matrix z(n_y, n_t, arma::fill::zeros);</p>
+<p>// simulated control signal ([=] V)
+Matrix u(n_u, n_t, arma::fill::zeros);</p>
+<p>// outputs, states and gain/disturbance params
+// *_hat indicates online estimates
+Matrix y_hat(n_y, n_t, arma::fill::zeros);
+Matrix x_hat(n_x, n_t, arma::fill::zeros);
+Matrix m_hat(n_y, n_t, arma::fill::zeros);</p>
+<p>// *_true indicates ground truth (system being controlled)
+Matrix y_true(n_y, n_t, arma::fill::zeros);
+Matrix x_true(n_x, n_t, arma::fill::zeros);
+Matrix m_true(n_y, n_t, arma::fill::zeros);</p>
+<p>// set initial val
+y_hat.col(0) = controller.sys().y();
+y_true.col(0) = controlled_system.y();</p>
+<p>x_hat.col(0) = controller.sys().x();
+x_true.col(0) = controlled_system.x();</p>
+<p>m_hat.col(0) = controller.sys().m();
+m_true.col(0) = controlled_system.m();</p>
+<p>cout &laquo; &ldquo;Starting &quot; &laquo; n_t * dt &laquo; &quot; sec simulation &hellip; \n&rdquo;;
+auto start = std::chrono::high_resolution_clock::now();
+for (size_t t = 1; t &lt; n_t; t++) {
+// simulate a stochastically switched disturbance
+Vector chance = arma::randu<Vector>(1);
+if (which_m == 0)  // low disturbance
+{
+if (chance[0] &lt; pr_lo2hi) {  // switches low -&gt; high disturbance
+m0_true = std::vector&lt;data_t&gt;(n_x, m_high);
+which_m = 1;
+}
+} else {                       // high disturbance
+if (chance[0] &lt; pr_hi2lo) {  // swithces high -&gt; low disturbance
+m0_true = std::vector&lt;data_t&gt;(n_x, m_low);
+which_m = 0;
+}
+}
+controlled_system.set_m(m0_true);</p>
+<pre><code>// e.g., use sinusoidal reference
+data_t f = 0.5;  // freq [=] Hz
+Vector t_vec = Vector(n_y, arma::fill::ones) * t;
+y_ref.col(t) +=
+    y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4);
+
+// Simulate the true system.
+z.col(t)=controlled_system.Simulate(u.col(t-1));
+
+// This method uses a steady-state solution to control problem to calculate
+// x_ref, u_ref from reference output y_ref. Notably, it does this in the
+// log-linear space (i.e., log(y)).
+//
+// Therefore, it is only applicable to regulation problems or cases where
+// reference trajectory changes slowly compared to system dynamics.
+controller.set_y_ref(y_ref.col(t));
+u.col(t)=controller.ControlOutputReference(z.col(t));
+
+y_true.col(t) = controlled_system.y();
+x_true.col(t) = controlled_system.x();
+m_true.col(t) = controlled_system.m();
+
+y_hat.col(t) = controller.sys().y();
+x_hat.col(t) = controller.sys().x();
+m_hat.col(t) = controller.sys().m();
+</code></pre>
+<p>}</p>
+<p>auto finish = std::chrono::high_resolution_clock::now();
+std::chrono::duration&lt;data_t, std::milli&gt; sim_time_ms = finish - start;
+cout &laquo; &ldquo;Finished simulation in &quot; &laquo; sim_time_ms.count() &laquo; &quot; ms.\n&rdquo;;
+cout &laquo; &ldquo;(app. &quot; &laquo; (sim_time_ms.count() / n_t) * 1e3 &laquo; &quot; us/time-step)\n&rdquo;;</p>
+<p>// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,
+// x_true, m_true saving with hdf5 via armadillo
+arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;</p>
+<p>auto dt_vec = Vector(1).fill(dt);
+dt_vec.save(arma::hdf5_name(&ldquo;eg_plds_ctrl.h5&rdquo;, &ldquo;dt&rdquo;));
+y_ref.save(arma::hdf5_name(&ldquo;eg_plds_ctrl.h5&rdquo;, &ldquo;y_ref&rdquo;, replace));
+u.save(arma::hdf5_name(&ldquo;eg_plds_ctrl.h5&rdquo;, &ldquo;u&rdquo;, replace));
+z.save(arma::hdf5_name(&ldquo;eg_plds_ctrl.h5&rdquo;, &ldquo;z&rdquo;, replace));
+x_true.save(arma::hdf5_name(&ldquo;eg_plds_ctrl.h5&rdquo;, &ldquo;x_true&rdquo;, replace));
+m_true.save(arma::hdf5_name(&ldquo;eg_plds_ctrl.h5&rdquo;, &ldquo;m_true&rdquo;, replace));
+y_true.save(arma::hdf5_name(&ldquo;eg_plds_ctrl.h5&rdquo;, &ldquo;y_true&rdquo;, replace));
+x_hat.save(arma::hdf5_name(&ldquo;eg_plds_ctrl.h5&rdquo;, &ldquo;x_hat&rdquo;, replace));
+m_hat.save(arma::hdf5_name(&ldquo;eg_plds_ctrl.h5&rdquo;, &ldquo;m_hat&rdquo;, replace));
+y_hat.save(arma::hdf5_name(&ldquo;eg_plds_ctrl.h5&rdquo;, &ldquo;y_hat&rdquo;, replace));</p>
+<p>return 0;
+}</p>
+<pre tabindex="0"><code>
+_Filename: eg_plds_ctrl.cpp_
+
+-------------------------------
+
+Updated on 31 March 2025 at 16:04:30 EDT
+</code></pre></article>
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+  <article class="markdown"><h1 id="eg_plds_estcpp">
+  eg_plds_est.cpp
+  <a class="anchor" href="#eg_plds_estcpp">#</a>
+</h1>
+<p>Example PLDS Estimation ```cpp</p>
+<p>//===&ndash; eg_plds_est.cpp - Example PLDS Estimation &mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;-===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the &ldquo;License&rdquo;);
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     <a href="http://www.apache.org/licenses/LICENSE-2.0">http://www.apache.org/licenses/LICENSE-2.0</a>
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an &ldquo;AS IS&rdquo; BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;-===//
+//===&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;-===//</p>
+<p>#include <ldsCtrlEst></p>
+<p>using lds::Matrix;
+using lds::Vector;
+using lds::data_t;
+using std::cout;</p>
+<p>// for generating random input
+Matrix random_walk(size_t n_t, const Matrix&amp; Q, const Vector&amp; x0);</p>
+<p>int main() {
+cout &laquo; &quot; ********** Example Poisson LDS Estimation ********** \n\n&quot;;</p>
+<p>// Make SISO system sampled at 1kHz
+data_t dt = 1e-3;
+size_t n_u = 1;                           // no. inputs
+size_t n_x = 1;                           // no. states
+size_t n_y = 1;                           // no. outputs
+auto n_t = static_cast&lt;size_t&gt;(30 / dt);  // no time steps for simulation.</p>
+<p>// construct ground truth system&hellip;
+lds::poisson::System system_true(n_u, n_x, n_y, dt);</p>
+<p>// Model parameters
+Matrix a_true(n_x, n_x, arma::fill::eye);
+a_true[0] = exp(-dt / 0.075);
+Matrix b_true = Matrix(n_x, n_u).fill(1e-2);
+Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2);  // disturbance
+Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) -
+a_true);  // initial state</p>
+<p>// Assign params.
+system_true.set_A(a_true);
+system_true.set_B(b_true);
+system_true.set_x0(x0_true);
+system_true.set_m(m0_true);
+system_true.Reset();</p>
+<p>// Construct system for estimation
+// e.g., will create a model with incorrect disturbance
+lds::poisson::System system_estimator(n_u, n_x, n_y, dt);</p>
+<p>// Can copy parameters from another system object
+system_estimator = system_true;</p>
+<p>// wrong disturbance
+Vector m0_est = m0_true * 2;
+system_estimator.set_m(m0_est);</p>
+<p>// set new initial conditions
+Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) -
+a_true);  // initial state
+system_estimator.set_x0(x0_est);
+system_estimator.Reset();  // reset to initial condition.</p>
+<p>// turn on adaptive disturbance estimation
+system_estimator.do_adapt_m = true;</p>
+<p>// set adaptation rate by changing covariance of assumed process noise acting
+// on random-walk evolution of m
+Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6;
+system_estimator.set_Q_m(q_m);</p>
+<p>cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;
+cout &laquo; &ldquo;estimator:\n&rdquo;;
+cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;
+system_estimator.Print();
+cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;</p>
+<p>// Set up simulation :
+// Simulated measurements
+Matrix z(n_y, n_t, arma::fill::zeros);</p>
+<p>// Stimulus (generate random stimulus)
+Matrix q_u =
+Matrix(n_u, n_u, arma::fill::eye) * 1e-3;  // cov of random walk
+Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));</p>
+<p>// create matrix to save outputs in&hellip;
+Matrix y_hat(n_y, n_t, arma::fill::zeros);
+Matrix y_true(n_y, n_t, arma::fill::zeros);</p>
+<p>// states and disturbance params
+Matrix x_hat(n_x, n_t, arma::fill::zeros);
+Matrix m_hat(n_x, n_t, arma::fill::zeros);</p>
+<p>Matrix x_true(n_x, n_t, arma::fill::zeros);
+Matrix m_true(n_x, n_t, arma::fill::zeros);</p>
+<p>// initial conditions
+y_hat.col(0) = system_estimator.y();
+y_true.col(0) = system_true.y();
+x_hat.col(0) = system_estimator.x();
+x_true.col(0) = system_true.x();
+m_hat.col(0) = system_estimator.m();
+m_true.col(0) = system_true.m();</p>
+<p>cout &laquo; &ldquo;Starting &quot; &laquo; n_t * dt &laquo; &quot; sec simlation &hellip; \n&rdquo;;
+auto start = std::chrono::high_resolution_clock::now();
+for (size_t t = 1; t &lt; n_t; t++) {
+// Simlate the true system.
+z.col(t) = system_true.Simulate(u.col(t - 1));</p>
+<pre><code>// Filter (predict -&gt; update)
+system_estimator.Filter(u.col(t - 1), z.col(t));
+
+// save signals
+y_hat.col(t) = system_estimator.y();
+y_true.col(t) = system_true.y();
+
+x_true.col(t) = system_true.x();
+m_true.col(t) = system_true.m();
+
+x_hat.col(t) = system_estimator.x();
+m_hat.col(t) = system_estimator.m();
+</code></pre>
+<p>}</p>
+<p>auto finish = std::chrono::high_resolution_clock::now();
+std::chrono::duration&lt;data_t, std::milli&gt; sim_time_ms = finish - start;
+cout &laquo; &ldquo;Finished simlation in &quot; &laquo; sim_time_ms.count() &laquo; &quot; ms.\n&rdquo;;
+cout &laquo; &ldquo;(app. &quot; &laquo; (sim_time_ms.count() / n_t) * 1e3 &laquo; &quot; us/time-step)\n&rdquo;;</p>
+<p>// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true,
+// x_true, m_true saving with hdf5 via armadillo
+arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;</p>
+<p>auto dt_vec = Vector(1).fill(dt);
+dt_vec.save(arma::hdf5_name(&ldquo;eg_plds_est.h5&rdquo;, &ldquo;dt&rdquo;));
+u.save(arma::hdf5_name(&ldquo;eg_plds_est.h5&rdquo;, &ldquo;u&rdquo;, replace));
+z.save(arma::hdf5_name(&ldquo;eg_plds_est.h5&rdquo;, &ldquo;z&rdquo;, replace));
+x_true.save(arma::hdf5_name(&ldquo;eg_plds_est.h5&rdquo;, &ldquo;x_true&rdquo;, replace));
+m_true.save(arma::hdf5_name(&ldquo;eg_plds_est.h5&rdquo;, &ldquo;m_true&rdquo;, replace));
+y_true.save(arma::hdf5_name(&ldquo;eg_plds_est.h5&rdquo;, &ldquo;y_true&rdquo;, replace));
+x_hat.save(arma::hdf5_name(&ldquo;eg_plds_est.h5&rdquo;, &ldquo;x_hat&rdquo;, replace));
+m_hat.save(arma::hdf5_name(&ldquo;eg_plds_est.h5&rdquo;, &ldquo;m_hat&rdquo;, replace));
+y_hat.save(arma::hdf5_name(&ldquo;eg_plds_est.h5&rdquo;, &ldquo;y_hat&rdquo;, replace));</p>
+<p>return 0;
+}</p>
+<p>// for generating random input
+Matrix random_walk(size_t n_t, const Matrix&amp; Q, const Vector&amp; x0) {
+size_t n = Q.n_rows;</p>
+<p>if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) {
+throw std::logic_error(&ldquo;Q must be <code>n</code> x <code>n</code>.&rdquo;);
+}</p>
+<p>Matrix x(n, n_t, arma::fill::zeros);
+x.col(0) = x0;
+for (size_t t = 1; t &lt; n_t; t++) {
+x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q);
+}</p>
+<p>return x;
+}</p>
+<pre tabindex="0"><code>
+_Filename: eg_plds_est.cpp_
+
+-------------------------------
+
+Updated on 31 March 2025 at 16:04:30 EDT
+</code></pre></article>
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+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
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+<li><strong>Tutorials</strong>
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+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
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+  <article class="markdown"><h1 id="eg_plds_switched_ctrlcpp">
+  eg_plds_switched_ctrl.cpp
+  <a class="anchor" href="#eg_plds_switched_ctrlcpp">#</a>
+</h1>
+<p>Example Switched PLDS Control ```cpp</p>
+<p>//===&ndash; eg_plds_switched_ctrl.cpp - Example Switched PLDS Control &mdash;===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the &ldquo;License&rdquo;);
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     <a href="http://www.apache.org/licenses/LICENSE-2.0">http://www.apache.org/licenses/LICENSE-2.0</a>
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an &ldquo;AS IS&rdquo; BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;-===//
+//===&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;&mdash;-===//</p>
+<p>#include <ldsCtrlEst></p>
+<p>using lds::data_t;
+using lds::Matrix;
+using lds::Vector;
+using std::cout;</p>
+<p>auto main() -&gt; int {
+cout &laquo; &quot; ********** Example Switched Poisson LDS Control ********** \n\n&quot;;</p>
+<p>// whether to do switched control
+bool do_switch_ctrl = true;</p>
+<p>// Make SISO system sampled at 1kHz
+data_t dt = 1e-3;
+size_t n_u = 1;
+size_t n_x = 1;
+size_t n_y = 1;</p>
+<p>// no time steps for simulation.
+auto n_t = static_cast&lt;size_t&gt;(30.0 / dt);</p>
+<p>// for simulating switching
+size_t which_mode = 1;
+data_t pr_21 = 1e-3;   // prob mode 1 -&gt; 2
+data_t pr_12 = pr_21;  // prob mode 2 -&gt; 1</p>
+<p>// simulated system being controlled
+lds::poisson::System controlled_system(n_u, n_x, n_y, dt);</p>
+<p>// <strong>Assume the system is not well characterized by one LDS, but is well
+// characterized by two LDS models with different input matrices.</strong>
+data_t scale_sys_b = 2;</p>
+<p>Matrix a(n_x, n_x, arma::fill::eye);
+a[0] = 0.985;
+Matrix b1 = Matrix(n_x, n_u).fill(0.05);
+Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));</p>
+<p>controlled_system.set_A(a);
+controlled_system.set_B(b1);
+controlled_system.set_d(d);
+controlled_system.Reset();  // reset to initial conditions</p>
+<p>// reference
+Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);</p>
+<p>// Let underlying system 1 be more sensitive than system 2
+Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);</p>
+<p>// create switched controller
+lds::poisson::SwitchedController switched_controller;
+lds::UniformMatrixList&lt;&gt; k_x;  // feedback controller gains
+{
+// create switched controller sub-systems
+// system 1
+lds::poisson::System sys1(controlled_system);</p>
+<pre><code>// set process noise covariance
+Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3;
+sys1.set_Q(q_controller);
+
+// adaptively estimate process disturbance (m)
+// n.b. using arbitrary default value for process noise if enabled.
+sys1.do_adapt_m = true;
+
+// setting initial mode to target to avoid large error at onset:
+Vector x0_controller = arma::log(y_ref0) - d;
+sys1.set_x0(x0_controller);
+sys1.Reset();  // reset to initial conditions
+
+cout &lt;&lt; &quot;.....................................\n&quot;;
+cout &lt;&lt; &quot;sys1:\n&quot;;
+cout &lt;&lt; &quot;.....................................\n&quot;;
+sys1.Print();
+cout &lt;&lt; &quot;.....................................\n&quot;;
+
+// system 2
+lds::poisson::System sys2 = sys1;
+
+// set parameters
+sys2.set_B(b2);
+
+cout &lt;&lt; &quot;.....................................\n&quot;;
+cout &lt;&lt; &quot;sys2:\n&quot;;
+cout &lt;&lt; &quot;.....................................\n&quot;;
+sys2.Print();
+cout &lt;&lt; &quot;.....................................\n&quot;;
+
+lds::UniformSystemList&lt;lds::poisson::System&gt; systems({sys1, sys2});
+
+// controller gains for underlying system s:
+Matrix k_x1(n_u, n_x, arma::fill::ones);
+Matrix k_x2 = scale_sys_b * k_x1;  // system2 is x-times less sensitive.
+k_x = lds::UniformMatrixList&lt;&gt;({k_x1, k_x2});
+
+data_t u_lb = 0.0;
+data_t u_ub = 5.0;
+switched_controller = std::move(
+    lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub));
+</code></pre>
+<p>}
+// Control variables
+size_t control_type = 0;  // no integral action, etc
+switched_controller.set_control_type(control_type);
+switched_controller.set_Kc(std::move(k_x));</p>
+<p>switched_controller.set_y_ref(y_ref0);</p>
+<p>std::vector<a href="lds::poisson::System">lds::poisson::System</a> systems_vec(3, lds::poisson::System());
+lds::UniformSystemList<a href="lds::poisson::System">lds::poisson::System</a> systems(std::move(systems_vec));</p>
+<p>cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;
+cout &laquo; &ldquo;switched_controller:\n&rdquo;;
+cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;
+switched_controller.Print();
+cout &laquo; &ldquo;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;&hellip;.\n&rdquo;;</p>
+<p>// Fake measurements
+Matrix z(n_y, n_t, arma::fill::zeros);</p>
+<p>// Will later contain control.
+Matrix u(n_u, n_t, arma::fill::zeros);</p>
+<p>// create Matrix to save outputs in&hellip;
+Matrix y_hat(n_y, n_t, arma::fill::zeros);
+Matrix y_true(n_y, n_t, arma::fill::zeros);
+Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);</p>
+<p>// modes and gain/disturbance params
+Matrix x_hat(n_x, n_t, arma::fill::zeros);
+Matrix x_true(n_x, n_t, arma::fill::zeros);
+Matrix mode(1, n_t, arma::fill::ones);</p>
+<p>// set initial val
+y_hat.col(0) = switched_controller.sys().y();
+y_true.col(0) = controlled_system.y();
+x_hat.col(0) = switched_controller.sys().x();
+x_true.col(0) = controlled_system.x();</p>
+<p>cout &laquo; &ldquo;Starting &quot; &laquo; n_t * dt &laquo; &quot; sec simulation &hellip; \n&rdquo;;
+auto start = std::chrono::high_resolution_clock::now();
+for (size_t t = 1; t &lt; n_t; t++) {
+// Let the controlled system stochastically change gain
+// Assume another algorithm decodes this mode change and signals the
+// switched_controller
+Vector chance(1, arma::fill::randu);
+if (which_mode == 1)  // mode1
+{
+if (chance[0] &lt; pr_21) {
+which_mode = 2;
+controlled_system.set_B(b2);
+if (do_switch_ctrl) {
+switched_controller.Switch(1);
+}
+}
+} else {  // mode2
+if (chance[0] &lt; pr_12) {
+which_mode = 1;
+controlled_system.set_B(b1);
+if (do_switch_ctrl) {
+switched_controller.Switch(0);
+}
+}
+}</p>
+<pre><code>// Simulate the true system.
+z.col(t) = controlled_system.Simulate(u.col(t - 1));
+
+// perform control
+u.col(t) = switched_controller.ControlOutputReference(z.col(t));
+
+mode.col(t) = which_mode;
+y_ref.col(t) = y_ref0;
+y_true.col(t) = controlled_system.y();
+x_true.col(t) = controlled_system.x();
+y_hat.col(t) = switched_controller.sys().y();
+x_hat.col(t) = switched_controller.sys().x();
+</code></pre>
+<p>}</p>
+<p>auto finish = std::chrono::high_resolution_clock::now();
+std::chrono::duration&lt;data_t, std::milli&gt; sim_time_ms = finish - start;
+cout &laquo; &ldquo;Finished simulation in &quot; &laquo; sim_time_ms.count() &laquo; &quot; ms.\n&rdquo;;
+cout &laquo; &ldquo;(app. &quot; &laquo; (sim_time_ms.count() / n_t) * 1e3 &laquo; &quot; us/time-step)\n&rdquo;;</p>
+<p>// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,
+// x_true, m_true saving with hdf5 via armadillo
+arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;</p>
+<p>auto dt_vec = Vector(1).fill(dt);
+dt_vec.save(arma::hdf5_name(&ldquo;eg_plds_switched_ctrl.h5&rdquo;, &ldquo;dt&rdquo;));
+y_ref.save(arma::hdf5_name(&ldquo;eg_plds_switched_ctrl.h5&rdquo;, &ldquo;y_ref&rdquo;, replace));
+u.save(arma::hdf5_name(&ldquo;eg_plds_switched_ctrl.h5&rdquo;, &ldquo;u&rdquo;, replace));
+z.save(arma::hdf5_name(&ldquo;eg_plds_switched_ctrl.h5&rdquo;, &ldquo;z&rdquo;, replace));
+x_true.save(arma::hdf5_name(&ldquo;eg_plds_switched_ctrl.h5&rdquo;, &ldquo;x_true&rdquo;, replace));
+y_true.save(arma::hdf5_name(&ldquo;eg_plds_switched_ctrl.h5&rdquo;, &ldquo;y_true&rdquo;, replace));
+x_hat.save(arma::hdf5_name(&ldquo;eg_plds_switched_ctrl.h5&rdquo;, &ldquo;x_hat&rdquo;, replace));
+y_hat.save(arma::hdf5_name(&ldquo;eg_plds_switched_ctrl.h5&rdquo;, &ldquo;y_hat&rdquo;, replace));
+mode.save(arma::hdf5_name(&ldquo;eg_plds_switched_ctrl.h5&rdquo;, &ldquo;mode&rdquo;, replace));</p>
+<p>return 0;
+}</p>
+<pre tabindex="0"><code>
+_Filename: eg_plds_switched_ctrl.cpp_
+
+-------------------------------
+
+Updated on 31 March 2025 at 16:04:30 EDT
+</code></pre></article>
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diff --git a/docs/docs/api/examples/index.html b/docs/docs/api/examples/index.html
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index 00000000..a07ea90a
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+eg_glds_ctrl.cpp Example GLDS Control.
+
+
+eg_glds_du_plds_ctrl.cpp Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).
+
+
+eg_plds_ctrl.cpp Example PLDS Control.
+
+
+eg_plds_est.cpp Example PLDS Estimation.
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+eg_plds_switched_ctrl.cpp Example Switched PLDS Control.
+
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+Updated on 31 March 2025 at 16:04:30 EDT">
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+eg_glds_du_plds_ctrl.cpp Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).
+eg_plds_ctrl.cpp Example PLDS Control.
+eg_plds_est.cpp Example PLDS Estimation.
+eg_plds_switched_ctrl.cpp Example Switched PLDS Control.
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+</li>
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+<p><strong><a href="/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/#example-eg-glds-du-plds-ctrl.cpp">eg_glds_du_plds_ctrl.cpp</a></strong> <br>Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/#example-eg-plds-ctrl.cpp">eg_plds_ctrl.cpp</a></strong> <br>Example PLDS Control.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/#example-eg-plds-est.cpp">eg_plds_est.cpp</a></strong> <br>Example PLDS Estimation.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/#example-eg-plds-switched-ctrl.cpp">eg_plds_switched_ctrl.cpp</a></strong> <br>Example Switched PLDS Control.</p>
+</li>
+</ul>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
+      
+
+      <footer class="book-footer">
+        
+  <div class="flex flex-wrap justify-between">
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diff --git a/docs/docs/api/examples/index.xml b/docs/docs/api/examples/index.xml
new file mode 100644
index 00000000..c128721d
--- /dev/null
+++ b/docs/docs/api/examples/index.xml
@@ -0,0 +1,46 @@
+<?xml version="1.0" encoding="utf-8" standalone="yes"?>
+<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
+  <channel>
+    <title>Examples on LDS C&amp;E</title>
+    <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/</link>
+    <description>Recent content in Examples on LDS C&amp;E</description>
+    <generator>Hugo</generator>
+    <language>en</language>
+    <atom:link href="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/index.xml" rel="self" type="application/rss+xml" />
+    <item>
+      <title>eg_glds_ctrl.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/</guid>
+      <description>Example GLDS Control.</description>
+    </item>
+    <item>
+      <title>eg_glds_du_plds_ctrl.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/</guid>
+      <description>Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).</description>
+    </item>
+    <item>
+      <title>eg_plds_ctrl.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/</guid>
+      <description>Example PLDS Control.</description>
+    </item>
+    <item>
+      <title>eg_plds_est.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/</guid>
+      <description>Example PLDS Estimation.</description>
+    </item>
+    <item>
+      <title>eg_plds_switched_ctrl.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/</guid>
+      <description>Example Switched PLDS Control.</description>
+    </item>
+  </channel>
+</rss>
diff --git a/docs/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/index.html b/docs/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/index.html
new file mode 100644
index 00000000..72d0728d
--- /dev/null
+++ b/docs/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/index.html
@@ -0,0 +1,437 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="
+  ldsCtrlEst_h
+  #
+
+
+  Files
+  #
+
+
+  
+      
+          Name
+      
+  
+  
+      
+          ldsCtrlEst_h/lds.h lds namespace
+      
+      
+          ldsCtrlEst_h/lds_ctrl.h Controller.
+      
+      
+          ldsCtrlEst_h/lds_fit.h LDS base fit type.
+      
+      
+          ldsCtrlEst_h/lds_fit_em.h subspace identification
+      
+      
+          ldsCtrlEst_h/lds_fit_ssid.h subspace identification
+      
+      
+          ldsCtrlEst_h/lds_gaussian.h glds namespace
+      
+      
+          ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.
+      
+      
+          ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.
+      
+      
+          ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.
+      
+      
+          ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.
+      
+      
+          ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.
+      
+      
+          ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.
+      
+      
+          ldsCtrlEst_h/lds_poisson.h plds namespace
+      
+      
+          ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.
+      
+      
+          ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.
+      
+      
+          ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.
+      
+      
+          ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.
+      
+      
+          ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.
+      
+      
+          ldsCtrlEst_h/lds_poisson_sys.h PLDS base type.
+      
+      
+          ldsCtrlEst_h/lds_sctrl.h SwitchedController type.
+      
+      
+          ldsCtrlEst_h/lds_sys.h LDS base type.
+      
+      
+          ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.
+      
+      
+          ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.
+      
+      
+          ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.
+      
+      
+          ldsCtrlEst_h/mex_c_util.h arma &lt;-&gt; mex interoperability utilities (Matlab C API)
+      
+      
+          ldsCtrlEst_h/mex_cpp_util.h arma &lt;-&gt; mex interoperability utilities (Matlab C&#43;&#43; API)
+      
+  
+
+
+Updated on 31 March 2025 at 16:04:30 EDT">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/">
+  <meta property="og:site_name" content="LDS C&E">
+  <meta property="og:title" content="ldsCtrlEst_h">
+  <meta property="og:description" content="ldsCtrlEst_h # Files # Name ldsCtrlEst_h/lds.h lds namespace ldsCtrlEst_h/lds_ctrl.h Controller. ldsCtrlEst_h/lds_fit.h LDS base fit type. ldsCtrlEst_h/lds_fit_em.h subspace identification ldsCtrlEst_h/lds_fit_ssid.h subspace identification ldsCtrlEst_h/lds_gaussian.h glds namespace ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller. ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type. ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type. ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type. ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type. ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type. ldsCtrlEst_h/lds_poisson.h plds namespace ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type. ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type. ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type. ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type. ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type. ldsCtrlEst_h/lds_poisson_sys.h PLDS base type. ldsCtrlEst_h/lds_sctrl.h SwitchedController type. ldsCtrlEst_h/lds_sys.h LDS base type. ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices. ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems. ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors. ldsCtrlEst_h/mex_c_util.h arma &lt;-&gt; mex interoperability utilities (Matlab C API) ldsCtrlEst_h/mex_cpp_util.h arma &lt;-&gt; mex interoperability utilities (Matlab C&#43;&#43; API) Updated on 31 March 2025 at 16:04:30 EDT">
+  <meta property="og:locale" content="en">
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+<h2 class="book-brand">
+  <a href="/lds-ctrl-est/"><img src="/lds-ctrl-est/ldsctrlest-logo.png" alt="Logo" /><span>LDS C&amp;E</span>
+  </a>
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+  <ul>
+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
+<li><a href="/lds-ctrl-est/docs/api/classes/">Classes</a></li>
+<li><a href="/lds-ctrl-est/docs/api/modules/">Modules</a></li>
+<li><a href="/lds-ctrl-est/docs/api/files/">Files</a></li>
+<li><a href="/lds-ctrl-est/docs/api/examples/">Examples</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/issues-contributing/"><strong>Reporting Issues &amp; Contributing</strong></a></li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
+</ul>
+<br />
+
+
+
+
+
+
+  
+<ul>
+  
+  <li>
+    <a href="https://github.com/stanley-rozell/lds-ctrl-est" target="_blank" rel="noopener">
+        Github
+      </a>
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+  
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+
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+    
+    <img src="/lds-ctrl-est/svg/toc.svg" class="book-icon" alt="Table of Contents" />
+    
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+</div>
+
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+  
+  <aside class="hidden clearfix">
+    
+  
+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#ldsctrlest_h">ldsCtrlEst_h</a>
+      <ul>
+        <li><a href="#files">Files</a></li>
+      </ul>
+    </li>
+  </ul>
+</nav>
+
+
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+  </aside>
+  
+ 
+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="ldsctrlest_h">
+  ldsCtrlEst_h
+  <a class="anchor" href="#ldsctrlest_h">#</a>
+</h1>
+<h2 id="files">
+  Files
+  <a class="anchor" href="#files">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds_8h/#file-lds.h">ldsCtrlEst_h/lds.h</a></strong> <br><code>lds</code> namespace</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__ctrl_8h/#file-lds-ctrl.h">ldsCtrlEst_h/lds_ctrl.h</a></strong> <br>Controller.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__fit_8h/#file-lds-fit.h">ldsCtrlEst_h/lds_fit.h</a></strong> <br>LDS base fit type.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__fit__em_8h/#file-lds-fit-em.h">ldsCtrlEst_h/lds_fit_em.h</a></strong> <br>subspace identification</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/#file-lds-fit-ssid.h">ldsCtrlEst_h/lds_fit_ssid.h</a></strong> <br>subspace identification</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__gaussian_8h/#file-lds-gaussian.h">ldsCtrlEst_h/lds_gaussian.h</a></strong> <br><code>glds</code> namespace</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/#file-lds-gaussian-ctrl.h">ldsCtrlEst_h/lds_gaussian_ctrl.h</a></strong> <br>GLDS Controller.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/#file-lds-gaussian-fit.h">ldsCtrlEst_h/lds_gaussian_fit.h</a></strong> <br>GLDS fit type.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/#file-lds-gaussian-fit-em.h">ldsCtrlEst_h/lds_gaussian_fit_em.h</a></strong> <br>GLDS E-M fit type.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/#file-lds-gaussian-fit-ssid.h">ldsCtrlEst_h/lds_gaussian_fit_ssid.h</a></strong> <br>GLDS SSID fit type.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/#file-lds-gaussian-sctrl.h">ldsCtrlEst_h/lds_gaussian_sctrl.h</a></strong> <br>GLDS switched controller type.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/#file-lds-gaussian-sys.h">ldsCtrlEst_h/lds_gaussian_sys.h</a></strong> <br>GLDS base type.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__poisson_8h/#file-lds-poisson.h">ldsCtrlEst_h/lds_poisson.h</a></strong> <br><code>plds</code> namespace</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/#file-lds-poisson-ctrl.h">ldsCtrlEst_h/lds_poisson_ctrl.h</a></strong> <br>PLDS controller type.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/#file-lds-poisson-fit.h">ldsCtrlEst_h/lds_poisson_fit.h</a></strong> <br>PLDS base fit type.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/#file-lds-poisson-fit-em.h">ldsCtrlEst_h/lds_poisson_fit_em.h</a></strong> <br>PLDS E-M fit type.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/#file-lds-poisson-fit-ssid.h">ldsCtrlEst_h/lds_poisson_fit_ssid.h</a></strong> <br>PLDS SSID fit type.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/#file-lds-poisson-sctrl.h">ldsCtrlEst_h/lds_poisson_sctrl.h</a></strong> <br>PLDS switched controller type.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/#file-lds-poisson-sys.h">ldsCtrlEst_h/lds_poisson_sys.h</a></strong> <br>PLDS base type.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__sctrl_8h/#file-lds-sctrl.h">ldsCtrlEst_h/lds_sctrl.h</a></strong> <br>SwitchedController type.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__sys_8h/#file-lds-sys.h">ldsCtrlEst_h/lds_sys.h</a></strong> <br>LDS base type.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/#file-lds-uniform-mats.h">ldsCtrlEst_h/lds_uniform_mats.h</a></strong> <br>List of uniformly sized matrices.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/#file-lds-uniform-systems.h">ldsCtrlEst_h/lds_uniform_systems.h</a></strong> <br>List of uniformly sized Systems.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/#file-lds-uniform-vecs.h">ldsCtrlEst_h/lds_uniform_vecs.h</a></strong> <br>List of uniformly sized vectors.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/mex__c__util_8h/#file-mex-c-util.h">ldsCtrlEst_h/mex_c_util.h</a></strong> <br>arma &lt;-&gt; mex interoperability utilities (Matlab C API)</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/#file-mex-cpp-util.h">ldsCtrlEst_h/mex_cpp_util.h</a></strong> <br>arma &lt;-&gt; mex interoperability utilities (Matlab C++ API)</td>
+      </tr>
+  </tbody>
+</table>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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diff --git a/docs/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/index.html b/docs/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/index.html
new file mode 100644
index 00000000..4e5f4394
--- /dev/null
+++ b/docs/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/index.html
@@ -0,0 +1,311 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
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+<meta name="viewport" content="width=device-width, initial-scale=1.0">
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+  src
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+
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+      
+          Name
+      
+  
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+      
+          src/lds.cpp misc lds namespace functions
+      
+      
+          src/lds_gaussian_sys.cpp GLDS base type.
+      
+      
+          src/lds_poisson_sys.cpp PLDS base type.
+      
+      
+          src/lds_sys.cpp LDS base type.
+      
+      
+          src/lds_uniform_vecs.cpp Uniformly sized vectors.
+      
+  
+
+
+Updated on 31 March 2025 at 16:04:30 EDT">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/">
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+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
+</ul>
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+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
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+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
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+<li><a href="/lds-ctrl-est/issues-contributing/"><strong>Reporting Issues &amp; Contributing</strong></a></li>
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+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
+</ul>
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+  <a class="anchor" href="#src">#</a>
+</h1>
+<h2 id="files">
+  Files
+  <a class="anchor" href="#files">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds_8cpp/#file-lds.cpp">src/lds.cpp</a></strong> <br>misc lds namespace functions</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/#file-lds-gaussian-sys.cpp">src/lds_gaussian_sys.cpp</a></strong> <br>GLDS base type.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/#file-lds-poisson-sys.cpp">src/lds_poisson_sys.cpp</a></strong> <br>PLDS base type.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__sys_8cpp/#file-lds-sys.cpp">src/lds_sys.cpp</a></strong> <br>LDS base type.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/#file-lds-uniform-vecs.cpp">src/lds_uniform_vecs.cpp</a></strong> <br>Uniformly sized vectors.</td>
+      </tr>
+  </tbody>
+</table>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
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diff --git a/docs/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/index.html b/docs/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/index.html
new file mode 100644
index 00000000..d6be595b
--- /dev/null
+++ b/docs/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/index.html
@@ -0,0 +1,311 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
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+  examples
+  #
+
+
+  Files
+  #
+
+
+  
+      
+          Name
+      
+  
+  
+      
+          examples/eg_glds_ctrl.cpp
+      
+      
+          examples/eg_glds_du_plds_ctrl.cpp
+      
+      
+          examples/eg_plds_ctrl.cpp
+      
+      
+          examples/eg_plds_est.cpp
+      
+      
+          examples/eg_plds_switched_ctrl.cpp
+      
+  
+
+
+Updated on 31 March 2025 at 16:04:30 EDT">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/">
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+
+
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+
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+
+  <ul>
+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
+<li><a href="/lds-ctrl-est/docs/api/classes/">Classes</a></li>
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+<li><a href="/lds-ctrl-est/issues-contributing/"><strong>Reporting Issues &amp; Contributing</strong></a></li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
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+  <aside class="hidden clearfix">
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+  Files
+  <a class="anchor" href="#files">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#file-eg-glds-ctrl.cpp">examples/eg_glds_ctrl.cpp</a></strong></td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#file-eg-glds-du-plds-ctrl.cpp">examples/eg_glds_du_plds_ctrl.cpp</a></strong></td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#file-eg-plds-ctrl.cpp">examples/eg_plds_ctrl.cpp</a></strong></td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#file-eg-plds-est.cpp">examples/eg_plds_est.cpp</a></strong></td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#file-eg-plds-switched-ctrl.cpp">examples/eg_plds_switched_ctrl.cpp</a></strong></td>
+      </tr>
+  </tbody>
+</table>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
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diff --git a/docs/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/index.html b/docs/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/index.html
new file mode 100644
index 00000000..bc851d8b
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+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
+<ul>
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+</ul>
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+<br />
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+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
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+<br />
+<ul>
+<li><strong>API Reference</strong>
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+    <a href="https://github.com/stanley-rozell/lds-ctrl-est" target="_blank" rel="noopener">
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+  <article class="markdown"><h1 id="include">
+  include
+  <a class="anchor" href="#include">#</a>
+</h1>
+<h2 id="directories">
+  Directories
+  <a class="anchor" href="#directories">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/#dir-ldsctrlest-h">ldsCtrlEst_h</a></strong></td>
+      </tr>
+  </tbody>
+</table>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
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diff --git a/docs/docs/api/files/eg__glds__ctrl_8cpp/index.html b/docs/docs/api/files/eg__glds__ctrl_8cpp/index.html
new file mode 100644
index 00000000..0e3ef361
--- /dev/null
+++ b/docs/docs/api/files/eg__glds__ctrl_8cpp/index.html
@@ -0,0 +1,679 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="
+  examples/eg_glds_ctrl.cpp
+  #
+
+
+  Types
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          using arma::Mat&lt; data_t &gt;
+          Matrix
+      
+      
+          using arma::Col&lt; data_t &gt;
+          Vector
+      
+      
+          using double
+          data_t
+      
+  
+
+
+  Functions
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          int
+          main()
+      
+  
+
+
+  Type Details
+  #
+
+
+  Matrix
+  #
+
+using lds::Matrix =  arma::Mat&lt;data_t&gt;;
+
+  Vector
+  #
+
+using lds::Vector =  arma::Col&lt;data_t&gt;;
+
+  data_t
+  #
+
+using lds::data_t =  double;
+Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/">
+  <meta property="og:site_name" content="LDS C&E">
+  <meta property="og:title" content="examples/eg_glds_ctrl.cpp">
+  <meta property="og:description" content="examples/eg_glds_ctrl.cpp # Types # Name using arma::Mat&lt; data_t &gt; Matrix using arma::Col&lt; data_t &gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat&lt;data_t&gt;; Vector # using lds::Vector = arma::Col&lt;data_t&gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.">
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+
+
+
+
+
+
+
+
+
+
+
+  <ul>
+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
+<li><a href="/lds-ctrl-est/docs/api/classes/">Classes</a></li>
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+<li><a href="/lds-ctrl-est/docs/api/examples/">Examples</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/issues-contributing/"><strong>Reporting Issues &amp; Contributing</strong></a></li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
+</ul>
+<br />
+
+
+
+
+
+
+  
+<ul>
+  
+  <li>
+    <a href="https://github.com/stanley-rozell/lds-ctrl-est" target="_blank" rel="noopener">
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+    <img src="/lds-ctrl-est/svg/menu.svg" class="book-icon" alt="Menu" />
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+
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+
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+    
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+    
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+
+
+  
+  <aside class="hidden clearfix">
+    
+  
+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#exampleseg_glds_ctrlcpp">examples/eg_glds_ctrl.cpp</a>
+      <ul>
+        <li><a href="#types">Types</a></li>
+        <li><a href="#functions">Functions</a></li>
+        <li><a href="#type-details">Type Details</a>
+          <ul>
+            <li><a href="#matrix">Matrix</a></li>
+            <li><a href="#vector">Vector</a></li>
+            <li><a href="#data_t">data_t</a></li>
+          </ul>
+        </li>
+        <li><a href="#function-details">Function Details</a>
+          <ul>
+            <li><a href="#main">main</a></li>
+          </ul>
+        </li>
+        <li><a href="#source-code">Source code</a></li>
+      </ul>
+    </li>
+  </ul>
+</nav>
+
+
+
+  </aside>
+  
+ 
+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="exampleseg_glds_ctrlcpp">
+  examples/eg_glds_ctrl.cpp
+  <a class="anchor" href="#exampleseg_glds_ctrlcpp">#</a>
+</h1>
+<h2 id="types">
+  Types
+  <a class="anchor" href="#types">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>using arma::Mat&lt; data_t &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#using-matrix">Matrix</a></strong></td>
+      </tr>
+      <tr>
+          <td>using arma::Col&lt; data_t &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#using-vector">Vector</a></strong></td>
+      </tr>
+      <tr>
+          <td>using double</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#using-data-t">data_t</a></strong></td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="functions">
+  Functions
+  <a class="anchor" href="#functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>int</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#function-main">main</a></strong>()</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="type-details">
+  Type Details
+  <a class="anchor" href="#type-details">#</a>
+</h2>
+<h3 id="matrix">
+  Matrix
+  <a class="anchor" href="#matrix">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Matrix <span style="color:#f92672">=</span>  arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>;
+</span></span></code></pre></div><h3 id="vector">
+  Vector
+  <a class="anchor" href="#vector">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Vector <span style="color:#f92672">=</span>  arma<span style="color:#f92672">::</span>Col<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>;
+</span></span></code></pre></div><h3 id="data_t">
+  data_t
+  <a class="anchor" href="#data_t">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>data_t <span style="color:#f92672">=</span>  <span style="color:#66d9ef">double</span>;
+</span></span></code></pre></div><p>Type of all data in library. If need 32b, change <code>double</code> to <code>float</code>. This could be potentially useful for large scale problems where there are memory constraints.</p>
+<h2 id="function-details">
+  Function Details
+  <a class="anchor" href="#function-details">#</a>
+</h2>
+<h3 id="main">
+  main
+  <a class="anchor" href="#main">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">int</span> main()
+</span></span></code></pre></div><p>Going to simulate a switching disturbance (m) acting on system</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;ldsCtrlEst&gt;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Matrix;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Vector;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>data_t;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>cout;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">auto</span> <span style="color:#a6e22e">main</span>() <span style="color:#f92672">-&gt;</span> <span style="color:#66d9ef">int</span> {
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; ********** Example Gaussian LDS Control ********** </span><span style="color:#ae81ff">\n\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Make 1st-order SISO system, sampled at 1kHz
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  data_t dt <span style="color:#f92672">=</span> <span style="color:#ae81ff">1e-3</span>;
+</span></span><span style="display:flex;"><span>  size_t n_u <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>  size_t n_x <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>  size_t n_y <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// no time steps for simulation.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">auto</span> n_t <span style="color:#f92672">=</span> <span style="color:#66d9ef">static_cast</span><span style="color:#f92672">&lt;</span>size_t<span style="color:#f92672">&gt;</span>(<span style="color:#ae81ff">5.0</span> <span style="color:#f92672">/</span> dt);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// construct ground truth system to be controlled...
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// initializes to random walk model with top-most n_y state observed
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  lds<span style="color:#f92672">::</span>gaussian<span style="color:#f92672">::</span>System controlled_system(n_u, n_x, n_y, dt);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Ground-truth parameters for the controlled system
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// (stand-in for physical system to be controlled)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix a_true(n_x, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye);
+</span></span><span style="display:flex;"><span>  a_true[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">=</span> exp(<span style="color:#f92672">-</span>dt <span style="color:#f92672">/</span> <span style="color:#ae81ff">0.01</span>);
+</span></span><span style="display:flex;"><span>  Matrix b_true <span style="color:#f92672">=</span> Matrix(n_x, n_u).fill(<span style="color:#ae81ff">2e-4</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// control signal to model input unit conversion e.g., V -&gt; mW/mm2:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector g_true <span style="color:#f92672">=</span> Vector(n_y).fill(<span style="color:#ae81ff">10.0</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// output noise covariance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix r_true <span style="color:#f92672">=</span> Matrix(n_y, n_y, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">*</span> <span style="color:#ae81ff">1e-4</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  size_t which_m <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;  <span style="color:#75715e">// whether low or high disturbance (0, 1)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  data_t m_low <span style="color:#f92672">=</span> <span style="color:#ae81ff">5</span> <span style="color:#f92672">*</span> dt <span style="color:#f92672">*</span> (<span style="color:#ae81ff">1</span> <span style="color:#f92672">-</span> a_true[<span style="color:#ae81ff">0</span>]);
+</span></span><span style="display:flex;"><span>  data_t pr_lo2hi <span style="color:#f92672">=</span> <span style="color:#ae81ff">1e-3</span>;  <span style="color:#75715e">// probability of going from low to high disturb.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  data_t m_high <span style="color:#f92672">=</span> <span style="color:#ae81ff">20</span> <span style="color:#f92672">*</span> dt <span style="color:#f92672">*</span> (<span style="color:#ae81ff">1</span> <span style="color:#f92672">-</span> a_true[<span style="color:#ae81ff">0</span>]);
+</span></span><span style="display:flex;"><span>  data_t pr_hi2lo <span style="color:#f92672">=</span> pr_lo2hi;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// initially let m be low
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector m0_true <span style="color:#f92672">=</span> Vector(n_x).fill(m_low);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Assign params.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  controlled_system.set_A(a_true);
+</span></span><span style="display:flex;"><span>  controlled_system.set_B(b_true);
+</span></span><span style="display:flex;"><span>  controlled_system.set_m(m0_true);
+</span></span><span style="display:flex;"><span>  controlled_system.set_g(g_true);
+</span></span><span style="display:flex;"><span>  controlled_system.set_R(r_true);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;controlled_system:</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  controlled_system.Print();
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// make a controller
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  lds<span style="color:#f92672">::</span>gaussian<span style="color:#f92672">::</span>Controller controller;
+</span></span><span style="display:flex;"><span>  {
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// Create **incorrect** model used for control.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// (e.g., imperfect model fitting)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Matrix b_controller <span style="color:#f92672">=</span> b_true <span style="color:#f92672">/</span> <span style="color:#ae81ff">2</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// let&#39;s assume zero process disturbance initially
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// (will be re-estimating)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Vector m_controller <span style="color:#f92672">=</span> Vector(n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// for this demo, just use arbitrary default R
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Matrix r_controller <span style="color:#f92672">=</span> Matrix(n_y, n_y, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">*</span> lds<span style="color:#f92672">::</span>kDefaultR0;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    lds<span style="color:#f92672">::</span>gaussian<span style="color:#f92672">::</span>System controller_system(controlled_system);
+</span></span><span style="display:flex;"><span>    controller_system.set_B(b_controller);
+</span></span><span style="display:flex;"><span>    controller_system.set_m(m_controller);
+</span></span><span style="display:flex;"><span>    controller_system.set_R(r_controller);
+</span></span><span style="display:flex;"><span>    controller_system.Reset();  <span style="color:#75715e">// reset to new m
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// going to adaptively re-estimate the disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    controller_system.do_adapt_m <span style="color:#f92672">=</span> true;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// set adaptation rate by changing covariance of assumed process noise
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// acting on random-walk evolution of m
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Matrix q_m <span style="color:#f92672">=</span> Matrix(n_x, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">*</span> <span style="color:#ae81ff">1e-6</span>;
+</span></span><span style="display:flex;"><span>    controller_system.set_Q_m(q_m);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// create controller
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// lower and upper bounds on control signal (e.g., in Volts)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    data_t u_lb <span style="color:#f92672">=</span> <span style="color:#ae81ff">0.0</span>;  <span style="color:#75715e">// [=] V
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    data_t u_ub <span style="color:#f92672">=</span> <span style="color:#ae81ff">5.0</span>;  <span style="color:#75715e">// [=] V
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    controller <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(
+</span></span><span style="display:flex;"><span>        lds<span style="color:#f92672">::</span>gaussian<span style="color:#f92672">::</span>Controller(std<span style="color:#f92672">::</span>move(controller_system), u_lb, u_ub));
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Control variables:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// if following enabled, adapts set point with re-estimated process
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// disturbance n.b., should not need integral action if this is enabled as the
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// adaptive estimator minimizes DC error
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">bool</span> do_adaptive_set_point <span style="color:#f92672">=</span> false;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Reference/target output, controller gains
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector y_ref0 <span style="color:#f92672">=</span> Vector(n_y).fill(<span style="color:#ae81ff">20.0</span> <span style="color:#f92672">*</span> dt);
+</span></span><span style="display:flex;"><span>  Matrix k_x <span style="color:#f92672">=</span> Matrix(n_u, n_x).fill(<span style="color:#ae81ff">100</span>);     <span style="color:#75715e">// gains on state error
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix k_inty <span style="color:#f92672">=</span> Matrix(n_u, n_y).fill(<span style="color:#ae81ff">1e3</span>);  <span style="color:#75715e">// gains on integrated err
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// setting initial state to target to avoid error at onset:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector x0 <span style="color:#f92672">=</span> Vector(n_x).fill(y_ref0[<span style="color:#ae81ff">0</span>]);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set up controller type bit mask so controller knows how to proceed
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  size_t control_type <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (do_adaptive_set_point) {
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// adapt set point with estimated disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    control_type <span style="color:#f92672">=</span> control_type <span style="color:#f92672">|</span> lds<span style="color:#f92672">::</span>kControlTypeAdaptM;
+</span></span><span style="display:flex;"><span>  } <span style="color:#66d9ef">else</span> {
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// use integral action to minimize DC error
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    control_type <span style="color:#f92672">=</span> control_type <span style="color:#f92672">|</span> lds<span style="color:#f92672">::</span>kControlTypeIntY;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set controller type
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  controller.set_control_type(control_type);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Let&#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector g_design <span style="color:#f92672">=</span> Vector(n_u).fill(<span style="color:#ae81ff">9</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Set params.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// **n.b. using arbitrary defaults for Q, R in this example. Really, these
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// should be set by users, as they tune characteristics of Kalman filter.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// Users can also choose not to recursively calculate the estimator gain and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// supply it (setKe) instead of covariances.**
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  controller.set_y_ref(y_ref0);
+</span></span><span style="display:flex;"><span>  controller.set_Kc(k_x);
+</span></span><span style="display:flex;"><span>  controller.set_Kc_inty(k_inty);
+</span></span><span style="display:flex;"><span>  controller.set_g_design(g_design);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;control system:</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  controller.Print();
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set up variables for simulation
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// create Matrix to save outputs in...
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix y_ref <span style="color:#f92672">=</span> Matrix(n_y, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>ones) <span style="color:#f92672">*</span> y_ref0[<span style="color:#ae81ff">0</span>];
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Simulated measurements
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix z(n_y, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// simulated control signal ([=] V)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix u(n_u, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// outputs, states and gain/disturbance params
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// *_hat indicates online estimates
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix y_hat(n_y, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix x_hat(n_x, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix m_hat(n_x, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// *_true indicates ground truth (system being controlled)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix y_true(n_y, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix x_true(n_x, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix m_true(n_x, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set initial val
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  y_hat.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_y <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, <span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> controller.sys().y();
+</span></span><span style="display:flex;"><span>  y_true.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_y <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, <span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> controlled_system.y();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  x_hat.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_x <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, <span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> controller.sys().x();
+</span></span><span style="display:flex;"><span>  x_true.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_x <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, <span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> controlled_system.x();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  m_hat.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_x <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, <span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> controller.sys().m();
+</span></span><span style="display:flex;"><span>  m_true.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_x <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, <span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> controlled_system.m();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Starting &#34;</span> <span style="color:#f92672">&lt;&lt;</span> n_t <span style="color:#f92672">*</span> dt <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; sec simulation ... </span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> start <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>chrono<span style="color:#f92672">::</span>high_resolution_clock<span style="color:#f92672">::</span>now();
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t t <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>; t <span style="color:#f92672">&lt;</span> n_t; t<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// simulate a stochastically switched disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Vector chance <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>randu<span style="color:#f92672">&lt;</span>Vector<span style="color:#f92672">&gt;</span>(<span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (which_m <span style="color:#f92672">==</span> <span style="color:#ae81ff">0</span>)  <span style="color:#75715e">// low disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    {
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">if</span> (chance[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;</span> pr_lo2hi) {  <span style="color:#75715e">// switches low -&gt; high disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>        m0_true <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>(n_x, m_high);
+</span></span><span style="display:flex;"><span>        which_m <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>    } <span style="color:#66d9ef">else</span> {                       <span style="color:#75715e">// high disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      <span style="color:#66d9ef">if</span> (chance[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;</span> pr_hi2lo) {  <span style="color:#75715e">// swithces high -&gt; low disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>        m0_true <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>(n_x, m_low);
+</span></span><span style="display:flex;"><span>        which_m <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>    controlled_system.set_m(m0_true);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// input
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Vector u_tm1(u.colptr(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>), u.n_rows, false, true);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// Simulate the true system.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    z.col(t) <span style="color:#f92672">=</span> controlled_system.Simulate(u_tm1);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// This method uses a steady-state solution to control problem to calculate
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// x_ref, u_ref from reference output y_ref. Therefore, it is only
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// applicable to regulation problems or cases where reference trajectory
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// changes slowly compared to system dynamics.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    u.col(t) <span style="color:#f92672">=</span> controller.ControlOutputReference(z.col(t));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// save the signals
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    y_true.col(t) <span style="color:#f92672">=</span> controlled_system.y();
+</span></span><span style="display:flex;"><span>    x_true.col(t) <span style="color:#f92672">=</span> controlled_system.x();
+</span></span><span style="display:flex;"><span>    m_true.col(t) <span style="color:#f92672">=</span> controlled_system.m();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    y_hat.col(t) <span style="color:#f92672">=</span> controller.sys().y();
+</span></span><span style="display:flex;"><span>    x_hat.col(t) <span style="color:#f92672">=</span> controller.sys().x();
+</span></span><span style="display:flex;"><span>    m_hat.col(t) <span style="color:#f92672">=</span> controller.sys().m();
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> finish <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>chrono<span style="color:#f92672">::</span>high_resolution_clock<span style="color:#f92672">::</span>now();
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>chrono<span style="color:#f92672">::</span>duration<span style="color:#f92672">&lt;</span>data_t, std<span style="color:#f92672">::</span>milli<span style="color:#f92672">&gt;</span> sim_time_ms <span style="color:#f92672">=</span> finish <span style="color:#f92672">-</span> start;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Finished simulation in &#34;</span> <span style="color:#f92672">&lt;&lt;</span> sim_time_ms.count() <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; ms.</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;(app. &#34;</span> <span style="color:#f92672">&lt;&lt;</span> (sim_time_ms.count() <span style="color:#f92672">/</span> n_t) <span style="color:#f92672">*</span> <span style="color:#ae81ff">1e3</span> <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; us/time-step)</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Saving simulation data to disk.</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// xTrue, mTrue saving with hdf5 via armadillo
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  arma<span style="color:#f92672">::</span>hdf5_opts<span style="color:#f92672">::</span>opts replace <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>hdf5_opts<span style="color:#f92672">::</span>replace;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> dt_vec <span style="color:#f92672">=</span> Vector(<span style="color:#ae81ff">1</span>).fill(dt);
+</span></span><span style="display:flex;"><span>  dt_vec.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_glds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;dt&#34;</span>));
+</span></span><span style="display:flex;"><span>  y_ref.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_glds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;y_ref&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  u.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_glds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;u&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  z.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_glds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;z&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  x_true.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_glds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;x_true&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  m_true.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_glds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;m_true&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  y_true.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_glds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;y_true&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  x_hat.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_glds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;x_hat&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  m_hat.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_glds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;m_hat&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  y_hat.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_glds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;y_hat&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;fin.</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>}
+</span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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+      <ul>
+        <li><a href="#types">Types</a></li>
+        <li><a href="#functions">Functions</a></li>
+        <li><a href="#type-details">Type Details</a>
+          <ul>
+            <li><a href="#matrix">Matrix</a></li>
+            <li><a href="#vector">Vector</a></li>
+            <li><a href="#data_t">data_t</a></li>
+          </ul>
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+        <li><a href="#function-details">Function Details</a>
+          <ul>
+            <li><a href="#main">main</a></li>
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diff --git a/docs/docs/api/files/eg__glds__du__plds__ctrl_8cpp/index.html b/docs/docs/api/files/eg__glds__du__plds__ctrl_8cpp/index.html
new file mode 100644
index 00000000..30e274c6
--- /dev/null
+++ b/docs/docs/api/files/eg__glds__du__plds__ctrl_8cpp/index.html
@@ -0,0 +1,691 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="
+  examples/eg_glds_du_plds_ctrl.cpp
+  #
+
+
+  Types
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          using double
+          data_t
+      
+      
+          using arma::Mat&lt; data_t &gt;
+          Matrix
+      
+      
+          using arma::Col&lt; data_t &gt;
+          Vector
+      
+  
+
+
+  Functions
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          int
+          main()
+      
+  
+
+
+  Type Details
+  #
+
+
+  data_t
+  #
+
+using lds::data_t =  double;
+Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.
+
+  Matrix
+  #
+
+using lds::Matrix =  arma::Mat&lt;data_t&gt;;
+
+  Vector
+  #
+
+using lds::Vector =  arma::Col&lt;data_t&gt;;
+
+  Function Details
+  #
+
+
+  main
+  #
+
+int main()
+Going to simulate a switching disturbance (m) acting on system">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/">
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+  <meta property="og:title" content="examples/eg_glds_du_plds_ctrl.cpp">
+  <meta property="og:description" content="examples/eg_glds_du_plds_ctrl.cpp # Types # Name using double data_t using arma::Mat&lt; data_t &gt; Matrix using arma::Col&lt; data_t &gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.
+Matrix # using lds::Matrix = arma::Mat&lt;data_t&gt;; Vector # using lds::Vector = arma::Col&lt;data_t&gt;; Function Details # main # int main() Going to simulate a switching disturbance (m) acting on system">
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+<ul>
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+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#exampleseg_glds_du_plds_ctrlcpp">examples/eg_glds_du_plds_ctrl.cpp</a>
+      <ul>
+        <li><a href="#types">Types</a></li>
+        <li><a href="#functions">Functions</a></li>
+        <li><a href="#type-details">Type Details</a>
+          <ul>
+            <li><a href="#data_t">data_t</a></li>
+            <li><a href="#matrix">Matrix</a></li>
+            <li><a href="#vector">Vector</a></li>
+          </ul>
+        </li>
+        <li><a href="#function-details">Function Details</a>
+          <ul>
+            <li><a href="#main">main</a></li>
+          </ul>
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+        <li><a href="#source-code">Source code</a></li>
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+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="exampleseg_glds_du_plds_ctrlcpp">
+  examples/eg_glds_du_plds_ctrl.cpp
+  <a class="anchor" href="#exampleseg_glds_du_plds_ctrlcpp">#</a>
+</h1>
+<h2 id="types">
+  Types
+  <a class="anchor" href="#types">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>using double</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#using-data-t">data_t</a></strong></td>
+      </tr>
+      <tr>
+          <td>using arma::Mat&lt; data_t &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#using-matrix">Matrix</a></strong></td>
+      </tr>
+      <tr>
+          <td>using arma::Col&lt; data_t &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#using-vector">Vector</a></strong></td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="functions">
+  Functions
+  <a class="anchor" href="#functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>int</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#function-main">main</a></strong>()</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="type-details">
+  Type Details
+  <a class="anchor" href="#type-details">#</a>
+</h2>
+<h3 id="data_t">
+  data_t
+  <a class="anchor" href="#data_t">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>data_t <span style="color:#f92672">=</span>  <span style="color:#66d9ef">double</span>;
+</span></span></code></pre></div><p>Type of all data in library. If need 32b, change <code>double</code> to <code>float</code>. This could be potentially useful for large scale problems where there are memory constraints.</p>
+<h3 id="matrix">
+  Matrix
+  <a class="anchor" href="#matrix">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Matrix <span style="color:#f92672">=</span>  arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>;
+</span></span></code></pre></div><h3 id="vector">
+  Vector
+  <a class="anchor" href="#vector">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Vector <span style="color:#f92672">=</span>  arma<span style="color:#f92672">::</span>Col<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>;
+</span></span></code></pre></div><h2 id="function-details">
+  Function Details
+  <a class="anchor" href="#function-details">#</a>
+</h2>
+<h3 id="main">
+  main
+  <a class="anchor" href="#main">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">int</span> main()
+</span></span></code></pre></div><p>Going to simulate a switching disturbance (m) acting on system</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;ldsCtrlEst&gt;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>data_t;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Matrix;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Vector;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>cout;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">auto</span> <span style="color:#a6e22e">main</span>() <span style="color:#f92672">-&gt;</span> <span style="color:#66d9ef">int</span> {
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; ********** Example Gaussian LDS du Control of PLDS ********** </span><span style="color:#ae81ff">\n\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Make 1st-order SISO system, sampled at 1kHz
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  data_t dt <span style="color:#f92672">=</span> <span style="color:#ae81ff">1e-3</span>;
+</span></span><span style="display:flex;"><span>  size_t n_u <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>  size_t n_x <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>  size_t n_y <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// no time steps for simulation.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">auto</span> n_t <span style="color:#f92672">=</span> <span style="color:#66d9ef">static_cast</span><span style="color:#f92672">&lt;</span>size_t<span style="color:#f92672">&gt;</span>(<span style="color:#ae81ff">5.0</span> <span style="color:#f92672">/</span> dt);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// construct ground truth system to be controlled...
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// initializes to random walk model with top-most n_y state observed
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>System controlled_system(n_u, n_x, n_y, dt);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Ground-truth parameters for the controlled system
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// (stand-in for physical system to be controlled)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix a_true(n_x, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye);
+</span></span><span style="display:flex;"><span>  a_true[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">=</span> exp(<span style="color:#f92672">-</span>dt <span style="color:#f92672">/</span> <span style="color:#ae81ff">0.01</span>);
+</span></span><span style="display:flex;"><span>  Matrix b_true <span style="color:#f92672">=</span> Matrix(n_x, n_u).fill(<span style="color:#ae81ff">2.5e-2</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// control signal to model input unit conversion e.g., V -&gt; mW/mm2:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector g_true <span style="color:#f92672">=</span> Vector(n_y).fill(<span style="color:#ae81ff">10.0</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  size_t which_m <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;  <span style="color:#75715e">// whether low or high disturbance (0, 1)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  data_t m_low <span style="color:#f92672">=</span> log(<span style="color:#ae81ff">1</span> <span style="color:#f92672">*</span> dt) <span style="color:#f92672">*</span> (<span style="color:#ae81ff">1</span> <span style="color:#f92672">-</span> a_true[<span style="color:#ae81ff">0</span>]);
+</span></span><span style="display:flex;"><span>  data_t pr_lo2hi <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      <span style="color:#ae81ff">0</span>;  <span style="color:#75715e">// 1e-3;  // probability of going from low to high disturb.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  data_t m_high <span style="color:#f92672">=</span> log(<span style="color:#ae81ff">20</span> <span style="color:#f92672">*</span> dt) <span style="color:#f92672">*</span> (<span style="color:#ae81ff">1</span> <span style="color:#f92672">-</span> a_true[<span style="color:#ae81ff">0</span>]);
+</span></span><span style="display:flex;"><span>  data_t pr_hi2lo <span style="color:#f92672">=</span> pr_lo2hi;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// initially let m be low
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector m0_true <span style="color:#f92672">=</span> Vector(n_x).fill(m_low);
+</span></span><span style="display:flex;"><span>  Vector x0_true <span style="color:#f92672">=</span> Vector(n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>ones) <span style="color:#f92672">*</span> log(<span style="color:#ae81ff">1</span> <span style="color:#f92672">*</span> dt);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Assign params.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  controlled_system.set_A(a_true);
+</span></span><span style="display:flex;"><span>  controlled_system.set_B(b_true);
+</span></span><span style="display:flex;"><span>  controlled_system.set_m(m0_true);
+</span></span><span style="display:flex;"><span>  controlled_system.set_g(g_true);
+</span></span><span style="display:flex;"><span>  controlled_system.set_x0(x0_true);
+</span></span><span style="display:flex;"><span>  controlled_system.Reset();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;controlled_system:</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  controlled_system.Print();
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// make a controller
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  lds<span style="color:#f92672">::</span>gaussian<span style="color:#f92672">::</span>Controller controller;
+</span></span><span style="display:flex;"><span>  {
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// Create **incorrect** model used for control.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// (e.g., imperfect model fitting)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Matrix b_controller <span style="color:#f92672">=</span> b_true <span style="color:#f92672">/</span> <span style="color:#ae81ff">50</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// let&#39;s assume zero process disturbance initially
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// (will be re-estimating)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Vector m_controller <span style="color:#f92672">=</span> Vector(n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// process noise covariance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Matrix q_controller <span style="color:#f92672">=</span> Matrix(n_y, n_y, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">*</span> <span style="color:#ae81ff">1e-8</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// output noise covariance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Matrix r_controller <span style="color:#f92672">=</span> Matrix(n_y, n_y, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">*</span> <span style="color:#ae81ff">1e-2</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    lds<span style="color:#f92672">::</span>gaussian<span style="color:#f92672">::</span>System controller_system(n_u, n_x, n_y, dt);
+</span></span><span style="display:flex;"><span>    controller_system.set_A(a_true);
+</span></span><span style="display:flex;"><span>    controller_system.set_B(b_controller);
+</span></span><span style="display:flex;"><span>    controller_system.set_g(g_true);
+</span></span><span style="display:flex;"><span>    controller_system.set_m(m_controller);
+</span></span><span style="display:flex;"><span>    controller_system.set_Q(q_controller);
+</span></span><span style="display:flex;"><span>    controller_system.set_R(r_controller);
+</span></span><span style="display:flex;"><span>    controller_system.Reset();  <span style="color:#75715e">// reset to new m
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// going to adaptively re-estimate the disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    controller_system.do_adapt_m <span style="color:#f92672">=</span> true;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// set adaptation rate by changing covariance of assumed process noise
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// acting on random-walk evolution of m
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Matrix q_m <span style="color:#f92672">=</span> Matrix(n_x, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">*</span> <span style="color:#ae81ff">1e-8</span>;
+</span></span><span style="display:flex;"><span>    controller_system.set_Q_m(q_m);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// create controller
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// lower and upper bounds on control signal (e.g., in Volts)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    data_t u_lb <span style="color:#f92672">=</span> <span style="color:#ae81ff">0.0</span>;  <span style="color:#75715e">// [=] V
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    data_t u_ub <span style="color:#f92672">=</span> <span style="color:#ae81ff">5.0</span>;  <span style="color:#75715e">// [=] V
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    controller <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(
+</span></span><span style="display:flex;"><span>        lds<span style="color:#f92672">::</span>gaussian<span style="color:#f92672">::</span>Controller(std<span style="color:#f92672">::</span>move(controller_system), u_lb, u_ub));
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Control variables:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// Reference/target output, controller gains
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector y_ref0 <span style="color:#f92672">=</span> Vector(n_y).fill(<span style="color:#ae81ff">20.0</span> <span style="color:#f92672">*</span> dt);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// to design for this example, augmented state with control and made the input
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du =
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// 1e-2.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix k_x <span style="color:#f92672">=</span> Matrix(n_u, n_x).fill(<span style="color:#ae81ff">2.44</span>);     <span style="color:#75715e">// gains on state error
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix k_inty <span style="color:#f92672">=</span> Matrix(n_u, n_y).fill(<span style="color:#ae81ff">97.4</span>);  <span style="color:#75715e">// gains on integrated err
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix k_u <span style="color:#f92672">=</span> Matrix(n_u, n_u).fill(<span style="color:#ae81ff">5.23e-2</span>);  <span style="color:#75715e">// gains on input amp
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set up controller type bit mask so controller knows how to proceed
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  size_t control_type <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// use integral action to minimize DC error
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  control_type <span style="color:#f92672">=</span> control_type <span style="color:#f92672">|</span> lds<span style="color:#f92672">::</span>kControlTypeIntY;
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// update change in control (LP filters control)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  control_type <span style="color:#f92672">=</span> control_type <span style="color:#f92672">|</span> lds<span style="color:#f92672">::</span>kControlTypeDeltaU;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set controller type
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  controller.set_control_type(control_type);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Let&#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector g_design <span style="color:#f92672">=</span> Vector(n_u).fill(<span style="color:#ae81ff">10</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Set params.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// **n.b. using arbitrary defaults for Q, R in this example. Really, these
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// should be set by users, as they tune characteristics of Kalman filter.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// Users can also choose not to recursively calculate the estimator gain and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// supply it (setKe) instead of covariances.**
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  controller.set_y_ref(y_ref0);
+</span></span><span style="display:flex;"><span>  controller.set_Kc(k_x);
+</span></span><span style="display:flex;"><span>  controller.set_Kc_inty(k_inty);
+</span></span><span style="display:flex;"><span>  controller.set_Kc_u(k_u);
+</span></span><span style="display:flex;"><span>  controller.set_g_design(g_design);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;control system:</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  controller.Print();
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set up variables for simulation
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// create Matrix to save outputs in...
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix y_ref <span style="color:#f92672">=</span> Matrix(n_y, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>ones) <span style="color:#f92672">*</span> y_ref0[<span style="color:#ae81ff">0</span>];
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Simulated measurements
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix z(n_y, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// simulated control signal ([=] V)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix u(n_u, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// outputs, states and gain/disturbance params
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// *_hat indicates online estimates
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix y_hat(n_y, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix x_hat(n_x, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix m_hat(n_x, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// *_true indicates ground truth (system being controlled)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix y_true(n_y, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix x_true(n_x, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix m_true(n_x, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// get initial val
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  y_hat.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_y <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, <span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> controller.sys().y();
+</span></span><span style="display:flex;"><span>  y_true.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_y <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, <span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> controlled_system.y();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  x_hat.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_x <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, <span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> controller.sys().x();
+</span></span><span style="display:flex;"><span>  x_true.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_x <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, <span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> controlled_system.x();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  m_hat.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_x <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, <span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> controller.sys().m();
+</span></span><span style="display:flex;"><span>  m_true.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_x <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, <span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> controlled_system.m();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Starting &#34;</span> <span style="color:#f92672">&lt;&lt;</span> n_t <span style="color:#f92672">*</span> dt <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; sec simulation ... </span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> start <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>chrono<span style="color:#f92672">::</span>high_resolution_clock<span style="color:#f92672">::</span>now();
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t t <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>; t <span style="color:#f92672">&lt;</span> n_t; t<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// simulate a stochastically switched disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Vector chance <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>randu<span style="color:#f92672">&lt;</span>Vector<span style="color:#f92672">&gt;</span>(<span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (which_m <span style="color:#f92672">==</span> <span style="color:#ae81ff">0</span>)  <span style="color:#75715e">// low disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    {
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">if</span> (chance[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;</span> pr_lo2hi) {  <span style="color:#75715e">// switches low -&gt; high disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>        m0_true <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>(n_x, m_high);
+</span></span><span style="display:flex;"><span>        which_m <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>    } <span style="color:#66d9ef">else</span> {                       <span style="color:#75715e">// high disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      <span style="color:#66d9ef">if</span> (chance[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;</span> pr_hi2lo) {  <span style="color:#75715e">// swithces high -&gt; low disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>        m0_true <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>(n_x, m_low);
+</span></span><span style="display:flex;"><span>        which_m <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>    controlled_system.set_m(m0_true);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// input
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Vector u_tm1(u.colptr(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>), u.n_rows, false, true);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// Simulate the true system.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    z.col(t) <span style="color:#f92672">=</span> controlled_system.Simulate(u_tm1);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// This method uses a steady-state solution to control problem to calculate
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// x_ref, u_ref from reference output y_ref. Therefore, it is only
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// applicable to regulation problems or cases where reference trajectory
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// changes slowly compared to system dynamics.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    u.col(t) <span style="color:#f92672">=</span> controller.ControlOutputReference(z.col(t));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// save the signals
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    y_true.col(t) <span style="color:#f92672">=</span> controlled_system.y();
+</span></span><span style="display:flex;"><span>    x_true.col(t) <span style="color:#f92672">=</span> controlled_system.x();
+</span></span><span style="display:flex;"><span>    m_true.col(t) <span style="color:#f92672">=</span> controlled_system.m();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    y_hat.col(t) <span style="color:#f92672">=</span> controller.sys().y();
+</span></span><span style="display:flex;"><span>    x_hat.col(t) <span style="color:#f92672">=</span> controller.sys().x();
+</span></span><span style="display:flex;"><span>    m_hat.col(t) <span style="color:#f92672">=</span> controller.sys().m();
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> finish <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>chrono<span style="color:#f92672">::</span>high_resolution_clock<span style="color:#f92672">::</span>now();
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>chrono<span style="color:#f92672">::</span>duration<span style="color:#f92672">&lt;</span>data_t, std<span style="color:#f92672">::</span>milli<span style="color:#f92672">&gt;</span> sim_time_ms <span style="color:#f92672">=</span> finish <span style="color:#f92672">-</span> start;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Finished simulation in &#34;</span> <span style="color:#f92672">&lt;&lt;</span> sim_time_ms.count() <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; ms.</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;(app. &#34;</span> <span style="color:#f92672">&lt;&lt;</span> (sim_time_ms.count() <span style="color:#f92672">/</span> n_t) <span style="color:#f92672">*</span> <span style="color:#ae81ff">1e3</span> <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; us/time-step)</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Saving simulation data to disk.</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// xTrue, mTrue saving with hdf5 via armadillo
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  arma<span style="color:#f92672">::</span>hdf5_opts<span style="color:#f92672">::</span>opts replace <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>hdf5_opts<span style="color:#f92672">::</span>replace;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> dt_vec <span style="color:#f92672">=</span> Vector(<span style="color:#ae81ff">1</span>).fill(dt);
+</span></span><span style="display:flex;"><span>  dt_vec.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_glds_du_plds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;dt&#34;</span>));
+</span></span><span style="display:flex;"><span>  y_ref.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_glds_du_plds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;y_ref&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  u.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_glds_du_plds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;u&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  z.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_glds_du_plds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;z&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  x_true.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_glds_du_plds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;x_true&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  m_true.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_glds_du_plds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;m_true&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  y_true.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_glds_du_plds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;y_true&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  x_hat.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_glds_du_plds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;x_hat&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  m_hat.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_glds_du_plds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;m_hat&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  y_hat.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_glds_du_plds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;y_hat&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;fin.</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>}
+</span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
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+        <li><a href="#types">Types</a></li>
+        <li><a href="#functions">Functions</a></li>
+        <li><a href="#type-details">Type Details</a>
+          <ul>
+            <li><a href="#data_t">data_t</a></li>
+            <li><a href="#matrix">Matrix</a></li>
+            <li><a href="#vector">Vector</a></li>
+          </ul>
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diff --git a/docs/docs/api/files/eg__plds__ctrl_8cpp/index.html b/docs/docs/api/files/eg__plds__ctrl_8cpp/index.html
new file mode 100644
index 00000000..a0bf2a5f
--- /dev/null
+++ b/docs/docs/api/files/eg__plds__ctrl_8cpp/index.html
@@ -0,0 +1,649 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="
+  examples/eg_plds_ctrl.cpp
+  #
+
+
+  Types
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          using arma::Mat&lt; data_t &gt;
+          Matrix
+      
+      
+          using arma::Col&lt; data_t &gt;
+          Vector
+      
+      
+          using double
+          data_t
+      
+  
+
+
+  Functions
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          int
+          main()
+      
+  
+
+
+  Type Details
+  #
+
+
+  Matrix
+  #
+
+using lds::Matrix =  arma::Mat&lt;data_t&gt;;
+
+  Vector
+  #
+
+using lds::Vector =  arma::Col&lt;data_t&gt;;
+
+  data_t
+  #
+
+using lds::data_t =  double;
+Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/">
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+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
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+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
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+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
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+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
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+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#exampleseg_plds_ctrlcpp">examples/eg_plds_ctrl.cpp</a>
+      <ul>
+        <li><a href="#types">Types</a></li>
+        <li><a href="#functions">Functions</a></li>
+        <li><a href="#type-details">Type Details</a>
+          <ul>
+            <li><a href="#matrix">Matrix</a></li>
+            <li><a href="#vector">Vector</a></li>
+            <li><a href="#data_t">data_t</a></li>
+          </ul>
+        </li>
+        <li><a href="#function-details">Function Details</a>
+          <ul>
+            <li><a href="#main">main</a></li>
+          </ul>
+        </li>
+        <li><a href="#source-code">Source code</a></li>
+      </ul>
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+  </aside>
+  
+ 
+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="exampleseg_plds_ctrlcpp">
+  examples/eg_plds_ctrl.cpp
+  <a class="anchor" href="#exampleseg_plds_ctrlcpp">#</a>
+</h1>
+<h2 id="types">
+  Types
+  <a class="anchor" href="#types">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>using arma::Mat&lt; data_t &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#using-matrix">Matrix</a></strong></td>
+      </tr>
+      <tr>
+          <td>using arma::Col&lt; data_t &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#using-vector">Vector</a></strong></td>
+      </tr>
+      <tr>
+          <td>using double</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#using-data-t">data_t</a></strong></td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="functions">
+  Functions
+  <a class="anchor" href="#functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>int</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#function-main">main</a></strong>()</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="type-details">
+  Type Details
+  <a class="anchor" href="#type-details">#</a>
+</h2>
+<h3 id="matrix">
+  Matrix
+  <a class="anchor" href="#matrix">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Matrix <span style="color:#f92672">=</span>  arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>;
+</span></span></code></pre></div><h3 id="vector">
+  Vector
+  <a class="anchor" href="#vector">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Vector <span style="color:#f92672">=</span>  arma<span style="color:#f92672">::</span>Col<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>;
+</span></span></code></pre></div><h3 id="data_t">
+  data_t
+  <a class="anchor" href="#data_t">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>data_t <span style="color:#f92672">=</span>  <span style="color:#66d9ef">double</span>;
+</span></span></code></pre></div><p>Type of all data in library. If need 32b, change <code>double</code> to <code>float</code>. This could be potentially useful for large scale problems where there are memory constraints.</p>
+<h2 id="function-details">
+  Function Details
+  <a class="anchor" href="#function-details">#</a>
+</h2>
+<h3 id="main">
+  main
+  <a class="anchor" href="#main">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">int</span> main()
+</span></span></code></pre></div><p>Going to simulate a switching disturbance (m) acting on system</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;ldsCtrlEst&gt;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Matrix;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Vector;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>data_t;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>cout;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">auto</span> <span style="color:#a6e22e">main</span>() <span style="color:#f92672">-&gt;</span> <span style="color:#66d9ef">int</span> {
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; ********** Example Poisson LDS Control ********** </span><span style="color:#ae81ff">\n\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Make SISO system sampled at 1kHz
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  data_t dt <span style="color:#f92672">=</span> <span style="color:#ae81ff">1e-3</span>;
+</span></span><span style="display:flex;"><span>  size_t n_u <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>  size_t n_x <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>  size_t n_y <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// no time steps for simulation.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">auto</span> n_t <span style="color:#f92672">=</span> <span style="color:#66d9ef">static_cast</span><span style="color:#f92672">&lt;</span>size_t<span style="color:#f92672">&gt;</span>(<span style="color:#ae81ff">10.0</span> <span style="color:#f92672">/</span> dt);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Control variables: _reference/target output, controller gains
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// n.b., Can either use Vector (arma::Col) or std::vector
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector y_ref0 <span style="color:#f92672">=</span> Vector(n_y, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>ones) <span style="color:#f92672">*</span> <span style="color:#ae81ff">30.0</span> <span style="color:#f92672">*</span> dt;
+</span></span><span style="display:flex;"><span>  Matrix k_x <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      Matrix(n_u, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros) <span style="color:#f92672">+</span> <span style="color:#ae81ff">1</span>;  <span style="color:#75715e">// gains on state error
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix k_inty <span style="color:#f92672">=</span> Matrix(n_u, n_y, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros) <span style="color:#f92672">+</span>
+</span></span><span style="display:flex;"><span>                   <span style="color:#ae81ff">10</span>;  <span style="color:#75715e">// gains on integrated output err
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Set control type bit mask, so controller knows what to do
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  size_t control_type <span style="color:#f92672">=</span> lds<span style="color:#f92672">::</span>kControlTypeIntY;  <span style="color:#75715e">// integral action
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Ground-truth parameters for the controlled system
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// (stand-in for physical system to be controlled)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix a_true(n_x, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye);
+</span></span><span style="display:flex;"><span>  a_true[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">=</span> <span style="color:#ae81ff">0.986</span>;
+</span></span><span style="display:flex;"><span>  Matrix b_true(n_x, n_u, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  b_true[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">=</span> <span style="color:#ae81ff">0.054</span>;
+</span></span><span style="display:flex;"><span>  Vector x0_true <span style="color:#f92672">=</span> Vector(n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>ones) <span style="color:#f92672">*</span> log(<span style="color:#ae81ff">1</span> <span style="color:#f92672">*</span> dt);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  size_t which_m <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>  data_t m_low <span style="color:#f92672">=</span> log(<span style="color:#ae81ff">1</span> <span style="color:#f92672">*</span> dt) <span style="color:#f92672">*</span> (<span style="color:#ae81ff">1</span> <span style="color:#f92672">-</span> a_true[<span style="color:#ae81ff">0</span>]);
+</span></span><span style="display:flex;"><span>  data_t pr_lo2hi <span style="color:#f92672">=</span> <span style="color:#ae81ff">1e-3</span>;
+</span></span><span style="display:flex;"><span>  data_t m_high <span style="color:#f92672">=</span> log(<span style="color:#ae81ff">20</span> <span style="color:#f92672">*</span> dt) <span style="color:#f92672">*</span> (<span style="color:#ae81ff">1</span> <span style="color:#f92672">-</span> a_true[<span style="color:#ae81ff">0</span>]);
+</span></span><span style="display:flex;"><span>  data_t pr_hi2lo <span style="color:#f92672">=</span> pr_lo2hi;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Vector m0_true <span style="color:#f92672">=</span> Vector(n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>ones) <span style="color:#f92672">*</span> m_low;
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// construct ground truth system to be controlled...
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>System controlled_system(n_u, n_x, n_y, dt);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Assign params.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  controlled_system.set_A(a_true);
+</span></span><span style="display:flex;"><span>  controlled_system.set_B(b_true);
+</span></span><span style="display:flex;"><span>  controlled_system.set_m(m0_true);
+</span></span><span style="display:flex;"><span>  controlled_system.set_x0(x0_true);
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// reset to initial conditions
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  controlled_system.Reset();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;controlled_system:</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  controlled_system.Print();
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Create the controller
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>Controller controller;
+</span></span><span style="display:flex;"><span>  {
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// Create model used for control.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>System controller_system(controlled_system);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// for this example, assume model correct, except disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Vector m0_controller <span style="color:#f92672">=</span> Vector(n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>ones) <span style="color:#f92672">*</span> m_low;
+</span></span><span style="display:flex;"><span>    Vector x0_controller <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>log(y_ref0);
+</span></span><span style="display:flex;"><span>    controller_system.set_m(m0_controller);
+</span></span><span style="display:flex;"><span>    controller_system.set_x0(x0_controller);
+</span></span><span style="display:flex;"><span>    controller_system.Reset(); <span style="color:#75715e">//reset to new init condition
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// adaptively re-estimate process disturbance (m)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    controller_system.do_adapt_m <span style="color:#f92672">=</span> true;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// set adaptation rate by changing covariance of assumed process noise
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// acting on random-walk evolution of m
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Matrix q_m <span style="color:#f92672">=</span> Matrix(n_x, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">*</span> <span style="color:#ae81ff">1e-5</span>;
+</span></span><span style="display:flex;"><span>    controller_system.set_Q_m(q_m);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    data_t u_lb <span style="color:#f92672">=</span> <span style="color:#ae81ff">0.0</span>;
+</span></span><span style="display:flex;"><span>    data_t u_ub <span style="color:#f92672">=</span> <span style="color:#ae81ff">5.0</span>;
+</span></span><span style="display:flex;"><span>    controller <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(
+</span></span><span style="display:flex;"><span>        lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>Controller(std<span style="color:#f92672">::</span>move(controller_system), u_lb, u_ub));
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set controller type
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  controller.set_control_type(control_type);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set controller gains
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  controller.set_Kc(k_x);
+</span></span><span style="display:flex;"><span>  controller.set_Kc_inty(k_inty);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// to protect against integral windup when output is consistently above
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// target:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  data_t tau_awu(<span style="color:#ae81ff">0.1</span>);
+</span></span><span style="display:flex;"><span>  controller.set_tau_awu(tau_awu);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;controller:</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  controller.Print();
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// create Matrix to save outputs in...
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix y_ref <span style="color:#f92672">=</span> Matrix(n_y, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  y_ref.each_col() <span style="color:#f92672">+=</span> y_ref0;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Simulated measurements
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix z(n_y, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// simulated control signal ([=] V)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix u(n_u, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// outputs, states and gain/disturbance params
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// *_hat indicates online estimates
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix y_hat(n_y, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix x_hat(n_x, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix m_hat(n_y, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// *_true indicates ground truth (system being controlled)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix y_true(n_y, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix x_true(n_x, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix m_true(n_y, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set initial val
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  y_hat.col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> controller.sys().y();
+</span></span><span style="display:flex;"><span>  y_true.col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> controlled_system.y();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  x_hat.col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> controller.sys().x();
+</span></span><span style="display:flex;"><span>  x_true.col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> controlled_system.x();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  m_hat.col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> controller.sys().m();
+</span></span><span style="display:flex;"><span>  m_true.col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> controlled_system.m();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Starting &#34;</span> <span style="color:#f92672">&lt;&lt;</span> n_t <span style="color:#f92672">*</span> dt <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; sec simulation ... </span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> start <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>chrono<span style="color:#f92672">::</span>high_resolution_clock<span style="color:#f92672">::</span>now();
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t t <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>; t <span style="color:#f92672">&lt;</span> n_t; t<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// simulate a stochastically switched disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Vector chance <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>randu<span style="color:#f92672">&lt;</span>Vector<span style="color:#f92672">&gt;</span>(<span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (which_m <span style="color:#f92672">==</span> <span style="color:#ae81ff">0</span>)  <span style="color:#75715e">// low disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    {
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">if</span> (chance[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;</span> pr_lo2hi) {  <span style="color:#75715e">// switches low -&gt; high disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>        m0_true <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>(n_x, m_high);
+</span></span><span style="display:flex;"><span>        which_m <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>    } <span style="color:#66d9ef">else</span> {                       <span style="color:#75715e">// high disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      <span style="color:#66d9ef">if</span> (chance[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;</span> pr_hi2lo) {  <span style="color:#75715e">// swithces high -&gt; low disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>        m0_true <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>(n_x, m_low);
+</span></span><span style="display:flex;"><span>        which_m <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>    controlled_system.set_m(m0_true);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// e.g., use sinusoidal reference
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    data_t f <span style="color:#f92672">=</span> <span style="color:#ae81ff">0.5</span>;  <span style="color:#75715e">// freq [=] Hz
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Vector t_vec <span style="color:#f92672">=</span> Vector(n_y, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>ones) <span style="color:#f92672">*</span> t;
+</span></span><span style="display:flex;"><span>    y_ref.col(t) <span style="color:#f92672">+=</span>
+</span></span><span style="display:flex;"><span>        y_ref0 <span style="color:#f92672">%</span> arma<span style="color:#f92672">::</span>sin(f <span style="color:#f92672">*</span> <span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> lds<span style="color:#f92672">::</span>kPi <span style="color:#f92672">*</span> dt <span style="color:#f92672">*</span> t_vec <span style="color:#f92672">-</span> lds<span style="color:#f92672">::</span>kPi <span style="color:#f92672">/</span> <span style="color:#ae81ff">4</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// Simulate the true system.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    z.col(t)<span style="color:#f92672">=</span>controlled_system.Simulate(u.col(t<span style="color:#f92672">-</span><span style="color:#ae81ff">1</span>));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// This method uses a steady-state solution to control problem to calculate
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// x_ref, u_ref from reference output y_ref. Notably, it does this in the
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// log-linear space (i.e., log(y)).
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// Therefore, it is only applicable to regulation problems or cases where
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// reference trajectory changes slowly compared to system dynamics.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    controller.set_y_ref(y_ref.col(t));
+</span></span><span style="display:flex;"><span>    u.col(t)<span style="color:#f92672">=</span>controller.ControlOutputReference(z.col(t));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    y_true.col(t) <span style="color:#f92672">=</span> controlled_system.y();
+</span></span><span style="display:flex;"><span>    x_true.col(t) <span style="color:#f92672">=</span> controlled_system.x();
+</span></span><span style="display:flex;"><span>    m_true.col(t) <span style="color:#f92672">=</span> controlled_system.m();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    y_hat.col(t) <span style="color:#f92672">=</span> controller.sys().y();
+</span></span><span style="display:flex;"><span>    x_hat.col(t) <span style="color:#f92672">=</span> controller.sys().x();
+</span></span><span style="display:flex;"><span>    m_hat.col(t) <span style="color:#f92672">=</span> controller.sys().m();
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> finish <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>chrono<span style="color:#f92672">::</span>high_resolution_clock<span style="color:#f92672">::</span>now();
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>chrono<span style="color:#f92672">::</span>duration<span style="color:#f92672">&lt;</span>data_t, std<span style="color:#f92672">::</span>milli<span style="color:#f92672">&gt;</span> sim_time_ms <span style="color:#f92672">=</span> finish <span style="color:#f92672">-</span> start;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Finished simulation in &#34;</span> <span style="color:#f92672">&lt;&lt;</span> sim_time_ms.count() <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; ms.</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;(app. &#34;</span> <span style="color:#f92672">&lt;&lt;</span> (sim_time_ms.count() <span style="color:#f92672">/</span> n_t) <span style="color:#f92672">*</span> <span style="color:#ae81ff">1e3</span> <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; us/time-step)</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// x_true, m_true saving with hdf5 via armadillo
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  arma<span style="color:#f92672">::</span>hdf5_opts<span style="color:#f92672">::</span>opts replace <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>hdf5_opts<span style="color:#f92672">::</span>replace;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> dt_vec <span style="color:#f92672">=</span> Vector(<span style="color:#ae81ff">1</span>).fill(dt);
+</span></span><span style="display:flex;"><span>  dt_vec.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;dt&#34;</span>));
+</span></span><span style="display:flex;"><span>  y_ref.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;y_ref&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  u.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;u&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  z.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;z&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  x_true.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;x_true&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  m_true.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;m_true&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  y_true.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;y_true&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  x_hat.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;x_hat&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  m_hat.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;m_hat&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  y_hat.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;y_hat&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>}
+</span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
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+        <li><a href="#functions">Functions</a></li>
+        <li><a href="#type-details">Type Details</a>
+          <ul>
+            <li><a href="#matrix">Matrix</a></li>
+            <li><a href="#vector">Vector</a></li>
+            <li><a href="#data_t">data_t</a></li>
+          </ul>
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+        <li><a href="#function-details">Function Details</a>
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diff --git a/docs/docs/api/files/eg__plds__est_8cpp/index.html b/docs/docs/api/files/eg__plds__est_8cpp/index.html
new file mode 100644
index 00000000..4a5b8ca0
--- /dev/null
+++ b/docs/docs/api/files/eg__plds__est_8cpp/index.html
@@ -0,0 +1,617 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="
+  examples/eg_plds_est.cpp
+  #
+
+
+  Types
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          using arma::Mat&lt; data_t &gt;
+          Matrix
+      
+      
+          using arma::Col&lt; data_t &gt;
+          Vector
+      
+      
+          using double
+          data_t
+      
+  
+
+
+  Functions
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          Matrix
+          random_walk(size_t n_t, const Matrix &amp; Q, const Vector &amp; x0)
+      
+      
+          int
+          main()
+      
+  
+
+
+  Type Details
+  #
+
+
+  Matrix
+  #
+
+using lds::Matrix =  arma::Mat&lt;data_t&gt;;
+
+  Vector
+  #
+
+using lds::Vector =  arma::Col&lt;data_t&gt;;
+
+  data_t
+  #
+
+using lds::data_t =  double;
+Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/">
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+  <aside class="hidden clearfix">
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+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#exampleseg_plds_estcpp">examples/eg_plds_est.cpp</a>
+      <ul>
+        <li><a href="#types">Types</a></li>
+        <li><a href="#functions">Functions</a></li>
+        <li><a href="#type-details">Type Details</a>
+          <ul>
+            <li><a href="#matrix">Matrix</a></li>
+            <li><a href="#vector">Vector</a></li>
+            <li><a href="#data_t">data_t</a></li>
+          </ul>
+        </li>
+        <li><a href="#function-details">Function Details</a>
+          <ul>
+            <li><a href="#random_walk">random_walk</a></li>
+            <li><a href="#main">main</a></li>
+          </ul>
+        </li>
+        <li><a href="#source-code">Source code</a></li>
+      </ul>
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+  </ul>
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+
+
+
+  </aside>
+  
+ 
+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="exampleseg_plds_estcpp">
+  examples/eg_plds_est.cpp
+  <a class="anchor" href="#exampleseg_plds_estcpp">#</a>
+</h1>
+<h2 id="types">
+  Types
+  <a class="anchor" href="#types">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>using arma::Mat&lt; data_t &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#using-matrix">Matrix</a></strong></td>
+      </tr>
+      <tr>
+          <td>using arma::Col&lt; data_t &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#using-vector">Vector</a></strong></td>
+      </tr>
+      <tr>
+          <td>using double</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#using-data-t">data_t</a></strong></td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="functions">
+  Functions
+  <a class="anchor" href="#functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#function-random-walk">random_walk</a></strong>(size_t n_t, const Matrix &amp; Q, const Vector &amp; x0)</td>
+      </tr>
+      <tr>
+          <td>int</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#function-main">main</a></strong>()</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="type-details">
+  Type Details
+  <a class="anchor" href="#type-details">#</a>
+</h2>
+<h3 id="matrix">
+  Matrix
+  <a class="anchor" href="#matrix">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Matrix <span style="color:#f92672">=</span>  arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>;
+</span></span></code></pre></div><h3 id="vector">
+  Vector
+  <a class="anchor" href="#vector">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Vector <span style="color:#f92672">=</span>  arma<span style="color:#f92672">::</span>Col<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>;
+</span></span></code></pre></div><h3 id="data_t">
+  data_t
+  <a class="anchor" href="#data_t">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>data_t <span style="color:#f92672">=</span>  <span style="color:#66d9ef">double</span>;
+</span></span></code></pre></div><p>Type of all data in library. If need 32b, change <code>double</code> to <code>float</code>. This could be potentially useful for large scale problems where there are memory constraints.</p>
+<h2 id="function-details">
+  Function Details
+  <a class="anchor" href="#function-details">#</a>
+</h2>
+<h3 id="random_walk">
+  random_walk
+  <a class="anchor" href="#random_walk">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix random_walk(
+</span></span><span style="display:flex;"><span>    size_t n_t,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> Q,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> x0
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><h3 id="main">
+  main
+  <a class="anchor" href="#main">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">int</span> main()
+</span></span></code></pre></div><h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;ldsCtrlEst&gt;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Matrix;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Vector;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>data_t;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>cout;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// for generating random input
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>Matrix <span style="color:#a6e22e">random_walk</span>(size_t n_t, <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> Q, <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> x0);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">int</span> <span style="color:#a6e22e">main</span>() {
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; ********** Example Poisson LDS Estimation ********** </span><span style="color:#ae81ff">\n\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Make SISO system sampled at 1kHz
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  data_t dt <span style="color:#f92672">=</span> <span style="color:#ae81ff">1e-3</span>;
+</span></span><span style="display:flex;"><span>  size_t n_u <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;                           <span style="color:#75715e">// no. inputs
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  size_t n_x <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;                           <span style="color:#75715e">// no. states
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  size_t n_y <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;                           <span style="color:#75715e">// no. outputs
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">auto</span> n_t <span style="color:#f92672">=</span> <span style="color:#66d9ef">static_cast</span><span style="color:#f92672">&lt;</span>size_t<span style="color:#f92672">&gt;</span>(<span style="color:#ae81ff">30</span> <span style="color:#f92672">/</span> dt);  <span style="color:#75715e">// no time steps for simulation.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// construct ground truth system...
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>System system_true(n_u, n_x, n_y, dt);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Model parameters
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix a_true(n_x, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye);
+</span></span><span style="display:flex;"><span>  a_true[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">=</span> exp(<span style="color:#f92672">-</span>dt <span style="color:#f92672">/</span> <span style="color:#ae81ff">0.075</span>);
+</span></span><span style="display:flex;"><span>  Matrix b_true <span style="color:#f92672">=</span> Matrix(n_x, n_u).fill(<span style="color:#ae81ff">1e-2</span>);
+</span></span><span style="display:flex;"><span>  Vector m0_true <span style="color:#f92672">=</span> Vector(n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros).fill(<span style="color:#f92672">-</span><span style="color:#ae81ff">7e-2</span>);  <span style="color:#75715e">// disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector x0_true <span style="color:#f92672">=</span> m0_true <span style="color:#f92672">*</span> arma<span style="color:#f92672">::</span>inv(Matrix(n_x, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">-</span>
+</span></span><span style="display:flex;"><span>                                        a_true);  <span style="color:#75715e">// initial state
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Assign params.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  system_true.set_A(a_true);
+</span></span><span style="display:flex;"><span>  system_true.set_B(b_true);
+</span></span><span style="display:flex;"><span>  system_true.set_x0(x0_true);
+</span></span><span style="display:flex;"><span>  system_true.set_m(m0_true);
+</span></span><span style="display:flex;"><span>  system_true.Reset();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Construct system for estimation
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// e.g., will create a model with incorrect disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>System system_estimator(n_u, n_x, n_y, dt);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Can copy parameters from another system object
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  system_estimator <span style="color:#f92672">=</span> system_true;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// wrong disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector m0_est <span style="color:#f92672">=</span> m0_true <span style="color:#f92672">*</span> <span style="color:#ae81ff">2</span>;
+</span></span><span style="display:flex;"><span>  system_estimator.set_m(m0_est);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set new initial conditions
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector x0_est <span style="color:#f92672">=</span> m0_est <span style="color:#f92672">*</span> arma<span style="color:#f92672">::</span>inv(Matrix(n_x, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">-</span>
+</span></span><span style="display:flex;"><span>                                      a_true);  <span style="color:#75715e">// initial state
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  system_estimator.set_x0(x0_est);
+</span></span><span style="display:flex;"><span>  system_estimator.Reset();  <span style="color:#75715e">// reset to initial condition.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// turn on adaptive disturbance estimation
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  system_estimator.do_adapt_m <span style="color:#f92672">=</span> true;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set adaptation rate by changing covariance of assumed process noise acting
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// on random-walk evolution of m
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix q_m <span style="color:#f92672">=</span> Matrix(n_x, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">*</span> <span style="color:#ae81ff">1e-6</span>;
+</span></span><span style="display:flex;"><span>  system_estimator.set_Q_m(q_m);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;estimator:</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  system_estimator.Print();
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Set up simulation :
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// Simulated measurements
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix z(n_y, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Stimulus (generate random stimulus)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix q_u <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      Matrix(n_u, n_u, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">*</span> <span style="color:#ae81ff">1e-3</span>;  <span style="color:#75715e">// cov of random walk
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix u <span style="color:#f92672">=</span> random_walk(n_t, q_u, Vector(n_u, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// create matrix to save outputs in...
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix y_hat(n_y, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix y_true(n_y, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// states and disturbance params
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix x_hat(n_x, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix m_hat(n_x, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Matrix x_true(n_x, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix m_true(n_x, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// initial conditions
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  y_hat.col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> system_estimator.y();
+</span></span><span style="display:flex;"><span>  y_true.col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> system_true.y();
+</span></span><span style="display:flex;"><span>  x_hat.col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> system_estimator.x();
+</span></span><span style="display:flex;"><span>  x_true.col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> system_true.x();
+</span></span><span style="display:flex;"><span>  m_hat.col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> system_estimator.m();
+</span></span><span style="display:flex;"><span>  m_true.col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> system_true.m();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Starting &#34;</span> <span style="color:#f92672">&lt;&lt;</span> n_t <span style="color:#f92672">*</span> dt <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; sec simlation ... </span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> start <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>chrono<span style="color:#f92672">::</span>high_resolution_clock<span style="color:#f92672">::</span>now();
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t t <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>; t <span style="color:#f92672">&lt;</span> n_t; t<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// Simlate the true system.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    z.col(t) <span style="color:#f92672">=</span> system_true.Simulate(u.col(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// Filter (predict -&gt; update)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    system_estimator.Filter(u.col(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>), z.col(t));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// save signals
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    y_hat.col(t) <span style="color:#f92672">=</span> system_estimator.y();
+</span></span><span style="display:flex;"><span>    y_true.col(t) <span style="color:#f92672">=</span> system_true.y();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    x_true.col(t) <span style="color:#f92672">=</span> system_true.x();
+</span></span><span style="display:flex;"><span>    m_true.col(t) <span style="color:#f92672">=</span> system_true.m();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    x_hat.col(t) <span style="color:#f92672">=</span> system_estimator.x();
+</span></span><span style="display:flex;"><span>    m_hat.col(t) <span style="color:#f92672">=</span> system_estimator.m();
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> finish <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>chrono<span style="color:#f92672">::</span>high_resolution_clock<span style="color:#f92672">::</span>now();
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>chrono<span style="color:#f92672">::</span>duration<span style="color:#f92672">&lt;</span>data_t, std<span style="color:#f92672">::</span>milli<span style="color:#f92672">&gt;</span> sim_time_ms <span style="color:#f92672">=</span> finish <span style="color:#f92672">-</span> start;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Finished simlation in &#34;</span> <span style="color:#f92672">&lt;&lt;</span> sim_time_ms.count() <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; ms.</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;(app. &#34;</span> <span style="color:#f92672">&lt;&lt;</span> (sim_time_ms.count() <span style="color:#f92672">/</span> n_t) <span style="color:#f92672">*</span> <span style="color:#ae81ff">1e3</span> <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; us/time-step)</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// x_true, m_true saving with hdf5 via armadillo
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  arma<span style="color:#f92672">::</span>hdf5_opts<span style="color:#f92672">::</span>opts replace <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>hdf5_opts<span style="color:#f92672">::</span>replace;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> dt_vec <span style="color:#f92672">=</span> Vector(<span style="color:#ae81ff">1</span>).fill(dt);
+</span></span><span style="display:flex;"><span>  dt_vec.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_est.h5&#34;</span>, <span style="color:#e6db74">&#34;dt&#34;</span>));
+</span></span><span style="display:flex;"><span>  u.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_est.h5&#34;</span>, <span style="color:#e6db74">&#34;u&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  z.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_est.h5&#34;</span>, <span style="color:#e6db74">&#34;z&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  x_true.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_est.h5&#34;</span>, <span style="color:#e6db74">&#34;x_true&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  m_true.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_est.h5&#34;</span>, <span style="color:#e6db74">&#34;m_true&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  y_true.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_est.h5&#34;</span>, <span style="color:#e6db74">&#34;y_true&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  x_hat.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_est.h5&#34;</span>, <span style="color:#e6db74">&#34;x_hat&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  m_hat.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_est.h5&#34;</span>, <span style="color:#e6db74">&#34;m_hat&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  y_hat.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_est.h5&#34;</span>, <span style="color:#e6db74">&#34;y_hat&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// for generating random input
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>Matrix <span style="color:#a6e22e">random_walk</span>(size_t n_t, <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> Q, <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> x0) {
+</span></span><span style="display:flex;"><span>  size_t n <span style="color:#f92672">=</span> Q.n_rows;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> ((n <span style="color:#f92672">!=</span> Q.n_cols) <span style="color:#f92672">||</span> (Q.n_cols <span style="color:#f92672">!=</span> Q.n_rows)) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>logic_error(<span style="color:#e6db74">&#34;Q must be `n` x `n`.&#34;</span>);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Matrix x(n, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  x.col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> x0;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t t <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>; t <span style="color:#f92672">&lt;</span> n_t; t<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    x.col(t) <span style="color:#f92672">=</span> x.col(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">+</span> arma<span style="color:#f92672">::</span>mvnrnd(Vector(n, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros), Q);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> x;
+</span></span><span style="display:flex;"><span>}
+</span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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+  <ul>
+    <li><a href="#exampleseg_plds_estcpp">examples/eg_plds_est.cpp</a>
+      <ul>
+        <li><a href="#types">Types</a></li>
+        <li><a href="#functions">Functions</a></li>
+        <li><a href="#type-details">Type Details</a>
+          <ul>
+            <li><a href="#matrix">Matrix</a></li>
+            <li><a href="#vector">Vector</a></li>
+            <li><a href="#data_t">data_t</a></li>
+          </ul>
+        </li>
+        <li><a href="#function-details">Function Details</a>
+          <ul>
+            <li><a href="#random_walk">random_walk</a></li>
+            <li><a href="#main">main</a></li>
+          </ul>
+        </li>
+        <li><a href="#source-code">Source code</a></li>
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diff --git a/docs/docs/api/files/eg__plds__switched__ctrl_8cpp/index.html b/docs/docs/api/files/eg__plds__switched__ctrl_8cpp/index.html
new file mode 100644
index 00000000..21e2396a
--- /dev/null
+++ b/docs/docs/api/files/eg__plds__switched__ctrl_8cpp/index.html
@@ -0,0 +1,880 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="
+  examples/eg_plds_switched_ctrl.cpp
+  #
+
+
+  Types
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          using double
+          data_t
+      
+      
+          using arma::Mat&lt; data_t &gt;
+          Matrix
+      
+      
+          using arma::Col&lt; data_t &gt;
+          Vector
+      
+  
+
+
+  Functions
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          int
+          main()
+      
+  
+
+
+  Type Details
+  #
+
+
+  data_t
+  #
+
+using lds::data_t =  double;
+Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.
+
+  Matrix
+  #
+
+using lds::Matrix =  arma::Mat&lt;data_t&gt;;
+
+  Vector
+  #
+
+using lds::Vector =  arma::Col&lt;data_t&gt;;
+
+  Function Details
+  #
+
+
+  main
+  #
+
+int main()
+
+  Source code
+  #
+
+//===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===//
+//
+// Copyright 2021 Michael Bolus
+// Copyright 2021 Georgia Institute of Technology
+//
+// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//===----------------------------------------------------------------------===//
+//===----------------------------------------------------------------------===//
+
+#include &lt;ldsCtrlEst&gt;
+
+using lds::data_t;
+using lds::Matrix;
+using lds::Vector;
+using std::cout;
+
+auto main() -&gt; int {
+  cout &lt;&lt; &#34; ********** Example Switched Poisson LDS Control ********** \n\n&#34;;
+
+  // whether to do switched control
+  bool do_switch_ctrl = true;
+
+  // Make SISO system sampled at 1kHz
+  data_t dt = 1e-3;
+  size_t n_u = 1;
+  size_t n_x = 1;
+  size_t n_y = 1;
+
+  // no time steps for simulation.
+  auto n_t = static_cast&lt;size_t&gt;(30.0 / dt);
+
+  // for simulating switching
+  size_t which_mode = 1;
+  data_t pr_21 = 1e-3;   // prob mode 1 -&gt; 2
+  data_t pr_12 = pr_21;  // prob mode 2 -&gt; 1
+
+  // simulated system being controlled
+  lds::poisson::System controlled_system(n_u, n_x, n_y, dt);
+
+  // **Assume the system is not well characterized by one LDS, but is well
+  // characterized by two LDS models with different input matrices.**
+  data_t scale_sys_b = 2;
+
+  Matrix a(n_x, n_x, arma::fill::eye);
+  a[0] = 0.985;
+  Matrix b1 = Matrix(n_x, n_u).fill(0.05);
+  Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));
+
+  controlled_system.set_A(a);
+  controlled_system.set_B(b1);
+  controlled_system.set_d(d);
+  controlled_system.Reset();  // reset to initial conditions
+
+  // reference
+  Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);
+
+  // Let underlying system 1 be more sensitive than system 2
+  Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);
+
+  // create switched controller
+  lds::poisson::SwitchedController switched_controller;
+  lds::UniformMatrixList&lt;&gt; k_x;  // feedback controller gains
+  {
+    // create switched controller sub-systems
+    // system 1
+    lds::poisson::System sys1(controlled_system);
+
+    // set process noise covariance
+    Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3;
+    sys1.set_Q(q_controller);
+
+    // adaptively estimate process disturbance (m)
+    // n.b. using arbitrary default value for process noise if enabled.
+    sys1.do_adapt_m = true;
+
+    // setting initial mode to target to avoid large error at onset:
+    Vector x0_controller = arma::log(y_ref0) - d;
+    sys1.set_x0(x0_controller);
+    sys1.Reset();  // reset to initial conditions
+
+    cout &lt;&lt; &#34;.....................................\n&#34;;
+    cout &lt;&lt; &#34;sys1:\n&#34;;
+    cout &lt;&lt; &#34;.....................................\n&#34;;
+    sys1.Print();
+    cout &lt;&lt; &#34;.....................................\n&#34;;
+
+    // system 2
+    lds::poisson::System sys2 = sys1;
+
+    // set parameters
+    sys2.set_B(b2);
+
+    cout &lt;&lt; &#34;.....................................\n&#34;;
+    cout &lt;&lt; &#34;sys2:\n&#34;;
+    cout &lt;&lt; &#34;.....................................\n&#34;;
+    sys2.Print();
+    cout &lt;&lt; &#34;.....................................\n&#34;;
+
+    lds::UniformSystemList&lt;lds::poisson::System&gt; systems({sys1, sys2});
+
+    // controller gains for underlying system s:
+    Matrix k_x1(n_u, n_x, arma::fill::ones);
+    Matrix k_x2 = scale_sys_b * k_x1;  // system2 is x-times less sensitive.
+    k_x = lds::UniformMatrixList&lt;&gt;({k_x1, k_x2});
+
+    data_t u_lb = 0.0;
+    data_t u_ub = 5.0;
+    switched_controller = std::move(
+        lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub));
+  }
+  // Control variables
+  size_t control_type = 0;  // no integral action, etc
+  switched_controller.set_control_type(control_type);
+  switched_controller.set_Kc(std::move(k_x));
+
+  switched_controller.set_y_ref(y_ref0);
+
+  std::vector&lt;lds::poisson::System&gt; systems_vec(3, lds::poisson::System());
+  lds::UniformSystemList&lt;lds::poisson::System&gt; systems(std::move(systems_vec));
+
+  cout &lt;&lt; &#34;.....................................\n&#34;;
+  cout &lt;&lt; &#34;switched_controller:\n&#34;;
+  cout &lt;&lt; &#34;.....................................\n&#34;;
+  switched_controller.Print();
+  cout &lt;&lt; &#34;.....................................\n&#34;;
+
+  // Fake measurements
+  Matrix z(n_y, n_t, arma::fill::zeros);
+
+  // Will later contain control.
+  Matrix u(n_u, n_t, arma::fill::zeros);
+
+  // create Matrix to save outputs in...
+  Matrix y_hat(n_y, n_t, arma::fill::zeros);
+  Matrix y_true(n_y, n_t, arma::fill::zeros);
+  Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);
+
+  // modes and gain/disturbance params
+  Matrix x_hat(n_x, n_t, arma::fill::zeros);
+  Matrix x_true(n_x, n_t, arma::fill::zeros);
+  Matrix mode(1, n_t, arma::fill::ones);
+
+  // set initial val
+  y_hat.col(0) = switched_controller.sys().y();
+  y_true.col(0) = controlled_system.y();
+  x_hat.col(0) = switched_controller.sys().x();
+  x_true.col(0) = controlled_system.x();
+
+  cout &lt;&lt; &#34;Starting &#34; &lt;&lt; n_t * dt &lt;&lt; &#34; sec simulation ... \n&#34;;
+  auto start = std::chrono::high_resolution_clock::now();
+  for (size_t t = 1; t &lt; n_t; t&#43;&#43;) {
+    // Let the controlled system stochastically change gain
+    // Assume another algorithm decodes this mode change and signals the
+    // switched_controller
+    Vector chance(1, arma::fill::randu);
+    if (which_mode == 1)  // mode1
+    {
+      if (chance[0] &lt; pr_21) {
+        which_mode = 2;
+        controlled_system.set_B(b2);
+        if (do_switch_ctrl) {
+          switched_controller.Switch(1);
+        }
+      }
+    } else {  // mode2
+      if (chance[0] &lt; pr_12) {
+        which_mode = 1;
+        controlled_system.set_B(b1);
+        if (do_switch_ctrl) {
+          switched_controller.Switch(0);
+        }
+      }
+    }
+
+    // Simulate the true system.
+    z.col(t) = controlled_system.Simulate(u.col(t - 1));
+
+    // perform control
+    u.col(t) = switched_controller.ControlOutputReference(z.col(t));
+
+    mode.col(t) = which_mode;
+    y_ref.col(t) = y_ref0;
+    y_true.col(t) = controlled_system.y();
+    x_true.col(t) = controlled_system.x();
+    y_hat.col(t) = switched_controller.sys().y();
+    x_hat.col(t) = switched_controller.sys().x();
+  }
+
+  auto finish = std::chrono::high_resolution_clock::now();
+  std::chrono::duration&lt;data_t, std::milli&gt; sim_time_ms = finish - start;
+  cout &lt;&lt; &#34;Finished simulation in &#34; &lt;&lt; sim_time_ms.count() &lt;&lt; &#34; ms.\n&#34;;
+  cout &lt;&lt; &#34;(app. &#34; &lt;&lt; (sim_time_ms.count() / n_t) * 1e3 &lt;&lt; &#34; us/time-step)\n&#34;;
+
+  // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,
+  // x_true, m_true saving with hdf5 via armadillo
+  arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
+
+  auto dt_vec = Vector(1).fill(dt);
+  dt_vec.save(arma::hdf5_name(&#34;eg_plds_switched_ctrl.h5&#34;, &#34;dt&#34;));
+  y_ref.save(arma::hdf5_name(&#34;eg_plds_switched_ctrl.h5&#34;, &#34;y_ref&#34;, replace));
+  u.save(arma::hdf5_name(&#34;eg_plds_switched_ctrl.h5&#34;, &#34;u&#34;, replace));
+  z.save(arma::hdf5_name(&#34;eg_plds_switched_ctrl.h5&#34;, &#34;z&#34;, replace));
+  x_true.save(arma::hdf5_name(&#34;eg_plds_switched_ctrl.h5&#34;, &#34;x_true&#34;, replace));
+  y_true.save(arma::hdf5_name(&#34;eg_plds_switched_ctrl.h5&#34;, &#34;y_true&#34;, replace));
+  x_hat.save(arma::hdf5_name(&#34;eg_plds_switched_ctrl.h5&#34;, &#34;x_hat&#34;, replace));
+  y_hat.save(arma::hdf5_name(&#34;eg_plds_switched_ctrl.h5&#34;, &#34;y_hat&#34;, replace));
+  mode.save(arma::hdf5_name(&#34;eg_plds_switched_ctrl.h5&#34;, &#34;mode&#34;, replace));
+
+  return 0;
+}
+
+Updated on 31 March 2025 at 16:04:30 EDT">
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+  <meta property="og:description" content="examples/eg_plds_switched_ctrl.cpp # Types # Name using double data_t using arma::Mat&lt; data_t &gt; Matrix using arma::Col&lt; data_t &gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.
+Matrix # using lds::Matrix = arma::Mat&lt;data_t&gt;; Vector # using lds::Vector = arma::Col&lt;data_t&gt;; Function Details # main # int main() Source code # //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the &#34;License&#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an &#34;AS IS&#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include &lt;ldsCtrlEst&gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -&gt; int { cout &lt;&lt; &#34; ********** Example Switched Poisson LDS Control ********** \n\n&#34;; // whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast&lt;size_t&gt;(30.0 / dt); // for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -&gt; 2 data_t pr_12 = pr_21; // prob mode 2 -&gt; 1 // simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions // reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList&lt;&gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout &lt;&lt; &#34;.....................................\n&#34;; cout &lt;&lt; &#34;sys1:\n&#34;; cout &lt;&lt; &#34;.....................................\n&#34;; sys1.Print(); cout &lt;&lt; &#34;.....................................\n&#34;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout &lt;&lt; &#34;.....................................\n&#34;; cout &lt;&lt; &#34;sys2:\n&#34;; cout &lt;&lt; &#34;.....................................\n&#34;; sys2.Print(); cout &lt;&lt; &#34;.....................................\n&#34;; lds::UniformSystemList&lt;lds::poisson::System&gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList&lt;&gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector&lt;lds::poisson::System&gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList&lt;lds::poisson::System&gt; systems(std::move(systems_vec)); cout &lt;&lt; &#34;.....................................\n&#34;; cout &lt;&lt; &#34;switched_controller:\n&#34;; cout &lt;&lt; &#34;.....................................\n&#34;; switched_controller.Print(); cout &lt;&lt; &#34;.....................................\n&#34;; // Fake measurements Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout &lt;&lt; &#34;Starting &#34; &lt;&lt; n_t * dt &lt;&lt; &#34; sec simulation ... \n&#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t &lt; n_t; t&#43;&#43;) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] &lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] &lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration&lt;data_t, std::milli&gt; sim_time_ms = finish - start; cout &lt;&lt; &#34;Finished simulation in &#34; &lt;&lt; sim_time_ms.count() &lt;&lt; &#34; ms.\n&#34;; cout &lt;&lt; &#34;(app. &#34; &lt;&lt; (sim_time_ms.count() / n_t) * 1e3 &lt;&lt; &#34; us/time-step)\n&#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(&#34;eg_plds_switched_ctrl.h5&#34;, &#34;dt&#34;)); y_ref.save(arma::hdf5_name(&#34;eg_plds_switched_ctrl.h5&#34;, &#34;y_ref&#34;, replace)); u.save(arma::hdf5_name(&#34;eg_plds_switched_ctrl.h5&#34;, &#34;u&#34;, replace)); z.save(arma::hdf5_name(&#34;eg_plds_switched_ctrl.h5&#34;, &#34;z&#34;, replace)); x_true.save(arma::hdf5_name(&#34;eg_plds_switched_ctrl.h5&#34;, &#34;x_true&#34;, replace)); y_true.save(arma::hdf5_name(&#34;eg_plds_switched_ctrl.h5&#34;, &#34;y_true&#34;, replace)); x_hat.save(arma::hdf5_name(&#34;eg_plds_switched_ctrl.h5&#34;, &#34;x_hat&#34;, replace)); y_hat.save(arma::hdf5_name(&#34;eg_plds_switched_ctrl.h5&#34;, &#34;y_hat&#34;, replace)); mode.save(arma::hdf5_name(&#34;eg_plds_switched_ctrl.h5&#34;, &#34;mode&#34;, replace)); return 0; } Updated on 31 March 2025 at 16:04:30 EDT">
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+    <li><a href="#exampleseg_plds_switched_ctrlcpp">examples/eg_plds_switched_ctrl.cpp</a>
+      <ul>
+        <li><a href="#types">Types</a></li>
+        <li><a href="#functions">Functions</a></li>
+        <li><a href="#type-details">Type Details</a>
+          <ul>
+            <li><a href="#data_t">data_t</a></li>
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+  <article class="markdown"><h1 id="exampleseg_plds_switched_ctrlcpp">
+  examples/eg_plds_switched_ctrl.cpp
+  <a class="anchor" href="#exampleseg_plds_switched_ctrlcpp">#</a>
+</h1>
+<h2 id="types">
+  Types
+  <a class="anchor" href="#types">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>using double</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#using-data-t">data_t</a></strong></td>
+      </tr>
+      <tr>
+          <td>using arma::Mat&lt; data_t &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#using-matrix">Matrix</a></strong></td>
+      </tr>
+      <tr>
+          <td>using arma::Col&lt; data_t &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#using-vector">Vector</a></strong></td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="functions">
+  Functions
+  <a class="anchor" href="#functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>int</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#function-main">main</a></strong>()</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="type-details">
+  Type Details
+  <a class="anchor" href="#type-details">#</a>
+</h2>
+<h3 id="data_t">
+  data_t
+  <a class="anchor" href="#data_t">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>data_t <span style="color:#f92672">=</span>  <span style="color:#66d9ef">double</span>;
+</span></span></code></pre></div><p>Type of all data in library. If need 32b, change <code>double</code> to <code>float</code>. This could be potentially useful for large scale problems where there are memory constraints.</p>
+<h3 id="matrix">
+  Matrix
+  <a class="anchor" href="#matrix">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Matrix <span style="color:#f92672">=</span>  arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>;
+</span></span></code></pre></div><h3 id="vector">
+  Vector
+  <a class="anchor" href="#vector">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Vector <span style="color:#f92672">=</span>  arma<span style="color:#f92672">::</span>Col<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>;
+</span></span></code></pre></div><h2 id="function-details">
+  Function Details
+  <a class="anchor" href="#function-details">#</a>
+</h2>
+<h3 id="main">
+  main
+  <a class="anchor" href="#main">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">int</span> main()
+</span></span></code></pre></div><h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;ldsCtrlEst&gt;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>data_t;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Matrix;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Vector;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>cout;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">auto</span> <span style="color:#a6e22e">main</span>() <span style="color:#f92672">-&gt;</span> <span style="color:#66d9ef">int</span> {
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; ********** Example Switched Poisson LDS Control ********** </span><span style="color:#ae81ff">\n\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// whether to do switched control
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">bool</span> do_switch_ctrl <span style="color:#f92672">=</span> true;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Make SISO system sampled at 1kHz
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  data_t dt <span style="color:#f92672">=</span> <span style="color:#ae81ff">1e-3</span>;
+</span></span><span style="display:flex;"><span>  size_t n_u <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>  size_t n_x <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>  size_t n_y <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// no time steps for simulation.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">auto</span> n_t <span style="color:#f92672">=</span> <span style="color:#66d9ef">static_cast</span><span style="color:#f92672">&lt;</span>size_t<span style="color:#f92672">&gt;</span>(<span style="color:#ae81ff">30.0</span> <span style="color:#f92672">/</span> dt);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// for simulating switching
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  size_t which_mode <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>  data_t pr_21 <span style="color:#f92672">=</span> <span style="color:#ae81ff">1e-3</span>;   <span style="color:#75715e">// prob mode 1 -&gt; 2
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  data_t pr_12 <span style="color:#f92672">=</span> pr_21;  <span style="color:#75715e">// prob mode 2 -&gt; 1
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// simulated system being controlled
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>System controlled_system(n_u, n_x, n_y, dt);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// **Assume the system is not well characterized by one LDS, but is well
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// characterized by two LDS models with different input matrices.**
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  data_t scale_sys_b <span style="color:#f92672">=</span> <span style="color:#ae81ff">2</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Matrix a(n_x, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye);
+</span></span><span style="display:flex;"><span>  a[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">=</span> <span style="color:#ae81ff">0.985</span>;
+</span></span><span style="display:flex;"><span>  Matrix b1 <span style="color:#f92672">=</span> Matrix(n_x, n_u).fill(<span style="color:#ae81ff">0.05</span>);
+</span></span><span style="display:flex;"><span>  Vector d <span style="color:#f92672">=</span> Vector(n_y, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros).fill(log(<span style="color:#ae81ff">1</span> <span style="color:#f92672">*</span> dt));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  controlled_system.set_A(a);
+</span></span><span style="display:flex;"><span>  controlled_system.set_B(b1);
+</span></span><span style="display:flex;"><span>  controlled_system.set_d(d);
+</span></span><span style="display:flex;"><span>  controlled_system.Reset();  <span style="color:#75715e">// reset to initial conditions
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// reference
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector y_ref0 <span style="color:#f92672">=</span> Vector(n_y, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros).fill(<span style="color:#ae81ff">25.0</span> <span style="color:#f92672">*</span> dt);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Let underlying system 1 be more sensitive than system 2
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix b2 <span style="color:#f92672">=</span> Matrix(n_x, n_u).fill(b1[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">/</span> scale_sys_b);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// create switched controller
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>SwitchedController switched_controller;
+</span></span><span style="display:flex;"><span>  lds<span style="color:#f92672">::</span>UniformMatrixList<span style="color:#f92672">&lt;&gt;</span> k_x;  <span style="color:#75715e">// feedback controller gains
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  {
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// create switched controller sub-systems
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// system 1
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>System sys1(controlled_system);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// set process noise covariance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Matrix q_controller <span style="color:#f92672">=</span> Matrix(n_x, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">*</span> <span style="color:#ae81ff">5e-3</span>;
+</span></span><span style="display:flex;"><span>    sys1.set_Q(q_controller);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// adaptively estimate process disturbance (m)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// n.b. using arbitrary default value for process noise if enabled.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    sys1.do_adapt_m <span style="color:#f92672">=</span> true;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// setting initial mode to target to avoid large error at onset:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Vector x0_controller <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>log(y_ref0) <span style="color:#f92672">-</span> d;
+</span></span><span style="display:flex;"><span>    sys1.set_x0(x0_controller);
+</span></span><span style="display:flex;"><span>    sys1.Reset();  <span style="color:#75715e">// reset to initial conditions
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>    cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>    cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;sys1:</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>    cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>    sys1.Print();
+</span></span><span style="display:flex;"><span>    cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// system 2
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>System sys2 <span style="color:#f92672">=</span> sys1;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// set parameters
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    sys2.set_B(b2);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>    cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;sys2:</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>    cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>    sys2.Print();
+</span></span><span style="display:flex;"><span>    cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    lds<span style="color:#f92672">::</span>UniformSystemList<span style="color:#f92672">&lt;</span>lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>System<span style="color:#f92672">&gt;</span> systems({sys1, sys2});
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// controller gains for underlying system s:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Matrix k_x1(n_u, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>ones);
+</span></span><span style="display:flex;"><span>    Matrix k_x2 <span style="color:#f92672">=</span> scale_sys_b <span style="color:#f92672">*</span> k_x1;  <span style="color:#75715e">// system2 is x-times less sensitive.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    k_x <span style="color:#f92672">=</span> lds<span style="color:#f92672">::</span>UniformMatrixList<span style="color:#f92672">&lt;&gt;</span>({k_x1, k_x2});
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    data_t u_lb <span style="color:#f92672">=</span> <span style="color:#ae81ff">0.0</span>;
+</span></span><span style="display:flex;"><span>    data_t u_ub <span style="color:#f92672">=</span> <span style="color:#ae81ff">5.0</span>;
+</span></span><span style="display:flex;"><span>    switched_controller <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(
+</span></span><span style="display:flex;"><span>        lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>SwitchedController(std<span style="color:#f92672">::</span>move(systems), u_lb, u_ub));
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Control variables
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  size_t control_type <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;  <span style="color:#75715e">// no integral action, etc
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  switched_controller.set_control_type(control_type);
+</span></span><span style="display:flex;"><span>  switched_controller.set_Kc(std<span style="color:#f92672">::</span>move(k_x));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  switched_controller.set_y_ref(y_ref0);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>System<span style="color:#f92672">&gt;</span> systems_vec(<span style="color:#ae81ff">3</span>, lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>System());
+</span></span><span style="display:flex;"><span>  lds<span style="color:#f92672">::</span>UniformSystemList<span style="color:#f92672">&lt;</span>lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>System<span style="color:#f92672">&gt;</span> systems(std<span style="color:#f92672">::</span>move(systems_vec));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;switched_controller:</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  switched_controller.Print();
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.....................................</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Fake measurements
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix z(n_y, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Will later contain control.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix u(n_u, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// create Matrix to save outputs in...
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix y_hat(n_y, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix y_true(n_y, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix y_ref <span style="color:#f92672">=</span> Matrix(n_y, n_t).fill(y_ref0[<span style="color:#ae81ff">0</span>]);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// modes and gain/disturbance params
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix x_hat(n_x, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix x_true(n_x, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix mode(<span style="color:#ae81ff">1</span>, n_t, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>ones);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set initial val
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  y_hat.col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> switched_controller.sys().y();
+</span></span><span style="display:flex;"><span>  y_true.col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> controlled_system.y();
+</span></span><span style="display:flex;"><span>  x_hat.col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> switched_controller.sys().x();
+</span></span><span style="display:flex;"><span>  x_true.col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> controlled_system.x();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Starting &#34;</span> <span style="color:#f92672">&lt;&lt;</span> n_t <span style="color:#f92672">*</span> dt <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; sec simulation ... </span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> start <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>chrono<span style="color:#f92672">::</span>high_resolution_clock<span style="color:#f92672">::</span>now();
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t t <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>; t <span style="color:#f92672">&lt;</span> n_t; t<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// Let the controlled system stochastically change gain
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// Assume another algorithm decodes this mode change and signals the
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// switched_controller
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Vector chance(<span style="color:#ae81ff">1</span>, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>randu);
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (which_mode <span style="color:#f92672">==</span> <span style="color:#ae81ff">1</span>)  <span style="color:#75715e">// mode1
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    {
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">if</span> (chance[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;</span> pr_21) {
+</span></span><span style="display:flex;"><span>        which_mode <span style="color:#f92672">=</span> <span style="color:#ae81ff">2</span>;
+</span></span><span style="display:flex;"><span>        controlled_system.set_B(b2);
+</span></span><span style="display:flex;"><span>        <span style="color:#66d9ef">if</span> (do_switch_ctrl) {
+</span></span><span style="display:flex;"><span>          switched_controller.Switch(<span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>        }
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>    } <span style="color:#66d9ef">else</span> {  <span style="color:#75715e">// mode2
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      <span style="color:#66d9ef">if</span> (chance[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;</span> pr_12) {
+</span></span><span style="display:flex;"><span>        which_mode <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>        controlled_system.set_B(b1);
+</span></span><span style="display:flex;"><span>        <span style="color:#66d9ef">if</span> (do_switch_ctrl) {
+</span></span><span style="display:flex;"><span>          switched_controller.Switch(<span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>        }
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// Simulate the true system.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    z.col(t) <span style="color:#f92672">=</span> controlled_system.Simulate(u.col(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// perform control
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    u.col(t) <span style="color:#f92672">=</span> switched_controller.ControlOutputReference(z.col(t));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    mode.col(t) <span style="color:#f92672">=</span> which_mode;
+</span></span><span style="display:flex;"><span>    y_ref.col(t) <span style="color:#f92672">=</span> y_ref0;
+</span></span><span style="display:flex;"><span>    y_true.col(t) <span style="color:#f92672">=</span> controlled_system.y();
+</span></span><span style="display:flex;"><span>    x_true.col(t) <span style="color:#f92672">=</span> controlled_system.x();
+</span></span><span style="display:flex;"><span>    y_hat.col(t) <span style="color:#f92672">=</span> switched_controller.sys().y();
+</span></span><span style="display:flex;"><span>    x_hat.col(t) <span style="color:#f92672">=</span> switched_controller.sys().x();
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> finish <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>chrono<span style="color:#f92672">::</span>high_resolution_clock<span style="color:#f92672">::</span>now();
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>chrono<span style="color:#f92672">::</span>duration<span style="color:#f92672">&lt;</span>data_t, std<span style="color:#f92672">::</span>milli<span style="color:#f92672">&gt;</span> sim_time_ms <span style="color:#f92672">=</span> finish <span style="color:#f92672">-</span> start;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Finished simulation in &#34;</span> <span style="color:#f92672">&lt;&lt;</span> sim_time_ms.count() <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; ms.</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;(app. &#34;</span> <span style="color:#f92672">&lt;&lt;</span> (sim_time_ms.count() <span style="color:#f92672">/</span> n_t) <span style="color:#f92672">*</span> <span style="color:#ae81ff">1e3</span> <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; us/time-step)</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// x_true, m_true saving with hdf5 via armadillo
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  arma<span style="color:#f92672">::</span>hdf5_opts<span style="color:#f92672">::</span>opts replace <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>hdf5_opts<span style="color:#f92672">::</span>replace;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> dt_vec <span style="color:#f92672">=</span> Vector(<span style="color:#ae81ff">1</span>).fill(dt);
+</span></span><span style="display:flex;"><span>  dt_vec.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_switched_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;dt&#34;</span>));
+</span></span><span style="display:flex;"><span>  y_ref.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_switched_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;y_ref&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  u.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_switched_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;u&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  z.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_switched_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;z&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  x_true.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_switched_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;x_true&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  y_true.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_switched_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;y_true&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  x_hat.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_switched_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;x_hat&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  y_hat.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_switched_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;y_hat&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>  mode.save(arma<span style="color:#f92672">::</span>hdf5_name(<span style="color:#e6db74">&#34;eg_plds_switched_ctrl.h5&#34;</span>, <span style="color:#e6db74">&#34;mode&#34;</span>, replace));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>}
+</span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
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+        <li><a href="#functions">Functions</a></li>
+        <li><a href="#type-details">Type Details</a>
+          <ul>
+            <li><a href="#data_t">data_t</a></li>
+            <li><a href="#matrix">Matrix</a></li>
+            <li><a href="#vector">Vector</a></li>
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diff --git a/docs/docs/api/files/index.html b/docs/docs/api/files/index.html
new file mode 100644
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+
+examples/eg_glds_ctrl.cpp
+
+
+examples/eg_glds_du_plds_ctrl.cpp
+
+
+examples/eg_plds_ctrl.cpp
+
+
+examples/eg_plds_est.cpp
+
+
+examples/eg_plds_switched_ctrl.cpp
+
+
+ldsCtrlEst_h/lds.h lds namespace
+
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+ldsCtrlEst_h/lds_ctrl.h Controller.
+
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+ldsCtrlEst_h/lds_fit.h LDS base fit type.
+
+
+ldsCtrlEst_h/lds_fit_em.h subspace identification
+
+
+ldsCtrlEst_h/lds_fit_ssid.h subspace identification
+
+
+ldsCtrlEst_h/lds_gaussian.h glds namespace
+
+
+ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.
+
+
+ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.
+
+
+ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.
+
+
+ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.
+
+
+ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.
+
+
+ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.
+
+
+ldsCtrlEst_h/lds_poisson.h plds namespace
+
+
+ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.
+
+
+ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.
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+ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.">
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+examples/eg_glds_du_plds_ctrl.cpp
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+examples/eg_plds_switched_ctrl.cpp
+ldsCtrlEst_h/lds.h lds namespace
+ldsCtrlEst_h/lds_ctrl.h Controller.
+ldsCtrlEst_h/lds_fit.h LDS base fit type.
+ldsCtrlEst_h/lds_fit_em.h subspace identification
+ldsCtrlEst_h/lds_fit_ssid.h subspace identification
+ldsCtrlEst_h/lds_gaussian.h glds namespace
+ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.
+ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.
+ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.
+ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.
+ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.
+ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.
+ldsCtrlEst_h/lds_poisson.h plds namespace
+ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.
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+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
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+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
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+<ul>
+<li><strong>API Reference</strong>
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+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
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+  <article class="markdown"><h1 id="files">
+  Files
+  <a class="anchor" href="#files">#</a>
+</h1>
+<ul>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#file-eg-glds-ctrl.cpp">examples/eg_glds_ctrl.cpp</a></strong></p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#file-eg-glds-du-plds-ctrl.cpp">examples/eg_glds_du_plds_ctrl.cpp</a></strong></p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#file-eg-plds-ctrl.cpp">examples/eg_plds_ctrl.cpp</a></strong></p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#file-eg-plds-est.cpp">examples/eg_plds_est.cpp</a></strong></p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#file-eg-plds-switched-ctrl.cpp">examples/eg_plds_switched_ctrl.cpp</a></strong></p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds_8h/#file-lds.h">ldsCtrlEst_h/lds.h</a></strong> <br><code>lds</code> namespace</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__ctrl_8h/#file-lds-ctrl.h">ldsCtrlEst_h/lds_ctrl.h</a></strong> <br>Controller.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__fit_8h/#file-lds-fit.h">ldsCtrlEst_h/lds_fit.h</a></strong> <br>LDS base fit type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__fit__em_8h/#file-lds-fit-em.h">ldsCtrlEst_h/lds_fit_em.h</a></strong> <br>subspace identification</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/#file-lds-fit-ssid.h">ldsCtrlEst_h/lds_fit_ssid.h</a></strong> <br>subspace identification</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__gaussian_8h/#file-lds-gaussian.h">ldsCtrlEst_h/lds_gaussian.h</a></strong> <br><code>glds</code> namespace</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/#file-lds-gaussian-ctrl.h">ldsCtrlEst_h/lds_gaussian_ctrl.h</a></strong> <br>GLDS Controller.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/#file-lds-gaussian-fit.h">ldsCtrlEst_h/lds_gaussian_fit.h</a></strong> <br>GLDS fit type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/#file-lds-gaussian-fit-em.h">ldsCtrlEst_h/lds_gaussian_fit_em.h</a></strong> <br>GLDS E-M fit type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/#file-lds-gaussian-fit-ssid.h">ldsCtrlEst_h/lds_gaussian_fit_ssid.h</a></strong> <br>GLDS SSID fit type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/#file-lds-gaussian-sctrl.h">ldsCtrlEst_h/lds_gaussian_sctrl.h</a></strong> <br>GLDS switched controller type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/#file-lds-gaussian-sys.h">ldsCtrlEst_h/lds_gaussian_sys.h</a></strong> <br>GLDS base type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__poisson_8h/#file-lds-poisson.h">ldsCtrlEst_h/lds_poisson.h</a></strong> <br><code>plds</code> namespace</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/#file-lds-poisson-ctrl.h">ldsCtrlEst_h/lds_poisson_ctrl.h</a></strong> <br>PLDS controller type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/#file-lds-poisson-fit.h">ldsCtrlEst_h/lds_poisson_fit.h</a></strong> <br>PLDS base fit type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/#file-lds-poisson-fit-em.h">ldsCtrlEst_h/lds_poisson_fit_em.h</a></strong> <br>PLDS E-M fit type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/#file-lds-poisson-fit-ssid.h">ldsCtrlEst_h/lds_poisson_fit_ssid.h</a></strong> <br>PLDS SSID fit type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/#file-lds-poisson-sctrl.h">ldsCtrlEst_h/lds_poisson_sctrl.h</a></strong> <br>PLDS switched controller type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/#file-lds-poisson-sys.h">ldsCtrlEst_h/lds_poisson_sys.h</a></strong> <br>PLDS base type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__sctrl_8h/#file-lds-sctrl.h">ldsCtrlEst_h/lds_sctrl.h</a></strong> <br>SwitchedController type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__sys_8h/#file-lds-sys.h">ldsCtrlEst_h/lds_sys.h</a></strong> <br>LDS base type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/#file-lds-uniform-mats.h">ldsCtrlEst_h/lds_uniform_mats.h</a></strong> <br>List of uniformly sized matrices.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/#file-lds-uniform-systems.h">ldsCtrlEst_h/lds_uniform_systems.h</a></strong> <br>List of uniformly sized Systems.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/#file-lds-uniform-vecs.h">ldsCtrlEst_h/lds_uniform_vecs.h</a></strong> <br>List of uniformly sized vectors.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/mex__c__util_8h/#file-mex-c-util.h">ldsCtrlEst_h/mex_c_util.h</a></strong> <br>arma &lt;-&gt; mex interoperability utilities (Matlab C API)</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/#file-mex-cpp-util.h">ldsCtrlEst_h/mex_cpp_util.h</a></strong> <br>arma &lt;-&gt; mex interoperability utilities (Matlab C++ API)</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds_8cpp/#file-lds.cpp">src/lds.cpp</a></strong> <br>misc lds namespace functions</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/#file-lds-gaussian-sys.cpp">src/lds_gaussian_sys.cpp</a></strong> <br>GLDS base type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/#file-lds-poisson-sys.cpp">src/lds_poisson_sys.cpp</a></strong> <br>PLDS base type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__sys_8cpp/#file-lds-sys.cpp">src/lds_sys.cpp</a></strong> <br>LDS base type.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/#file-lds-uniform-vecs.cpp">src/lds_uniform_vecs.cpp</a></strong> <br>Uniformly sized vectors.</p>
+</li>
+</ul>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
+      
+
+      <footer class="book-footer">
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+      <div class="book-toc-content">
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diff --git a/docs/docs/api/files/index.xml b/docs/docs/api/files/index.xml
new file mode 100644
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--- /dev/null
+++ b/docs/docs/api/files/index.xml
@@ -0,0 +1,291 @@
+<?xml version="1.0" encoding="utf-8" standalone="yes"?>
+<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
+  <channel>
+    <title>Files on LDS C&amp;E</title>
+    <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/</link>
+    <description>Recent content in Files on LDS C&amp;E</description>
+    <generator>Hugo</generator>
+    <language>en</language>
+    <atom:link href="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/index.xml" rel="self" type="application/rss+xml" />
+    <item>
+      <title>examples</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/</guid>
+      <description>&lt;h1 id=&#34;examples&#34;&gt;&#xA;  examples&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#examples&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;h2 id=&#34;files&#34;&gt;&#xA;  Files&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#files&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#file-eg-glds-ctrl.cpp&#34;&gt;examples/eg_glds_ctrl.cpp&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#file-eg-glds-du-plds-ctrl.cpp&#34;&gt;examples/eg_glds_du_plds_ctrl.cpp&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#file-eg-plds-ctrl.cpp&#34;&gt;examples/eg_plds_ctrl.cpp&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#file-eg-plds-est.cpp&#34;&gt;examples/eg_plds_est.cpp&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#file-eg-plds-switched-ctrl.cpp&#34;&gt;examples/eg_plds_switched_ctrl.cpp&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;hr&gt;&#xA;&lt;p&gt;Updated on 31 March 2025 at 16:04:30 EDT&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>examples/eg_glds_ctrl.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/</guid>
+      <description>&lt;h1 id=&#34;exampleseg_glds_ctrlcpp&#34;&gt;&#xA;  examples/eg_glds_ctrl.cpp&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#exampleseg_glds_ctrlcpp&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;h2 id=&#34;types&#34;&gt;&#xA;  Types&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#types&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using arma::Mat&amp;lt; data_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#using-matrix&#34;&gt;Matrix&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using arma::Col&amp;lt; data_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#using-vector&#34;&gt;Vector&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using double&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;functions&#34;&gt;&#xA;  Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;int&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#function-main&#34;&gt;main&lt;/a&gt;&lt;/strong&gt;()&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;type-details&#34;&gt;&#xA;  Type Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#type-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;matrix&#34;&gt;&#xA;  Matrix&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#matrix&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Matrix &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Mat&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h3 id=&#34;vector&#34;&gt;&#xA;  Vector&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#vector&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Vector &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Col&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h3 id=&#34;data_t&#34;&gt;&#xA;  data_t&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#data_t&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;data_t &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;double&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;Type of all data in library. If need 32b, change &lt;code&gt;double&lt;/code&gt; to &lt;code&gt;float&lt;/code&gt;. This could be potentially useful for large scale problems where there are memory constraints.&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>examples/eg_glds_du_plds_ctrl.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/</guid>
+      <description>&lt;h1 id=&#34;exampleseg_glds_du_plds_ctrlcpp&#34;&gt;&#xA;  examples/eg_glds_du_plds_ctrl.cpp&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#exampleseg_glds_du_plds_ctrlcpp&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;h2 id=&#34;types&#34;&gt;&#xA;  Types&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#types&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using double&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using arma::Mat&amp;lt; data_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#using-matrix&#34;&gt;Matrix&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using arma::Col&amp;lt; data_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#using-vector&#34;&gt;Vector&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;functions&#34;&gt;&#xA;  Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;int&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#function-main&#34;&gt;main&lt;/a&gt;&lt;/strong&gt;()&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;type-details&#34;&gt;&#xA;  Type Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#type-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;data_t&#34;&gt;&#xA;  data_t&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#data_t&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;data_t &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;double&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;Type of all data in library. If need 32b, change &lt;code&gt;double&lt;/code&gt; to &lt;code&gt;float&lt;/code&gt;. This could be potentially useful for large scale problems where there are memory constraints.&lt;/p&gt;&#xA;&lt;h3 id=&#34;matrix&#34;&gt;&#xA;  Matrix&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#matrix&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Matrix &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Mat&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h3 id=&#34;vector&#34;&gt;&#xA;  Vector&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#vector&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Vector &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Col&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h2 id=&#34;function-details&#34;&gt;&#xA;  Function Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#function-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;main&#34;&gt;&#xA;  main&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#main&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;int&lt;/span&gt; main()&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;Going to simulate a switching disturbance (m) acting on system&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>examples/eg_plds_ctrl.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/</guid>
+      <description>&lt;h1 id=&#34;exampleseg_plds_ctrlcpp&#34;&gt;&#xA;  examples/eg_plds_ctrl.cpp&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#exampleseg_plds_ctrlcpp&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;h2 id=&#34;types&#34;&gt;&#xA;  Types&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#types&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using arma::Mat&amp;lt; data_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#using-matrix&#34;&gt;Matrix&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using arma::Col&amp;lt; data_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#using-vector&#34;&gt;Vector&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using double&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;functions&#34;&gt;&#xA;  Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;int&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#function-main&#34;&gt;main&lt;/a&gt;&lt;/strong&gt;()&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;type-details&#34;&gt;&#xA;  Type Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#type-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;matrix&#34;&gt;&#xA;  Matrix&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#matrix&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Matrix &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Mat&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h3 id=&#34;vector&#34;&gt;&#xA;  Vector&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#vector&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Vector &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Col&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h3 id=&#34;data_t&#34;&gt;&#xA;  data_t&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#data_t&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;data_t &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;double&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;Type of all data in library. If need 32b, change &lt;code&gt;double&lt;/code&gt; to &lt;code&gt;float&lt;/code&gt;. This could be potentially useful for large scale problems where there are memory constraints.&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>examples/eg_plds_est.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/</guid>
+      <description>&lt;h1 id=&#34;exampleseg_plds_estcpp&#34;&gt;&#xA;  examples/eg_plds_est.cpp&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#exampleseg_plds_estcpp&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;h2 id=&#34;types&#34;&gt;&#xA;  Types&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#types&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using arma::Mat&amp;lt; data_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#using-matrix&#34;&gt;Matrix&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using arma::Col&amp;lt; data_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#using-vector&#34;&gt;Vector&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using double&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;functions&#34;&gt;&#xA;  Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#function-random-walk&#34;&gt;random_walk&lt;/a&gt;&lt;/strong&gt;(size_t n_t, const Matrix &amp;amp; Q, const Vector &amp;amp; x0)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;int&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#function-main&#34;&gt;main&lt;/a&gt;&lt;/strong&gt;()&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;type-details&#34;&gt;&#xA;  Type Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#type-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;matrix&#34;&gt;&#xA;  Matrix&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#matrix&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Matrix &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Mat&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h3 id=&#34;vector&#34;&gt;&#xA;  Vector&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#vector&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Vector &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Col&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h3 id=&#34;data_t&#34;&gt;&#xA;  data_t&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#data_t&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;data_t &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;double&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;Type of all data in library. If need 32b, change &lt;code&gt;double&lt;/code&gt; to &lt;code&gt;float&lt;/code&gt;. This could be potentially useful for large scale problems where there are memory constraints.&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>examples/eg_plds_switched_ctrl.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/</guid>
+      <description>&lt;h1 id=&#34;exampleseg_plds_switched_ctrlcpp&#34;&gt;&#xA;  examples/eg_plds_switched_ctrl.cpp&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#exampleseg_plds_switched_ctrlcpp&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;h2 id=&#34;types&#34;&gt;&#xA;  Types&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#types&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using double&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using arma::Mat&amp;lt; data_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#using-matrix&#34;&gt;Matrix&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using arma::Col&amp;lt; data_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#using-vector&#34;&gt;Vector&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;functions&#34;&gt;&#xA;  Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;int&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#function-main&#34;&gt;main&lt;/a&gt;&lt;/strong&gt;()&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;type-details&#34;&gt;&#xA;  Type Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#type-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;data_t&#34;&gt;&#xA;  data_t&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#data_t&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;data_t &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;double&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;Type of all data in library. If need 32b, change &lt;code&gt;double&lt;/code&gt; to &lt;code&gt;float&lt;/code&gt;. This could be potentially useful for large scale problems where there are memory constraints.&lt;/p&gt;&#xA;&lt;h3 id=&#34;matrix&#34;&gt;&#xA;  Matrix&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#matrix&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Matrix &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Mat&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h3 id=&#34;vector&#34;&gt;&#xA;  Vector&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#vector&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Vector &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Col&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h2 id=&#34;function-details&#34;&gt;&#xA;  Function Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#function-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;main&#34;&gt;&#xA;  main&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#main&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;int&lt;/span&gt; main()&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h2 id=&#34;source-code&#34;&gt;&#xA;  Source code&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#source-code&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;//===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===//&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;//&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;// Copyright 2021 Michael Bolus&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;// Copyright 2021 Georgia Institute of Technology&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;//&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;// Licensed under the Apache License, Version 2.0 (the &amp;#34;License&amp;#34;);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;// you may not use this file except in compliance with the License.&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;// You may obtain a copy of the License at&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;//&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;//     http://www.apache.org/licenses/LICENSE-2.0&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;//&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;// Unless required by applicable law or agreed to in writing, software&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;// distributed under the License is distributed on an &amp;#34;AS IS&amp;#34; BASIS,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;// See the License for the specific language governing permissions and&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;// limitations under the License.&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;//&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;//===----------------------------------------------------------------------===//&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;//===----------------------------------------------------------------------===//&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;#include&lt;/span&gt; &lt;span style=&#34;color:#75715e&#34;&gt;&amp;lt;ldsCtrlEst&amp;gt;&lt;/span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;data_t;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Matrix;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Vector;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;cout;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;auto&lt;/span&gt; &lt;span style=&#34;color:#a6e22e&#34;&gt;main&lt;/span&gt;() &lt;span style=&#34;color:#f92672&#34;&gt;-&amp;gt;&lt;/span&gt; &lt;span style=&#34;color:#66d9ef&#34;&gt;int&lt;/span&gt; {&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34; ********** Example Switched Poisson LDS Control ********** &lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// whether to do switched control&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;bool&lt;/span&gt; do_switch_ctrl &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; true;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// Make SISO system sampled at 1kHz&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  data_t dt &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1e-3&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  size_t n_u &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  size_t n_x &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  size_t n_y &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// no time steps for simulation.&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;auto&lt;/span&gt; n_t &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#66d9ef&#34;&gt;static_cast&lt;/span&gt;&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;size_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;(&lt;span style=&#34;color:#ae81ff&#34;&gt;30.0&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;/&lt;/span&gt; dt);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// for simulating switching&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  size_t which_mode &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  data_t pr_21 &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1e-3&lt;/span&gt;;   &lt;span style=&#34;color:#75715e&#34;&gt;// prob mode 1 -&amp;gt; 2&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  data_t pr_12 &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; pr_21;  &lt;span style=&#34;color:#75715e&#34;&gt;// prob mode 2 -&amp;gt; 1&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// simulated system being controlled&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;poisson&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;System controlled_system(n_u, n_x, n_y, dt);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// **Assume the system is not well characterized by one LDS, but is well&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// characterized by two LDS models with different input matrices.**&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  data_t scale_sys_b &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;2&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  Matrix a(n_x, n_x, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;eye);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  a[&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;] &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;0.985&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  Matrix b1 &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; Matrix(n_x, n_u).fill(&lt;span style=&#34;color:#ae81ff&#34;&gt;0.05&lt;/span&gt;);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  Vector d &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; Vector(n_y, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;zeros).fill(log(&lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;*&lt;/span&gt; dt));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  controlled_system.set_A(a);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  controlled_system.set_B(b1);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  controlled_system.set_d(d);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  controlled_system.Reset();  &lt;span style=&#34;color:#75715e&#34;&gt;// reset to initial conditions&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// reference&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  Vector y_ref0 &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; Vector(n_y, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;zeros).fill(&lt;span style=&#34;color:#ae81ff&#34;&gt;25.0&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;*&lt;/span&gt; dt);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// Let underlying system 1 be more sensitive than system 2&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  Matrix b2 &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; Matrix(n_x, n_u).fill(b1[&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;] &lt;span style=&#34;color:#f92672&#34;&gt;/&lt;/span&gt; scale_sys_b);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// create switched controller&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;poisson&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;SwitchedController switched_controller;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;UniformMatrixList&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;gt;&lt;/span&gt; k_x;  &lt;span style=&#34;color:#75715e&#34;&gt;// feedback controller gains&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  {&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// create switched controller sub-systems&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// system 1&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;poisson&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;System sys1(controlled_system);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// set process noise covariance&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    Matrix q_controller &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; Matrix(n_x, n_x, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;eye) &lt;span style=&#34;color:#f92672&#34;&gt;*&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;5e-3&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    sys1.set_Q(q_controller);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// adaptively estimate process disturbance (m)&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// n.b. using arbitrary default value for process noise if enabled.&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    sys1.do_adapt_m &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; true;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// setting initial mode to target to avoid large error at onset:&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    Vector x0_controller &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;log(y_ref0) &lt;span style=&#34;color:#f92672&#34;&gt;-&lt;/span&gt; d;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    sys1.set_x0(x0_controller);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    sys1.Reset();  &lt;span style=&#34;color:#75715e&#34;&gt;// reset to initial conditions&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;.....................................&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;sys1:&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;.....................................&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    sys1.Print();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;.....................................&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// system 2&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;poisson&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;System sys2 &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; sys1;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// set parameters&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    sys2.set_B(b2);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;.....................................&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;sys2:&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;.....................................&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    sys2.Print();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;.....................................&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;UniformSystemList&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;poisson&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;System&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; systems({sys1, sys2});&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// controller gains for underlying system s:&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    Matrix k_x1(n_u, n_x, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;ones);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    Matrix k_x2 &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; scale_sys_b &lt;span style=&#34;color:#f92672&#34;&gt;*&lt;/span&gt; k_x1;  &lt;span style=&#34;color:#75715e&#34;&gt;// system2 is x-times less sensitive.&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    k_x &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;UniformMatrixList&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;gt;&lt;/span&gt;({k_x1, k_x2});&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    data_t u_lb &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;0.0&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    data_t u_ub &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;5.0&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    switched_controller &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;move(&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;        lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;poisson&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;SwitchedController(std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;move(systems), u_lb, u_ub));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  }&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// Control variables&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  size_t control_type &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;;  &lt;span style=&#34;color:#75715e&#34;&gt;// no integral action, etc&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  switched_controller.set_control_type(control_type);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  switched_controller.set_Kc(std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;move(k_x));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  switched_controller.set_y_ref(y_ref0);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;vector&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;poisson&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;System&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; systems_vec(&lt;span style=&#34;color:#ae81ff&#34;&gt;3&lt;/span&gt;, lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;poisson&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;System());&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;UniformSystemList&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;poisson&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;System&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; systems(std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;move(systems_vec));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;.....................................&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;switched_controller:&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;.....................................&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  switched_controller.Print();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;.....................................&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// Fake measurements&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  Matrix z(n_y, n_t, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;zeros);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// Will later contain control.&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  Matrix u(n_u, n_t, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;zeros);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// create Matrix to save outputs in...&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  Matrix y_hat(n_y, n_t, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;zeros);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  Matrix y_true(n_y, n_t, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;zeros);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  Matrix y_ref &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; Matrix(n_y, n_t).fill(y_ref0[&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;]);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// modes and gain/disturbance params&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  Matrix x_hat(n_x, n_t, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;zeros);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  Matrix x_true(n_x, n_t, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;zeros);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  Matrix mode(&lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;, n_t, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;ones);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// set initial val&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  y_hat.col(&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; switched_controller.sys().y();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  y_true.col(&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; controlled_system.y();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  x_hat.col(&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; switched_controller.sys().x();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  x_true.col(&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; controlled_system.x();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;Starting &amp;#34;&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; n_t &lt;span style=&#34;color:#f92672&#34;&gt;*&lt;/span&gt; dt &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34; sec simulation ... &lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;auto&lt;/span&gt; start &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;chrono&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;high_resolution_clock&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;now();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;for&lt;/span&gt; (size_t t &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;; t &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; n_t; t&lt;span style=&#34;color:#f92672&#34;&gt;++&lt;/span&gt;) {&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// Let the controlled system stochastically change gain&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// Assume another algorithm decodes this mode change and signals the&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// switched_controller&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    Vector chance(&lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;randu);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#66d9ef&#34;&gt;if&lt;/span&gt; (which_mode &lt;span style=&#34;color:#f92672&#34;&gt;==&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;)  &lt;span style=&#34;color:#75715e&#34;&gt;// mode1&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    {&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;      &lt;span style=&#34;color:#66d9ef&#34;&gt;if&lt;/span&gt; (chance[&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;] &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; pr_21) {&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;        which_mode &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;2&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;        controlled_system.set_B(b2);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;        &lt;span style=&#34;color:#66d9ef&#34;&gt;if&lt;/span&gt; (do_switch_ctrl) {&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;          switched_controller.Switch(&lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;        }&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;      }&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    } &lt;span style=&#34;color:#66d9ef&#34;&gt;else&lt;/span&gt; {  &lt;span style=&#34;color:#75715e&#34;&gt;// mode2&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;      &lt;span style=&#34;color:#66d9ef&#34;&gt;if&lt;/span&gt; (chance[&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;] &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; pr_12) {&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;        which_mode &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;        controlled_system.set_B(b1);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;        &lt;span style=&#34;color:#66d9ef&#34;&gt;if&lt;/span&gt; (do_switch_ctrl) {&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;          switched_controller.Switch(&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;        }&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;      }&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    }&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// Simulate the true system.&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    z.col(t) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; controlled_system.Simulate(u.col(t &lt;span style=&#34;color:#f92672&#34;&gt;-&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// perform control&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    u.col(t) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; switched_controller.ControlOutputReference(z.col(t));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    mode.col(t) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; which_mode;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    y_ref.col(t) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; y_ref0;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    y_true.col(t) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; controlled_system.y();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    x_true.col(t) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; controlled_system.x();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    y_hat.col(t) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; switched_controller.sys().y();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    x_hat.col(t) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; switched_controller.sys().x();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  }&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;auto&lt;/span&gt; finish &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;chrono&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;high_resolution_clock&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;now();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;chrono&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;duration&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t, std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;milli&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; sim_time_ms &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; finish &lt;span style=&#34;color:#f92672&#34;&gt;-&lt;/span&gt; start;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;Finished simulation in &amp;#34;&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; sim_time_ms.count() &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34; ms.&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;(app. &amp;#34;&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; (sim_time_ms.count() &lt;span style=&#34;color:#f92672&#34;&gt;/&lt;/span&gt; n_t) &lt;span style=&#34;color:#f92672&#34;&gt;*&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1e3&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34; us/time-step)&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// x_true, m_true saving with hdf5 via armadillo&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_opts&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;opts replace &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_opts&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;replace;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;auto&lt;/span&gt; dt_vec &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; Vector(&lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;).fill(dt);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  dt_vec.save(arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_name(&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;eg_plds_switched_ctrl.h5&amp;#34;&lt;/span&gt;, &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;dt&amp;#34;&lt;/span&gt;));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  y_ref.save(arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_name(&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;eg_plds_switched_ctrl.h5&amp;#34;&lt;/span&gt;, &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;y_ref&amp;#34;&lt;/span&gt;, replace));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  u.save(arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_name(&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;eg_plds_switched_ctrl.h5&amp;#34;&lt;/span&gt;, &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;u&amp;#34;&lt;/span&gt;, replace));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  z.save(arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_name(&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;eg_plds_switched_ctrl.h5&amp;#34;&lt;/span&gt;, &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;z&amp;#34;&lt;/span&gt;, replace));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  x_true.save(arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_name(&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;eg_plds_switched_ctrl.h5&amp;#34;&lt;/span&gt;, &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;x_true&amp;#34;&lt;/span&gt;, replace));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  y_true.save(arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_name(&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;eg_plds_switched_ctrl.h5&amp;#34;&lt;/span&gt;, &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;y_true&amp;#34;&lt;/span&gt;, replace));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  x_hat.save(arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_name(&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;eg_plds_switched_ctrl.h5&amp;#34;&lt;/span&gt;, &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;x_hat&amp;#34;&lt;/span&gt;, replace));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  y_hat.save(arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_name(&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;eg_plds_switched_ctrl.h5&amp;#34;&lt;/span&gt;, &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;y_hat&amp;#34;&lt;/span&gt;, replace));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  mode.save(arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_name(&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;eg_plds_switched_ctrl.h5&amp;#34;&lt;/span&gt;, &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;mode&amp;#34;&lt;/span&gt;, replace));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;return&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;}&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;p&gt;Updated on 31 March 2025 at 16:04:30 EDT&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>include</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/</guid>
+      <description>&lt;h1 id=&#34;include&#34;&gt;&#xA;  include&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#include&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;h2 id=&#34;directories&#34;&gt;&#xA;  Directories&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#directories&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/#dir-ldsctrlest-h&#34;&gt;ldsCtrlEst_h&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;hr&gt;&#xA;&lt;p&gt;Updated on 31 March 2025 at 16:04:30 EDT&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/</guid>
+      <description>&lt;h1 id=&#34;ldsctrlest_h&#34;&gt;&#xA;  ldsCtrlEst_h&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#ldsctrlest_h&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;h2 id=&#34;files&#34;&gt;&#xA;  Files&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#files&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds_8h/#file-lds.h&#34;&gt;ldsCtrlEst_h/lds.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;&lt;code&gt;lds&lt;/code&gt; namespace&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__ctrl_8h/#file-lds-ctrl.h&#34;&gt;ldsCtrlEst_h/lds_ctrl.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;Controller.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit_8h/#file-lds-fit.h&#34;&gt;ldsCtrlEst_h/lds_fit.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;LDS base fit type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit__em_8h/#file-lds-fit-em.h&#34;&gt;ldsCtrlEst_h/lds_fit_em.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;subspace identification&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/#file-lds-fit-ssid.h&#34;&gt;ldsCtrlEst_h/lds_fit_ssid.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;subspace identification&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian_8h/#file-lds-gaussian.h&#34;&gt;ldsCtrlEst_h/lds_gaussian.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;&lt;code&gt;glds&lt;/code&gt; namespace&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/#file-lds-gaussian-ctrl.h&#34;&gt;ldsCtrlEst_h/lds_gaussian_ctrl.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;GLDS Controller.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/#file-lds-gaussian-fit.h&#34;&gt;ldsCtrlEst_h/lds_gaussian_fit.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;GLDS fit type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/#file-lds-gaussian-fit-em.h&#34;&gt;ldsCtrlEst_h/lds_gaussian_fit_em.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;GLDS E-M fit type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/#file-lds-gaussian-fit-ssid.h&#34;&gt;ldsCtrlEst_h/lds_gaussian_fit_ssid.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;GLDS SSID fit type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/#file-lds-gaussian-sctrl.h&#34;&gt;ldsCtrlEst_h/lds_gaussian_sctrl.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;GLDS switched controller type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/#file-lds-gaussian-sys.h&#34;&gt;ldsCtrlEst_h/lds_gaussian_sys.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;GLDS base type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson_8h/#file-lds-poisson.h&#34;&gt;ldsCtrlEst_h/lds_poisson.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;&lt;code&gt;plds&lt;/code&gt; namespace&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/#file-lds-poisson-ctrl.h&#34;&gt;ldsCtrlEst_h/lds_poisson_ctrl.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;PLDS controller type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/#file-lds-poisson-fit.h&#34;&gt;ldsCtrlEst_h/lds_poisson_fit.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;PLDS base fit type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/#file-lds-poisson-fit-em.h&#34;&gt;ldsCtrlEst_h/lds_poisson_fit_em.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;PLDS E-M fit type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/#file-lds-poisson-fit-ssid.h&#34;&gt;ldsCtrlEst_h/lds_poisson_fit_ssid.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;PLDS SSID fit type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/#file-lds-poisson-sctrl.h&#34;&gt;ldsCtrlEst_h/lds_poisson_sctrl.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;PLDS switched controller type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/#file-lds-poisson-sys.h&#34;&gt;ldsCtrlEst_h/lds_poisson_sys.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;PLDS base type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sctrl_8h/#file-lds-sctrl.h&#34;&gt;ldsCtrlEst_h/lds_sctrl.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;SwitchedController type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8h/#file-lds-sys.h&#34;&gt;ldsCtrlEst_h/lds_sys.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;LDS base type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/#file-lds-uniform-mats.h&#34;&gt;ldsCtrlEst_h/lds_uniform_mats.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;List of uniformly sized matrices.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/#file-lds-uniform-systems.h&#34;&gt;ldsCtrlEst_h/lds_uniform_systems.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;List of uniformly sized Systems.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/#file-lds-uniform-vecs.h&#34;&gt;ldsCtrlEst_h/lds_uniform_vecs.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;List of uniformly sized vectors.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/mex__c__util_8h/#file-mex-c-util.h&#34;&gt;ldsCtrlEst_h/mex_c_util.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;arma &amp;lt;-&amp;gt; mex interoperability utilities (Matlab C API)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/#file-mex-cpp-util.h&#34;&gt;ldsCtrlEst_h/mex_cpp_util.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;arma &amp;lt;-&amp;gt; mex interoperability utilities (Matlab C++ API)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;hr&gt;&#xA;&lt;p&gt;Updated on 31 March 2025 at 16:04:30 EDT&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_ctrl.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__ctrl_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__ctrl_8h/</guid>
+      <description>Controller.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_fit_em.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit__em_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit__em_8h/</guid>
+      <description>subspace identification</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_fit_ssid.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/</guid>
+      <description>subspace identification</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_fit.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit_8h/</guid>
+      <description>LDS base fit type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_gaussian_ctrl.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/</guid>
+      <description>GLDS Controller.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_gaussian_fit_em.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/</guid>
+      <description>GLDS E-M fit type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_gaussian_fit_ssid.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/</guid>
+      <description>GLDS SSID fit type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_gaussian_fit.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/</guid>
+      <description>GLDS fit type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_gaussian_sctrl.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/</guid>
+      <description>GLDS switched controller type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_gaussian_sys.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/</guid>
+      <description>GLDS base type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_gaussian.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian_8h/</guid>
+      <description>glds namespace</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_poisson_ctrl.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/</guid>
+      <description>PLDS controller type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_poisson_fit_em.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/</guid>
+      <description>PLDS E-M fit type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_poisson_fit_ssid.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/</guid>
+      <description>PLDS SSID fit type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_poisson_fit.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/</guid>
+      <description>PLDS base fit type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_poisson_sctrl.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/</guid>
+      <description>PLDS switched controller type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_poisson_sys.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/</guid>
+      <description>PLDS base type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_poisson.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson_8h/</guid>
+      <description>plds namespace</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_sctrl.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sctrl_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sctrl_8h/</guid>
+      <description>SwitchedController type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_sys.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8h/</guid>
+      <description>LDS base type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_uniform_mats.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/</guid>
+      <description>List of uniformly sized matrices.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_uniform_systems.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/</guid>
+      <description>List of uniformly sized Systems.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_uniform_vecs.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/</guid>
+      <description>List of uniformly sized vectors.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds_8h/</guid>
+      <description>lds namespace</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/mex_c_util.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/mex__c__util_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/mex__c__util_8h/</guid>
+      <description>arma &amp;lt;-&amp;gt; mex interoperability utilities (Matlab C API)</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/mex_cpp_util.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/</guid>
+      <description>arma &amp;lt;-&amp;gt; mex interoperability utilities (Matlab C++ API)</description>
+    </item>
+    <item>
+      <title>src</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/</guid>
+      <description>&lt;h1 id=&#34;src&#34;&gt;&#xA;  src&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#src&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;h2 id=&#34;files&#34;&gt;&#xA;  Files&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#files&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds_8cpp/#file-lds.cpp&#34;&gt;src/lds.cpp&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;misc lds namespace functions&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/#file-lds-gaussian-sys.cpp&#34;&gt;src/lds_gaussian_sys.cpp&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;GLDS base type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/#file-lds-poisson-sys.cpp&#34;&gt;src/lds_poisson_sys.cpp&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;PLDS base type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8cpp/#file-lds-sys.cpp&#34;&gt;src/lds_sys.cpp&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;LDS base type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/#file-lds-uniform-vecs.cpp&#34;&gt;src/lds_uniform_vecs.cpp&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;Uniformly sized vectors.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;hr&gt;&#xA;&lt;p&gt;Updated on 31 March 2025 at 16:04:30 EDT&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>src/lds_gaussian_sys.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/</guid>
+      <description>GLDS base type.</description>
+    </item>
+    <item>
+      <title>src/lds_poisson_sys.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/</guid>
+      <description>PLDS base type.</description>
+    </item>
+    <item>
+      <title>src/lds_sys.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8cpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8cpp/</guid>
+      <description>LDS base type.</description>
+    </item>
+    <item>
+      <title>src/lds_uniform_vecs.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/</guid>
+      <description>Uniformly sized vectors.</description>
+    </item>
+    <item>
+      <title>src/lds.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds_8cpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds_8cpp/</guid>
+      <description>misc lds namespace functions</description>
+    </item>
+  </channel>
+</rss>
diff --git a/docs/docs/api/files/lds_8cpp/index.html b/docs/docs/api/files/lds_8cpp/index.html
new file mode 100644
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+  <article class="markdown"><h1 id="srcldscpp">
+  src/lds.cpp
+  <a class="anchor" href="#srcldscpp">#</a>
+</h1>
+<p>misc lds namespace functions  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file implements miscellaneous lds namespace functions not bound to a class.</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- lds.cpp - LDS -----------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;ldsCtrlEst_h/lds.h&gt;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// insert any necessary function definitions here.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> <span style="color:#a6e22e">ForceSymPD</span>(Matrix<span style="color:#f92672">&amp;</span> X) {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (X.is_sympd() <span style="color:#f92672">||</span> <span style="color:#f92672">!</span>X.is_square()) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span>;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// make symmetric
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  X <span style="color:#f92672">=</span> (X <span style="color:#f92672">+</span> X.t()) <span style="color:#f92672">/</span> <span style="color:#ae81ff">2</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// for eigenval decomp
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">bool</span> did_succeed(true);
+</span></span><span style="display:flex;"><span>  Vector d;
+</span></span><span style="display:flex;"><span>  Matrix u;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// see first method (which may not be ideal):
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  size_t k(<span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">bool</span> is_sympd <span style="color:#f92672">=</span> X.is_sympd();
+</span></span><span style="display:flex;"><span>  Matrix id <span style="color:#f92672">=</span> Matrix(X.n_rows, X.n_cols, fill<span style="color:#f92672">::</span>eye);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">while</span> (<span style="color:#f92672">!</span>is_sympd) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (k <span style="color:#f92672">&gt;</span> <span style="color:#ae81ff">100</span>) {
+</span></span><span style="display:flex;"><span>      did_succeed <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>eig_sym(d, u, X, <span style="color:#e6db74">&#34;std&#34;</span>);
+</span></span><span style="display:flex;"><span>      data_t min_eig <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>min(d);
+</span></span><span style="display:flex;"><span>      std<span style="color:#f92672">::</span>cerr <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;After multiple iterations, min eigen val = &#34;</span> <span style="color:#f92672">&lt;&lt;</span> min_eig
+</span></span><span style="display:flex;"><span>                <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;.</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(
+</span></span><span style="display:flex;"><span>          <span style="color:#e6db74">&#34;Failed to make matrix symmetric positive definite.&#34;</span>);
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">return</span>;
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// Limit(d, arma::eps(0), kInf);  // force to be positive...
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// Matrix d_diag = arma::diagmat(d);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// X = u * d_diag * u.t();
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>    did_succeed <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>eig_sym(d, u, X, <span style="color:#e6db74">&#34;std&#34;</span>);
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>did_succeed) {
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(<span style="color:#e6db74">&#34;ForceSymPD failed.&#34;</span>);
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    data_t min_eig <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>min(d);
+</span></span><span style="display:flex;"><span>    X <span style="color:#f92672">+=</span> id <span style="color:#f92672">*</span> abs(min_eig) <span style="color:#f92672">+</span> arma<span style="color:#f92672">::</span>datum<span style="color:#f92672">::</span>eps;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// make sure symm:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    X <span style="color:#f92672">=</span> (X <span style="color:#f92672">+</span> X.t()) <span style="color:#f92672">/</span> <span style="color:#ae81ff">2</span>;
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// double check eigenvals positive after symmetrizing:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    arma<span style="color:#f92672">::</span>eig_sym(d, u, X, <span style="color:#e6db74">&#34;std&#34;</span>);
+</span></span><span style="display:flex;"><span>    min_eig <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>min(d);
+</span></span><span style="display:flex;"><span>    is_sympd <span style="color:#f92672">=</span> min_eig <span style="color:#f92672">&gt;</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>    k<span style="color:#f92672">++</span>;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> <span style="color:#a6e22e">ForceSymMinEig</span>(Matrix<span style="color:#f92672">&amp;</span> X, data_t eig_min) {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>X.is_square()) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span>;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// make symmetric
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  X <span style="color:#f92672">=</span> (X <span style="color:#f92672">+</span> X.t()) <span style="color:#f92672">/</span> <span style="color:#ae81ff">2</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">bool</span> did_succeed(true);
+</span></span><span style="display:flex;"><span>  Vector d;
+</span></span><span style="display:flex;"><span>  Matrix u;
+</span></span><span style="display:flex;"><span>  did_succeed <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>eig_sym(d, u, X, <span style="color:#e6db74">&#34;std&#34;</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>did_succeed) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(<span style="color:#e6db74">&#34;ForceSymMinEig failed.&#34;</span>);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  Limit(d, eig_min <span style="color:#f92672">+</span> arma<span style="color:#f92672">::</span>eps(eig_min), kInf);  <span style="color:#75715e">// enforce lower bound
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix d_diag <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>diagmat(d);
+</span></span><span style="display:flex;"><span>  X <span style="color:#f92672">=</span> u <span style="color:#f92672">*</span> d_diag <span style="color:#f92672">*</span> u.t();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// double check symmetric
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  X <span style="color:#f92672">=</span> (X <span style="color:#f92672">+</span> X.t()) <span style="color:#f92672">/</span> <span style="color:#ae81ff">2</span>;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> <span style="color:#a6e22e">lq</span>(Matrix<span style="color:#f92672">&amp;</span> L, Matrix<span style="color:#f92672">&amp;</span> Qt, <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> X) {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">bool</span> did_succeed(true);
+</span></span><span style="display:flex;"><span>  did_succeed <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>qr_econ(Qt, L, X.t());
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>did_succeed) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(<span style="color:#e6db74">&#34;LQ decomposition failed.&#34;</span>);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  arma<span style="color:#f92672">::</span>inplace_trans(L);
+</span></span><span style="display:flex;"><span>  arma<span style="color:#f92672">::</span>inplace_trans(Qt);
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>Matrix <span style="color:#a6e22e">calcCov</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> A, <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> B) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// subtract out mean
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">auto</span> m_a <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>mean(A, <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  Matrix a0 <span style="color:#f92672">=</span> A;
+</span></span><span style="display:flex;"><span>  a0.each_col() <span style="color:#f92672">-=</span> m_a;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> m_b <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>mean(B, <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  Matrix b0 <span style="color:#f92672">=</span> B;
+</span></span><span style="display:flex;"><span>  b0.each_col() <span style="color:#f92672">-=</span> m_b;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Matrix cov <span style="color:#f92672">=</span> a0 <span style="color:#f92672">*</span> b0.t() <span style="color:#f92672">/</span> a0.n_cols;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> cov;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace lds
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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+  <article class="markdown"><h1 id="ldsctrlest_hldsh">
+  ldsCtrlEst_h/lds.h
+  <a class="anchor" href="#ldsctrlest_hldsh">#</a>
+</h1>
+<p><code>lds</code> namespace  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file defines the <code>lds</code> namespace, which will be an umbrella for linear dynamical systems with Gaussian (<code>[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)</code>) or Poisson (<code>[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)</code>) observations.</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// #ifndef LDSCTRLEST
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// #include &lt;ldsCtrlEst&gt;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// #endif
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;armadillo&gt;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> data_t <span style="color:#f92672">=</span> <span style="color:#66d9ef">double</span>;  <span style="color:#75715e">// may change to float (but breaks mex functions)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#66d9ef">using</span> Vector <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>Col<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> Matrix <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> Cube <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>Cube<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> View <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>subview<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> fill <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>fill;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">static</span> <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>size_t kControlTypeDeltaU <span style="color:#f92672">=</span> <span style="color:#ae81ff">0x1</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">static</span> <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>size_t kControlTypeIntY <span style="color:#f92672">=</span> kControlTypeDeltaU <span style="color:#f92672">&lt;&lt;</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">static</span> <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>size_t kControlTypeAdaptM <span style="color:#f92672">=</span> kControlTypeDeltaU <span style="color:#f92672">&lt;&lt;</span> <span style="color:#ae81ff">2</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">static</span> <span style="color:#66d9ef">const</span> data_t kInf <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>numeric_limits<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;::</span>infinity();
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">static</span> <span style="color:#66d9ef">const</span> data_t kPi <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>datum<span style="color:#f92672">::</span>pi;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">static</span> <span style="color:#66d9ef">const</span> data_t kDefaultP0 <span style="color:#f92672">=</span> <span style="color:#ae81ff">1e-6</span>;  
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">static</span> <span style="color:#66d9ef">const</span> data_t kDefaultQ0 <span style="color:#f92672">=</span> <span style="color:#ae81ff">1e-6</span>;  
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">static</span> <span style="color:#66d9ef">const</span> data_t kDefaultR0 <span style="color:#f92672">=</span> <span style="color:#ae81ff">1e-2</span>;  
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">enum</span> <span style="color:#a6e22e">SSIDWt</span> {
+</span></span><span style="display:flex;"><span>  kSSIDNone,   
+</span></span><span style="display:flex;"><span>  kSSIDMOESP,  
+</span></span><span style="display:flex;"><span>  kSSIDCVA     
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">enum</span> <span style="color:#a6e22e">MatrixListFreeDim</span> {
+</span></span><span style="display:flex;"><span>  kMatFreeDimNone,  
+</span></span><span style="display:flex;"><span>  kMatFreeDim1,     
+</span></span><span style="display:flex;"><span>  kMatFreeDim2      
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// TODO(mfbolus): for SwitchedController, may want systems to have differing
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// numbers of states. Use this enum as template parameter?
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// enum SystemListFreeDim {
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//   kSysFreeDimNone,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//   kSysFreeDimX  ///&lt; allow state dim (x) of systems in list to be hetero
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// };
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// place hard limits on contents of vecors/mats
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Limit</span>(std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;&amp;</span> x, data_t lb, data_t ub);
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Limit</span>(Vector<span style="color:#f92672">&amp;</span> x, data_t lb, data_t ub);
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Limit</span>(Matrix<span style="color:#f92672">&amp;</span> x, data_t lb, data_t ub);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// in-place assign that errs if there are dimension mismatches:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Reassign</span>(Vector<span style="color:#f92672">&amp;</span> some, <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> other,
+</span></span><span style="display:flex;"><span>              <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>string<span style="color:#f92672">&amp;</span> parenthetical <span style="color:#f92672">=</span> <span style="color:#e6db74">&#34;Reassign&#34;</span>);
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Reassign</span>(Matrix<span style="color:#f92672">&amp;</span> some, <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> other,
+</span></span><span style="display:flex;"><span>              <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>string<span style="color:#f92672">&amp;</span> parenthetical <span style="color:#f92672">=</span> <span style="color:#e6db74">&#34;Reassign&#34;</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// TODO(mfbolus): this is a fudge, but for some reason, cov mats often going
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// numerically asymm.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> <span style="color:#a6e22e">ForceSymPD</span>(Matrix<span style="color:#f92672">&amp;</span> X);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> <span style="color:#a6e22e">ForceSymMinEig</span>(Matrix<span style="color:#f92672">&amp;</span> X, data_t eig_min <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> <span style="color:#a6e22e">lq</span>(Matrix<span style="color:#f92672">&amp;</span> L, Matrix<span style="color:#f92672">&amp;</span> Qt, <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> X);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>Matrix <span style="color:#a6e22e">calcCov</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> A, <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> B);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Limit</span>(std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;&amp;</span> x, data_t lb, data_t ub) {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (data_t<span style="color:#f92672">&amp;</span> el : x) {
+</span></span><span style="display:flex;"><span>    el <span style="color:#f92672">=</span> el <span style="color:#f92672">&lt;</span> lb <span style="color:#f92672">?</span> lb : el;
+</span></span><span style="display:flex;"><span>    el <span style="color:#f92672">=</span> el <span style="color:#f92672">&gt;</span> ub <span style="color:#f92672">?</span> ub : el;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Limit</span>(Vector<span style="color:#f92672">&amp;</span> x, data_t lb, data_t ub) {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (data_t<span style="color:#f92672">&amp;</span> el : x) {
+</span></span><span style="display:flex;"><span>    el <span style="color:#f92672">=</span> el <span style="color:#f92672">&lt;</span> lb <span style="color:#f92672">?</span> lb : el;
+</span></span><span style="display:flex;"><span>    el <span style="color:#f92672">=</span> el <span style="color:#f92672">&gt;</span> ub <span style="color:#f92672">?</span> ub : el;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Limit</span>(Matrix<span style="color:#f92672">&amp;</span> x, data_t lb, data_t ub) {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (data_t<span style="color:#f92672">&amp;</span> el : x) {
+</span></span><span style="display:flex;"><span>    el <span style="color:#f92672">=</span> el <span style="color:#f92672">&lt;</span> lb <span style="color:#f92672">?</span> lb : el;
+</span></span><span style="display:flex;"><span>    el <span style="color:#f92672">=</span> el <span style="color:#f92672">&gt;</span> ub <span style="color:#f92672">?</span> ub : el;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Reassign</span>(Vector<span style="color:#f92672">&amp;</span> some, <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> other,
+</span></span><span style="display:flex;"><span>                     <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>string<span style="color:#f92672">&amp;</span> parenthetical) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// check dimensions
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">if</span> (other.n_elem <span style="color:#f92672">!=</span> some.n_elem) {
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>ostringstream ss;
+</span></span><span style="display:flex;"><span>    ss <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;cannot reassign vector of size &#34;</span> <span style="color:#f92672">&lt;&lt;</span> some.n_elem
+</span></span><span style="display:flex;"><span>       <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; with vector of size &#34;</span> <span style="color:#f92672">&lt;&lt;</span> other.n_elem <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;(&#34;</span> <span style="color:#f92672">&lt;&lt;</span> parenthetical
+</span></span><span style="display:flex;"><span>       <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;)&#34;</span>;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(ss.str());
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t k <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; k <span style="color:#f92672">&lt;</span> some.n_elem; k<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    some[k] <span style="color:#f92672">=</span> other[k];
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Reassign</span>(Matrix<span style="color:#f92672">&amp;</span> some, <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> other,
+</span></span><span style="display:flex;"><span>                     <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>string<span style="color:#f92672">&amp;</span> parenthetical) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// check dimensions
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">if</span> ((other.n_rows <span style="color:#f92672">!=</span> some.n_rows) <span style="color:#f92672">||</span> (other.n_cols <span style="color:#f92672">!=</span> some.n_cols)) {
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>ostringstream ss;
+</span></span><span style="display:flex;"><span>    ss <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;cannot reassign matrix of size &#34;</span> <span style="color:#f92672">&lt;&lt;</span> some.n_rows <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;x&#34;</span> <span style="color:#f92672">&lt;&lt;</span> some.n_cols
+</span></span><span style="display:flex;"><span>       <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; with matrix of size &#34;</span> <span style="color:#f92672">&lt;&lt;</span> other.n_rows <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;x&#34;</span> <span style="color:#f92672">&lt;&lt;</span> other.n_cols <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;(&#34;</span>
+</span></span><span style="display:flex;"><span>       <span style="color:#f92672">&lt;&lt;</span> parenthetical <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;)&#34;</span>;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(ss.str());
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t k <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; k <span style="color:#f92672">&lt;</span> some.n_elem; k<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    some[k] <span style="color:#f92672">=</span> other[k];
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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diff --git a/docs/docs/api/files/lds__ctrl_8h/index.html b/docs/docs/api/files/lds__ctrl_8h/index.html
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+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="Controller.">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__ctrl_8h/">
+  <meta property="og:site_name" content="LDS C&E">
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+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
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+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
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+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
+<li><a href="/lds-ctrl-est/docs/api/classes/">Classes</a></li>
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+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
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+  <li>
+    <a href="https://github.com/stanley-rozell/lds-ctrl-est" target="_blank" rel="noopener">
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+  <article class="markdown"><h1 id="ldsctrlest_hlds_ctrlh">
+  ldsCtrlEst_h/lds_ctrl.h
+  <a class="anchor" href="#ldsctrlest_hlds_ctrlh">#</a>
+</h1>
+<p>Controller.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">lds::Controller</a></strong></td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file declares the type for control of a linear dynamical system (<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">lds::Controller</a>).</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_CTRL_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_CTRL_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// namespace
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">// system type
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_sys.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">Controller</span> {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">static_assert</span>(std<span style="color:#f92672">::</span>is_base_of<span style="color:#f92672">&lt;</span>lds<span style="color:#f92672">::</span>System, System<span style="color:#f92672">&gt;::</span>value,
+</span></span><span style="display:flex;"><span>                <span style="color:#e6db74">&#34;System must be derived from lds::System type.&#34;</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">public</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  Controller() <span style="color:#f92672">=</span> <span style="color:#66d9ef">default</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Controller(<span style="color:#66d9ef">const</span> System<span style="color:#f92672">&amp;</span> sys, data_t u_lb, data_t u_ub,
+</span></span><span style="display:flex;"><span>             size_t control_type <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Controller(System<span style="color:#f92672">&amp;&amp;</span> sys, data_t u_lb, data_t u_ub, size_t control_type <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> Control(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> z, <span style="color:#66d9ef">bool</span> do_control <span style="color:#f92672">=</span> true,
+</span></span><span style="display:flex;"><span>                        <span style="color:#66d9ef">bool</span> do_lock_control <span style="color:#f92672">=</span> false,
+</span></span><span style="display:flex;"><span>                        data_t sigma_soft_start <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>, data_t sigma_u_noise <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>,
+</span></span><span style="display:flex;"><span>                        <span style="color:#66d9ef">bool</span> do_reset_at_control_onset <span style="color:#f92672">=</span> true);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> ControlOutputReference(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> z, <span style="color:#66d9ef">bool</span> do_control <span style="color:#f92672">=</span> true,
+</span></span><span style="display:flex;"><span>                                       <span style="color:#66d9ef">bool</span> do_estimation <span style="color:#f92672">=</span> true,
+</span></span><span style="display:flex;"><span>                                       <span style="color:#66d9ef">bool</span> do_lock_control <span style="color:#f92672">=</span> false,
+</span></span><span style="display:flex;"><span>                                       data_t sigma_soft_start <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>,
+</span></span><span style="display:flex;"><span>                                       data_t sigma_u_noise <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>,
+</span></span><span style="display:flex;"><span>                                       <span style="color:#66d9ef">bool</span> do_reset_at_control_onset <span style="color:#f92672">=</span> true);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// get methods:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">const</span> System<span style="color:#f92672">&amp;</span> sys() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> sys_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> Kc() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> Kc_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> Kc_inty() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> Kc_inty_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> Kc_u() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> Kc_u_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> g_design() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> g_design_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> u_ref() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> u_ref_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> x_ref() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> x_ref_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> y_ref() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> y_ref_; };
+</span></span><span style="display:flex;"><span>  size_t <span style="color:#a6e22e">control_type</span>() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> control_type_; };
+</span></span><span style="display:flex;"><span>  data_t <span style="color:#a6e22e">tau_awu</span>() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> tau_awu_; };
+</span></span><span style="display:flex;"><span>  data_t <span style="color:#a6e22e">u_lb</span>() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> u_lb_; };
+</span></span><span style="display:flex;"><span>  data_t <span style="color:#a6e22e">u_ub</span>() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> u_ub_; };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set methods
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_sys</span>(<span style="color:#66d9ef">const</span> System<span style="color:#f92672">&amp;</span> sys) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> does_match <span style="color:#f92672">=</span> sys_.n_u() <span style="color:#f92672">==</span> sys.n_u();
+</span></span><span style="display:flex;"><span>    does_match <span style="color:#f92672">=</span> does_match <span style="color:#f92672">&amp;&amp;</span> (sys_.n_x() <span style="color:#f92672">==</span> sys.n_x());
+</span></span><span style="display:flex;"><span>    does_match <span style="color:#f92672">=</span> does_match <span style="color:#f92672">&amp;&amp;</span> (sys_.n_y() <span style="color:#f92672">==</span> sys.n_y());
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (does_match) {
+</span></span><span style="display:flex;"><span>      sys_ <span style="color:#f92672">=</span> sys;
+</span></span><span style="display:flex;"><span>    } <span style="color:#66d9ef">else</span> {
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(
+</span></span><span style="display:flex;"><span>          <span style="color:#e6db74">&#34;new system argument to `set_sys` does not match dimensionality of &#34;</span>
+</span></span><span style="display:flex;"><span>          <span style="color:#e6db74">&#34;existing system&#34;</span>);
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_g_design</span>(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> g_design) { Reassign(g_design_, g_design); };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_u_ref</span>(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> u_ref) { Reassign(u_ref_, u_ref); };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_x_ref</span>(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> x_ref) {
+</span></span><span style="display:flex;"><span>    Reassign(x_ref_, x_ref);
+</span></span><span style="display:flex;"><span>    cx_ref_ <span style="color:#f92672">=</span> sys_.C() <span style="color:#f92672">*</span> x_ref_;
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// y_ref needs to be handled differently depending on output fn.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// (need to populate cx_ref_ too, which depends on output fn)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_y_ref</span>(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> y_ref) { Reassign(y_ref_, y_ref); };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_Kc</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> Kc) { Reassign(Kc_, Kc); };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_Kc_inty</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> Kc_inty) { Reassign(Kc_inty_, Kc_inty); };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_Kc_u</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> Kc_u) { Reassign(Kc_u_, Kc_u); };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_tau_awu</span>(data_t tau) {
+</span></span><span style="display:flex;"><span>    tau_awu_ <span style="color:#f92672">=</span> tau;
+</span></span><span style="display:flex;"><span>    k_awu_ <span style="color:#f92672">=</span> sys_.dt() <span style="color:#f92672">/</span> tau_awu_;
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_control_type</span>(size_t control_type);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// There is no reason u_lb/ub should not be public, but making set methods
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// anyway.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_u_lb</span>(data_t u_lb) { u_lb_ <span style="color:#f92672">=</span> u_lb; };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_u_ub</span>(data_t u_ub) { u_ub_ <span style="color:#f92672">=</span> u_ub; };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Reset</span>() {
+</span></span><span style="display:flex;"><span>    sys_.Reset();
+</span></span><span style="display:flex;"><span>    u_ref_.zeros();
+</span></span><span style="display:flex;"><span>    u_ref_prev_.zeros();
+</span></span><span style="display:flex;"><span>    int_e_.zeros();
+</span></span><span style="display:flex;"><span>    int_e_awu_adjust_.zeros();
+</span></span><span style="display:flex;"><span>    u_sat_.zeros();
+</span></span><span style="display:flex;"><span>    u_saturated_ <span style="color:#f92672">=</span> false;
+</span></span><span style="display:flex;"><span>    t_since_control_onset_ <span style="color:#f92672">=</span> <span style="color:#ae81ff">0.0</span>;
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Print</span>() {
+</span></span><span style="display:flex;"><span>    sys_.Print();
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;g_design : &#34;</span> <span style="color:#f92672">&lt;&lt;</span> g_design_ <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;u_lb : &#34;</span> <span style="color:#f92672">&lt;&lt;</span> u_lb_ <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;u_ub : &#34;</span> <span style="color:#f92672">&lt;&lt;</span> u_ub_ <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">protected</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  System sys_;  
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Vector u_;         
+</span></span><span style="display:flex;"><span>  Vector u_return_;  
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Vector g_design_;  
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">//  reference signals
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector u_ref_;  
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// create no set method for this:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector u_ref_prev_;  
+</span></span><span style="display:flex;"><span>  Vector x_ref_;       
+</span></span><span style="display:flex;"><span>  Vector y_ref_;       
+</span></span><span style="display:flex;"><span>  Vector cx_ref_;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Controller gains
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix Kc_;  
+</span></span><span style="display:flex;"><span>  Matrix
+</span></span><span style="display:flex;"><span>      Kc_u_;  
+</span></span><span style="display:flex;"><span>  Matrix Kc_inty_;  
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// control after g inversion
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// do not need set methods for these.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector du_ref_;
+</span></span><span style="display:flex;"><span>  Vector dv_ref_;
+</span></span><span style="display:flex;"><span>  Vector v_ref_;
+</span></span><span style="display:flex;"><span>  Vector dv_;
+</span></span><span style="display:flex;"><span>  Vector v_;  
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// integral error
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// do not need set method for this
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector int_e_;             
+</span></span><span style="display:flex;"><span>  Vector int_e_awu_adjust_;  
+</span></span><span style="display:flex;"><span>  Vector u_sat_;  
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">bool</span> do_control_prev_ <span style="color:#f92672">=</span> false;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">bool</span> do_lock_control_prev_ <span style="color:#f92672">=</span> false;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// whether the g of system has become inverted from what you think it is
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// (gain_ref)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">bool</span> u_saturated_ <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      false;  
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// should be safe to have references here bc nothing needs to be done
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// (like reset vars) when it changes...
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  data_t u_lb_{};  
+</span></span><span style="display:flex;"><span>  data_t u_ub_{};  
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  data_t tau_awu_{};  
+</span></span><span style="display:flex;"><span>  data_t k_awu_ <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  data_t t_since_control_onset_ <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;  
+</span></span><span style="display:flex;"><span>  size_t control_type_{};             
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">private</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> CalcControl(<span style="color:#66d9ef">bool</span> do_control <span style="color:#f92672">=</span> true, <span style="color:#66d9ef">bool</span> do_estimation <span style="color:#f92672">=</span> true,
+</span></span><span style="display:flex;"><span>                   <span style="color:#66d9ef">bool</span> do_lock_control <span style="color:#f92672">=</span> false, data_t sigma_soft_start <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>,
+</span></span><span style="display:flex;"><span>                   data_t sigma_u_noise <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>,
+</span></span><span style="display:flex;"><span>                   <span style="color:#66d9ef">bool</span> do_reset_at_control_onset <span style="color:#f92672">=</span> true);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">CalcSteadyStateSetPoint</span>();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">AntiWindup</span>();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">InitVars</span>(size_t control_type);
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// Implement the above:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Controller(<span style="color:#66d9ef">const</span> System<span style="color:#f92672">&amp;</span> sys, data_t u_lb,
+</span></span><span style="display:flex;"><span>                                      data_t u_ub, size_t control_type)
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> sys_(sys),
+</span></span><span style="display:flex;"><span>      u_lb_(u_lb),
+</span></span><span style="display:flex;"><span>      u_ub_(u_ub),
+</span></span><span style="display:flex;"><span>      tau_awu_(lds<span style="color:#f92672">::</span>kInf) {
+</span></span><span style="display:flex;"><span>  InitVars(control_type);
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Controller(System<span style="color:#f92672">&amp;&amp;</span> sys, data_t u_lb, data_t u_ub,
+</span></span><span style="display:flex;"><span>                                      size_t control_type)
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> sys_(std<span style="color:#f92672">::</span>move(sys)),
+</span></span><span style="display:flex;"><span>      u_lb_(u_lb),
+</span></span><span style="display:flex;"><span>      u_ub_(u_ub),
+</span></span><span style="display:flex;"><span>      tau_awu_(lds<span style="color:#f92672">::</span>kInf) {
+</span></span><span style="display:flex;"><span>  InitVars(control_type);
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_control_type(size_t control_type) {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (control_type_ <span style="color:#f92672">==</span> control_type) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span>;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// creating a blank slate...
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  control_type_ <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>  Kc_inty_.zeros(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>  Kc_u_.zeros(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>  int_e_.zeros(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>  int_e_awu_adjust_.zeros(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// controller was designed to minimize integral error
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">if</span> (control_type <span style="color:#f92672">&amp;</span> kControlTypeIntY) {
+</span></span><span style="display:flex;"><span>    Kc_inty_.zeros(sys_.n_u(), sys_.n_y());
+</span></span><span style="display:flex;"><span>    int_e_.zeros(sys_.n_y());
+</span></span><span style="display:flex;"><span>    int_e_awu_adjust_.zeros(sys_.n_u());
+</span></span><span style="display:flex;"><span>    control_type_ <span style="color:#f92672">=</span> control_type_ <span style="color:#f92672">|</span> kControlTypeIntY;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// controller was designed to minimize deltaU
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// (i.e. state augmented with u)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">if</span> (control_type <span style="color:#f92672">&amp;</span> kControlTypeDeltaU) {
+</span></span><span style="display:flex;"><span>    Kc_u_.zeros(sys_.n_u(), sys_.n_u());
+</span></span><span style="display:flex;"><span>    control_type_ <span style="color:#f92672">=</span> control_type_ <span style="color:#f92672">|</span> kControlTypeDeltaU;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// whether to adapt set point calculate with (re-estimated) process
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// disturbance (m)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">if</span> (control_type <span style="color:#f92672">&amp;</span> kControlTypeAdaptM) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (sys_.do_adapt_m)  <span style="color:#75715e">// only if adapting m...
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    {
+</span></span><span style="display:flex;"><span>      control_type_ <span style="color:#f92672">=</span> control_type_ <span style="color:#f92672">|</span> kControlTypeAdaptM;
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// set_control_type
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Control(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> z, <span style="color:#66d9ef">bool</span> do_control, <span style="color:#66d9ef">bool</span> do_lock_control,
+</span></span><span style="display:flex;"><span>    data_t sigma_soft_start, data_t sigma_u_noise,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> do_reset_at_control_onset) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// update state estimates, given latest measurement
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  sys_.Filter(u_, z);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">bool</span> do_estimation <span style="color:#f92672">=</span> true;  <span style="color:#75715e">// always have estimator on in this case
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// calculate control signal
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start,
+</span></span><span style="display:flex;"><span>              sigma_u_noise, do_reset_at_control_onset);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> u_return_;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>ControlOutputReference(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> z, <span style="color:#66d9ef">bool</span> do_control, <span style="color:#66d9ef">bool</span> do_estimation, <span style="color:#66d9ef">bool</span> do_lock_control,
+</span></span><span style="display:flex;"><span>    data_t sigma_soft_start, data_t sigma_u_noise,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> do_reset_at_control_onset) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// update state estimates, given latest measurement
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">if</span> (do_estimation) {
+</span></span><span style="display:flex;"><span>    sys_.Filter(u_, z);
+</span></span><span style="display:flex;"><span>  } <span style="color:#66d9ef">else</span> {
+</span></span><span style="display:flex;"><span>    sys_.f(u_);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// calculate the set point
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// solves for u_ref and x_ref when output is at y_ref at steady state.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">if</span> (do_control) {
+</span></span><span style="display:flex;"><span>    CalcSteadyStateSetPoint();
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// calculate control signal
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start,
+</span></span><span style="display:flex;"><span>              sigma_u_noise, do_reset_at_control_onset);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> u_return_;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>CalcControl(<span style="color:#66d9ef">bool</span> do_control, <span style="color:#66d9ef">bool</span> do_estimation,
+</span></span><span style="display:flex;"><span>                                            <span style="color:#66d9ef">bool</span> do_lock_control,
+</span></span><span style="display:flex;"><span>                                            data_t sigma_soft_start,
+</span></span><span style="display:flex;"><span>                                            data_t sigma_u_noise,
+</span></span><span style="display:flex;"><span>                                            <span style="color:#66d9ef">bool</span> do_reset_at_control_onset) {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (do_control <span style="color:#f92672">&amp;&amp;</span> do_estimation) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>do_control_prev_) {
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">if</span> (do_reset_at_control_onset) {
+</span></span><span style="display:flex;"><span>        Reset();
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>      t_since_control_onset_ <span style="color:#f92672">=</span> <span style="color:#ae81ff">0.0</span>;
+</span></span><span style="display:flex;"><span>    } <span style="color:#66d9ef">else</span> {
+</span></span><span style="display:flex;"><span>      t_since_control_onset_ <span style="color:#f92672">+=</span> sys_.dt();
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// enforce softstart on control vars.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#66d9ef">if</span> (sigma_soft_start <span style="color:#f92672">&gt;</span> <span style="color:#ae81ff">0</span>) {
+</span></span><span style="display:flex;"><span>      <span style="color:#75715e">// half-Gaussian soft-start scaling factor
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      data_t soft_start_sf <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span> <span style="color:#f92672">-</span> exp(<span style="color:#f92672">-</span>pow(t_since_control_onset_, <span style="color:#ae81ff">2</span>) <span style="color:#f92672">/</span>
+</span></span><span style="display:flex;"><span>                                     (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> pow(sigma_soft_start, <span style="color:#ae81ff">2</span>)));
+</span></span><span style="display:flex;"><span>      u_ref_ <span style="color:#f92672">*=</span> soft_start_sf;
+</span></span><span style="display:flex;"><span>      <span style="color:#75715e">// TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      <span style="color:#75715e">// x_ref_ *= soft_start_sf;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      <span style="color:#75715e">// cx_ref_ *= soft_start_sf;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      <span style="color:#75715e">// y_ref_ *= soft_start_sf;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>do_lock_control) {
+</span></span><span style="display:flex;"><span>      <span style="color:#75715e">// first do u -&gt; v change of vars. (v = g.*u)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      <span style="color:#75715e">// e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      <span style="color:#75715e">// control voltage u[=]V)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      v_ref_ <span style="color:#f92672">=</span> g_design_ <span style="color:#f92672">%</span> u_ref_;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>      <span style="color:#75715e">// Given FB, calc. the change in control
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      <span style="color:#66d9ef">if</span> (control_type_ <span style="color:#f92672">&amp;</span> kControlTypeDeltaU) {
+</span></span><span style="display:flex;"><span>        <span style="color:#75715e">// if control designed to minimize not u but deltaU (i.e. state aug with
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>        <span style="color:#75715e">// u):
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>        <span style="color:#75715e">// TODO(mfbolus): Commented out for now. See note below.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>        <span style="color:#75715e">// du_ref_ = u_ref_ - u_ref_prev_;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>        <span style="color:#75715e">// dv_ref_ = g_design_ % du_ref_;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>        <span style="color:#75715e">// TODO(mfbolus): Assuming users want *smooth* control signals if using
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>        <span style="color:#75715e">// kControlTypeDeltaU, it should be the case that dv_ref_ is --&gt; 0. May
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>        <span style="color:#75715e">// want to revisit, but I am going to force it to be zero unless a
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>        <span style="color:#75715e">// situation arises that argues for keeping the above.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>        dv_ref_.zeros();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>        dv_ <span style="color:#f92672">=</span> dv_ref_;                     <span style="color:#75715e">// nominally-optimal.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>        dv_ <span style="color:#f92672">-=</span> Kc_ <span style="color:#f92672">*</span> (sys_.x() <span style="color:#f92672">-</span> x_ref_);  <span style="color:#75715e">// instantaneous state error
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>        dv_ <span style="color:#f92672">-=</span> Kc_u_ <span style="color:#f92672">*</span> (v_ <span style="color:#f92672">-</span> v_ref_);      <span style="color:#75715e">// penalty on amp u (rel to ref)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>        <span style="color:#66d9ef">if</span> (control_type_ <span style="color:#f92672">&amp;</span> kControlTypeIntY) {
+</span></span><span style="display:flex;"><span>          <span style="color:#75715e">// TODO(mfbolus): one approach to protection against integral windup
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>          <span style="color:#75715e">// would be to not integrate error when control signal saturated:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>          <span style="color:#75715e">// if(!uSaturated)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>          int_e_ <span style="color:#f92672">+=</span> (sys_.cx() <span style="color:#f92672">-</span> cx_ref_) <span style="color:#f92672">*</span> sys_.dt();  <span style="color:#75715e">// integrated error
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>          dv_ <span style="color:#f92672">-=</span> Kc_inty_ <span style="color:#f92672">*</span> int_e_;  <span style="color:#75715e">// control for integrated error
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>        }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>        <span style="color:#75715e">// update the control
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>        v_ <span style="color:#f92672">+=</span> dv_;
+</span></span><span style="display:flex;"><span>      } <span style="color:#66d9ef">else</span> {
+</span></span><span style="display:flex;"><span>        v_ <span style="color:#f92672">=</span> v_ref_;                      <span style="color:#75715e">// nominally-optimal.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>        v_ <span style="color:#f92672">-=</span> Kc_ <span style="color:#f92672">*</span> (sys_.x() <span style="color:#f92672">-</span> x_ref_);  <span style="color:#75715e">// instantaneous state error
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>        <span style="color:#66d9ef">if</span> (control_type_ <span style="color:#f92672">&amp;</span> kControlTypeIntY) {
+</span></span><span style="display:flex;"><span>          <span style="color:#75715e">// TODO(mfbolus): one approach to protection against integral windup
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>          <span style="color:#75715e">// would be to not integrate error when control signal saturated:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>          <span style="color:#75715e">// if (!uSaturated)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>          int_e_ <span style="color:#f92672">+=</span> (sys_.cx() <span style="color:#f92672">-</span> cx_ref_) <span style="color:#f92672">*</span> sys_.dt();  <span style="color:#75715e">// integrated error
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>          v_ <span style="color:#f92672">-=</span> Kc_inty_ <span style="color:#f92672">*</span> int_e_;  <span style="color:#75715e">// control for integrated error
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>        }
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>      <span style="color:#75715e">// convert back to control voltage u[=]V
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      u_ <span style="color:#f92672">=</span> v_ <span style="color:#f92672">/</span> sys_.g();
+</span></span><span style="display:flex;"><span>    }       <span style="color:#75715e">// else do nothing until lock is low
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  } <span style="color:#66d9ef">else</span> {  <span style="color:#75715e">// if not control
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// feed through u_ref in open loop
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    u_ <span style="color:#f92672">=</span> u_ref_ <span style="color:#f92672">%</span> g_design_ <span style="color:#f92672">/</span> sys_.g();
+</span></span><span style="display:flex;"><span>    v_ <span style="color:#f92672">=</span> sys_.g() <span style="color:#f92672">%</span> u_;
+</span></span><span style="display:flex;"><span>    u_ref_.zeros();
+</span></span><span style="display:flex;"><span>    int_e_.zeros();
+</span></span><span style="display:flex;"><span>    int_e_awu_adjust_.zeros();
+</span></span><span style="display:flex;"><span>    u_sat_.zeros();
+</span></span><span style="display:flex;"><span>  }  <span style="color:#75715e">// ends do_control
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// enforce box constraints (and antiwindup)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  AntiWindup();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// add noise to input?
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// The value for u that is *returned* to user after addition of any noise,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// while keeping controller/estimator blind to this addition.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  u_return_ <span style="color:#f92672">=</span> u_;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> ((sigma_u_noise <span style="color:#f92672">&gt;</span> <span style="color:#ae81ff">0.0</span>) <span style="color:#f92672">&amp;&amp;</span> (do_control <span style="color:#f92672">&amp;&amp;</span> <span style="color:#f92672">!</span>do_lock_control)) {
+</span></span><span style="display:flex;"><span>    u_return_ <span style="color:#f92672">+=</span> sigma_u_noise <span style="color:#f92672">*</span> Vector(sys_.n_u(), fill<span style="color:#f92672">::</span>randn);
+</span></span><span style="display:flex;"><span>    Limit(u_return_, u_lb_, u_ub_);
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// For next time step:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  u_ref_prev_ <span style="color:#f92672">=</span> u_ref_;
+</span></span><span style="display:flex;"><span>  do_control_prev_ <span style="color:#f92672">=</span> do_control;
+</span></span><span style="display:flex;"><span>  do_lock_control_prev_ <span style="color:#f92672">=</span> do_lock_control;
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// CalcControl
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>CalcSteadyStateSetPoint() {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Linearly-constrained least squares (ls).
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// _reference:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//  Boyd &amp; Vandenberghe (2018) Introduction to Applied Linear Algebra
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix a_ls <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill<span style="color:#f92672">::</span>zeros));
+</span></span><span style="display:flex;"><span>  Vector b_ls <span style="color:#f92672">=</span> cx_ref_;
+</span></span><span style="display:flex;"><span>  Matrix c_ls <span style="color:#f92672">=</span> join_horiz(sys_.A() <span style="color:#f92672">-</span> Matrix(sys_.n_x(), sys_.n_x(), fill<span style="color:#f92672">::</span>eye),
+</span></span><span style="display:flex;"><span>                           sys_.B() <span style="color:#f92672">*</span> arma<span style="color:#f92672">::</span>diagmat(sys_.g()));
+</span></span><span style="display:flex;"><span>  Vector d_ls <span style="color:#f92672">=</span> <span style="color:#f92672">-</span>sys_.m0();
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (control_type_ <span style="color:#f92672">&amp;</span> kControlTypeAdaptM) {
+</span></span><span style="display:flex;"><span>    d_ls <span style="color:#f92672">=</span> <span style="color:#f92672">-</span>sys_.m();  <span style="color:#75715e">// adapt setpoint calc with disturbance?
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Matrix a_ls_t <span style="color:#f92672">=</span> a_ls.t();  <span style="color:#75715e">// TODO(mfbolus): not sure why but causes seg
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>                             <span style="color:#75715e">// fault if I do not do this.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix phi_ls <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      join_vert(join_horiz(<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> a_ls_t <span style="color:#f92672">*</span> a_ls, c_ls.t()),
+</span></span><span style="display:flex;"><span>                join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill<span style="color:#f92672">::</span>zeros)));
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// TODO(mfbolus): should be actual inverse, rather than pseudo-inverse:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix inv_phi <span style="color:#f92672">=</span> pinv(phi_ls);
+</span></span><span style="display:flex;"><span>  Vector xulam <span style="color:#f92672">=</span> inv_phi <span style="color:#f92672">*</span> join_vert(<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> a_ls_t <span style="color:#f92672">*</span> b_ls, d_ls);
+</span></span><span style="display:flex;"><span>  x_ref_ <span style="color:#f92672">=</span> xulam.subvec(<span style="color:#ae81ff">0</span>, sys_.n_x() <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  u_ref_ <span style="color:#f92672">=</span> xulam.subvec(sys_.n_x(), sys_.n_x() <span style="color:#f92672">+</span> sys_.n_u() <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  cx_ref_ <span style="color:#f92672">=</span> sys_.C() <span style="color:#f92672">*</span> x_ref_;
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// CalcSteadyStateSetPoint
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>AntiWindup() {
+</span></span><span style="display:flex;"><span>  u_saturated_ <span style="color:#f92672">=</span> false;
+</span></span><span style="display:flex;"><span>  u_sat_ <span style="color:#f92672">=</span> u_;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// limit u and flag whether saturated
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">for</span> (size_t k <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; k <span style="color:#f92672">&lt;</span> u_.n_elem; k<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (u_[k] <span style="color:#f92672">&lt;</span> u_lb_) {
+</span></span><span style="display:flex;"><span>      u_sat_[k] <span style="color:#f92672">=</span> u_lb_;
+</span></span><span style="display:flex;"><span>      u_saturated_ <span style="color:#f92672">=</span> true;
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (u_[k] <span style="color:#f92672">&gt;</span> u_ub_) {
+</span></span><span style="display:flex;"><span>      u_sat_[k] <span style="color:#f92672">=</span> u_ub_;
+</span></span><span style="display:flex;"><span>      u_saturated_ <span style="color:#f92672">=</span> true;
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> ((control_type_ <span style="color:#f92672">&amp;</span> kControlTypeIntY) <span style="color:#f92672">&amp;&amp;</span> (tau_awu_ <span style="color:#f92672">&lt;</span> lds<span style="color:#f92672">::</span>kInf)) {
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// one-step back-calculation (calculate intE for u=u_sat)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// (Astroem, Rundqwist 1989 warn against using this...)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// int_e_awu_adjust_ =
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">//     solve(Kc_inty_, (u_ - u_sat_));  // pinv(Kc_inty) * (u-uSat);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// gradual: see Astroem, Rundqwist 1989
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// this is a fudge for doing MIMO gradual
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// n.b., went ahead and multiplied 1/T by dt so don&#39;t have to do that here.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    int_e_awu_adjust_ <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>        k_awu_ <span style="color:#f92672">*</span> (sign(Kc_inty_).t() <span style="color:#f92672">/</span> sys_.n_u()) <span style="color:#f92672">*</span> (u_ <span style="color:#f92672">-</span> u_sat_);
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// int_e_awu_adjust_ = k_awu_ * (u_-u_sat_);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>    int_e_ <span style="color:#f92672">+=</span> int_e_awu_adjust_;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set u to saturated version
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  u_ <span style="color:#f92672">=</span> u_sat_;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>InitVars(size_t control_type) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// initialize to default values
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  u_ref_ <span style="color:#f92672">=</span> Vector(sys_.n_u(), fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  u_ref_prev_ <span style="color:#f92672">=</span> Vector(sys_.n_u(), fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  x_ref_ <span style="color:#f92672">=</span> Vector(sys_.n_x(), fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  y_ref_ <span style="color:#f92672">=</span> Vector(sys_.n_y(), fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  cx_ref_ <span style="color:#f92672">=</span> Vector(sys_.n_y(), fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  u_ <span style="color:#f92672">=</span> Vector(sys_.n_u(), fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  u_return_ <span style="color:#f92672">=</span> Vector(sys_.n_u(), fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  u_sat_ <span style="color:#f92672">=</span> Vector(sys_.n_u(), fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Might not need all these, so zero elements until later.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Kc_ <span style="color:#f92672">=</span> Matrix(sys_.n_u(), sys_.n_x(), fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Kc_u_ <span style="color:#f92672">=</span> Matrix(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Kc_inty_ <span style="color:#f92672">=</span> Matrix(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  g_design_ <span style="color:#f92672">=</span> sys_.g();  <span style="color:#75715e">// by default, same as model
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  dv_ <span style="color:#f92672">=</span> Vector(sys_.n_u(), fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  v_ <span style="color:#f92672">=</span> Vector(sys_.n_u(), fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  du_ref_ <span style="color:#f92672">=</span> Vector(sys_.n_u(), fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  dv_ref_ <span style="color:#f92672">=</span> Vector(sys_.n_u(), fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  v_ref_ <span style="color:#f92672">=</span> Vector(sys_.n_u(), fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  int_e_ <span style="color:#f92672">=</span> Vector(<span style="color:#ae81ff">0</span>, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  int_e_awu_adjust_ <span style="color:#f92672">=</span> Vector(<span style="color:#ae81ff">0</span>, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  set_control_type(control_type);
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
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+    <li><a href="#ldsctrlest_hlds_fith">ldsCtrlEst_h/lds_fit.h</a>
+      <ul>
+        <li><a href="#namespaces">Namespaces</a></li>
+        <li><a href="#classes">Classes</a></li>
+        <li><a href="#detailed-description">Detailed Description</a></li>
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+  <article class="markdown"><h1 id="ldsctrlest_hlds_fith">
+  ldsCtrlEst_h/lds_fit.h
+  <a class="anchor" href="#ldsctrlest_hlds_fith">#</a>
+</h1>
+<p>LDS base fit type.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">lds::Fit</a></strong> <br>LDS <a href="">Fit</a> Type.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDS_FIT_HPP
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDS_FIT_HPP
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// namespace
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_uniform_mats.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">Fit</span> {
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">public</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  Fit() <span style="color:#f92672">=</span> <span style="color:#66d9ef">default</span>;
+</span></span><span style="display:flex;"><span>  Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">virtual</span> <span style="color:#f92672">~</span>Fit() <span style="color:#f92672">=</span> <span style="color:#66d9ef">default</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// get methods
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  size_t <span style="color:#a6e22e">n_u</span>() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> n_u_; };
+</span></span><span style="display:flex;"><span>  size_t <span style="color:#a6e22e">n_x</span>() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> n_x_; };
+</span></span><span style="display:flex;"><span>  size_t <span style="color:#a6e22e">n_y</span>() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> n_y_; };
+</span></span><span style="display:flex;"><span>  data_t <span style="color:#a6e22e">dt</span>() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> dt_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> A() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> A_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> B() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> B_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> g() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> g_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> m() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> m_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> Q() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> Q_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> x0() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> x0_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> P0() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> P0_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> C() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> C_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> d() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> d_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// gets measurement noise
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> R() <span style="color:#66d9ef">const</span> <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set methods (e.g., seeding initial fit values)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_A</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> A) { Reassign(A_, A); };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_B</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> B) { Reassign(B_, B); };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_g</span>(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> g) { Reassign(g_, g); };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_m</span>(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> m) { Reassign(m_, m); };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_Q</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> Q) {
+</span></span><span style="display:flex;"><span>    Reassign(Q_, Q);
+</span></span><span style="display:flex;"><span>    ForceSymPD(Q_);
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_R</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> R) <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_x0</span>(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> x0) { Reassign(x0_, x0); };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_P0</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> P0) {
+</span></span><span style="display:flex;"><span>    Reassign(P0_, P0);
+</span></span><span style="display:flex;"><span>    ForceSymPD(P0_);
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_C</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> C) { Reassign(C_, C); };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_d</span>(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> d) { Reassign(d_, d); };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  View <span style="color:#a6e22e">f</span>(Matrix<span style="color:#f92672">&amp;</span> x, <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> u, size_t t) {
+</span></span><span style="display:flex;"><span>    x.col(t) <span style="color:#f92672">=</span> A_ <span style="color:#f92672">*</span> x.col(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">+</span> B_ <span style="color:#f92672">*</span> (g_ <span style="color:#f92672">%</span> u.col(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>)) <span style="color:#f92672">+</span> m_;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span> x.col(t);
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  View <span style="color:#a6e22e">f</span>(Matrix<span style="color:#f92672">&amp;</span> x_pre, <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> x_post, <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> u, size_t t) {
+</span></span><span style="display:flex;"><span>    x_pre.col(t) <span style="color:#f92672">=</span> A_ <span style="color:#f92672">*</span> x_post.col(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">+</span> B_ <span style="color:#f92672">*</span> (g_ <span style="color:#f92672">%</span> u.col(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>)) <span style="color:#f92672">+</span> m_;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span> x_pre.col(t);
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">virtual</span> View <span style="color:#a6e22e">h</span>(Matrix<span style="color:#f92672">&amp;</span> y, <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> x, size_t t) <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">protected</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  data_t dt_{};  
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Dynamics
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix A_;  
+</span></span><span style="display:flex;"><span>  Matrix B_;  
+</span></span><span style="display:flex;"><span>  Vector g_;  
+</span></span><span style="display:flex;"><span>  Vector m_;  
+</span></span><span style="display:flex;"><span>  Matrix Q_;  
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Output
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix C_;  
+</span></span><span style="display:flex;"><span>  Vector d_;  
+</span></span><span style="display:flex;"><span>  Matrix R_; 
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// initial conditions
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector x0_;  
+</span></span><span style="display:flex;"><span>  Matrix P0_;  
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  size_t n_u_{};  
+</span></span><span style="display:flex;"><span>  size_t n_x_{};  
+</span></span><span style="display:flex;"><span>  size_t n_y_{};  
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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+  ldsCtrlEst_h/lds_fit_em.h
+  <a class="anchor" href="#ldsctrlest_hlds_fit_emh">#</a>
+</h1>
+<p>subspace identification  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">lds::EM</a></strong></td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file declares the type for fitting a linear dynamical system by expectation-maximization (<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">lds::EM</a>).</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_EMAX_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_EMAX_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_fit.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">EM</span> {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">static_assert</span>(std<span style="color:#f92672">::</span>is_base_of<span style="color:#f92672">&lt;</span>lds<span style="color:#f92672">::</span>Fit, Fit<span style="color:#f92672">&gt;::</span>value,
+</span></span><span style="display:flex;"><span>                <span style="color:#e6db74">&#34;Fit must be derived from lds::Fit type.&#34;</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">public</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  EM() <span style="color:#f92672">=</span> <span style="color:#66d9ef">default</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  EM(size_t n_x, data_t dt, UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;&amp;&amp;</span> u_train,
+</span></span><span style="display:flex;"><span>     UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;&amp;&amp;</span> z_train);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  EM(<span style="color:#66d9ef">const</span> Fit<span style="color:#f92672">&amp;</span> fit0, UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;&amp;&amp;</span> u_train,
+</span></span><span style="display:flex;"><span>     UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;&amp;&amp;</span> z_train);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">virtual</span> <span style="color:#f92672">~</span>EM() <span style="color:#f92672">=</span> <span style="color:#66d9ef">default</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Fit<span style="color:#f92672">&amp;</span> Run(<span style="color:#66d9ef">bool</span> calc_dynamics <span style="color:#f92672">=</span> true, <span style="color:#66d9ef">bool</span> calc_Q <span style="color:#f92672">=</span> true,
+</span></span><span style="display:flex;"><span>                 <span style="color:#66d9ef">bool</span> calc_init <span style="color:#f92672">=</span> true, <span style="color:#66d9ef">bool</span> calc_output <span style="color:#f92672">=</span> true,
+</span></span><span style="display:flex;"><span>                 <span style="color:#66d9ef">bool</span> calc_measurement <span style="color:#f92672">=</span> true, size_t max_iter <span style="color:#f92672">=</span> <span style="color:#ae81ff">100</span>,
+</span></span><span style="display:flex;"><span>                 data_t tol <span style="color:#f92672">=</span> <span style="color:#ae81ff">1e-2</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>tuple<span style="color:#f92672">&lt;</span>UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;</span>, UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;&gt;</span>
+</span></span><span style="display:flex;"><span>  ReturnData() {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">auto</span> tuple <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>make_tuple(std<span style="color:#f92672">::</span>move(u_), std<span style="color:#f92672">::</span>move(z_));
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// auto tuple = std::make_tuple(u_, z_);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    u_ <span style="color:#f92672">=</span> UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;</span>();
+</span></span><span style="display:flex;"><span>    z_ <span style="color:#f92672">=</span> UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;</span>();
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span> tuple;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;&amp;</span> x() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> x_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;&amp;</span> y() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> y_; };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> sum_E_x_t_x_t() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> sum_E_x_t_x_t_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> sum_E_xu_tm1_xu_tm1() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> sum_E_xu_tm1_xu_tm1_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> sum_E_xu_t_xu_tm1() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> sum_E_xu_t_xu_tm1_; };
+</span></span><span style="display:flex;"><span>  size_t <span style="color:#a6e22e">n_t_tot</span>() { <span style="color:#66d9ef">return</span> n_t_tot_; }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> theta() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> theta_; };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">protected</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> Expectation(<span style="color:#66d9ef">bool</span> force_common_initial <span style="color:#f92672">=</span> false);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Maximization</span>(<span style="color:#66d9ef">bool</span> calc_dynamics <span style="color:#f92672">=</span> true, <span style="color:#66d9ef">bool</span> calc_Q <span style="color:#f92672">=</span> true,
+</span></span><span style="display:flex;"><span>                    <span style="color:#66d9ef">bool</span> calc_init <span style="color:#f92672">=</span> false, <span style="color:#66d9ef">bool</span> calc_output <span style="color:#f92672">=</span> false,
+</span></span><span style="display:flex;"><span>                    <span style="color:#66d9ef">bool</span> calc_measurement <span style="color:#f92672">=</span> false);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">MaximizeDynamics</span>();
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">MaximizeQ</span>();
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">MaximizeInitial</span>();
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">MaximizeOutput</span>() <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">MaximizeMeasurement</span>() <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Smooth</span>(<span style="color:#66d9ef">bool</span> force_common_initial);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">RecurseKe</span>(Matrix<span style="color:#f92672">&amp;</span> Ke, Cube<span style="color:#f92672">&amp;</span> P_pre, Cube<span style="color:#f92672">&amp;</span> P_post, size_t t) <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Reset</span>();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">InitVars</span>();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Vector <span style="color:#a6e22e">UpdateTheta</span>();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// input/output training data
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;</span> u_;  
+</span></span><span style="display:flex;"><span>  UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;</span> z_;  
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;</span> x_;              
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Cube<span style="color:#f92672">&gt;</span> P_;                
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Cube<span style="color:#f92672">&gt;</span> P_t_tm1_;          
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;</span> y_;              
+</span></span><span style="display:flex;"><span>  Matrix diag_y_;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// expectations calculated in E-step
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix sum_E_x_t_x_t_;        
+</span></span><span style="display:flex;"><span>  Matrix sum_E_xu_tm1_xu_tm1_;  
+</span></span><span style="display:flex;"><span>  Matrix sum_E_xu_t_xu_tm1_;    
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Fit fit_;
+</span></span><span style="display:flex;"><span>  Vector theta_;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  data_t dt_{};              
+</span></span><span style="display:flex;"><span>  size_t n_u_{};             
+</span></span><span style="display:flex;"><span>  size_t n_x_{};             
+</span></span><span style="display:flex;"><span>  size_t n_y_{};             
+</span></span><span style="display:flex;"><span>  size_t n_trials_{};        
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>size_t<span style="color:#f92672">&gt;</span> n_t_;  
+</span></span><span style="display:flex;"><span>  size_t n_t_tot_{};         
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>EM<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>EM(size_t n_x, data_t dt, UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;&amp;&amp;</span> u_train,
+</span></span><span style="display:flex;"><span>            UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;&amp;&amp;</span> z_train) {
+</span></span><span style="display:flex;"><span>  n_u_ <span style="color:#f92672">=</span> u_train.at(<span style="color:#ae81ff">0</span>).n_rows;
+</span></span><span style="display:flex;"><span>  n_y_ <span style="color:#f92672">=</span> z_train.at(<span style="color:#ae81ff">0</span>).n_rows;
+</span></span><span style="display:flex;"><span>  fit_ <span style="color:#f92672">=</span> Fit(n_u_, n_x, n_y_, dt);
+</span></span><span style="display:flex;"><span>  u_ <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(u_train);
+</span></span><span style="display:flex;"><span>  z_ <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(z_train);
+</span></span><span style="display:flex;"><span>  InitVars();
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>EM<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>EM(<span style="color:#66d9ef">const</span> Fit<span style="color:#f92672">&amp;</span> fit0, UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;&amp;&amp;</span> u_train,
+</span></span><span style="display:flex;"><span>            UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;&amp;&amp;</span> z_train) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// make sure fit dims match I/O data
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">if</span> (fit0.n_u() <span style="color:#f92672">!=</span> u_train.at(<span style="color:#ae81ff">0</span>).n_rows) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(
+</span></span><span style="display:flex;"><span>        <span style="color:#e6db74">&#34;Initial fit and input training data have inconsistent dimensions&#34;</span>);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (fit0.n_y() <span style="color:#f92672">!=</span> z_train.at(<span style="color:#ae81ff">0</span>).n_rows) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(
+</span></span><span style="display:flex;"><span>        <span style="color:#e6db74">&#34;Initial fit and output training data have inconsistent dimensions&#34;</span>);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  fit_ <span style="color:#f92672">=</span> fit0;
+</span></span><span style="display:flex;"><span>  u_ <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(u_train);
+</span></span><span style="display:flex;"><span>  z_ <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(z_train);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  InitVars();
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> EM<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>InitVars() {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// check input/output data dimensions are consistent
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">if</span> (z_.size() <span style="color:#f92672">!=</span> u_.size()) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(
+</span></span><span style="display:flex;"><span>        <span style="color:#e6db74">&#34;I/O training data have different number of trials.&#34;</span>);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  n_trials_ <span style="color:#f92672">=</span> u_.size();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  n_t_tot_ <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>  n_t_ <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>size_t<span style="color:#f92672">&gt;</span>(n_trials_);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t trial <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; trial <span style="color:#f92672">&lt;</span> n_trials_; trial<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (z_.at(trial).n_cols <span style="color:#f92672">!=</span> u_.at(trial).n_cols) {
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(
+</span></span><span style="display:flex;"><span>          <span style="color:#e6db74">&#34;I/O training data have different number of time steps.&#34;</span>);
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>    n_t_[trial] <span style="color:#f92672">=</span> u_.at(trial).n_cols;
+</span></span><span style="display:flex;"><span>    n_t_tot_ <span style="color:#f92672">+=</span> n_t_[trial];
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  n_u_ <span style="color:#f92672">=</span> fit_.n_u();
+</span></span><span style="display:flex;"><span>  n_x_ <span style="color:#f92672">=</span> fit_.n_x();
+</span></span><span style="display:flex;"><span>  n_y_ <span style="color:#f92672">=</span> fit_.n_y();
+</span></span><span style="display:flex;"><span>  dt_ <span style="color:#f92672">=</span> fit_.dt();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  x_ <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;</span>(n_trials_);
+</span></span><span style="display:flex;"><span>  P_ <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Cube<span style="color:#f92672">&gt;</span>(n_trials_);
+</span></span><span style="display:flex;"><span>  P_t_tm1_ <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Cube<span style="color:#f92672">&gt;</span>(n_trials_);
+</span></span><span style="display:flex;"><span>  y_ <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;</span>(n_trials_);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t trial <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; trial <span style="color:#f92672">&lt;</span> n_trials_; trial<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    x_[trial] <span style="color:#f92672">=</span> Matrix(n_x_, n_t_[trial], fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>    P_[trial] <span style="color:#f92672">=</span> Cube(n_x_, n_x_, n_t_[trial], fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>    P_t_tm1_[trial] <span style="color:#f92672">=</span> Cube(n_x_, n_x_, n_t_[trial], fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>    y_[trial] <span style="color:#f92672">=</span> Matrix(n_y_, n_t_[trial], fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  diag_y_ <span style="color:#f92672">=</span> Matrix(n_y_, n_y_, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// covariances in expectation step
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  sum_E_x_t_x_t_ <span style="color:#f92672">=</span> Matrix(n_x_, n_x_, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  sum_E_xu_tm1_xu_tm1_ <span style="color:#f92672">=</span> Matrix(n_x_ <span style="color:#f92672">+</span> n_u_, n_x_ <span style="color:#f92672">+</span> n_u_, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  sum_E_xu_t_xu_tm1_ <span style="color:#f92672">=</span> Matrix(n_x_ <span style="color:#f92672">+</span> n_u_, n_x_ <span style="color:#f92672">+</span> n_u_, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">const</span> Fit<span style="color:#f92672">&amp;</span> EM<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>Run(<span style="color:#66d9ef">bool</span> calc_dynamics, <span style="color:#66d9ef">bool</span> calc_Q, <span style="color:#66d9ef">bool</span> calc_init,
+</span></span><span style="display:flex;"><span>                        <span style="color:#66d9ef">bool</span> calc_output, <span style="color:#66d9ef">bool</span> calc_measurement,
+</span></span><span style="display:flex;"><span>                        size_t max_iter, data_t tol) {
+</span></span><span style="display:flex;"><span>  Reset();  <span style="color:#75715e">// to initial conditions
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  size_t n_params <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      <span style="color:#ae81ff">3</span> <span style="color:#f92672">*</span> n_x_ <span style="color:#f92672">*</span> n_x_ <span style="color:#f92672">+</span> n_x_ <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_x_ <span style="color:#f92672">+</span> n_y_ <span style="color:#f92672">*</span> n_x_ <span style="color:#f92672">+</span> n_y_ <span style="color:#f92672">*</span> n_y_;
+</span></span><span style="display:flex;"><span>  Vector <span style="color:#a6e22e">theta</span>(n_params);
+</span></span><span style="display:flex;"><span>  Vector <span style="color:#a6e22e">theta_new</span>(n_params);
+</span></span><span style="display:flex;"><span>  data_t max_dtheta <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// if solving for initial conditions, allow them be varied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// otherwise, freeze at provided values.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">bool</span> force_common_initial <span style="color:#f92672">=</span> <span style="color:#f92672">!</span>calc_init;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// go until parameter convergence
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">for</span> (size_t l <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; l <span style="color:#f92672">&lt;</span> max_iter; l<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    theta_ <span style="color:#f92672">=</span> UpdateTheta();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Iteration &#34;</span> <span style="color:#f92672">&lt;&lt;</span> l <span style="color:#f92672">+</span> <span style="color:#ae81ff">1</span> <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;/&#34;</span> <span style="color:#f92672">&lt;&lt;</span> max_iter <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; ...</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    Expectation(force_common_initial);
+</span></span><span style="display:flex;"><span>    Maximization(calc_dynamics, calc_Q, calc_init, calc_output,
+</span></span><span style="display:flex;"><span>                 calc_measurement);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// check convergence
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    theta_new <span style="color:#f92672">=</span> UpdateTheta();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    Vector dtheta <span style="color:#f92672">=</span> abs(theta_new <span style="color:#f92672">-</span> theta_) <span style="color:#f92672">/</span> abs(theta_);
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// some parameters could be zero...
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    arma<span style="color:#f92672">::</span>uvec ubi_finite <span style="color:#f92672">=</span> find_finite(dtheta);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    max_dtheta <span style="color:#f92672">=</span> max(dtheta.elem(ubi_finite));
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;max dtheta: &#34;</span> <span style="color:#f92672">&lt;&lt;</span> max_dtheta <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (max_dtheta <span style="color:#f92672">&lt;</span> tol) {
+</span></span><span style="display:flex;"><span>      std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Converged.</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">break</span>;
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> fit_;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> EM<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>Smooth(<span style="color:#66d9ef">bool</span> force_common_initial) {
+</span></span><span style="display:flex;"><span>  Matrix <span style="color:#a6e22e">k_e</span>(n_x_, n_y_);  <span style="color:#75715e">// estimator gain
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Cube k_backfilt;         <span style="color:#75715e">// back-filtering gains
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// TODO(mfbolus): this loop could be made parallel
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">for</span> (size_t trial <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; trial <span style="color:#f92672">&lt;</span> z_.size(); trial<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    Matrix <span style="color:#a6e22e">x_pre</span>(n_x_, n_t_[trial], fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>    Cube <span style="color:#a6e22e">p_pre</span>(n_x_, n_x_, n_t_[trial], fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>    Matrix <span style="color:#a6e22e">x_post</span>(n_x_, n_t_[trial], fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>    Cube <span style="color:#a6e22e">p_post</span>(n_x_, n_x_, n_t_[trial], fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (force_common_initial)  <span style="color:#75715e">// forces all trials to have same initial
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>                               <span style="color:#75715e">// conditions.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    {
+</span></span><span style="display:flex;"><span>      x_[trial].col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> fit_.x0();
+</span></span><span style="display:flex;"><span>      P_[trial].slice(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> fit_.P0();
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>    y_[trial].col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> fit_.C() <span style="color:#f92672">*</span> x_[trial].col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">+</span> fit_.d();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// This *should not* be necessary but make sure P is symmetric.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    ForceSymPD(P_[trial].slice(<span style="color:#ae81ff">0</span>));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    x_pre.col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> x_[trial].col(<span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>    p_pre.slice(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> P_[trial].slice(<span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    x_post.col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> x_[trial].col(<span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>    p_post.slice(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> P_[trial].slice(<span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// filter
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#66d9ef">for</span> (size_t t <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>; t <span style="color:#f92672">&lt;</span> n_t_[trial]; t<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>      <span style="color:#75715e">// predict
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      fit_.f(x_pre, x_post, u_.at(trial), t);
+</span></span><span style="display:flex;"><span>      fit_.h(y_[trial], x_pre, t);
+</span></span><span style="display:flex;"><span>      diag_y_.diag() <span style="color:#f92672">=</span> y_[trial].col(t);  <span style="color:#75715e">// TODO(mfbolus): change if parallel
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>      <span style="color:#75715e">// update --&gt; posterior estimation
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      RecurseKe(k_e, p_pre, p_post, t);
+</span></span><span style="display:flex;"><span>      x_post.col(t) <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>          x_pre.col(t) <span style="color:#f92672">+</span> k_e <span style="color:#f92672">*</span> (z_.at(trial).col(t) <span style="color:#f92672">-</span> y_[trial].col(t));
+</span></span><span style="display:flex;"><span>      y_[trial].col(t) <span style="color:#f92672">=</span> fit_.C() <span style="color:#f92672">*</span> x_post.col(t) <span style="color:#f92672">+</span> fit_.d();
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// backfilter -&gt; Smoothed estimate
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// Reference:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// Shumway et Stoffer (1982)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    ForceSymPD(p_post.slice(n_t_[trial] <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>));
+</span></span><span style="display:flex;"><span>    k_backfilt <span style="color:#f92672">=</span> Cube(n_x_, n_x_, n_t_[trial], fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>    x_[trial].col(n_t_[trial] <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span> x_post.col(n_t_[trial] <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>    P_[trial].slice(n_t_[trial] <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span> p_post.slice(n_t_[trial] <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">for</span> (size_t t <span style="color:#f92672">=</span> (n_t_[trial] <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>); t <span style="color:#f92672">&gt;</span> <span style="color:#ae81ff">0</span>; t<span style="color:#f92672">--</span>) {
+</span></span><span style="display:flex;"><span>      <span style="color:#75715e">// TODO(mfmbolus): should not be necessary to force symm positive def
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      ForceSymPD(p_pre.slice(t));
+</span></span><span style="display:flex;"><span>      ForceSymPD(p_post.slice(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>));
+</span></span><span style="display:flex;"><span>      ForceSymPD(P_[trial].slice(t));
+</span></span><span style="display:flex;"><span>      k_backfilt.slice(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>          p_post.slice(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">*</span> fit_.A().t() <span style="color:#f92672">*</span> inv_sympd(p_pre.slice(t));
+</span></span><span style="display:flex;"><span>      x_[trial].col(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>          x_post.col(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">+</span>
+</span></span><span style="display:flex;"><span>          k_backfilt.slice(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">*</span> (x_[trial].col(t) <span style="color:#f92672">-</span> x_pre.col(t));
+</span></span><span style="display:flex;"><span>      P_[trial].slice(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>          p_post.slice(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">+</span> k_backfilt.slice(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">*</span>
+</span></span><span style="display:flex;"><span>                                    (P_[trial].slice(t) <span style="color:#f92672">-</span> p_pre.slice(t)) <span style="color:#f92672">*</span>
+</span></span><span style="display:flex;"><span>                                    k_backfilt.slice(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>).t();
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// do the same for P_t_tm1
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Matrix <span style="color:#a6e22e">id</span>(n_x_, n_x_, fill<span style="color:#f92672">::</span>eye);
+</span></span><span style="display:flex;"><span>    P_t_tm1_[trial].slice(n_t_[trial] <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>        (id <span style="color:#f92672">-</span> k_e <span style="color:#f92672">*</span> fit_.C()) <span style="color:#f92672">*</span> fit_.A() <span style="color:#f92672">*</span> p_post.slice(n_t_[trial] <span style="color:#f92672">-</span> <span style="color:#ae81ff">2</span>);
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">for</span> (size_t t <span style="color:#f92672">=</span> (n_t_[trial] <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>); t <span style="color:#f92672">&gt;</span> <span style="color:#ae81ff">1</span>; t<span style="color:#f92672">--</span>) {
+</span></span><span style="display:flex;"><span>      P_t_tm1_[trial].slice(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>          p_post.slice(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">*</span> k_backfilt.slice(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">2</span>).t() <span style="color:#f92672">+</span>
+</span></span><span style="display:flex;"><span>          k_backfilt.slice(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">*</span>
+</span></span><span style="display:flex;"><span>              (P_t_tm1_[trial].slice(t) <span style="color:#f92672">-</span> fit_.A() <span style="color:#f92672">*</span> p_post.slice(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>)) <span style="color:#f92672">*</span>
+</span></span><span style="display:flex;"><span>              k_backfilt.slice(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">2</span>).t();
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// finally, get smoothed estimate of output
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#66d9ef">for</span> (size_t t <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; t <span style="color:#f92672">&lt;</span> n_t_[trial]; t<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>      fit_.h(y_[trial], x_[trial], t);
+</span></span><span style="display:flex;"><span>    }  <span style="color:#75715e">// samps loop
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  }    <span style="color:#75715e">// trial loop
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}  <span style="color:#75715e">// Smooth
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// template &lt;typename Fit&gt;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// void EM&lt;Fit&gt;::RecurseKe(Matrix&amp; Ke, Cube&amp; P_pre, Cube&amp; P_post, size_t t) {
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//   // predict covar
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//   P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q();
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">//   // update Ke
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//   Ke = P_pre.slice(t) * fit_.C().t() *
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//        inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R());
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">//   // update cov
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//   // Reference: Ghahramani et Hinton (1996)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//   P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">//   // // n.b. for poisson :
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//   // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() +
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//   fit_.Q();
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//   // // update cov
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//   // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ *
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//   // fit_.C());
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//   // // update Ke
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//   // Ke = P_post.slice(t) * fit_.C();
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// }
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> EM<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>Expectation(<span style="color:#66d9ef">bool</span> force_common_initial) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// calculate the mean/cov of state needed for maximizing E[pr(z|theta)]
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Smooth(force_common_initial);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// now get the various forms of sum(E[xx&#39;]) needed
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// n.b. Going to start at t=1 rather than 0 bc most max terms need that.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// so really &#34;n_t_tot_&#34; is (n_t_tot_-1)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  n_t_tot_ <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>  sum_E_x_t_x_t_ <span style="color:#f92672">=</span> Matrix(n_x_, n_x_, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  sum_E_xu_tm1_xu_tm1_ <span style="color:#f92672">=</span> Matrix(n_x_ <span style="color:#f92672">+</span> n_u_, n_x_ <span style="color:#f92672">+</span> n_u_, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  sum_E_xu_t_xu_tm1_ <span style="color:#f92672">=</span> Matrix(n_x_ <span style="color:#f92672">+</span> n_u_, n_x_ <span style="color:#f92672">+</span> n_u_, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Vector <span style="color:#a6e22e">xu_tm1</span>(n_x_ <span style="color:#f92672">+</span> n_u_, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Vector <span style="color:#a6e22e">xu_t</span>(n_x_ <span style="color:#f92672">+</span> n_u_, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t trial <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; trial <span style="color:#f92672">&lt;</span> z_.size(); trial<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">for</span> (size_t t <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>; t <span style="color:#f92672">&lt;</span> n_t_[trial]; t<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>      <span style="color:#75715e">// ------------ sum_E_x_t_x_t ------------
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      sum_E_x_t_x_t_ <span style="color:#f92672">+=</span> x_[trial].col(t) <span style="color:#f92672">*</span> x_[trial].col(t).t();
+</span></span><span style="display:flex;"><span>      sum_E_x_t_x_t_ <span style="color:#f92672">+=</span> P_[trial].slice(t);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>      <span style="color:#75715e">// ------------ sum_E_xu_tm1_xu_tm1 ------------
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      xu_tm1 <span style="color:#f92672">=</span> join_vert(x_[trial].col(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>), u_.at(trial).col(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>));
+</span></span><span style="display:flex;"><span>      sum_E_xu_tm1_xu_tm1_ <span style="color:#f92672">+=</span> xu_tm1 <span style="color:#f92672">*</span> xu_tm1.t();
+</span></span><span style="display:flex;"><span>      sum_E_xu_tm1_xu_tm1_.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">+=</span>
+</span></span><span style="display:flex;"><span>          P_[trial].slice(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>      <span style="color:#75715e">// ------------ sum_E_xu_t_xu_tm1 ------------
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      xu_t <span style="color:#f92672">=</span> join_vert(x_[trial].col(t), u_.at(trial).col(t));
+</span></span><span style="display:flex;"><span>      sum_E_xu_t_xu_tm1_ <span style="color:#f92672">+=</span> xu_t <span style="color:#f92672">*</span> xu_tm1.t();
+</span></span><span style="display:flex;"><span>      sum_E_xu_t_xu_tm1_.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">+=</span>
+</span></span><span style="display:flex;"><span>          P_t_tm1_[trial].slice(t);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>      n_t_tot_ <span style="color:#f92672">+=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>    }  <span style="color:#75715e">// time
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  }    <span style="color:#75715e">// trial
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}  <span style="color:#75715e">// Expectation
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> EM<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>Maximization(<span style="color:#66d9ef">bool</span> calc_dynamics, <span style="color:#66d9ef">bool</span> calc_Q, <span style="color:#66d9ef">bool</span> calc_init,
+</span></span><span style="display:flex;"><span>                           <span style="color:#66d9ef">bool</span> calc_output, <span style="color:#66d9ef">bool</span> calc_measurement) {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (calc_output) {
+</span></span><span style="display:flex;"><span>    MaximizeOutput();
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (calc_measurement) {
+</span></span><span style="display:flex;"><span>    MaximizeMeasurement();
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (calc_dynamics) {
+</span></span><span style="display:flex;"><span>    MaximizeDynamics();
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (calc_Q) {
+</span></span><span style="display:flex;"><span>    MaximizeQ();
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (calc_init) {
+</span></span><span style="display:flex;"><span>    MaximizeInitial();
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// Maximization
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> EM<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>MaximizeDynamics() {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Shumway, Stoffer (1982); Ghahgramani, Hinton (1996)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix ab <span style="color:#f92672">=</span> sum_E_xu_t_xu_tm1_.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_x_ <span style="color:#f92672">+</span> n_u_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">*</span>
+</span></span><span style="display:flex;"><span>              inv_sympd(sum_E_xu_tm1_xu_tm1_);
+</span></span><span style="display:flex;"><span>  fit_.set_A(ab.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>));
+</span></span><span style="display:flex;"><span>  fit_.set_B(ab.submat(<span style="color:#ae81ff">0</span>, n_x_, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_x_ <span style="color:#f92672">+</span> n_u_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>));
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;A_new[0]: &#34;</span> <span style="color:#f92672">&lt;&lt;</span> fit_.A()[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;B_new[0]: &#34;</span> <span style="color:#f92672">&lt;&lt;</span> fit_.B()[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> EM<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>MaximizeQ() {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// View sum_e_x_t_xu_tm1 =
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 *
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//                                inv_sympd(sum_E_xu_tm1_xu_tm1_) *
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//                                sum_e_x_t_xu_tm1.t();
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// q /= n_t_tot_;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// this way is same as above iff dynamics were just updated:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// View sum_e_x_t_xu_tm1 =
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// Matrix ab = arma::join_horiz(fit_.A(), fit_.B());
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t();
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// q /= n_t_tot_;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// From scratch method:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// Q is covariance of the error between state and dynamics-predicted state
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// (aka process noise)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})&#39;]
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// t-1 terms:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  View sum_e_x_tm1_x_tm1 <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      sum_E_xu_tm1_xu_tm1_.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  View sum_e_u_tm1_u_tm1 <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ <span style="color:#f92672">+</span> n_u_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_x_ <span style="color:#f92672">+</span> n_u_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  View sum_e_x_tm1_u_tm1 <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      sum_E_xu_tm1_xu_tm1_.submat(<span style="color:#ae81ff">0</span>, n_x_, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_x_ <span style="color:#f92672">+</span> n_u_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// t, t-1 terms:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  View sum_e_x_t_x_tm1 <span style="color:#f92672">=</span> sum_E_xu_t_xu_tm1_.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  View sum_e_x_t_u_tm1 <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      sum_E_xu_t_xu_tm1_.submat(<span style="color:#ae81ff">0</span>, n_x_, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_x_ <span style="color:#f92672">+</span> n_u_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Matrix q <span style="color:#f92672">=</span> sum_E_x_t_x_t_;
+</span></span><span style="display:flex;"><span>  q <span style="color:#f92672">+=</span> fit_.A() <span style="color:#f92672">*</span> sum_e_x_tm1_x_tm1 <span style="color:#f92672">*</span> fit_.A().t();
+</span></span><span style="display:flex;"><span>  q <span style="color:#f92672">-=</span> sum_e_x_t_x_tm1 <span style="color:#f92672">*</span> fit_.A().t();
+</span></span><span style="display:flex;"><span>  q <span style="color:#f92672">-=</span> fit_.A() <span style="color:#f92672">*</span> sum_e_x_t_x_tm1.t();
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// input-related terms:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  q <span style="color:#f92672">+=</span> fit_.B() <span style="color:#f92672">*</span> sum_e_u_tm1_u_tm1 <span style="color:#f92672">*</span> fit_.B().t();
+</span></span><span style="display:flex;"><span>  q <span style="color:#f92672">-=</span> sum_e_x_t_u_tm1 <span style="color:#f92672">*</span> fit_.B().t();
+</span></span><span style="display:flex;"><span>  q <span style="color:#f92672">-=</span> fit_.B() <span style="color:#f92672">*</span> sum_e_x_t_u_tm1.t();
+</span></span><span style="display:flex;"><span>  q <span style="color:#f92672">+=</span> fit_.A() <span style="color:#f92672">*</span> sum_e_x_tm1_u_tm1 <span style="color:#f92672">*</span> fit_.B().t();
+</span></span><span style="display:flex;"><span>  q <span style="color:#f92672">+=</span> fit_.B() <span style="color:#f92672">*</span> sum_e_x_tm1_u_tm1.t() <span style="color:#f92672">*</span> fit_.A().t();
+</span></span><span style="display:flex;"><span>  q <span style="color:#f92672">/=</span> n_t_tot_;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  fit_.set_Q(q);
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Q_new[0]: &#34;</span> <span style="color:#f92672">&lt;&lt;</span> fit_.Q()[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// std::cout &lt;&lt; &#34;Q_new: \n&#34; &lt;&lt; fit_.Q() &lt;&lt; &#34;\n&#34;;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> EM<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>MaximizeInitial() {
+</span></span><span style="display:flex;"><span>  Vector x0 <span style="color:#f92672">=</span> fit_.x0();
+</span></span><span style="display:flex;"><span>  x0.zeros();
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t trial <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; trial <span style="color:#f92672">&lt;</span> z_.size(); trial<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    x0 <span style="color:#f92672">+=</span> x_[trial].col(<span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  x0 <span style="color:#f92672">/=</span> z_.size();
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;x0_new[0]: &#34;</span> <span style="color:#f92672">&lt;&lt;</span> x0[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// always recalc P0 even if the initial state is fixed (at zero, for
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// example)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix <span style="color:#a6e22e">e_var</span>(n_x_, n_x_, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t trial <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; trial <span style="color:#f92672">&lt;</span> z_.size(); trial<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    e_var <span style="color:#f92672">+=</span> (x_[trial].col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">-</span> x0) <span style="color:#f92672">*</span> (x_[trial].col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">-</span> x0).t();
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  e_var <span style="color:#f92672">/=</span> z_.size();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// go ahead and subtract x0*x0&#39; so don&#39;t have to below.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  e_var <span style="color:#f92672">-=</span> x0 <span style="color:#f92672">*</span> x0.t();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// To get P0, going to get initial P_ per trial and average.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// (which might be wrong, but need a single number)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix p0 <span style="color:#f92672">=</span> fit_.P0();
+</span></span><span style="display:flex;"><span>  p0.zeros();
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t trial <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; trial <span style="color:#f92672">&lt;</span> z_.size(); trial<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    p0 <span style="color:#f92672">+=</span>
+</span></span><span style="display:flex;"><span>        (x_[trial].col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">*</span> x_[trial].col(<span style="color:#ae81ff">0</span>).t()) <span style="color:#f92672">+</span> P_[trial].slice(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">+</span> e_var;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  p0 <span style="color:#f92672">/=</span> z_.size();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  fit_.set_P0(p0);
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;P0_new[0]: &#34;</span> <span style="color:#f92672">&lt;&lt;</span> fit_.P0()[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> EM<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>MaximizeOutput() {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// solve for C+d:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix <span style="color:#a6e22e">sum_zx</span>(n_y_, n_x_ <span style="color:#f92672">+</span> <span style="color:#ae81ff">1</span>, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Vector <span style="color:#a6e22e">x1</span>(n_x_ <span style="color:#f92672">+</span> <span style="color:#ae81ff">1</span>, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  x1[n_x_] <span style="color:#f92672">=</span> <span style="color:#ae81ff">1.0</span>;  <span style="color:#75715e">// augment with one to solve for bias
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix <span style="color:#a6e22e">sum_e_x1_x1</span>(n_x_ <span style="color:#f92672">+</span> <span style="color:#ae81ff">1</span>, n_x_ <span style="color:#f92672">+</span> <span style="color:#ae81ff">1</span>, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t trial <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; trial <span style="color:#f92672">&lt;</span> z_.size(); trial<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">for</span> (size_t t <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>; t <span style="color:#f92672">&lt;</span> n_t_[trial]; t<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>      x1.subvec(<span style="color:#ae81ff">0</span>, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span> x_[trial].col(t);
+</span></span><span style="display:flex;"><span>      sum_zx <span style="color:#f92672">+=</span> z_.at(trial).col(t) <span style="color:#f92672">*</span> x1.t();
+</span></span><span style="display:flex;"><span>      sum_e_x1_x1 <span style="color:#f92672">+=</span> x1 <span style="color:#f92672">*</span> x1.t();
+</span></span><span style="display:flex;"><span>      sum_e_x1_x1.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">+=</span> P_[trial].slice(t);
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  Matrix cd <span style="color:#f92672">=</span> sum_zx <span style="color:#f92672">*</span> inv_sympd(sum_e_x1_x1);
+</span></span><span style="display:flex;"><span>  fit_.set_C(cd.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>));
+</span></span><span style="display:flex;"><span>  fit_.set_d(vectorise(cd.submat(<span style="color:#ae81ff">0</span>, n_x_, n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_x_)));
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;C_new[0]: &#34;</span> <span style="color:#f92672">&lt;&lt;</span> fit_.C()[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;d_new[0]: &#34;</span> <span style="color:#f92672">&lt;&lt;</span> fit_.d()[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> EM<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>MaximizeMeasurement() {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Solve for measurement noise covar
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  size_t n_t_tot <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Ghahgramani, Hinton 1996:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix <span style="color:#a6e22e">sum_zz</span>(n_y_, n_y_, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix <span style="color:#a6e22e">sum_yz</span>(n_y_, n_y_, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t trial <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; trial <span style="color:#f92672">&lt;</span> z_.size(); trial<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">for</span> (size_t t <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>; t <span style="color:#f92672">&lt;</span> n_t_[trial]; t<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>      sum_zz <span style="color:#f92672">+=</span> z_.at(trial).col(t) <span style="color:#f92672">*</span> z_.at(trial).col(t).t();
+</span></span><span style="display:flex;"><span>      <span style="color:#75715e">// Use Cnew:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      sum_yz <span style="color:#f92672">+=</span>
+</span></span><span style="display:flex;"><span>          (fit_.C() <span style="color:#f92672">*</span> x_[trial].col(t) <span style="color:#f92672">+</span> fit_.d()) <span style="color:#f92672">*</span> z_.at(trial).col(t).t();
+</span></span><span style="display:flex;"><span>      n_t_tot <span style="color:#f92672">+=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  fit_.set_R((sum_zz <span style="color:#f92672">-</span> sum_yz) <span style="color:#f92672">/</span> n_t_tot);
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;R_new[0]: &#34;</span> <span style="color:#f92672">&lt;&lt;</span> fit_.R()[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> EM<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>Reset() {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// reset to initial conditions
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">for</span> (size_t trial <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; trial <span style="color:#f92672">&lt;</span> n_trials_; trial<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    x_[trial].col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> fit_.x0();
+</span></span><span style="display:flex;"><span>    P_[trial].slice(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> fit_.P0();
+</span></span><span style="display:flex;"><span>    y_[trial].col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">=</span> fit_.C() <span style="color:#f92672">*</span> x_[trial].col(<span style="color:#ae81ff">0</span>) <span style="color:#f92672">+</span> fit_.d();
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>Vector EM<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>UpdateTheta() {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// TODO(mfbolus): This should include n_y_ more params for d.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  size_t n_params <span style="color:#f92672">=</span> <span style="color:#ae81ff">3</span> <span style="color:#f92672">*</span> n_x_ <span style="color:#f92672">*</span> n_x_ <span style="color:#f92672">+</span> n_x_ <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_x_ <span style="color:#f92672">+</span> n_y_ <span style="color:#f92672">*</span> n_x_ <span style="color:#f92672">+</span> n_y_;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (fit_.R().n_elem <span style="color:#f92672">&gt;</span> <span style="color:#ae81ff">0</span>) {
+</span></span><span style="display:flex;"><span>    n_params <span style="color:#f92672">+=</span> n_y_ <span style="color:#f92672">*</span> n_y_;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  Vector <span style="color:#a6e22e">theta</span>(n_params);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  size_t idx_start <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>  theta.subvec(idx_start, idx_start <span style="color:#f92672">+</span> n_x_ <span style="color:#f92672">*</span> n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span> vectorise(fit_.A());
+</span></span><span style="display:flex;"><span>  idx_start <span style="color:#f92672">+=</span> n_x_ <span style="color:#f92672">*</span> n_x_;
+</span></span><span style="display:flex;"><span>  theta.subvec(idx_start, idx_start <span style="color:#f92672">+</span> n_x_ <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span> vectorise(fit_.B());
+</span></span><span style="display:flex;"><span>  idx_start <span style="color:#f92672">+=</span> n_x_ <span style="color:#f92672">*</span> n_u_;
+</span></span><span style="display:flex;"><span>  theta.subvec(idx_start, idx_start <span style="color:#f92672">+</span> n_x_ <span style="color:#f92672">*</span> n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span> vectorise(fit_.Q());
+</span></span><span style="display:flex;"><span>  idx_start <span style="color:#f92672">+=</span> n_x_ <span style="color:#f92672">*</span> n_x_;
+</span></span><span style="display:flex;"><span>  theta.subvec(idx_start, idx_start <span style="color:#f92672">+</span> n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span> vectorise(fit_.x0());
+</span></span><span style="display:flex;"><span>  idx_start <span style="color:#f92672">+=</span> n_x_;
+</span></span><span style="display:flex;"><span>  theta.subvec(idx_start, idx_start <span style="color:#f92672">+</span> n_x_ <span style="color:#f92672">*</span> n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span> vectorise(fit_.P0());
+</span></span><span style="display:flex;"><span>  idx_start <span style="color:#f92672">+=</span> n_x_ <span style="color:#f92672">*</span> n_x_;
+</span></span><span style="display:flex;"><span>  theta.subvec(idx_start, idx_start <span style="color:#f92672">+</span> n_y_ <span style="color:#f92672">*</span> n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span> vectorise(fit_.C());
+</span></span><span style="display:flex;"><span>  idx_start <span style="color:#f92672">+=</span> n_y_ <span style="color:#f92672">*</span> n_x_;
+</span></span><span style="display:flex;"><span>  theta.subvec(idx_start, idx_start <span style="color:#f92672">+</span> n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span> vectorise(fit_.d());
+</span></span><span style="display:flex;"><span>  idx_start <span style="color:#f92672">+=</span> n_y_;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (fit_.R().n_elem <span style="color:#f92672">&gt;</span> <span style="color:#ae81ff">0</span>) {
+</span></span><span style="display:flex;"><span>    theta.subvec(idx_start, idx_start <span style="color:#f92672">+</span> n_y_ <span style="color:#f92672">*</span> n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span> vectorise(fit_.R());
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> theta;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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+  <article class="markdown"><h1 id="ldsctrlest_hlds_fit_ssidh">
+  ldsCtrlEst_h/lds_fit_ssid.h
+  <a class="anchor" href="#ldsctrlest_hlds_fit_ssidh">#</a>
+</h1>
+<p>subspace identification  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">lds::SSID</a></strong></td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm (<code>[lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)&lt;Fit&gt;</code>).</p>
+<p>References: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_FIT_SSID_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_FIT_SSID_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_fit.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">SSID</span> {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">static_assert</span>(std<span style="color:#f92672">::</span>is_base_of<span style="color:#f92672">&lt;</span>lds<span style="color:#f92672">::</span>Fit, Fit<span style="color:#f92672">&gt;::</span>value,
+</span></span><span style="display:flex;"><span>                <span style="color:#e6db74">&#34;Fit must be derived from lds::Fit type.&#34;</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">public</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  SSID() <span style="color:#f92672">=</span> <span style="color:#66d9ef">default</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  SSID(size_t n_x, size_t n_h, data_t dt,
+</span></span><span style="display:flex;"><span>       UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;&amp;&amp;</span> u_train,
+</span></span><span style="display:flex;"><span>       UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;&amp;&amp;</span> z_train,
+</span></span><span style="display:flex;"><span>       <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> d <span style="color:#f92672">=</span> Vector(<span style="color:#ae81ff">1</span>).fill(<span style="color:#f92672">-</span>kInf));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>tuple<span style="color:#f92672">&lt;</span>Fit, Vector<span style="color:#f92672">&gt;</span> Run(SSIDWt ssid_wt);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>tuple<span style="color:#f92672">&lt;</span>UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;</span>, UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;&gt;</span>
+</span></span><span style="display:flex;"><span>  ReturnData() {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">auto</span> tuple <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>make_tuple(std<span style="color:#f92672">::</span>move(u_), std<span style="color:#f92672">::</span>move(z_));
+</span></span><span style="display:flex;"><span>    u_ <span style="color:#f92672">=</span> UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;</span>();
+</span></span><span style="display:flex;"><span>    z_ <span style="color:#f92672">=</span> UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;</span>();
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span> tuple;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">protected</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> CalcD(data_t t_silence <span style="color:#f92672">=</span> <span style="color:#ae81ff">0.1</span>, data_t thresh_silence <span style="color:#f92672">=</span> <span style="color:#ae81ff">0.001</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">CreateHankelDataMat</span>();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">DecomposeData</span>() <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">CalcSVD</span>(SSIDWt wt);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Solve</span>(data_t wt_dc);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">RecomputeExtObs</span>();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// input/output training data
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;</span> u_;  
+</span></span><span style="display:flex;"><span>  UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;</span> z_;  
+</span></span><span style="display:flex;"><span>  Matrix D_;                           
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Fit fit_;      
+</span></span><span style="display:flex;"><span>  Matrix g_dc_;  
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  data_t dt_{};   
+</span></span><span style="display:flex;"><span>  size_t n_u_{};  
+</span></span><span style="display:flex;"><span>  size_t n_x_{};  
+</span></span><span style="display:flex;"><span>  size_t n_y_{};  
+</span></span><span style="display:flex;"><span>  size_t n_h_{};
+</span></span><span style="display:flex;"><span>  size_t n_trials_{};        
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>size_t<span style="color:#f92672">&gt;</span> n_t_;  
+</span></span><span style="display:flex;"><span>  size_t n_t_tot_{};         
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Matrix L_;          
+</span></span><span style="display:flex;"><span>  Vector s_;          
+</span></span><span style="display:flex;"><span>  Matrix ext_obs_t_;  
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>SSID<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>SSID(size_t n_x, size_t n_h, data_t dt,
+</span></span><span style="display:flex;"><span>                UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;&amp;&amp;</span> u_train,
+</span></span><span style="display:flex;"><span>                UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;&amp;&amp;</span> z_train, <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> d) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// check input/output data dimensions are consistent
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">if</span> (z_train.size() <span style="color:#f92672">!=</span> u_train.size()) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(
+</span></span><span style="display:flex;"><span>        <span style="color:#e6db74">&#34;I/O training data have different number of trials.&#34;</span>);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  n_trials_ <span style="color:#f92672">=</span> u_train.size();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  n_t_tot_ <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>  n_t_ <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>size_t<span style="color:#f92672">&gt;</span>(n_trials_);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t trial <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; trial <span style="color:#f92672">&lt;</span> n_trials_; trial<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (z_train.at(trial).n_cols <span style="color:#f92672">!=</span> u_train.at(trial).n_cols) {
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(
+</span></span><span style="display:flex;"><span>          <span style="color:#e6db74">&#34;I/O training data have different number of time steps.&#34;</span>);
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>    n_t_[trial] <span style="color:#f92672">=</span> u_train.at(trial).n_cols;
+</span></span><span style="display:flex;"><span>    n_t_tot_ <span style="color:#f92672">+=</span> n_t_[trial];
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  dt_ <span style="color:#f92672">=</span> dt;
+</span></span><span style="display:flex;"><span>  n_x_ <span style="color:#f92672">=</span> n_x;
+</span></span><span style="display:flex;"><span>  n_u_ <span style="color:#f92672">=</span> u_train.at(<span style="color:#ae81ff">0</span>).n_rows;
+</span></span><span style="display:flex;"><span>  n_y_ <span style="color:#f92672">=</span> z_train.at(<span style="color:#ae81ff">0</span>).n_rows;
+</span></span><span style="display:flex;"><span>  n_h_ <span style="color:#f92672">=</span> n_h;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// dimensionality check for eventual block-hankel data matrix
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  size_t len <span style="color:#f92672">=</span> n_t_tot_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">+</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (len <span style="color:#f92672">&lt;</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">*</span> (n_u_ <span style="color:#f92672">+</span> n_y_))) {
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>ostringstream ss;
+</span></span><span style="display:flex;"><span>    ss <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Dataset problem! More rows than columns in block-hankel data &#34;</span>
+</span></span><span style="display:flex;"><span>          <span style="color:#e6db74">&#34;matrix: 2*(n_u+n_y)*n_h &gt; data-length! Need higher data-length or &#34;</span>
+</span></span><span style="display:flex;"><span>          <span style="color:#e6db74">&#34;lower n_h.&#34;</span>;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(ss.str());
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  fit_ <span style="color:#f92672">=</span> Fit(n_u_, n_x_, n_y_, dt_);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  u_ <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(u_train);
+</span></span><span style="display:flex;"><span>  z_ <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(z_train);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>d.is_finite() <span style="color:#f92672">||</span> (d.n_rows <span style="color:#f92672">!=</span> n_y_)) {
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// TODO(mfbolus): implement least-square solution for impulse response with
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// a second input of ones. Data-driven way of accounting for offset *not*
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// driven by an input.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// For now, calculate output bias (d) as the
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// output wherever the stimulus has not been on for some amount of time.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// convolve u with rectangle and take all samples. This is a reasonable
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// approach, since often when autonomous systems are fit (i.e., systems with
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// no input), they will subtract off the mean of the output. This
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// essentially amounts to setting output bias to the mean of the output when
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// there is no stimulation.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    data_t t_silence <span style="color:#f92672">=</span> <span style="color:#ae81ff">0.1</span>;
+</span></span><span style="display:flex;"><span>    data_t thresh_silence <span style="color:#f92672">=</span> <span style="color:#ae81ff">0.001</span>;
+</span></span><span style="display:flex;"><span>    CalcD(t_silence, thresh_silence);
+</span></span><span style="display:flex;"><span>  } <span style="color:#66d9ef">else</span> {
+</span></span><span style="display:flex;"><span>    fit_.set_d(d);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>std<span style="color:#f92672">::</span>tuple<span style="color:#f92672">&lt;</span>Fit, Vector<span style="color:#f92672">&gt;</span> SSID<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>Run(SSIDWt ssid_wt) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// the weight on minimizing dc I/O gain only works for gaussian,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// and hopefully not necessary with appropriate dataset.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  data_t wt_dc <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// std::cout &lt;&lt; &#34;creating hankel mat\n&#34;;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  CreateHankelDataMat();
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// std::cout &lt;&lt; &#34;decomposing data\n&#34;;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  DecomposeData();
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// std::cout &lt;&lt; &#34;calculating svd\n&#34;;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  CalcSVD(ssid_wt);
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// std::cout &lt;&lt; &#34;solving for params\n&#34;;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Solve(wt_dc);
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// std::cout &lt;&lt; &#34;fin\n&#34;;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">return</span> std<span style="color:#f92672">::</span>make_tuple(fit_, s_);
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> SSID<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>CalcD(data_t t_silence, data_t thresh_silence) {
+</span></span><span style="display:flex;"><span>  Vector <span style="color:#a6e22e">d</span>(z_.at(<span style="color:#ae81ff">0</span>).n_rows, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Vector <span style="color:#a6e22e">win</span>(<span style="color:#66d9ef">static_cast</span><span style="color:#f92672">&lt;</span>size_t<span style="color:#f92672">&gt;</span>(t_silence <span style="color:#f92672">/</span> dt_), fill<span style="color:#f92672">::</span>ones);
+</span></span><span style="display:flex;"><span>  Vector <span style="color:#a6e22e">sum_z_silence</span>(n_y_, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  size_t <span style="color:#a6e22e">n_silence</span>(<span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t trial <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; trial <span style="color:#f92672">&lt;</span> u_.size(); trial<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// find silent samples
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// start by convolving with
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Vector sum_u <span style="color:#f92672">=</span> vectorise(sum(abs(u_.at(trial)), <span style="color:#ae81ff">0</span>));
+</span></span><span style="display:flex;"><span>    Vector u_conv <span style="color:#f92672">=</span> conv(sum_u, win, <span style="color:#e6db74">&#34;same&#34;</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// get only the samples that are silent...
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    arma<span style="color:#f92672">::</span>uvec ubi_silence <span style="color:#f92672">=</span> find(u_conv <span style="color:#f92672">&lt;=</span> thresh_silence);
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (ubi_silence.n_elem <span style="color:#f92672">&gt;</span> <span style="color:#ae81ff">0</span>) {
+</span></span><span style="display:flex;"><span>      sum_z_silence <span style="color:#f92672">+=</span> arma<span style="color:#f92672">::</span>sum(z_.at(trial).cols(ubi_silence), <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>      n_silence <span style="color:#f92672">+=</span> ubi_silence.n_elem;
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (n_silence <span style="color:#f92672">&gt;</span> <span style="color:#ae81ff">0</span>) {
+</span></span><span style="display:flex;"><span>    d <span style="color:#f92672">=</span> sum_z_silence <span style="color:#f92672">/</span> n_silence;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  fit_.set_d(d);
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> SSID<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>CreateHankelDataMat() {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// temporary copy of data
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix <span style="color:#a6e22e">z</span>(n_y_, n_t_tot_, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix <span style="color:#a6e22e">u</span>(n_u_, n_t_tot_, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  size_t <span style="color:#a6e22e">so_far</span>(<span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t trial <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; trial <span style="color:#f92672">&lt;</span> z_.size(); trial<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    z.submat(<span style="color:#ae81ff">0</span>, so_far, n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, so_far <span style="color:#f92672">+</span> n_t_.at(trial) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span> z_.at(trial);
+</span></span><span style="display:flex;"><span>    u.submat(<span style="color:#ae81ff">0</span>, so_far, n_u_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, so_far <span style="color:#f92672">+</span> n_t_.at(trial) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span> u_.at(trial);
+</span></span><span style="display:flex;"><span>    so_far <span style="color:#f92672">+=</span> n_t_.at(trial);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// remove output bias
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  z.each_col() <span style="color:#f92672">-=</span> fit_.d();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// calculate I/O gain @ DC while data in convenient form
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  g_dc_ <span style="color:#f92672">=</span> z <span style="color:#f92672">*</span> pinv(u);
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// std::cout &lt;&lt; &#34;G0_data = &#34; &lt;&lt; g_dc_ &lt;&lt; &#34;\n&#34;;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// create hankel data matrix
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  size_t len <span style="color:#f92672">=</span> z.n_cols <span style="color:#f92672">-</span> <span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">+</span> <span style="color:#ae81ff">1</span>;  <span style="color:#75715e">// data length in hankel mat
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// block-hankel data matrix
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  D_ <span style="color:#f92672">=</span> Matrix(<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">*</span> (n_u_ <span style="color:#f92672">+</span> n_y_), len, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// past input
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">auto</span> u_p <span style="color:#f92672">=</span> D_.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_h_ <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, len <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// future input
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">auto</span> u_f <span style="color:#f92672">=</span> D_.submat(n_h_ <span style="color:#f92672">*</span> n_u_, <span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, len <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// past output
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">auto</span> y_p <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      D_.submat(<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">*</span> n_u_, <span style="color:#ae81ff">0</span>, n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, len <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// future output
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">auto</span> y_f <span style="color:#f92672">=</span> D_.submat(n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_), <span style="color:#ae81ff">0</span>,
+</span></span><span style="display:flex;"><span>                       <span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">*</span> (n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, len <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  size_t idx <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t k <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; k <span style="color:#f92672">&lt;</span> len; k<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    idx <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">for</span> (size_t kk <span style="color:#f92672">=</span> k; kk <span style="color:#f92672">&lt;</span> (n_h_ <span style="color:#f92672">+</span> k); kk<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>      u_p.col(k).subvec(idx, idx <span style="color:#f92672">+</span> n_u_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span> u.col(kk);
+</span></span><span style="display:flex;"><span>      idx <span style="color:#f92672">+=</span> n_u_;
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    idx <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">for</span> (size_t kk <span style="color:#f92672">=</span> (n_h_ <span style="color:#f92672">+</span> k); kk <span style="color:#f92672">&lt;</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">+</span> k); kk<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>      u_f.col(k).subvec(idx, idx <span style="color:#f92672">+</span> n_u_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span> u.col(kk);
+</span></span><span style="display:flex;"><span>      idx <span style="color:#f92672">+=</span> n_u_;
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    idx <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">for</span> (size_t kk <span style="color:#f92672">=</span> k; kk <span style="color:#f92672">&lt;</span> (n_h_ <span style="color:#f92672">+</span> k); kk<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>      y_p.col(k).subvec(idx, idx <span style="color:#f92672">+</span> n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span> z.col(kk);
+</span></span><span style="display:flex;"><span>      idx <span style="color:#f92672">+=</span> n_y_;
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    idx <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">for</span> (size_t kk <span style="color:#f92672">=</span> (n_h_ <span style="color:#f92672">+</span> k); kk <span style="color:#f92672">&lt;</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">+</span> k); kk<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>      y_f.col(k).subvec(idx, idx <span style="color:#f92672">+</span> n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span> z.col(kk);
+</span></span><span style="display:flex;"><span>      idx <span style="color:#f92672">+=</span> n_y_;
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  D_ <span style="color:#f92672">/=</span> sqrt(<span style="color:#66d9ef">static_cast</span><span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>(len));
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// template &lt;typename Fit&gt;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// void SSID&lt;Fit&gt;::DecomposeData() {
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//   // do LQ decomp instead of calculating covariance expensive way
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//   // Note that &#34;R&#34; in van Overschee is lower-triangular (L), not &#34;R&#34; in QR
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//   // decomp. Very confusing.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//   Matrix q_t;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//   lq(L_, q_t, D_);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//   // van Overschee zeros out the other elements.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//   L_ = trimatl(L_);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// }
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> SSID<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>CalcSVD(SSIDWt wt) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// submats that will be needed:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">auto</span> R_14_14 <span style="color:#f92672">=</span> L_.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>,
+</span></span><span style="display:flex;"><span>                           n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> R_11_14 <span style="color:#f92672">=</span> L_.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_h_ <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> R_11_13 <span style="color:#f92672">=</span> L_.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_h_ <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> R_23_13 <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      L_.submat(n_h_ <span style="color:#f92672">*</span> n_u_, <span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, <span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> R_44_14 <span style="color:#f92672">=</span> L_.submat(<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">*</span> n_h_, <span style="color:#ae81ff">0</span>, n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>,
+</span></span><span style="display:flex;"><span>                           n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> R_44_13 <span style="color:#f92672">=</span> L_.submat(<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">*</span> n_h_, <span style="color:#ae81ff">0</span>, n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>,
+</span></span><span style="display:flex;"><span>                           n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> R_44 <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      L_.submat(<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">*</span> n_h_, <span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">*</span> n_h_, n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>,
+</span></span><span style="display:flex;"><span>                n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> R_56_14 <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      L_.submat(n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_), <span style="color:#ae81ff">0</span>, n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> <span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_y_) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>,
+</span></span><span style="display:flex;"><span>                n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Matrix Lup_Luf_Lyp <span style="color:#f92672">=</span> R_56_14 <span style="color:#f92672">*</span> pinv(R_14_14);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> Lup <span style="color:#f92672">=</span> Lup_Luf_Lyp.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_h_ <span style="color:#f92672">*</span> n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_h_ <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> Luf <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      Lup_Luf_Lyp.submat(<span style="color:#ae81ff">0</span>, n_h_ <span style="color:#f92672">*</span> n_u_, n_h_ <span style="color:#f92672">*</span> n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, <span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> Lyp <span style="color:#f92672">=</span> Lup_Luf_Lyp.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">*</span> n_u_, n_h_ <span style="color:#f92672">*</span> n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>,
+</span></span><span style="display:flex;"><span>                                n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// aka: R_f
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix R_56_16 <span style="color:#f92672">=</span> L_.submat(n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_), <span style="color:#ae81ff">0</span>,
+</span></span><span style="display:flex;"><span>                             <span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">*</span> (n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, L_.n_cols <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// from van Overschee subid.m:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// Rf = R((2*m+l)*i+1:2*(m+l)*i,:);   % Future outputs
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  Matrix U;
+</span></span><span style="display:flex;"><span>  Matrix V;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">switch</span> (wt) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">case</span> kSSIDNone: {
+</span></span><span style="display:flex;"><span>      <span style="color:#75715e">// No weighting. (what van Overschee calls &#34;N4SID&#34;)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      Matrix O_k_sans_Qt <span style="color:#f92672">=</span> Lup <span style="color:#f92672">*</span> R_11_14 <span style="color:#f92672">+</span> Lyp <span style="color:#f92672">*</span> R_44_14;
+</span></span><span style="display:flex;"><span>      arma<span style="color:#f92672">::</span>svd(U, s_, V, O_k_sans_Qt, <span style="color:#e6db74">&#34;std&#34;</span>);
+</span></span><span style="display:flex;"><span>    } <span style="color:#66d9ef">break</span>;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">case</span> kSSIDMOESP: {
+</span></span><span style="display:flex;"><span>      <span style="color:#75715e">// MOESP weighting
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      <span style="color:#75715e">// This is what they use in the &#34;robust&#34; algorithm van Overschee, de Moor
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      <span style="color:#75715e">// 1996
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      Matrix Pi <span style="color:#f92672">=</span> Matrix(<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">*</span> n_u_, <span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">*</span> n_u_, fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">-</span>
+</span></span><span style="display:flex;"><span>                  R_23_13.t() <span style="color:#f92672">*</span> inv(R_23_13 <span style="color:#f92672">*</span> R_23_13.t()) <span style="color:#f92672">*</span> R_23_13;
+</span></span><span style="display:flex;"><span>      Matrix O_k_ortho_Uf_sans_Qt <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>          join_horiz((Lup <span style="color:#f92672">*</span> R_11_13 <span style="color:#f92672">+</span> Lyp <span style="color:#f92672">*</span> R_44_13) <span style="color:#f92672">*</span> Pi, Lyp <span style="color:#f92672">*</span> R_44);
+</span></span><span style="display:flex;"><span>      svd(U, s_, V, O_k_ortho_Uf_sans_Qt, <span style="color:#e6db74">&#34;std&#34;</span>);
+</span></span><span style="display:flex;"><span>    } <span style="color:#66d9ef">break</span>;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">case</span> kSSIDCVA: {
+</span></span><span style="display:flex;"><span>      <span style="color:#75715e">// CVA weighting
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      <span style="color:#75715e">// See van Overschee&#39;s matlab code (subid.m):
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      <span style="color:#75715e">// https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      Matrix Pi <span style="color:#f92672">=</span> Matrix(<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">*</span> n_u_, <span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">*</span> n_u_, fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">-</span>
+</span></span><span style="display:flex;"><span>                  R_23_13.t() <span style="color:#f92672">*</span> inv(R_23_13 <span style="color:#f92672">*</span> R_23_13.t()) <span style="color:#f92672">*</span> R_23_13;
+</span></span><span style="display:flex;"><span>      Matrix O_k_ortho_Uf_sans_Qt <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>          join_horiz((Lup <span style="color:#f92672">*</span> R_11_13 <span style="color:#f92672">+</span> Lyp <span style="color:#f92672">*</span> R_44_13) <span style="color:#f92672">*</span> Pi, Lyp <span style="color:#f92672">*</span> R_44);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>      Matrix inv_w1;
+</span></span><span style="display:flex;"><span>      Matrix qt1;
+</span></span><span style="display:flex;"><span>      lq(inv_w1, qt1, R_56_16);  <span style="color:#75715e">// lq decomp of R_f (future output data)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      inv_w1 <span style="color:#f92672">=</span> trimatl(inv_w1);
+</span></span><span style="display:flex;"><span>      inv_w1 <span style="color:#f92672">=</span> inv_w1.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_y_ <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_y_ <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>      Matrix w_o_w <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>solve(
+</span></span><span style="display:flex;"><span>          inv_w1, O_k_ortho_Uf_sans_Qt);  <span style="color:#75715e">// alternatively
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>                                          <span style="color:#75715e">// pinv(inv_W1)*O_k_ortho_Uf_sans_Qt
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>      svd(U, s_, V, w_o_w, <span style="color:#e6db74">&#34;std&#34;</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>      U <span style="color:#f92672">=</span> inv_w1 <span style="color:#f92672">*</span> U;
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">break</span>;
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Truncate to model order (heart of ssid method)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">auto</span> s_hat <span style="color:#f92672">=</span> s_.subvec(<span style="color:#ae81ff">0</span>, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  Matrix diag_sqrt_s <span style="color:#f92672">=</span> diagmat(sqrt(s_hat));
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> u_hat <span style="color:#f92672">=</span> U.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, U.n_rows <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// get extended observability and controllability mats
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  ext_obs_t_ <span style="color:#f92672">=</span> u_hat <span style="color:#f92672">*</span> diag_sqrt_s;  <span style="color:#75715e">// extended observability matrix
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> SSID<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>Solve(data_t wt_dc) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// required submats
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">auto</span> R_56_14 <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      L_.submat(n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_), <span style="color:#ae81ff">0</span>, n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> <span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_y_) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>,
+</span></span><span style="display:flex;"><span>                n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> R_23_15 <span style="color:#f92672">=</span> L_.submat(n_h_ <span style="color:#f92672">*</span> n_u_, <span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>,
+</span></span><span style="display:flex;"><span>                           n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">+</span> n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> R_66_15 <span style="color:#f92672">=</span> L_.submat(n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">+</span> n_y_, <span style="color:#ae81ff">0</span>,
+</span></span><span style="display:flex;"><span>                           <span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">*</span> (n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>,
+</span></span><span style="display:flex;"><span>                           n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">+</span> n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> R_55_15 <span style="color:#f92672">=</span> L_.submat(n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_), <span style="color:#ae81ff">0</span>,
+</span></span><span style="display:flex;"><span>                           n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">+</span> n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>,
+</span></span><span style="display:flex;"><span>                           n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">+</span> n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">auto</span> R_56_15 <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      L_.submat(n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_), <span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_h_ <span style="color:#f92672">*</span> (n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>,
+</span></span><span style="display:flex;"><span>                n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">+</span> n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Solve for params using appropriate algorithm:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// robust deterministic/stochastic algorithm in van Overschee 1996
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// algorithm that the authors say &#34;works&#34; in practice.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">auto</span> ext_obs_tm1 <span style="color:#f92672">=</span> ext_obs_t_.submat(
+</span></span><span style="display:flex;"><span>      <span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, ext_obs_t_.n_rows <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span> <span style="color:#f92672">-</span> n_y_,
+</span></span><span style="display:flex;"><span>      ext_obs_t_.n_cols <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);  <span style="color:#75715e">// extended observability matrix
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// This is what textbook (1996) says:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// HOWEVER, do not know why but have to fill the last place with zeros like
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// authors&#39; matlab implementation (see `subid.m`)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// Otherwise, get ridiculous covariances (although A,C estimates are close to
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// same...)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix Tr <span style="color:#f92672">=</span> join_vert(
+</span></span><span style="display:flex;"><span>      join_horiz(pinv(ext_obs_t_) <span style="color:#f92672">*</span> R_56_14, Matrix(n_x_, n_y_, fill<span style="color:#f92672">::</span>zeros)),
+</span></span><span style="display:flex;"><span>      R_23_15);
+</span></span><span style="display:flex;"><span>  Matrix Tl <span style="color:#f92672">=</span> join_vert(pinv(ext_obs_tm1) <span style="color:#f92672">*</span> R_66_15, R_55_15);
+</span></span><span style="display:flex;"><span>  Matrix S <span style="color:#f92672">=</span> Tl <span style="color:#f92672">*</span> pinv(Tr);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Use alternative in van Overschee 1996, p. 129. Apparently, should ensure
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// stability.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  fit_.set_C(ext_obs_t_.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, ext_obs_t_.n_cols <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>));
+</span></span><span style="display:flex;"><span>  Matrix ext_obs_t_p1 <span style="color:#f92672">=</span> join_vert(
+</span></span><span style="display:flex;"><span>      ext_obs_t_.submat(n_y_, <span style="color:#ae81ff">0</span>, ext_obs_t_.n_rows <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, ext_obs_t_.n_cols <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>),
+</span></span><span style="display:flex;"><span>      Matrix(n_y_, ext_obs_t_.n_cols, fill<span style="color:#f92672">::</span>zeros));
+</span></span><span style="display:flex;"><span>  fit_.set_A(pinv(ext_obs_t_) <span style="color:#f92672">*</span> ext_obs_t_p1);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// At this point, van Overschee &amp; de Moor suggest re-calculating ext_obs_t_,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// ext_obs_tm1 from (A, C) because it was just an approximation. This is
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  RecomputeExtObs();
+</span></span><span style="display:flex;"><span>  ext_obs_tm1 <span style="color:#f92672">=</span> ext_obs_t_.submat(
+</span></span><span style="display:flex;"><span>      <span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, ext_obs_t_.n_rows <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span> <span style="color:#f92672">-</span> n_y_,
+</span></span><span style="display:flex;"><span>      ext_obs_t_.n_cols <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);  <span style="color:#75715e">// extended observability matrix
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Tl <span style="color:#f92672">=</span> join_vert(pinv(ext_obs_tm1) <span style="color:#f92672">*</span> R_66_15, R_55_15);
+</span></span><span style="display:flex;"><span>  Tr <span style="color:#f92672">=</span> join_vert(
+</span></span><span style="display:flex;"><span>      join_horiz(pinv(ext_obs_t_) <span style="color:#f92672">*</span> R_56_14, Matrix(n_x_, n_y_, fill<span style="color:#f92672">::</span>zeros)),
+</span></span><span style="display:flex;"><span>      R_23_15);
+</span></span><span style="display:flex;"><span>  S <span style="color:#f92672">=</span> Tl <span style="color:#f92672">*</span> pinv(Tr);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Matrix Lcurly <span style="color:#f92672">=</span> S.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_x_ <span style="color:#f92672">+</span> n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">*</span> pinv(ext_obs_t_);
+</span></span><span style="display:flex;"><span>  Matrix Mcurly <span style="color:#f92672">=</span> pinv(ext_obs_tm1);
+</span></span><span style="display:flex;"><span>  Matrix Pcurly <span style="color:#f92672">=</span> Tl <span style="color:#f92672">-</span> Lcurly <span style="color:#f92672">*</span> R_56_15;
+</span></span><span style="display:flex;"><span>  Vector Pvec <span style="color:#f92672">=</span> vectorise(Pcurly);
+</span></span><span style="display:flex;"><span>  Matrix Qcurly <span style="color:#f92672">=</span> R_23_15;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Identify [D; B], assuming D=0 and ensuring DC gain is correct
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix <span style="color:#a6e22e">sum_QcurlyT_kron_Ncurly</span>(
+</span></span><span style="display:flex;"><span>      (n_h_ <span style="color:#f92672">*</span> (<span style="color:#ae81ff">2</span> <span style="color:#f92672">*</span> n_u_ <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">+</span> n_y_) <span style="color:#f92672">*</span> (n_y_ <span style="color:#f92672">+</span> n_x_), n_u_ <span style="color:#f92672">*</span> (n_y_ <span style="color:#f92672">+</span> n_x_),
+</span></span><span style="display:flex;"><span>      fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Matrix <span style="color:#a6e22e">eye_ext_obs_tm1</span>(n_y_ <span style="color:#f92672">+</span> ext_obs_tm1.n_rows, n_y_ <span style="color:#f92672">+</span> ext_obs_tm1.n_cols,
+</span></span><span style="display:flex;"><span>                         fill<span style="color:#f92672">::</span>eye);
+</span></span><span style="display:flex;"><span>  eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>,
+</span></span><span style="display:flex;"><span>                         eye_ext_obs_tm1.n_cols <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span> ext_obs_tm1;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// van Overschee (1996) p. 126
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix N1_Tl <span style="color:#f92672">=</span> <span style="color:#f92672">-</span>Lcurly;
+</span></span><span style="display:flex;"><span>  N1_Tl.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, N1_Tl.n_cols <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">+=</span>
+</span></span><span style="display:flex;"><span>      join_horiz(Matrix(n_x_, n_y_, fill<span style="color:#f92672">::</span>zeros), Mcurly);
+</span></span><span style="display:flex;"><span>  N1_Tl.submat(n_x_, <span style="color:#ae81ff">0</span>, n_x_ <span style="color:#f92672">+</span> n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">+=</span>
+</span></span><span style="display:flex;"><span>      Matrix(n_y_, n_y_, fill<span style="color:#f92672">::</span>eye);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Matrix <span style="color:#a6e22e">Nk_Tl</span>(N1_Tl.n_rows, N1_Tl.n_cols, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  Matrix N_k;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t k <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; k <span style="color:#f92672">&lt;</span> n_h_; k<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">auto</span> Qcurly_k <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>        Qcurly.submat(n_u_ <span style="color:#f92672">*</span> k, <span style="color:#ae81ff">0</span>, n_u_ <span style="color:#f92672">*</span> (k <span style="color:#f92672">+</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, Qcurly.n_cols <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    Nk_Tl.zeros();
+</span></span><span style="display:flex;"><span>    Nk_Tl.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_x_ <span style="color:#f92672">+</span> n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, Nk_Tl.n_cols <span style="color:#f92672">-</span> k <span style="color:#f92672">*</span> n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>        N1_Tl.submat(<span style="color:#ae81ff">0</span>, k <span style="color:#f92672">*</span> n_y_, N1_Tl.n_rows <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, N1_Tl.n_cols <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>    N_k <span style="color:#f92672">=</span> Nk_Tl <span style="color:#f92672">*</span> eye_ext_obs_tm1;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    sum_QcurlyT_kron_Ncurly <span style="color:#f92672">+=</span> kron(Qcurly_k.t(), N_k);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Matrix err_vec;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (wt_dc <span style="color:#f92672">&gt;</span> <span style="color:#ae81ff">0</span>) {
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// Constraints enforced by weighted least squares
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// Reference:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// Excited Industrial Systems. Conference in Decision and Control.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// constraint 1: assume D=0 --&gt; remove the components for Dvec (this is
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// actually a hard constraint in that it ignores D)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Matrix sum_QcurlyT_kron_Ncurly_db <span style="color:#f92672">=</span> sum_QcurlyT_kron_Ncurly;
+</span></span><span style="display:flex;"><span>    sum_QcurlyT_kron_Ncurly <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>        Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ <span style="color:#f92672">*</span> n_u_);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    size_t kkk <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">for</span> (size_t k <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>; k <span style="color:#f92672">&lt;</span> (n_u_ <span style="color:#f92672">+</span> <span style="color:#ae81ff">1</span>); k<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>      size_t start_idx <span style="color:#f92672">=</span> k <span style="color:#f92672">*</span> (n_y_ <span style="color:#f92672">+</span> n_x_) <span style="color:#f92672">-</span> n_x_;
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">for</span> (size_t kk <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; kk <span style="color:#f92672">&lt;</span> n_x_; kk<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>        sum_QcurlyT_kron_Ncurly.col(kkk) <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>            sum_QcurlyT_kron_Ncurly_db.col(start_idx <span style="color:#f92672">+</span> kk);
+</span></span><span style="display:flex;"><span>        kkk<span style="color:#f92672">++</span>;
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// constraint 2: Make sure DC I/O gain is correct
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Matrix b_to_g0 <span style="color:#f92672">=</span> fit_.C() <span style="color:#f92672">*</span> inv(Matrix(n_x_, n_x_, fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">-</span> fit_.A());
+</span></span><span style="display:flex;"><span>    Matrix Pvec_Gvec <span style="color:#f92672">=</span> join_vert(Pvec, vectorise(g_dc_));
+</span></span><span style="display:flex;"><span>    Matrix eye_kron_b_to_g0 <span style="color:#f92672">=</span> kron(Matrix(n_u_, n_u_, fill<span style="color:#f92672">::</span>eye), b_to_g0);
+</span></span><span style="display:flex;"><span>    Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>        join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// WEIGHTED LS
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// Important in practice because I care a lot about at least getting the DC
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// gain correct. Put x weight on minimizing error at DC, relative to others
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Matrix <span style="color:#a6e22e">w</span>(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows,
+</span></span><span style="display:flex;"><span>             sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill<span style="color:#f92672">::</span>eye);
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// Make weight on minimizing DC error immense so at least that
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// should be nailed.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    size_t start_row <span style="color:#f92672">=</span> sum_QcurlyT_kron_Ncurly.n_rows;
+</span></span><span style="display:flex;"><span>    size_t start_col <span style="color:#f92672">=</span> sum_QcurlyT_kron_Ncurly.n_rows;
+</span></span><span style="display:flex;"><span>    size_t stop_row <span style="color:#f92672">=</span> w.n_rows <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>    size_t stop_col <span style="color:#f92672">=</span> w.n_cols <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// weight with data length?
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    w.submat(start_row, start_col, stop_row, stop_col) <span style="color:#f92672">*=</span> wt_dc;
+</span></span><span style="display:flex;"><span>    Vector b_vec <span style="color:#f92672">=</span> inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() <span style="color:#f92672">*</span> w <span style="color:#f92672">*</span>
+</span></span><span style="display:flex;"><span>                       sum_QcurlyT_kron_Ncurly_b_to_g0) <span style="color:#f92672">*</span>
+</span></span><span style="display:flex;"><span>                   sum_QcurlyT_kron_Ncurly_b_to_g0.t() <span style="color:#f92672">*</span> w <span style="color:#f92672">*</span> Pvec_Gvec;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// Calculate residuals and their cov.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// Because I&#39;ve added constraints, I need to re-calculate the right term
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// with b_vec instead of how van Overschee do in final algorithm.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    err_vec <span style="color:#f92672">=</span> Pvec <span style="color:#f92672">-</span> sum_QcurlyT_kron_Ncurly <span style="color:#f92672">*</span> b_vec;
+</span></span><span style="display:flex;"><span>  } <span style="color:#66d9ef">else</span> {
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// default way: *no* constraint on G0 or D=0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Vector db_vec <span style="color:#f92672">=</span> pinv(sum_QcurlyT_kron_Ncurly) <span style="color:#f92672">*</span> Pvec;
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// TODO(mfbolus) n.b., this gets thrown away...
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    fit_.set_B(Matrix(db_vec.memptr() <span style="color:#f92672">+</span> (n_u_ <span style="color:#f92672">*</span> n_y_), n_x_, n_u_));
+</span></span><span style="display:flex;"><span>    err_vec <span style="color:#f92672">=</span> Pvec <span style="color:#f92672">-</span> sum_QcurlyT_kron_Ncurly <span style="color:#f92672">*</span> db_vec;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// TODO(mfbolus): Something is wrong with the error calculation above.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// Use the way van overschee does it in `subid.m`
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// WARNING: this ignores any above constraints, so Q, R will be approximate...
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix err <span style="color:#f92672">=</span> Tl <span style="color:#f92672">-</span> S <span style="color:#f92672">*</span> Tr;
+</span></span><span style="display:flex;"><span>  Matrix cov_err <span style="color:#f92672">=</span> err <span style="color:#f92672">*</span> err.t();
+</span></span><span style="display:flex;"><span>  fit_.set_Q(cov_err.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_x_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>));
+</span></span><span style="display:flex;"><span>  fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ <span style="color:#f92672">+</span> n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, n_x_ <span style="color:#f92672">+</span> n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>));
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> Fit<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> SSID<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>RecomputeExtObs() {
+</span></span><span style="display:flex;"><span>  ext_obs_t_.submat(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, ext_obs_t_.n_cols <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span> fit_.C();
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t k <span style="color:#f92672">=</span> <span style="color:#ae81ff">2</span>; k <span style="color:#f92672">&lt;</span> (n_h_ <span style="color:#f92672">+</span> <span style="color:#ae81ff">1</span>); k<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    ext_obs_t_.submat((k <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">*</span> n_y_, <span style="color:#ae81ff">0</span>, k <span style="color:#f92672">*</span> n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>, ext_obs_t_.n_cols <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>        ext_obs_t_.submat((k <span style="color:#f92672">-</span> <span style="color:#ae81ff">2</span>) <span style="color:#f92672">*</span> n_y_, <span style="color:#ae81ff">0</span>, (k <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">*</span> n_y_ <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>,
+</span></span><span style="display:flex;"><span>                          ext_obs_t_.n_cols <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>) <span style="color:#f92672">*</span>
+</span></span><span style="display:flex;"><span>        fit_.A();
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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+  <article class="markdown"><h1 id="ldsctrlest_hlds_gaussianh">
+  ldsCtrlEst_h/lds_gaussian.h
+  <a class="anchor" href="#ldsctrlest_hlds_gaussianh">#</a>
+</h1>
+<p><code>glds</code> namespace  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/">lds::gaussian</a></strong> <br>Linear Dynamical Systems with Gaussian observations.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations (<code>[lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)</code>).</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_GAUSSIAN_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_GAUSSIAN_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// namespace
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> gaussian {
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// insert any Gaussian-specific things here...
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}  <span style="color:#75715e">// namespace gaussian
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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+  <article class="markdown"><h1 id="ldsctrlest_hlds_gaussian_ctrlh">
+  ldsCtrlEst_h/lds_gaussian_ctrl.h
+  <a class="anchor" href="#ldsctrlest_hlds_gaussian_ctrlh">#</a>
+</h1>
+<p>GLDS Controller.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/">lds::gaussian</a></strong> <br>Linear Dynamical Systems with Gaussian observations.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/">lds::gaussian::Controller</a></strong> <br>Gaussian-observation <a href="">Controller</a> Type.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/">lds::gaussian::Controller</a>). It inherits functionality from the underlying GLDS model type (<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/">lds::gaussian::System</a>), including state estimation.</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// namespace
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_gaussian.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">// system
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_gaussian_sys.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">// controller
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_ctrl.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> gaussian {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">Controller</span> <span style="color:#f92672">:</span> <span style="color:#66d9ef">public</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;</span> {
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">public</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> set_y_ref(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> y_ref) <span style="color:#66d9ef">override</span> {
+</span></span><span style="display:flex;"><span>    Reassign(y_ref_,y_ref);
+</span></span><span style="display:flex;"><span>    cx_ref_ <span style="color:#f92672">=</span> y_ref <span style="color:#f92672">-</span> sys_.d();
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// make sure base class template methods available
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Controller;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Control;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>ControlOutputReference;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>sys;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Kc;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Kc_inty;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Kc_u;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>g_design;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>u_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>x_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>y_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>control_type;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_sys;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_g_design;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_u_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_x_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_y_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_Kc;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_Kc_inty;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_Kc_u;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_tau_awu;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_control_type;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Reset;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Print;
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace gaussian
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
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diff --git a/docs/docs/api/files/lds__gaussian__fit_8h/index.html b/docs/docs/api/files/lds__gaussian__fit_8h/index.html
new file mode 100644
index 00000000..297c7577
--- /dev/null
+++ b/docs/docs/api/files/lds__gaussian__fit_8h/index.html
@@ -0,0 +1,353 @@
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+  <article class="markdown"><h1 id="ldsctrlest_hlds_gaussian_fith">
+  ldsCtrlEst_h/lds_gaussian_fit.h
+  <a class="anchor" href="#ldsctrlest_hlds_gaussian_fith">#</a>
+</h1>
+<p>GLDS fit type.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/">lds::gaussian</a></strong> <br>Linear Dynamical Systems with Gaussian observations.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/">lds::gaussian::Fit</a></strong> <br>GLDS <a href="">Fit</a> Type.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_GAUSSIAN_FIT_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// namespace
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_gaussian.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">// fit type
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_fit.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> gaussian {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">Fit</span> <span style="color:#f92672">:</span> <span style="color:#66d9ef">public</span> lds<span style="color:#f92672">::</span>Fit {
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">public</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  Fit() <span style="color:#f92672">=</span> <span style="color:#66d9ef">default</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> R() <span style="color:#66d9ef">const</span> <span style="color:#66d9ef">override</span> { <span style="color:#66d9ef">return</span> R_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_R</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> R) <span style="color:#66d9ef">override</span> {
+</span></span><span style="display:flex;"><span>    Reassign(R_, R);
+</span></span><span style="display:flex;"><span>    ForceSymPD(R_);
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  View <span style="color:#a6e22e">h</span>(Matrix<span style="color:#f92672">&amp;</span> y, <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> x, size_t t) <span style="color:#66d9ef">override</span> {
+</span></span><span style="display:flex;"><span>    y.col(t) <span style="color:#f92672">=</span> C_ <span style="color:#f92672">*</span> x.col(t) <span style="color:#f92672">+</span> d_;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span> y.col(t);
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>};  <span style="color:#75715e">// namespace gaussian
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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diff --git a/docs/docs/api/files/lds__gaussian__fit__em_8h/index.html b/docs/docs/api/files/lds__gaussian__fit__em_8h/index.html
new file mode 100644
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+        <li><a href="#namespaces">Namespaces</a></li>
+        <li><a href="#classes">Classes</a></li>
+        <li><a href="#detailed-description">Detailed Description</a></li>
+        <li><a href="#source-code">Source code</a></li>
+      </ul>
+    </li>
+  </ul>
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+  <article class="markdown"><h1 id="ldsctrlest_hlds_gaussian_fit_emh">
+  ldsCtrlEst_h/lds_gaussian_fit_em.h
+  <a class="anchor" href="#ldsctrlest_hlds_gaussian_fit_emh">#</a>
+</h1>
+<p>GLDS E-M fit type.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/">lds::gaussian</a></strong> <br>Linear Dynamical Systems with Gaussian observations.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/">lds::gaussian::FitEM</a></strong> <br>GLDS E-M <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/">Fit</a> Type.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (<code>lds::gaussian::emFit_t</code>).</p>
+<p>References: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).</p>
+<p>[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_fit_em.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_gaussian_fit.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> gaussian {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">FitEM</span> <span style="color:#f92672">:</span> <span style="color:#66d9ef">public</span> EM<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;</span> {
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">public</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> EM<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>EM;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">private</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> MaximizeOutput() <span style="color:#66d9ef">override</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">MaximizeMeasurement</span>() <span style="color:#66d9ef">override</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">RecurseKe</span>(Matrix<span style="color:#f92672">&amp;</span> Ke, Cube<span style="color:#f92672">&amp;</span> P_pre, Cube<span style="color:#f92672">&amp;</span> P_post, size_t t) <span style="color:#66d9ef">override</span>;
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace gaussian
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
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+  ldsCtrlEst_h/lds_gaussian_fit_ssid.h
+  <a class="anchor" href="#ldsctrlest_hlds_gaussian_fit_ssidh">#</a>
+</h1>
+<p>GLDS SSID fit type.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/">lds::gaussian</a></strong> <br>Linear Dynamical Systems with Gaussian observations.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/">lds::gaussian::FitSSID</a></strong> <br>Subspace Identification (<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">SSID</a>) for GLDS.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (<code>lds::gaussian::ssidFit_t</code>).</p>
+<p>References: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_fit_ssid.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_gaussian_fit.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> gaussian {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">FitSSID</span> <span style="color:#f92672">:</span> <span style="color:#66d9ef">public</span> SSID<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;</span> {
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">public</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> SSID<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>SSID;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> SSID<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>Run;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">private</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> SSID<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>CreateHankelDataMat;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> SSID<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>CalcSVD;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> SSID<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>Solve;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">DecomposeData</span>() <span style="color:#66d9ef">override</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">SolveVanOverschee</span>();
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace gaussian
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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+  ldsCtrlEst_h/lds_gaussian_sctrl.h
+  <a class="anchor" href="#ldsctrlest_hlds_gaussian_sctrlh">#</a>
+</h1>
+<p>GLDS switched controller type.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/">lds::gaussian</a></strong> <br>Linear Dynamical Systems with Gaussian observations.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/">lds::gaussian::SwitchedController</a></strong> <br>Gaussian-observation <a href="">SwitchedController</a> Type.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/">lds::gaussian::SwitchedController</a>).</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// controller type
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_gaussian_ctrl.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">// switched controller
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_sctrl.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> gaussian {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">SwitchedController</span> <span style="color:#f92672">:</span> <span style="color:#66d9ef">public</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;</span> {
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">public</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> set_y_ref(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> y_ref) <span style="color:#66d9ef">override</span> {
+</span></span><span style="display:flex;"><span>    Reassign(y_ref_, y_ref);
+</span></span><span style="display:flex;"><span>    cx_ref_ <span style="color:#f92672">=</span> y_ref <span style="color:#f92672">-</span> sys_.d();
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// make sure base class template methods available
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>SwitchedController;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Switch;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Control;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>ControlOutputReference;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>sys;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Kc;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Kc_inty;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Kc_u;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>g_design;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>u_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>x_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>y_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>control_type;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_g_design;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_u_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_x_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_y_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_Kc;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_Kc_inty;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_Kc_u;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_tau_awu;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_control_type;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Reset;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Print;
+</span></span><span style="display:flex;"><span>};  <span style="color:#75715e">// SwitchedController
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}  <span style="color:#75715e">// namespace gaussian
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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+  src/lds_gaussian_sys.cpp
+  <a class="anchor" href="#srclds_gaussian_syscpp">#</a>
+</h1>
+<p>GLDS base type.  <a href="#detailed-description">More&hellip;</a></p>
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+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (<code>lds::gaussian::sys_t</code>). It inherits functionality from the underlying linear dynamical system (<code>lds::sys_t</code>).</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;ldsCtrlEst_h/lds_gaussian_sys.h&gt;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>lds<span style="color:#f92672">::</span>gaussian<span style="color:#f92672">::</span>System<span style="color:#f92672">::</span>System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0,
+</span></span><span style="display:flex;"><span>                   data_t q0, data_t r0)
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> lds<span style="color:#f92672">::</span>System(n_u, n_x, n_y, dt, p0, q0) {
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  R_.zeros(n_y, n_y);
+</span></span><span style="display:flex;"><span>  R_.diag().fill(r0);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  do_recurse_Ke_<span style="color:#f92672">=</span>true;
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// recursively estimate Ke
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#66d9ef">void</span> lds<span style="color:#f92672">::</span>gaussian<span style="color:#f92672">::</span>System<span style="color:#f92672">::</span>RecurseKe() {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>do_recurse_Ke_) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span>;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// predict covariance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  P_ <span style="color:#f92672">=</span> A_ <span style="color:#f92672">*</span> P_ <span style="color:#f92672">*</span> A_.t() <span style="color:#f92672">+</span> Q_;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// calc Kalman gain
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Ke_ <span style="color:#f92672">=</span> P_ <span style="color:#f92672">*</span> C_.t() <span style="color:#f92672">*</span> inv_sympd(C_ <span style="color:#f92672">*</span> P_ <span style="color:#f92672">*</span> C_.t() <span style="color:#f92672">+</span> R_);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// update covariance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// Reference: Ghahramani et Hinton (1996)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  P_ <span style="color:#f92672">=</span> P_ <span style="color:#f92672">-</span> Ke_ <span style="color:#f92672">*</span> C_ <span style="color:#f92672">*</span> P_;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (do_adapt_m) {
+</span></span><span style="display:flex;"><span>    P_m_ <span style="color:#f92672">+=</span> Q_m_;  <span style="color:#75715e">// A_m = I (i.e., random walk)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Ke_m_ <span style="color:#f92672">=</span> P_m_ <span style="color:#f92672">*</span> C_.t() <span style="color:#f92672">*</span> inv_sympd(C_ <span style="color:#f92672">*</span> P_m_ <span style="color:#f92672">*</span> C_.t() <span style="color:#f92672">+</span> R_);
+</span></span><span style="display:flex;"><span>    P_m_ <span style="color:#f92672">=</span> P_m_ <span style="color:#f92672">-</span> Ke_m_ <span style="color:#f92672">*</span> C_ <span style="color:#f92672">*</span> P_m_;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// Simulate
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#66d9ef">const</span> lds<span style="color:#f92672">::</span>Vector<span style="color:#f92672">&amp;</span> lds<span style="color:#f92672">::</span>gaussian<span style="color:#f92672">::</span>System<span style="color:#f92672">::</span>Simulate(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> u_tm1){
+</span></span><span style="display:flex;"><span>  f(u_tm1, true);<span style="color:#75715e">//simulate dynamics with noise added
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  h();<span style="color:#75715e">//output
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  z_ <span style="color:#f92672">=</span> y_ <span style="color:#f92672">+</span> arma<span style="color:#f92672">::</span>mvnrnd(Vector(n_y_).fill(<span style="color:#ae81ff">0</span>), R_);<span style="color:#75715e">//measure
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">return</span> z_;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> lds<span style="color:#f92672">::</span>gaussian<span style="color:#f92672">::</span>System<span style="color:#f92672">::</span>Print() {
+</span></span><span style="display:flex;"><span>  lds<span style="color:#f92672">::</span>System<span style="color:#f92672">::</span>Print();
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;R: </span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span> <span style="color:#f92672">&lt;&lt;</span> R_ <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>}
+</span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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diff --git a/docs/docs/api/files/lds__gaussian__sys_8h/index.html b/docs/docs/api/files/lds__gaussian__sys_8h/index.html
new file mode 100644
index 00000000..de1f5c23
--- /dev/null
+++ b/docs/docs/api/files/lds__gaussian__sys_8h/index.html
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+  <article class="markdown"><h1 id="ldsctrlest_hlds_gaussian_sysh">
+  ldsCtrlEst_h/lds_gaussian_sys.h
+  <a class="anchor" href="#ldsctrlest_hlds_gaussian_sysh">#</a>
+</h1>
+<p>GLDS base type.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/">lds::gaussian</a></strong> <br>Linear Dynamical Systems with Gaussian observations.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/">lds::gaussian::System</a></strong> <br>Gaussian LDS Type.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (<code>[lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/)</code>). It inherits functionality from the underlying linear dynamical system (<code>[lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)</code>).</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_GAUSSIAN_SYS_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// namespace
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_gaussian.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">// system
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_sys.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> gaussian {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">System</span> <span style="color:#f92672">:</span> <span style="color:#66d9ef">public</span> lds<span style="color:#f92672">::</span>System {
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">public</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  System() <span style="color:#f92672">=</span> <span style="color:#66d9ef">default</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  System(std<span style="color:#f92672">::</span>size_t n_u, std<span style="color:#f92672">::</span>size_t n_x, std<span style="color:#f92672">::</span>size_t n_y, data_t dt,
+</span></span><span style="display:flex;"><span>         data_t p0 <span style="color:#f92672">=</span> kDefaultP0, data_t q0 <span style="color:#f92672">=</span> kDefaultQ0,
+</span></span><span style="display:flex;"><span>         data_t r0 <span style="color:#f92672">=</span> kDefaultR0);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> Simulate(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> u_tm1) <span style="color:#66d9ef">override</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// get methods
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> R() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> R_; };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set methods
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_Q</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> Q) {
+</span></span><span style="display:flex;"><span>    lds<span style="color:#f92672">::</span>System<span style="color:#f92672">::</span>set_Q(Q);
+</span></span><span style="display:flex;"><span>    do_recurse_Ke_ <span style="color:#f92672">=</span> true;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_R</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> R) {
+</span></span><span style="display:flex;"><span>    Reassign(R_, R);
+</span></span><span style="display:flex;"><span>    do_recurse_Ke_ <span style="color:#f92672">=</span> true;
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_Ke</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> Ke) {
+</span></span><span style="display:flex;"><span>    Reassign(Ke_, Ke);
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// if users have set Ke, they must not want to calculate it online.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    do_recurse_Ke_ <span style="color:#f92672">=</span> false;
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_Ke_m</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> Ke_m) {
+</span></span><span style="display:flex;"><span>    Reassign(Ke_m_, Ke_m);
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// if users have set Ke, they must not want to calculate it online.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    do_recurse_Ke_ <span style="color:#f92672">=</span> false;
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Print</span>();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">protected</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> h() <span style="color:#66d9ef">override</span> {
+</span></span><span style="display:flex;"><span>    cx_ <span style="color:#f92672">=</span> C_ <span style="color:#f92672">*</span> x_;
+</span></span><span style="display:flex;"><span>    y_ <span style="color:#f92672">=</span> cx_ <span style="color:#f92672">+</span> d_;
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Vector <span style="color:#a6e22e">h_</span>(Vector x) <span style="color:#66d9ef">override</span> { <span style="color:#66d9ef">return</span> C_ <span style="color:#f92672">*</span> x <span style="color:#f92672">+</span> d_; };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">RecurseKe</span>() <span style="color:#66d9ef">override</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Gaussian-output-specific
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix R_;              
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">bool</span> do_recurse_Ke_{};  
+</span></span><span style="display:flex;"><span>};  <span style="color:#75715e">// System
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}  <span style="color:#75715e">// namespace gaussian
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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diff --git a/docs/docs/api/files/lds__poisson_8h/index.html b/docs/docs/api/files/lds__poisson_8h/index.html
new file mode 100644
index 00000000..5f177a82
--- /dev/null
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+  <article class="markdown"><h1 id="ldsctrlest_hlds_poissonh">
+  ldsCtrlEst_h/lds_poisson.h
+  <a class="anchor" href="#ldsctrlest_hlds_poissonh">#</a>
+</h1>
+<p><code>plds</code> namespace  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/">lds::poisson</a></strong> <br>Linear Dynamical Systems with Poisson observations.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file declares and partially defines the namespace for linear dynamical systems with Poisson observations (<code>[lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)</code>).</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_POISSON_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_POISSON_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> poisson {
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// TODO(mfbolus): Not sure if defining these as static here makes the most
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// sense. Is there a downside to letting multiple poisson System objects share a
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// common random number generator?
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#66d9ef">static</span> std<span style="color:#f92672">::</span>random_device rd;  
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">static</span> std<span style="color:#f92672">::</span>mt19937 rng <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>mt19937(
+</span></span><span style="display:flex;"><span>    rd());  
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace poisson
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
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+  ldsCtrlEst_h/lds_poisson_ctrl.h
+  <a class="anchor" href="#ldsctrlest_hlds_poisson_ctrlh">#</a>
+</h1>
+<p>PLDS controller type.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/">lds::poisson</a></strong> <br>Linear Dynamical Systems with Poisson observations.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/">lds::poisson::Controller</a></strong> <br>PLDS <a href="">Controller</a> Type.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system (<code>[lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/)</code>). It inherits functionality from the underlying PLDS model type (<code>[lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)</code>), including state estimation.</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_POISSON_CTRL_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_POISSON_CTRL_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// namespace
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_poisson.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">// system type
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_poisson_sys.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">// control type
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_ctrl.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> poisson {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">Controller</span> <span style="color:#f92672">:</span> <span style="color:#66d9ef">public</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;</span> {
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">public</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> set_y_ref(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> y_ref) <span style="color:#66d9ef">override</span> {
+</span></span><span style="display:flex;"><span>    Reassign(y_ref_, y_ref);
+</span></span><span style="display:flex;"><span>    lds<span style="color:#f92672">::</span>Limit(y_ref_, kYRefLb, lds<span style="color:#f92672">::</span>kInf);
+</span></span><span style="display:flex;"><span>    cx_ref_ <span style="color:#f92672">=</span> log(y_ref_) <span style="color:#f92672">-</span> sys_.d();
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// make sure base class template methods available
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Controller;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Control;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>ControlOutputReference;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>sys;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Kc;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Kc_inty;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Kc_u;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>g_design;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>u_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>x_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>y_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>control_type;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_sys;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_g_design;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_u_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_x_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_y_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_Kc;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_Kc_inty;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_Kc_u;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_tau_awu;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_control_type;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Reset;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Print;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">private</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">constexpr</span> <span style="color:#66d9ef">static</span> <span style="color:#66d9ef">const</span> data_t kYRefLb <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      <span style="color:#ae81ff">1e-4</span>;  
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace poisson
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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diff --git a/docs/docs/api/files/lds__poisson__fit_8h/index.html b/docs/docs/api/files/lds__poisson__fit_8h/index.html
new file mode 100644
index 00000000..e4cba33c
--- /dev/null
+++ b/docs/docs/api/files/lds__poisson__fit_8h/index.html
@@ -0,0 +1,361 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
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+  <meta charset="UTF-8">
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+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
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+  <article class="markdown"><h1 id="ldsctrlest_hlds_poisson_fith">
+  ldsCtrlEst_h/lds_poisson_fit.h
+  <a class="anchor" href="#ldsctrlest_hlds_poisson_fith">#</a>
+</h1>
+<p>PLDS base fit type.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/">lds::poisson</a></strong> <br>Linear Dynamical Systems with Poisson observations.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/">lds::poisson::Fit</a></strong> <br>PLDS <a href="">Fit</a> Type.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_POISSON_FIT_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_POISSON_FIT_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// namespace
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_poisson.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">// fit
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_fit.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> poisson {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">Fit</span> <span style="color:#f92672">:</span> <span style="color:#66d9ef">public</span> lds<span style="color:#f92672">::</span>Fit {
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">public</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  Fit() <span style="color:#f92672">=</span> <span style="color:#66d9ef">default</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)
+</span></span><span style="display:flex;"><span>      <span style="color:#f92672">:</span> lds<span style="color:#f92672">::</span>Fit(n_u, n_x, n_y, dt){};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  View <span style="color:#a6e22e">h</span>(Matrix<span style="color:#f92672">&amp;</span> y, <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> x, size_t t) <span style="color:#66d9ef">override</span> {
+</span></span><span style="display:flex;"><span>    y.col(t) <span style="color:#f92672">=</span> exp(C_ <span style="color:#f92672">*</span> x.col(t) <span style="color:#f92672">+</span> d_);
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span> y.col(t);
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_R</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> R) <span style="color:#66d9ef">override</span> {
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>cerr
+</span></span><span style="display:flex;"><span>        <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;WARNING: Cannot set R (R[0] = &#34;</span> <span style="color:#f92672">&lt;&lt;</span> R.at(<span style="color:#ae81ff">0</span>)
+</span></span><span style="display:flex;"><span>        <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;). No Gaussian measurement noise in Poisson observation model.</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> R() <span style="color:#66d9ef">const</span> <span style="color:#66d9ef">override</span> {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span> R_;
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>};  <span style="color:#75715e">// namespace poisson
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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+  <article class="markdown"><h1 id="ldsctrlest_hlds_poisson_fit_emh">
+  ldsCtrlEst_h/lds_poisson_fit_em.h
+  <a class="anchor" href="#ldsctrlest_hlds_poisson_fit_emh">#</a>
+</h1>
+<p>PLDS E-M fit type.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/">lds::poisson</a></strong> <br>Linear Dynamical Systems with Poisson observations.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/">lds::poisson::FitEM</a></strong> <br>PLDS E-M <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/">Fit</a> Type.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (<code>lds::gaussian::emFit_t</code>).</p>
+<p>References: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).</p>
+<p>[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.</p>
+<p>[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_POISSON_FIT_EM_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_fit_em.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_poisson_fit.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> poisson {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">FitEM</span> <span style="color:#f92672">:</span> <span style="color:#66d9ef">public</span> EM<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;</span> {
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">public</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> EM<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>EM;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">private</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> MaximizeOutput() <span style="color:#66d9ef">override</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">MaximizeMeasurement</span>() <span style="color:#66d9ef">override</span>{};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">RecurseKe</span>(Matrix<span style="color:#f92672">&amp;</span> Ke, Cube<span style="color:#f92672">&amp;</span> P_pre, Cube<span style="color:#f92672">&amp;</span> P_post, size_t t) <span style="color:#66d9ef">override</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  data_t <span style="color:#a6e22e">NewtonSolveC</span>();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">AnalyticalSolveD</span>();
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace poisson
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
+      
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+  <article class="markdown"><h1 id="ldsctrlest_hlds_poisson_fit_ssidh">
+  ldsCtrlEst_h/lds_poisson_fit_ssid.h
+  <a class="anchor" href="#ldsctrlest_hlds_poisson_fit_ssidh">#</a>
+</h1>
+<p>PLDS SSID fit type.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/">lds::poisson</a></strong> <br>Linear Dynamical Systems with Poisson observations.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/">lds::poisson::FitSSID</a></strong> <br>Subspace Identification (<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">SSID</a>) for PLDS.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm (<code>[lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/)</code>).</p>
+<p>References: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_POISSON_FIT_SSID_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_fit_ssid.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_poisson_fit.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> poisson {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">FitSSID</span> <span style="color:#f92672">:</span> <span style="color:#66d9ef">public</span> SSID<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;</span> {
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">public</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> SSID<span style="color:#f92672">&lt;</span>Fit<span style="color:#f92672">&gt;::</span>SSID;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">private</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> DecomposeData() <span style="color:#66d9ef">override</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">CalcCov</span>();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">PoissonToGaussianMoments</span>();
+</span></span><span style="display:flex;"><span>  Matrix cov_;
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace poisson
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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+  <article class="markdown"><h1 id="ldsctrlest_hlds_poisson_sctrlh">
+  ldsCtrlEst_h/lds_poisson_sctrl.h
+  <a class="anchor" href="#ldsctrlest_hlds_poisson_sctrlh">#</a>
+</h1>
+<p>PLDS switched controller type.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/">lds::poisson</a></strong> <br>Linear Dynamical Systems with Poisson observations.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/">lds::poisson::SwitchedController</a></strong> <br>Poisson-observation <a href="">SwitchedController</a> Type.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/">lds::poisson::SwitchedController</a>).</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_poisson_ctrl.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_sctrl.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> poisson {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">SwitchedController</span> <span style="color:#f92672">:</span> <span style="color:#66d9ef">public</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;</span> {
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">public</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> set_y_ref(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> y_ref) <span style="color:#66d9ef">override</span> {
+</span></span><span style="display:flex;"><span>    Reassign(y_ref_,y_ref);
+</span></span><span style="display:flex;"><span>    lds<span style="color:#f92672">::</span>Limit(y_ref_, kYRefLB, lds<span style="color:#f92672">::</span>kInf);
+</span></span><span style="display:flex;"><span>    cx_ref_ <span style="color:#f92672">=</span> log(y_ref_) <span style="color:#f92672">-</span> sys_.d();
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// make sure base class template methods available
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>SwitchedController;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Switch;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Control;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>ControlOutputReference;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>sys;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Kc;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Kc_inty;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Kc_u;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>g_design;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>u_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>x_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>y_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>control_type;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_g_design;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_u_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_x_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_y_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_Kc;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_Kc_inty;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_Kc_u;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_tau_awu;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_control_type;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Reset;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Print;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">private</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">constexpr</span> <span style="color:#66d9ef">static</span> data_t kYRefLB <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      <span style="color:#ae81ff">1e-4</span>;  
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace poisson
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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+  <article class="markdown"><h1 id="srclds_poisson_syscpp">
+  src/lds_poisson_sys.cpp
+  <a class="anchor" href="#srclds_poisson_syscpp">#</a>
+</h1>
+<p>PLDS base type.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (<code>lds::poisson::sys_t</code>). It inherits functionality from the underlying linear dynamical system (<code>lds::sys_t</code>).</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;ldsCtrlEst_h/lds_poisson_sys.h&gt;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>System<span style="color:#f92672">::</span>System(size_t n_u, size_t n_x, size_t n_y, data_t dt,
+</span></span><span style="display:flex;"><span>                             data_t p0, data_t q0)
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> lds<span style="color:#f92672">::</span>System(n_u, n_x, n_y, dt, p0, q0) {
+</span></span><span style="display:flex;"><span>  diag_y_ <span style="color:#f92672">=</span> diagmat(y_);
+</span></span><span style="display:flex;"><span>  pd_ <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>poisson_distribution<span style="color:#f92672">&lt;</span>size_t<span style="color:#f92672">&gt;</span>(<span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// Correct: Given measurement (z) and current input (u), update estimate of the
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// state, covar, output.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// see Eden et al. 2004
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#66d9ef">void</span> lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>System<span style="color:#f92672">::</span>RecurseKe() {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// predict covariance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  P_ <span style="color:#f92672">=</span> A_ <span style="color:#f92672">*</span> P_ <span style="color:#f92672">*</span> A_.t() <span style="color:#f92672">+</span> Q_;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// update cov
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  P_ <span style="color:#f92672">=</span> pinv(pinv(P_) <span style="color:#f92672">+</span> C_.t() <span style="color:#f92672">*</span> diag_y_ <span style="color:#f92672">*</span> C_);
+</span></span><span style="display:flex;"><span>  Ke_ <span style="color:#f92672">=</span> P_ <span style="color:#f92672">*</span> C_.t();
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (do_adapt_m) {
+</span></span><span style="display:flex;"><span>    P_m_ <span style="color:#f92672">+=</span> Q_m_;                                     <span style="color:#75715e">// predict (A_m = I)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    P_m_ <span style="color:#f92672">=</span> pinv(pinv(P_m_) <span style="color:#f92672">+</span> C_.t() <span style="color:#f92672">*</span> diag_y_ <span style="color:#f92672">*</span> C_);  <span style="color:#75715e">// update
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Ke_m_ <span style="color:#f92672">=</span> P_m_ <span style="color:#f92672">*</span> C_.t();
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// Simulate Measurement: z ~ Poisson(y)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#66d9ef">const</span> lds<span style="color:#f92672">::</span>Vector<span style="color:#f92672">&amp;</span> lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>System<span style="color:#f92672">::</span>Simulate(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> u_tm1) {
+</span></span><span style="display:flex;"><span>  f(u_tm1, true);  <span style="color:#75715e">// simulate dynamics with noise added
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  h();             <span style="color:#75715e">// output
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  z_.zeros();
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (std<span style="color:#f92672">::</span>size_t k <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; k <span style="color:#f92672">&lt;</span> n_y_; k<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// construct a Poisson distribution object with mean y[k]
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    pd_ <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>poisson_distribution<span style="color:#f92672">&lt;</span>size_t<span style="color:#f92672">&gt;</span>(y_[k]);
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// pull random sample from this distribution
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    z_[k] <span style="color:#f92672">=</span> pd_(rng);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> z_;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// ******************* SYS_T *******************
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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+  ldsCtrlEst_h/lds_poisson_sys.h
+  <a class="anchor" href="#ldsctrlest_hlds_poisson_sysh">#</a>
+</h1>
+<p>PLDS base type.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/">lds::poisson</a></strong> <br>Linear Dynamical Systems with Poisson observations.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/">lds::poisson::System</a></strong> <br>Poisson <a href="">System</a> type.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (<code>[lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)</code>). It inherits functionality from the underlying linear dynamical system (<code>[lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)</code>).</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_POISSON_SYS_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_POISSON_SYS_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// namespace
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_poisson.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">// system
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_sys.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// needed for Poisson random number generation
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;random&gt;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> poisson {
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">System</span> <span style="color:#f92672">:</span> <span style="color:#66d9ef">public</span> lds<span style="color:#f92672">::</span>System {
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">public</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  System() <span style="color:#f92672">=</span> <span style="color:#66d9ef">default</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  System(std<span style="color:#f92672">::</span>size_t n_u, std<span style="color:#f92672">::</span>size_t n_x, std<span style="color:#f92672">::</span>size_t n_y, data_t dt,
+</span></span><span style="display:flex;"><span>         data_t p0 <span style="color:#f92672">=</span> kDefaultP0, data_t q0 <span style="color:#f92672">=</span> kDefaultQ0);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> Simulate(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> u_tm1) <span style="color:#66d9ef">override</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">protected</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> h() <span style="color:#66d9ef">override</span> {
+</span></span><span style="display:flex;"><span>    cx_ <span style="color:#f92672">=</span> C_ <span style="color:#f92672">*</span> x_;
+</span></span><span style="display:flex;"><span>    y_ <span style="color:#f92672">=</span> exp(cx_ <span style="color:#f92672">+</span> d_);
+</span></span><span style="display:flex;"><span>    diag_y_.diag() <span style="color:#f92672">=</span> y_;
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Vector <span style="color:#a6e22e">h_</span>(Vector x) <span style="color:#66d9ef">override</span> { <span style="color:#66d9ef">return</span> exp(C_ <span style="color:#f92672">*</span> x <span style="color:#f92672">+</span> d_); };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">RecurseKe</span>() <span style="color:#66d9ef">override</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">private</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Poisson-output-specific
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix diag_y_;  
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>poisson_distribution<span style="color:#f92672">&lt;</span>size_t<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>      pd_;  
+</span></span><span style="display:flex;"><span>};  <span style="color:#75715e">// System
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}  <span style="color:#75715e">// namespace poisson
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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diff --git a/docs/docs/api/files/lds__sctrl_8h/index.html b/docs/docs/api/files/lds__sctrl_8h/index.html
new file mode 100644
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+<html lang="en" dir="ltr">
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+<meta name="viewport" content="width=device-width, initial-scale=1.0">
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+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sctrl_8h/">
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+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
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+</ul>
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+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
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+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
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+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
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+</ul>
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+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
+</ul>
+<br />
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+    <a href="https://github.com/stanley-rozell/lds-ctrl-est" target="_blank" rel="noopener">
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+  <article class="markdown"><h1 id="ldsctrlest_hlds_sctrlh">
+  ldsCtrlEst_h/lds_sctrl.h
+  <a class="anchor" href="#ldsctrlest_hlds_sctrlh">#</a>
+</h1>
+<p>SwitchedController type.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/">lds::SwitchedController</a></strong> <br><a href="">SwitchedController</a> Type.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/">lds::gaussian::SwitchedController</a>).</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_SCTRL_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_SCTRL_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_ctrl.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_uniform_mats.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_uniform_vecs.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">SwitchedController</span> <span style="color:#f92672">:</span> <span style="color:#66d9ef">public</span> Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;</span> {
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">public</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  SwitchedController() <span style="color:#f92672">=</span> <span style="color:#66d9ef">default</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  SwitchedController(<span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;&amp;</span> systems, data_t u_lb,
+</span></span><span style="display:flex;"><span>                     data_t u_ub, size_t control_type <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  SwitchedController(std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;&amp;&amp;</span> systems, data_t u_lb, data_t u_ub,
+</span></span><span style="display:flex;"><span>                     size_t control_type <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Switch</span>(size_t idx, <span style="color:#66d9ef">bool</span> do_force_switch <span style="color:#f92672">=</span> false);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_Kc</span>(<span style="color:#66d9ef">const</span> UniformMatrixList<span style="color:#f92672">&lt;&gt;&amp;</span> Kc) {
+</span></span><span style="display:flex;"><span>    Kc_list_ <span style="color:#f92672">=</span> Kc;
+</span></span><span style="display:flex;"><span>    Kc_ <span style="color:#f92672">=</span> Kc_list_.at(<span style="color:#ae81ff">0</span>);  <span style="color:#75715e">// set to first
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#66d9ef">if</span> (idx_ <span style="color:#f92672">!=</span> <span style="color:#ae81ff">0</span>) {
+</span></span><span style="display:flex;"><span>      Switch(idx_, true);
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_Kc</span>(UniformMatrixList<span style="color:#f92672">&lt;&gt;&amp;&amp;</span> Kc) {
+</span></span><span style="display:flex;"><span>    Kc_list_ <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(Kc);
+</span></span><span style="display:flex;"><span>    Kc_ <span style="color:#f92672">=</span> Kc_list_.at(<span style="color:#ae81ff">0</span>);  <span style="color:#75715e">// set to first
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#66d9ef">if</span> (idx_ <span style="color:#f92672">!=</span> <span style="color:#ae81ff">0</span>) {
+</span></span><span style="display:flex;"><span>      Switch(idx_, true);
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_Kc_inty</span>(<span style="color:#66d9ef">const</span> UniformMatrixList<span style="color:#f92672">&lt;&gt;&amp;</span> Kc_inty) {
+</span></span><span style="display:flex;"><span>    Kc_inty_list_ <span style="color:#f92672">=</span> Kc_inty;
+</span></span><span style="display:flex;"><span>    Kc_inty_ <span style="color:#f92672">=</span> Kc_inty_list_.at(<span style="color:#ae81ff">0</span>);  <span style="color:#75715e">// set to first
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#66d9ef">if</span> (idx_ <span style="color:#f92672">!=</span> <span style="color:#ae81ff">0</span>) {
+</span></span><span style="display:flex;"><span>      Switch(idx_, true);
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_Kc_inty</span>(UniformMatrixList<span style="color:#f92672">&lt;&gt;&amp;&amp;</span> Kc_inty) {
+</span></span><span style="display:flex;"><span>    Kc_inty_list_ <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(Kc_inty);
+</span></span><span style="display:flex;"><span>    Kc_inty_ <span style="color:#f92672">=</span> Kc_inty_list_.at(<span style="color:#ae81ff">0</span>);  <span style="color:#75715e">// set to first
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#66d9ef">if</span> (idx_ <span style="color:#f92672">!=</span> <span style="color:#ae81ff">0</span>) {
+</span></span><span style="display:flex;"><span>      Switch(idx_, true);
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_Kc_u</span>(<span style="color:#66d9ef">const</span> UniformMatrixList<span style="color:#f92672">&lt;&gt;&amp;</span> Kc_u) {
+</span></span><span style="display:flex;"><span>    Kc_u_list_ <span style="color:#f92672">=</span> Kc_u;
+</span></span><span style="display:flex;"><span>    Kc_u_ <span style="color:#f92672">=</span> Kc_u_list_.at(<span style="color:#ae81ff">0</span>);  <span style="color:#75715e">// set to first
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#66d9ef">if</span> (idx_ <span style="color:#f92672">!=</span> <span style="color:#ae81ff">0</span>) {
+</span></span><span style="display:flex;"><span>      Switch(idx_, true);
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_Kc_u</span>(UniformMatrixList<span style="color:#f92672">&lt;&gt;&amp;&amp;</span> Kc_u) {
+</span></span><span style="display:flex;"><span>    Kc_u_list_ <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(Kc_u);
+</span></span><span style="display:flex;"><span>    Kc_u_ <span style="color:#f92672">=</span> Kc_u_list_.at(<span style="color:#ae81ff">0</span>);  <span style="color:#75715e">// set to first
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#66d9ef">if</span> (idx_ <span style="color:#f92672">!=</span> <span style="color:#ae81ff">0</span>) {
+</span></span><span style="display:flex;"><span>      Switch(idx_, true);
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_g_design</span>(<span style="color:#66d9ef">const</span> UniformVectorList<span style="color:#f92672">&amp;</span> g) {
+</span></span><span style="display:flex;"><span>    g_design_list_ <span style="color:#f92672">=</span> g;
+</span></span><span style="display:flex;"><span>    g_design_ <span style="color:#f92672">=</span> g_design_list_.at(<span style="color:#ae81ff">0</span>);  <span style="color:#75715e">// set to first
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#66d9ef">if</span> (idx_ <span style="color:#f92672">!=</span> <span style="color:#ae81ff">0</span>) {
+</span></span><span style="display:flex;"><span>      Switch(idx_, true);
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_g_design</span>(UniformVectorList<span style="color:#f92672">&amp;&amp;</span> g) {
+</span></span><span style="display:flex;"><span>    g_design_list_ <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(g);
+</span></span><span style="display:flex;"><span>    g_design_ <span style="color:#f92672">=</span> g_design_list_.at(<span style="color:#ae81ff">0</span>);  <span style="color:#75715e">// set to first
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#66d9ef">if</span> (idx_ <span style="color:#f92672">!=</span> <span style="color:#ae81ff">0</span>) {
+</span></span><span style="display:flex;"><span>      Switch(idx_, true);
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// make sure base class template methods available
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Controller;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Control;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>ControlOutputReference;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>sys;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Kc;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Kc_inty;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Kc_u;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>g_design;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>u_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>x_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>y_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>control_type;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_u_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_x_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_y_ref;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_tau_awu;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_control_type;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Reset;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Print;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">protected</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>      systems_;     
+</span></span><span style="display:flex;"><span>  size_t n_sys_{};  
+</span></span><span style="display:flex;"><span>  size_t idx_{};    
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// controller gains could be different for each
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  UniformMatrixList<span style="color:#f92672">&lt;&gt;</span> Kc_list_;
+</span></span><span style="display:flex;"><span>  UniformMatrixList<span style="color:#f92672">&lt;&gt;</span> Kc_inty_list_;
+</span></span><span style="display:flex;"><span>  UniformMatrixList<span style="color:#f92672">&lt;&gt;</span> Kc_u_list_;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// design-phase input gain could also be different
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  UniformVectorList g_design_list_;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// TODO(mfbolus): not sure why I need to do this.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">using</span> Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Kc_;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Kc_inty_;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Kc_u_;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>g_design_;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>sys_;
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// using Controller&lt;System&gt;::u_ref_;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// using Controller&lt;System&gt;::x_ref_;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// using Controller&lt;System&gt;::y_ref_;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">using</span> Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>control_type_;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">private</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> InitVars();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>set_sys;
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// using Controller&lt;System&gt;::set_Kc;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// using Controller&lt;System&gt;::set_Kc_inty;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// using Controller&lt;System&gt;::set_Kc_u;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// using Controller&lt;System&gt;::set_g_design;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>SwitchedController(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;&amp;</span> systems, data_t u_lb, data_t u_ub,
+</span></span><span style="display:flex;"><span>    size_t control_type)
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;</span>(systems.at(<span style="color:#ae81ff">0</span>), u_lb, u_ub, control_type),
+</span></span><span style="display:flex;"><span>      systems_(systems) {
+</span></span><span style="display:flex;"><span>  InitVars();
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>SwitchedController(
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;&amp;&amp;</span> systems, data_t u_lb, data_t u_ub,
+</span></span><span style="display:flex;"><span>    size_t control_type)
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> Controller<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;</span>(System(systems.at(<span style="color:#ae81ff">0</span>).n_u(), systems.at(<span style="color:#ae81ff">0</span>).n_x(),
+</span></span><span style="display:flex;"><span>                                systems.at(<span style="color:#ae81ff">0</span>).n_y(), systems.at(<span style="color:#ae81ff">0</span>).dt()),
+</span></span><span style="display:flex;"><span>                         u_lb, u_ub, control_type),
+</span></span><span style="display:flex;"><span>      systems_(std<span style="color:#f92672">::</span>move(systems)) {
+</span></span><span style="display:flex;"><span>  InitVars();
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>InitVars() {
+</span></span><span style="display:flex;"><span>  n_sys_ <span style="color:#f92672">=</span> systems_.size();
+</span></span><span style="display:flex;"><span>  sys_ <span style="color:#f92672">=</span> systems_.at(<span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Kc_list_ <span style="color:#f92672">=</span> UniformMatrixList<span style="color:#f92672">&lt;&gt;</span>(std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;</span>(n_sys_, Kc_));
+</span></span><span style="display:flex;"><span>  Kc_inty_list_ <span style="color:#f92672">=</span> UniformMatrixList<span style="color:#f92672">&lt;&gt;</span>(std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;</span>(n_sys_, Kc_inty_));
+</span></span><span style="display:flex;"><span>  Kc_u_list_ <span style="color:#f92672">=</span> UniformMatrixList<span style="color:#f92672">&lt;&gt;</span>(std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;</span>(n_sys_, Kc_inty_));
+</span></span><span style="display:flex;"><span>  g_design_list_ <span style="color:#f92672">=</span> UniformVectorList(std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Vector<span style="color:#f92672">&gt;</span>(n_sys_, g_design_));
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> SwitchedController<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Switch(size_t idx,
+</span></span><span style="display:flex;"><span>                                               <span style="color:#66d9ef">bool</span> do_force_switch) {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> ((idx <span style="color:#f92672">==</span> idx_) <span style="color:#f92672">&amp;&amp;</span> <span style="color:#f92672">!</span>do_force_switch) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span>;  <span style="color:#75715e">// already there.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// put old up and get new one out
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  systems_.at(idx_) <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(sys_);
+</span></span><span style="display:flex;"><span>  sys_ <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(systems_.at(idx));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set the state of this system to that of the previous system
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// TODO(mfbolus): This will only work as intended if state matrix is the same.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  sys_.set_m(systems_.at(idx_).m(), true);
+</span></span><span style="display:flex;"><span>  sys_.set_x(systems_.at(idx_).x());
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// swap controller gains
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Kc_list_.Swap(Kc_, idx_);
+</span></span><span style="display:flex;"><span>  Kc_list_.Swap(Kc_, idx);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (control_type_ <span style="color:#f92672">&amp;</span> kControlTypeIntY) {
+</span></span><span style="display:flex;"><span>    Kc_inty_list_.Swap(Kc_inty_, idx_);
+</span></span><span style="display:flex;"><span>    Kc_inty_list_.Swap(Kc_inty_, idx);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (control_type_ <span style="color:#f92672">&amp;</span> kControlTypeDeltaU) {
+</span></span><span style="display:flex;"><span>    Kc_u_list_.Swap(Kc_u_, idx_);
+</span></span><span style="display:flex;"><span>    Kc_u_list_.Swap(Kc_u_, idx);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  g_design_list_.Swap(g_design_, idx_);
+</span></span><span style="display:flex;"><span>  g_design_list_.Swap(g_design_, idx);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  idx_ <span style="color:#f92672">=</span> idx;
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// Switch
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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+  <article class="markdown"><h1 id="srclds_syscpp">
+  src/lds_sys.cpp
+  <a class="anchor" href="#srclds_syscpp">#</a>
+</h1>
+<p>LDS base type.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file implements the base type for linear dynamical systems (<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">lds::System</a>). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- lds_sys.cpp - LDS -------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;ldsCtrlEst_h/lds_sys.h&gt;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;vector&gt;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>lds<span style="color:#f92672">::</span>System<span style="color:#f92672">::</span>System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0,
+</span></span><span style="display:flex;"><span>                    data_t q0)
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) {
+</span></span><span style="display:flex;"><span>  InitVars(p0, q0);
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> lds<span style="color:#f92672">::</span>System<span style="color:#f92672">::</span>InitVars(data_t p0, data_t q0) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// initial conditions.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  x0_ <span style="color:#f92672">=</span> Vector(n_x_, fill<span style="color:#f92672">::</span>zeros);  <span style="color:#75715e">// includes bias (nY) and g (nU)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  P0_ <span style="color:#f92672">=</span> p0 <span style="color:#f92672">*</span> Matrix(n_x_, n_x_, fill<span style="color:#f92672">::</span>eye);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  m0_ <span style="color:#f92672">=</span> x0_;
+</span></span><span style="display:flex;"><span>  P0_m_ <span style="color:#f92672">=</span> P0_;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// signals
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  x_ <span style="color:#f92672">=</span> x0_;
+</span></span><span style="display:flex;"><span>  P_ <span style="color:#f92672">=</span> P0_;
+</span></span><span style="display:flex;"><span>  m_ <span style="color:#f92672">=</span> m0_;
+</span></span><span style="display:flex;"><span>  P_m_ <span style="color:#f92672">=</span> P0_m_;
+</span></span><span style="display:flex;"><span>  y_ <span style="color:#f92672">=</span> Vector(n_y_, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  cx_ <span style="color:#f92672">=</span> Vector(n_y_, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  z_ <span style="color:#f92672">=</span> Vector(n_y_, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// By default, random walk where each state is independent
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// In this way, provides independent estimates of rate per channel of output.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  A_ <span style="color:#f92672">=</span> Matrix(n_x_, n_x_, fill<span style="color:#f92672">::</span>eye);
+</span></span><span style="display:flex;"><span>  B_ <span style="color:#f92672">=</span> Matrix(n_x_, n_u_, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  g_ <span style="color:#f92672">=</span> Vector(n_u_, fill<span style="color:#f92672">::</span>ones);
+</span></span><span style="display:flex;"><span>  Q_ <span style="color:#f92672">=</span> q0 <span style="color:#f92672">*</span> Matrix(n_x_, n_x_, fill<span style="color:#f92672">::</span>eye);
+</span></span><span style="display:flex;"><span>  Q_m_ <span style="color:#f92672">=</span> Q_;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  C_ <span style="color:#f92672">=</span> Matrix(n_y_, n_x_, fill<span style="color:#f92672">::</span>eye);  <span style="color:#75715e">// each state will map to an output by
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  d_ <span style="color:#f92672">=</span> Vector(n_y_, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Ke_ <span style="color:#f92672">=</span> Matrix(n_x_, n_y_, fill<span style="color:#f92672">::</span>zeros);    <span style="color:#75715e">// estimator gain.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Ke_m_ <span style="color:#f92672">=</span> Matrix(n_x_, n_y_, fill<span style="color:#f92672">::</span>zeros);  <span style="color:#75715e">// estimator gain for m adaptation.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  do_adapt_m <span style="color:#f92672">=</span> false;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// and update estimate of the state, covar, output using Kalman filter
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#66d9ef">void</span> lds<span style="color:#f92672">::</span>System<span style="color:#f92672">::</span>Filter(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> u_tm1, <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> z_t) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// predict mean
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  f(u_tm1);  <span style="color:#75715e">// dynamics
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  h();  <span style="color:#75715e">// output
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// recursively calculate esimator gains (or just keep existing values)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// (also predicts+updates estimate covariance)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  RecurseKe();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// update
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  x_ <span style="color:#f92672">+=</span> Ke_ <span style="color:#f92672">*</span> (z_t <span style="color:#f92672">-</span> y_);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (do_adapt_m) {
+</span></span><span style="display:flex;"><span>    m_ <span style="color:#f92672">+=</span> Ke_m_ <span style="color:#f92672">*</span> (z_t <span style="color:#f92672">-</span> y_);  <span style="color:#75715e">// adaptively estimating disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// With new state, estimate output.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  h();  <span style="color:#75715e">// --&gt; posterior
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> lds<span style="color:#f92672">::</span>System<span style="color:#f92672">::</span>Reset() {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// reset to initial conditions
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  x_ <span style="color:#f92672">=</span> x0_;      <span style="color:#75715e">// mean
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  P_ <span style="color:#f92672">=</span> P0_;      <span style="color:#75715e">// cov of state estimate
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  m_ <span style="color:#f92672">=</span> m0_;      <span style="color:#75715e">// process disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  P_m_ <span style="color:#f92672">=</span> P0_m_;  <span style="color:#75715e">// cov of disturbance estimate
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  h();
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>lds<span style="color:#f92672">::</span>UniformMatrixList<span style="color:#f92672">&lt;</span>lds<span style="color:#f92672">::</span>kMatFreeDim2<span style="color:#f92672">&gt;&gt;</span>
+</span></span><span style="display:flex;"><span>lds<span style="color:#f92672">::</span>System<span style="color:#f92672">::</span>nstep_pred_block(lds<span style="color:#f92672">::</span>UniformMatrixList<span style="color:#f92672">&lt;</span>lds<span style="color:#f92672">::</span>kMatFreeDim2<span style="color:#f92672">&gt;</span> u,
+</span></span><span style="display:flex;"><span>                              lds<span style="color:#f92672">::</span>UniformMatrixList<span style="color:#f92672">&lt;</span>lds<span style="color:#f92672">::</span>kMatFreeDim2<span style="color:#f92672">&gt;</span> z,
+</span></span><span style="display:flex;"><span>                              size_t n_pred) {
+</span></span><span style="display:flex;"><span>  lds<span style="color:#f92672">::</span>UniformMatrixList<span style="color:#f92672">&lt;</span>lds<span style="color:#f92672">::</span>kMatFreeDim2<span style="color:#f92672">&gt;</span> x_filt;
+</span></span><span style="display:flex;"><span>  lds<span style="color:#f92672">::</span>UniformMatrixList<span style="color:#f92672">&lt;</span>lds<span style="color:#f92672">::</span>kMatFreeDim2<span style="color:#f92672">&gt;</span> x_pred;
+</span></span><span style="display:flex;"><span>  lds<span style="color:#f92672">::</span>UniformMatrixList<span style="color:#f92672">&lt;</span>lds<span style="color:#f92672">::</span>kMatFreeDim2<span style="color:#f92672">&gt;</span> y_pred;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t k <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; k <span style="color:#f92672">&lt;</span> u.size(); k<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    Reset();
+</span></span><span style="display:flex;"><span>    size_t n_t <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>size(u[k])[<span style="color:#ae81ff">1</span>];
+</span></span><span style="display:flex;"><span>    Matrix <span style="color:#a6e22e">x_filt_k</span>(n_x_, n_t, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>    Matrix <span style="color:#a6e22e">x_pred_k</span>(n_x_, n_t <span style="color:#f92672">-</span> n_pred, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>    Matrix <span style="color:#a6e22e">y_pred_k</span>(n_y_, n_t <span style="color:#f92672">-</span> n_pred, fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">for</span> (size_t t <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; t <span style="color:#f92672">&lt;</span> n_t <span style="color:#f92672">-</span> n_pred; t<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>      Vector x_pred_ahead <span style="color:#f92672">=</span> x_;
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">for</span> (size_t t_u <span style="color:#f92672">=</span> t; t_u <span style="color:#f92672">&lt;</span> t <span style="color:#f92672">+</span> n_pred; t_u<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>        x_pred_ahead <span style="color:#f92672">=</span> A_ <span style="color:#f92672">*</span> x_pred_ahead <span style="color:#f92672">+</span> B_ <span style="color:#f92672">*</span> u[k].col(t_u);
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>      x_pred_k.col(t) <span style="color:#f92672">=</span> x_pred_ahead;
+</span></span><span style="display:flex;"><span>      y_pred_k.col(t) <span style="color:#f92672">=</span> h_(x_pred_ahead);
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">if</span> (t <span style="color:#f92672">&gt;</span> <span style="color:#ae81ff">0</span>) {
+</span></span><span style="display:flex;"><span>        Filter(u[k].col(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>), z[k].col(t));
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>      x_filt_k.col(t) <span style="color:#f92672">=</span> x_;  <span style="color:#75715e">// given previous measurment
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">for</span> (size_t t <span style="color:#f92672">=</span> n_t <span style="color:#f92672">-</span> n_pred; t <span style="color:#f92672">&lt;</span> n_t; t<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">if</span> (t <span style="color:#f92672">&gt;</span> <span style="color:#ae81ff">0</span>) {
+</span></span><span style="display:flex;"><span>        Filter(u[k].col(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>), z[k].col(t));
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>      x_filt_k.col(t) <span style="color:#f92672">=</span> x_;
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    x_filt.append(x_filt_k);
+</span></span><span style="display:flex;"><span>    x_pred.append(x_pred_k);
+</span></span><span style="display:flex;"><span>    y_pred.append(y_pred_k);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> {x_filt, x_pred, y_pred};
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> lds<span style="color:#f92672">::</span>System<span style="color:#f92672">::</span>Print() {
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74"> ********** SYSTEM ********** </span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;x: </span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span> <span style="color:#f92672">&lt;&lt;</span> x_ <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;P: </span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span> <span style="color:#f92672">&lt;&lt;</span> P_ <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;A: </span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span> <span style="color:#f92672">&lt;&lt;</span> A_ <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;B: </span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span> <span style="color:#f92672">&lt;&lt;</span> B_ <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;g: </span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span> <span style="color:#f92672">&lt;&lt;</span> g_ <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;m: </span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span> <span style="color:#f92672">&lt;&lt;</span> m_ <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Q: </span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span> <span style="color:#f92672">&lt;&lt;</span> Q_ <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Q_m: </span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span> <span style="color:#f92672">&lt;&lt;</span> Q_m_ <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;d: </span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span> <span style="color:#f92672">&lt;&lt;</span> d_ <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;C: </span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span> <span style="color:#f92672">&lt;&lt;</span> C_ <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>cout <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;y: </span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span> <span style="color:#f92672">&lt;&lt;</span> y_ <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">//******************* SYS_T *******************
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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+        <li><a href="#namespaces">Namespaces</a></li>
+        <li><a href="#classes">Classes</a></li>
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+  <article class="markdown"><h1 id="ldsctrlest_hlds_sysh">
+  ldsCtrlEst_h/lds_sys.h
+  <a class="anchor" href="#ldsctrlest_hlds_sysh">#</a>
+</h1>
+<p>LDS base type.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">lds::System</a></strong> <br>Linear Dynamical <a href="">System</a> Type.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file declares and partially defines the base type for linear dynamical systems (<code>[lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)</code>). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_SYS_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_SYS_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_uniform_mats.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">System</span> {
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">public</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  System() <span style="color:#f92672">=</span> <span style="color:#66d9ef">default</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 <span style="color:#f92672">=</span> kDefaultP0,
+</span></span><span style="display:flex;"><span>         data_t q0 <span style="color:#f92672">=</span> kDefaultQ0);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">virtual</span> <span style="color:#f92672">~</span>System() {}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Filter</span>(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> u_tm1, <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> z);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> Simulate(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> u_tm1) <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">f</span>(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> u, <span style="color:#66d9ef">bool</span> do_add_noise <span style="color:#f92672">=</span> false) {
+</span></span><span style="display:flex;"><span>    x_ <span style="color:#f92672">=</span> A_ <span style="color:#f92672">*</span> x_ <span style="color:#f92672">+</span> B_ <span style="color:#f92672">*</span> (g_ <span style="color:#f92672">%</span> u) <span style="color:#f92672">+</span> m_;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (do_add_noise) {
+</span></span><span style="display:flex;"><span>      x_ <span style="color:#f92672">+=</span> arma<span style="color:#f92672">::</span>mvnrnd(Vector(n_x_).fill(<span style="color:#ae81ff">0</span>), Q_);
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">h</span>() <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">virtual</span> Vector <span style="color:#a6e22e">h_</span>(Vector x) <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  size_t <span style="color:#a6e22e">n_u</span>() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> n_u_; };
+</span></span><span style="display:flex;"><span>  size_t <span style="color:#a6e22e">n_x</span>() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> n_x_; };
+</span></span><span style="display:flex;"><span>  size_t <span style="color:#a6e22e">n_y</span>() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> n_y_; };
+</span></span><span style="display:flex;"><span>  data_t <span style="color:#a6e22e">dt</span>() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> dt_; };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> x() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> x_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> P() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> P_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> m() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> m_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> P_m() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> P_m_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> cx() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> cx_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> y() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> y_; };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> x0() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> x0_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> m0() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> m0_; };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> A() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> A_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> B() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> B_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> g() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> g_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> C() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> C_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> d() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> d_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> Ke() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> Ke_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> Ke_m() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> Ke_m_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> Q() { <span style="color:#66d9ef">return</span> Q_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> Q_m() { <span style="color:#66d9ef">return</span> Q_m_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> P0() { <span style="color:#66d9ef">return</span> P0_; };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> P0_m() { <span style="color:#66d9ef">return</span> P0_m_; };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_A</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> A) { Reassign(A_, A); };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_B</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> B) { Reassign(B_, B); };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_m</span>(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> m, <span style="color:#66d9ef">bool</span> do_force_assign <span style="color:#f92672">=</span> false) {
+</span></span><span style="display:flex;"><span>    Reassign(m0_, m);
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> ((<span style="color:#f92672">!</span>do_adapt_m) <span style="color:#f92672">||</span> do_force_assign) {
+</span></span><span style="display:flex;"><span>      Reassign(m_, m);
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_g</span>(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> g) { Reassign(g_, g); };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_Q</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> Q) { Reassign(Q_, Q); };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_Q_m</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> Q_m) { Reassign(Q_m_, Q_m); };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_x0</span>(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> x0) { Reassign(x0_, x0); };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_P0</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> P0) { Reassign(P0_, P0); };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_P0_m</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> P0_m) { Reassign(P0_m_, P0_m); };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_C</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> C) { Reassign(C_, C); };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_d</span>(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> d) { Reassign(d_, d); };
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">set_x</span>(<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> x) {
+</span></span><span style="display:flex;"><span>    Reassign(x_, x);
+</span></span><span style="display:flex;"><span>    h();
+</span></span><span style="display:flex;"><span>  };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Reset</span>();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;&gt;</span> nstep_pred_block(
+</span></span><span style="display:flex;"><span>      UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;</span> u, UniformMatrixList<span style="color:#f92672">&lt;</span>kMatFreeDim2<span style="color:#f92672">&gt;</span> z,
+</span></span><span style="display:flex;"><span>      size_t n_pred <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Print</span>();
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// safe to leave this public and non-const
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">bool</span> do_adapt_m{};  
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">protected</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">virtual</span> <span style="color:#66d9ef">void</span> RecurseKe() <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">InitVars</span>(data_t p0 <span style="color:#f92672">=</span> kDefaultP0, data_t q0 <span style="color:#f92672">=</span> kDefaultQ0);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>size_t n_x_{};  
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>size_t n_u_{};  
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>size_t n_y_{};  
+</span></span><span style="display:flex;"><span>  data_t dt_{};        
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Signals:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector x_;    
+</span></span><span style="display:flex;"><span>  Matrix P_;    
+</span></span><span style="display:flex;"><span>  Vector m_;    
+</span></span><span style="display:flex;"><span>  Matrix P_m_;  
+</span></span><span style="display:flex;"><span>  Vector cx_;   
+</span></span><span style="display:flex;"><span>  Vector y_;    
+</span></span><span style="display:flex;"><span>  Vector z_;    
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Parameters:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector x0_;    
+</span></span><span style="display:flex;"><span>  Matrix P0_;    
+</span></span><span style="display:flex;"><span>  Vector m0_;    
+</span></span><span style="display:flex;"><span>  Matrix P0_m_;  
+</span></span><span style="display:flex;"><span>  Matrix A_;     
+</span></span><span style="display:flex;"><span>  Matrix B_;     
+</span></span><span style="display:flex;"><span>  Vector g_;     
+</span></span><span style="display:flex;"><span>  Matrix Q_;     
+</span></span><span style="display:flex;"><span>  Matrix Q_m_;   
+</span></span><span style="display:flex;"><span>  Matrix C_;     
+</span></span><span style="display:flex;"><span>  Vector d_;     
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Matrix Ke_;    
+</span></span><span style="display:flex;"><span>  Matrix Ke_m_;  
+</span></span><span style="display:flex;"><span>};  <span style="color:#75715e">// System
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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+  ldsCtrlEst_h/lds_uniform_mats.h
+  <a class="anchor" href="#ldsctrlest_hlds_uniform_matsh">#</a>
+</h1>
+<p>List of uniformly sized matrices.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">lds::UniformMatrixList</a></strong></td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_UNIFORM_MATS_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;array&gt;</span><span style="color:#75715e">   </span><span style="color:#75715e">// std::array
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;vector&gt;</span><span style="color:#75715e">  </span><span style="color:#75715e">// std::vector
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span>MatrixListFreeDim D <span style="color:#f92672">=</span> kMatFreeDimNone<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">UniformMatrixList</span> <span style="color:#f92672">:</span> <span style="color:#66d9ef">public</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;</span> {
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">private</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// TODO(mfbolus): would rather *uncomment* the below for sake of conversion
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// using std::vector&lt;Matrix&gt;::vector;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// don&#39;t allow push_back to be used since it doesn&#39;t check dims
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;::</span>push_back;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">public</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;::</span><span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;::</span><span style="color:#66d9ef">operator</span>[];
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;::</span>begin;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;::</span>end;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;::</span>size;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;::</span>at;
+</span></span><span style="display:flex;"><span>  UniformMatrixList() <span style="color:#f92672">=</span> <span style="color:#66d9ef">default</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">explicit</span> <span style="color:#a6e22e">UniformMatrixList</span>(<span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;&amp;</span> mats,
+</span></span><span style="display:flex;"><span>                             std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">2</span><span style="color:#f92672">&gt;</span> dim <span style="color:#f92672">=</span> {<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>});
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">explicit</span> <span style="color:#a6e22e">UniformMatrixList</span>(std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;&amp;&amp;</span> mats,
+</span></span><span style="display:flex;"><span>                             std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">2</span><span style="color:#f92672">&gt;</span> dim <span style="color:#f92672">=</span> {<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>});
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  UniformMatrixList(std<span style="color:#f92672">::</span>initializer_list<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;</span> mats,
+</span></span><span style="display:flex;"><span>                    std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">2</span><span style="color:#f92672">&gt;</span> dim <span style="color:#f92672">=</span> {<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>});
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  UniformMatrixList(<span style="color:#66d9ef">const</span> UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;&amp;</span> that);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  UniformMatrixList(UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;&amp;&amp;</span> that) <span style="color:#66d9ef">noexcept</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#f92672">~</span>UniformMatrixList() <span style="color:#f92672">=</span> <span style="color:#66d9ef">default</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">2</span><span style="color:#f92672">&gt;&amp;</span> dim(size_t n <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>) <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> dim_.at(n); }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  size_t <span style="color:#a6e22e">size</span>() { <span style="color:#66d9ef">return</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;::</span>size(); };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> at(size_t n) { <span style="color:#66d9ef">return</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;::</span>at(n); };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Swap</span>(Matrix<span style="color:#f92672">&amp;</span> that, size_t n);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;&amp;</span> <span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>(<span style="color:#66d9ef">const</span> UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;&amp;</span> that);
+</span></span><span style="display:flex;"><span>  UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;&amp;</span> <span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>(UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;&amp;&amp;</span> that) <span style="color:#66d9ef">noexcept</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">append</span>(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> mat);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">private</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> CheckDimensions(std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">2</span><span style="color:#f92672">&gt;</span> dim);
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">2</span><span style="color:#f92672">&gt;&gt;</span> dim_;
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span>MatrixListFreeDim D<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;::</span>Swap(Matrix<span style="color:#f92672">&amp;</span> that, size_t n) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// make sure request in range
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">if</span> (n <span style="color:#f92672">&gt;=</span> <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>size()) {
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>cerr
+</span></span><span style="display:flex;"><span>        <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Requested UniformMatrixList element out of bounds. Skipping.</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span>;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// check dim
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">bool</span> does_match <span style="color:#f92672">=</span> true;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>(D <span style="color:#f92672">==</span> kMatFreeDim1)) {
+</span></span><span style="display:flex;"><span>    does_match <span style="color:#f92672">=</span> does_match <span style="color:#f92672">&amp;&amp;</span> (dim_[<span style="color:#ae81ff">0</span>][<span style="color:#ae81ff">0</span>] <span style="color:#f92672">==</span> that.n_rows);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>(D <span style="color:#f92672">==</span> kMatFreeDim2)) {
+</span></span><span style="display:flex;"><span>    does_match <span style="color:#f92672">=</span> does_match <span style="color:#f92672">&amp;&amp;</span> (dim_[<span style="color:#ae81ff">0</span>][<span style="color:#ae81ff">1</span>] <span style="color:#f92672">==</span> that.n_cols);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>does_match) {
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>cerr <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Cannot swap a UniformMatrixList element for an element of &#34;</span>
+</span></span><span style="display:flex;"><span>                 <span style="color:#e6db74">&#34;different size. Skipping.</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span>;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// if checks pass, perform swap
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// not moving, since it causes memory issues.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// so this method isn&#39;t a memory-saver as designed for now
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix tmp <span style="color:#f92672">=</span> (<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>)[n];
+</span></span><span style="display:flex;"><span>  (<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>)[n] <span style="color:#f92672">=</span> that;
+</span></span><span style="display:flex;"><span>  that <span style="color:#f92672">=</span> tmp;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (D <span style="color:#f92672">==</span> kMatFreeDim1) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>dim_[n][<span style="color:#ae81ff">0</span>] <span style="color:#f92672">=</span> (<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>)[n].n_rows;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (D <span style="color:#f92672">==</span> kMatFreeDim2) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>dim_[n][<span style="color:#ae81ff">1</span>] <span style="color:#f92672">=</span> (<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>)[n].n_cols;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span>MatrixListFreeDim D<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;::</span>append(<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> mat) {
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">2</span><span style="color:#f92672">&gt;</span> dim({mat.n_rows, mat.n_cols});
+</span></span><span style="display:flex;"><span>  CheckDimensions(dim);
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;::</span>push_back(mat);
+</span></span><span style="display:flex;"><span>  dim_.push_back(dim);
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span>MatrixListFreeDim D<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;&amp;</span> UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;::</span><span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;&amp;</span> that) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// make sure dim_ vector is initialized
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">if</span> (dim_.empty()) {
+</span></span><span style="display:flex;"><span>    dim_ <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">2</span><span style="color:#f92672">&gt;&gt;</span>(that.size(), {<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>});
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// check dimensions
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span><span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>empty()) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (<span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>size() <span style="color:#f92672">!=</span> that.size()) {
+</span></span><span style="display:flex;"><span>      std<span style="color:#f92672">::</span>ostringstream ss;
+</span></span><span style="display:flex;"><span>      ss <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;cannot reassign &#34;</span> <span style="color:#f92672">&lt;&lt;</span> <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>size() <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; matrices with &#34;</span>
+</span></span><span style="display:flex;"><span>         <span style="color:#f92672">&lt;&lt;</span> that.size() <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; matrices&#34;</span>;
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(ss.str());
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// if dimensions a not zero and do not match, skip move with error message.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#66d9ef">bool</span> dims_nonzero <span style="color:#f92672">=</span> true;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">for</span> (<span style="color:#66d9ef">auto</span> d : dim_) {
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>(D <span style="color:#f92672">==</span> kMatFreeDim1) <span style="color:#f92672">&amp;&amp;</span> d[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;</span> <span style="color:#ae81ff">1</span>) {
+</span></span><span style="display:flex;"><span>        dims_nonzero <span style="color:#f92672">=</span> false;
+</span></span><span style="display:flex;"><span>        <span style="color:#66d9ef">break</span>;
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>(D <span style="color:#f92672">==</span> kMatFreeDim2) <span style="color:#f92672">&amp;&amp;</span> d[<span style="color:#ae81ff">1</span>] <span style="color:#f92672">&lt;</span> <span style="color:#ae81ff">1</span>) {
+</span></span><span style="display:flex;"><span>        dims_nonzero <span style="color:#f92672">=</span> false;
+</span></span><span style="display:flex;"><span>        <span style="color:#66d9ef">break</span>;
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (dims_nonzero) {
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">bool</span> does_match <span style="color:#f92672">=</span> true;
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>(D <span style="color:#f92672">==</span> kMatFreeDim1)) {
+</span></span><span style="display:flex;"><span>        does_match <span style="color:#f92672">=</span> does_match <span style="color:#f92672">&amp;&amp;</span> (dim_[<span style="color:#ae81ff">0</span>][<span style="color:#ae81ff">0</span>] <span style="color:#f92672">==</span> that.at(<span style="color:#ae81ff">0</span>).n_rows);
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>(D <span style="color:#f92672">==</span> kMatFreeDim2)) {
+</span></span><span style="display:flex;"><span>        does_match <span style="color:#f92672">=</span> does_match <span style="color:#f92672">&amp;&amp;</span> (dim_[<span style="color:#ae81ff">0</span>][<span style="color:#ae81ff">1</span>] <span style="color:#f92672">==</span> that.at(<span style="color:#ae81ff">0</span>).n_cols);
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>does_match) {
+</span></span><span style="display:flex;"><span>        std<span style="color:#f92672">::</span>ostringstream ss;
+</span></span><span style="display:flex;"><span>        ss <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;cannot reassign matrices of size &#34;</span> <span style="color:#f92672">&lt;&lt;</span> dim_[<span style="color:#ae81ff">0</span>][<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;x&#34;</span>
+</span></span><span style="display:flex;"><span>           <span style="color:#f92672">&lt;&lt;</span> dim_[<span style="color:#ae81ff">0</span>][<span style="color:#ae81ff">1</span>] <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; with matrices of size &#34;</span> <span style="color:#f92672">&lt;&lt;</span> that.at(<span style="color:#ae81ff">0</span>).n_rows
+</span></span><span style="display:flex;"><span>           <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;x&#34;</span> <span style="color:#f92672">&lt;&lt;</span> that.at(<span style="color:#ae81ff">0</span>).n_cols;
+</span></span><span style="display:flex;"><span>        <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(ss.str());
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t k <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; k <span style="color:#f92672">&lt;</span> <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>size(); k<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    (<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>)[k] <span style="color:#f92672">=</span> that[k];
+</span></span><span style="display:flex;"><span>    dim_[k] <span style="color:#f92672">=</span> that.dim(k);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> (<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>);
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span>MatrixListFreeDim D<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;&amp;</span> UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;::</span><span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>(
+</span></span><span style="display:flex;"><span>    UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;&amp;&amp;</span> that) <span style="color:#66d9ef">noexcept</span> {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// // check dimensions
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// // if empty, assume a default constructed object and safe to move
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// if (!this-&gt;empty()) {
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//   if (this-&gt;size() != that.size()) {
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     std::cerr &lt;&lt; &#34;Cannot reassign &#34; &lt;&lt; this-&gt;size() &lt;&lt; &#34; matrices with &#34;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//               &lt;&lt; that.size() &lt;&lt; &#34; matrices. Skipping.\n&#34;;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     return (*this);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//   }
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//   // if dimensions a not zero and do not match, skip move with error
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//   message. bool dims_nonzero = true; for (auto d : dim_) {
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     if (!(D == kMatFreeDim1) &amp;&amp; (d[0] &lt; 1)) {
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//       dims_nonzero = false;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//       break;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     }
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     if (!(D == kMatFreeDim2) &amp;&amp; (d[1] &lt; 1)) {
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//       dims_nonzero = false;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//       break;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     }
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//   }
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//   if (dims_nonzero) {
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     bool does_match = true;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     if (!(D == kMatFreeDim1)) {
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//       does_match = does_match &amp;&amp; (dim_[0][0] == that.at(0).n_rows);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     }
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     if (!(D == kMatFreeDim2)) {
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//       does_match = does_match &amp;&amp; (dim_[0][1] == that.at(0).n_cols);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     }
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     if (!does_match) {
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//       this-&gt;at(0).print(&#34;this[0] = &#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//       that.at(0).print(&#34;that[0] = &#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//       std::cerr
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//           &lt;&lt; &#34;Cannot move a UniformMatrixList element of size (&#34; &lt;&lt;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//           that.at(0).n_rows &lt;&lt; &#34;,&#34; &lt;&lt; that.at(0).n_cols &lt;&lt; &#34;) for an
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//           element of size (&#34; &lt;&lt; dim_[0][0] &lt;&lt; &#34;,&#34; &lt;&lt; dim_[0][1] &lt;&lt; &#34;).
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//           Skipping.\n&#34;;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//       return (*this);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     }
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//   }
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// }
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  dim_ <span style="color:#f92672">=</span> that.dim_;
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;::</span><span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>(std<span style="color:#f92672">::</span>move(that));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> (<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>);
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span>MatrixListFreeDim D<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;::</span>UniformMatrixList(<span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;&amp;</span> mats,
+</span></span><span style="display:flex;"><span>                                        std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">2</span><span style="color:#f92672">&gt;</span> dim)
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> vector(mats) {
+</span></span><span style="display:flex;"><span>  CheckDimensions(dim);
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span>MatrixListFreeDim D<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;::</span>UniformMatrixList(std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;&amp;&amp;</span> mats,
+</span></span><span style="display:flex;"><span>                                        std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">2</span><span style="color:#f92672">&gt;</span> dim)
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> vector(std<span style="color:#f92672">::</span>move(mats)) {
+</span></span><span style="display:flex;"><span>  CheckDimensions(dim);
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span>MatrixListFreeDim D<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;::</span>UniformMatrixList(std<span style="color:#f92672">::</span>initializer_list<span style="color:#f92672">&lt;</span>Matrix<span style="color:#f92672">&gt;</span> mats,
+</span></span><span style="display:flex;"><span>                                        std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">2</span><span style="color:#f92672">&gt;</span> dim)
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> vector(mats) {
+</span></span><span style="display:flex;"><span>  CheckDimensions(dim);
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span>MatrixListFreeDim D<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;::</span>UniformMatrixList(<span style="color:#66d9ef">const</span> UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;&amp;</span> that)
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> vector(that) {
+</span></span><span style="display:flex;"><span>  (<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>) <span style="color:#f92672">=</span> that;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span>MatrixListFreeDim D<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;::</span>UniformMatrixList(UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;&amp;&amp;</span> that) <span style="color:#66d9ef">noexcept</span>
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> vector(std<span style="color:#f92672">::</span>move(that)) {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t k <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; k <span style="color:#f92672">&lt;</span> <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>size(); k<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">2</span><span style="color:#f92672">&gt;</span> dim_k({<span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>at(k).n_rows, <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>at(k).n_cols});
+</span></span><span style="display:flex;"><span>    dim_.push_back(dim_k);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span>MatrixListFreeDim D<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> UniformMatrixList<span style="color:#f92672">&lt;</span>D<span style="color:#f92672">&gt;::</span>CheckDimensions(std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">2</span><span style="color:#f92672">&gt;</span> dim) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// change behavior based on free dim D
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">if</span> ((dim[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">==</span> <span style="color:#ae81ff">0</span>) <span style="color:#f92672">&amp;&amp;</span> <span style="color:#f92672">!</span>(D <span style="color:#f92672">==</span> kMatFreeDim1)) {
+</span></span><span style="display:flex;"><span>    dim[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">=</span> <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>at(<span style="color:#ae81ff">0</span>).n_rows;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> ((dim[<span style="color:#ae81ff">1</span>] <span style="color:#f92672">==</span> <span style="color:#ae81ff">0</span>) <span style="color:#f92672">&amp;&amp;</span> <span style="color:#f92672">!</span>(D <span style="color:#f92672">==</span> kMatFreeDim2)) {
+</span></span><span style="display:flex;"><span>    dim[<span style="color:#ae81ff">1</span>] <span style="color:#f92672">=</span> <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>at(<span style="color:#ae81ff">0</span>).n_cols;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// make sure dimensiolaties are all uniform
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">bool</span> <span style="color:#a6e22e">does_match</span>(true);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (<span style="color:#66d9ef">const</span> Matrix<span style="color:#f92672">&amp;</span> mat : <span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>(D <span style="color:#f92672">==</span> kMatFreeDim1)) {
+</span></span><span style="display:flex;"><span>      does_match <span style="color:#f92672">=</span> does_match <span style="color:#f92672">&amp;&amp;</span> (mat.n_rows <span style="color:#f92672">==</span> dim[<span style="color:#ae81ff">0</span>]);
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>(D <span style="color:#f92672">==</span> kMatFreeDim2)) {
+</span></span><span style="display:flex;"><span>      does_match <span style="color:#f92672">=</span> does_match <span style="color:#f92672">&amp;&amp;</span> (mat.n_cols <span style="color:#f92672">==</span> dim[<span style="color:#ae81ff">1</span>]);
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>does_match) {
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(
+</span></span><span style="display:flex;"><span>          <span style="color:#e6db74">&#34;Dimensionality of one or more input matrices are not uniform.&#34;</span>);
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  dim_ <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">2</span><span style="color:#f92672">&gt;&gt;</span>(<span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>size(), dim);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t k <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; k <span style="color:#f92672">&lt;</span> <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>size(); k<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    dim_[k][<span style="color:#ae81ff">0</span>] <span style="color:#f92672">=</span> (<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>)[k].n_rows;
+</span></span><span style="display:flex;"><span>    dim_[k][<span style="color:#ae81ff">1</span>] <span style="color:#f92672">=</span> (<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>)[k].n_cols;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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diff --git a/docs/docs/api/files/lds__uniform__systems_8h/index.html b/docs/docs/api/files/lds__uniform__systems_8h/index.html
new file mode 100644
index 00000000..42dc8a9f
--- /dev/null
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+    <li><a href="#ldsctrlest_hlds_uniform_systemsh">ldsCtrlEst_h/lds_uniform_systems.h</a>
+      <ul>
+        <li><a href="#namespaces">Namespaces</a></li>
+        <li><a href="#classes">Classes</a></li>
+        <li><a href="#detailed-description">Detailed Description</a></li>
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+  <article class="markdown"><h1 id="ldsctrlest_hlds_uniform_systemsh">
+  ldsCtrlEst_h/lds_uniform_systems.h
+  <a class="anchor" href="#ldsctrlest_hlds_uniform_systemsh">#</a>
+</h1>
+<p>List of uniformly sized Systems.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/">lds::UniformSystemList</a></strong></td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file provides a container for uniformly sized Systems.</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;array&gt;</span><span style="color:#75715e">   </span><span style="color:#75715e">// std::array
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;vector&gt;</span><span style="color:#75715e">  </span><span style="color:#75715e">// std::vector
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// namespace
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">// System type
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds_sys.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">UniformSystemList</span> <span style="color:#f92672">:</span> <span style="color:#66d9ef">public</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;</span> {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">static_assert</span>(std<span style="color:#f92672">::</span>is_base_of<span style="color:#f92672">&lt;</span>lds<span style="color:#f92672">::</span>System, System<span style="color:#f92672">&gt;::</span>value,
+</span></span><span style="display:flex;"><span>                <span style="color:#e6db74">&#34;System must be derived from lds::System type.&#34;</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">private</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// TODO(mfbolus): would rather *uncomment* the below for sake of conversion
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// using std::vector&lt;System&gt;::vector;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span><span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span><span style="color:#66d9ef">operator</span>[];
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>at;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>begin;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>end;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>size;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">public</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  UniformSystemList() <span style="color:#f92672">=</span> <span style="color:#66d9ef">default</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">explicit</span> <span style="color:#a6e22e">UniformSystemList</span>(<span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;&amp;</span> systems,
+</span></span><span style="display:flex;"><span>                             std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">3</span><span style="color:#f92672">&gt;</span> dim <span style="color:#f92672">=</span> {<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>});
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">explicit</span> <span style="color:#a6e22e">UniformSystemList</span>(std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;&amp;&amp;</span> systems,
+</span></span><span style="display:flex;"><span>                             std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">3</span><span style="color:#f92672">&gt;</span> dim <span style="color:#f92672">=</span> {<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>});
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  UniformSystemList(std<span style="color:#f92672">::</span>initializer_list<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;</span> systems,
+</span></span><span style="display:flex;"><span>                    std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">3</span><span style="color:#f92672">&gt;</span> dim <span style="color:#f92672">=</span> {<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">0</span>});
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  UniformSystemList(<span style="color:#66d9ef">const</span> UniformSystemList<span style="color:#f92672">&amp;</span> that);
+</span></span><span style="display:flex;"><span>  UniformSystemList(UniformSystemList<span style="color:#f92672">&amp;&amp;</span> that) <span style="color:#66d9ef">noexcept</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#f92672">~</span>UniformSystemList() <span style="color:#f92672">=</span> <span style="color:#66d9ef">default</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">3</span><span style="color:#f92672">&gt;&amp;</span> dim() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> dim_; }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  size_t <span style="color:#a6e22e">size</span>() { <span style="color:#66d9ef">return</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>size(); };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> System<span style="color:#f92672">&amp;</span> at(size_t n) { <span style="color:#66d9ef">return</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>at(n); };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Swap</span>(System<span style="color:#f92672">&amp;</span> that, size_t n);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  UniformSystemList<span style="color:#f92672">&amp;</span> <span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>(<span style="color:#66d9ef">const</span> UniformSystemList<span style="color:#f92672">&amp;</span> that);
+</span></span><span style="display:flex;"><span>  UniformSystemList<span style="color:#f92672">&amp;</span> <span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>(UniformSystemList<span style="color:#f92672">&amp;&amp;</span> that) <span style="color:#66d9ef">noexcept</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">private</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> CheckDimensions(std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">3</span><span style="color:#f92672">&gt;</span> dim);
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">3</span><span style="color:#f92672">&gt;</span> dim_{};
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> UniformSystemList<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>Swap(System<span style="color:#f92672">&amp;</span> that, size_t n) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// make sure request in range
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">if</span> (n <span style="color:#f92672">&gt;=</span> <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>size()) {
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>cerr
+</span></span><span style="display:flex;"><span>        <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Requested UniformSystemList element out of bounds. Skipping.</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span>;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// check dim
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">bool</span> does_match <span style="color:#f92672">=</span> (dim_[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">==</span> that.n_u()) <span style="color:#f92672">&amp;&amp;</span> (dim_[<span style="color:#ae81ff">1</span>] <span style="color:#f92672">==</span> that.n_x()) <span style="color:#f92672">&amp;&amp;</span>
+</span></span><span style="display:flex;"><span>                    (dim_[<span style="color:#ae81ff">2</span>] <span style="color:#f92672">==</span> that.n_y());
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>does_match) {
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>cerr <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Cannot swap a UniformSystemList element for an element of &#34;</span>
+</span></span><span style="display:flex;"><span>                 <span style="color:#e6db74">&#34;different size. Skipping.</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span>;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// if checks pass, perform swap
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  System tmp <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move((<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>)[n]);
+</span></span><span style="display:flex;"><span>  (<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>)[n] <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(that);
+</span></span><span style="display:flex;"><span>  that <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(tmp);
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> UniformSystemList<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;&amp;</span> UniformSystemList<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span><span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> UniformSystemList<span style="color:#f92672">&amp;</span> that) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// check dimensions
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span><span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>empty()) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (<span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>size() <span style="color:#f92672">!=</span> that.size()) {
+</span></span><span style="display:flex;"><span>      std<span style="color:#f92672">::</span>ostringstream ss;
+</span></span><span style="display:flex;"><span>      ss <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;cannot reassign &#34;</span> <span style="color:#f92672">&lt;&lt;</span> <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>size() <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; systems with &#34;</span>
+</span></span><span style="display:flex;"><span>         <span style="color:#f92672">&lt;&lt;</span> that.size() <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; systems&#34;</span>;
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(ss.str());
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (dim_[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">+</span> dim_[<span style="color:#ae81ff">1</span>] <span style="color:#f92672">+</span> dim_[<span style="color:#ae81ff">2</span>]) {
+</span></span><span style="display:flex;"><span>      std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">3</span><span style="color:#f92672">&gt;</span> other_dim(that.dim());
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">if</span> (dim_ <span style="color:#f92672">!=</span> other_dim) {
+</span></span><span style="display:flex;"><span>        std<span style="color:#f92672">::</span>ostringstream ss;
+</span></span><span style="display:flex;"><span>        ss <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;cannot reassign systems of size &#34;</span> <span style="color:#f92672">&lt;&lt;</span> dim_[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;x&#34;</span> <span style="color:#f92672">&lt;&lt;</span> dim_[<span style="color:#ae81ff">1</span>]
+</span></span><span style="display:flex;"><span>           <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;x&#34;</span> <span style="color:#f92672">&lt;&lt;</span> dim_[<span style="color:#ae81ff">2</span>] <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; with systems of size &#34;</span> <span style="color:#f92672">&lt;&lt;</span> other_dim[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;x&#34;</span>
+</span></span><span style="display:flex;"><span>           <span style="color:#f92672">&lt;&lt;</span> other_dim[<span style="color:#ae81ff">1</span>] <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;x&#34;</span> <span style="color:#f92672">&lt;&lt;</span> dim_[<span style="color:#ae81ff">2</span>];
+</span></span><span style="display:flex;"><span>        <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(ss.str());
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t k <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; k <span style="color:#f92672">&lt;</span> <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>size(); k<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    (<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>)[k] <span style="color:#f92672">=</span> that[k];
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> (<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>);
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> UniformSystemList<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;&amp;</span> UniformSystemList<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span><span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>(
+</span></span><span style="display:flex;"><span>    UniformSystemList<span style="color:#f92672">&amp;&amp;</span> that) <span style="color:#66d9ef">noexcept</span> {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// // check dimensions
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// // if empty, assume a default constructed object and safe to move
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// if (!this-&gt;empty()) {
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//   if (this-&gt;size() != that.size()) {
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     std::cerr &lt;&lt; &#34;Cannot reassign &#34; &lt;&lt; this-&gt;size() &lt;&lt; &#34; systems with &#34;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//               &lt;&lt; that.size() &lt;&lt; &#34; systems. Skipping.\n&#34;;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     return (*this);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//   }
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//   // if dimensions a not zero and do not match, skip move with error
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//   message. if (dim_[0] + dim_[1] + dim_[2]) {
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     bool does_match = (dim_[0] == that.at(0).n_u()) &amp;&amp;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//                       (dim_[1] == that.at(0).n_x()) &amp;&amp;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//                       (dim_[2] == that.at(0).n_y());
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     if (!does_match) {
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//       std::cerr
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//           &lt;&lt; &#34;Cannot move a UniformSystemList element for an element of &#34;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//              &#34;different size. Skipping.\n&#34;;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//       return (*this);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     }
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//   }
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// }
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  dim_ <span style="color:#f92672">=</span> that.dim_;
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span><span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>(std<span style="color:#f92672">::</span>move(that));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> (<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>);
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>UniformSystemList<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>UniformSystemList(<span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;&amp;</span> systems,
+</span></span><span style="display:flex;"><span>                                             std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">3</span><span style="color:#f92672">&gt;</span> dim)
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;</span>(systems) {
+</span></span><span style="display:flex;"><span>  CheckDimensions(dim);
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>UniformSystemList<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>UniformSystemList(std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;&amp;&amp;</span> systems,
+</span></span><span style="display:flex;"><span>                                             std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">3</span><span style="color:#f92672">&gt;</span> dim)
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;</span>(std<span style="color:#f92672">::</span>move(systems)) {
+</span></span><span style="display:flex;"><span>  CheckDimensions(dim);
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>UniformSystemList<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>UniformSystemList(
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>initializer_list<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;</span> systems, std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">3</span><span style="color:#f92672">&gt;</span> dim)
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;</span>(systems) {
+</span></span><span style="display:flex;"><span>  CheckDimensions(dim);
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>UniformSystemList<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>UniformSystemList(<span style="color:#66d9ef">const</span> UniformSystemList<span style="color:#f92672">&amp;</span> that)
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;</span>(that) {
+</span></span><span style="display:flex;"><span>  (<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>) <span style="color:#f92672">=</span> that;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>UniformSystemList<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>UniformSystemList(UniformSystemList<span style="color:#f92672">&amp;&amp;</span> that) <span style="color:#66d9ef">noexcept</span>
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;</span>(std<span style="color:#f92672">::</span>move(that)) {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>dim_[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">=</span> <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>at(<span style="color:#ae81ff">0</span>).n_u();
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>dim_[<span style="color:#ae81ff">1</span>] <span style="color:#f92672">=</span> <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>at(<span style="color:#ae81ff">0</span>).n_x();
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>dim_[<span style="color:#ae81ff">2</span>] <span style="color:#f92672">=</span> <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>at(<span style="color:#ae81ff">0</span>).n_y();
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> System<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> UniformSystemList<span style="color:#f92672">&lt;</span>System<span style="color:#f92672">&gt;::</span>CheckDimensions(std<span style="color:#f92672">::</span>array<span style="color:#f92672">&lt;</span>size_t, <span style="color:#ae81ff">3</span><span style="color:#f92672">&gt;</span> dim) {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (dim[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">+</span> dim[<span style="color:#ae81ff">1</span>] <span style="color:#f92672">+</span> dim[<span style="color:#ae81ff">2</span>]) {
+</span></span><span style="display:flex;"><span>    dim_ <span style="color:#f92672">=</span> dim;
+</span></span><span style="display:flex;"><span>  } <span style="color:#66d9ef">else</span> {
+</span></span><span style="display:flex;"><span>    dim_[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">=</span> <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>at(<span style="color:#ae81ff">0</span>).n_u();
+</span></span><span style="display:flex;"><span>    dim_[<span style="color:#ae81ff">1</span>] <span style="color:#f92672">=</span> <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>at(<span style="color:#ae81ff">0</span>).n_x();
+</span></span><span style="display:flex;"><span>    dim_[<span style="color:#ae81ff">2</span>] <span style="color:#f92672">=</span> <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>at(<span style="color:#ae81ff">0</span>).n_y();
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// make sure dimensiolaties are all uniform
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">bool</span> <span style="color:#a6e22e">does_match</span>(true);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (<span style="color:#66d9ef">const</span> System<span style="color:#f92672">&amp;</span> sys : <span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>) {
+</span></span><span style="display:flex;"><span>    does_match <span style="color:#f92672">=</span> does_match <span style="color:#f92672">&amp;&amp;</span> (sys.n_u() <span style="color:#f92672">==</span> dim_[<span style="color:#ae81ff">0</span>]);
+</span></span><span style="display:flex;"><span>    does_match <span style="color:#f92672">=</span> does_match <span style="color:#f92672">&amp;&amp;</span> (sys.n_x() <span style="color:#f92672">==</span> dim_[<span style="color:#ae81ff">1</span>]);
+</span></span><span style="display:flex;"><span>    does_match <span style="color:#f92672">=</span> does_match <span style="color:#f92672">&amp;&amp;</span> (sys.n_y() <span style="color:#f92672">==</span> dim_[<span style="color:#ae81ff">2</span>]);
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>does_match) {
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(
+</span></span><span style="display:flex;"><span>          <span style="color:#e6db74">&#34;Dimensionality of one or more input systems are not uniform.&#34;</span>);
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
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new file mode 100644
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+  <article class="markdown"><h1 id="srclds_uniform_vecscpp">
+  src/lds_uniform_vecs.cpp
+  <a class="anchor" href="#srclds_uniform_vecscpp">#</a>
+</h1>
+<p>Uniformly sized vectors.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/">lds</a></strong> <br>Linear Dynamical Systems (LDS) namespace.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file provides a container for uniformly sized vectors.</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;ldsCtrlEst_h/lds_uniform_vecs.h&gt;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>UniformVectorList<span style="color:#f92672">::</span>UniformVectorList(<span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Vector<span style="color:#f92672">&gt;&amp;</span> vecs,
+</span></span><span style="display:flex;"><span>                                     size_t dim)
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> vector(vecs) {
+</span></span><span style="display:flex;"><span>  CheckDimensions(dim);
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>UniformVectorList<span style="color:#f92672">::</span>UniformVectorList(std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Vector<span style="color:#f92672">&gt;&amp;&amp;</span> vecs, size_t dim)
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> vector(std<span style="color:#f92672">::</span>move(vecs)) {
+</span></span><span style="display:flex;"><span>  CheckDimensions(dim);
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>UniformVectorList<span style="color:#f92672">::</span>UniformVectorList(std<span style="color:#f92672">::</span>initializer_list<span style="color:#f92672">&lt;</span>Vector<span style="color:#f92672">&gt;</span> vecs,
+</span></span><span style="display:flex;"><span>                                     size_t dim)
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> vector(vecs) {
+</span></span><span style="display:flex;"><span>  CheckDimensions(dim);
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>UniformVectorList<span style="color:#f92672">::</span>UniformVectorList(<span style="color:#66d9ef">const</span> UniformVectorList<span style="color:#f92672">&amp;</span> that)
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> vector(that) {
+</span></span><span style="display:flex;"><span>  (<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>) <span style="color:#f92672">=</span> that;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>UniformVectorList<span style="color:#f92672">::</span>UniformVectorList(UniformVectorList<span style="color:#f92672">&amp;&amp;</span> that) <span style="color:#66d9ef">noexcept</span>
+</span></span><span style="display:flex;"><span>    <span style="color:#f92672">:</span> vector(std<span style="color:#f92672">::</span>move(that)) {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>dim_ <span style="color:#f92672">=</span> <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>at(<span style="color:#ae81ff">0</span>).n_elem;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">void</span> UniformVectorList<span style="color:#f92672">::</span>CheckDimensions(size_t dim) {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (dim) {
+</span></span><span style="display:flex;"><span>    dim_ <span style="color:#f92672">=</span> dim;
+</span></span><span style="display:flex;"><span>  } <span style="color:#66d9ef">else</span> {
+</span></span><span style="display:flex;"><span>    dim_ <span style="color:#f92672">=</span> <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>at(<span style="color:#ae81ff">0</span>).n_elem;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// make sure dimensiolaties are all uniform
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">bool</span> <span style="color:#a6e22e">does_match</span>(true);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (<span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> vec : <span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>) {
+</span></span><span style="display:flex;"><span>    does_match <span style="color:#f92672">=</span> does_match <span style="color:#f92672">&amp;&amp;</span> (vec.n_elem <span style="color:#f92672">==</span> dim_);
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>does_match) {
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(
+</span></span><span style="display:flex;"><span>          <span style="color:#e6db74">&#34;Dimensionality of one or more input matrices are not uniform.&#34;</span>);
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace lds
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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+  ldsCtrlEst_h/lds_uniform_vecs.h
+  <a class="anchor" href="#ldsctrlest_hlds_uniform_vecsh">#</a>
+</h1>
+<p>List of uniformly sized vectors.  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
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+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">lds::UniformVectorList</a></strong></td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file provides a container for uniformly sized vectors.</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_LDS_UNIFORM_VECS_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;array&gt;</span><span style="color:#75715e">   </span><span style="color:#75715e">// std::array
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;vector&gt;</span><span style="color:#75715e">  </span><span style="color:#75715e">// std::vector
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;lds.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> lds {
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">UniformVectorList</span> <span style="color:#f92672">:</span> <span style="color:#66d9ef">public</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Vector<span style="color:#f92672">&gt;</span> {
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">private</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// TODO(mfbolus): would rather *uncomment* the below for sake of conversion
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// using std::vector&lt;Vector&gt;::vector;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Vector<span style="color:#f92672">&gt;::</span><span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Vector<span style="color:#f92672">&gt;::</span><span style="color:#66d9ef">operator</span>[];
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Vector<span style="color:#f92672">&gt;::</span>at;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Vector<span style="color:#f92672">&gt;::</span>begin;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Vector<span style="color:#f92672">&gt;::</span>end;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Vector<span style="color:#f92672">&gt;::</span>size;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">public</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  UniformVectorList() <span style="color:#f92672">=</span> <span style="color:#66d9ef">default</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">explicit</span> <span style="color:#a6e22e">UniformVectorList</span>(<span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Vector<span style="color:#f92672">&gt;&amp;</span> vecs, size_t dim <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">explicit</span> <span style="color:#a6e22e">UniformVectorList</span>(std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Vector<span style="color:#f92672">&gt;&amp;&amp;</span> vecs, size_t dim <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  UniformVectorList(std<span style="color:#f92672">::</span>initializer_list<span style="color:#f92672">&lt;</span>Vector<span style="color:#f92672">&gt;</span> vecs, size_t dim <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  UniformVectorList(<span style="color:#66d9ef">const</span> UniformVectorList<span style="color:#f92672">&amp;</span> that);
+</span></span><span style="display:flex;"><span>  UniformVectorList(UniformVectorList<span style="color:#f92672">&amp;&amp;</span> that) <span style="color:#66d9ef">noexcept</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#f92672">~</span>UniformVectorList() <span style="color:#f92672">=</span> <span style="color:#66d9ef">default</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  size_t <span style="color:#a6e22e">dim</span>() <span style="color:#66d9ef">const</span> { <span style="color:#66d9ef">return</span> dim_; }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  size_t <span style="color:#a6e22e">size</span>() { <span style="color:#66d9ef">return</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Vector<span style="color:#f92672">&gt;::</span>size(); };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> Vector<span style="color:#f92672">&amp;</span> at(size_t n) { <span style="color:#66d9ef">return</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Vector<span style="color:#f92672">&gt;::</span>at(n); };
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> <span style="color:#a6e22e">Swap</span>(Vector<span style="color:#f92672">&amp;</span> that, size_t n);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  UniformVectorList<span style="color:#f92672">&amp;</span> <span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>(<span style="color:#66d9ef">const</span> UniformVectorList<span style="color:#f92672">&amp;</span> that);
+</span></span><span style="display:flex;"><span>  UniformVectorList<span style="color:#f92672">&amp;</span> <span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>(UniformVectorList<span style="color:#f92672">&amp;&amp;</span> that) <span style="color:#66d9ef">noexcept</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">private</span><span style="color:#f92672">:</span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">void</span> CheckDimensions(size_t dim);
+</span></span><span style="display:flex;"><span>  size_t dim_{};
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> UniformVectorList<span style="color:#f92672">::</span>Swap(Vector<span style="color:#f92672">&amp;</span> that, size_t n) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// make sure request in range
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">if</span> (n <span style="color:#f92672">&gt;=</span> <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>size()) {
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>cerr
+</span></span><span style="display:flex;"><span>        <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Requested UniformMatrixList element out of bounds. Skipping.</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span>;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// check dim
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">bool</span> does_match <span style="color:#f92672">=</span> dim_ <span style="color:#f92672">==</span> that.n_elem;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span>does_match) {
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>cerr <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;Cannot swap a UniformMatrixList element for an element of &#34;</span>
+</span></span><span style="display:flex;"><span>                 <span style="color:#e6db74">&#34;different size. Skipping.</span><span style="color:#ae81ff">\n</span><span style="color:#e6db74">&#34;</span>;
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span>;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// if checks pass, perform swap
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector tmp <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move((<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>)[n]);
+</span></span><span style="display:flex;"><span>  (<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>)[n] <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(that);
+</span></span><span style="display:flex;"><span>  that <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(tmp);
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> UniformVectorList<span style="color:#f92672">&amp;</span> UniformVectorList<span style="color:#f92672">::</span><span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> UniformVectorList<span style="color:#f92672">&amp;</span> that) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// check dimensions
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">if</span> (<span style="color:#f92672">!</span><span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>empty()) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (<span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>size() <span style="color:#f92672">!=</span> that.size()) {
+</span></span><span style="display:flex;"><span>      std<span style="color:#f92672">::</span>ostringstream ss;
+</span></span><span style="display:flex;"><span>      ss <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;cannot reassign &#34;</span> <span style="color:#f92672">&lt;&lt;</span> <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>size() <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; vectors with &#34;</span>
+</span></span><span style="display:flex;"><span>         <span style="color:#f92672">&lt;&lt;</span> that.size() <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; vectors&#34;</span>;
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(ss.str());
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (dim_) {
+</span></span><span style="display:flex;"><span>      size_t <span style="color:#a6e22e">other_dim</span>(that.dim());
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">if</span> (dim_ <span style="color:#f92672">!=</span> other_dim) {
+</span></span><span style="display:flex;"><span>        std<span style="color:#f92672">::</span>ostringstream ss;
+</span></span><span style="display:flex;"><span>        ss <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34;cannot reassign vectors of size &#34;</span> <span style="color:#f92672">&lt;&lt;</span> dim_
+</span></span><span style="display:flex;"><span>           <span style="color:#f92672">&lt;&lt;</span> <span style="color:#e6db74">&#34; with vectors of size &#34;</span> <span style="color:#f92672">&lt;&lt;</span> other_dim;
+</span></span><span style="display:flex;"><span>        <span style="color:#66d9ef">throw</span> std<span style="color:#f92672">::</span>runtime_error(ss.str());
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t k <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; k <span style="color:#f92672">&lt;</span> <span style="color:#66d9ef">this</span><span style="color:#f92672">-&gt;</span>size(); k<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    (<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>)[k] <span style="color:#f92672">=</span> that[k];
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> (<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>);
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> UniformVectorList<span style="color:#f92672">&amp;</span> UniformVectorList<span style="color:#f92672">::</span><span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>(
+</span></span><span style="display:flex;"><span>    UniformVectorList<span style="color:#f92672">&amp;&amp;</span> that) <span style="color:#66d9ef">noexcept</span> {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// // check dimensions
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// if (!this-&gt;empty()) {
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//   if (this-&gt;size() != that.size()) {
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     std::cerr &lt;&lt; &#34;Cannot reassign &#34; &lt;&lt; this-&gt;size() &lt;&lt; &#34; vectors with &#34;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//               &lt;&lt; that.size() &lt;&lt; &#34; vectors. Skipping.\n&#34;;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     return (*this);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//   }
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//   if (dim_) {
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     size_t other_dim(that.dim());
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     if (dim_ != other_dim) {
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//       std::cerr &lt;&lt; &#34;Cannot reassign vectors of size &#34; &lt;&lt; dim_
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//                 &lt;&lt; &#34; with matrices of size &#34; &lt;&lt; other_dim &lt;&lt; &#34;.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//                 Skipping.\n&#34;;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//       return (*this);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//     }
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//   }
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// }
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  dim_ <span style="color:#f92672">=</span> that.dim_;
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>Vector<span style="color:#f92672">&gt;::</span><span style="color:#66d9ef">operator</span><span style="color:#f92672">=</span>(std<span style="color:#f92672">::</span>move(that));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> (<span style="color:#f92672">*</span><span style="color:#66d9ef">this</span>);
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace lds
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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+  <article class="markdown"><h1 id="ldsctrlest_hmex_c_utilh">
+  ldsCtrlEst_h/mex_c_util.h
+  <a class="anchor" href="#ldsctrlest_hmex_c_utilh">#</a>
+</h1>
+<p>arma &lt;-&gt; mex interoperability utilities (Matlab C API)  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/">armamexc</a></strong> <br>arma/mex interface using Matlab C API</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file defines utility functions for interoperability between armadillo and Matlab/Octave&rsquo;s C mex API.</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_MEXC_UTIL_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_MEXC_UTIL_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;ldsCtrlEst&gt;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;mex.h&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// // If Matlab_FOUND, include matrix.h.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// // (Octave does not need/have it.)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// #ifdef Matlab_FOUND
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// #include &#34;matrix.h&#34;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// #endif
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> armamexc {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">T</span><span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">auto</span> m2T_scalar(<span style="color:#66d9ef">const</span> mxArray <span style="color:#f92672">*</span>matlab_scalar) <span style="color:#f92672">-&gt;</span> T {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (mxGetData(matlab_scalar)) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span> <span style="color:#66d9ef">static_cast</span><span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span>(mxGetScalar(matlab_scalar));
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  mexErrMsgTxt(<span style="color:#e6db74">&#34;No data available.&#34;</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">T</span><span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">auto</span> m2a_mat(<span style="color:#66d9ef">const</span> mxArray <span style="color:#f92672">*</span>matlab_mat, <span style="color:#66d9ef">bool</span> copy_aux_mem <span style="color:#f92672">=</span> false,
+</span></span><span style="display:flex;"><span>                    <span style="color:#66d9ef">bool</span> strict <span style="color:#f92672">=</span> true) <span style="color:#f92672">-&gt;</span> arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span> {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (mxGetData(matlab_mat)) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> mwSize n_dim <span style="color:#f92672">=</span> mxGetNumberOfDimensions(matlab_mat);
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (n_dim <span style="color:#f92672">==</span> <span style="color:#ae81ff">2</span>) {
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">return</span> arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span>(<span style="color:#66d9ef">static_cast</span><span style="color:#f92672">&lt;</span>T <span style="color:#f92672">*&gt;</span>(mxGetData(matlab_mat)),
+</span></span><span style="display:flex;"><span>                          mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem,
+</span></span><span style="display:flex;"><span>                          strict);
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>    mexErrMsgTxt(<span style="color:#e6db74">&#34;Number of dimensions must be 2.&#34;</span>);
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span> arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span>();
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  mexErrMsgTxt(<span style="color:#e6db74">&#34;No data available.&#34;</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span>();
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">// TODO(mfbolus): make these templated.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> T<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">auto</span> a2m_mat(arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span> <span style="color:#66d9ef">const</span> <span style="color:#f92672">&amp;</span>arma_mat) <span style="color:#f92672">-&gt;</span> mxArray <span style="color:#f92672">*</span> {
+</span></span><span style="display:flex;"><span>  mxArray <span style="color:#f92672">*</span>matlab_mat <span style="color:#f92672">=</span> mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols,
+</span></span><span style="display:flex;"><span>                                              mxDOUBLE_CLASS, mxREAL);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (matlab_mat) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">auto</span> <span style="color:#f92672">*</span>dst_pointer <span style="color:#f92672">=</span> <span style="color:#66d9ef">static_cast</span><span style="color:#f92672">&lt;</span>T <span style="color:#f92672">*&gt;</span>(mxGetData(matlab_mat));
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> <span style="color:#66d9ef">auto</span> <span style="color:#f92672">*</span>src_pointer <span style="color:#f92672">=</span> <span style="color:#66d9ef">const_cast</span><span style="color:#f92672">&lt;</span>T <span style="color:#f92672">*&gt;</span>(arma_mat.memptr());
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// TODO(mfbolus): I just want to MOVE the data, not copy.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    std<span style="color:#f92672">::</span>memcpy(dst_pointer, src_pointer, <span style="color:#66d9ef">sizeof</span>(T) <span style="color:#f92672">*</span> arma_mat.n_elem);
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span> matlab_mat;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  mexErrMsgTxt(<span style="color:#e6db74">&#34;Failed to create matlab mat from arma::Mat.&#34;</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> <span style="color:#66d9ef">nullptr</span>;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> T<span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">auto</span> a2m_vec(arma<span style="color:#f92672">::</span>Col<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span> <span style="color:#66d9ef">const</span> <span style="color:#f92672">&amp;</span>arma_vec) <span style="color:#f92672">-&gt;</span> mxArray <span style="color:#f92672">*</span> {
+</span></span><span style="display:flex;"><span>  mxArray <span style="color:#f92672">*</span>matlab_mat <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      mxCreateNumericMatrix(arma_vec.n_elem, <span style="color:#ae81ff">1</span>, mxDOUBLE_CLASS, mxREAL);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (matlab_mat) {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">auto</span> <span style="color:#f92672">*</span>dst_pointer <span style="color:#f92672">=</span> <span style="color:#66d9ef">static_cast</span><span style="color:#f92672">&lt;</span>T <span style="color:#f92672">*&gt;</span>(mxGetData(matlab_mat));
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> <span style="color:#66d9ef">auto</span> <span style="color:#f92672">*</span>src_pointer <span style="color:#f92672">=</span> <span style="color:#66d9ef">const_cast</span><span style="color:#f92672">&lt;</span>T <span style="color:#f92672">*&gt;</span>(arma_vec.memptr());
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// TODO(mfbolus): I just want to MOVE the data, not copy.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    std<span style="color:#f92672">::</span>memcpy(dst_pointer, src_pointer, <span style="color:#66d9ef">sizeof</span>(T) <span style="color:#f92672">*</span> arma_vec.n_elem);
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span> matlab_mat;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  mexErrMsgTxt(<span style="color:#e6db74">&#34;Failed to create matlab mat from arma::Col.&#34;</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> <span style="color:#66d9ef">nullptr</span>;
+</span></span><span style="display:flex;"><span>}
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace armamexc
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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+  ldsCtrlEst_h/mex_cpp_util.h
+  <a class="anchor" href="#ldsctrlest_hmex_cpp_utilh">#</a>
+</h1>
+<p>arma &lt;-&gt; mex interoperability utilities (Matlab C++ API)  <a href="#detailed-description">More&hellip;</a></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/">armamexcpp</a></strong> <br>arma/mex interface using Matlab C++ API</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>This file defines utility functions for interoperability between armadillo and Matlab&rsquo;s C++ mex API.</p>
+<h2 id="source-code">
+  Source code
+  <a class="anchor" href="#source-code">#</a>
+</h2>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">//===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Michael Bolus
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Copyright 2021 Georgia Institute of Technology
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Licensed under the Apache License, Version 2.0 (the &#34;License&#34;);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// you may not use this file except in compliance with the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// You may obtain a copy of the License at
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//     http://www.apache.org/licenses/LICENSE-2.0
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// Unless required by applicable law or agreed to in writing, software
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// distributed under the License is distributed on an &#34;AS IS&#34; BASIS,
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// See the License for the specific language governing permissions and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">// limitations under the License.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">//===----------------------------------------------------------------------===//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#ifndef LDSCTRLEST_MEXCPP_UTIL_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#define LDSCTRLEST_MEXCPP_UTIL_H
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;ldsCtrlEst&gt;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;mex.hpp&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&#34;mexAdapter.hpp&#34;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">namespace</span> armamexcpp {
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">T</span><span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;&gt;</span> m2a_cellmat(matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>CellArray<span style="color:#f92672">&amp;</span> matlab_cell) {
+</span></span><span style="display:flex;"><span>  size_t n_cells <span style="color:#f92672">=</span> matlab_cell.getNumberOfElements();
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;&gt;</span> arma_mat(n_cells,
+</span></span><span style="display:flex;"><span>                                     arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span>(<span style="color:#ae81ff">1</span>, <span style="color:#ae81ff">1</span>, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros));
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t k <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; k <span style="color:#f92672">&lt;</span> n_cells; k<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>TypedArray<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span> matlab_mat <span style="color:#f92672">=</span> matlab_cell[k];
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">auto</span> dims <span style="color:#f92672">=</span> matlab_mat.getDimensions();
+</span></span><span style="display:flex;"><span>    arma_mat[k] <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span>(matlab_mat.release().get(), dims[<span style="color:#ae81ff">0</span>], dims[<span style="color:#ae81ff">1</span>]);
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> arma_mat;
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">T</span><span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span> m2s_vec(matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>TypedArray<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;&amp;</span> matlab_array) {
+</span></span><span style="display:flex;"><span>  size_t n_elem <span style="color:#f92672">=</span> matlab_array.getNumberOfElements();
+</span></span><span style="display:flex;"><span>  T<span style="color:#f92672">*</span> ptr <span style="color:#f92672">=</span> matlab_array.release().get();
+</span></span><span style="display:flex;"><span>  std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span> vec(ptr, ptr <span style="color:#f92672">+</span> n_elem);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> vec;
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">T</span><span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>arma<span style="color:#f92672">::</span>Col<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span> m2a_vec(matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>TypedArray<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span> matlab_array) {
+</span></span><span style="display:flex;"><span>  size_t n_elem <span style="color:#f92672">=</span> matlab_array.getNumberOfElements();
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// T* ptr = matlab_array.release().get();
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// arma::Col&lt;T&gt; vec(ptr, n_elem);  //, false);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// TODO(mfbolus): for some reason, using the above pointer at times leads to
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// getting garbage values. matlab array values may be stored in non-contiguous
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// memory?
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  arma<span style="color:#f92672">::</span>Col<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span> vec(n_elem, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (size_t k <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>; k <span style="color:#f92672">&lt;</span> n_elem; k<span style="color:#f92672">++</span>) {
+</span></span><span style="display:flex;"><span>    vec[k] <span style="color:#f92672">=</span> matlab_array[k];
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> vec;
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">T</span><span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span> m2a_mat(matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>TypedArray<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span> matlab_array) {
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// ArrayDimensions == std::vector&lt;size_t&gt;
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">auto</span> dims <span style="color:#f92672">=</span> matlab_array.getDimensions();
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// T* ptr = matlab_array.release().get();
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// arma::Mat&lt;T&gt; mat(ptr, dims[0], dims[1]);  //, false);
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// TODO(mfbolus): for some reason, using the above pointer at times leads to
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// getting garbage values. matlab array values may be stored in non-contiguous
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// memory?
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">//
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// armadillo and matlab both use column-major ordering, so this should work:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  size_t n_elem <span style="color:#f92672">=</span> dims[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">*</span> dims[<span style="color:#ae81ff">1</span>];
+</span></span><span style="display:flex;"><span>  arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span> mat(dims[<span style="color:#ae81ff">0</span>], dims[<span style="color:#ae81ff">1</span>], arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>  size_t <span style="color:#a6e22e">k</span>(<span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">for</span> (<span style="color:#66d9ef">auto</span> m: matlab_array) {
+</span></span><span style="display:flex;"><span>    mat[k] <span style="color:#f92672">=</span> m;
+</span></span><span style="display:flex;"><span>    k<span style="color:#f92672">++</span>;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> mat;
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">T</span><span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>TypedArray<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span> a2m_mat(<span style="color:#66d9ef">const</span> arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;&amp;</span> arma_mat,
+</span></span><span style="display:flex;"><span>                                    matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>ArrayFactory<span style="color:#f92672">&amp;</span> factory) {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>TypedArray<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span> matlab_mat <span style="color:#f92672">=</span> factory.createArray<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span>(
+</span></span><span style="display:flex;"><span>      {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(),
+</span></span><span style="display:flex;"><span>      arma_mat.memptr() <span style="color:#f92672">+</span> arma_mat.n_elem);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> matlab_mat;
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">T</span><span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>TypedArray<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span> a2m_vec(<span style="color:#66d9ef">const</span> arma<span style="color:#f92672">::</span>Col<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;&amp;</span> arma_vec,
+</span></span><span style="display:flex;"><span>                                    matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>ArrayFactory<span style="color:#f92672">&amp;</span> factory) {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>TypedArray<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span> matlab_mat <span style="color:#f92672">=</span>
+</span></span><span style="display:flex;"><span>      factory.createArray<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span>({arma_vec.n_elem, <span style="color:#ae81ff">1</span>}, arma_vec.memptr(),
+</span></span><span style="display:flex;"><span>                             arma_vec.memptr() <span style="color:#f92672">+</span> arma_vec.n_elem);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> matlab_mat;
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">T</span><span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>TypedArray<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span> s2m_vec(<span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;&amp;</span> std_vec,
+</span></span><span style="display:flex;"><span>                                    matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>ArrayFactory<span style="color:#f92672">&amp;</span> factory) {
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">const</span> matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>TypedArray<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span> matlab_mat <span style="color:#f92672">=</span> factory.createArray<span style="color:#f92672">&lt;</span>T<span style="color:#f92672">&gt;</span>(
+</span></span><span style="display:flex;"><span>      {std_vec.size(), <span style="color:#ae81ff">1</span>}, std_vec.data(), std_vec.data() <span style="color:#f92672">+</span> std_vec.size());
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">return</span> matlab_mat;
+</span></span><span style="display:flex;"><span>};
+</span></span><span style="display:flex;"><span>}  <span style="color:#75715e">// namespace armamexcpp
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#75715e">#endif
+</span></span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
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diff --git a/docs/docs/api/modules/group__control__masks/index.html b/docs/docs/api/modules/group__control__masks/index.html
new file mode 100644
index 00000000..724736db
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+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
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+      <ul>
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+        <li><a href="#detailed-description">Detailed Description</a></li>
+        <li><a href="#attribute-details">Attribute Details</a>
+          <ul>
+            <li><a href="#kcontroltypedeltau">kControlTypeDeltaU</a></li>
+            <li><a href="#kcontroltypeinty">kControlTypeIntY</a></li>
+            <li><a href="#kcontroltypeadaptm">kControlTypeAdaptM</a></li>
+          </ul>
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+      </ul>
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+  
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+      </header>
+
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+      
+  <article class="markdown"><h1 id="control-mode-bit-masks">
+  Control Mode Bit Masks
+  <a class="anchor" href="#control-mode-bit-masks">#</a>
+</h1>
+<p>provides fill types for constructing new armadillo vectors, matrices <br> <br><a href="#detailed-description">More&hellip;</a>
+<br></p>
+<h2 id="attributes">
+  Attributes
+  <a class="anchor" href="#attributes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>const std::size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/modules/group__control__masks/#variable-kcontroltypedeltau">kControlTypeDeltaU</a></strong> <br>control designed to penalize change in input</td>
+      </tr>
+      <tr>
+          <td>const std::size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/modules/group__control__masks/#variable-kcontroltypeinty">kControlTypeIntY</a></strong> <br>control using integral action</td>
+      </tr>
+      <tr>
+          <td>const std::size_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/modules/group__control__masks/#variable-kcontroltypeadaptm">kControlTypeAdaptM</a></strong> <br>adapt control setpoint with re-estimated disturbance <code>m</code></td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>Control mode bit masks. These can be bit-wise OR&rsquo;d to use in combination.</p>
+<h2 id="attribute-details">
+  Attribute Details
+  <a class="anchor" href="#attribute-details">#</a>
+</h2>
+<h3 id="kcontroltypedeltau">
+  kControlTypeDeltaU
+  <a class="anchor" href="#kcontroltypedeltau">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">static</span> <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>size_t kControlTypeDeltaU <span style="color:#f92672">=</span> <span style="color:#ae81ff">0x1</span>;
+</span></span></code></pre></div><p>Control was designed to penalize change in input (i.e., the state was augmented with input <code>u</code>)</p>
+<h3 id="kcontroltypeinty">
+  kControlTypeIntY
+  <a class="anchor" href="#kcontroltypeinty">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">static</span> <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>size_t kControlTypeIntY <span style="color:#f92672">=</span> kControlTypeDeltaU <span style="color:#f92672">&lt;&lt;</span> <span style="color:#ae81ff">1</span>;
+</span></span></code></pre></div><p>Control using integral action (i.e., the state was augmented with output <code>y</code> during design)</p>
+<h3 id="kcontroltypeadaptm">
+  kControlTypeAdaptM
+  <a class="anchor" href="#kcontroltypeadaptm">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">static</span> <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>size_t kControlTypeAdaptM <span style="color:#f92672">=</span> kControlTypeDeltaU <span style="color:#f92672">&lt;&lt;</span> <span style="color:#ae81ff">2</span>;
+</span></span></code></pre></div><p>Adapt control setpoint adapted with re-estimated process disturbance <code>m</code>.</p>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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+        <li><a href="#attribute-details">Attribute Details</a>
+          <ul>
+            <li><a href="#kcontroltypedeltau">kControlTypeDeltaU</a></li>
+            <li><a href="#kcontroltypeinty">kControlTypeIntY</a></li>
+            <li><a href="#kcontroltypeadaptm">kControlTypeAdaptM</a></li>
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diff --git a/docs/docs/api/modules/group__defaults/index.html b/docs/docs/api/modules/group__defaults/index.html
new file mode 100644
index 00000000..21096776
--- /dev/null
+++ b/docs/docs/api/modules/group__defaults/index.html
@@ -0,0 +1,376 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="
+  Defaults
+  #
+
+ More&hellip;
+
+
+  Attributes
+  #
+
+
+  
+      
+          
+          Name
+      
+  
+  
+      
+          const data_t
+          kDefaultP0 default state estimate covar
+      
+      
+          const data_t
+          kDefaultQ0 default process noise covar
+      
+      
+          const data_t
+          kDefaultR0 default output noise covar
+      
+  
+
+
+  Detailed Description
+  #
+
+Default values for common variables (e.g., default diagonal elements of covariances)
+
+  Attribute Details
+  #
+
+
+  kDefaultP0
+  #
+
+static const data_t kDefaultP0 = 1e-6;
+
+  kDefaultQ0
+  #
+
+static const data_t kDefaultQ0 = 1e-6;
+
+  kDefaultR0
+  #
+
+static const data_t kDefaultR0 = 1e-2;
+
+Updated on 31 March 2025 at 16:04:30 EDT">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__defaults/">
+  <meta property="og:site_name" content="LDS C&E">
+  <meta property="og:title" content="Defaults">
+  <meta property="og:description" content="Defaults # More… Attributes # Name const data_t kDefaultP0 default state estimate covar const data_t kDefaultQ0 default process noise covar const data_t kDefaultR0 default output noise covar Detailed Description # Default values for common variables (e.g., default diagonal elements of covariances)
+Attribute Details # kDefaultP0 # static const data_t kDefaultP0 = 1e-6; kDefaultQ0 # static const data_t kDefaultQ0 = 1e-6; kDefaultR0 # static const data_t kDefaultR0 = 1e-2; Updated on 31 March 2025 at 16:04:30 EDT">
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+    <li><a href="#defaults">Defaults</a>
+      <ul>
+        <li><a href="#attributes">Attributes</a></li>
+        <li><a href="#detailed-description">Detailed Description</a></li>
+        <li><a href="#attribute-details">Attribute Details</a>
+          <ul>
+            <li><a href="#kdefaultp0">kDefaultP0</a></li>
+            <li><a href="#kdefaultq0">kDefaultQ0</a></li>
+            <li><a href="#kdefaultr0">kDefaultR0</a></li>
+          </ul>
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+  </aside>
+  
+ 
+      </header>
+
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+      
+  <article class="markdown"><h1 id="defaults">
+  Defaults
+  <a class="anchor" href="#defaults">#</a>
+</h1>
+<p><br> <br><a href="#detailed-description">More&hellip;</a>
+<br></p>
+<h2 id="attributes">
+  Attributes
+  <a class="anchor" href="#attributes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>const data_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/modules/group__defaults/#variable-kdefaultp0">kDefaultP0</a></strong> <br>default state estimate covar</td>
+      </tr>
+      <tr>
+          <td>const data_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/modules/group__defaults/#variable-kdefaultq0">kDefaultQ0</a></strong> <br>default process noise covar</td>
+      </tr>
+      <tr>
+          <td>const data_t</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/modules/group__defaults/#variable-kdefaultr0">kDefaultR0</a></strong> <br>default output noise covar</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>Default values for common variables (e.g., default diagonal elements of covariances)</p>
+<h2 id="attribute-details">
+  Attribute Details
+  <a class="anchor" href="#attribute-details">#</a>
+</h2>
+<h3 id="kdefaultp0">
+  kDefaultP0
+  <a class="anchor" href="#kdefaultp0">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">static</span> <span style="color:#66d9ef">const</span> data_t kDefaultP0 <span style="color:#f92672">=</span> <span style="color:#ae81ff">1e-6</span>;
+</span></span></code></pre></div><h3 id="kdefaultq0">
+  kDefaultQ0
+  <a class="anchor" href="#kdefaultq0">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">static</span> <span style="color:#66d9ef">const</span> data_t kDefaultQ0 <span style="color:#f92672">=</span> <span style="color:#ae81ff">1e-6</span>;
+</span></span></code></pre></div><h3 id="kdefaultr0">
+  kDefaultR0
+  <a class="anchor" href="#kdefaultr0">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">static</span> <span style="color:#66d9ef">const</span> data_t kDefaultR0 <span style="color:#f92672">=</span> <span style="color:#ae81ff">1e-2</span>;
+</span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
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+      <div class="book-toc-content">
+        
+  
+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#defaults">Defaults</a>
+      <ul>
+        <li><a href="#attributes">Attributes</a></li>
+        <li><a href="#detailed-description">Detailed Description</a></li>
+        <li><a href="#attribute-details">Attribute Details</a>
+          <ul>
+            <li><a href="#kdefaultp0">kDefaultP0</a></li>
+            <li><a href="#kdefaultq0">kDefaultQ0</a></li>
+            <li><a href="#kdefaultr0">kDefaultR0</a></li>
+          </ul>
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diff --git a/docs/docs/api/modules/index.html b/docs/docs/api/modules/index.html
new file mode 100644
index 00000000..2e880b25
--- /dev/null
+++ b/docs/docs/api/modules/index.html
@@ -0,0 +1,265 @@
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+  Modules
+  #
+
+
+
+Control Mode Bit Masks provides fill types for constructing new armadillo vectors, matrices
+
+
+Defaults
+
+
+
+Updated on 31 March 2025 at 16:04:30 EDT">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/">
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+Defaults
+Updated on 31 March 2025 at 16:04:30 EDT">
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+
+  <ul>
+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
+</ul>
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+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
+</ul>
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+</ul>
+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
+<li><a href="/lds-ctrl-est/docs/api/classes/">Classes</a></li>
+<li><a href="/lds-ctrl-est/docs/api/modules/"class=active>Modules</a></li>
+<li><a href="/lds-ctrl-est/docs/api/files/">Files</a></li>
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+<ul>
+<li><a href="/lds-ctrl-est/issues-contributing/"><strong>Reporting Issues &amp; Contributing</strong></a></li>
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+      </header>
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+      
+      
+  <article class="markdown"><h1 id="modules">
+  Modules
+  <a class="anchor" href="#modules">#</a>
+</h1>
+<ul>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/modules/group__control__masks/">Control Mode Bit Masks</a></strong> <br>provides fill types for constructing new armadillo vectors, matrices</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/modules/group__defaults/">Defaults</a></strong></p>
+</li>
+</ul>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
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diff --git a/docs/docs/api/modules/index.xml b/docs/docs/api/modules/index.xml
new file mode 100644
index 00000000..1ba5bc5c
--- /dev/null
+++ b/docs/docs/api/modules/index.xml
@@ -0,0 +1,25 @@
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+  <channel>
+    <title>Modules on LDS C&amp;E</title>
+    <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/</link>
+    <description>Recent content in Modules on LDS C&amp;E</description>
+    <generator>Hugo</generator>
+    <language>en</language>
+    <atom:link href="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/index.xml" rel="self" type="application/rss+xml" />
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+      <title>Control Mode Bit Masks</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__control__masks/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__control__masks/</guid>
+      <description>provides fill types for constructing new armadillo vectors, matrices</description>
+    </item>
+    <item>
+      <title>Defaults</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__defaults/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__defaults/</guid>
+      <description>&lt;h1 id=&#34;defaults&#34;&gt;&#xA;  Defaults&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#defaults&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;&lt;br&gt; &lt;br&gt;&lt;a href=&#34;#detailed-description&#34;&gt;More&amp;hellip;&lt;/a&gt;&#xA;&lt;br&gt;&lt;/p&gt;&#xA;&lt;h2 id=&#34;attributes&#34;&gt;&#xA;  Attributes&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#attributes&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const data_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__defaults/#variable-kdefaultp0&#34;&gt;kDefaultP0&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;default state estimate covar&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const data_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__defaults/#variable-kdefaultq0&#34;&gt;kDefaultQ0&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;default process noise covar&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const data_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__defaults/#variable-kdefaultr0&#34;&gt;kDefaultR0&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;default output noise covar&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;detailed-description&#34;&gt;&#xA;  Detailed Description&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#detailed-description&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;p&gt;Default values for common variables (e.g., default diagonal elements of covariances)&lt;/p&gt;&#xA;&lt;h2 id=&#34;attribute-details&#34;&gt;&#xA;  Attribute Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#attribute-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;kdefaultp0&#34;&gt;&#xA;  kDefaultP0&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#kdefaultp0&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;static&lt;/span&gt; &lt;span style=&#34;color:#66d9ef&#34;&gt;const&lt;/span&gt; data_t kDefaultP0 &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1e-6&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h3 id=&#34;kdefaultq0&#34;&gt;&#xA;  kDefaultQ0&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#kdefaultq0&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;static&lt;/span&gt; &lt;span style=&#34;color:#66d9ef&#34;&gt;const&lt;/span&gt; data_t kDefaultQ0 &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1e-6&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h3 id=&#34;kdefaultr0&#34;&gt;&#xA;  kDefaultR0&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#kdefaultr0&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;static&lt;/span&gt; &lt;span style=&#34;color:#66d9ef&#34;&gt;const&lt;/span&gt; data_t kDefaultR0 &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1e-2&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;p&gt;Updated on 31 March 2025 at 16:04:30 EDT&lt;/p&gt;</description>
+    </item>
+  </channel>
+</rss>
diff --git a/docs/docs/api/namespaces/index.html b/docs/docs/api/namespaces/index.html
new file mode 100644
index 00000000..8e414384
--- /dev/null
+++ b/docs/docs/api/namespaces/index.html
@@ -0,0 +1,286 @@
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+  <meta charset="UTF-8">
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+  Namespaces
+  #
+
+
+
+armamexc arma/mex interface using Matlab C API
+
+
+armamexcpp arma/mex interface using Matlab C&#43;&#43; API
+
+
+lds::gaussian Linear Dynamical Systems with Gaussian observations.
+
+
+lds::poisson Linear Dynamical Systems with Poisson observations.
+
+
+std
+
+
+
+Updated on 31 March 2025 at 16:04:30 EDT">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/">
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+armamexcpp arma/mex interface using Matlab C&#43;&#43; API
+lds::gaussian Linear Dynamical Systems with Gaussian observations.
+lds::poisson Linear Dynamical Systems with Poisson observations.
+std
+Updated on 31 March 2025 at 16:04:30 EDT">
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+<title>Namespaces | LDS C&amp;E</title>
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+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
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+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
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+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/"class=active>Namespaces</a></li>
+<li><a href="/lds-ctrl-est/docs/api/classes/">Classes</a></li>
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+<li><a href="/lds-ctrl-est/issues-contributing/"><strong>Reporting Issues &amp; Contributing</strong></a></li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
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+<br />
+
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+    
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+    
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+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#namespaces">Namespaces</a></li>
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+  </aside>
+  
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+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h1>
+<ul>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/">armamexc</a></strong> <br>arma/mex interface using Matlab C API</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/">armamexcpp</a></strong> <br>arma/mex interface using Matlab C++ API</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/">lds::gaussian</a></strong> <br>Linear Dynamical Systems with Gaussian observations.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/">lds::poisson</a></strong> <br>Linear Dynamical Systems with Poisson observations.</p>
+</li>
+<li>
+<p><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacestd/">std</a></strong></p>
+</li>
+</ul>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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diff --git a/docs/docs/api/namespaces/index.xml b/docs/docs/api/namespaces/index.xml
new file mode 100644
index 00000000..a07424c7
--- /dev/null
+++ b/docs/docs/api/namespaces/index.xml
@@ -0,0 +1,53 @@
+<?xml version="1.0" encoding="utf-8" standalone="yes"?>
+<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
+  <channel>
+    <title>Namespaces on LDS C&amp;E</title>
+    <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/</link>
+    <description>Recent content in Namespaces on LDS C&amp;E</description>
+    <generator>Hugo</generator>
+    <language>en</language>
+    <atom:link href="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/index.xml" rel="self" type="application/rss+xml" />
+    <item>
+      <title>armamexc</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/</guid>
+      <description>arma/mex interface using Matlab C API</description>
+    </item>
+    <item>
+      <title>armamexcpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/</guid>
+      <description>arma/mex interface using Matlab C++ API</description>
+    </item>
+    <item>
+      <title>lds</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/</guid>
+      <description>Linear Dynamical Systems (LDS) namespace.</description>
+    </item>
+    <item>
+      <title>lds::gaussian</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/</guid>
+      <description>Linear Dynamical Systems with Gaussian observations.</description>
+    </item>
+    <item>
+      <title>lds::poisson</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/</guid>
+      <description>Linear Dynamical Systems with Poisson observations.</description>
+    </item>
+    <item>
+      <title>std</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacestd/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacestd/</guid>
+      <description>&lt;h1 id=&#34;std&#34;&gt;&#xA;  std&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#std&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;br&gt;&#xA;&lt;hr&gt;&#xA;&lt;p&gt;Updated on 31 March 2025 at 16:04:30 EDT&lt;/p&gt;</description>
+    </item>
+  </channel>
+</rss>
diff --git a/docs/docs/api/namespaces/namespacearmamexc/index.html b/docs/docs/api/namespaces/namespacearmamexc/index.html
new file mode 100644
index 00000000..4aad9cd5
--- /dev/null
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+
+  <label for="toc-control">
+    
+    <img src="/lds-ctrl-est/svg/toc.svg" class="book-icon" alt="Table of Contents" />
+    
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+    
+  
+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#armamexc">armamexc</a>
+      <ul>
+        <li><a href="#functions">Functions</a></li>
+        <li><a href="#detailed-description">Detailed Description</a></li>
+        <li><a href="#function-details">Function Details</a>
+          <ul>
+            <li><a href="#m2t_scalar">m2T_scalar</a></li>
+            <li><a href="#m2a_mat">m2a_mat</a></li>
+            <li><a href="#a2m_mat">a2m_mat</a></li>
+            <li><a href="#a2m_vec">a2m_vec</a></li>
+          </ul>
+        </li>
+      </ul>
+    </li>
+  </ul>
+</nav>
+
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+  
+ 
+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="armamexc">
+  armamexc
+  <a class="anchor" href="#armamexc">#</a>
+</h1>
+<p>arma/mex interface using Matlab C API <br> <br><a href="#detailed-description">More&hellip;</a>
+<br></p>
+<h2 id="functions">
+  Functions
+  <a class="anchor" href="#functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>template &lt;class T &gt; <br>T</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/#function-m2t-scalar">m2T_scalar</a></strong>(const mxArray * matlab_scalar)<br>Convert Matlab mxArray to scalar of type T.</td>
+      </tr>
+      <tr>
+          <td>template &lt;class T &gt; <br>arma::Mat&lt; T &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/#function-m2a-mat">m2a_mat</a></strong>(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)<br>Convert matlab matrix to armadillo.</td>
+      </tr>
+      <tr>
+          <td>template &lt;typename T &gt; <br>mxArray *</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/#function-a2m-mat">a2m_mat</a></strong>(arma::Mat&lt; T &gt; const &amp; arma_mat)<br>Convert armadillo to matlab matrix.</td>
+      </tr>
+      <tr>
+          <td>template &lt;typename T &gt; <br>mxArray *</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/#function-a2m-vec">a2m_vec</a></strong>(arma::Col&lt; T &gt; const &amp; arma_vec)<br>Convert armadillo to matlab vector.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>Utilities for arma/mex interface <em>using Matlab C API</em></p>
+<h2 id="function-details">
+  Function Details
+  <a class="anchor" href="#function-details">#</a>
+</h2>
+<h3 id="m2t_scalar">
+  m2T_scalar
+  <a class="anchor" href="#m2t_scalar">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">T</span> <span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> T m2T_scalar(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> mxArray <span style="color:#f92672">*</span> matlab_scalar
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>matlab_scalar</strong> matlab scalar</li>
+</ul>
+<p><strong>Template Parameters</strong>:</p>
+<ul>
+<li><strong>T</strong> type</li>
+</ul>
+<p><strong>Return</strong>: scalar of type T</p>
+<h3 id="m2a_mat">
+  m2a_mat
+  <a class="anchor" href="#m2a_mat">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">T</span> <span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span> T <span style="color:#f92672">&gt;</span> m2a_mat(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> mxArray <span style="color:#f92672">*</span> matlab_mat,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> copy_aux_mem <span style="color:#f92672">=</span>false,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">bool</span> strict <span style="color:#f92672">=</span>true
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>matlab_mat</strong> matlab matrix</li>
+<li><strong>copy_aux_mem</strong> [optional] whether to copy auxiliary memory</li>
+<li><strong>strict</strong> [optional] strictly enforce the above</li>
+</ul>
+<p><strong>Template Parameters</strong>:</p>
+<ul>
+<li><strong>T</strong> type</li>
+</ul>
+<p><strong>Return</strong>: armadillo matrix of type T</p>
+<h3 id="a2m_mat">
+  a2m_mat
+  <a class="anchor" href="#a2m_mat">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> T <span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> mxArray <span style="color:#f92672">*</span> a2m_mat(
+</span></span><span style="display:flex;"><span>    arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span> T <span style="color:#f92672">&gt;</span> <span style="color:#66d9ef">const</span> <span style="color:#f92672">&amp;</span> arma_mat
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>arma_mat</strong> armadillo matrix</li>
+</ul>
+<p><strong>Return</strong>: matlab matrix</p>
+<h3 id="a2m_vec">
+  a2m_vec
+  <a class="anchor" href="#a2m_vec">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">typename</span> T <span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> mxArray <span style="color:#f92672">*</span> a2m_vec(
+</span></span><span style="display:flex;"><span>    arma<span style="color:#f92672">::</span>Col<span style="color:#f92672">&lt;</span> T <span style="color:#f92672">&gt;</span> <span style="color:#66d9ef">const</span> <span style="color:#f92672">&amp;</span> arma_vec
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>arma_vec</strong> armadillo vector</li>
+</ul>
+<p><strong>Return</strong>: matlab vector</p>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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+    <li><a href="#armamexc">armamexc</a>
+      <ul>
+        <li><a href="#functions">Functions</a></li>
+        <li><a href="#detailed-description">Detailed Description</a></li>
+        <li><a href="#function-details">Function Details</a>
+          <ul>
+            <li><a href="#m2t_scalar">m2T_scalar</a></li>
+            <li><a href="#m2a_mat">m2a_mat</a></li>
+            <li><a href="#a2m_mat">a2m_mat</a></li>
+            <li><a href="#a2m_vec">a2m_vec</a></li>
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diff --git a/docs/docs/api/namespaces/namespacearmamexcpp/index.html b/docs/docs/api/namespaces/namespacearmamexcpp/index.html
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+  <ul>
+    <li><a href="#armamexcpp">armamexcpp</a>
+      <ul>
+        <li><a href="#functions">Functions</a></li>
+        <li><a href="#detailed-description">Detailed Description</a></li>
+        <li><a href="#function-details">Function Details</a>
+          <ul>
+            <li><a href="#m2a_cellmat">m2a_cellmat</a></li>
+            <li><a href="#m2s_vec">m2s_vec</a></li>
+            <li><a href="#m2a_vec">m2a_vec</a></li>
+            <li><a href="#m2a_mat">m2a_mat</a></li>
+            <li><a href="#a2m_mat">a2m_mat</a></li>
+            <li><a href="#a2m_vec">a2m_vec</a></li>
+            <li><a href="#s2m_vec">s2m_vec</a></li>
+          </ul>
+        </li>
+      </ul>
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+  </ul>
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+  </aside>
+  
+ 
+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="armamexcpp">
+  armamexcpp
+  <a class="anchor" href="#armamexcpp">#</a>
+</h1>
+<p>arma/mex interface using Matlab C++ API <br> <br><a href="#detailed-description">More&hellip;</a>
+<br></p>
+<h2 id="functions">
+  Functions
+  <a class="anchor" href="#functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>template &lt;class T &gt; <br>std::vector&lt; arma::Mat&lt; T &gt; &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/#function-m2a-cellmat">m2a_cellmat</a></strong>(matlab::data::CellArray &amp; matlab_cell)<br>Convert matlab cell array to vector of armadillo matrices.</td>
+      </tr>
+      <tr>
+          <td>template &lt;class T &gt; <br>std::vector&lt; T &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/#function-m2s-vec">m2s_vec</a></strong>(matlab::data::TypedArray&lt; T &gt; &amp; matlab_array)<br>Convert matlab matrix to a vector of scalars.</td>
+      </tr>
+      <tr>
+          <td>template &lt;class T &gt; <br>arma::Col&lt; T &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/#function-m2a-vec">m2a_vec</a></strong>(matlab::data::TypedArray&lt; T &gt; matlab_array)<br>Convert matlab to armadillo vector.</td>
+      </tr>
+      <tr>
+          <td>template &lt;class T &gt; <br>arma::Mat&lt; T &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/#function-m2a-mat">m2a_mat</a></strong>(matlab::data::TypedArray&lt; T &gt; matlab_array)<br>Convert matlab to armadillo matrix.</td>
+      </tr>
+      <tr>
+          <td>template &lt;class T &gt; <br>matlab::data::TypedArray&lt; T &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/#function-a2m-mat">a2m_mat</a></strong>(const arma::Mat&lt; T &gt; &amp; arma_mat, matlab::data::ArrayFactory &amp; factory)<br>Convert armadillo to matlab matrix.</td>
+      </tr>
+      <tr>
+          <td>template &lt;class T &gt; <br>matlab::data::TypedArray&lt; T &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/#function-a2m-vec">a2m_vec</a></strong>(const arma::Col&lt; T &gt; &amp; arma_vec, matlab::data::ArrayFactory &amp; factory)<br>Convert armadillo to matlab vector.</td>
+      </tr>
+      <tr>
+          <td>template &lt;class T &gt; <br>matlab::data::TypedArray&lt; T &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/#function-s2m-vec">s2m_vec</a></strong>(const std::vector&lt; T &gt; &amp; std_vec, matlab::data::ArrayFactory &amp; factory)<br>Convert vector of scalar T to matlab matrix.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="detailed-description">
+  Detailed Description
+  <a class="anchor" href="#detailed-description">#</a>
+</h2>
+<p>utilities for arma/mex interface <em>using Matlab C++ API</em></p>
+<h2 id="function-details">
+  Function Details
+  <a class="anchor" href="#function-details">#</a>
+</h2>
+<h3 id="m2a_cellmat">
+  m2a_cellmat
+  <a class="anchor" href="#m2a_cellmat">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">T</span> <span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span> T <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&gt;</span> m2a_cellmat(
+</span></span><span style="display:flex;"><span>    matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>CellArray <span style="color:#f92672">&amp;</span> matlab_cell
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>matlab_cell</strong> matlab cell</li>
+</ul>
+<p><strong>Template Parameters</strong>:</p>
+<ul>
+<li><strong>T</strong> type</li>
+</ul>
+<p><strong>Return</strong>: vector of armadillo matrices of type T</p>
+<h3 id="m2s_vec">
+  m2s_vec
+  <a class="anchor" href="#m2s_vec">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">T</span> <span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> T <span style="color:#f92672">&gt;</span> m2s_vec(
+</span></span><span style="display:flex;"><span>    matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>TypedArray<span style="color:#f92672">&lt;</span> T <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;</span> matlab_array
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>matlab_array</strong> matlab array</li>
+</ul>
+<p><strong>Template Parameters</strong>:</p>
+<ul>
+<li><strong>T</strong> type</li>
+</ul>
+<p><strong>Return</strong>: vector of type T</p>
+<h3 id="m2a_vec">
+  m2a_vec
+  <a class="anchor" href="#m2a_vec">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">T</span> <span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>arma<span style="color:#f92672">::</span>Col<span style="color:#f92672">&lt;</span> T <span style="color:#f92672">&gt;</span> m2a_vec(
+</span></span><span style="display:flex;"><span>    matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>TypedArray<span style="color:#f92672">&lt;</span> T <span style="color:#f92672">&gt;</span> matlab_array
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>matlab_array</strong> matlab array</li>
+</ul>
+<p><strong>Template Parameters</strong>:</p>
+<ul>
+<li><strong>T</strong> type</li>
+</ul>
+<p><strong>Return</strong>: armadillo vector of type T</p>
+<h3 id="m2a_mat">
+  m2a_mat
+  <a class="anchor" href="#m2a_mat">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">T</span> <span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span> T <span style="color:#f92672">&gt;</span> m2a_mat(
+</span></span><span style="display:flex;"><span>    matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>TypedArray<span style="color:#f92672">&lt;</span> T <span style="color:#f92672">&gt;</span> matlab_array
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>matlab_array</strong> matlab matrix</li>
+</ul>
+<p><strong>Template Parameters</strong>:</p>
+<ul>
+<li><strong>T</strong> type</li>
+</ul>
+<p><strong>Return</strong>: armadillo matrix of type T</p>
+<h3 id="a2m_mat">
+  a2m_mat
+  <a class="anchor" href="#a2m_mat">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">T</span> <span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>TypedArray<span style="color:#f92672">&lt;</span> T <span style="color:#f92672">&gt;</span> a2m_mat(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span> T <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;</span> arma_mat,
+</span></span><span style="display:flex;"><span>    matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>ArrayFactory <span style="color:#f92672">&amp;</span> factory
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>arma_mat</strong> arma matrix</li>
+<li><strong>factory</strong> matlab &ldquo;array factory&rdquo;</li>
+</ul>
+<p><strong>Template Parameters</strong>:</p>
+<ul>
+<li><strong>T</strong> type</li>
+</ul>
+<p><strong>Return</strong>: matlab matrix</p>
+<h3 id="a2m_vec">
+  a2m_vec
+  <a class="anchor" href="#a2m_vec">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">T</span> <span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>TypedArray<span style="color:#f92672">&lt;</span> T <span style="color:#f92672">&gt;</span> a2m_vec(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> arma<span style="color:#f92672">::</span>Col<span style="color:#f92672">&lt;</span> T <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;</span> arma_vec,
+</span></span><span style="display:flex;"><span>    matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>ArrayFactory <span style="color:#f92672">&amp;</span> factory
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>arma_vec</strong> armadillo vector</li>
+<li><strong>factory</strong> matlab &ldquo;array factory&rdquo;</li>
+</ul>
+<p><strong>Template Parameters</strong>:</p>
+<ul>
+<li><strong>T</strong> type</li>
+</ul>
+<p><strong>Return</strong>: matlab matrix</p>
+<h3 id="s2m_vec">
+  s2m_vec
+  <a class="anchor" href="#s2m_vec">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">template</span> <span style="color:#f92672">&lt;</span><span style="color:#66d9ef">class</span> <span style="color:#a6e22e">T</span> <span style="color:#f92672">&gt;</span>
+</span></span><span style="display:flex;"><span>matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>TypedArray<span style="color:#f92672">&lt;</span> T <span style="color:#f92672">&gt;</span> s2m_vec(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> T <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;</span> std_vec,
+</span></span><span style="display:flex;"><span>    matlab<span style="color:#f92672">::</span>data<span style="color:#f92672">::</span>ArrayFactory <span style="color:#f92672">&amp;</span> factory
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>std_vec</strong> standard vector</li>
+<li><strong>factory</strong> matlab &ldquo;array factory&rdquo;</li>
+</ul>
+<p><strong>Template Parameters</strong>:</p>
+<ul>
+<li><strong>T</strong> type</li>
+</ul>
+<p><strong>Return</strong>: matlab matrix</p>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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diff --git a/docs/docs/api/namespaces/namespacelds/index.html b/docs/docs/api/namespaces/namespacelds/index.html
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+<li><strong>Library Terminology</strong>
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+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
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+<li><strong>Tutorials</strong>
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+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
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+</ul>
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+</ul>
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+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
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+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
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+  <ul>
+    <li><a href="#lds">lds</a>
+      <ul>
+        <li><a href="#namespaces">Namespaces</a></li>
+        <li><a href="#classes">Classes</a></li>
+        <li><a href="#types">Types</a></li>
+        <li><a href="#functions">Functions</a></li>
+        <li><a href="#attributes">Attributes</a></li>
+        <li><a href="#type-details">Type Details</a>
+          <ul>
+            <li><a href="#ssidwt">SSIDWt</a></li>
+            <li><a href="#matrixlistfreedim">MatrixListFreeDim</a></li>
+            <li><a href="#data_t">data_t</a></li>
+            <li><a href="#vector">Vector</a></li>
+            <li><a href="#matrix">Matrix</a></li>
+            <li><a href="#cube">Cube</a></li>
+            <li><a href="#view">View</a></li>
+          </ul>
+        </li>
+        <li><a href="#function-details">Function Details</a>
+          <ul>
+            <li><a href="#limit">Limit</a></li>
+            <li><a href="#limit-1">Limit</a></li>
+            <li><a href="#limit-2">Limit</a></li>
+            <li><a href="#reassign">Reassign</a></li>
+            <li><a href="#reassign-1">Reassign</a></li>
+            <li><a href="#forcesympd">ForceSymPD</a></li>
+            <li><a href="#forcesymmineig">ForceSymMinEig</a></li>
+            <li><a href="#lq">lq</a></li>
+            <li><a href="#calccov">calcCov</a></li>
+          </ul>
+        </li>
+        <li><a href="#attribute-details">Attribute Details</a>
+          <ul>
+            <li><a href="#kinf">kInf</a></li>
+            <li><a href="#kpi">kPi</a></li>
+          </ul>
+        </li>
+      </ul>
+    </li>
+  </ul>
+</nav>
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+  </aside>
+  
+ 
+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="lds">
+  lds
+  <a class="anchor" href="#lds">#</a>
+</h1>
+<p>Linear Dynamical Systems (LDS) namespace. <br></p>
+<h2 id="namespaces">
+  Namespaces
+  <a class="anchor" href="#namespaces">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/">lds::gaussian</a></strong> <br>Linear Dynamical Systems with Gaussian observations.</td>
+      </tr>
+      <tr>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/">lds::poisson</a></strong> <br>Linear Dynamical Systems with Poisson observations.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/">lds::Controller</a></strong></td>
+      </tr>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/">lds::EM</a></strong></td>
+      </tr>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/">lds::Fit</a></strong> <br>LDS <a href="">Fit</a> Type.</td>
+      </tr>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">lds::SSID</a></strong></td>
+      </tr>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/">lds::SwitchedController</a></strong> <br><a href="">SwitchedController</a> Type.</td>
+      </tr>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_system/">lds::System</a></strong> <br>Linear Dynamical <a href="">System</a> Type.</td>
+      </tr>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/">lds::UniformMatrixList</a></strong></td>
+      </tr>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/">lds::UniformSystemList</a></strong></td>
+      </tr>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/">lds::UniformVectorList</a></strong></td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="types">
+  Types
+  <a class="anchor" href="#types">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>enum</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt">SSIDWt</a></strong> { kSSIDNone, kSSIDMOESP, kSSIDCVA}<br>weighting options for <a href="">SSID</a></td>
+      </tr>
+      <tr>
+          <td>enum</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-matrixlistfreedim">MatrixListFreeDim</a></strong> { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2}</td>
+      </tr>
+      <tr>
+          <td>using double</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></strong></td>
+      </tr>
+      <tr>
+          <td>using arma::Col&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-vector">Vector</a></strong></td>
+      </tr>
+      <tr>
+          <td>using arma::Mat&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-matrix">Matrix</a></strong></td>
+      </tr>
+      <tr>
+          <td>using arma::Cube&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-cube">Cube</a></strong></td>
+      </tr>
+      <tr>
+          <td>using arma::subview&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> &gt;</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-view">View</a></strong></td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="functions">
+  Functions
+  <a class="anchor" href="#functions">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-limit">Limit</a></strong>(std::vector&lt; <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> &gt; &amp; x, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> lb, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> ub)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-limit">Limit</a></strong>(Vector &amp; x, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> lb, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> ub)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-limit">Limit</a></strong>(Matrix &amp; x, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> lb, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> ub)</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-reassign">Reassign</a></strong>(Vector &amp; some, const Vector &amp; other, const std::string &amp; parenthetical =&ldquo;Reassign&rdquo;)<br>reassigns contents of some Vector in place</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-reassign">Reassign</a></strong>(Matrix &amp; some, const Matrix &amp; other, const std::string &amp; parenthetical =&ldquo;Reassign&rdquo;)<br>reassigns contents of some Matrix in place</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-forcesympd">ForceSymPD</a></strong>(Matrix &amp; X)<br>forces matrix to be symmetric positive-definite</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-forcesymmineig">ForceSymMinEig</a></strong>(Matrix &amp; X, <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a> eig_min =0)<br>forces matrix to be symmetric and have a minimum eigenvalue</td>
+      </tr>
+      <tr>
+          <td>void</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-lq">lq</a></strong>(Matrix &amp; L, Matrix &amp; Qt, const Matrix &amp; X)<br>LQ decomposition.</td>
+      </tr>
+      <tr>
+          <td>Matrix</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#function-calccov">calcCov</a></strong>(const Matrix &amp; A, const Matrix &amp; B)<br>Calculate covariance matrix.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="attributes">
+  Attributes
+  <a class="anchor" href="#attributes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>const <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#variable-kinf">kInf</a></strong> <br>Some useful numbers.</td>
+      </tr>
+      <tr>
+          <td>const <a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t">data_t</a></td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds/#variable-kpi">kPi</a></strong></td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="type-details">
+  Type Details
+  <a class="anchor" href="#type-details">#</a>
+</h2>
+<h3 id="ssidwt">
+  SSIDWt
+  <a class="anchor" href="#ssidwt">#</a>
+</h3>
+<table>
+  <thead>
+      <tr>
+          <th>Enumerator</th>
+          <th>Value</th>
+          <th>Description</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>kSSIDNone</td>
+          <td></td>
+          <td>None.</td>
+      </tr>
+      <tr>
+          <td>kSSIDMOESP</td>
+          <td></td>
+          <td>MOESP (AKA &ldquo;robust method&rdquo; in van Overschee 1996)</td>
+      </tr>
+      <tr>
+          <td>kSSIDCVA</td>
+          <td></td>
+          <td>CVA &ldquo;Canonical Variate Analysis&rdquo;.</td>
+      </tr>
+  </tbody>
+</table>
+<p>Weighting options for singular value decomposition performed during subspace identification (<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">SSID</a>)</p>
+<p>Reference:</p>
+<p>van Overschee, de Moor. 1996. Subspace Identification for Linear Systems.</p>
+<h3 id="matrixlistfreedim">
+  MatrixListFreeDim
+  <a class="anchor" href="#matrixlistfreedim">#</a>
+</h3>
+<table>
+  <thead>
+      <tr>
+          <th>Enumerator</th>
+          <th>Value</th>
+          <th>Description</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>kMatFreeDimNone</td>
+          <td></td>
+          <td>neither dim free to be hetero in mat list</td>
+      </tr>
+      <tr>
+          <td>kMatFreeDim1</td>
+          <td></td>
+          <td>allow 1st dim of mats in list to be hetero</td>
+      </tr>
+      <tr>
+          <td>kMatFreeDim2</td>
+          <td></td>
+          <td>allow 2nd dim of mats in list to be hetero</td>
+      </tr>
+  </tbody>
+</table>
+<h3 id="data_t">
+  data_t
+  <a class="anchor" href="#data_t">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>data_t <span style="color:#f92672">=</span>  <span style="color:#66d9ef">double</span>;
+</span></span></code></pre></div><p>Type of all data in library. If need 32b, change <code>double</code> to <code>float</code>. This could be potentially useful for large scale problems where there are memory constraints.</p>
+<h3 id="vector">
+  Vector
+  <a class="anchor" href="#vector">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Vector <span style="color:#f92672">=</span>  arma<span style="color:#f92672">::</span>Col<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>;
+</span></span></code></pre></div><h3 id="matrix">
+  Matrix
+  <a class="anchor" href="#matrix">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Matrix <span style="color:#f92672">=</span>  arma<span style="color:#f92672">::</span>Mat<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>;
+</span></span></code></pre></div><h3 id="cube">
+  Cube
+  <a class="anchor" href="#cube">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Cube <span style="color:#f92672">=</span>  arma<span style="color:#f92672">::</span>Cube<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>;
+</span></span></code></pre></div><h3 id="view">
+  View
+  <a class="anchor" href="#view">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>View <span style="color:#f92672">=</span>  arma<span style="color:#f92672">::</span>subview<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;</span>;
+</span></span></code></pre></div><h2 id="function-details">
+  Function Details
+  <a class="anchor" href="#function-details">#</a>
+</h2>
+<h3 id="limit">
+  Limit
+  <a class="anchor" href="#limit">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> Limit(
+</span></span><span style="display:flex;"><span>    std<span style="color:#f92672">::</span>vector<span style="color:#f92672">&lt;</span> data_t <span style="color:#f92672">&gt;</span> <span style="color:#f92672">&amp;</span> x,
+</span></span><span style="display:flex;"><span>    data_t lb,
+</span></span><span style="display:flex;"><span>    data_t ub
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><h3 id="limit-1">
+  Limit
+  <a class="anchor" href="#limit-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> Limit(
+</span></span><span style="display:flex;"><span>    Vector <span style="color:#f92672">&amp;</span> x,
+</span></span><span style="display:flex;"><span>    data_t lb,
+</span></span><span style="display:flex;"><span>    data_t ub
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><h3 id="limit-2">
+  Limit
+  <a class="anchor" href="#limit-2">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> Limit(
+</span></span><span style="display:flex;"><span>    Matrix <span style="color:#f92672">&amp;</span> x,
+</span></span><span style="display:flex;"><span>    data_t lb,
+</span></span><span style="display:flex;"><span>    data_t ub
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><h3 id="reassign">
+  Reassign
+  <a class="anchor" href="#reassign">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> Reassign(
+</span></span><span style="display:flex;"><span>    Vector <span style="color:#f92672">&amp;</span> some,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Vector <span style="color:#f92672">&amp;</span> other,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>string <span style="color:#f92672">&amp;</span> parenthetical <span style="color:#f92672">=</span><span style="color:#e6db74">&#34;Reassign&#34;</span>
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>some</strong> some Vector</li>
+<li><strong>other</strong> other Vector</li>
+<li><strong>parenthetical</strong> optional description provided by caller to ease debugging</li>
+</ul>
+<h3 id="reassign-1">
+  Reassign
+  <a class="anchor" href="#reassign-1">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">inline</span> <span style="color:#66d9ef">void</span> Reassign(
+</span></span><span style="display:flex;"><span>    Matrix <span style="color:#f92672">&amp;</span> some,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> other,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> std<span style="color:#f92672">::</span>string <span style="color:#f92672">&amp;</span> parenthetical <span style="color:#f92672">=</span><span style="color:#e6db74">&#34;Reassign&#34;</span>
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>some</strong> some Matrix</li>
+<li><strong>other</strong> other Matrix</li>
+<li><strong>parenthetical</strong> optional description provided by caller to ease debugging</li>
+</ul>
+<h3 id="forcesympd">
+  ForceSymPD
+  <a class="anchor" href="#forcesympd">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> ForceSymPD(
+</span></span><span style="display:flex;"><span>    Matrix <span style="color:#f92672">&amp;</span> X
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>X</strong> mutated matrix</li>
+</ul>
+<h3 id="forcesymmineig">
+  ForceSymMinEig
+  <a class="anchor" href="#forcesymmineig">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> ForceSymMinEig(
+</span></span><span style="display:flex;"><span>    Matrix <span style="color:#f92672">&amp;</span> X,
+</span></span><span style="display:flex;"><span>    data_t eig_min <span style="color:#f92672">=</span><span style="color:#ae81ff">0</span>
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>X</strong> mutated matrix</li>
+<li><strong>eig_min</strong> [optional] minimum eigen value</li>
+</ul>
+<h3 id="lq">
+  lq
+  <a class="anchor" href="#lq">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">void</span> lq(
+</span></span><span style="display:flex;"><span>    Matrix <span style="color:#f92672">&amp;</span> L,
+</span></span><span style="display:flex;"><span>    Matrix <span style="color:#f92672">&amp;</span> Qt,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> X
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>L</strong> lower triangle matrix</li>
+<li><strong>Qt</strong> orthonormal matrix (transposed cf QR decomp)</li>
+<li><strong>X</strong> matrix being decomposed</li>
+</ul>
+<h3 id="calccov">
+  calcCov
+  <a class="anchor" href="#calccov">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>Matrix calcCov(
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> A,
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">const</span> Matrix <span style="color:#f92672">&amp;</span> B
+</span></span><span style="display:flex;"><span>)
+</span></span></code></pre></div><p><strong>Parameters</strong>:</p>
+<ul>
+<li><strong>A</strong> some matrix</li>
+<li><strong>B</strong> some other matrix</li>
+</ul>
+<p><strong>Return</strong>: covariance</p>
+<h2 id="attribute-details">
+  Attribute Details
+  <a class="anchor" href="#attribute-details">#</a>
+</h2>
+<h3 id="kinf">
+  kInf
+  <a class="anchor" href="#kinf">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">static</span> <span style="color:#66d9ef">const</span> data_t kInf <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>numeric_limits<span style="color:#f92672">&lt;</span>data_t<span style="color:#f92672">&gt;::</span>infinity();
+</span></span></code></pre></div><h3 id="kpi">
+  kPi
+  <a class="anchor" href="#kpi">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">static</span> <span style="color:#66d9ef">const</span> data_t kPi <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>datum<span style="color:#f92672">::</span>pi;
+</span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
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+        <li><a href="#type-details">Type Details</a>
+          <ul>
+            <li><a href="#ssidwt">SSIDWt</a></li>
+            <li><a href="#matrixlistfreedim">MatrixListFreeDim</a></li>
+            <li><a href="#data_t">data_t</a></li>
+            <li><a href="#vector">Vector</a></li>
+            <li><a href="#matrix">Matrix</a></li>
+            <li><a href="#cube">Cube</a></li>
+            <li><a href="#view">View</a></li>
+          </ul>
+        </li>
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+          <ul>
+            <li><a href="#limit">Limit</a></li>
+            <li><a href="#limit-1">Limit</a></li>
+            <li><a href="#limit-2">Limit</a></li>
+            <li><a href="#reassign">Reassign</a></li>
+            <li><a href="#reassign-1">Reassign</a></li>
+            <li><a href="#forcesympd">ForceSymPD</a></li>
+            <li><a href="#forcesymmineig">ForceSymMinEig</a></li>
+            <li><a href="#lq">lq</a></li>
+            <li><a href="#calccov">calcCov</a></li>
+          </ul>
+        </li>
+        <li><a href="#attribute-details">Attribute Details</a>
+          <ul>
+            <li><a href="#kinf">kInf</a></li>
+            <li><a href="#kpi">kPi</a></li>
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diff --git a/docs/docs/api/namespaces/namespacelds_1_1gaussian/index.html b/docs/docs/api/namespaces/namespacelds_1_1gaussian/index.html
new file mode 100644
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+      
+  <article class="markdown"><h1 id="ldsgaussian">
+  lds::gaussian
+  <a class="anchor" href="#ldsgaussian">#</a>
+</h1>
+<p>Linear Dynamical Systems with Gaussian observations. <br></p>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/">lds::gaussian::Controller</a></strong> <br>Gaussian-observation <a href="">Controller</a> Type.</td>
+      </tr>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/">lds::gaussian::Fit</a></strong> <br>GLDS <a href="">Fit</a> Type.</td>
+      </tr>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/">lds::gaussian::FitEM</a></strong> <br>GLDS E-M <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/">Fit</a> Type.</td>
+      </tr>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/">lds::gaussian::FitSSID</a></strong> <br>Subspace Identification (<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">SSID</a>) for GLDS.</td>
+      </tr>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/">lds::gaussian::SwitchedController</a></strong> <br>Gaussian-observation <a href="">SwitchedController</a> Type.</td>
+      </tr>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/">lds::gaussian::System</a></strong> <br>Gaussian LDS Type.</td>
+      </tr>
+  </tbody>
+</table>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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diff --git a/docs/docs/api/namespaces/namespacelds_1_1poisson/index.html b/docs/docs/api/namespaces/namespacelds_1_1poisson/index.html
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+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
+</ul>
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+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
+<li><a href="/lds-ctrl-est/docs/api/classes/">Classes</a></li>
+<li><a href="/lds-ctrl-est/docs/api/modules/">Modules</a></li>
+<li><a href="/lds-ctrl-est/docs/api/files/">Files</a></li>
+<li><a href="/lds-ctrl-est/docs/api/examples/">Examples</a></li>
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+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/issues-contributing/"><strong>Reporting Issues &amp; Contributing</strong></a></li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
+</ul>
+<br />
+
+
+
+
+
+
+  
+<ul>
+  
+  <li>
+    <a href="https://github.com/stanley-rozell/lds-ctrl-est" target="_blank" rel="noopener">
+        Github
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+    
+    <img src="/lds-ctrl-est/svg/toc.svg" class="book-icon" alt="Table of Contents" />
+    
+  </label>
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+  <aside class="hidden clearfix">
+    
+  
+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#ldspoisson">lds::poisson</a>
+      <ul>
+        <li><a href="#classes">Classes</a></li>
+        <li><a href="#attributes">Attributes</a></li>
+        <li><a href="#attribute-details">Attribute Details</a>
+          <ul>
+            <li><a href="#rd">rd</a></li>
+            <li><a href="#rng">rng</a></li>
+          </ul>
+        </li>
+      </ul>
+    </li>
+  </ul>
+</nav>
+
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+
+  </aside>
+  
+ 
+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="ldspoisson">
+  lds::poisson
+  <a class="anchor" href="#ldspoisson">#</a>
+</h1>
+<p>Linear Dynamical Systems with Poisson observations. <br></p>
+<h2 id="classes">
+  Classes
+  <a class="anchor" href="#classes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/">lds::poisson::Controller</a></strong> <br>PLDS <a href="">Controller</a> Type.</td>
+      </tr>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/">lds::poisson::Fit</a></strong> <br>PLDS <a href="">Fit</a> Type.</td>
+      </tr>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/">lds::poisson::FitEM</a></strong> <br>PLDS E-M <a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/">Fit</a> Type.</td>
+      </tr>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/">lds::poisson::FitSSID</a></strong> <br>Subspace Identification (<a href="/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/">SSID</a>) for PLDS.</td>
+      </tr>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/">lds::poisson::SwitchedController</a></strong> <br>Poisson-observation <a href="">SwitchedController</a> Type.</td>
+      </tr>
+      <tr>
+          <td>class</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/">lds::poisson::System</a></strong> <br>Poisson <a href="">System</a> type.</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="attributes">
+  Attributes
+  <a class="anchor" href="#attributes">#</a>
+</h2>
+<table>
+  <thead>
+      <tr>
+          <th></th>
+          <th>Name</th>
+      </tr>
+  </thead>
+  <tbody>
+      <tr>
+          <td>std::random_device</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/#variable-rd">rd</a></strong> <br>random device for simulating poisson data</td>
+      </tr>
+      <tr>
+          <td>std::mt19937</td>
+          <td><strong><a href="/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/#variable-rng">rng</a></strong> <br>random number generator for simulating poisson data</td>
+      </tr>
+  </tbody>
+</table>
+<h2 id="attribute-details">
+  Attribute Details
+  <a class="anchor" href="#attribute-details">#</a>
+</h2>
+<h3 id="rd">
+  rd
+  <a class="anchor" href="#rd">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">static</span> std<span style="color:#f92672">::</span>random_device rd;
+</span></span></code></pre></div><h3 id="rng">
+  rng
+  <a class="anchor" href="#rng">#</a>
+</h3>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#66d9ef">static</span> std<span style="color:#f92672">::</span>mt19937 rng <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>mt19937(
+</span></span><span style="display:flex;"><span>    rd());
+</span></span></code></pre></div><hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
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+          <ul>
+            <li><a href="#rd">rd</a></li>
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diff --git a/docs/docs/api/namespaces/namespacestd/index.html b/docs/docs/api/namespaces/namespacestd/index.html
new file mode 100644
index 00000000..5989be1d
--- /dev/null
+++ b/docs/docs/api/namespaces/namespacestd/index.html
@@ -0,0 +1,249 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="
+  std
+  #
+
+
+
+Updated on 31 March 2025 at 16:04:30 EDT">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacestd/">
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+  <meta property="og:title" content="std">
+  <meta property="og:description" content="std # Updated on 31 March 2025 at 16:04:30 EDT">
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+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
+</ul>
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+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
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+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
+<li><a href="/lds-ctrl-est/docs/api/classes/">Classes</a></li>
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+<li><a href="/lds-ctrl-est/issues-contributing/"><strong>Reporting Issues &amp; Contributing</strong></a></li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
+</ul>
+<br />
+
+
+
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+  <aside class="hidden clearfix">
+    
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+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#std">std</a></li>
+  </ul>
+</nav>
+
+
+
+  </aside>
+  
+ 
+      </header>
+
+      
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+  <article class="markdown"><h1 id="std">
+  std
+  <a class="anchor" href="#std">#</a>
+</h1>
+<br>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
+      
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diff --git a/docs/docs/api/pages/index.html b/docs/docs/api/pages/index.html
new file mode 100644
index 00000000..0b6bb617
--- /dev/null
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+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="
+  Pages
+  #
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+Updated on 31 March 2025 at 16:04:30 EDT">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/pages/">
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+  <meta property="og:locale" content="en">
+  <meta property="og:type" content="website">
+<title>Pages | LDS C&amp;E</title>
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+<link rel="alternate" type="application/rss+xml" href="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/pages/index.xml" title="LDS C&E" />
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+  <input type="text" id="book-search-input" placeholder="Search" aria-label="Search" maxlength="64" data-hotkeys="s/" />
+  <div class="book-search-spinner hidden"></div>
+  <ul id="book-search-results"></ul>
+</div>
+
+
+
+
+
+
+
+
+
+
+
+  <ul>
+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
+<li><a href="/lds-ctrl-est/docs/api/classes/">Classes</a></li>
+<li><a href="/lds-ctrl-est/docs/api/modules/">Modules</a></li>
+<li><a href="/lds-ctrl-est/docs/api/files/">Files</a></li>
+<li><a href="/lds-ctrl-est/docs/api/examples/">Examples</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/issues-contributing/"><strong>Reporting Issues &amp; Contributing</strong></a></li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
+</ul>
+<br />
+
+
+
+
+
+
+  
+<ul>
+  
+  <li>
+    <a href="https://github.com/stanley-rozell/lds-ctrl-est" target="_blank" rel="noopener">
+        Github
+      </a>
+  </li>
+  
+</ul>
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+
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+
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+
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+      </div>
+    </aside>
+
+    <div class="book-page">
+      <header class="book-header">
+        
+  <div class="flex align-center justify-between">
+  <label for="menu-control">
+    <img src="/lds-ctrl-est/svg/menu.svg" class="book-icon" alt="Menu" />
+  </label>
+
+  <strong>Pages</strong>
+
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+    
+    <img src="/lds-ctrl-est/svg/toc.svg" class="book-icon" alt="Table of Contents" />
+    
+  </label>
+</div>
+
+
+  
+  <aside class="hidden clearfix">
+    
+  
+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#pages">Pages</a></li>
+  </ul>
+</nav>
+
+
+
+  </aside>
+  
+ 
+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="pages">
+  Pages
+  <a class="anchor" href="#pages">#</a>
+</h1>
+<hr>
+<p>Updated on 31 March 2025 at 16:04:30 EDT</p>
+</article>
+ 
+      
+
+      <footer class="book-footer">
+        
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+    </div>
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+      <div class="book-toc-content">
+        
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+    <li><a href="#pages">Pages</a></li>
+  </ul>
+</nav>
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+    
+  </main>
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diff --git a/docs/docs/api/pages/index.xml b/docs/docs/api/pages/index.xml
new file mode 100644
index 00000000..4ef68faa
--- /dev/null
+++ b/docs/docs/api/pages/index.xml
@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="utf-8" standalone="yes"?>
+<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
+  <channel>
+    <title>Pages on LDS C&amp;E</title>
+    <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/pages/</link>
+    <description>Recent content in Pages on LDS C&amp;E</description>
+    <generator>Hugo</generator>
+    <language>en</language>
+    <atom:link href="https://stanley-rozell.github.io/lds-ctrl-est/docs/api/pages/index.xml" rel="self" type="application/rss+xml" />
+  </channel>
+</rss>
diff --git a/docs/docs/getting-started/getting-started/index.html b/docs/docs/getting-started/getting-started/index.html
new file mode 100644
index 00000000..11e5c6ee
--- /dev/null
+++ b/docs/docs/getting-started/getting-started/index.html
@@ -0,0 +1,405 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="
+  Getting Started
+  #
+
+This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.
+ldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C&#43;&#43; package manager which allows us to easily install and use the dependencies in isolation.
+
+  Tested Configurations
+  #
+
+Building C&#43;&#43; libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the RelWithDebInfo build type in the CMake configure command (-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo):">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/getting-started/getting-started/">
+  <meta property="og:site_name" content="LDS C&E">
+  <meta property="og:title" content="LDS C&E">
+  <meta property="og:description" content="Getting Started # This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.
+ldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C&#43;&#43; package manager which allows us to easily install and use the dependencies in isolation.
+Tested Configurations # Building C&#43;&#43; libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the RelWithDebInfo build type in the CMake configure command (-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo):">
+  <meta property="og:locale" content="en">
+  <meta property="og:type" content="article">
+    <meta property="article:section" content="docs">
+<title>Getting Started | LDS C&amp;E</title>
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+  </a>
+</h2>
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+<div class="book-search">
+  <input type="text" id="book-search-input" placeholder="Search" aria-label="Search" maxlength="64" data-hotkeys="s/" />
+  <div class="book-search-spinner hidden"></div>
+  <ul id="book-search-results"></ul>
+</div>
+
+
+
+
+
+
+
+
+
+
+
+  <ul>
+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"class=active><strong>Getting Started</strong></a>
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
+<li><a href="/lds-ctrl-est/docs/api/classes/">Classes</a></li>
+<li><a href="/lds-ctrl-est/docs/api/modules/">Modules</a></li>
+<li><a href="/lds-ctrl-est/docs/api/files/">Files</a></li>
+<li><a href="/lds-ctrl-est/docs/api/examples/">Examples</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/issues-contributing/"><strong>Reporting Issues &amp; Contributing</strong></a></li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
+</ul>
+<br />
+
+
+
+
+
+
+  
+<ul>
+  
+  <li>
+    <a href="https://github.com/stanley-rozell/lds-ctrl-est" target="_blank" rel="noopener">
+        Github
+      </a>
+  </li>
+  
+</ul>
+
+
+
+
+
+
+</nav>
+
+
+
+
+  <script>(function(){var e=document.querySelector("aside .book-menu-content");addEventListener("beforeunload",function(){localStorage.setItem("menu.scrollTop",e.scrollTop)}),e.scrollTop=localStorage.getItem("menu.scrollTop")})()</script>
+
+
+ 
+      </div>
+    </aside>
+
+    <div class="book-page">
+      <header class="book-header">
+        
+  <div class="flex align-center justify-between">
+  <label for="menu-control">
+    <img src="/lds-ctrl-est/svg/menu.svg" class="book-icon" alt="Menu" />
+  </label>
+
+  <strong>Getting Started</strong>
+
+  <label for="toc-control">
+    
+    <img src="/lds-ctrl-est/svg/toc.svg" class="book-icon" alt="Table of Contents" />
+    
+  </label>
+</div>
+
+
+  
+  <aside class="hidden clearfix">
+    
+  
+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#getting-started">Getting Started</a>
+      <ul>
+        <li><a href="#tested-configurations">Tested Configurations</a></li>
+        <li><a href="#mac-pre-requisities">Mac Pre-requisities</a></li>
+        <li><a href="#linux-pre-requisites">Linux Pre-requisites</a></li>
+        <li><a href="#windows-installation">Windows Installation</a></li>
+        <li><a href="#downloading-the-library">Downloading the Library</a></li>
+        <li><a href="#compilation--installation">Compilation + Installation</a></li>
+        <li><a href="#options">Options</a></li>
+        <li><a href="#python-bindings-package-ldsctrlest">Python bindings package <code>ldsctrlest</code></a></li>
+        <li><a href="#common-issues">Common issues</a></li>
+      </ul>
+    </li>
+  </ul>
+</nav>
+
+
+
+  </aside>
+  
+ 
+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="getting-started">
+  Getting Started
+  <a class="anchor" href="#getting-started">#</a>
+</h1>
+<p>This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.</p>
+<p><code>ldsCtrlEst</code> requires <a href="http://arma.sourceforge.net/">Armadillo</a> for linear algebra as well as <a href="https://www.hdfgroup.org/downloads/hdf5/">HDF5</a> for saving output. <a href="https://vcpkg.io/"><code>vcpkg</code></a> is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.</p>
+<h2 id="tested-configurations">
+  Tested Configurations
+  <a class="anchor" href="#tested-configurations">#</a>
+</h2>
+<p>Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the <code>RelWithDebInfo</code> build type in the CMake configure command (<code>-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo</code>):</p>
+<ul>
+<li>Ubuntu 18.04 with GCC 7.5 compiler</li>
+<li>macOS 11 (Big Sur) with Apple Clang 12 compiler</li>
+<li>Windows 10 with Visual Studio 16.11 (2019 release) and Clang 12 compiler</li>
+</ul>
+<p>That being said, if you want to debug a build for a single platform, here are some things you can try:</p>
+<ul>
+<li>Use different compilers (or even different versions of a single compiler)</li>
+<li>Use different versions of vcpkg (which you can control by checking out a different commit in the vcpkg submodule)</li>
+</ul>
+<h2 id="mac-pre-requisities">
+  Mac Pre-requisities
+  <a class="anchor" href="#mac-pre-requisities">#</a>
+</h2>
+<p>Xcode Command Line Tools will get you clang, gcc, make, and git:</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-shell" data-lang="shell"><span style="display:flex;"><span>xcode-select --install
+</span></span></code></pre></div><p>Homebrew is &ldquo;The Missing Package Manager for macOS&rdquo; which will make installing lots of things easy. Install like this:</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-shell" data-lang="shell"><span style="display:flex;"><span>/bin/bash -c <span style="color:#e6db74">&#34;</span><span style="color:#66d9ef">$(</span>curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh<span style="color:#66d9ef">)</span><span style="color:#e6db74">&#34;</span>
+</span></span></code></pre></div><p>You can then use it to install CMake, gfortran, and pkg-config:</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-shell" data-lang="shell"><span style="display:flex;"><span>brew install cmake gfortran pkg-config
+</span></span></code></pre></div><h2 id="linux-pre-requisites">
+  Linux Pre-requisites
+  <a class="anchor" href="#linux-pre-requisites">#</a>
+</h2>
+<p>You&rsquo;ll need Git, CMake, GCC, gfortran, etc.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-shell" data-lang="shell"><span style="display:flex;"><span>sudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential ninja-build
+</span></span></code></pre></div><h2 id="windows-installation">
+  Windows Installation
+  <a class="anchor" href="#windows-installation">#</a>
+</h2>
+<p>Look <a href="/lds-ctrl-est/docs/getting-started/windows/">here</a> for Windows-specific instructions.</p>
+<h2 id="downloading-the-library">
+  Downloading the Library
+  <a class="anchor" href="#downloading-the-library">#</a>
+</h2>
+<p>First, clone the repository along with submodules:</p>
+<pre tabindex="0"><code>git clone https://github.com/cloctools/lds-ctrl-est.git 
+cd lds-ctrl-est
+git submodule update --init
+</code></pre><h2 id="compilation--installation">
+  Compilation + Installation
+  <a class="anchor" href="#compilation--installation">#</a>
+</h2>
+<p>Now generate the cache and build using your IDE or from the command line as follows.</p>
+<pre tabindex="0"><code class="language-source" data-lang="source">mkdir build &amp;&amp; cd build
+cmake ..
+cmake --build .
+</code></pre><p>The first time, <code>vcpkg</code> will automatically install dependencies into <code>[build directory]/vcpkg_installed/</code>, which will likely take about 10-20 minutes.</p>
+<p>If you want to use <code>vcpkg</code> set up somewhere besides this repo&rsquo;s submodule, add <code>-DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake</code> to the <code>cmake</code> command directly or <a href="https://github.com/microsoft/vcpkg#using-vcpkg-with-cmake">through your IDE&rsquo;s settings</a>.</p>
+<p>You can verify the build is working by running <code>ctest</code> from the build folder, which runs all the example scripts.</p>
+<h2 id="options">
+  Options
+  <a class="anchor" href="#options">#</a>
+</h2>
+<p>This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.</p>
+<ol>
+<li><code>LDSCTRLEST_BUILD_EXAMPLES</code>  : [default=ON] whether to build example programs located under <code>examples/</code> in the source tree</li>
+<li><code>LDSCTRLEST_BUILD_FIT</code>       : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation</li>
+<li><code>LDSCTRLEST_BUILD_STATIC</code>    : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use</li>
+</ol>
+<p><em>n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.</em></p>
+<p>Below are example usages of <code>cmake</code> to configure/build the library.</p>
+<ul>
+<li>
+<p>For basic project build &amp; install</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-shell" data-lang="shell"><span style="display:flex;"><span>cd /path/to/repository
+</span></span><span style="display:flex;"><span>mkdir build <span style="color:#f92672">&amp;&amp;</span> cd build
+</span></span><span style="display:flex;"><span>cmake .. <span style="color:#75715e">#configure build</span>
+</span></span><span style="display:flex;"><span>cmake --build <span style="color:#75715e">#build the project</span>
+</span></span><span style="display:flex;"><span>sudo make install <span style="color:#75715e">#[optional] installs to default location (OS-specific)</span>
+</span></span></code></pre></div></li>
+<li>
+<p>To set the install prefix</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-shell" data-lang="shell"><span style="display:flex;"><span>cd /path/to/repository
+</span></span><span style="display:flex;"><span>mkdir build <span style="color:#f92672">&amp;&amp;</span> cd build
+</span></span><span style="display:flex;"><span>cmake -DCMAKE_INSTALL_PREFIX<span style="color:#f92672">=</span>/your/install/prefix .. <span style="color:#75715e">#configure build with chosen install location</span>
+</span></span><span style="display:flex;"><span>cmake --build <span style="color:#75715e">#build the project</span>
+</span></span><span style="display:flex;"><span>make install <span style="color:#75715e">#install to /your/install/prefix</span>
+</span></span></code></pre></div></li>
+<li>
+<p>To build the <em>bare bones</em> project, excluding fit code and Matlab mex code.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-shell" data-lang="shell"><span style="display:flex;"><span>cd /path/to/repository
+</span></span><span style="display:flex;"><span>mkdir build <span style="color:#f92672">&amp;&amp;</span> cd build
+</span></span><span style="display:flex;"><span>cmake -DLDSCTRLEST_BUILD_FIT<span style="color:#f92672">=</span><span style="color:#ae81ff">0</span> .. <span style="color:#75715e">#configure not to build the fitting portion of library</span>
+</span></span><span style="display:flex;"><span>make <span style="color:#75715e">#build the project</span>
+</span></span></code></pre></div><p><em>n.b.</em>, If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.</p>
+<ol>
+<li><code>LD_LIBRARY_PATH</code>: search path for dynamically loaded libraries</li>
+<li><code>PKG_CONFIG_PATH</code>: search path for <code>pkg-config</code> tool</li>
+</ol>
+</li>
+</ul>
+<p>On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.</p>
+<h2 id="python-bindings-package-ldsctrlest">
+  Python bindings package <code>ldsctrlest</code>
+  <a class="anchor" href="#python-bindings-package-ldsctrlest">#</a>
+</h2>
+<p>With the <code>LDSCTRLEST_BUILD_PYTHON</code> setting (off by default) and the <code>pybind11</code> submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a <code>-DPython3_ROOT_DIR=[path/to/install/dir]</code> argument to the CMake cache generation command (the first one) so CMake doesn&rsquo;t use an undesired version. That environment needs to have NumPy installed.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-shell" data-lang="shell"><span style="display:flex;"><span>cmake --build . --target python_modules
+</span></span></code></pre></div><p>The bindings need to be generated just once per Python version. Once the build is complete, navigate to the <code>[build location]/python</code> folder and run <code>pip install .</code> to make it importable anywhere for your current environment. <em>The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though.</em> You can verify the installation was successful by running <code>pytest</code> from the <code>build/python</code> directory (<code>pip install pytest matplotlib</code> first if you need to).</p>
+<p>See <code>python/ldsctrlest/README.md</code> for usage details.</p>
+<p>Also, beware that a single build will probably not work for both the standalone library and the Python package, since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with <code>-DLDSCTRLEST_BUILD_PYTHON=ON</code>, install the package, then again with  <code>-DLDSCTRLEST_BUILD_PYTHON=OFF</code> for the pure C++ build to work correctly.</p>
+<h2 id="common-issues">
+  Common issues
+  <a class="anchor" href="#common-issues">#</a>
+</h2>
+<ol>
+<li>&ldquo;I have built the library and installed it in a non-default location. In building my own project linking against <code>ldsCtrlEst</code>, <code>cmake</code> or <code>pkg-config</code> cannot find the library or its configuration information.&rdquo;</li>
+</ol>
+<p>If <code>cmake</code> and/or <code>pkg-config</code> cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For <code>cmake</code> this means adding your chosen install prefix to the environment variable <code>CMAKE_PREFIX_PATH</code>. Similarly, for <code>pkg-config</code> you need to add <code>your/install/prefix/lib/pkgconfig</code> to its search path, <code>PKG_CONFIG_PATH</code>. Assuming a Unix shell whose login startup file is <code>~/.profile</code> and ldsCtrlEst was installed using prefix <code>your/install/prefix</code>, add the following to <code>.profile</code>.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-shell" data-lang="shell"><span style="display:flex;"><span>export CMAKE_PREFIX_PATH<span style="color:#f92672">=</span>$CMAKE_PREFIX_PATH:/your/install/prefix
+</span></span><span style="display:flex;"><span>export PKG_CONFIG_PATH<span style="color:#f92672">=</span>$PKG_CONFIG_PATH:/your/install/prefix
+</span></span></code></pre></div><ol start="2">
+<li>vcpkg fails on configuration</li>
+</ol>
+<p>Try running <code>./bootstrap-vcpkg</code> from the <code>vcpkg</code> folder and try again. If that doesn&rsquo;t work, try updating vcpkg to a newer version (in the source control tab, click on the commit hash by the vcpkg repo then select from the dropdown) and running <code>boostsrap-vcpkg</code> again. You can also try upgrading your system (e.g., <code>apt update</code>, <code>apt upgrade</code>).</p>
+<ol start="3">
+<li>
+<p><code>Could not find Python3 (missing: Python3_NumPy_INCLUDE_DIRS NumPy)</code></p>
+<p>Make sure NumPy is installed in the Python environment you specified. If CMake still can&rsquo;t find it, you may need to tell CMake exactly where to find it by adding an argument to the configure command: <code>-DPython3_NumPy_INCLUDE_DIR=...</code>. You can find that location like this: <code>python -c 'import numpy; print(numpy.get_include())'</code></p>
+</li>
+</ol>
+</article>
+ 
+      
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+  <ul>
+    <li><a href="#getting-started">Getting Started</a>
+      <ul>
+        <li><a href="#tested-configurations">Tested Configurations</a></li>
+        <li><a href="#mac-pre-requisities">Mac Pre-requisities</a></li>
+        <li><a href="#linux-pre-requisites">Linux Pre-requisites</a></li>
+        <li><a href="#windows-installation">Windows Installation</a></li>
+        <li><a href="#downloading-the-library">Downloading the Library</a></li>
+        <li><a href="#compilation--installation">Compilation + Installation</a></li>
+        <li><a href="#options">Options</a></li>
+        <li><a href="#python-bindings-package-ldsctrlest">Python bindings package <code>ldsctrlest</code></a></li>
+        <li><a href="#common-issues">Common issues</a></li>
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diff --git a/docs/docs/getting-started/windows/index.html b/docs/docs/getting-started/windows/index.html
new file mode 100644
index 00000000..ad5f272e
--- /dev/null
+++ b/docs/docs/getting-started/windows/index.html
@@ -0,0 +1,320 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="
+  Windows Installation
+  #
+
+
+  Windows Pre-requisites
+  #
+
+Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:
+Set-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time
+iwr get.scoop.sh | Invoke-Expression
+Install Git and CMake if you don&rsquo;t already have them:
+scoop install git cmake
+If that didn&rsquo;t work, follow more detailed instructions here.
+The easiest way to compile C&#43;&#43; project on Windows is with Visual Studio&rsquo;s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on &ldquo;Desktop development with C&#43;&#43;.&rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the &ldquo;Installation details&rdquo; sidebar under optional features.">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/getting-started/windows/">
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+  <meta property="og:title" content="LDS C&E">
+  <meta property="og:description" content="Windows Installation # Windows Pre-requisites # Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:
+Set-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time iwr get.scoop.sh | Invoke-Expression Install Git and CMake if you don’t already have them:
+scoop install git cmake If that didn’t work, follow more detailed instructions here.
+The easiest way to compile C&#43;&#43; project on Windows is with Visual Studio’s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on “Desktop development with C&#43;&#43;.” If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the “Installation details” sidebar under optional features.">
+  <meta property="og:locale" content="en">
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+<!--
+Made with Book Theme
+https://github.com/alex-shpak/hugo-book
+-->
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+</head>
+<body dir="ltr">
+  <input type="checkbox" class="hidden toggle" id="menu-control" />
+  <input type="checkbox" class="hidden toggle" id="toc-control" />
+  <main class="container flex">
+    <aside class="book-menu">
+      <div class="book-menu-content">
+        
+  <nav>
+<h2 class="book-brand">
+  <a href="/lds-ctrl-est/"><img src="/lds-ctrl-est/ldsctrlest-logo.png" alt="Logo" /><span>LDS C&amp;E</span>
+  </a>
+</h2>
+
+
+<div class="book-search">
+  <input type="text" id="book-search-input" placeholder="Search" aria-label="Search" maxlength="64" data-hotkeys="s/" />
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+</div>
+
+
+
+
+
+
+
+
+
+
+
+  <ul>
+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/windows/"class=active><strong>Windows</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
+<li><a href="/lds-ctrl-est/docs/api/classes/">Classes</a></li>
+<li><a href="/lds-ctrl-est/docs/api/modules/">Modules</a></li>
+<li><a href="/lds-ctrl-est/docs/api/files/">Files</a></li>
+<li><a href="/lds-ctrl-est/docs/api/examples/">Examples</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/issues-contributing/"><strong>Reporting Issues &amp; Contributing</strong></a></li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
+</ul>
+<br />
+
+
+
+
+
+
+  
+<ul>
+  
+  <li>
+    <a href="https://github.com/stanley-rozell/lds-ctrl-est" target="_blank" rel="noopener">
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+  </label>
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+  
+  <aside class="hidden clearfix">
+    
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+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#windows-installation">Windows Installation</a>
+      <ul>
+        <li><a href="#windows-pre-requisites">Windows Pre-requisites</a></li>
+        <li><a href="#downloading-the-library">Downloading the Library</a></li>
+        <li><a href="#installation">Installation</a></li>
+        <li><a href="#considerations">Considerations</a></li>
+        <li><a href="#troubleshooting">Troubleshooting</a></li>
+      </ul>
+    </li>
+  </ul>
+</nav>
+
+
+
+  </aside>
+  
+ 
+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="windows-installation">
+  Windows Installation
+  <a class="anchor" href="#windows-installation">#</a>
+</h1>
+<h2 id="windows-pre-requisites">
+  Windows Pre-requisites
+  <a class="anchor" href="#windows-pre-requisites">#</a>
+</h2>
+<p>Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-shell" data-lang="shell"><span style="display:flex;"><span>Set-ExecutionPolicy RemoteSigned -Scope CurrentUser <span style="color:#75715e"># Optional: Needed to run a remote script the first time</span>
+</span></span><span style="display:flex;"><span>iwr get.scoop.sh | Invoke-Expression
+</span></span></code></pre></div><p>Install Git and CMake if you don&rsquo;t already have them:</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-shell" data-lang="shell"><span style="display:flex;"><span>scoop install git cmake
+</span></span></code></pre></div><p>If that didn&rsquo;t work, follow more detailed instructions <a href="https://github.com/ScoopInstaller/Install#readme">here</a>.</p>
+<p>The easiest way to compile C++ project on Windows is with Visual Studio&rsquo;s build tools, which you can download <a href="https://visualstudio.microsoft.com/downloads/">here</a> (or <a href="https://visualstudio.microsoft.com/vs/older-downloads/">here</a> for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on &ldquo;Desktop development with C++.&rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the &ldquo;Installation details&rdquo; sidebar under optional features.</p>
+<p>And the easiest way to use Visual Studio&rsquo;s build tools is with <a href="https://code.visualstudio.com/">VS Code</a>, along with the <a href="https://marketplace.visualstudio.com/items?itemName=ms-vscode.cmake-tools">CMake Tools extension</a>. Install them and you should be ready to go.</p>
+<h2 id="downloading-the-library">
+  Downloading the Library
+  <a class="anchor" href="#downloading-the-library">#</a>
+</h2>
+<p>First, clone the repository, either from VS Code or the command line:</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-shell" data-lang="shell"><span style="display:flex;"><span>git clone https://github.com/cloctools/lds-ctrl-est.git 
+</span></span><span style="display:flex;"><span>cd lds-ctrl-est
+</span></span></code></pre></div><p>You&rsquo;ll need to initialize the submodules from the command line after the repo is cloned:</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-shell" data-lang="shell"><span style="display:flex;"><span>git submodule update --init
+</span></span></code></pre></div><h2 id="installation">
+  Installation
+  <a class="anchor" href="#installation">#</a>
+</h2>
+<p>When you open the folder in VS Code, you will like be prompted by the CMake Tools extension to configure the project. Make sure you select the kit (you&rsquo;ll be prompted when you configure&ndash;else there&rsquo;s an icon in the bar on the bottom of the window or type <code>Ctrl+Shift+P</code>, then &ldquo;cmake select kit&rdquo;). Choose <code>Clang [latest version] with GNU CLI ... amd64</code> assuming you are running a 64-bit OS. (MSVC may work okay too if you don&rsquo;t need to build Python bindings.)</p>
+<p>Follow along with the &ldquo;Getting Started&rdquo; instructions, but where you see config options specified as <code>-DLDSCTREST_BUILD_STATIC=OFF</code> or <code>-DPython3_ROOT_DIR=...</code>, you will enter those in settings: open with <code>Ctrl+,</code>, click &ldquo;workspace&rdquo;, then search for &ldquo;CMake: Configure Args&rdquo; and enter each of your desired arguments as a separate item.</p>
+<p>To configure, use <code>Ctrl+Shift+P</code> and search for the &ldquo;CMake: Configure&rdquo; command. To build, click the &ldquo;Build&rdquo; button on the bottom bar. Then click the &ldquo;CTest&rdquo; button to run the example scripts.</p>
+<h2 id="considerations">
+  Considerations
+  <a class="anchor" href="#considerations">#</a>
+</h2>
+<p>Development on Windows has been more prone to bugs than on Unix systems, so if you encounter many problems, consider switching—<a href="https://docs.microsoft.com/en-us/windows/wsl/install">WSL (Windows Subsystem for Linux)</a> is a good option for Windows users who don&rsquo;t want to work on a different machine.</p>
+<p>Compilation has been successfully tested in VS Code using the following kit, <strong>using the &ldquo;RelWithDebInfo&rdquo; config</strong>:</p>
+<pre tabindex="0"><code>Clang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64)
+</code></pre><h2 id="troubleshooting">
+  Troubleshooting
+  <a class="anchor" href="#troubleshooting">#</a>
+</h2>
+<ol>
+<li>The build appears to work, but tests fail with code <code>0xc0000135</code> OR &ldquo;I have built the library and installed it in a non-default location. In building my own project linking against <code>ldsCtrlEst</code>, <code>cmake</code> or <code>pkg-config</code> cannot find the library or its configuration information.&rdquo;</li>
+</ol>
+<p>Have you installed the library? In VS Code, use Shift+F7 to build a specific target, in this case <code>INSTALL</code>. If that doesn&rsquo;t solve your problem, you will likely need to add the build or install folder to your <code>PATH</code> environment variable, which you can do using the settings GUI (search for &ldquo;Edit the system environment variables&rdquo;).</p>
+<ol start="2">
+<li>On Windows, &ldquo;Generate CMake Cache&rdquo; step errs because creating symbolic links is not permitted.</li>
+</ol>
+<p>Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to <em>Control Panel -&gt; Administrative Tools -&gt; Local Policies -&gt; User Rights Assignment -&gt; Create Symbolic Links</em>.</p>
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+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
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+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
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+    <title>LDS C&#43;E Documentation on LDS C&amp;E</title>
+    <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/</link>
+    <description>Recent content in LDS C&#43;E Documentation on LDS C&amp;E</description>
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+    <atom:link href="https://stanley-rozell.github.io/lds-ctrl-est/docs/index.xml" rel="self" type="application/rss+xml" />
+    <item>
+      <title></title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/getting-started/getting-started/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/getting-started/getting-started/</guid>
+      <description>&lt;h1 id=&#34;getting-started&#34;&gt;&#xA;  Getting Started&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#getting-started&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.&lt;/p&gt;&#xA;&lt;p&gt;&lt;code&gt;ldsCtrlEst&lt;/code&gt; requires &lt;a href=&#34;http://arma.sourceforge.net/&#34;&gt;Armadillo&lt;/a&gt; for linear algebra as well as &lt;a href=&#34;https://www.hdfgroup.org/downloads/hdf5/&#34;&gt;HDF5&lt;/a&gt; for saving output. &lt;a href=&#34;https://vcpkg.io/&#34;&gt;&lt;code&gt;vcpkg&lt;/code&gt;&lt;/a&gt; is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.&lt;/p&gt;&#xA;&lt;h2 id=&#34;tested-configurations&#34;&gt;&#xA;  Tested Configurations&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#tested-configurations&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;p&gt;Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the &lt;code&gt;RelWithDebInfo&lt;/code&gt; build type in the CMake configure command (&lt;code&gt;-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo&lt;/code&gt;):&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title></title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/getting-started/windows/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/getting-started/windows/</guid>
+      <description>&lt;h1 id=&#34;windows-installation&#34;&gt;&#xA;  Windows Installation&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#windows-installation&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;h2 id=&#34;windows-pre-requisites&#34;&gt;&#xA;  Windows Pre-requisites&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#windows-pre-requisites&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;p&gt;Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:&lt;/p&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-shell&#34; data-lang=&#34;shell&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;Set-ExecutionPolicy RemoteSigned -Scope CurrentUser &lt;span style=&#34;color:#75715e&#34;&gt;# Optional: Needed to run a remote script the first time&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;iwr get.scoop.sh | Invoke-Expression&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;Install Git and CMake if you don&amp;rsquo;t already have them:&lt;/p&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-shell&#34; data-lang=&#34;shell&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;scoop install git cmake&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;If that didn&amp;rsquo;t work, follow more detailed instructions &lt;a href=&#34;https://github.com/ScoopInstaller/Install#readme&#34;&gt;here&lt;/a&gt;.&lt;/p&gt;&#xA;&lt;p&gt;The easiest way to compile C++ project on Windows is with Visual Studio&amp;rsquo;s build tools, which you can download &lt;a href=&#34;https://visualstudio.microsoft.com/downloads/&#34;&gt;here&lt;/a&gt; (or &lt;a href=&#34;https://visualstudio.microsoft.com/vs/older-downloads/&#34;&gt;here&lt;/a&gt; for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on &amp;ldquo;Desktop development with C++.&amp;rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the &amp;ldquo;Installation details&amp;rdquo; sidebar under optional features.&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>C&amp;E</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/terminology/control-estimation/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/terminology/control-estimation/</guid>
+      <description>&lt;h1 id=&#34;control--estimation&#34;&gt;&#xA;  Control &amp;amp; Estimation&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#control--estimation&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., &lt;em&gt;filtering&lt;/em&gt;). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>Models</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/terminology/model/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/terminology/model/</guid>
+      <description>&lt;h1 id=&#34;model-definitions&#34;&gt;&#xA;  Model Definitions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#model-definitions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form:&#xA;&#xA;&lt;link rel=&#34;stylesheet&#34; href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/katex/katex.min.css&#34; /&gt;&#xA;&lt;script defer src=&#34;https://stanley-rozell.github.io/lds-ctrl-est/katex/katex.min.js&#34;&gt;&lt;/script&gt;&#xA;&lt;script defer src=&#34;https://stanley-rozell.github.io/lds-ctrl-est/katex/auto-render.min.js&#34; onload=&#34;renderMathInElement(document.body);&#34;&gt;&lt;/script&gt;&lt;span&gt;&#xA;  \[\mathbf{x}_{t&amp;#43;1} = f\left( \mathbf{x}_{t}, \mathbf{v}_{t} \right) = \mathbf{A} \mathbf{x}_{t} &amp;#43; \mathbf{B} \mathbf{v}_{t} &amp;#43; \mathbf{m}_{t} &amp;#43; \mathbf{w}_{t}\]&#xA;&lt;/span&gt;&#xA;&lt;/p&gt;&#xA;&lt;span&gt;&#xA;  \[\mathbf{y}_{t} = h\left( \mathbf{x}_{t} \right)\]&#xA;&lt;/span&gt;&#xA;&#xA;&lt;pre&gt;&lt;code&gt;t           : time index&#xA;x           : system state&#xA;v = g%u     : input (e.g., in physical units used for model fit)&#xA;u           : control signal sent to actuator (e.g., in Volts)&#xA;y           : system output&#xA;m           : process disturbance&#xA;w ~ N(0, Q) : process noise/disturbance&#xA;&#xA;A           : state matrix&#xA;B           : input coupling matrix&#xA;g           : input gain (e.g., for converting to control signal actuator voltage)&#xA;              n.b., assumes this conversion is linear&#xA;Q           : process noise covariance&#xA;&#xA;%           : element-wise multiplication&#xA;&lt;/code&gt;&lt;/pre&gt;&#xA;&lt;h2 id=&#34;lds-with-gaussian-observations&#34;&gt;&#xA;  LDS with Gaussian Observations&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#lds-with-gaussian-observations&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;p&gt;For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.&lt;/p&gt;</description>
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+  </channel>
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diff --git a/docs/docs/terminology/control-estimation/index.html b/docs/docs/terminology/control-estimation/index.html
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+  Control &amp; Estimation
+  #
+
+The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.">
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+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"class=active><strong>Control &amp; Estimation</strong></a></li>
+</ul>
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+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
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+<li><strong>API Reference</strong>
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+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
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+    <li><a href="#control--estimation">Control &amp; Estimation</a>
+      <ul>
+        <li><a href="#state-estimation">State estimation</a></li>
+        <li><a href="#adaptive-estimation-of-process-disturbance">Adaptive estimation of process disturbance</a></li>
+        <li><a href="#control">Control</a></li>
+        <li><a href="#calculating-reference-state-control-from-output">Calculating reference state-control from output</a></li>
+        <li><a href="#model-predictive-control">Model Predictive Control</a></li>
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+      
+  <article class="markdown"><h1 id="control--estimation">
+  Control &amp; Estimation
+  <a class="anchor" href="#control--estimation">#</a>
+</h1>
+<p>The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., <em>filtering</em>). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.</p>
+<h2 id="state-estimation">
+  State estimation
+  <a class="anchor" href="#state-estimation">#</a>
+</h2>
+<p>In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by
+
+<link rel="stylesheet" href="/lds-ctrl-est/katex/katex.min.css" />
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+<script defer src="/lds-ctrl-est/katex/auto-render.min.js" onload="renderMathInElement(document.body);"></script><span>
+  \[\widehat{\mathbf{x}}_{t|t-1} = \mathbf{A}\widehat{\mathbf{x}}_{t-1|t-1} &#43; \mathbf{B} u_{t-1} &#43; \mathbf{m}_{t-1} \;,\]
+</span>
+</p>
+<span>
+  \[\widehat{\mathbf{x}}_{t|t} = \widehat{\mathbf{x}}_{t|t-1} &#43; \mathbf{K}^{\rm e}_t \left(\mathbf{z}_t - \widehat{\mathbf{y}}_{t|t-1}\right)\;,\]
+</span>
+
+<p>where <span>
+  \( \hat{\left(\cdot\right)}_{t|j} \)
+</span>
+ indicates an estimate at time <span>
+  \( t \)
+</span>
+ given data up to time <span>
+  \( j \)
+</span>
+ inclusive, <span>
+  \( \mathbf{K}^{\rm e} \)
+</span>
+ is the estimator gain, and</p>
+<span>
+  \[ \widehat{\mathbf{y}}_{t|t-1} = h\left( \widehat{\mathbf{x}}_{t|t-1} \right) \; .\]
+</span>
+
+<p>In the case of GLDS models, the estimator gain (called <code>Ke</code> in library) is calculated recursively by <a href="https://en.wikipedia.org/wiki/Kalman_filter">Kalman filtering</a>, which requires knowledge of the process noise and measurement noise covariances (<code>Q</code>, <code>R</code>) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the <code>lds::gaussian::System::set_Ke</code> mutator.</p>
+<p>In the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (<a href="http://www.stat.columbia.edu/~liam/teaching/neurostat-spr11/papers/brown-et-al/eden2004.pdf">Eden et al. 2004</a>). This nonlinear filter recursively updates <code>Ke</code> at each time step and requires an estimate of the process noise covariance (<code>Q</code>) as well.</p>
+<h2 id="adaptive-estimation-of-process-disturbance">
+  Adaptive estimation of process disturbance
+  <a class="anchor" href="#adaptive-estimation-of-process-disturbance">#</a>
+</h2>
+<p>Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (<code>m</code>) is adaptively re-estimated when the <code>lds::System::do_adapt_m</code> property is set to <code>true</code>. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.</p>
+<p>When parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk
+<span>
+  \[\mathbf{m}_{t} = \mathbf{m}_{t-1} &#43; \mathbf{w}^m_{t-1} \;,\]
+</span>
+
+where <span>
+  \( \mathbf{w}^m \sim \mathcal{N}\left(0, \mathbf{Q}_m\right)\)
+</span>
+. Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.</p>
+<h2 id="control">
+  Control
+  <a class="anchor" href="#control">#</a>
+</h2>
+<p>Given the estimated state, the controller updates the inputs to the system according to the following law:
+<span>
+  \[\mathbf{u}_{t} = \mathbf{u}^{\rm ref}_t - \mathbf{K}^c_x \left( \widehat{\mathbf{x}}_t - \mathbf{x}^{\rm ref}_t\right)\;,\]
+</span>
+</p>
+<p>where <span>
+  \( \left( \cdot \right)^{\rm ref} \)
+</span>
+ correspond to reference/target signals and <span>
+  \( \mathbf{K}^c_x \)
+</span>
+ is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.</p>
+<p>If users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as</p>
+<span>
+  \[\mathbf{u}_{t} = \mathbf{u}^{\rm ref}_t - \mathbf{K}^c_x \left( \widehat{\mathbf{x}}_t - \mathbf{x}^{\rm ref}_t\right) - \mathbf{K}^c_{\rm inty} \sum_{j=1}^{t}\left( \widehat{\mathbf{y}}_j - \mathbf{y}^{\rm ref}_j \right) \;.\]
+</span>
+
+<p>An additional option available to users is a control law that updates the <em>change</em> in <code>u</code>,</p>
+<span>
+  \[\Delta\mathbf{u}_{t} = -\mathbf{K}^c_u \left(\mathbf{u}_{t-1} - \mathbf{u}^{\rm ref}_{t-1} \right) - \mathbf{K}^c_x \left( \widehat{\mathbf{x}}_t - \mathbf{x}^{\rm ref}_t\right)\;,\]
+</span>
+
+<span>
+  \[\mathbf{u}_{t} = \mathbf{u}_{t-1} &#43; \Delta\mathbf{u}_{t} \; .\]
+</span>
+
+<p>Notice that this takes the form of a first-order difference equation for updating control (<em>i.e.</em>, <span>
+  \( \Delta\mathbf{u}_{t} = -\mathbf{K}^c_u \mathbf{u}_{t-1} &#43; \epsilon_{t-1} \)
+</span>
+), effectively low-pass filtering the input depending on the characteristics of <span>
+  \( \mathbf{K}^c_u \)
+</span>
+. This can be useful in cases where users have designed the controller gains by LQR to minimize <em>not</em> the amplitude of the input, but the <em>change</em> in input, by augmenting the state vector with the input during LQR design.</p>
+<p>Integral action and the <span>
+  \( \Delta \mathbf{u} \)
+</span>
+ control law can be combined. The library keeps track of the controller type by way of <a href="/lds-ctrl-est/docs/api/modules/group__control__masks/">bit masks</a> which can be bit-wise OR&rsquo;d to use in combination.</p>
+<h2 id="calculating-reference-state-control-from-output">
+  Calculating reference state-control from output
+  <a class="anchor" href="#calculating-reference-state-control-from-output">#</a>
+</h2>
+<p>In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce <span>
+  \( \mathbf{x}^{\rm ref} \)
+</span>
+ and <span>
+  \( \mathbf{u}^{\rm ref} \)
+</span>
+. Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (<code>lds::Controller&lt;System&gt;::ControlOutputReference</code>). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.</p>
+<h2 id="model-predictive-control">
+  Model Predictive Control
+  <a class="anchor" href="#model-predictive-control">#</a>
+</h2>
+<p>Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.</p>
+<p>In the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.</p>
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+        <li><a href="#calculating-reference-state-control-from-output">Calculating reference state-control from output</a></li>
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diff --git a/docs/docs/terminology/model/index.html b/docs/docs/terminology/model/index.html
new file mode 100644
index 00000000..22aada64
--- /dev/null
+++ b/docs/docs/terminology/model/index.html
@@ -0,0 +1,361 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="
+  Model Definitions
+  #
+
+This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form:
+
+
+
+
+  \[\mathbf{x}_{t&#43;1} = f\left( \mathbf{x}_{t}, \mathbf{v}_{t} \right) = \mathbf{A} \mathbf{x}_{t} &#43; \mathbf{B} \mathbf{v}_{t} &#43; \mathbf{m}_{t} &#43; \mathbf{w}_{t}\]
+
+
+
+  \[\mathbf{y}_{t} = h\left( \mathbf{x}_{t} \right)\]
+
+
+t           : time index
+x           : system state
+v = g%u     : input (e.g., in physical units used for model fit)
+u           : control signal sent to actuator (e.g., in Volts)
+y           : system output
+m           : process disturbance
+w ~ N(0, Q) : process noise/disturbance
+
+A           : state matrix
+B           : input coupling matrix
+g           : input gain (e.g., for converting to control signal actuator voltage)
+              n.b., assumes this conversion is linear
+Q           : process noise covariance
+
+%           : element-wise multiplication
+
+
+  LDS with Gaussian Observations
+  #
+
+For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/terminology/model/">
+  <meta property="og:site_name" content="LDS C&E">
+  <meta property="og:title" content="Models">
+  <meta property="og:description" content="Model Definitions # This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form: \[\mathbf{x}_{t&#43;1} = f\left( \mathbf{x}_{t}, \mathbf{v}_{t} \right) = \mathbf{A} \mathbf{x}_{t} &#43; \mathbf{B} \mathbf{v}_{t} &#43; \mathbf{m}_{t} &#43; \mathbf{w}_{t}\] \[\mathbf{y}_{t} = h\left( \mathbf{x}_{t} \right)\] t : time index x : system state v = g%u : input (e.g., in physical units used for model fit) u : control signal sent to actuator (e.g., in Volts) y : system output m : process disturbance w ~ N(0, Q) : process noise/disturbance A : state matrix B : input coupling matrix g : input gain (e.g., for converting to control signal actuator voltage) n.b., assumes this conversion is linear Q : process noise covariance % : element-wise multiplication LDS with Gaussian Observations # For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.">
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+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"class=active><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
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+  <ul>
+    <li><a href="#model-definitions">Model Definitions</a>
+      <ul>
+        <li><a href="#lds-with-gaussian-observations">LDS with Gaussian Observations</a></li>
+        <li><a href="#lds-with-poisson-observations">LDS with Poisson Observations</a></li>
+        <li><a href="#model-predictive-control-mpc">Model Predictive Control (MPC)</a></li>
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+  <article class="markdown"><h1 id="model-definitions">
+  Model Definitions
+  <a class="anchor" href="#model-definitions">#</a>
+</h1>
+<p>This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form:
+
+<link rel="stylesheet" href="/lds-ctrl-est/katex/katex.min.css" />
+<script defer src="/lds-ctrl-est/katex/katex.min.js"></script>
+<script defer src="/lds-ctrl-est/katex/auto-render.min.js" onload="renderMathInElement(document.body);"></script><span>
+  \[\mathbf{x}_{t&#43;1} = f\left( \mathbf{x}_{t}, \mathbf{v}_{t} \right) = \mathbf{A} \mathbf{x}_{t} &#43; \mathbf{B} \mathbf{v}_{t} &#43; \mathbf{m}_{t} &#43; \mathbf{w}_{t}\]
+</span>
+</p>
+<span>
+  \[\mathbf{y}_{t} = h\left( \mathbf{x}_{t} \right)\]
+</span>
+
+<pre><code>t           : time index
+x           : system state
+v = g%u     : input (e.g., in physical units used for model fit)
+u           : control signal sent to actuator (e.g., in Volts)
+y           : system output
+m           : process disturbance
+w ~ N(0, Q) : process noise/disturbance
+
+A           : state matrix
+B           : input coupling matrix
+g           : input gain (e.g., for converting to control signal actuator voltage)
+              n.b., assumes this conversion is linear
+Q           : process noise covariance
+
+%           : element-wise multiplication
+</code></pre>
+<h2 id="lds-with-gaussian-observations">
+  LDS with Gaussian Observations
+  <a class="anchor" href="#lds-with-gaussian-observations">#</a>
+</h2>
+<p>For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.</p>
+<span>
+  \[\mathbf{y}_{t} = \mathbf{C} \mathbf{x}_{t} &#43; \mathbf{d}\]
+</span>
+
+<span>
+  \[\mathbf{z}_{t} \sim \mathcal{N}\left(\mathbf{y}_{t} , \mathbf{R} \right)\]
+</span>
+
+<pre><code>z           : measurement
+
+C           : output matrix
+d           : output bias
+R           : measurement noise covariance
+</code></pre>
+<h2 id="lds-with-poisson-observations">
+  LDS with Poisson Observations
+  <a class="anchor" href="#lds-with-poisson-observations">#</a>
+</h2>
+<p>For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.</p>
+<span>
+  \[y_{t}^{i} = \exp \left(\mathbf{c}^i \mathbf{x}_{t} &#43; d^i\right)\]
+</span>
+
+<span>
+  \[z_{t}^i \sim \rm{Poisson} \left(y_{t}^i \right)\]
+</span>
+
+<pre><code>i           : output index
+
+z           : measurement (count data)
+
+c           : i^th row of output matrix (C)
+d           : output bias
+</code></pre>
+<h2 id="model-predictive-control-mpc">
+  Model Predictive Control (MPC)
+  <a class="anchor" href="#model-predictive-control-mpc">#</a>
+</h2>
+<p>Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.</p>
+<p>In the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.</p>
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diff --git a/docs/docs/tutorials/eg_glds_control/index.html b/docs/docs/tutorials/eg_glds_control/index.html
new file mode 100644
index 00000000..89c73f49
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+  GLDS Control Tutorial
+  #
+
+This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.">
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+  <meta property="og:title" content="GLDS Control">
+  <meta property="og:description" content="GLDS Control Tutorial # This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.">
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+    <img src="/lds-ctrl-est/svg/toc.svg" class="book-icon" alt="Table of Contents" />
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+    <li><a href="#glds-control-tutorial">GLDS Control Tutorial</a>
+      <ul>
+        <li><a href="#preamble">Preamble</a></li>
+        <li><a href="#creating-a-simulated-system">Creating a simulated system</a></li>
+        <li><a href="#creating-the-controller">Creating the controller</a></li>
+        <li><a href="#simulating-control">Simulating control</a></li>
+        <li><a href="#example-simulation-result">Example simulation result</a></li>
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+  <article class="markdown"><h1 id="glds-control-tutorial">
+  GLDS Control Tutorial
+  <a class="anchor" href="#glds-control-tutorial">#</a>
+</h1>
+<p>This tutorial shows how to use this library to control a system with a Gaussian LDS controller (<code>lds::gaussian::Controller</code>). In place of a physical system, a GLDS model (<code>lds::gaussian::System</code>) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.</p>
+<p>The full code for this can be found <a href="/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/">here</a>.</p>
+<h2 id="preamble">
+  Preamble
+  <a class="anchor" href="#preamble">#</a>
+</h2>
+<p>In addition to including the main <code>ldsCtrlEst</code> header, this tutorial will use some shorthand.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;ldsCtrlEst&gt;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Matrix;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Vector;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>data_t;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>cout;
+</span></span></code></pre></div><p>Note that <code>lds::Matrix</code> and <code>lds::Vector</code> are typedefs for <code>arma::Mat&lt;data_t&gt;</code> and <code>arma::Col&lt;data_t&gt;</code>, where the data type is <code>double</code> by default. May be changed to <code>float</code> in <code>include/ldsCtrlEst_h/lds.h</code> if there are memory constraints (e.g., large-scale MIMO control problems).</p>
+<h2 id="creating-a-simulated-system">
+  Creating a simulated system
+  <a class="anchor" href="#creating-a-simulated-system">#</a>
+</h2>
+<p>A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>  <span style="color:#75715e">// Make 1st-order SISO system, sampled at 1kHz
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  data_t dt <span style="color:#f92672">=</span> <span style="color:#ae81ff">1e-3</span>;
+</span></span><span style="display:flex;"><span>  size_t n_u <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>  size_t n_x <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>  size_t n_y <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// no time steps for simulation.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">auto</span> n_t <span style="color:#f92672">=</span> <span style="color:#66d9ef">static_cast</span><span style="color:#f92672">&lt;</span>size_t<span style="color:#f92672">&gt;</span>(<span style="color:#ae81ff">5.0</span> <span style="color:#f92672">/</span> dt);
+</span></span></code></pre></div><p>When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>  <span style="color:#75715e">// construct ground truth system to be controlled...
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// initializes to random walk model with top-most n_y state observed
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  lds<span style="color:#f92672">::</span>gaussian<span style="color:#f92672">::</span>System controlled_system(n_u, n_x, n_y, dt);
+</span></span></code></pre></div><p>This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top <code>min(n_x, n_y)</code> states are observed at the output. i.e., for this example,
+
+<link rel="stylesheet" href="/lds-ctrl-est/katex/katex.min.css" />
+<script defer src="/lds-ctrl-est/katex/katex.min.js"></script>
+<script defer src="/lds-ctrl-est/katex/auto-render.min.js" onload="renderMathInElement(document.body);"></script><span>
+  \[
+x_{t&#43;1} = x_t &#43; w_t
\]
+</span>
+</p>
+<span>
+  \[
+y_{t} = x_t
\]
+</span>
+
+<p>where <span>
+  \( w_{t} \sim \mathcal{N}\left( 0, Q \right) \)
+</span>
+.</p>
+<p>Now, create non-default parameters for this model.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>  <span style="color:#75715e">// Ground-truth parameters for the controlled system
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// (stand-in for physical system to be controlled)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix <span style="color:#a6e22e">a_true</span>(n_x, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye);
+</span></span><span style="display:flex;"><span>  a_true[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">=</span> exp(<span style="color:#f92672">-</span>dt <span style="color:#f92672">/</span> <span style="color:#ae81ff">0.01</span>);
+</span></span><span style="display:flex;"><span>  Matrix b_true <span style="color:#f92672">=</span> Matrix(n_x, n_u).fill(<span style="color:#ae81ff">2e-4</span>);
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// control signal to model input unit conversion e.g., V -&gt; mW/mm2:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector g_true <span style="color:#f92672">=</span> Vector(n_y).fill(<span style="color:#ae81ff">10.0</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// output noise covariance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix r_true <span style="color:#f92672">=</span> Matrix(n_y, n_y, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">*</span> <span style="color:#ae81ff">1e-4</span>;
+</span></span></code></pre></div><p>As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>  <span style="color:#75715e">/// Going to simulate a switching disturbance (m) acting on system
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  size_t which_m <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;  <span style="color:#75715e">// whether low or high disturbance (0, 1)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  data_t m_low <span style="color:#f92672">=</span> <span style="color:#ae81ff">5</span> <span style="color:#f92672">*</span> dt <span style="color:#f92672">*</span> (<span style="color:#ae81ff">1</span> <span style="color:#f92672">-</span> a_true[<span style="color:#ae81ff">0</span>]);
+</span></span><span style="display:flex;"><span>  data_t pr_lo2hi <span style="color:#f92672">=</span> <span style="color:#ae81ff">1e-3</span>;  <span style="color:#75715e">// probability of going from low to high disturb.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  data_t m_high <span style="color:#f92672">=</span> <span style="color:#ae81ff">20</span> <span style="color:#f92672">*</span> dt <span style="color:#f92672">*</span> (<span style="color:#ae81ff">1</span> <span style="color:#f92672">-</span> a_true[<span style="color:#ae81ff">0</span>]);
+</span></span><span style="display:flex;"><span>  data_t pr_hi2lo <span style="color:#f92672">=</span> pr_lo2hi;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// initially let m be low
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector m0_true <span style="color:#f92672">=</span> Vector(n_y).fill(m_low);
+</span></span></code></pre></div><p>Finally, assign the parameters using corresponding set-methods.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span> <span style="color:#75715e">// Assign params.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  controlled_system.set_A(a_true);
+</span></span><span style="display:flex;"><span>  controlled_system.set_B(b_true);
+</span></span><span style="display:flex;"><span>  controlled_system.set_m(m0_true);
+</span></span><span style="display:flex;"><span>  controlled_system.set_g(g_true);
+</span></span><span style="display:flex;"><span>  controlled_system.set_R(r_true);
+</span></span></code></pre></div><h2 id="creating-the-controller">
+  Creating the controller
+  <a class="anchor" href="#creating-the-controller">#</a>
+</h2>
+<p>Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this <code>lds::gaussian::System</code> object and upper/lower bounds on the control signal (<code>u_lb</code>, <code>u_ub</code> below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does <strong>not</strong> saturate somehow, simply set the lower and upper bounds to <code>-lds::kInf</code> and <code>lds::kInf</code>, respectively. Simple saturation is currently the only actuator model in this library.</p>
+<p>For the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>  <span style="color:#75715e">// make a controller
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  lds<span style="color:#f92672">::</span>gaussian<span style="color:#f92672">::</span>Controller controller;
+</span></span><span style="display:flex;"><span>  {
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// Create **incorrect** model used for control.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// (e.g., imperfect model fitting)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Matrix b_controller <span style="color:#f92672">=</span> b_true <span style="color:#f92672">/</span> <span style="color:#ae81ff">2</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// let&#39;s assume zero process disturbance initially
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// (will be re-estimating)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Vector m_controller <span style="color:#f92672">=</span> Vector(n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// for this demo, just use arbitrary default R
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Matrix r_controller <span style="color:#f92672">=</span> Matrix(n_y, n_y, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">*</span> lds<span style="color:#f92672">::</span>kDefaultR0;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    lds<span style="color:#f92672">::</span>gaussian<span style="color:#f92672">::</span>System controller_system(controlled_system);
+</span></span><span style="display:flex;"><span>    controller_system.set_B(b_controller);
+</span></span><span style="display:flex;"><span>    controller_system.set_m(m_controller);
+</span></span><span style="display:flex;"><span>    controller_system.set_R(r_controller);
+</span></span><span style="display:flex;"><span>    controller_system.Reset();  <span style="color:#75715e">// reset to new m
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// going to adaptively re-estimate the disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    controller_system.do_adapt_m <span style="color:#f92672">=</span> true;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// set adaptation rate by changing covariance of assumed process noise
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// acting on random-walk evolution of m
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Matrix q_m <span style="color:#f92672">=</span> Matrix(n_x, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">*</span> <span style="color:#ae81ff">1e-6</span>;
+</span></span><span style="display:flex;"><span>    controller_system.set_Q_m(q_m);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// create controller
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// lower and upper bounds on control signal (e.g., in Volts)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    data_t u_lb <span style="color:#f92672">=</span> <span style="color:#ae81ff">0.0</span>;  <span style="color:#75715e">// [=] V
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    data_t u_ub <span style="color:#f92672">=</span> <span style="color:#ae81ff">5.0</span>;  <span style="color:#75715e">// [=] V
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    controller <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(
+</span></span><span style="display:flex;"><span>        lds<span style="color:#f92672">::</span>gaussian<span style="color:#f92672">::</span>Controller(std<span style="color:#f92672">::</span>move(controller_system), u_lb, u_ub));
+</span></span><span style="display:flex;"><span>  }
+</span></span></code></pre></div><p>Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.</p>
+<p>With the controller constructed, control variables may be set.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>  <span style="color:#75715e">// Control variables:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// if following enabled, adapts set point with re-estimated process
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// disturbance n.b., should not need integral action if this is enabled as the
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// adaptive estimator minimizes DC error
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">bool</span> do_adaptive_set_point <span style="color:#f92672">=</span> false;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Reference/target output, controller gains
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector y_ref0 <span style="color:#f92672">=</span> Vector(n_y).fill(<span style="color:#ae81ff">20.0</span> <span style="color:#f92672">*</span> dt);
+</span></span><span style="display:flex;"><span>  Matrix k_x <span style="color:#f92672">=</span> Matrix(n_u, n_x).fill(<span style="color:#ae81ff">100</span>);     <span style="color:#75715e">// gains on state error
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix k_inty <span style="color:#f92672">=</span> Matrix(n_u, n_y).fill(<span style="color:#ae81ff">1e3</span>);  <span style="color:#75715e">// gains on integrated err
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// setting initial state to target to avoid error at onset:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector x0 <span style="color:#f92672">=</span> Vector(n_x).fill(y_ref0[<span style="color:#ae81ff">0</span>]);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set up controller type bit mask so controller knows how to proceed
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  size_t control_type <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (do_adaptive_set_point) {
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// adapt set point with estimated disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    control_type <span style="color:#f92672">=</span> control_type <span style="color:#f92672">|</span> lds<span style="color:#f92672">::</span>kControlTypeAdaptM;
+</span></span><span style="display:flex;"><span>  } <span style="color:#66d9ef">else</span> {
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// use integral action to minimize DC error
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    control_type <span style="color:#f92672">=</span> control_type <span style="color:#f92672">|</span> lds<span style="color:#f92672">::</span>kControlTypeIntY;
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set controller type
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  controller.set_control_type(control_type);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Let&#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2):
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector g_design <span style="color:#f92672">=</span> Vector(n_u).fill(<span style="color:#ae81ff">9</span>);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Set params.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// **n.b. using arbitrary defaults for Q, R in this example. Really, these
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// should be set by users, as they tune characteristics of Kalman filter.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// Users can also choose not to recursively calculate the estimator gain and
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// supply it (setKe) instead of covariances.**
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  controller.set_y_ref(y_ref0);
+</span></span><span style="display:flex;"><span>  controller.set_Kc(k_x);
+</span></span><span style="display:flex;"><span>  controller.set_Kc_inty(k_inty);
+</span></span><span style="display:flex;"><span>  controller.set_g_design(g_design);
+</span></span></code></pre></div><h2 id="simulating-control">
+  Simulating control
+  <a class="anchor" href="#simulating-control">#</a>
+</h2>
+<p>In this demonstration, we will use the <code>ControlOutputReference</code> method which allows users to simply set the reference output and supply the current measurement <code>z</code>. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.</p>
+<p>The control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>  <span style="color:#75715e">// Simulate the true system.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  z.col(t) <span style="color:#f92672">=</span> controlled_system.Simulate(u_tm1);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// This method uses a steady-state solution to control problem to calculate
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// x_ref, u_ref from reference output y_ref. Therefore, it is only
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// applicable to regulation problems or cases where reference trajectory
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// changes slowly compared to system dynamics.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  u.col(t) <span style="color:#f92672">=</span> controller.ControlOutputReference(z.col(t));
+</span></span></code></pre></div><h2 id="example-simulation-result">
+  Example simulation result
+  <a class="anchor" href="#example-simulation-result">#</a>
+</h2>
+<p>Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller&rsquo;s online estimates of the output, state, and disturbance are given in purple.</p>
+<p><img src="/lds-ctrl-est/eg_glds_ctrl_output.png" alt="example control output" /></p>
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diff --git a/docs/docs/tutorials/eg_plds_state_estimation/index.html b/docs/docs/tutorials/eg_plds_state_estimation/index.html
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+  PLDS State Estimation Tutorial
+  #
+
+This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.
+The full code for this can be found here.">
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+  <meta property="og:description" content="PLDS State Estimation Tutorial # This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.
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+    <li><a href="#plds-state-estimation-tutorial">PLDS State Estimation Tutorial</a>
+      <ul>
+        <li><a href="#preamble">Preamble</a></li>
+        <li><a href="#creating-a-simulated-system">Creating a simulated system</a></li>
+        <li><a href="#creating-the-estimator">Creating the estimator</a></li>
+        <li><a href="#simulating-estimation">Simulating estimation</a></li>
+        <li><a href="#example-simulation-result">Example simulation result</a></li>
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+  <article class="markdown"><h1 id="plds-state-estimation-tutorial">
+  PLDS State Estimation Tutorial
+  <a class="anchor" href="#plds-state-estimation-tutorial">#</a>
+</h1>
+<p>This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (<code>lds::poisson::System</code>) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.</p>
+<p>The full code for this can be found <a href="/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/">here</a>.</p>
+<h2 id="preamble">
+  Preamble
+  <a class="anchor" href="#preamble">#</a>
+</h2>
+<p>In addition to including the main <code>ldsCtrlEst</code> header, this tutorial will use some shorthand.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;ldsCtrlEst&gt;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Matrix;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Vector;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>data_t;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>cout;
+</span></span></code></pre></div><p>Note that <code>lds::Matrix</code> and <code>lds::Vector</code> are typedefs for <code>arma::Mat&lt;data_t&gt;</code> and <code>arma::Col&lt;data_t&gt;</code>, where the data type is <code>double</code> by default. May be changed to <code>float</code> in <code>include/ldsCtrlEst_h/lds.h</code> if there are memory constraints (e.g., large-scale MIMO control problems).</p>
+<h2 id="creating-a-simulated-system">
+  Creating a simulated system
+  <a class="anchor" href="#creating-a-simulated-system">#</a>
+</h2>
+<p>A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>  <span style="color:#75715e">// Make SISO system sampled at 1kHz
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  data_t dt <span style="color:#f92672">=</span> <span style="color:#ae81ff">1e-3</span>;
+</span></span><span style="display:flex;"><span>  size_t n_u <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;                           <span style="color:#75715e">// no. inputs
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  size_t n_x <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;                           <span style="color:#75715e">// no. states
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  size_t n_y <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;                           <span style="color:#75715e">// no. outputs
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">auto</span> n_t <span style="color:#f92672">=</span> <span style="color:#66d9ef">static_cast</span><span style="color:#f92672">&lt;</span>size_t<span style="color:#f92672">&gt;</span>(<span style="color:#ae81ff">30</span> <span style="color:#f92672">/</span> dt);  <span style="color:#75715e">// no time steps for simulation.
+</span></span></span></code></pre></div><p>When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>  <span style="color:#75715e">// construct ground truth system...
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>System system_true(n_u, n_x, n_y, dt);
+</span></span></code></pre></div><p>This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top <code>min(n_x, n_y)</code> states are observed at the output. i.e., for this example,
+
+<link rel="stylesheet" href="/lds-ctrl-est/katex/katex.min.css" />
+<script defer src="/lds-ctrl-est/katex/katex.min.js"></script>
+<script defer src="/lds-ctrl-est/katex/auto-render.min.js" onload="renderMathInElement(document.body);"></script><span>
+  \[x_{t&#43;1} = x_t &#43; w_t\]
+</span>
+</p>
+<span>
+  \[y_{t} = \exp\left(x_t\right)\]
+</span>
+
+<p>where <span>
+  \( w_{t} \sim \mathcal{N}\left( 0, Q \right) \)
+</span>
+.</p>
+<p>Now, create non-default parameters for this model.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>  <span style="color:#75715e">// Model parameters
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix <span style="color:#a6e22e">a_true</span>(n_x, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye);
+</span></span><span style="display:flex;"><span>  a_true[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">=</span> exp(<span style="color:#f92672">-</span>dt <span style="color:#f92672">/</span> <span style="color:#ae81ff">0.075</span>);
+</span></span><span style="display:flex;"><span>  Matrix b_true <span style="color:#f92672">=</span> Matrix(n_x, n_u).fill(<span style="color:#ae81ff">1e-2</span>);
+</span></span><span style="display:flex;"><span>  Vector m0_true <span style="color:#f92672">=</span> Vector(n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros).fill(<span style="color:#f92672">-</span><span style="color:#ae81ff">7e-2</span>);  <span style="color:#75715e">// disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector x0_true <span style="color:#f92672">=</span> m0_true <span style="color:#f92672">*</span> arma<span style="color:#f92672">::</span>inv(Matrix(n_x, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">-</span>
+</span></span><span style="display:flex;"><span>                                        a_true);  <span style="color:#75715e">// initial state
+</span></span></span></code></pre></div><p>Finally, assign the parameters using corresponding set-methods.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>  <span style="color:#75715e">// Assign params.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  system_true.set_A(a_true);
+</span></span><span style="display:flex;"><span>  system_true.set_B(b_true);
+</span></span><span style="display:flex;"><span>  system_true.set_x0(x0_true);
+</span></span><span style="display:flex;"><span>  system_true.set_m(m0_true);
+</span></span><span style="display:flex;"><span>  system_true.Reset();
+</span></span></code></pre></div><h2 id="creating-the-estimator">
+  Creating the estimator
+  <a class="anchor" href="#creating-the-estimator">#</a>
+</h2>
+<p>Now, create the estimator. The system type includes filtering functionality for state estimation, so create another <code>lds::poisson::System</code>. As noted above, the only parameter mismatch in this simulation will be the process disturbance.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>  <span style="color:#75715e">// Construct system for estimation
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// e.g., will create a model with incorrect disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>System system_estimator(n_u, n_x, n_y, dt);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Can copy parameters from another system object
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  system_estimator <span style="color:#f92672">=</span> system_true;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// wrong disturbance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector m0_est <span style="color:#f92672">=</span> m0_true <span style="color:#f92672">*</span> <span style="color:#ae81ff">2</span>;
+</span></span><span style="display:flex;"><span>  system_estimator.set_m(m0_est);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set new initial conditions
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector x0_est <span style="color:#f92672">=</span> m0_est <span style="color:#f92672">*</span> arma<span style="color:#f92672">::</span>inv(Matrix(n_x, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">-</span>
+</span></span><span style="display:flex;"><span>                                      a_true);  <span style="color:#75715e">// initial state
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  system_estimator.set_x0(x0_est);
+</span></span><span style="display:flex;"><span>  system_estimator.Reset();  <span style="color:#75715e">// reset to initial condition.
+</span></span></span></code></pre></div><p>To ensure robust estimates, adaptively re-estimate the process disturbance.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>  <span style="color:#75715e">// turn on adaptive disturbance estimation
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  system_estimator.do_adapt_m <span style="color:#f92672">=</span> true;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// set adaptation rate by changing covariance of assumed process noise acting
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// on random-walk evolution of m
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Matrix q_m <span style="color:#f92672">=</span> Matrix(n_x, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">*</span> <span style="color:#ae81ff">1e-6</span>;
+</span></span><span style="display:flex;"><span>  system_estimator.set_Q_m(q_m);
+</span></span></code></pre></div><h2 id="simulating-estimation">
+  Simulating estimation
+  <a class="anchor" href="#simulating-estimation">#</a>
+</h2>
+<p>In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>    <span style="color:#75715e">// Simlate the true system.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    z.col(t) <span style="color:#f92672">=</span> system_true.Simulate(u.col(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// Filter (predict -&gt; update)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    system_estimator.Filter(u.col(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>), z.col(t));
+</span></span></code></pre></div><h2 id="example-simulation-result">
+  Example simulation result
+  <a class="anchor" href="#example-simulation-result">#</a>
+</h2>
+<p>Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.</p>
+<p><img src="/lds-ctrl-est/eg_plds_est_output.png" alt="example estimator output" /></p>
+<p>With this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.</p>
+<p><img src="/lds-ctrl-est/eg_plds_est_output_hist.png" alt="example estimator output histogram" /></p>
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diff --git a/docs/docs/tutorials/eg_switched_plds_control/index.html b/docs/docs/tutorials/eg_switched_plds_control/index.html
new file mode 100644
index 00000000..ec4911b7
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+++ b/docs/docs/tutorials/eg_switched_plds_control/index.html
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+  PLDS Switched Control Tutorial
+  #
+
+This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.
+In the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">
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+  <meta property="og:description" content="PLDS Switched Control Tutorial # This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.
+In the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.">
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+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/"class=active>Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
+<li><a href="/lds-ctrl-est/docs/api/classes/">Classes</a></li>
+<li><a href="/lds-ctrl-est/docs/api/modules/">Modules</a></li>
+<li><a href="/lds-ctrl-est/docs/api/files/">Files</a></li>
+<li><a href="/lds-ctrl-est/docs/api/examples/">Examples</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/issues-contributing/"><strong>Reporting Issues &amp; Contributing</strong></a></li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
+</ul>
+<br />
+
+
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+  
+<ul>
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+  <li>
+    <a href="https://github.com/stanley-rozell/lds-ctrl-est" target="_blank" rel="noopener">
+        Github
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+  
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+  <strong>PLDS Switched Control</strong>
+
+  <label for="toc-control">
+    
+    <img src="/lds-ctrl-est/svg/toc.svg" class="book-icon" alt="Table of Contents" />
+    
+  </label>
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+  <aside class="hidden clearfix">
+    
+  
+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#plds-switched-control-tutorial">PLDS Switched Control Tutorial</a>
+      <ul>
+        <li><a href="#preamble">Preamble</a></li>
+        <li><a href="#creating-the-simulated-system">Creating the simulated system</a></li>
+        <li><a href="#creating-the-controller">Creating the controller</a></li>
+        <li><a href="#simulating-control">Simulating control</a></li>
+        <li><a href="#example-simulation-result">Example simulation result</a></li>
+      </ul>
+    </li>
+  </ul>
+</nav>
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+  </aside>
+  
+ 
+      </header>
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+      
+      
+  <article class="markdown"><h1 id="plds-switched-control-tutorial">
+  PLDS Switched Control Tutorial
+  <a class="anchor" href="#plds-switched-control-tutorial">#</a>
+</h1>
+<p>This tutorial shows how to use this library to control a system with a switched PLDS controller (<code>lds::poisson::SwitchedController</code>). This type of controller is applicable in scenarios where a physical system is not accurately captured by a <strong>single</strong> LDS but has <strong>multiple</strong> discrete operating modes where the dynamics can be well-approximated as linear.</p>
+<p>In the example that follows, another PLDS model (<code>lds::poisson::System</code>) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.</p>
+<p>The full code for this can be found <a href="/lds-ctrl-est/docs/api/examples/eg_plds_switched_log_lin_ctrl_8cpp-example/">here</a>.</p>
+<h2 id="preamble">
+  Preamble
+  <a class="anchor" href="#preamble">#</a>
+</h2>
+<p>In addition to including the main <code>ldsCtrlEst</code> header, this tutorial will use some shorthand.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span><span style="color:#75715e">#include</span> <span style="color:#75715e">&lt;ldsCtrlEst&gt;</span><span style="color:#75715e">
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Matrix;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>Vector;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> lds<span style="color:#f92672">::</span>data_t;
+</span></span><span style="display:flex;"><span><span style="color:#66d9ef">using</span> std<span style="color:#f92672">::</span>cout;
+</span></span></code></pre></div><p>Note that <code>lds::Matrix</code> and <code>lds::Vector</code> are typedefs for <code>arma::Mat&lt;data_t&gt;</code> and <code>arma::Col&lt;data_t&gt;</code>, where the data type is <code>double</code> by default. May be changed to <code>float</code> in <code>include/ldsCtrlEst_h/lds.h</code> if there are memory constraints (e.g., large-scale MIMO control problems).</p>
+<h2 id="creating-the-simulated-system">
+  Creating the simulated system
+  <a class="anchor" href="#creating-the-simulated-system">#</a>
+</h2>
+<p>A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>  <span style="color:#75715e">// whether to do switched control
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">bool</span> do_switch_ctrl <span style="color:#f92672">=</span> true;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Make SISO system sampled at 1kHz
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  data_t dt <span style="color:#f92672">=</span> <span style="color:#ae81ff">1e-3</span>;
+</span></span><span style="display:flex;"><span>  size_t n_u <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>  size_t n_x <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>  size_t n_y <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// no time steps for simulation.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#66d9ef">auto</span> n_t <span style="color:#f92672">=</span> <span style="color:#66d9ef">static_cast</span><span style="color:#f92672">&lt;</span>size_t<span style="color:#f92672">&gt;</span>(<span style="color:#ae81ff">30.0</span> <span style="color:#f92672">/</span> dt);
+</span></span></code></pre></div><p>The system&rsquo;s input matrix (<code>B</code>) will be switched stochastically from one value (<code>b1</code>) to a less sensitive value (<code>b2</code>) according to the following probabilities.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>  <span style="color:#75715e">// for simulating switching
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  size_t which_mode <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>  data_t pr_21 <span style="color:#f92672">=</span> <span style="color:#ae81ff">1e-3</span>;   <span style="color:#75715e">// prob mode 1 -&gt; 2
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  data_t pr_12 <span style="color:#f92672">=</span> pr_21;  <span style="color:#75715e">// prob mode 2 -&gt; 1
+</span></span></span></code></pre></div><p>Initially, the system will be in &ldquo;mode&rdquo; 1, where <code>B = b1</code>.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>  <span style="color:#75715e">// simulated system being controlled
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>System controlled_system(n_u, n_x, n_y, dt);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// **Assume the system is not well characterized by one LDS, but is well
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// characterized by two LDS models with different input matrices.**
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  data_t scale_sys_b <span style="color:#f92672">=</span> <span style="color:#ae81ff">2</span>;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  Matrix <span style="color:#a6e22e">a</span>(n_x, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye);
+</span></span><span style="display:flex;"><span>  a[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">=</span> <span style="color:#ae81ff">0.985</span>;
+</span></span><span style="display:flex;"><span>  Matrix b1 <span style="color:#f92672">=</span> Matrix(n_x, n_u).fill(<span style="color:#ae81ff">0.05</span>);
+</span></span><span style="display:flex;"><span>  Vector d <span style="color:#f92672">=</span> Vector(n_y, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>zeros).fill(log(<span style="color:#ae81ff">1</span> <span style="color:#f92672">*</span> dt));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  controlled_system.set_A(a);
+</span></span><span style="display:flex;"><span>  controlled_system.set_B(b1);
+</span></span><span style="display:flex;"><span>  controlled_system.set_d(d);
+</span></span><span style="display:flex;"><span>  controlled_system.Reset();  <span style="color:#75715e">// reset to initial conditions
+</span></span></span></code></pre></div><p>See the <a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a> and <a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS State Estimation</a> tutorials for more detail about creating <code>System</code> objects.</p>
+<h2 id="creating-the-controller">
+  Creating the controller
+  <a class="anchor" href="#creating-the-controller">#</a>
+</h2>
+<p>Now, create the controller. A switched-system controller (<code>SwitchedController</code>) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (<code>sys1</code>, <code>sys2</code>), which are the same save for their input gains.</p>
+<p>Similar to a non-switched controller, constructing a <code>SwitchedController</code> requires these system models and upper/lower bounds on control. See the <a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a> tutorial for more details. In the case of a <code>SwitchedController</code>, it needs a <strong>list</strong> of systems, using the <code>std::vector</code> container.</p>
+<p>Moreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides <code>UniformMatrixList</code> and <code>UniformVectorList</code> containers that should be used when setting <code>Kc</code>, <code>Kc_inty</code>, <code>g_design</code>. These containers are <code>std::vector</code>s whose contents are uniformly sized.</p>
+<p>Putting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>  <span style="color:#75715e">// create switched controller
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>SwitchedController switched_controller;
+</span></span><span style="display:flex;"><span>  lds<span style="color:#f92672">::</span>UniformMatrixList<span style="color:#f92672">&lt;&gt;</span> k_x; <span style="color:#75715e">// feedback controller gains
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  {
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// create switched controller sub-systems
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// system 1
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>System sys1(controlled_system);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// set process noise covariance
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Matrix q_controller <span style="color:#f92672">=</span> Matrix(n_x, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>eye) <span style="color:#f92672">*</span> <span style="color:#ae81ff">5e-3</span>;
+</span></span><span style="display:flex;"><span>    sys1.set_Q(q_controller);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// adaptively estimate process disturbance (m)
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#75715e">// n.b. using arbitrary default value for process noise if enabled.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    sys1.do_adapt_m <span style="color:#f92672">=</span> true;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// setting initial mode to target to avoid large error at onset:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Vector x0_controller <span style="color:#f92672">=</span> arma<span style="color:#f92672">::</span>log(y_ref0) <span style="color:#f92672">-</span> d;
+</span></span><span style="display:flex;"><span>    sys1.set_x0(x0_controller);
+</span></span><span style="display:flex;"><span>    sys1.Reset();  <span style="color:#75715e">// reset to initial conditions
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// system 2
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>System sys2 <span style="color:#f92672">=</span> sys1;
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// set parameters
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    sys2.set_B(b2);
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    lds<span style="color:#f92672">::</span>UniformSystemList<span style="color:#f92672">&lt;</span>lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>System<span style="color:#f92672">&gt;</span> systems({sys1, sys2});
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    <span style="color:#75715e">// controller gains for underlying systems:
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    Matrix <span style="color:#a6e22e">k_x1</span>(n_u, n_x, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>ones);
+</span></span><span style="display:flex;"><span>    Matrix k_x2 <span style="color:#f92672">=</span> scale_sys_b <span style="color:#f92672">*</span> k_x1;  <span style="color:#75715e">// system2 is x-times less sensitive.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    k_x <span style="color:#f92672">=</span> lds<span style="color:#f92672">::</span>UniformMatrixList<span style="color:#f92672">&lt;&gt;</span>({k_x1, k_x2});
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>    data_t u_lb <span style="color:#f92672">=</span> <span style="color:#ae81ff">0.0</span>;
+</span></span><span style="display:flex;"><span>    data_t u_ub <span style="color:#f92672">=</span> <span style="color:#ae81ff">5.0</span>;
+</span></span><span style="display:flex;"><span>    switched_controller <span style="color:#f92672">=</span> std<span style="color:#f92672">::</span>move(
+</span></span><span style="display:flex;"><span>        lds<span style="color:#f92672">::</span>poisson<span style="color:#f92672">::</span>SwitchedController(std<span style="color:#f92672">::</span>move(systems), u_lb, u_ub));
+</span></span><span style="display:flex;"><span>  }
+</span></span></code></pre></div><p>Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.</p>
+<p>Now that the <code>SwitchedController</code> is instantiated, assign its parameters.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>    <span style="color:#75715e">// Control variables
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  size_t control_type <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span>;  <span style="color:#75715e">// no integral action, etc
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  switched_controller.set_control_type(control_type);
+</span></span><span style="display:flex;"><span>  switched_controller.set_Kc(std<span style="color:#f92672">::</span>move(k_x));
+</span></span><span style="display:flex;"><span>  switched_controller.set_y_ref(y_ref0);
+</span></span></code></pre></div><h2 id="simulating-control">
+  Simulating control
+  <a class="anchor" href="#simulating-control">#</a>
+</h2>
+<p>In this demonstration, we will use the <code>ControlOutputReference</code> method which allows users to simply set the reference output event rate (<code>y_ref</code>) and supply the current measurement <code>z</code>. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).</p>
+<p>The control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.</p>
+<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-cpp" data-lang="cpp"><span style="display:flex;"><span>  <span style="color:#75715e">// Let the controlled system stochastically change gain
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// Assume another algorithm decodes this mode change and signals the
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  <span style="color:#75715e">// switched_controller
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  Vector <span style="color:#a6e22e">chance</span>(<span style="color:#ae81ff">1</span>, arma<span style="color:#f92672">::</span>fill<span style="color:#f92672">::</span>randu);
+</span></span><span style="display:flex;"><span>  <span style="color:#66d9ef">if</span> (which_mode <span style="color:#f92672">==</span> <span style="color:#ae81ff">1</span>)  <span style="color:#75715e">// mode1
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  {
+</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">if</span> (chance[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;</span> pr_21) {
+</span></span><span style="display:flex;"><span>      which_mode <span style="color:#f92672">=</span> <span style="color:#ae81ff">2</span>;
+</span></span><span style="display:flex;"><span>      controlled_system.set_B(b2);
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">if</span> (do_switch_ctrl) {
+</span></span><span style="display:flex;"><span>        switched_controller.Switch(<span style="color:#ae81ff">1</span>);
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  } <span style="color:#66d9ef">else</span> {  <span style="color:#75715e">// mode2
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>    <span style="color:#66d9ef">if</span> (chance[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">&lt;</span> pr_12) {
+</span></span><span style="display:flex;"><span>      which_mode <span style="color:#f92672">=</span> <span style="color:#ae81ff">1</span>;
+</span></span><span style="display:flex;"><span>      controlled_system.set_B(b1);
+</span></span><span style="display:flex;"><span>      <span style="color:#66d9ef">if</span> (do_switch_ctrl) {
+</span></span><span style="display:flex;"><span>        switched_controller.Switch(<span style="color:#ae81ff">0</span>);
+</span></span><span style="display:flex;"><span>      }
+</span></span><span style="display:flex;"><span>    }
+</span></span><span style="display:flex;"><span>  }
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// Simulate the true system.
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  z.col(t) <span style="color:#f92672">=</span> controlled_system.Simulate(u.col(t <span style="color:#f92672">-</span> <span style="color:#ae81ff">1</span>));
+</span></span><span style="display:flex;"><span>
+</span></span><span style="display:flex;"><span>  <span style="color:#75715e">// perform control
+</span></span></span><span style="display:flex;"><span><span style="color:#75715e"></span>  u.col(t) <span style="color:#f92672">=</span> switched_controller.ControlOutputReference(z.col(t));
+</span></span></code></pre></div><p>Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system&rsquo;s operating mode and call the <code>Switch</code> method accordingly. Such a decoder is not currently included in this library.</p>
+<h2 id="example-simulation-result">
+  Example simulation result
+  <a class="anchor" href="#example-simulation-result">#</a>
+</h2>
+<p>Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller&rsquo;s online estimates of the output and state are shown in purple.</p>
+<p><img src="/lds-ctrl-est/eg_plds_switched_ctrl_output.png" alt="example control output" /></p>
+<p>Note that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.</p>
+</article>
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+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
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+<br />
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
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+<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
+</ul>
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+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
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diff --git a/docs/docs/tutorials/index.xml b/docs/docs/tutorials/index.xml
new file mode 100644
index 00000000..232f8ad3
--- /dev/null
+++ b/docs/docs/tutorials/index.xml
@@ -0,0 +1,32 @@
+<?xml version="1.0" encoding="utf-8" standalone="yes"?>
+<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
+  <channel>
+    <title>LDS C&#43;E Examples on LDS C&amp;E</title>
+    <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/</link>
+    <description>Recent content in LDS C&#43;E Examples on LDS C&amp;E</description>
+    <generator>Hugo</generator>
+    <language>en</language>
+    <atom:link href="https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/index.xml" rel="self" type="application/rss+xml" />
+    <item>
+      <title>GLDS Control</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_glds_control/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_glds_control/</guid>
+      <description>&lt;h1 id=&#34;glds-control-tutorial&#34;&gt;&#xA;  GLDS Control Tutorial&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#glds-control-tutorial&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;This tutorial shows how to use this library to control a system with a Gaussian LDS controller (&lt;code&gt;lds::gaussian::Controller&lt;/code&gt;). In place of a physical system, a GLDS model (&lt;code&gt;lds::gaussian::System&lt;/code&gt;) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>PLDS State Estimation</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/</guid>
+      <description>&lt;h1 id=&#34;plds-state-estimation-tutorial&#34;&gt;&#xA;  PLDS State Estimation Tutorial&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#plds-state-estimation-tutorial&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (&lt;code&gt;lds::poisson::System&lt;/code&gt;) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.&lt;/p&gt;&#xA;&lt;p&gt;The full code for this can be found &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/&#34;&gt;here&lt;/a&gt;.&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>PLDS Switched Control</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/</guid>
+      <description>&lt;h1 id=&#34;plds-switched-control-tutorial&#34;&gt;&#xA;  PLDS Switched Control Tutorial&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#plds-switched-control-tutorial&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;This tutorial shows how to use this library to control a system with a switched PLDS controller (&lt;code&gt;lds::poisson::SwitchedController&lt;/code&gt;). This type of controller is applicable in scenarios where a physical system is not accurately captured by a &lt;strong&gt;single&lt;/strong&gt; LDS but has &lt;strong&gt;multiple&lt;/strong&gt; discrete operating modes where the dynamics can be well-approximated as linear.&lt;/p&gt;&#xA;&lt;p&gt;In the example that follows, another PLDS model (&lt;code&gt;lds::poisson::System&lt;/code&gt;) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.&lt;/p&gt;</description>
+    </item>
+  </channel>
+</rss>
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diff --git a/docs/eg_plds_ctrl_output.png b/docs/eg_plds_ctrl_output.png
new file mode 100644
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+[{"id":0,"href":"/lds-ctrl-est/docs/","title":"LDS C+E Documentation","section":"LDS Control \u0026 Estimation","content":" LDS Control \u0026amp; Estimation Documentation # "},{"id":1,"href":"/lds-ctrl-est/docs/tutorials/","title":"LDS C+E Examples","section":"LDS C+E Documentation","content":" Examples # "},{"id":2,"href":"/lds-ctrl-est/acknowledgements/","title":"Acknowledgements","section":"LDS Control \u0026 Estimation","content":" Acknowledgements # Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.\n"},{"id":3,"href":"/lds-ctrl-est/docs/getting-started/getting-started/","title":"Getting Started","section":"LDS C+E Documentation","content":" Getting Started # This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.\nldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.\nTested Configurations # Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the RelWithDebInfo build type in the CMake configure command (-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo):\nUbuntu 18.04 with GCC 7.5 compiler macOS 11 (Big Sur) with Apple Clang 12 compiler Windows 10 with Visual Studio 16.11 (2019 release) and Clang 12 compiler That being said, if you want to debug a build for a single platform, here are some things you can try:\nUse different compilers (or even different versions of a single compiler) Use different versions of vcpkg (which you can control by checking out a different commit in the vcpkg submodule) Mac Pre-requisities # Xcode Command Line Tools will get you clang, gcc, make, and git:\nxcode-select --install Homebrew is \u0026ldquo;The Missing Package Manager for macOS\u0026rdquo; which will make installing lots of things easy. Install like this:\n/bin/bash -c \u0026#34;$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\u0026#34; You can then use it to install CMake, gfortran, and pkg-config:\nbrew install cmake gfortran pkg-config Linux Pre-requisites # You\u0026rsquo;ll need Git, CMake, GCC, gfortran, etc.\nsudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential ninja-build Windows Installation # Look here for Windows-specific instructions.\nDownloading the Library # First, clone the repository along with submodules:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est\rgit submodule update --init Compilation + Installation # Now generate the cache and build using your IDE or from the command line as follows.\nmkdir build \u0026amp;\u0026amp; cd build\rcmake ..\rcmake --build . The first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.\nIf you want to use vcpkg set up somewhere besides this repo\u0026rsquo;s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE\u0026rsquo;s settings.\nYou can verify the build is working by running ctest from the build folder, which runs all the example scripts.\nOptions # This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\nLDSCTRLEST_BUILD_EXAMPLES : [default=ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake to configure/build the library.\nFor basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build cmake --build #build the project sudo make install #[optional] installs to default location (OS-specific) To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location cmake --build #build the project make install #install to /your/install/prefix To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\nLD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.\nPython bindings package ldsctrlest # With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython3_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn\u0026rsquo;t use an undesired version. That environment needs to have NumPy installed.\ncmake --build . --target python_modules The bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory (pip install pytest matplotlib first if you need to).\nSee python/ldsctrlest/README.md for usage details.\nAlso, beware that a single build will probably not work for both the standalone library and the Python package, since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.\nCommon issues # \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.\nexport CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix vcpkg fails on configuration Try running ./bootstrap-vcpkg from the vcpkg folder and try again. If that doesn\u0026rsquo;t work, try updating vcpkg to a newer version (in the source control tab, click on the commit hash by the vcpkg repo then select from the dropdown) and running boostsrap-vcpkg again. You can also try upgrading your system (e.g., apt update, apt upgrade).\nCould not find Python3 (missing: Python3_NumPy_INCLUDE_DIRS NumPy)\nMake sure NumPy is installed in the Python environment you specified. If CMake still can\u0026rsquo;t find it, you may need to tell CMake exactly where to find it by adding an argument to the configure command: -DPython3_NumPy_INCLUDE_DIR=.... You can find that location like this: python -c 'import numpy; print(numpy.get_include())'\n"},{"id":4,"href":"/lds-ctrl-est/docs/getting-started/windows/","title":"Windows","section":"LDS C+E Documentation","content":" Windows Installation # Windows Pre-requisites # Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:\nSet-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time iwr get.scoop.sh | Invoke-Expression Install Git and CMake if you don\u0026rsquo;t already have them:\nscoop install git cmake If that didn\u0026rsquo;t work, follow more detailed instructions here.\nThe easiest way to compile C++ project on Windows is with Visual Studio\u0026rsquo;s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on \u0026ldquo;Desktop development with C++.\u0026rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the \u0026ldquo;Installation details\u0026rdquo; sidebar under optional features.\nAnd the easiest way to use Visual Studio\u0026rsquo;s build tools is with VS Code, along with the CMake Tools extension. Install them and you should be ready to go.\nDownloading the Library # First, clone the repository, either from VS Code or the command line:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est You\u0026rsquo;ll need to initialize the submodules from the command line after the repo is cloned:\ngit submodule update --init Installation # When you open the folder in VS Code, you will like be prompted by the CMake Tools extension to configure the project. Make sure you select the kit (you\u0026rsquo;ll be prompted when you configure\u0026ndash;else there\u0026rsquo;s an icon in the bar on the bottom of the window or type Ctrl+Shift+P, then \u0026ldquo;cmake select kit\u0026rdquo;). Choose Clang [latest version] with GNU CLI ... amd64 assuming you are running a 64-bit OS. (MSVC may work okay too if you don\u0026rsquo;t need to build Python bindings.)\nFollow along with the \u0026ldquo;Getting Started\u0026rdquo; instructions, but where you see config options specified as -DLDSCTREST_BUILD_STATIC=OFF or -DPython3_ROOT_DIR=..., you will enter those in settings: open with Ctrl+,, click \u0026ldquo;workspace\u0026rdquo;, then search for \u0026ldquo;CMake: Configure Args\u0026rdquo; and enter each of your desired arguments as a separate item.\nTo configure, use Ctrl+Shift+P and search for the \u0026ldquo;CMake: Configure\u0026rdquo; command. To build, click the \u0026ldquo;Build\u0026rdquo; button on the bottom bar. Then click the \u0026ldquo;CTest\u0026rdquo; button to run the example scripts.\nConsiderations # Development on Windows has been more prone to bugs than on Unix systems, so if you encounter many problems, consider switching—WSL (Windows Subsystem for Linux) is a good option for Windows users who don\u0026rsquo;t want to work on a different machine.\nCompilation has been successfully tested in VS Code using the following kit, using the \u0026ldquo;RelWithDebInfo\u0026rdquo; config:\nClang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64) Troubleshooting # The build appears to work, but tests fail with code 0xc0000135 OR \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; Have you installed the library? In VS Code, use Shift+F7 to build a specific target, in this case INSTALL. If that doesn\u0026rsquo;t solve your problem, you will likely need to add the build or install folder to your PATH environment variable, which you can do using the settings GUI (search for \u0026ldquo;Edit the system environment variables\u0026rdquo;).\nOn Windows, \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted. Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":5,"href":"/lds-ctrl-est/issues-contributing/","title":"Issues Contributing","section":"LDS Control \u0026 Estimation","content":" Reporting Issues # If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.\nContributing # We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.\n"},{"id":6,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/","title":"armamexc","section":"Namespaces","content":" armamexc # arma/mex interface using Matlab C API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; T m2T_scalar(const mxArray * matlab_scalar)\nConvert Matlab mxArray to scalar of type T. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)\nConvert matlab matrix to armadillo. template \u0026lt;typename T \u0026gt; mxArray * a2m_mat(arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat)\nConvert armadillo to matlab matrix. template \u0026lt;typename T \u0026gt; mxArray * a2m_vec(arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec)\nConvert armadillo to matlab vector. Detailed Description # Utilities for arma/mex interface using Matlab C API\nFunction Details # m2T_scalar # template \u0026lt;class T \u0026gt; inline T m2T_scalar( const mxArray * matlab_scalar ) Parameters:\nmatlab_scalar matlab scalar Template Parameters:\nT type Return: scalar of type T\nm2a_mat # template \u0026lt;class T \u0026gt; inline arma::Mat\u0026lt; T \u0026gt; m2a_mat( const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true ) Parameters:\nmatlab_mat matlab matrix copy_aux_mem [optional] whether to copy auxiliary memory strict [optional] strictly enforce the above Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_mat( arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat ) Parameters:\narma_mat armadillo matrix Return: matlab matrix\na2m_vec # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_vec( arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec ) Parameters:\narma_vec armadillo vector Return: matlab vector\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":7,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/","title":"armamexcpp","section":"Namespaces","content":" armamexcpp # arma/mex interface using Matlab C++ API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat(matlab::data::CellArray \u0026amp; matlab_cell)\nConvert matlab cell array to vector of armadillo matrices. template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array)\nConvert matlab matrix to a vector of scalars. template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo vector. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat(const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec(const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab vector. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec(const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert vector of scalar T to matlab matrix. Detailed Description # utilities for arma/mex interface using Matlab C++ API\nFunction Details # m2a_cellmat # template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat( matlab::data::CellArray \u0026amp; matlab_cell ) Parameters:\nmatlab_cell matlab cell Template Parameters:\nT type Return: vector of armadillo matrices of type T\nm2s_vec # template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: vector of type T\nm2a_vec # template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: armadillo vector of type T\nm2a_mat # template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab matrix Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat( const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_mat arma matrix factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\na2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec( const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_vec armadillo vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\ns2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec( const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\nstd_vec standard vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":8,"href":"/lds-ctrl-est/docs/terminology/control-estimation/","title":"C\u0026E","section":"LDS C+E Documentation","content":" Control \u0026amp; Estimation # The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.\nState estimation # In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by \\[\\widehat{\\mathbf{x}}_{t|t-1} = \\mathbf{A}\\widehat{\\mathbf{x}}_{t-1|t-1} \u0026#43; \\mathbf{B} u_{t-1} \u0026#43; \\mathbf{m}_{t-1} \\;,\\] \\[\\widehat{\\mathbf{x}}_{t|t} = \\widehat{\\mathbf{x}}_{t|t-1} \u0026#43; \\mathbf{K}^{\\rm e}_t \\left(\\mathbf{z}_t - \\widehat{\\mathbf{y}}_{t|t-1}\\right)\\;,\\] where \\( \\hat{\\left(\\cdot\\right)}_{t|j} \\) indicates an estimate at time \\( t \\) given data up to time \\( j \\) inclusive, \\( \\mathbf{K}^{\\rm e} \\) is the estimator gain, and\n\\[ \\widehat{\\mathbf{y}}_{t|t-1} = h\\left( \\widehat{\\mathbf{x}}_{t|t-1} \\right) \\; .\\] In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.\nIn the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.\nAdaptive estimation of process disturbance # Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.\nWhen parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk \\[\\mathbf{m}_{t} = \\mathbf{m}_{t-1} \u0026#43; \\mathbf{w}^m_{t-1} \\;,\\] where \\( \\mathbf{w}^m \\sim \\mathcal{N}\\left(0, \\mathbf{Q}_m\\right)\\) . Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.\nControl # Given the estimated state, the controller updates the inputs to the system according to the following law: \\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] where \\( \\left( \\cdot \\right)^{\\rm ref} \\) correspond to reference/target signals and \\( \\mathbf{K}^c_x \\) is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.\nIf users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as\n\\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right) - \\mathbf{K}^c_{\\rm inty} \\sum_{j=1}^{t}\\left( \\widehat{\\mathbf{y}}_j - \\mathbf{y}^{\\rm ref}_j \\right) \\;.\\] An additional option available to users is a control law that updates the change in u,\n\\[\\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\left(\\mathbf{u}_{t-1} - \\mathbf{u}^{\\rm ref}_{t-1} \\right) - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] \\[\\mathbf{u}_{t} = \\mathbf{u}_{t-1} \u0026#43; \\Delta\\mathbf{u}_{t} \\; .\\] Notice that this takes the form of a first-order difference equation for updating control (i.e., \\( \\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\mathbf{u}_{t-1} \u0026#43; \\epsilon_{t-1} \\) ), effectively low-pass filtering the input depending on the characteristics of \\( \\mathbf{K}^c_u \\) . This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.\nIntegral action and the \\( \\Delta \\mathbf{u} \\) control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR\u0026rsquo;d to use in combination.\nCalculating reference state-control from output # In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce \\( \\mathbf{x}^{\\rm ref} \\) and \\( \\mathbf{u}^{\\rm ref} \\) . Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.\n"},{"id":9,"href":"/lds-ctrl-est/docs/api/classes/","title":"Classes","section":"LDS C+E Documentation","content":" Classes # lds::Controller\nlds::EM\nlds::Fit LDS Fit Type.\nlds::SSID\nlds::SwitchedController SwitchedController Type.\nlds::System Linear Dynamical System Type.\nlds::UniformMatrixList\nlds::UniformSystemList\nlds::UniformVectorList\nlds::gaussian::Controller Gaussian-observation Controller Type.\nlds::gaussian::Fit GLDS Fit Type.\nlds::gaussian::FitEM GLDS E-M Fit Type.\nlds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.\nlds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.\nlds::gaussian::System Gaussian LDS Type.\nlds::poisson::Controller PLDS Controller Type.\nlds::poisson::Fit PLDS Fit Type.\nlds::poisson::FitEM PLDS E-M Fit Type.\nlds::poisson::FitSSID Subspace Identification (SSID) for PLDS.\nlds::poisson::SwitchedController Poisson-observation SwitchedController Type.\nlds::poisson::System Poisson System type.\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":10,"href":"/lds-ctrl-est/docs/api/modules/group__control__masks/","title":"Control Mode Bit Masks","section":"Modules","content":" Control Mode Bit Masks # provides fill types for constructing new armadillo vectors, matrices More\u0026hellip; Attributes # Name const std::size_t kControlTypeDeltaU control designed to penalize change in input const std::size_t kControlTypeIntY control using integral action const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m Detailed Description # Control mode bit masks. These can be bit-wise OR\u0026rsquo;d to use in combination.\nAttribute Details # kControlTypeDeltaU # static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY # static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM # static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":11,"href":"/lds-ctrl-est/docs/api/modules/group__defaults/","title":"Defaults","section":"Modules","content":" Defaults # More\u0026hellip; Attributes # Name const data_t kDefaultP0 default state estimate covar const data_t kDefaultQ0 default process noise covar const data_t kDefaultR0 default output noise covar Detailed Description # Default values for common variables (e.g., default diagonal elements of covariances)\nAttribute Details # kDefaultP0 # static const data_t kDefaultP0 = 1e-6; kDefaultQ0 # static const data_t kDefaultQ0 = 1e-6; kDefaultR0 # static const data_t kDefaultR0 = 1e-2; Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":12,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/","title":"eg_glds_ctrl.cpp","section":"Examples","content":" eg_glds_ctrl.cpp # Example GLDS Control ```cpp\n//===\u0026ndash; eg_glds_ctrl.cpp - Example GLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS Control ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\n// output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false;\n// Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err\n// setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]);\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; }\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:32:33 EST "},{"id":13,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/","title":"eg_glds_du_plds_ctrl.cpp","section":"Examples","content":" eg_glds_du_plds_ctrl.cpp # Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u). ```cpp\n//===\u0026ndash; eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt);\n// to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU;\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_du_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:32:33 EST "},{"id":14,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/","title":"eg_plds_ctrl.cpp","section":"Examples","content":" eg_plds_ctrl.cpp # Example PLDS Control ```cpp\n//===\u0026ndash; eg_plds_ctrl.cpp - Example PLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Control ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt);\n// Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err\n// Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\nsize_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\nVector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled\u0026hellip; lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system);\n// for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type);\n// set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty);\n// to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0;\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros);\n// set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y();\nx_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x();\nm_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:32:33 EST "},{"id":15,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/","title":"eg_plds_est.cpp","section":"Examples","content":" eg_plds_est.cpp # Example PLDS Estimation ```cpp\n//===\u0026ndash; eg_plds_est.cpp - Example PLDS Estimation \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0);\nint main() { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.\n// construct ground truth system\u0026hellip; lds::poisson::System system_true(n_u, n_x, n_y, dt);\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset();\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt);\n// Can copy parameters from another system object system_estimator = system_true;\n// wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est);\n// set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true;\n// set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;estimator:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; system_estimator.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));\n// create matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros);\n// states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\nMatrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simlation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1));\n// Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simlation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows;\nif ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026ldquo;Q must be n x n.\u0026rdquo;); }\nMatrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); }\nreturn x; }\n_Filename: eg_plds_est.cpp_ ------------------------------- Updated on 5 March 2025 at 16:32:33 EST "},{"id":16,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/","title":"eg_plds_switched_ctrl.cpp","section":"Examples","content":" eg_plds_switched_ctrl.cpp # Example Switched PLDS Control ```cpp\n//===\u0026ndash; eg_plds_switched_ctrl.cpp - Example Switched PLDS Control \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026quot;;\n// whether to do switched control bool do_switch_ctrl = true;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt);\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices. data_t scale_sys_b = 2;\nMatrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));\ncontrolled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions\n// reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);\n// Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system);\n// set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys1:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys2:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x));\nswitched_controller.set_y_ref(y_ref0);\nstd::vectorlds::poisson::System systems_vec(3, lds::poisson::System()); lds::UniformSystemListlds::poisson::System systems(std::move(systems_vec));\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;switched_controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; switched_controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Fake measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros);\n// create Matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);\n// modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones);\n// set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } }\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace)); mode.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;mode\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_switched_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:32:33 EST "},{"id":17,"href":"/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/","title":"examples","section":"Files","content":" examples # Files # Name examples/eg_glds_ctrl.cpp examples/eg_glds_du_plds_ctrl.cpp examples/eg_plds_ctrl.cpp examples/eg_plds_est.cpp examples/eg_plds_switched_ctrl.cpp Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":18,"href":"/lds-ctrl-est/docs/api/examples/","title":"Examples","section":"LDS C+E Documentation","content":" Examples # eg_glds_ctrl.cpp Example GLDS Control.\neg_glds_du_plds_ctrl.cpp Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).\neg_plds_ctrl.cpp Example PLDS Control.\neg_plds_est.cpp Example PLDS Estimation.\neg_plds_switched_ctrl.cpp Example Switched PLDS Control.\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":19,"href":"/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/","title":"examples/eg_glds_ctrl.cpp","section":"Files","content":" examples/eg_glds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":20,"href":"/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/","title":"examples/eg_glds_du_plds_ctrl.cpp","section":"Files","content":" examples/eg_glds_du_plds_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU; // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":21,"href":"/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/","title":"examples/eg_plds_ctrl.cpp","section":"Files","content":" examples/eg_plds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err // Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled... lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type); // set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":22,"href":"/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/","title":"examples/eg_plds_est.cpp","section":"Files","content":" examples/eg_plds_est.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name Matrix random_walk(size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0) int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # random_walk # Matrix random_walk( size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0 ) main # int main() Source code # //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. // construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state // Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. // turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; } Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":23,"href":"/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/","title":"examples/eg_plds_switched_ctrl.cpp","section":"Files","content":" examples/eg_plds_switched_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Source code # //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 // simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions // reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":24,"href":"/lds-ctrl-est/docs/api/files/","title":"Files","section":"LDS C+E Documentation","content":" Files # examples/eg_glds_ctrl.cpp\nexamples/eg_glds_du_plds_ctrl.cpp\nexamples/eg_plds_ctrl.cpp\nexamples/eg_plds_est.cpp\nexamples/eg_plds_switched_ctrl.cpp\nldsCtrlEst_h/lds.h lds namespace\nldsCtrlEst_h/lds_ctrl.h Controller.\nldsCtrlEst_h/lds_fit.h LDS base fit type.\nldsCtrlEst_h/lds_fit_em.h subspace identification\nldsCtrlEst_h/lds_fit_ssid.h subspace identification\nldsCtrlEst_h/lds_gaussian.h glds namespace\nldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.\nldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.\nldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.\nldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.\nldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.\nldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.\nldsCtrlEst_h/lds_poisson.h plds namespace\nldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.\nldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.\nldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.\nldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.\nldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.\nldsCtrlEst_h/lds_poisson_sys.h PLDS base type.\nldsCtrlEst_h/lds_sctrl.h SwitchedController type.\nldsCtrlEst_h/lds_sys.h LDS base type.\nldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.\nldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.\nldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.\nldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)\nldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)\nsrc/lds.cpp misc lds namespace functions\nsrc/lds_gaussian_sys.cpp GLDS base type.\nsrc/lds_poisson_sys.cpp PLDS base type.\nsrc/lds_sys.cpp LDS base type.\nsrc/lds_uniform_vecs.cpp Uniformly sized vectors.\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":25,"href":"/lds-ctrl-est/docs/tutorials/eg_glds_control/","title":"GLDS Control","section":"LDS C+E Examples","content":" GLDS Control Tutorial # This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\] \\[\ry_{t} = x_t\r\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.\n/// Going to simulate a switching disturbance (m) acting on system size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_y).fill(m_low); Finally, assign the parameters using corresponding set-methods.\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); Creating the controller # Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.\nFor the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.\n// make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nWith the controller constructed, control variables may be set.\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.\nThe control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller\u0026rsquo;s online estimates of the output, state, and disturbance are given in purple.\n"},{"id":26,"href":"/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/","title":"include","section":"Files","content":" include # Directories # Name ldsCtrlEst_h Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":27,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds/","title":"lds","section":"Namespaces","content":" lds # Linear Dynamical Systems (LDS) namespace. Namespaces # Name lds::gaussian Linear Dynamical Systems with Gaussian observations. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::Controller class lds::EM class lds::Fit LDS Fit Type. class lds::SSID class lds::SwitchedController SwitchedController Type. class lds::System Linear Dynamical System Type. class lds::UniformMatrixList class lds::UniformSystemList class lds::UniformVectorList Types # Name enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}\nweighting options for SSID enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2} using double data_t using arma::Col\u0026lt; data_t \u0026gt; Vector using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Cube\u0026lt; data_t \u0026gt; Cube using arma::subview\u0026lt; data_t \u0026gt; View Functions # Name void Limit(std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub) void Limit(Vector \u0026amp; x, data_t lb, data_t ub) void Limit(Matrix \u0026amp; x, data_t lb, data_t ub) void Reassign(Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Vector in place void Reassign(Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Matrix in place void ForceSymPD(Matrix \u0026amp; X)\nforces matrix to be symmetric positive-definite void ForceSymMinEig(Matrix \u0026amp; X, data_t eig_min =0)\nforces matrix to be symmetric and have a minimum eigenvalue void lq(Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X)\nLQ decomposition. Matrix calcCov(const Matrix \u0026amp; A, const Matrix \u0026amp; B)\nCalculate covariance matrix. Attributes # Name const data_t kInf Some useful numbers. const data_t kPi Type Details # SSIDWt # Enumerator Value Description kSSIDNone None. kSSIDMOESP MOESP (AKA \u0026ldquo;robust method\u0026rdquo; in van Overschee 1996) kSSIDCVA CVA \u0026ldquo;Canonical Variate Analysis\u0026rdquo;. Weighting options for singular value decomposition performed during subspace identification (SSID)\nReference:\nvan Overschee, de Moor. 1996. Subspace Identification for Linear Systems.\nMatrixListFreeDim # Enumerator Value Description kMatFreeDimNone neither dim free to be hetero in mat list kMatFreeDim1 allow 1st dim of mats in list to be hetero kMatFreeDim2 allow 2nd dim of mats in list to be hetero data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nVector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Cube # using lds::Cube = arma::Cube\u0026lt;data_t\u0026gt;; View # using lds::View = arma::subview\u0026lt;data_t\u0026gt;; Function Details # Limit # inline void Limit( std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Vector \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Matrix \u0026amp; x, data_t lb, data_t ub ) Reassign # inline void Reassign( Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Vector other other Vector parenthetical optional description provided by caller to ease debugging Reassign # inline void Reassign( Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Matrix other other Matrix parenthetical optional description provided by caller to ease debugging ForceSymPD # void ForceSymPD( Matrix \u0026amp; X ) Parameters:\nX mutated matrix ForceSymMinEig # void ForceSymMinEig( Matrix \u0026amp; X, data_t eig_min =0 ) Parameters:\nX mutated matrix eig_min [optional] minimum eigen value lq # void lq( Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X ) Parameters:\nL lower triangle matrix Qt orthonormal matrix (transposed cf QR decomp) X matrix being decomposed calcCov # Matrix calcCov( const Matrix \u0026amp; A, const Matrix \u0026amp; B ) Parameters:\nA some matrix B some other matrix Return: covariance\nAttribute Details # kInf # static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); kPi # static const data_t kPi = arma::datum::pi; Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":28,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/","title":"lds::Controller","section":"Classes","content":" lds::Controller # More\u0026hellip;\nInherited by lds::SwitchedController\u0026lt; System \u0026gt;, lds::gaussian::Controller, lds::poisson::Controller\nPublic Functions # Name Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes # Name System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::Controller; Public Function Details # Controller # Controller() =default Controller # inline Controller( const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Controller # inline Controller( System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Control # inline const Vector \u0026amp; Control( const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step). This is the most flexible option, but requires user to have set the controller\u0026rsquo;s y_ref, x_ref, and u_ref variables.\nControlOutputReference # inline const Vector \u0026amp; ControlOutputReference( const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_estimation [optional] whether to update state estimate (if false, effectively open-loop control) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.\nsys # inline const System \u0026amp; sys() const Kc # inline const Matrix \u0026amp; Kc() const Kc_inty # inline const Matrix \u0026amp; Kc_inty() const Kc_u # inline const Matrix \u0026amp; Kc_u() const g_design # inline const Vector \u0026amp; g_design() const u_ref # inline const Vector \u0026amp; u_ref() const x_ref # inline const Vector \u0026amp; x_ref() const y_ref # inline const Vector \u0026amp; y_ref() const control_type # inline size_t control_type() const tau_awu # inline data_t tau_awu() const u_lb # inline data_t u_lb() const u_ub # inline data_t u_ub() const set_sys # inline void set_sys( const System \u0026amp; sys ) set_g_design # inline void set_g_design( const Vector \u0026amp; g_design ) set_u_ref # inline void set_u_ref( const Vector \u0026amp; u_ref ) set_x_ref # inline void set_x_ref( const Vector \u0026amp; x_ref ) set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) Reimplemented by: lds::gaussian::Controller::set_y_ref, lds::gaussian::SwitchedController::set_y_ref, lds::poisson::Controller::set_y_ref, lds::poisson::SwitchedController::set_y_ref\nset_Kc # inline void set_Kc( const Matrix \u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const Matrix \u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const Matrix \u0026amp; Kc_u ) set_tau_awu # inline void set_tau_awu( data_t tau ) set_control_type # inline void set_control_type( size_t control_type ) Parameters:\ncontrol_type control type bit mask Template Parameters:\nSystem type derived from lds::System set_u_lb # inline void set_u_lb( data_t u_lb ) Parameters:\nu_lb control lower bound set_u_ub # inline void set_u_ub( data_t u_ub ) Parameters:\nu_ub control upper bound Reset # inline void Reset() Print # inline void Print() Protected Attribute Details # sys_ # System sys_; u_ # Vector u_; u_return_ # Vector u_return_; g_design_ # Vector g_design_; u_ref_ # Vector u_ref_; u_ref_prev_ # Vector u_ref_prev_; x_ref_ # Vector x_ref_; y_ref_ # Vector y_ref_; cx_ref_ # Vector cx_ref_; Kc_ # Matrix Kc_; Kc_u_ # Matrix Kc_u_; Kc_inty_ # Matrix Kc_inty_; du_ref_ # Vector du_ref_; dv_ref_ # Vector dv_ref_; v_ref_ # Vector v_ref_; dv_ # Vector dv_; v_ # Vector v_; int_e_ # Vector int_e_; int_e_awu_adjust_ # Vector int_e_awu_adjust_; u_sat_ # Vector u_sat_; do_control_prev_ # bool do_control_prev_ = false; do_lock_control_prev_ # bool do_lock_control_prev_ = false; u_saturated_ # bool u_saturated_ = false; u_lb_ # data_t u_lb_ {}; u_ub_ # data_t u_ub_ {}; tau_awu_ # data_t tau_awu_ {}; k_awu_ # data_t k_awu_ = 0; t_since_control_onset_ # data_t t_since_control_onset_ = 0; control_type_ # size_t control_type_ {}; Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":29,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/","title":"lds::EM","section":"Classes","content":" lds::EM # More\u0026hellip;\nInherited by lds::gaussian::FitEM, lds::poisson::FitEM\nPublic Functions # Name EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions # Name void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() virtual void MaximizeOutput() =0 virtual void MaximizeMeasurement() =0 void Smooth(bool force_common_initial)\nget smoothed estimates virtual void RecurseKe(Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t) =0\nrecursively update estimator gain Ke void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::EM; Public Function Details # EM # EM() =default EM # EM( size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nn_x number of states dt sample period u_train input training data z_train measurement training data EM # EM( const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nfit0 initial fit u_train input training data z_train measurement training data ~EM # virtual ~EM() =default Run # const Fit \u0026amp; Run( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2 ) Parameters:\ncalc_dynamics [optional] whether to calculate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law max_iter max number of iterations tol convergence tolerance (max fractional abs change) Return: Fit\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nx # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const y # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const sum_E_x_t_x_t # inline const Matrix \u0026amp; sum_E_x_t_x_t() const sum_E_xu_tm1_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const sum_E_xu_t_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const n_t_tot # inline size_t n_t_tot() theta # inline const Vector \u0026amp; theta() const Protected Function Details # Expectation # void Expectation( bool force_common_initial =false ) Parameters:\nforce_common_initial whether to force common initial condition for all trials Maximization # void Maximization( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false ) Parameters:\ncalc_dynamics [optional] whether to caclulate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law MaximizeDynamics # void MaximizeDynamics() MaximizeQ # void MaximizeQ() MaximizeInitial # void MaximizeInitial() MaximizeOutput # virtual void MaximizeOutput() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput\nMaximizeMeasurement # virtual void MaximizeMeasurement() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement\nSmooth # void Smooth( bool force_common_initial ) Parameters:\nforce_common_initial whether to force common initial conditions RecurseKe # virtual void RecurseKe( Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t ) =0 Parameters:\nKe estimator gain P_pre cov of predicted state est. P_post cov of postior sate est. t time Reimplemented by: lds::gaussian::FitEM::RecurseKe, lds::poisson::FitEM::RecurseKe\nReset # void Reset() InitVars # void InitVars() UpdateTheta # Vector UpdateTheta() Return: parameter list\nProtected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; x_ # std::vector\u0026lt; Matrix \u0026gt; x_; P_ # std::vector\u0026lt; Cube \u0026gt; P_; P_t_tm1_ # std::vector\u0026lt; Cube \u0026gt; P_t_tm1_; y_ # std::vector\u0026lt; Matrix \u0026gt; y_; diag_y_ # Matrix diag_y_; sum_E_x_t_x_t_ # Matrix sum_E_x_t_x_t_; sum_E_xu_tm1_xu_tm1_ # Matrix sum_E_xu_tm1_xu_tm1_; sum_E_xu_t_xu_tm1_ # Matrix sum_E_xu_t_xu_tm1_; fit_ # Fit fit_; theta_ # Vector theta_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":30,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/","title":"lds::Fit","section":"Classes","content":" lds::Fit # LDS Fit Type. #include \u0026lt;lds_fit.h\u0026gt;\nInherited by lds::gaussian::Fit, lds::poisson::Fit\nPublic Functions # Name Fit() =default\nConstructs a new Fit. Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias virtual const Matrix \u0026amp; R() const =0 void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance virtual void set_R(const Matrix \u0026amp; R) =0\nsets output noise covariance (if any) void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) =0\noutput function Protected Attributes # Name data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period ~Fit # virtual ~Fit() =default n_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const m # inline const Vector \u0026amp; m() const Q # inline const Matrix \u0026amp; Q() const x0 # inline const Vector \u0026amp; x0() const P0 # inline const Matrix \u0026amp; P0() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const R # virtual const Matrix \u0026amp; R() const =0 Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R\nset_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_g # inline void set_g( const Vector \u0026amp; g ) set_m # inline void set_m( const Vector \u0026amp; m ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # virtual void set_R( const Matrix \u0026amp; R ) =0 Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R\nset_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) f # inline View f( Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t ) Parameters:\nx state estimate (over time) u input (over time) t time index Return: view of updated state\nf # inline View f( Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t ) Parameters:\nx_pre predicted state est. x_post posterior state est. u input (over time) t time index Return: view of predicted state\nh # virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) =0 Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplemented by: lds::gaussian::Fit::h, lds::poisson::Fit::h\nProtected Attribute Details # dt_ # data_t dt_ {}; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; m_ # Vector m_; Q_ # Matrix Q_; C_ # Matrix C_; d_ # Vector d_; R_ # Matrix R_; x0_ # Vector x0_; P0_ # Matrix P0_; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":31,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/","title":"lds::gaussian","section":"Namespaces","content":" lds::gaussian # Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. class lds::gaussian::Fit GLDS Fit Type. class lds::gaussian::FitEM GLDS E-M Fit Type. class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. class lds::gaussian::System Gaussian LDS Type. Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":32,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/","title":"lds::gaussian::Controller","section":"Classes","content":" lds::gaussian::Controller # Gaussian-observation Controller Type. #include \u0026lt;lds_gaussian_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":33,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/","title":"lds::gaussian::Fit","section":"Classes","content":" lds::gaussian::Fit # GLDS Fit Type. #include \u0026lt;lds_gaussian_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual const Matrix \u0026amp; R() const override\ngets measurement noise covariance virtual void set_R(const Matrix \u0026amp; R) override\nsets measurement noise covariance virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period R # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nh # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":34,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/","title":"lds::gaussian::FitEM","section":"Classes","content":" lds::gaussian::FitEM # GLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_gaussian_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::gaussian::FitEM; This type is used in the process of fitting GLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":35,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/","title":"lds::gaussian::FitSSID","section":"Classes","content":" lds::gaussian::FitSSID # Subspace Identification (SSID) for GLDS. #include \u0026lt;lds_gaussian_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":36,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/","title":"lds::gaussian::SwitchedController","section":"Classes","content":" lds::gaussian::SwitchedController # Gaussian-observation SwitchedController Type. #include \u0026lt;lds_gaussian_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":37,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/","title":"lds::gaussian::System","section":"Classes","content":" lds::gaussian::System # Gaussian LDS Type. #include \u0026lt;lds_gaussian_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)\nConstructs a new Gaussian System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. const Matrix \u0026amp; R() const\nGet output noise covariance. void set_Q(const Matrix \u0026amp; Q) void set_R(const Matrix \u0026amp; R)\nSet output noise covariance. void set_Ke(const Matrix \u0026amp; Ke)\nSet estimator gain. void set_Ke_m(const Matrix \u0026amp; Ke_m)\nSet disturbance estimator gain. void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively update estimator gain. Protected Attributes # Name Matrix R_ covariance of output noise bool do_recurse_Ke_ whether to recursively calculate estimator gain Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0 ) Parameters:\nn_u number of inputs (u) n_x number of states (x) n_y number of outputs (y) dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) r0 [optional] initial diagonal elements of output noise covariance (R) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nR # inline const Matrix \u0026amp; R() const set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # inline void set_R( const Matrix \u0026amp; R ) set_Ke # inline void set_Ke( const Matrix \u0026amp; Ke ) set_Ke_m # inline void set_Ke_m( const Matrix \u0026amp; Ke_m ) Print # void Print() Protected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nProtected Attribute Details # R_ # Matrix R_; do_recurse_Ke_ # bool do_recurse_Ke_ {}; Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":38,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/","title":"lds::poisson","section":"Namespaces","content":" lds::poisson # Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. class lds::poisson::Fit PLDS Fit Type. class lds::poisson::FitEM PLDS E-M Fit Type. class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. class lds::poisson::System Poisson System type. Attributes # Name std::random_device rd random device for simulating poisson data std::mt19937 rng random number generator for simulating poisson data Attribute Details # rd # static std::random_device rd; rng # static std::mt19937 rng = std::mt19937( rd()); Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":39,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/","title":"lds::poisson::Controller","section":"Classes","content":" lds::poisson::Controller # PLDS Controller Type. #include \u0026lt;lds_poisson_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":40,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/","title":"lds::poisson::Fit","section":"Classes","content":" lds::poisson::Fit # PLDS Fit Type. #include \u0026lt;lds_poisson_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function virtual void set_R(const Matrix \u0026amp; R) override\nsets output noise covariance (if any) virtual const Matrix \u0026amp; R() const override Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # inline Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period h # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nR # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":41,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/","title":"lds::poisson::FitEM","section":"Classes","content":" lds::poisson::FitEM # PLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_poisson_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::poisson::FitEM; This type is used in the process of fitting PLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":42,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/","title":"lds::poisson::FitSSID","section":"Classes","content":" lds::poisson::FitSSID # Subspace Identification (SSID) for PLDS. #include \u0026lt;lds_poisson_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":43,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/","title":"lds::poisson::SwitchedController","section":"Classes","content":" lds::poisson::SwitchedController # Poisson-observation SwitchedController Type. #include \u0026lt;lds_poisson_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":44,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/","title":"lds::poisson::System","section":"Classes","content":" lds::poisson::System # Poisson System type. #include \u0026lt;lds_poisson_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nConstructs a new Poisson System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively recalculate estimator gain (Ke) Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nProtected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nRecursively recalculate estimator gain (Ke).\nReferences:\nSmith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.\nEden UT, \u0026hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":45,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/","title":"lds::SSID","section":"Classes","content":" lds::SSID # More\u0026hellip;\nInherited by lds::gaussian::FitSSID, lds::poisson::FitSSID\nPublic Functions # Name SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions # Name void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. virtual void DecomposeData() =0\nDecompose data to lower-triangular matrix (used in Solve) void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::SSID; Public Function Details # SSID # SSID() =default SSID # SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf) ) Parameters:\nn_x number of states n_h size of block-hankel data matrix dt sample period u_train input training data z_train measurement training data d output bias Run # std::tuple\u0026lt; Fit, Vector \u0026gt; Run( SSIDWt ssid_wt ) Parameters:\nssid_wt weight for singular value decomp Return: tuple (Fit, singular values)\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nProtected Function Details # CalcD # void CalcD( data_t t_silence =0.1, data_t thresh_silence =0.001 ) Parameters:\nt_silence threshold on period of time that qualifies as \u0026ldquo;silence\u0026rdquo; thresh_silence threshold on input amplitude u that qualifies as \u0026ldquo;silence\u0026rdquo; CreateHankelDataMat # void CreateHankelDataMat() Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.\nDecomposeData # virtual void DecomposeData() =0 Reimplemented by: lds::gaussian::FitSSID::DecomposeData, lds::poisson::FitSSID::DecomposeData\nCalcSVD # void CalcSVD( SSIDWt wt ) Parameters:\nssid_wt weight for SVD Solve # void Solve( data_t wt_dc ) Parameters:\nwt_dc weight placed on getting correct DC I/O gain RecomputeExtObs # void RecomputeExtObs() Protected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; D_ # Matrix D_; fit_ # Fit fit_; g_dc_ # Matrix g_dc_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_h_ # size_t n_h_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; L_ # Matrix L_; s_ # Vector s_; ext_obs_t_ # Matrix ext_obs_t_; Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":46,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/","title":"lds::SwitchedController","section":"Classes","content":" lds::SwitchedController # SwitchedController Type. More\u0026hellip;\n#include \u0026lt;lds_sctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nInherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController\nPublic Functions # Name SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes # Name std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::SwitchedController; Public Function Details # SwitchedController # SwitchedController() =default SwitchedController # inline SwitchedController( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask SwitchedController # inline SwitchedController( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Switch # inline void Switch( size_t idx, bool do_force_switch =false ) Parameters:\nidx index do_force_switch whether to force a system switch even if already there. set_Kc # inline void set_Kc( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc ) set_Kc # inline void set_Kc( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty ) set_Kc_inty # inline void set_Kc_inty( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u ) set_Kc_u # inline void set_Kc_u( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u ) set_g_design # inline void set_g_design( const UniformVectorList \u0026amp; g ) set_g_design # inline void set_g_design( UniformVectorList \u0026amp;\u0026amp; g ) Protected Attribute Details # systems_ # std::vector\u0026lt; System \u0026gt; systems_; n_sys_ # size_t n_sys_ {}; idx_ # size_t idx_ {}; Kc_list_ # UniformMatrixList Kc_list_; Kc_inty_list_ # UniformMatrixList Kc_inty_list_; Kc_u_list_ # UniformMatrixList Kc_u_list_; g_design_list_ # UniformVectorList g_design_list_; Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":47,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_system/","title":"lds::System","section":"Classes","content":" lds::System # Linear Dynamical System Type. #include \u0026lt;lds_sys.h\u0026gt;\nInherited by lds::gaussian::System, lds::poisson::System\nPublic Functions # Name System() =default\nConstructs a new System. System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nconstructs a new System virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) =0\nsimulates system (single time step) void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function virtual void h() =0\nsystem output function virtual Vector h_(Vector x) =0\nsystem output function (stateless) size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void RecurseKe() =0\nRecursively recalculate estimator gain (Ke) void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes # Name bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes # Name std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 diagonal elements for state estimate covariance q0 diagonal elements for process noise covariance ~System # inline virtual ~System() Filter # void Filter( const Vector \u0026amp; u_tm1, const Vector \u0026amp; z ) Parameters:\nu_tm1 input at t-minus-1 z_t current measurement Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.\nSimulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) =0 Parameters:\nu_tm1 input at time t-1 Return: simulated measurement at time t\nReimplemented by: lds::gaussian::System::Simulate, lds::poisson::System::Simulate\nf # inline void f( const Vector \u0026amp; u, bool do_add_noise =false ) Parameters:\nu input do_add_noise whether to add simulated process noise h # virtual void h() =0 Reimplemented by: lds::gaussian::System::h, lds::poisson::System::h\nh_ # virtual Vector h_( Vector x ) =0 Parameters:\nx_t state at time t Return: predicted state at time t + 1\nReimplemented by: lds::gaussian::System::h_, lds::poisson::System::h_\nn_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const x # inline const Vector \u0026amp; x() const P # inline const Matrix \u0026amp; P() const m # inline const Vector \u0026amp; m() const P_m # inline const Matrix \u0026amp; P_m() const cx # inline const Vector \u0026amp; cx() const y # inline const Vector \u0026amp; y() const x0 # inline const Vector \u0026amp; x0() const m0 # inline const Vector \u0026amp; m0() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const Ke # inline const Matrix \u0026amp; Ke() const Ke_m # inline const Matrix \u0026amp; Ke_m() const Q # inline const Matrix \u0026amp; Q() Q_m # inline const Matrix \u0026amp; Q_m() P0 # inline const Matrix \u0026amp; P0() P0_m # inline const Matrix \u0026amp; P0_m() set_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_m # inline void set_m( const Vector \u0026amp; m, bool do_force_assign =false ) set_g # inline void set_g( const Vector \u0026amp; g ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_Q_m # inline void set_Q_m( const Matrix \u0026amp; Q_m ) set_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_P0_m # inline void set_P0_m( const Matrix \u0026amp; P0_m ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) set_x # inline void set_x( const Vector \u0026amp; x ) Reset # void Reset() nstep_pred_block # std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block( UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1 ) Print # void Print() Protected Function Details # RecurseKe # virtual void RecurseKe() =0 Reimplemented by: lds::gaussian::System::RecurseKe, lds::poisson::System::RecurseKe\nInitVars # void InitVars( data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Public Attribute Details # do_adapt_m # bool do_adapt_m {}; Protected Attribute Details # n_x_ # std::size_t n_x_ {}; n_u_ # std::size_t n_u_ {}; n_y_ # std::size_t n_y_ {}; dt_ # data_t dt_ {}; x_ # Vector x_; P_ # Matrix P_; m_ # Vector m_; P_m_ # Matrix P_m_; cx_ # Vector cx_; y_ # Vector y_; z_ # Vector z_; x0_ # Vector x0_; P0_ # Matrix P0_; m0_ # Vector m0_; P0_m_ # Matrix P0_m_; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; Q_ # Matrix Q_; Q_m_ # Matrix Q_m_; C_ # Matrix C_; d_ # Vector d_; Ke_ # Matrix Ke_; Ke_m_ # Matrix Ke_m_; Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":48,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/","title":"lds::UniformMatrixList","section":"Classes","content":" lds::UniformMatrixList # More\u0026hellip;\nInherits from std::vector\u0026lt; Matrix \u0026gt;\nPublic Functions # Name UniformMatrixList() =default\nConstructs a new UniformMatrixList. UniformMatrixList(const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent. UniformMatrixList(std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent. UniformMatrixList(std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent. UniformMatrixList(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nConstructs a new UniformMatrixList (copy). UniformMatrixList(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nConstructs a new UniformMatrixList (move). ~UniformMatrixList() =default\nDestroys the object. const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim(size_t n =0) const\ngets dimensions of uniformly sized matrices size_t size()\nsize of container const Matrix \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Matrix \u0026amp; that, size_t n)\nswaps input matrix with n^th matrix of list UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nassigns the contents (copy) UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nassigns the contents (move) void append(const Matrix \u0026amp; mat)\nappends a matrix to the list Detailed Description # template \u0026lt;MatrixListFreeDim D =kMatFreeDimNone\u0026gt; class lds::UniformMatrixList; Public Function Details # UniformMatrixList # UniformMatrixList() =default UniformMatrixList # explicit UniformMatrixList( const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # explicit UniformMatrixList( std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList UniformMatrixList # UniformMatrixList( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList ~UniformMatrixList # ~UniformMatrixList() =default dim # inline const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim( size_t n =0 ) const Parameters:\nn [optional] index in list of matrices Return: dimensions\nsize # inline size_t size() at # inline const Matrix \u0026amp; at( size_t n ) Swap # inline void Swap( Matrix \u0026amp; that, size_t n ) Parameters:\nthat input matrix n index where the matrix is moved operator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\noperator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\nappend # void append( const Matrix \u0026amp; mat ) Parameters:\nmat input matrix Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":49,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/","title":"lds::UniformSystemList","section":"Classes","content":" lds::UniformSystemList # More\u0026hellip;\nInherits from std::vector\u0026lt; System \u0026gt;\nPublic Functions # Name UniformSystemList() =default\nConstructs a new UniformSystemList. UniformSystemList(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by copying existing vector of System if dimensions consistent. UniformSystemList(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by moving existing vector of System if dimensions consistent. UniformSystemList(std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList from initializer_list of System if dimensions consistent. UniformSystemList(const UniformSystemList \u0026amp; that)\nConstructs a new UniformSystemList (copy). UniformSystemList(UniformSystemList \u0026amp;\u0026amp; that)\nConstructs a new UniformSystemList (move). ~UniformSystemList() =default\nDestroys the object. const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const\ngets dimensions of the uniformly sized systems size_t size()\nsize of container const System \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(System \u0026amp; that, size_t n)\nswaps input system with n^th system of list UniformSystemList \u0026amp; operator=(const UniformSystemList \u0026amp; that)\nassigns the contents (copy) UniformSystemList \u0026amp; operator=(UniformSystemList \u0026amp;\u0026amp; that)\nassigns the contents (move) Detailed Description # template \u0026lt;typename System \u0026gt; class lds::UniformSystemList; Public Function Details # UniformSystemList # UniformSystemList() =default UniformSystemList # explicit UniformSystemList( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # explicit UniformSystemList( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList UniformSystemList # UniformSystemList( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList ~UniformSystemList # ~UniformSystemList() =default dim # inline const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const size # inline size_t size() at # inline const System \u0026amp; at( size_t n ) Swap # inline void Swap( System \u0026amp; that, size_t n ) Parameters:\nthat input system n index where the system is moved operator= # inline UniformSystemList \u0026amp; operator=( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\noperator= # inline UniformSystemList \u0026amp; operator=( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":50,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/","title":"lds::UniformVectorList","section":"Classes","content":" lds::UniformVectorList # Inherits from std::vector\u0026lt; Vector \u0026gt;\nPublic Functions # Name UniformVectorList() =default\nConstructs a new UniformVectorList. UniformVectorList(const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent. UniformVectorList(std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent. UniformVectorList(std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0)\nConstructs a new UniformVectorList from initializer_list of Vector if dimensions consistent. UniformVectorList(const UniformVectorList \u0026amp; that)\nConstructs a new UniformVectorList (copy) UniformVectorList(UniformVectorList \u0026amp;\u0026amp; that)\nConstructs a new UniformVectorList (move) ~UniformVectorList() =default\nDestroys the object. size_t dim() const\ngets dimensions of the uniformly sized matrices size_t size()\nsize of container const Vector \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Vector \u0026amp; that, size_t n)\nswaps input matrix with n^th vector of list UniformVectorList \u0026amp; operator=(const UniformVectorList \u0026amp; that)\nassigns the contents (copy) UniformVectorList \u0026amp; operator=(UniformVectorList \u0026amp;\u0026amp; that)\nassigns the contents (move) Public Function Details # UniformVectorList # UniformVectorList() =default UniformVectorList # explicit UniformVectorList( const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dims dimension UniformVectorList # explicit UniformVectorList( std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList UniformVectorList # UniformVectorList( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList ~UniformVectorList # ~UniformVectorList() =default dim # inline size_t dim() const size # inline size_t size() at # inline const Vector \u0026amp; at( size_t n ) Swap # inline void Swap( Vector \u0026amp; that, size_t n ) Parameters:\nthat input vector n index where the vector is moved operator= # inline UniformVectorList \u0026amp; operator=( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\noperator= # inline UniformVectorList \u0026amp; operator=( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":51,"href":"/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/","title":"ldsCtrlEst_h","section":"Files","content":" ldsCtrlEst_h # Files # Name ldsCtrlEst_h/lds.h lds namespace ldsCtrlEst_h/lds_ctrl.h Controller. ldsCtrlEst_h/lds_fit.h LDS base fit type. ldsCtrlEst_h/lds_fit_em.h subspace identification ldsCtrlEst_h/lds_fit_ssid.h subspace identification ldsCtrlEst_h/lds_gaussian.h glds namespace ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller. ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type. ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type. ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type. ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type. ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type. ldsCtrlEst_h/lds_poisson.h plds namespace ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type. ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type. ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type. ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type. ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type. ldsCtrlEst_h/lds_poisson_sys.h PLDS base type. ldsCtrlEst_h/lds_sctrl.h SwitchedController type. ldsCtrlEst_h/lds_sys.h LDS base type. ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices. ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems. ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors. ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":52,"href":"/lds-ctrl-est/docs/api/files/lds__ctrl_8h/","title":"ldsCtrlEst_h/lds_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_ctrl.h # Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Controller Detailed Description # This file declares the type for control of a linear dynamical system (lds::Controller).\nSource code # //===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_CTRL_H #define LDSCTRLEST_LDS_CTRL_H // namespace #include \u0026#34;lds.h\u0026#34; // system type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class Controller { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); public: Controller() = default; Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); const Vector\u0026amp; Control(const Vector\u0026amp; z, bool do_control = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); const Vector\u0026amp; ControlOutputReference(const Vector\u0026amp; z, bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); // get methods: const System\u0026amp; sys() const { return sys_; }; const Matrix\u0026amp; Kc() const { return Kc_; }; const Matrix\u0026amp; Kc_inty() const { return Kc_inty_; }; const Matrix\u0026amp; Kc_u() const { return Kc_u_; }; const Vector\u0026amp; g_design() const { return g_design_; }; const Vector\u0026amp; u_ref() const { return u_ref_; }; const Vector\u0026amp; x_ref() const { return x_ref_; }; const Vector\u0026amp; y_ref() const { return y_ref_; }; size_t control_type() const { return control_type_; }; data_t tau_awu() const { return tau_awu_; }; data_t u_lb() const { return u_lb_; }; data_t u_ub() const { return u_ub_; }; // set methods void set_sys(const System\u0026amp; sys) { bool does_match = sys_.n_u() == sys.n_u(); does_match = does_match \u0026amp;\u0026amp; (sys_.n_x() == sys.n_x()); does_match = does_match \u0026amp;\u0026amp; (sys_.n_y() == sys.n_y()); if (does_match) { sys_ = sys; } else { throw std::runtime_error( \u0026#34;new system argument to `set_sys` does not match dimensionality of \u0026#34; \u0026#34;existing system\u0026#34;); } }; void set_g_design(const Vector\u0026amp; g_design) { Reassign(g_design_, g_design); }; void set_u_ref(const Vector\u0026amp; u_ref) { Reassign(u_ref_, u_ref); }; void set_x_ref(const Vector\u0026amp; x_ref) { Reassign(x_ref_, x_ref); cx_ref_ = sys_.C() * x_ref_; }; // y_ref needs to be handled differently depending on output fn. // (need to populate cx_ref_ too, which depends on output fn) virtual void set_y_ref(const Vector\u0026amp; y_ref) { Reassign(y_ref_, y_ref); }; void set_Kc(const Matrix\u0026amp; Kc) { Reassign(Kc_, Kc); }; void set_Kc_inty(const Matrix\u0026amp; Kc_inty) { Reassign(Kc_inty_, Kc_inty); }; void set_Kc_u(const Matrix\u0026amp; Kc_u) { Reassign(Kc_u_, Kc_u); }; void set_tau_awu(data_t tau) { tau_awu_ = tau; k_awu_ = sys_.dt() / tau_awu_; }; void set_control_type(size_t control_type); // There is no reason u_lb/ub should not be public, but making set methods // anyway. void set_u_lb(data_t u_lb) { u_lb_ = u_lb; }; void set_u_ub(data_t u_ub) { u_ub_ = u_ub; }; void Reset() { sys_.Reset(); u_ref_.zeros(); u_ref_prev_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); u_saturated_ = false; t_since_control_onset_ = 0.0; }; void Print() { sys_.Print(); std::cout \u0026lt;\u0026lt; \u0026#34;g_design : \u0026#34; \u0026lt;\u0026lt; g_design_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_lb : \u0026#34; \u0026lt;\u0026lt; u_lb_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_ub : \u0026#34; \u0026lt;\u0026lt; u_ub_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }; protected: System sys_; Vector u_; Vector u_return_; Vector g_design_; // reference signals Vector u_ref_; // create no set method for this: Vector u_ref_prev_; Vector x_ref_; Vector y_ref_; Vector cx_ref_; // Controller gains Matrix Kc_; Matrix Kc_u_; Matrix Kc_inty_; // control after g inversion // do not need set methods for these. Vector du_ref_; Vector dv_ref_; Vector v_ref_; Vector dv_; Vector v_; // integral error // do not need set method for this Vector int_e_; Vector int_e_awu_adjust_; Vector u_sat_; bool do_control_prev_ = false; bool do_lock_control_prev_ = false; // whether the g of system has become inverted from what you think it is // (gain_ref) bool u_saturated_ = false; // should be safe to have references here bc nothing needs to be done // (like reset vars) when it changes... data_t u_lb_{}; data_t u_ub_{}; data_t tau_awu_{}; data_t k_awu_ = 0; data_t t_since_control_onset_ = 0; size_t control_type_{}; private: void CalcControl(bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); void CalcSteadyStateSetPoint(); void AntiWindup(); void InitVars(size_t control_type); }; // Implement the above: template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(sys), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(std::move(sys)), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::set_control_type(size_t control_type) { if (control_type_ == control_type) { return; } // creating a blank slate... control_type_ = 0; Kc_inty_.zeros(0, 0); Kc_u_.zeros(0, 0); int_e_.zeros(0, 0); int_e_awu_adjust_.zeros(0, 0); // controller was designed to minimize integral error if (control_type \u0026amp; kControlTypeIntY) { Kc_inty_.zeros(sys_.n_u(), sys_.n_y()); int_e_.zeros(sys_.n_y()); int_e_awu_adjust_.zeros(sys_.n_u()); control_type_ = control_type_ | kControlTypeIntY; } // controller was designed to minimize deltaU // (i.e. state augmented with u) if (control_type \u0026amp; kControlTypeDeltaU) { Kc_u_.zeros(sys_.n_u(), sys_.n_u()); control_type_ = control_type_ | kControlTypeDeltaU; } // whether to adapt set point calculate with (re-estimated) process // disturbance (m) if (control_type \u0026amp; kControlTypeAdaptM) { if (sys_.do_adapt_m) // only if adapting m... { control_type_ = control_type_ | kControlTypeAdaptM; } } } // set_control_type template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::Control( const Vector\u0026amp; z, bool do_control, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement sys_.Filter(u_, z); bool do_estimation = true; // always have estimator on in this case // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::ControlOutputReference( const Vector\u0026amp; z, bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement if (do_estimation) { sys_.Filter(u_, z); } else { sys_.f(u_); } // calculate the set point // solves for u_ref and x_ref when output is at y_ref at steady state. if (do_control) { CalcSteadyStateSetPoint(); } // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcControl(bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { if (do_control \u0026amp;\u0026amp; do_estimation) { if (!do_control_prev_) { if (do_reset_at_control_onset) { Reset(); } t_since_control_onset_ = 0.0; } else { t_since_control_onset_ += sys_.dt(); } // enforce softstart on control vars. if (sigma_soft_start \u0026gt; 0) { // half-Gaussian soft-start scaling factor data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) / (2 * pow(sigma_soft_start, 2))); u_ref_ *= soft_start_sf; // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too // x_ref_ *= soft_start_sf; // cx_ref_ *= soft_start_sf; // y_ref_ *= soft_start_sf; } if (!do_lock_control) { // first do u -\u0026gt; v change of vars. (v = g.*u) // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver // control voltage u[=]V) v_ref_ = g_design_ % u_ref_; // Given FB, calc. the change in control if (control_type_ \u0026amp; kControlTypeDeltaU) { // if control designed to minimize not u but deltaU (i.e. state aug with // u): // TODO(mfbolus): Commented out for now. See note below. // du_ref_ = u_ref_ - u_ref_prev_; // dv_ref_ = g_design_ % du_ref_; // TODO(mfbolus): Assuming users want *smooth* control signals if using // kControlTypeDeltaU, it should be the case that dv_ref_ is --\u0026gt; 0. May // want to revisit, but I am going to force it to be zero unless a // situation arises that argues for keeping the above. dv_ref_.zeros(); dv_ = dv_ref_; // nominally-optimal. dv_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error dv_ -= Kc_u_ * (v_ - v_ref_); // penalty on amp u (rel to ref) if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if(!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error dv_ -= Kc_inty_ * int_e_; // control for integrated error } // update the control v_ += dv_; } else { v_ = v_ref_; // nominally-optimal. v_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if (!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error v_ -= Kc_inty_ * int_e_; // control for integrated error } } // convert back to control voltage u[=]V u_ = v_ / sys_.g(); } // else do nothing until lock is low } else { // if not control // feed through u_ref in open loop u_ = u_ref_ % g_design_ / sys_.g(); v_ = sys_.g() % u_; u_ref_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); } // ends do_control // enforce box constraints (and antiwindup) AntiWindup(); // add noise to input? // The value for u that is *returned* to user after addition of any noise, // while keeping controller/estimator blind to this addition. u_return_ = u_; if ((sigma_u_noise \u0026gt; 0.0) \u0026amp;\u0026amp; (do_control \u0026amp;\u0026amp; !do_lock_control)) { u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn); Limit(u_return_, u_lb_, u_ub_); }; // For next time step: u_ref_prev_ = u_ref_; do_control_prev_ = do_control; do_lock_control_prev_ = do_lock_control; } // CalcControl template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcSteadyStateSetPoint() { // Linearly-constrained least squares (ls). // // _reference: // Boyd \u0026amp; Vandenberghe (2018) Introduction to Applied Linear Algebra // Matrix a_ls = join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros)); Vector b_ls = cx_ref_; Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye), sys_.B() * arma::diagmat(sys_.g())); Vector d_ls = -sys_.m0(); if (control_type_ \u0026amp; kControlTypeAdaptM) { d_ls = -sys_.m(); // adapt setpoint calc with disturbance? } Matrix a_ls_t = a_ls.t(); // TODO(mfbolus): not sure why but causes seg // fault if I do not do this. Matrix phi_ls = join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()), join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros))); // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse: Matrix inv_phi = pinv(phi_ls); Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls); x_ref_ = xulam.subvec(0, sys_.n_x() - 1); u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1); cx_ref_ = sys_.C() * x_ref_; } // CalcSteadyStateSetPoint template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::AntiWindup() { u_saturated_ = false; u_sat_ = u_; // limit u and flag whether saturated for (size_t k = 0; k \u0026lt; u_.n_elem; k++) { if (u_[k] \u0026lt; u_lb_) { u_sat_[k] = u_lb_; u_saturated_ = true; } if (u_[k] \u0026gt; u_ub_) { u_sat_[k] = u_ub_; u_saturated_ = true; } } if ((control_type_ \u0026amp; kControlTypeIntY) \u0026amp;\u0026amp; (tau_awu_ \u0026lt; lds::kInf)) { // one-step back-calculation (calculate intE for u=u_sat) // (Astroem, Rundqwist 1989 warn against using this...) // int_e_awu_adjust_ = // solve(Kc_inty_, (u_ - u_sat_)); // pinv(Kc_inty) * (u-uSat); // gradual: see Astroem, Rundqwist 1989 // this is a fudge for doing MIMO gradual // n.b., went ahead and multiplied 1/T by dt so don\u0026#39;t have to do that here. int_e_awu_adjust_ = k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_); // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_); int_e_ += int_e_awu_adjust_; } // set u to saturated version u_ = u_sat_; } template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::InitVars(size_t control_type) { // initialize to default values u_ref_ = Vector(sys_.n_u(), fill::zeros); u_ref_prev_ = Vector(sys_.n_u(), fill::zeros); x_ref_ = Vector(sys_.n_x(), fill::zeros); y_ref_ = Vector(sys_.n_y(), fill::zeros); cx_ref_ = Vector(sys_.n_y(), fill::zeros); u_ = Vector(sys_.n_u(), fill::zeros); u_return_ = Vector(sys_.n_u(), fill::zeros); u_sat_ = Vector(sys_.n_u(), fill::zeros); // Might not need all these, so zero elements until later. Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros); Kc_u_ = Matrix(0, 0, fill::zeros); Kc_inty_ = Matrix(0, 0, fill::zeros); g_design_ = sys_.g(); // by default, same as model dv_ = Vector(sys_.n_u(), fill::zeros); v_ = Vector(sys_.n_u(), fill::zeros); du_ref_ = Vector(sys_.n_u(), fill::zeros); dv_ref_ = Vector(sys_.n_u(), fill::zeros); v_ref_ = Vector(sys_.n_u(), fill::zeros); int_e_ = Vector(0, fill::zeros); int_e_awu_adjust_ = Vector(0, fill::zeros); set_control_type(control_type); } } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":53,"href":"/lds-ctrl-est/docs/api/files/lds__fit__em_8h/","title":"ldsCtrlEst_h/lds_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_em.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::EM Detailed Description # This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).\nSource code # //===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_EMAX_H #define LDSCTRLEST_LDS_EMAX_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class EM { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: EM() = default; EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); virtual ~EM() = default; const Fit\u0026amp; Run(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = true, bool calc_output = true, bool calc_measurement = true, size_t max_iter = 100, data_t tol = 1e-2); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); // auto tuple = std::make_tuple(u_, z_); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; x() const { return x_; }; const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; y() const { return y_; }; const Matrix\u0026amp; sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; }; const Matrix\u0026amp; sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; }; const Matrix\u0026amp; sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; }; size_t n_t_tot() { return n_t_tot_; } const Vector\u0026amp; theta() const { return theta_; }; protected: void Expectation(bool force_common_initial = false); void Maximization(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = false, bool calc_output = false, bool calc_measurement = false); void MaximizeDynamics(); void MaximizeQ(); void MaximizeInitial(); virtual void MaximizeOutput() = 0; virtual void MaximizeMeasurement() = 0; void Smooth(bool force_common_initial); virtual void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) = 0; void Reset(); void InitVars(); Vector UpdateTheta(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; std::vector\u0026lt;Matrix\u0026gt; x_; std::vector\u0026lt;Cube\u0026gt; P_; std::vector\u0026lt;Cube\u0026gt; P_t_tm1_; std::vector\u0026lt;Matrix\u0026gt; y_; Matrix diag_y_; // expectations calculated in E-step Matrix sum_E_x_t_x_t_; Matrix sum_E_xu_tm1_xu_tm1_; Matrix sum_E_xu_t_xu_tm1_; Fit fit_; Vector theta_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; }; template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; fit_ = Fit(n_u_, n_x, n_y_, dt); u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { // make sure fit dims match I/O data if (fit0.n_u() != u_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and input training data have inconsistent dimensions\u0026#34;); } if (fit0.n_y() != z_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and output training data have inconsistent dimensions\u0026#34;); } fit_ = fit0; u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::InitVars() { // check input/output data dimensions are consistent if (z_.size() != u_.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_.at(trial).n_cols != u_.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } n_u_ = fit_.n_u(); n_x_ = fit_.n_x(); n_y_ = fit_.n_y(); dt_ = fit_.dt(); x_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); P_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); P_t_tm1_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); y_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros); P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros); } diag_y_ = Matrix(n_y_, n_y_, fill::zeros); // covariances in expectation step sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); } template \u0026lt;typename Fit\u0026gt; const Fit\u0026amp; EM\u0026lt;Fit\u0026gt;::Run(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement, size_t max_iter, data_t tol) { Reset(); // to initial conditions size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_; Vector theta(n_params); Vector theta_new(n_params); data_t max_dtheta = 1; // if solving for initial conditions, allow them be varied. // otherwise, freeze at provided values. bool force_common_initial = !calc_init; // go until parameter convergence for (size_t l = 0; l \u0026lt; max_iter; l++) { theta_ = UpdateTheta(); std::cout \u0026lt;\u0026lt; \u0026#34;Iteration \u0026#34; \u0026lt;\u0026lt; l + 1 \u0026lt;\u0026lt; \u0026#34;/\u0026#34; \u0026lt;\u0026lt; max_iter \u0026lt;\u0026lt; \u0026#34; ...\\n\u0026#34;; Expectation(force_common_initial); Maximization(calc_dynamics, calc_Q, calc_init, calc_output, calc_measurement); // check convergence theta_new = UpdateTheta(); Vector dtheta = abs(theta_new - theta_) / abs(theta_); // some parameters could be zero... arma::uvec ubi_finite = find_finite(dtheta); max_dtheta = max(dtheta.elem(ubi_finite)); std::cout \u0026lt;\u0026lt; \u0026#34;max dtheta: \u0026#34; \u0026lt;\u0026lt; max_dtheta \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; if (max_dtheta \u0026lt; tol) { std::cout \u0026lt;\u0026lt; \u0026#34;Converged.\\n\u0026#34;; break; } std::cout \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } return fit_; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Smooth(bool force_common_initial) { Matrix k_e(n_x_, n_y_); // estimator gain Cube k_backfilt; // back-filtering gains // TODO(mfbolus): this loop could be made parallel for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { Matrix x_pre(n_x_, n_t_[trial], fill::zeros); Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros); Matrix x_post(n_x_, n_t_[trial], fill::zeros); Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros); if (force_common_initial) // forces all trials to have same initial // conditions. { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); } y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); // This *should not* be necessary but make sure P is symmetric. ForceSymPD(P_[trial].slice(0)); x_pre.col(0) = x_[trial].col(0); p_pre.slice(0) = P_[trial].slice(0); x_post.col(0) = x_[trial].col(0); p_post.slice(0) = P_[trial].slice(0); // filter for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // predict fit_.f(x_pre, x_post, u_.at(trial), t); fit_.h(y_[trial], x_pre, t); diag_y_.diag() = y_[trial].col(t); // TODO(mfbolus): change if parallel // update --\u0026gt; posterior estimation RecurseKe(k_e, p_pre, p_post, t); x_post.col(t) = x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t)); y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d(); } // backfilter -\u0026gt; Smoothed estimate // Reference: // Shumway et Stoffer (1982) ForceSymPD(p_post.slice(n_t_[trial] - 1)); k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1); P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 0; t--) { // TODO(mfmbolus): should not be necessary to force symm positive def ForceSymPD(p_pre.slice(t)); ForceSymPD(p_post.slice(t - 1)); ForceSymPD(P_[trial].slice(t)); k_backfilt.slice(t - 1) = p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t)); x_[trial].col(t - 1) = x_post.col(t - 1) + k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t)); P_[trial].slice(t - 1) = p_post.slice(t - 1) + k_backfilt.slice(t - 1) * (P_[trial].slice(t) - p_pre.slice(t)) * k_backfilt.slice(t - 1).t(); } // do the same for P_t_tm1 Matrix id(n_x_, n_x_, fill::eye); P_t_tm1_[trial].slice(n_t_[trial] - 1) = (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 1; t--) { P_t_tm1_[trial].slice(t - 1) = p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() + k_backfilt.slice(t - 1) * (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) * k_backfilt.slice(t - 2).t(); } // finally, get smoothed estimate of output for (size_t t = 0; t \u0026lt; n_t_[trial]; t++) { fit_.h(y_[trial], x_[trial], t); } // samps loop } // trial loop } // Smooth // template \u0026lt;typename Fit\u0026gt; // void EM\u0026lt;Fit\u0026gt;::RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) { // // predict covar // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q(); // // update Ke // Ke = P_pre.slice(t) * fit_.C().t() * // inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R()); // // update cov // // Reference: Ghahramani et Hinton (1996) // P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t); // // // n.b. for poisson : // // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + // fit_.Q(); // // // update cov // // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ * // // fit_.C()); // // // update Ke // // Ke = P_post.slice(t) * fit_.C(); // } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Expectation(bool force_common_initial) { // calculate the mean/cov of state needed for maximizing E[pr(z|theta)] Smooth(force_common_initial); // now get the various forms of sum(E[xx\u0026#39;]) needed // n.b. Going to start at t=1 rather than 0 bc most max terms need that. // so really \u0026#34;n_t_tot_\u0026#34; is (n_t_tot_-1) n_t_tot_ = 0; sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); Vector xu_tm1(n_x_ + n_u_, fill::zeros); Vector xu_t(n_x_ + n_u_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // ------------ sum_E_x_t_x_t ------------ sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t(); sum_E_x_t_x_t_ += P_[trial].slice(t); // ------------ sum_E_xu_tm1_xu_tm1 ------------ xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1)); sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t(); sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t - 1); // ------------ sum_E_xu_t_xu_tm1 ------------ xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t)); sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t(); sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_t_tm1_[trial].slice(t); n_t_tot_ += 1; } // time } // trial } // Expectation template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement) { if (calc_output) { MaximizeOutput(); } if (calc_measurement) { MaximizeMeasurement(); } if (calc_dynamics) { MaximizeDynamics(); } if (calc_Q) { MaximizeQ(); } if (calc_init) { MaximizeInitial(); } } // Maximization template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeDynamics() { // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) * inv_sympd(sum_E_xu_tm1_xu_tm1_); fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1)); std::cout \u0026lt;\u0026lt; \u0026#34;A_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.A()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.B()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeQ() { // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 * // inv_sympd(sum_E_xu_tm1_xu_tm1_) * // sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // this way is same as above iff dynamics were just updated: // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix ab = arma::join_horiz(fit_.A(), fit_.B()); // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // From scratch method: // Q is covariance of the error between state and dynamics-predicted state // (aka process noise) // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})\u0026#39;] // t-1 terms: View sum_e_x_tm1_x_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_u_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1); View sum_e_x_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); // t, t-1 terms: View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_x_t_u_tm1 = sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); Matrix q = sum_E_x_t_x_t_; q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t(); q -= sum_e_x_t_x_tm1 * fit_.A().t(); q -= fit_.A() * sum_e_x_t_x_tm1.t(); // input-related terms: q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t(); q -= sum_e_x_t_u_tm1 * fit_.B().t(); q -= fit_.B() * sum_e_x_t_u_tm1.t(); q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t(); q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t(); q /= n_t_tot_; fit_.set_Q(q); std::cout \u0026lt;\u0026lt; \u0026#34;Q_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.Q()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // std::cout \u0026lt;\u0026lt; \u0026#34;Q_new: \\n\u0026#34; \u0026lt;\u0026lt; fit_.Q() \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeInitial() { Vector x0 = fit_.x0(); x0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { x0 += x_[trial].col(0); } x0 /= z_.size(); std::cout \u0026lt;\u0026lt; \u0026#34;x0_new[0]: \u0026#34; \u0026lt;\u0026lt; x0[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // always recalc P0 even if the initial state is fixed (at zero, for // example) Matrix e_var(n_x_, n_x_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t(); } e_var /= z_.size(); // go ahead and subtract x0*x0\u0026#39; so don\u0026#39;t have to below. e_var -= x0 * x0.t(); // To get P0, going to get initial P_ per trial and average. // (which might be wrong, but need a single number) Matrix p0 = fit_.P0(); p0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { p0 += (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var; } p0 /= z_.size(); fit_.set_P0(p0); std::cout \u0026lt;\u0026lt; \u0026#34;P0_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.P0()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeOutput() { // solve for C+d: Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros); Vector x1(n_x_ + 1, fill::zeros); x1[n_x_] = 1.0; // augment with one to solve for bias Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { x1.subvec(0, n_x_ - 1) = x_[trial].col(t); sum_zx += z_.at(trial).col(t) * x1.t(); sum_e_x1_x1 += x1 * x1.t(); sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t); } } Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1); fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1)); fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_))); std::cout \u0026lt;\u0026lt; \u0026#34;C_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.C()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.d()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeMeasurement() { // Solve for measurement noise covar size_t n_t_tot = 0; // Ghahgramani, Hinton 1996: Matrix sum_zz(n_y_, n_y_, fill::zeros); Matrix sum_yz(n_y_, n_y_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t(); // Use Cnew: sum_yz += (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t(); n_t_tot += 1; } } fit_.set_R((sum_zz - sum_yz) / n_t_tot); std::cout \u0026lt;\u0026lt; \u0026#34;R_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.R()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Reset() { // reset to initial conditions for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); } } template \u0026lt;typename Fit\u0026gt; Vector EM\u0026lt;Fit\u0026gt;::UpdateTheta() { // TODO(mfbolus): This should include n_y_ more params for d. size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_; if (fit_.R().n_elem \u0026gt; 0) { n_params += n_y_ * n_y_; } Vector theta(n_params); size_t idx_start = 0; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B()); idx_start += n_x_ * n_u_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0()); idx_start += n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C()); idx_start += n_y_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d()); idx_start += n_y_; if (fit_.R().n_elem \u0026gt; 0) { theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R()); } return theta; } } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":54,"href":"/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_ssid.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SSID Detailed Description # This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)\u0026lt;Fit\u0026gt;).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_FIT_SSID_H #define LDSCTRLEST_LDS_FIT_SSID_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class SSID { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: SSID() = default; SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d = Vector(1).fill(-kInf)); std::tuple\u0026lt;Fit, Vector\u0026gt; Run(SSIDWt ssid_wt); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } protected: void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001); void CreateHankelDataMat(); virtual void DecomposeData() = 0; void CalcSVD(SSIDWt wt); void Solve(data_t wt_dc); void RecomputeExtObs(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; Matrix D_; Fit fit_; Matrix g_dc_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_h_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; Matrix L_; Vector s_; Matrix ext_obs_t_; }; template \u0026lt;typename Fit\u0026gt; SSID\u0026lt;Fit\u0026gt;::SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d) { // check input/output data dimensions are consistent if (z_train.size() != u_train.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_train.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_train.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } dt_ = dt; n_x_ = n_x; n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; n_h_ = n_h; // dimensionality check for eventual block-hankel data matrix size_t len = n_t_tot_ - 2 * n_h_ + 1; if (len \u0026lt; (2 * n_h_ * (n_u_ + n_y_))) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;Dataset problem! More rows than columns in block-hankel data \u0026#34; \u0026#34;matrix: 2*(n_u+n_y)*n_h \u0026gt; data-length! Need higher data-length or \u0026#34; \u0026#34;lower n_h.\u0026#34;; throw std::runtime_error(ss.str()); } fit_ = Fit(n_u_, n_x_, n_y_, dt_); u_ = std::move(u_train); z_ = std::move(z_train); if (!d.is_finite() || (d.n_rows != n_y_)) { // TODO(mfbolus): implement least-square solution for impulse response with // a second input of ones. Data-driven way of accounting for offset *not* // driven by an input. // // For now, calculate output bias (d) as the // output wherever the stimulus has not been on for some amount of time. // convolve u with rectangle and take all samples. This is a reasonable // approach, since often when autonomous systems are fit (i.e., systems with // no input), they will subtract off the mean of the output. This // essentially amounts to setting output bias to the mean of the output when // there is no stimulation. data_t t_silence = 0.1; data_t thresh_silence = 0.001; CalcD(t_silence, thresh_silence); } else { fit_.set_d(d); } } template \u0026lt;typename Fit\u0026gt; std::tuple\u0026lt;Fit, Vector\u0026gt; SSID\u0026lt;Fit\u0026gt;::Run(SSIDWt ssid_wt) { // the weight on minimizing dc I/O gain only works for gaussian, // and hopefully not necessary with appropriate dataset. data_t wt_dc = 0; // std::cout \u0026lt;\u0026lt; \u0026#34;creating hankel mat\\n\u0026#34;; CreateHankelDataMat(); // std::cout \u0026lt;\u0026lt; \u0026#34;decomposing data\\n\u0026#34;; DecomposeData(); // std::cout \u0026lt;\u0026lt; \u0026#34;calculating svd\\n\u0026#34;; CalcSVD(ssid_wt); // std::cout \u0026lt;\u0026lt; \u0026#34;solving for params\\n\u0026#34;; Solve(wt_dc); // std::cout \u0026lt;\u0026lt; \u0026#34;fin\\n\u0026#34;; return std::make_tuple(fit_, s_); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcD(data_t t_silence, data_t thresh_silence) { Vector d(z_.at(0).n_rows, fill::zeros); Vector win(static_cast\u0026lt;size_t\u0026gt;(t_silence / dt_), fill::ones); Vector sum_z_silence(n_y_, fill::zeros); size_t n_silence(0); for (size_t trial = 0; trial \u0026lt; u_.size(); trial++) { // find silent samples // start by convolving with Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0)); Vector u_conv = conv(sum_u, win, \u0026#34;same\u0026#34;); // get only the samples that are silent... arma::uvec ubi_silence = find(u_conv \u0026lt;= thresh_silence); if (ubi_silence.n_elem \u0026gt; 0) { sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1); n_silence += ubi_silence.n_elem; } } if (n_silence \u0026gt; 0) { d = sum_z_silence / n_silence; } fit_.set_d(d); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat() { // temporary copy of data Matrix z(n_y_, n_t_tot_, fill::zeros); Matrix u(n_u_, n_t_tot_, fill::zeros); size_t so_far(0); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial); u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial); so_far += n_t_.at(trial); } // remove output bias z.each_col() -= fit_.d(); // calculate I/O gain @ DC while data in convenient form g_dc_ = z * pinv(u); // std::cout \u0026lt;\u0026lt; \u0026#34;G0_data = \u0026#34; \u0026lt;\u0026lt; g_dc_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // create hankel data matrix size_t len = z.n_cols - 2 * n_h_ + 1; // data length in hankel mat // block-hankel data matrix D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros); // past input auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1); // future input auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1); // past output auto y_p = D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1); // future output auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, len - 1); size_t idx = 0; for (size_t k = 0; k \u0026lt; len; k++) { idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } } D_ /= sqrt(static_cast\u0026lt;data_t\u0026gt;(len)); } // template \u0026lt;typename Fit\u0026gt; // void SSID\u0026lt;Fit\u0026gt;::DecomposeData() { // // do LQ decomp instead of calculating covariance expensive way // // Note that \u0026#34;R\u0026#34; in van Overschee is lower-triangular (L), not \u0026#34;R\u0026#34; in QR // // decomp. Very confusing. // Matrix q_t; // lq(L_, q_t, D_); // // van Overschee zeros out the other elements. // L_ = trimatl(L_); // } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcSVD(SSIDWt wt) { // submats that will be needed: auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1); auto R_23_13 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1); auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_) - 1); auto R_44 = L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14); auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1); auto Luf = Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1); auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); // aka: R_f Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1); // from van Overschee subid.m: // Rf = R((2*m+l)*i+1:2*(m+l)*i,:); % Future outputs Matrix U; Matrix V; switch (wt) { case kSSIDNone: { // No weighting. (what van Overschee calls \u0026#34;N4SID\u0026#34;) Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14; arma::svd(U, s_, V, O_k_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDMOESP: { // MOESP weighting // This is what they use in the \u0026#34;robust\u0026#34; algorithm van Overschee, de Moor // 1996 Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); svd(U, s_, V, O_k_ortho_Uf_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDCVA: { // CVA weighting // See van Overschee\u0026#39;s matlab code (subid.m): // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); Matrix inv_w1; Matrix qt1; lq(inv_w1, qt1, R_56_16); // lq decomp of R_f (future output data) inv_w1 = trimatl(inv_w1); inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1); Matrix w_o_w = arma::solve( inv_w1, O_k_ortho_Uf_sans_Qt); // alternatively // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt svd(U, s_, V, w_o_w, \u0026#34;std\u0026#34;); U = inv_w1 * U; break; } } // Truncate to model order (heart of ssid method) auto s_hat = s_.subvec(0, n_x_ - 1); Matrix diag_sqrt_s = diagmat(sqrt(s_hat)); auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1); // get extended observability and controllability mats ext_obs_t_ = u_hat * diag_sqrt_s; // extended observability matrix } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::Solve(data_t wt_dc) { // required submats auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_56_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); // Solve for params using appropriate algorithm: // robust deterministic/stochastic algorithm in van Overschee 1996 // algorithm that the authors say \u0026#34;works\u0026#34; in practice. auto ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix // This is what textbook (1996) says: // // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15); // // HOWEVER, do not know why but have to fill the last place with zeros like // authors\u0026#39; matlab implementation (see `subid.m`) // Otherwise, get ridiculous covariances (although A,C estimates are close to // same...) Matrix Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Matrix S = Tl * pinv(Tr); // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure // stability. fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1)); Matrix ext_obs_t_p1 = join_vert( ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1), Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros)); fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1); // At this point, van Overschee \u0026amp; de Moor suggest re-calculating ext_obs_t_, // ext_obs_tm1 from (A, C) because it was just an approximation. This is RecomputeExtObs(); ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); S = Tl * pinv(Tr); Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_); Matrix Mcurly = pinv(ext_obs_tm1); Matrix Pcurly = Tl - Lcurly * R_56_15; Vector Pvec = vectorise(Pcurly); Matrix Qcurly = R_23_15; // Identify [D; B], assuming D=0 and ensuring DC gain is correct Matrix sum_QcurlyT_kron_Ncurly( (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_), fill::zeros); Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols, fill::eye); eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1, eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1; // van Overschee (1996) p. 126 Matrix N1_Tl = -Lcurly; N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) += join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly); N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) += Matrix(n_y_, n_y_, fill::eye); Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros); Matrix N_k; for (size_t k = 0; k \u0026lt; n_h_; k++) { auto Qcurly_k = Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1); Nk_Tl.zeros(); Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) = N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1); N_k = Nk_Tl * eye_ext_obs_tm1; sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k); } Matrix err_vec; if (wt_dc \u0026gt; 0) { // Constraints enforced by weighted least squares // // Reference: // // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly // Excited Industrial Systems. Conference in Decision and Control. // constraint 1: assume D=0 --\u0026gt; remove the components for Dvec (this is // actually a hard constraint in that it ignores D) Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly; sum_QcurlyT_kron_Ncurly = Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_); size_t kkk = 0; for (size_t k = 1; k \u0026lt; (n_u_ + 1); k++) { size_t start_idx = k * (n_y_ + n_x_) - n_x_; for (size_t kk = 0; kk \u0026lt; n_x_; kk++) { sum_QcurlyT_kron_Ncurly.col(kkk) = sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk); kkk++; } } // constraint 2: Make sure DC I/O gain is correct Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A()); Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_)); Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0); Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 = join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0); // WEIGHTED LS // Important in practice because I care a lot about at least getting the DC // gain correct. Put x weight on minimizing error at DC, relative to others Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye); // Make weight on minimizing DC error immense so at least that // should be nailed. size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows; size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows; size_t stop_row = w.n_rows - 1; size_t stop_col = w.n_cols - 1; // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale // weight with data length? w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc; Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * sum_QcurlyT_kron_Ncurly_b_to_g0) * sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec; fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_)); // Calculate residuals and their cov. // Because I\u0026#39;ve added constraints, I need to re-calculate the right term // with b_vec instead of how van Overschee do in final algorithm. err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec; } else { // default way: *no* constraint on G0 or D=0 Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec; // TODO(mfbolus) n.b., this gets thrown away... // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_); fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_)); err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec; } // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols); // TODO(mfbolus): Something is wrong with the error calculation above. // Use the way van overschee does it in `subid.m` // WARNING: this ignores any above constraints, so Q, R will be approximate... Matrix err = Tl - S * Tr; Matrix cov_err = err * err.t(); fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1)); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::RecomputeExtObs() { ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C(); for (size_t k = 2; k \u0026lt; (n_h_ + 1); k++) { ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) = ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1, ext_obs_t_.n_cols - 1) * fit_.A(); } } } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":55,"href":"/lds-ctrl-est/docs/api/files/lds__fit_8h/","title":"ldsCtrlEst_h/lds_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_fit.h # LDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Fit LDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.\nSource code # //===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDS_FIT_HPP #define LDS_FIT_HPP // namespace #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); virtual ~Fit() = default; // get methods size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; Q() const { return Q_; }; const Vector\u0026amp; x0() const { return x0_; }; const Matrix\u0026amp; P0() const { return P0_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; // gets measurement noise virtual const Matrix\u0026amp; R() const = 0; // set methods (e.g., seeding initial fit values) void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_m(const Vector\u0026amp; m) { Reassign(m_, m); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); ForceSymPD(Q_); }; virtual void set_R(const Matrix\u0026amp; R) = 0; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); ForceSymPD(P0_); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; View f(Matrix\u0026amp; x, const Matrix\u0026amp; u, size_t t) { x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x.col(t); }; View f(Matrix\u0026amp; x_pre, const Matrix\u0026amp; x_post, const Matrix\u0026amp; u, size_t t) { x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x_pre.col(t); }; virtual View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) = 0; protected: data_t dt_{}; // Dynamics Matrix A_; Matrix B_; Vector g_; Vector m_; Matrix Q_; // Output Matrix C_; Vector d_; Matrix R_; // initial conditions Vector x0_; Matrix P0_; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; }; } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":56,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_ctrl.h # GLDS Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. Detailed Description # This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_gaussian_sys.h\u0026#34; // controller #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace gaussian { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); cx_ref_ = y_ref - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":57,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_em.h # GLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitEM GLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":58,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_ssid.h # GLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; using SSID\u0026lt;Fit\u0026gt;::Run; private: using SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat; using SSID\u0026lt;Fit\u0026gt;::CalcSVD; using SSID\u0026lt;Fit\u0026gt;::Solve; void DecomposeData() override; void SolveVanOverschee(); }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":59,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit.h # GLDS fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Fit GLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // fit type #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace gaussian { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); const Matrix\u0026amp; R() const override { return R_; }; void set_R(const Matrix\u0026amp; R) override { Reassign(R_, R); ForceSymPD(R_); }; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = C_ * x.col(t) + d_; return y.col(t); }; }; }; // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":60,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sctrl.h # GLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H // controller type #include \u0026#34;lds_gaussian_ctrl.h\u0026#34; // switched controller #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace gaussian { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); cx_ref_ = y_ref - sys_.d(); } // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; }; // SwitchedController } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":61,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/","title":"ldsCtrlEst_h/lds_gaussian_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sys.h # GLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::System Gaussian LDS Type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H #define LDSCTRLEST_LDS_GAUSSIAN_SYS_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; namespace lds { namespace gaussian { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0, data_t r0 = kDefaultR0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; // get methods const Matrix\u0026amp; R() const { return R_; }; // set methods void set_Q(const Matrix\u0026amp; Q) { lds::System::set_Q(Q); do_recurse_Ke_ = true; } void set_R(const Matrix\u0026amp; R) { Reassign(R_, R); do_recurse_Ke_ = true; }; void set_Ke(const Matrix\u0026amp; Ke) { Reassign(Ke_, Ke); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void set_Ke_m(const Matrix\u0026amp; Ke_m) { Reassign(Ke_m_, Ke_m); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void Print(); protected: void h() override { cx_ = C_ * x_; y_ = cx_ + d_; }; Vector h_(Vector x) override { return C_ * x + d_; }; void RecurseKe() override; // Gaussian-output-specific Matrix R_; bool do_recurse_Ke_{}; }; // System } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":62,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian_8h/","title":"ldsCtrlEst_h/lds_gaussian.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian.h # glds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_H #define LDSCTRLEST_LDS_GAUSSIAN_H // namespace #include \u0026#34;lds.h\u0026#34; namespace lds { namespace gaussian { // insert any Gaussian-specific things here... } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":63,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_ctrl.h # PLDS controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. Detailed Description # This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_CTRL_H #define LDSCTRLEST_LDS_POISSON_CTRL_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system type #include \u0026#34;lds_poisson_sys.h\u0026#34; // control type #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace poisson { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); lds::Limit(y_ref_, kYRefLb, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; private: constexpr static const data_t kYRefLb = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":64,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_em.h # PLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitEM PLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\n[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H #define LDSCTRLEST_LDS_POISSON_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override{}; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; data_t NewtonSolveC(); void AnalyticalSolveD(); }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":65,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_ssid.h # PLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. Detailed Description # This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/)).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H #define LDSCTRLEST_LDS_POISSON_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; private: void DecomposeData() override; void CalcCov(); void PoissonToGaussianMoments(); Matrix cov_; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":66,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/","title":"ldsCtrlEst_h/lds_poisson_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit.h # PLDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Fit PLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_H #define LDSCTRLEST_LDS_POISSON_FIT_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // fit #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace poisson { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) : lds::Fit(n_u, n_x, n_y, dt){}; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = exp(C_ * x.col(t) + d_); return y.col(t); }; void set_R(const Matrix\u0026amp; R) override { std::cerr \u0026lt;\u0026lt; \u0026#34;WARNING: Cannot set R (R[0] = \u0026#34; \u0026lt;\u0026lt; R.at(0) \u0026lt;\u0026lt; \u0026#34;). No Gaussian measurement noise in Poisson observation model.\\n\u0026#34;; }; const Matrix\u0026amp; R() const override { return R_; }; }; }; // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":67,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sctrl.h # PLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #include \u0026#34;lds_poisson_ctrl.h\u0026#34; #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace poisson { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); lds::Limit(y_ref_, kYRefLB, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; private: constexpr static data_t kYRefLB = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":68,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/","title":"ldsCtrlEst_h/lds_poisson_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sys.h # PLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::System Poisson System type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SYS_H #define LDSCTRLEST_LDS_POISSON_SYS_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; // needed for Poisson random number generation #include \u0026lt;random\u0026gt; namespace lds { namespace poisson { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; protected: void h() override { cx_ = C_ * x_; y_ = exp(cx_ + d_); diag_y_.diag() = y_; }; Vector h_(Vector x) override { return exp(C_ * x + d_); }; void RecurseKe() override; private: // Poisson-output-specific Matrix diag_y_; std::poisson_distribution\u0026lt;size_t\u0026gt; pd_; }; // System } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":69,"href":"/lds-ctrl-est/docs/api/files/lds__poisson_8h/","title":"ldsCtrlEst_h/lds_poisson.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson.h # plds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_H #define LDSCTRLEST_LDS_POISSON_H #include \u0026#34;lds.h\u0026#34; namespace lds { namespace poisson { // TODO(mfbolus): Not sure if defining these as static here makes the most // sense. Is there a downside to letting multiple poisson System objects share a // common random number generator? static std::random_device rd; static std::mt19937 rng = std::mt19937( rd()); } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":70,"href":"/lds-ctrl-est/docs/api/files/lds__sctrl_8h/","title":"ldsCtrlEst_h/lds_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_sctrl.h # SwitchedController type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SwitchedController SwitchedController Type. Detailed Description # This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SCTRL_H #define LDSCTRLEST_LDS_SCTRL_H #include \u0026#34;lds_ctrl.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; #include \u0026#34;lds_uniform_vecs.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class SwitchedController : public Controller\u0026lt;System\u0026gt; { public: SwitchedController() = default; SwitchedController(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); SwitchedController(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); void Switch(size_t idx, bool do_force_switch = false); void set_Kc(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc) { Kc_list_ = Kc; Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc) { Kc_list_ = std::move(Kc); Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_inty) { Kc_inty_list_ = Kc_inty; Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_inty) { Kc_inty_list_ = std::move(Kc_inty); Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_u) { Kc_u_list_ = Kc_u; Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_u) { Kc_u_list_ = std::move(Kc_u); Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(const UniformVectorList\u0026amp; g) { g_design_list_ = g; g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(UniformVectorList\u0026amp;\u0026amp; g) { g_design_list_ = std::move(g); g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; protected: std::vector\u0026lt;System\u0026gt; systems_; size_t n_sys_{}; size_t idx_{}; // controller gains could be different for each UniformMatrixList\u0026lt;\u0026gt; Kc_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_inty_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_u_list_; // design-phase input gain could also be different UniformVectorList g_design_list_; // TODO(mfbolus): not sure why I need to do this. using Controller\u0026lt;System\u0026gt;::Kc_; using Controller\u0026lt;System\u0026gt;::Kc_inty_; using Controller\u0026lt;System\u0026gt;::Kc_u_; using Controller\u0026lt;System\u0026gt;::g_design_; using Controller\u0026lt;System\u0026gt;::sys_; // using Controller\u0026lt;System\u0026gt;::u_ref_; // using Controller\u0026lt;System\u0026gt;::x_ref_; // using Controller\u0026lt;System\u0026gt;::y_ref_; // using Controller\u0026lt;System\u0026gt;::control_type_; private: void InitVars(); using lds::Controller\u0026lt;System\u0026gt;::set_sys; // using Controller\u0026lt;System\u0026gt;::set_Kc; // using Controller\u0026lt;System\u0026gt;::set_Kc_inty; // using Controller\u0026lt;System\u0026gt;::set_Kc_u; // using Controller\u0026lt;System\u0026gt;::set_g_design; }; template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(systems.at(0), u_lb, u_ub, control_type), systems_(systems) { InitVars(); } template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(System(systems.at(0).n_u(), systems.at(0).n_x(), systems.at(0).n_y(), systems.at(0).dt()), u_lb, u_ub, control_type), systems_(std::move(systems)) { InitVars(); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::InitVars() { n_sys_ = systems_.size(); sys_ = systems_.at(0); Kc_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_)); Kc_inty_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); Kc_u_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); g_design_list_ = UniformVectorList(std::vector\u0026lt;Vector\u0026gt;(n_sys_, g_design_)); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::Switch(size_t idx, bool do_force_switch) { if ((idx == idx_) \u0026amp;\u0026amp; !do_force_switch) { return; // already there. } // put old up and get new one out systems_.at(idx_) = std::move(sys_); sys_ = std::move(systems_.at(idx)); // set the state of this system to that of the previous system // TODO(mfbolus): This will only work as intended if state matrix is the same. // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp. sys_.set_m(systems_.at(idx_).m(), true); sys_.set_x(systems_.at(idx_).x()); // swap controller gains Kc_list_.Swap(Kc_, idx_); Kc_list_.Swap(Kc_, idx); if (control_type_ \u0026amp; kControlTypeIntY) { Kc_inty_list_.Swap(Kc_inty_, idx_); Kc_inty_list_.Swap(Kc_inty_, idx); } if (control_type_ \u0026amp; kControlTypeDeltaU) { Kc_u_list_.Swap(Kc_u_, idx_); Kc_u_list_.Swap(Kc_u_, idx); } g_design_list_.Swap(g_design_, idx_); g_design_list_.Swap(g_design_, idx); idx_ = idx; } // Switch } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":71,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8h/","title":"ldsCtrlEst_h/lds_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_sys.h # LDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::System Linear Dynamical System Type. Detailed Description # This file declares and partially defines the base type for linear dynamical systems ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SYS_H #define LDSCTRLEST_LDS_SYS_H #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class System { public: System() = default; System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); virtual ~System() {} void Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z); virtual const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) = 0; void f(const Vector\u0026amp; u, bool do_add_noise = false) { x_ = A_ * x_ + B_ * (g_ % u) + m_; if (do_add_noise) { x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_); } }; virtual void h() = 0; virtual Vector h_(Vector x) = 0; size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Vector\u0026amp; x() const { return x_; }; const Matrix\u0026amp; P() const { return P_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; P_m() const { return P_m_; }; const Vector\u0026amp; cx() const { return cx_; }; const Vector\u0026amp; y() const { return y_; }; const Vector\u0026amp; x0() const { return x0_; }; const Vector\u0026amp; m0() const { return m0_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; const Matrix\u0026amp; Ke() const { return Ke_; }; const Matrix\u0026amp; Ke_m() const { return Ke_m_; }; const Matrix\u0026amp; Q() { return Q_; }; const Matrix\u0026amp; Q_m() { return Q_m_; }; const Matrix\u0026amp; P0() { return P0_; }; const Matrix\u0026amp; P0_m() { return P0_m_; }; void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_m(const Vector\u0026amp; m, bool do_force_assign = false) { Reassign(m0_, m); if ((!do_adapt_m) || do_force_assign) { Reassign(m_, m); } }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); }; void set_Q_m(const Matrix\u0026amp; Q_m) { Reassign(Q_m_, Q_m); }; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); }; void set_P0_m(const Matrix\u0026amp; P0_m) { Reassign(P0_m_, P0_m); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; void set_x(const Vector\u0026amp; x) { Reassign(x_, x); h(); }; void Reset(); std::vector\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; nstep_pred_block( UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z, size_t n_pred = 1); void Print(); // safe to leave this public and non-const bool do_adapt_m{}; protected: virtual void RecurseKe() = 0; void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); std::size_t n_x_{}; std::size_t n_u_{}; std::size_t n_y_{}; data_t dt_{}; // Signals: Vector x_; Matrix P_; Vector m_; Matrix P_m_; Vector cx_; Vector y_; Vector z_; // Parameters: Vector x0_; Matrix P0_; Vector m0_; Matrix P0_m_; Matrix A_; Matrix B_; Vector g_; Matrix Q_; Matrix Q_m_; Matrix C_; Vector d_; Matrix Ke_; Matrix Ke_m_; }; // System } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":72,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/","title":"ldsCtrlEst_h/lds_uniform_mats.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_mats.h # List of uniformly sized matrices. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformMatrixList Detailed Description # This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H #define LDSCTRLEST_LDS_UNIFORM_MATS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { template \u0026lt;MatrixListFreeDim D = kMatFreeDimNone\u0026gt; class UniformMatrixList : public std::vector\u0026lt;Matrix\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Matrix\u0026gt;::vector; // don\u0026#39;t allow push_back to be used since it doesn\u0026#39;t check dims using std::vector\u0026lt;Matrix\u0026gt;::push_back; public: using std::vector\u0026lt;Matrix\u0026gt;::operator=; using std::vector\u0026lt;Matrix\u0026gt;::operator[]; using std::vector\u0026lt;Matrix\u0026gt;::begin; using std::vector\u0026lt;Matrix\u0026gt;::end; using std::vector\u0026lt;Matrix\u0026gt;::size; using std::vector\u0026lt;Matrix\u0026gt;::at; UniformMatrixList() = default; explicit UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); explicit UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; ~UniformMatrixList() = default; const std::array\u0026lt;size_t, 2\u0026gt;\u0026amp; dim(size_t n = 0) const { return dim_.at(n); } size_t size() { return std::vector\u0026lt;Matrix\u0026gt;::size(); }; const Matrix\u0026amp; at(size_t n) { return std::vector\u0026lt;Matrix\u0026gt;::at(n); }; void Swap(Matrix\u0026amp; that, size_t n); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; void append(const Matrix\u0026amp; mat); private: void CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim); std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt; dim_; }; template \u0026lt;MatrixListFreeDim D\u0026gt; inline void UniformMatrixList\u0026lt;D\u0026gt;::Swap(Matrix\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.n_cols); } if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap // not moving, since it causes memory issues. // so this method isn\u0026#39;t a memory-saver as designed for now Matrix tmp = (*this)[n]; (*this)[n] = that; that = tmp; if (D == kMatFreeDim1) { this-\u0026gt;dim_[n][0] = (*this)[n].n_rows; } if (D == kMatFreeDim2) { this-\u0026gt;dim_[n][1] = (*this)[n].n_cols; } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::append(const Matrix\u0026amp; mat) { std::array\u0026lt;size_t, 2\u0026gt; dim({mat.n_rows, mat.n_cols}); CheckDimensions(dim); std::vector\u0026lt;Matrix\u0026gt;::push_back(mat); dim_.push_back(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) { // make sure dim_ vector is initialized if (dim_.empty()) { dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(that.size(), {0, 0}); } // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices\u0026#34;; throw std::runtime_error(ss.str()); } // if dimensions a not zero and do not match, skip move with error message. bool dims_nonzero = true; for (auto d : dim_) { if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; d[0] \u0026lt; 1) { dims_nonzero = false; break; } if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; d[1] \u0026lt; 1) { dims_nonzero = false; break; } } if (dims_nonzero) { bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); } if (!does_match) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrices of size \u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; dim_[k] = that.dim(k); } return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. bool dims_nonzero = true; for (auto d : dim_) { // if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; (d[0] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; (d[1] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // } // // if (dims_nonzero) { // bool does_match = true; // if (!(D == kMatFreeDim1)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); // } // // if (!(D == kMatFreeDim2)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); // } // // if (!does_match) { // this-\u0026gt;at(0).print(\u0026#34;this[0] = \u0026#34;); // that.at(0).print(\u0026#34;that[0] = \u0026#34;); // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformMatrixList element of size (\u0026#34; \u0026lt;\u0026lt; // that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols \u0026lt;\u0026lt; \u0026#34;) for an // element of size (\u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34;). // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Matrix\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(std::move(mats)) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) : vector(that) { (*this) = that; } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { std::array\u0026lt;size_t, 2\u0026gt; dim_k({this-\u0026gt;at(k).n_rows, this-\u0026gt;at(k).n_cols}); dim_.push_back(dim_k); } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim) { // change behavior based on free dim D if ((dim[0] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim1)) { dim[0] = this-\u0026gt;at(0).n_rows; } if ((dim[1] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim2)) { dim[1] = this-\u0026gt;at(0).n_cols; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Matrix\u0026amp; mat : *this) { if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_rows == dim[0]); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_cols == dim[1]); } if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(this-\u0026gt;size(), dim); for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { dim_[k][0] = (*this)[k].n_rows; dim_[k][1] = (*this)[k].n_cols; } } } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":73,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/","title":"ldsCtrlEst_h/lds_uniform_systems.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_systems.h # List of uniformly sized Systems. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformSystemList Detailed Description # This file provides a container for uniformly sized Systems.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector // namespace #include \u0026#34;lds.h\u0026#34; // System type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class UniformSystemList : public std::vector\u0026lt;System\u0026gt; { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;System\u0026gt;::vector; using std::vector\u0026lt;System\u0026gt;::operator=; using std::vector\u0026lt;System\u0026gt;::operator[]; using std::vector\u0026lt;System\u0026gt;::at; using std::vector\u0026lt;System\u0026gt;::begin; using std::vector\u0026lt;System\u0026gt;::end; using std::vector\u0026lt;System\u0026gt;::size; public: UniformSystemList() = default; explicit UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); explicit UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(const UniformSystemList\u0026amp; that); UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept; ~UniformSystemList() = default; const std::array\u0026lt;size_t, 3\u0026gt;\u0026amp; dim() const { return dim_; } size_t size() { return std::vector\u0026lt;System\u0026gt;::size(); }; const System\u0026amp; at(size_t n) { return std::vector\u0026lt;System\u0026gt;::at(n); }; void Swap(System\u0026amp; that, size_t n); UniformSystemList\u0026amp; operator=(const UniformSystemList\u0026amp; that); UniformSystemList\u0026amp; operator=(UniformSystemList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim); std::array\u0026lt;size_t, 3\u0026gt; dim_{}; }; template \u0026lt;typename System\u0026gt; inline void UniformSystemList\u0026lt;System\u0026gt;::Swap(System\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformSystemList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = (dim_[0] == that.n_u()) \u0026amp;\u0026amp; (dim_[1] == that.n_x()) \u0026amp;\u0026amp; (dim_[2] == that.n_y()); if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformSystemList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap System tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( const UniformSystemList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_[0] + dim_[1] + dim_[2]) { std::array\u0026lt;size_t, 3\u0026gt; other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign systems of size \u0026#34; \u0026lt;\u0026lt; dim_[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2] \u0026lt;\u0026lt; \u0026#34; with systems of size \u0026#34; \u0026lt;\u0026lt; other_dim[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other_dim[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2]; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( UniformSystemList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. if (dim_[0] + dim_[1] + dim_[2]) { // bool does_match = (dim_[0] == that.at(0).n_u()) \u0026amp;\u0026amp; // (dim_[1] == that.at(0).n_x()) \u0026amp;\u0026amp; // (dim_[2] == that.at(0).n_y()); // if (!does_match) { // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformSystemList element for an element of \u0026#34; // \u0026#34;different size. Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;System\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(std::move(systems)) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList( std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const UniformSystemList\u0026amp; that) : std::vector\u0026lt;System\u0026gt;(that) { (*this) = that; } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept : std::vector\u0026lt;System\u0026gt;(std::move(that)) { this-\u0026gt;dim_[0] = this-\u0026gt;at(0).n_u(); this-\u0026gt;dim_[1] = this-\u0026gt;at(0).n_x(); this-\u0026gt;dim_[2] = this-\u0026gt;at(0).n_y(); } template \u0026lt;typename System\u0026gt; void UniformSystemList\u0026lt;System\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim) { if (dim[0] + dim[1] + dim[2]) { dim_ = dim; } else { dim_[0] = this-\u0026gt;at(0).n_u(); dim_[1] = this-\u0026gt;at(0).n_x(); dim_[2] = this-\u0026gt;at(0).n_y(); } // make sure dimensiolaties are all uniform bool does_match(true); for (const System\u0026amp; sys : *this) { does_match = does_match \u0026amp;\u0026amp; (sys.n_u() == dim_[0]); does_match = does_match \u0026amp;\u0026amp; (sys.n_x() == dim_[1]); does_match = does_match \u0026amp;\u0026amp; (sys.n_y() == dim_[2]); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input systems are not uniform.\u0026#34;); } } } } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":74,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/","title":"ldsCtrlEst_h/lds_uniform_vecs.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_vecs.h # List of uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformVectorList Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H #define LDSCTRLEST_LDS_UNIFORM_VECS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { class UniformVectorList : public std::vector\u0026lt;Vector\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Vector\u0026gt;::vector; using std::vector\u0026lt;Vector\u0026gt;::operator=; using std::vector\u0026lt;Vector\u0026gt;::operator[]; using std::vector\u0026lt;Vector\u0026gt;::at; using std::vector\u0026lt;Vector\u0026gt;::begin; using std::vector\u0026lt;Vector\u0026gt;::end; using std::vector\u0026lt;Vector\u0026gt;::size; public: UniformVectorList() = default; explicit UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim = 0); explicit UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim = 0); UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim = 0); UniformVectorList(const UniformVectorList\u0026amp; that); UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept; ~UniformVectorList() = default; size_t dim() const { return dim_; } size_t size() { return std::vector\u0026lt;Vector\u0026gt;::size(); }; const Vector\u0026amp; at(size_t n) { return std::vector\u0026lt;Vector\u0026gt;::at(n); }; void Swap(Vector\u0026amp; that, size_t n); UniformVectorList\u0026amp; operator=(const UniformVectorList\u0026amp; that); UniformVectorList\u0026amp; operator=(UniformVectorList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(size_t dim); size_t dim_{}; }; inline void UniformVectorList::Swap(Vector\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = dim_ == that.n_elem; if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap Vector tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( const UniformVectorList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_) { size_t other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ \u0026lt;\u0026lt; \u0026#34; with vectors of size \u0026#34; \u0026lt;\u0026lt; other_dim; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( UniformVectorList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors. Skipping.\\n\u0026#34;; // return (*this); // } // // if (dim_) { // size_t other_dim(that.dim()); // if (dim_ != other_dim) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ // \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; other_dim \u0026lt;\u0026lt; \u0026#34;. // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Vector\u0026gt;::operator=(std::move(that)); return (*this); } } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":75,"href":"/lds-ctrl-est/docs/api/files/lds_8h/","title":"ldsCtrlEst_h/lds.h","section":"Files","content":" ldsCtrlEst_h/lds.h # lds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)) observations.\nSource code # //===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_H #define LDSCTRLEST_LDS_H // #ifndef LDSCTRLEST // #include \u0026lt;ldsCtrlEst\u0026gt; // #endif #include \u0026lt;armadillo\u0026gt; namespace lds { using data_t = double; // may change to float (but breaks mex functions) using Vector = arma::Col\u0026lt;data_t\u0026gt;; using Matrix = arma::Mat\u0026lt;data_t\u0026gt;; using Cube = arma::Cube\u0026lt;data_t\u0026gt;; using View = arma::subview\u0026lt;data_t\u0026gt;; namespace fill = arma::fill; static const std::size_t kControlTypeDeltaU = 0x1; static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); static const data_t kPi = arma::datum::pi; static const data_t kDefaultP0 = 1e-6; static const data_t kDefaultQ0 = 1e-6; static const data_t kDefaultR0 = 1e-2; enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA }; enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2 }; // TODO(mfbolus): for SwitchedController, may want systems to have differing // numbers of states. Use this enum as template parameter? // enum SystemListFreeDim { // kSysFreeDimNone, // kSysFreeDimX ///\u0026lt; allow state dim (x) of systems in list to be hetero // }; // place hard limits on contents of vecors/mats void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub); void Limit(Vector\u0026amp; x, data_t lb, data_t ub); void Limit(Matrix\u0026amp; x, data_t lb, data_t ub); // in-place assign that errs if there are dimension mismatches: void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); // TODO(mfbolus): this is a fudge, but for some reason, cov mats often going // numerically asymm. void ForceSymPD(Matrix\u0026amp; X); void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min = 0); void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X); Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B); inline void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Vector\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Matrix\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if (other.n_elem != some.n_elem) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vector of size \u0026#34; \u0026lt;\u0026lt; some.n_elem \u0026lt;\u0026lt; \u0026#34; with vector of size \u0026#34; \u0026lt;\u0026lt; other.n_elem \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } inline void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrix of size \u0026#34; \u0026lt;\u0026lt; some.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; some.n_cols \u0026lt;\u0026lt; \u0026#34; with matrix of size \u0026#34; \u0026lt;\u0026lt; other.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other.n_cols \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":76,"href":"/lds-ctrl-est/docs/api/files/mex__c__util_8h/","title":"ldsCtrlEst_h/mex_c_util.h","section":"Files","content":" ldsCtrlEst_h/mex_c_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) More\u0026hellip;\nNamespaces # Name armamexc arma/mex interface using Matlab C API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab/Octave\u0026rsquo;s C mex API.\nSource code # //===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXC_UTIL_H #define LDSCTRLEST_MEXC_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.h\u0026#34; // // If Matlab_FOUND, include matrix.h. // // (Octave does not need/have it.) // #ifdef Matlab_FOUND // #include \u0026#34;matrix.h\u0026#34; // #endif namespace armamexc { template \u0026lt;class T\u0026gt; inline auto m2T_scalar(const mxArray *matlab_scalar) -\u0026gt; T { if (mxGetData(matlab_scalar)) { return static_cast\u0026lt;T\u0026gt;(mxGetScalar(matlab_scalar)); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return 0; } template \u0026lt;class T\u0026gt; inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false, bool strict = true) -\u0026gt; arma::Mat\u0026lt;T\u0026gt; { if (mxGetData(matlab_mat)) { const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat); if (n_dim == 2) { return arma::Mat\u0026lt;T\u0026gt;(static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)), mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem, strict); } mexErrMsgTxt(\u0026#34;Number of dimensions must be 2.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } // TODO(mfbolus): make these templated. template \u0026lt;typename T\u0026gt; inline auto a2m_mat(arma::Mat\u0026lt;T\u0026gt; const \u0026amp;arma_mat) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_mat.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Mat.\u0026#34;); return nullptr; } template \u0026lt;typename T\u0026gt; inline auto a2m_vec(arma::Col\u0026lt;T\u0026gt; const \u0026amp;arma_vec) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_vec.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Col.\u0026#34;); return nullptr; } } // namespace armamexc #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":77,"href":"/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/","title":"ldsCtrlEst_h/mex_cpp_util.h","section":"Files","content":" ldsCtrlEst_h/mex_cpp_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) More\u0026hellip;\nNamespaces # Name armamexcpp arma/mex interface using Matlab C++ API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab\u0026rsquo;s C++ mex API.\nSource code # //===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXCPP_UTIL_H #define LDSCTRLEST_MEXCPP_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.hpp\u0026#34; #include \u0026#34;mexAdapter.hpp\u0026#34; namespace armamexcpp { template \u0026lt;class T\u0026gt; std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; m2a_cellmat(matlab::data::CellArray\u0026amp; matlab_cell) { size_t n_cells = matlab_cell.getNumberOfElements(); std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; arma_mat(n_cells, arma::Mat\u0026lt;T\u0026gt;(1, 1, arma::fill::zeros)); for (size_t k = 0; k \u0026lt; n_cells; k++) { matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = matlab_cell[k]; auto dims = matlab_mat.getDimensions(); arma_mat[k] = arma::Mat\u0026lt;T\u0026gt;(matlab_mat.release().get(), dims[0], dims[1]); } return arma_mat; }; template \u0026lt;class T\u0026gt; std::vector\u0026lt;T\u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt;T\u0026gt;\u0026amp; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); T* ptr = matlab_array.release().get(); std::vector\u0026lt;T\u0026gt; vec(ptr, ptr + n_elem); return vec; }; template \u0026lt;class T\u0026gt; arma::Col\u0026lt;T\u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); // T* ptr = matlab_array.release().get(); // arma::Col\u0026lt;T\u0026gt; vec(ptr, n_elem); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? arma::Col\u0026lt;T\u0026gt; vec(n_elem, arma::fill::zeros); for (size_t k = 0; k \u0026lt; n_elem; k++) { vec[k] = matlab_array[k]; } return vec; }; template \u0026lt;class T\u0026gt; arma::Mat\u0026lt;T\u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { // ArrayDimensions == std::vector\u0026lt;size_t\u0026gt; auto dims = matlab_array.getDimensions(); // T* ptr = matlab_array.release().get(); // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false) // arma::Mat\u0026lt;T\u0026gt; mat(ptr, dims[0], dims[1]); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? // // armadillo and matlab both use column-major ordering, so this should work: size_t n_elem = dims[0] * dims[1]; arma::Mat\u0026lt;T\u0026gt; mat(dims[0], dims[1], arma::fill::zeros); size_t k(0); for (auto m: matlab_array) { mat[k] = m; k++; } return mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_mat(const arma::Mat\u0026lt;T\u0026gt;\u0026amp; arma_mat, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(), arma_mat.memptr() + arma_mat.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_vec(const arma::Col\u0026lt;T\u0026gt;\u0026amp; arma_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;({arma_vec.n_elem, 1}, arma_vec.memptr(), arma_vec.memptr() + arma_vec.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; s2m_vec(const std::vector\u0026lt;T\u0026gt;\u0026amp; std_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size()); return matlab_mat; }; } // namespace armamexcpp #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":78,"href":"/lds-ctrl-est/docs/terminology/model/","title":"Models","section":"LDS C+E Documentation","content":" Model Definitions # This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form: \\[\\mathbf{x}_{t\u0026#43;1} = f\\left( \\mathbf{x}_{t}, \\mathbf{v}_{t} \\right) = \\mathbf{A} \\mathbf{x}_{t} \u0026#43; \\mathbf{B} \\mathbf{v}_{t} \u0026#43; \\mathbf{m}_{t} \u0026#43; \\mathbf{w}_{t}\\] \\[\\mathbf{y}_{t} = h\\left( \\mathbf{x}_{t} \\right)\\] t : time index x : system state v = g%u : input (e.g., in physical units used for model fit) u : control signal sent to actuator (e.g., in Volts) y : system output m : process disturbance w ~ N(0, Q) : process noise/disturbance A : state matrix B : input coupling matrix g : input gain (e.g., for converting to control signal actuator voltage) n.b., assumes this conversion is linear Q : process noise covariance % : element-wise multiplication LDS with Gaussian Observations # For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.\n\\[\\mathbf{y}_{t} = \\mathbf{C} \\mathbf{x}_{t} \u0026#43; \\mathbf{d}\\] \\[\\mathbf{z}_{t} \\sim \\mathcal{N}\\left(\\mathbf{y}_{t} , \\mathbf{R} \\right)\\] z : measurement C : output matrix d : output bias R : measurement noise covariance LDS with Poisson Observations # For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.\n\\[y_{t}^{i} = \\exp \\left(\\mathbf{c}^i \\mathbf{x}_{t} \u0026#43; d^i\\right)\\] \\[z_{t}^i \\sim \\rm{Poisson} \\left(y_{t}^i \\right)\\] i : output index z : measurement (count data) c : i^th row of output matrix (C) d : output bias Model Predictive Control (MPC) # Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.\nIn the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.\n"},{"id":79,"href":"/lds-ctrl-est/docs/api/modules/","title":"Modules","section":"LDS C+E Documentation","content":" Modules # Control Mode Bit Masks provides fill types for constructing new armadillo vectors, matrices\nDefaults\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":80,"href":"/lds-ctrl-est/docs/api/namespaces/","title":"Namespaces","section":"LDS C+E Documentation","content":" Namespaces # armamexc arma/mex interface using Matlab C API\narmamexcpp arma/mex interface using Matlab C++ API\nlds::gaussian Linear Dynamical Systems with Gaussian observations.\nlds::poisson Linear Dynamical Systems with Poisson observations.\nstd\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":81,"href":"/lds-ctrl-est/docs/api/pages/","title":"Pages","section":"LDS C+E Documentation","content":" Pages # Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":82,"href":"/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/","title":"PLDS State Estimation","section":"LDS C+E Examples","content":" PLDS State Estimation Tutorial # This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[x_{t\u0026#43;1} = x_t \u0026#43; w_t\\] \\[y_{t} = \\exp\\left(x_t\\right)\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state Finally, assign the parameters using corresponding set-methods.\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); Creating the estimator # Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. To ensure robust estimates, adaptively re-estimate the process disturbance.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); Simulating estimation # In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.\n// Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.\nWith this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.\n"},{"id":83,"href":"/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/","title":"PLDS Switched Control","section":"LDS C+E Examples","content":" PLDS Switched Control Tutorial # This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.\nIn the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating the simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); The system\u0026rsquo;s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 Initially, the system will be in \u0026ldquo;mode\u0026rdquo; 1, where B = b1.\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.\nCreating the controller # Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.\nSimilar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.\nMoreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.\nPutting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying systems: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nNow that the SwitchedController is instantiated, assign its parameters.\n// Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).\nThe control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.\n// Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system\u0026rsquo;s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.\nExample simulation result # Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller\u0026rsquo;s online estimates of the output and state are shown in purple.\nNote that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.\n"},{"id":84,"href":"/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/","title":"src","section":"Files","content":" src # Files # Name src/lds.cpp misc lds namespace functions src/lds_gaussian_sys.cpp GLDS base type. src/lds_poisson_sys.cpp PLDS base type. src/lds_sys.cpp LDS base type. src/lds_uniform_vecs.cpp Uniformly sized vectors. Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":85,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/","title":"src/lds_gaussian_sys.cpp","section":"Files","content":" src/lds_gaussian_sys.cpp # GLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_gaussian_sys.h\u0026gt; lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0, data_t r0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { R_.zeros(n_y, n_y); R_.diag().fill(r0); do_recurse_Ke_=true; }; // recursively estimate Ke void lds::gaussian::System::RecurseKe() { if (!do_recurse_Ke_) { return; } // predict covariance P_ = A_ * P_ * A_.t() + Q_; // calc Kalman gain Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_); // update covariance // Reference: Ghahramani et Hinton (1996) P_ = P_ - Ke_ * C_ * P_; if (do_adapt_m) { P_m_ += Q_m_; // A_m = I (i.e., random walk) Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_); P_m_ = P_m_ - Ke_m_ * C_ * P_m_; } } // Simulate const lds::Vector\u0026amp; lds::gaussian::System::Simulate(const Vector\u0026amp; u_tm1){ f(u_tm1, true);//simulate dynamics with noise added h();//output z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure return z_; } void lds::gaussian::System::Print() { lds::System::Print(); std::cout \u0026lt;\u0026lt; \u0026#34;R: \\n\u0026#34; \u0026lt;\u0026lt; R_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":86,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/","title":"src/lds_poisson_sys.cpp","section":"Files","content":" src/lds_poisson_sys.cpp # PLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_poisson_sys.h\u0026gt; lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { diag_y_ = diagmat(y_); pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(0); }; // Correct: Given measurement (z) and current input (u), update estimate of the // state, covar, output. // // see Eden et al. 2004 void lds::poisson::System::RecurseKe() { // predict covariance P_ = A_ * P_ * A_.t() + Q_; // update cov P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_); Ke_ = P_ * C_.t(); if (do_adapt_m) { P_m_ += Q_m_; // predict (A_m = I) P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_); // update Ke_m_ = P_m_ * C_.t(); } } // Simulate Measurement: z ~ Poisson(y) const lds::Vector\u0026amp; lds::poisson::System::Simulate(const Vector\u0026amp; u_tm1) { f(u_tm1, true); // simulate dynamics with noise added h(); // output z_.zeros(); for (std::size_t k = 0; k \u0026lt; n_y_; k++) { // construct a Poisson distribution object with mean y[k] pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(y_[k]); // pull random sample from this distribution z_[k] = pd_(rng); } return z_; } // ******************* SYS_T ******************* Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":87,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8cpp/","title":"src/lds_sys.cpp","section":"Files","content":" src/lds_sys.cpp # LDS base type. More\u0026hellip;\nDetailed Description # This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- lds_sys.cpp - LDS -------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_sys.h\u0026gt; #include \u0026lt;vector\u0026gt; lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) { InitVars(p0, q0); } void lds::System::InitVars(data_t p0, data_t q0) { // initial conditions. x0_ = Vector(n_x_, fill::zeros); // includes bias (nY) and g (nU) P0_ = p0 * Matrix(n_x_, n_x_, fill::eye); m0_ = x0_; P0_m_ = P0_; // signals x_ = x0_; P_ = P0_; m_ = m0_; P_m_ = P0_m_; y_ = Vector(n_y_, fill::zeros); cx_ = Vector(n_y_, fill::zeros); z_ = Vector(n_y_, fill::zeros); // By default, random walk where each state is independent // In this way, provides independent estimates of rate per channel of output. A_ = Matrix(n_x_, n_x_, fill::eye); B_ = Matrix(n_x_, n_u_, fill::zeros); g_ = Vector(n_u_, fill::ones); Q_ = q0 * Matrix(n_x_, n_x_, fill::eye); Q_m_ = Q_; C_ = Matrix(n_y_, n_x_, fill::eye); // each state will map to an output by d_ = Vector(n_y_, fill::zeros); Ke_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain. Ke_m_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain for m adaptation. do_adapt_m = false; } // Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state // and update estimate of the state, covar, output using Kalman filter void lds::System::Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z_t) { // predict mean f(u_tm1); // dynamics h(); // output // recursively calculate esimator gains (or just keep existing values) // (also predicts+updates estimate covariance) RecurseKe(); // update x_ += Ke_ * (z_t - y_); if (do_adapt_m) { m_ += Ke_m_ * (z_t - y_); // adaptively estimating disturbance } // With new state, estimate output. h(); // --\u0026gt; posterior } void lds::System::Reset() { // reset to initial conditions x_ = x0_; // mean P_ = P0_; // cov of state estimate m_ = m0_; // process disturbance P_m_ = P0_m_; // cov of disturbance estimate h(); } std::vector\u0026lt;lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt;\u0026gt; lds::System::nstep_pred_block(lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; u, lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; z, size_t n_pred) { lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_filt; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_pred; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; y_pred; for (size_t k = 0; k \u0026lt; u.size(); k++) { Reset(); size_t n_t = arma::size(u[k])[1]; Matrix x_filt_k(n_x_, n_t, fill::zeros); Matrix x_pred_k(n_x_, n_t - n_pred, fill::zeros); Matrix y_pred_k(n_y_, n_t - n_pred, fill::zeros); for (size_t t = 0; t \u0026lt; n_t - n_pred; t++) { Vector x_pred_ahead = x_; for (size_t t_u = t; t_u \u0026lt; t + n_pred; t_u++) { x_pred_ahead = A_ * x_pred_ahead + B_ * u[k].col(t_u); } x_pred_k.col(t) = x_pred_ahead; y_pred_k.col(t) = h_(x_pred_ahead); if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; // given previous measurment } for (size_t t = n_t - n_pred; t \u0026lt; n_t; t++) { if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; } x_filt.append(x_filt_k); x_pred.append(x_pred_k); y_pred.append(y_pred_k); } return {x_filt, x_pred, y_pred}; } void lds::System::Print() { std::cout \u0026lt;\u0026lt; \u0026#34;\\n ********** SYSTEM ********** \\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;x: \\n\u0026#34; \u0026lt;\u0026lt; x_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;P: \\n\u0026#34; \u0026lt;\u0026lt; P_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;A: \\n\u0026#34; \u0026lt;\u0026lt; A_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B: \\n\u0026#34; \u0026lt;\u0026lt; B_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;g: \\n\u0026#34; \u0026lt;\u0026lt; g_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;m: \\n\u0026#34; \u0026lt;\u0026lt; m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q: \\n\u0026#34; \u0026lt;\u0026lt; Q_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q_m: \\n\u0026#34; \u0026lt;\u0026lt; Q_m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d: \\n\u0026#34; \u0026lt;\u0026lt; d_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;C: \\n\u0026#34; \u0026lt;\u0026lt; C_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;y: \\n\u0026#34; \u0026lt;\u0026lt; y_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } //******************* SYS_T ******************* Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":88,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/","title":"src/lds_uniform_vecs.cpp","section":"Files","content":" src/lds_uniform_vecs.cpp # Uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_uniform_vecs.h\u0026gt; namespace lds { UniformVectorList::UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); } UniformVectorList::UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim) : vector(std::move(vecs)) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(const UniformVectorList\u0026amp; that) : vector(that) { (*this) = that; } UniformVectorList::UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { this-\u0026gt;dim_ = this-\u0026gt;at(0).n_elem; } void UniformVectorList::CheckDimensions(size_t dim) { if (dim) { dim_ = dim; } else { dim_ = this-\u0026gt;at(0).n_elem; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Vector\u0026amp; vec : *this) { does_match = does_match \u0026amp;\u0026amp; (vec.n_elem == dim_); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } } } // namespace lds Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":89,"href":"/lds-ctrl-est/docs/api/files/lds_8cpp/","title":"src/lds.cpp","section":"Files","content":" src/lds.cpp # misc lds namespace functions More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file implements miscellaneous lds namespace functions not bound to a class.\nSource code # //===-- lds.cpp - LDS -----------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds.h\u0026gt; // insert any necessary function definitions here. namespace lds { void ForceSymPD(Matrix\u0026amp; X) { if (X.is_sympd() || !X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; // for eigenval decomp bool did_succeed(true); Vector d; Matrix u; // see first method (which may not be ideal): // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/ size_t k(1); bool is_sympd = X.is_sympd(); Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye); while (!is_sympd) { if (k \u0026gt; 100) { did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); data_t min_eig = arma::min(d); std::cerr \u0026lt;\u0026lt; \u0026#34;After multiple iterations, min eigen val = \u0026#34; \u0026lt;\u0026lt; min_eig \u0026lt;\u0026lt; \u0026#34;.\\n\u0026#34;; throw std::runtime_error( \u0026#34;Failed to make matrix symmetric positive definite.\u0026#34;); return; } // Limit(d, arma::eps(0), kInf); // force to be positive... // Matrix d_diag = arma::diagmat(d); // X = u * d_diag * u.t(); did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymPD failed.\u0026#34;); } data_t min_eig = arma::min(d); X += id * abs(min_eig) + arma::datum::eps; // make sure symm: X = (X + X.t()) / 2; // double check eigenvals positive after symmetrizing: arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); min_eig = arma::min(d); is_sympd = min_eig \u0026gt; 0; k++; } } void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min) { if (!X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; bool did_succeed(true); Vector d; Matrix u; did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymMinEig failed.\u0026#34;); } Limit(d, eig_min + arma::eps(eig_min), kInf); // enforce lower bound Matrix d_diag = arma::diagmat(d); X = u * d_diag * u.t(); // double check symmetric X = (X + X.t()) / 2; } void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X) { bool did_succeed(true); did_succeed = arma::qr_econ(Qt, L, X.t()); if (!did_succeed) { throw std::runtime_error(\u0026#34;LQ decomposition failed.\u0026#34;); } arma::inplace_trans(L); arma::inplace_trans(Qt); } Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B) { // subtract out mean auto m_a = arma::mean(A, 1); Matrix a0 = A; a0.each_col() -= m_a; auto m_b = arma::mean(B, 1); Matrix b0 = B; b0.each_col() -= m_b; Matrix cov = a0 * b0.t() / a0.n_cols; return cov; } } // namespace lds Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":90,"href":"/lds-ctrl-est/docs/api/namespaces/namespacestd/","title":"std","section":"Namespaces","content":" std # Updated on 5 March 2025 at 16:32:33 EST\n"}]
\ No newline at end of file
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+[{"id":0,"href":"/docs/","title":"LDS C+E Documentation","section":"LDS Control and Estimation","content":" LDS Control \u0026amp; Estimation Documentation # "},{"id":1,"href":"/docs/tutorials/","title":"LDS C+E Examples","section":"LDS C+E Documentation","content":" Examples # "},{"id":2,"href":"/docs/api/","title":"API Reference","section":"LDS C+E Documentation","content":" API Reference # The API documentation is organized into the following sections:\nClasses - Documentation for all classes Namespaces - Documentation for all namespaces Files - Documentation for all files Modules - Documentation for all modules Examples - Documentation for all examples "},{"id":3,"href":"/acknowledgements/","title":"Acknowledgements","section":"LDS Control and Estimation","content":" Acknowledgements # Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.\n"},{"id":4,"href":"/docs/getting-started/getting-started/","title":"Getting Started","section":"LDS C+E Documentation","content":" Getting Started # This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.\nldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.\nTested Configurations # Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the RelWithDebInfo build type in the CMake configure command (-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo):\nUbuntu 18.04 with GCC 7.5 compiler macOS 11 (Big Sur) with Apple Clang 12 compiler Windows 10 with Visual Studio 16.11 (2019 release) and Clang 12 compiler That being said, if you want to debug a build for a single platform, here are some things you can try:\nUse different compilers (or even different versions of a single compiler) Use different versions of vcpkg (which you can control by checking out a different commit in the vcpkg submodule) Mac Pre-requisities # Xcode Command Line Tools will get you clang, gcc, make, and git:\nxcode-select --install Homebrew is \u0026ldquo;The Missing Package Manager for macOS\u0026rdquo; which will make installing lots of things easy. Install like this:\n/bin/bash -c \u0026#34;$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\u0026#34; You can then use it to install CMake, gfortran, and pkg-config:\nbrew install cmake gfortran pkg-config Linux Pre-requisites # You\u0026rsquo;ll need Git, CMake, GCC, gfortran, etc.\nsudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential ninja-build Windows Installation # Look here for Windows-specific instructions.\nDownloading the Library # First, clone the repository along with submodules:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est\rgit submodule update --init Compilation + Installation # Now generate the cache and build using your IDE or from the command line as follows.\nmkdir build \u0026amp;\u0026amp; cd build\rcmake ..\rcmake --build . The first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.\nIf you want to use vcpkg set up somewhere besides this repo\u0026rsquo;s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE\u0026rsquo;s settings.\nYou can verify the build is working by running ctest from the build folder, which runs all the example scripts.\nOptions # This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\nLDSCTRLEST_BUILD_EXAMPLES : [default=ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake to configure/build the library.\nFor basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build cmake --build #build the project sudo make install #[optional] installs to default location (OS-specific) To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location cmake --build #build the project make install #install to /your/install/prefix To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\nLD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.\nPython bindings package ldsctrlest # With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython3_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn\u0026rsquo;t use an undesired version. That environment needs to have NumPy installed.\ncmake --build . --target python_modules The bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory (pip install pytest matplotlib first if you need to).\nSee python/ldsctrlest/README.md for usage details.\nAlso, beware that a single build will probably not work for both the standalone library and the Python package, since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.\nCommon issues # \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.\nexport CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix vcpkg fails on configuration Try running ./bootstrap-vcpkg from the vcpkg folder and try again. If that doesn\u0026rsquo;t work, try updating vcpkg to a newer version (in the source control tab, click on the commit hash by the vcpkg repo then select from the dropdown) and running boostsrap-vcpkg again. You can also try upgrading your system (e.g., apt update, apt upgrade).\nCould not find Python3 (missing: Python3_NumPy_INCLUDE_DIRS NumPy)\nMake sure NumPy is installed in the Python environment you specified. If CMake still can\u0026rsquo;t find it, you may need to tell CMake exactly where to find it by adding an argument to the configure command: -DPython3_NumPy_INCLUDE_DIR=.... You can find that location like this: python -c 'import numpy; print(numpy.get_include())'\n"},{"id":5,"href":"/docs/getting-started/windows/","title":"Windows","section":"LDS C+E Documentation","content":" Windows Installation # Windows Pre-requisites # Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:\nSet-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time iwr get.scoop.sh | Invoke-Expression Install Git and CMake if you don\u0026rsquo;t already have them:\nscoop install git cmake If that didn\u0026rsquo;t work, follow more detailed instructions here.\nThe easiest way to compile C++ project on Windows is with Visual Studio\u0026rsquo;s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on \u0026ldquo;Desktop development with C++.\u0026rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the \u0026ldquo;Installation details\u0026rdquo; sidebar under optional features.\nAnd the easiest way to use Visual Studio\u0026rsquo;s build tools is with VS Code, along with the CMake Tools extension. Install them and you should be ready to go.\nDownloading the Library # First, clone the repository, either from VS Code or the command line:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est You\u0026rsquo;ll need to initialize the submodules from the command line after the repo is cloned:\ngit submodule update --init Installation # When you open the folder in VS Code, you will like be prompted by the CMake Tools extension to configure the project. Make sure you select the kit (you\u0026rsquo;ll be prompted when you configure\u0026ndash;else there\u0026rsquo;s an icon in the bar on the bottom of the window or type Ctrl+Shift+P, then \u0026ldquo;cmake select kit\u0026rdquo;). Choose Clang [latest version] with GNU CLI ... amd64 assuming you are running a 64-bit OS. (MSVC may work okay too if you don\u0026rsquo;t need to build Python bindings.)\nFollow along with the \u0026ldquo;Getting Started\u0026rdquo; instructions, but where you see config options specified as -DLDSCTREST_BUILD_STATIC=OFF or -DPython3_ROOT_DIR=..., you will enter those in settings: open with Ctrl+,, click \u0026ldquo;workspace\u0026rdquo;, then search for \u0026ldquo;CMake: Configure Args\u0026rdquo; and enter each of your desired arguments as a separate item.\nTo configure, use Ctrl+Shift+P and search for the \u0026ldquo;CMake: Configure\u0026rdquo; command. To build, click the \u0026ldquo;Build\u0026rdquo; button on the bottom bar. Then click the \u0026ldquo;CTest\u0026rdquo; button to run the example scripts.\nConsiderations # Development on Windows has been more prone to bugs than on Unix systems, so if you encounter many problems, consider switching—WSL (Windows Subsystem for Linux) is a good option for Windows users who don\u0026rsquo;t want to work on a different machine.\nCompilation has been successfully tested in VS Code using the following kit, using the \u0026ldquo;RelWithDebInfo\u0026rdquo; config:\nClang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64) Troubleshooting # The build appears to work, but tests fail with code 0xc0000135 OR \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; Have you installed the library? In VS Code, use Shift+F7 to build a specific target, in this case INSTALL. If that doesn\u0026rsquo;t solve your problem, you will likely need to add the build or install folder to your PATH environment variable, which you can do using the settings GUI (search for \u0026ldquo;Edit the system environment variables\u0026rdquo;).\nOn Windows, \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted. Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":6,"href":"/issues-contributing/","title":"Issues Contributing","section":"LDS Control and Estimation","content":" Reporting Issues # If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.\nContributing # We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.\n"},{"id":7,"href":"/docs/api/namespaces/namespacearmamexc/","title":"armamexc","section":"Namespaces","content":" armamexc # arma/mex interface using Matlab C API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; T m2T_scalar(const mxArray * matlab_scalar)\nConvert Matlab mxArray to scalar of type T. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)\nConvert matlab matrix to armadillo. template \u0026lt;typename T \u0026gt; mxArray * a2m_mat(arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat)\nConvert armadillo to matlab matrix. template \u0026lt;typename T \u0026gt; mxArray * a2m_vec(arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec)\nConvert armadillo to matlab vector. Detailed Description # Utilities for arma/mex interface using Matlab C API\nFunction Details # m2T_scalar # template \u0026lt;class T \u0026gt; inline T m2T_scalar( const mxArray * matlab_scalar ) Parameters:\nmatlab_scalar matlab scalar Template Parameters:\nT type Return: scalar of type T\nm2a_mat # template \u0026lt;class T \u0026gt; inline arma::Mat\u0026lt; T \u0026gt; m2a_mat( const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true ) Parameters:\nmatlab_mat matlab matrix copy_aux_mem [optional] whether to copy auxiliary memory strict [optional] strictly enforce the above Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_mat( arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat ) Parameters:\narma_mat armadillo matrix Return: matlab matrix\na2m_vec # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_vec( arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec ) Parameters:\narma_vec armadillo vector Return: matlab vector\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":8,"href":"/docs/api/namespaces/namespacearmamexcpp/","title":"armamexcpp","section":"Namespaces","content":" armamexcpp # arma/mex interface using Matlab C++ API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat(matlab::data::CellArray \u0026amp; matlab_cell)\nConvert matlab cell array to vector of armadillo matrices. template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array)\nConvert matlab matrix to a vector of scalars. template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo vector. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat(const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec(const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab vector. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec(const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert vector of scalar T to matlab matrix. Detailed Description # utilities for arma/mex interface using Matlab C++ API\nFunction Details # m2a_cellmat # template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat( matlab::data::CellArray \u0026amp; matlab_cell ) Parameters:\nmatlab_cell matlab cell Template Parameters:\nT type Return: vector of armadillo matrices of type T\nm2s_vec # template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: vector of type T\nm2a_vec # template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: armadillo vector of type T\nm2a_mat # template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab matrix Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat( const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_mat arma matrix factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\na2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec( const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_vec armadillo vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\ns2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec( const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\nstd_vec standard vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":9,"href":"/docs/terminology/control-estimation/","title":"C\u0026E","section":"LDS C+E Documentation","content":" Control \u0026amp; Estimation # The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.\nState estimation # In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by \\[\\widehat{\\mathbf{x}}_{t|t-1} = \\mathbf{A}\\widehat{\\mathbf{x}}_{t-1|t-1} \u0026#43; \\mathbf{B} u_{t-1} \u0026#43; \\mathbf{m}_{t-1} \\;,\\] \\[\\widehat{\\mathbf{x}}_{t|t} = \\widehat{\\mathbf{x}}_{t|t-1} \u0026#43; \\mathbf{K}^{\\rm e}_t \\left(\\mathbf{z}_t - \\widehat{\\mathbf{y}}_{t|t-1}\\right)\\;,\\] where \\( \\hat{\\left(\\cdot\\right)}_{t|j} \\) indicates an estimate at time \\( t \\) given data up to time \\( j \\) inclusive, \\( \\mathbf{K}^{\\rm e} \\) is the estimator gain, and\n\\[ \\widehat{\\mathbf{y}}_{t|t-1} = h\\left( \\widehat{\\mathbf{x}}_{t|t-1} \\right) \\; .\\] In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.\nIn the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.\nAdaptive estimation of process disturbance # Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.\nWhen parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk \\[\\mathbf{m}_{t} = \\mathbf{m}_{t-1} \u0026#43; \\mathbf{w}^m_{t-1} \\;,\\] where \\( \\mathbf{w}^m \\sim \\mathcal{N}\\left(0, \\mathbf{Q}_m\\right)\\) . Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.\nControl # Given the estimated state, the controller updates the inputs to the system according to the following law: \\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] where \\( \\left( \\cdot \\right)^{\\rm ref} \\) correspond to reference/target signals and \\( \\mathbf{K}^c_x \\) is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.\nIf users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as\n\\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right) - \\mathbf{K}^c_{\\rm inty} \\sum_{j=1}^{t}\\left( \\widehat{\\mathbf{y}}_j - \\mathbf{y}^{\\rm ref}_j \\right) \\;.\\] An additional option available to users is a control law that updates the change in u,\n\\[\\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\left(\\mathbf{u}_{t-1} - \\mathbf{u}^{\\rm ref}_{t-1} \\right) - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] \\[\\mathbf{u}_{t} = \\mathbf{u}_{t-1} \u0026#43; \\Delta\\mathbf{u}_{t} \\; .\\] Notice that this takes the form of a first-order difference equation for updating control (i.e., \\( \\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\mathbf{u}_{t-1} \u0026#43; \\epsilon_{t-1} \\) ), effectively low-pass filtering the input depending on the characteristics of \\( \\mathbf{K}^c_u \\) . This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.\nIntegral action and the \\( \\Delta \\mathbf{u} \\) control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR\u0026rsquo;d to use in combination.\nCalculating reference state-control from output # In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce \\( \\mathbf{x}^{\\rm ref} \\) and \\( \\mathbf{u}^{\\rm ref} \\) . Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.\n"},{"id":10,"href":"/docs/api/classes/","title":"Classes","section":"API Reference","content":" Classes # "},{"id":11,"href":"/docs/api/modules/group__control__masks/","title":"Control Mode Bit Masks","section":"Modules","content":" Control Mode Bit Masks # provides fill types for constructing new armadillo vectors, matrices More\u0026hellip; Attributes # Name const std::size_t kControlTypeDeltaU control designed to penalize change in input const std::size_t kControlTypeIntY control using integral action const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m Detailed Description # Control mode bit masks. These can be bit-wise OR\u0026rsquo;d to use in combination.\nAttribute Details # kControlTypeDeltaU # static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY # static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM # static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":12,"href":"/docs/api/modules/group__defaults/","title":"Defaults","section":"Modules","content":" Defaults # More\u0026hellip; Attributes # Name const data_t kDefaultP0 default state estimate covar const data_t kDefaultQ0 default process noise covar const data_t kDefaultR0 default output noise covar Detailed Description # Default values for common variables (e.g., default diagonal elements of covariances)\nAttribute Details # kDefaultP0 # static const data_t kDefaultP0 = 1e-6; kDefaultQ0 # static const data_t kDefaultQ0 = 1e-6; kDefaultR0 # static const data_t kDefaultR0 = 1e-2; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":13,"href":"/docs/api/examples/eg_glds_ctrl_8cpp-example/","title":"eg_glds_ctrl.cpp","section":"Examples","content":" eg_glds_ctrl.cpp # Example GLDS Control ```cpp\n//===\u0026ndash; eg_glds_ctrl.cpp - Example GLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS Control ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\n// output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false;\n// Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err\n// setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]);\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; }\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:35:01 EST "},{"id":14,"href":"/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/","title":"eg_glds_du_plds_ctrl.cpp","section":"Examples","content":" eg_glds_du_plds_ctrl.cpp # Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u). ```cpp\n//===\u0026ndash; eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt);\n// to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU;\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_du_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:35:01 EST "},{"id":15,"href":"/docs/api/examples/eg_plds_ctrl_8cpp-example/","title":"eg_plds_ctrl.cpp","section":"Examples","content":" eg_plds_ctrl.cpp # Example PLDS Control ```cpp\n//===\u0026ndash; eg_plds_ctrl.cpp - Example PLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Control ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt);\n// Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err\n// Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\nsize_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\nVector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled\u0026hellip; lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system);\n// for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type);\n// set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty);\n// to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0;\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros);\n// set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y();\nx_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x();\nm_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:35:01 EST "},{"id":16,"href":"/docs/api/examples/eg_plds_est_8cpp-example/","title":"eg_plds_est.cpp","section":"Examples","content":" eg_plds_est.cpp # Example PLDS Estimation ```cpp\n//===\u0026ndash; eg_plds_est.cpp - Example PLDS Estimation \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0);\nint main() { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.\n// construct ground truth system\u0026hellip; lds::poisson::System system_true(n_u, n_x, n_y, dt);\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset();\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt);\n// Can copy parameters from another system object system_estimator = system_true;\n// wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est);\n// set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true;\n// set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;estimator:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; system_estimator.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));\n// create matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros);\n// states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\nMatrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simlation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1));\n// Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simlation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows;\nif ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026ldquo;Q must be n x n.\u0026rdquo;); }\nMatrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); }\nreturn x; }\n_Filename: eg_plds_est.cpp_ ------------------------------- Updated on 5 March 2025 at 16:35:01 EST "},{"id":17,"href":"/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/","title":"eg_plds_switched_ctrl.cpp","section":"Examples","content":" eg_plds_switched_ctrl.cpp # Example Switched PLDS Control ```cpp\n//===\u0026ndash; eg_plds_switched_ctrl.cpp - Example Switched PLDS Control \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026quot;;\n// whether to do switched control bool do_switch_ctrl = true;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt);\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices. data_t scale_sys_b = 2;\nMatrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));\ncontrolled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions\n// reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);\n// Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system);\n// set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys1:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys2:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x));\nswitched_controller.set_y_ref(y_ref0);\nstd::vectorlds::poisson::System systems_vec(3, lds::poisson::System()); lds::UniformSystemListlds::poisson::System systems(std::move(systems_vec));\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;switched_controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; switched_controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Fake measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros);\n// create Matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);\n// modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones);\n// set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } }\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace)); mode.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;mode\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_switched_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:35:01 EST "},{"id":18,"href":"/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/","title":"examples","section":"Files","content":" examples # Files # Name examples/eg_glds_ctrl.cpp examples/eg_glds_du_plds_ctrl.cpp examples/eg_plds_ctrl.cpp examples/eg_plds_est.cpp examples/eg_plds_switched_ctrl.cpp Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":19,"href":"/docs/api/examples/","title":"Examples","section":"API Reference","content":" Examples # "},{"id":20,"href":"/docs/api/files/eg__glds__ctrl_8cpp/","title":"examples/eg_glds_ctrl.cpp","section":"Files","content":" examples/eg_glds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":21,"href":"/docs/api/files/eg__glds__du__plds__ctrl_8cpp/","title":"examples/eg_glds_du_plds_ctrl.cpp","section":"Files","content":" examples/eg_glds_du_plds_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU; // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":22,"href":"/docs/api/files/eg__plds__ctrl_8cpp/","title":"examples/eg_plds_ctrl.cpp","section":"Files","content":" examples/eg_plds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err // Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled... lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type); // set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":23,"href":"/docs/api/files/eg__plds__est_8cpp/","title":"examples/eg_plds_est.cpp","section":"Files","content":" examples/eg_plds_est.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name Matrix random_walk(size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0) int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # random_walk # Matrix random_walk( size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0 ) main # int main() Source code # //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. // construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state // Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. // turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":24,"href":"/docs/api/files/eg__plds__switched__ctrl_8cpp/","title":"examples/eg_plds_switched_ctrl.cpp","section":"Files","content":" examples/eg_plds_switched_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Source code # //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 // simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions // reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":25,"href":"/docs/api/files/","title":"Files","section":"API Reference","content":" Files # "},{"id":26,"href":"/docs/tutorials/eg_glds_control/","title":"GLDS Control","section":"LDS C+E Examples","content":" GLDS Control Tutorial # This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\] \\[\ry_{t} = x_t\r\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.\n/// Going to simulate a switching disturbance (m) acting on system size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_y).fill(m_low); Finally, assign the parameters using corresponding set-methods.\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); Creating the controller # Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.\nFor the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.\n// make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nWith the controller constructed, control variables may be set.\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.\nThe control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller\u0026rsquo;s online estimates of the output, state, and disturbance are given in purple.\n"},{"id":27,"href":"/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/","title":"include","section":"Files","content":" include # Directories # Name ldsCtrlEst_h Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":28,"href":"/docs/api/namespaces/namespacelds/","title":"lds","section":"Namespaces","content":" lds # Linear Dynamical Systems (LDS) namespace. Namespaces # Name lds::gaussian Linear Dynamical Systems with Gaussian observations. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::Controller class lds::EM class lds::Fit LDS Fit Type. class lds::SSID class lds::SwitchedController SwitchedController Type. class lds::System Linear Dynamical System Type. class lds::UniformMatrixList class lds::UniformSystemList class lds::UniformVectorList Types # Name enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}\nweighting options for SSID enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2} using double data_t using arma::Col\u0026lt; data_t \u0026gt; Vector using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Cube\u0026lt; data_t \u0026gt; Cube using arma::subview\u0026lt; data_t \u0026gt; View Functions # Name void Limit(std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub) void Limit(Vector \u0026amp; x, data_t lb, data_t ub) void Limit(Matrix \u0026amp; x, data_t lb, data_t ub) void Reassign(Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Vector in place void Reassign(Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Matrix in place void ForceSymPD(Matrix \u0026amp; X)\nforces matrix to be symmetric positive-definite void ForceSymMinEig(Matrix \u0026amp; X, data_t eig_min =0)\nforces matrix to be symmetric and have a minimum eigenvalue void lq(Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X)\nLQ decomposition. Matrix calcCov(const Matrix \u0026amp; A, const Matrix \u0026amp; B)\nCalculate covariance matrix. Attributes # Name const data_t kInf Some useful numbers. const data_t kPi Type Details # SSIDWt # Enumerator Value Description kSSIDNone None. kSSIDMOESP MOESP (AKA \u0026ldquo;robust method\u0026rdquo; in van Overschee 1996) kSSIDCVA CVA \u0026ldquo;Canonical Variate Analysis\u0026rdquo;. Weighting options for singular value decomposition performed during subspace identification (SSID)\nReference:\nvan Overschee, de Moor. 1996. Subspace Identification for Linear Systems.\nMatrixListFreeDim # Enumerator Value Description kMatFreeDimNone neither dim free to be hetero in mat list kMatFreeDim1 allow 1st dim of mats in list to be hetero kMatFreeDim2 allow 2nd dim of mats in list to be hetero data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nVector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Cube # using lds::Cube = arma::Cube\u0026lt;data_t\u0026gt;; View # using lds::View = arma::subview\u0026lt;data_t\u0026gt;; Function Details # Limit # inline void Limit( std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Vector \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Matrix \u0026amp; x, data_t lb, data_t ub ) Reassign # inline void Reassign( Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Vector other other Vector parenthetical optional description provided by caller to ease debugging Reassign # inline void Reassign( Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Matrix other other Matrix parenthetical optional description provided by caller to ease debugging ForceSymPD # void ForceSymPD( Matrix \u0026amp; X ) Parameters:\nX mutated matrix ForceSymMinEig # void ForceSymMinEig( Matrix \u0026amp; X, data_t eig_min =0 ) Parameters:\nX mutated matrix eig_min [optional] minimum eigen value lq # void lq( Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X ) Parameters:\nL lower triangle matrix Qt orthonormal matrix (transposed cf QR decomp) X matrix being decomposed calcCov # Matrix calcCov( const Matrix \u0026amp; A, const Matrix \u0026amp; B ) Parameters:\nA some matrix B some other matrix Return: covariance\nAttribute Details # kInf # static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); kPi # static const data_t kPi = arma::datum::pi; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":29,"href":"/docs/api/classes/classlds_1_1_controller/","title":"lds::Controller","section":"Classes","content":" lds::Controller # More\u0026hellip;\nInherited by lds::SwitchedController\u0026lt; System \u0026gt;, lds::gaussian::Controller, lds::poisson::Controller\nPublic Functions # Name Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes # Name System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::Controller; Public Function Details # Controller # Controller() =default Controller # inline Controller( const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Controller # inline Controller( System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Control # inline const Vector \u0026amp; Control( const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step). This is the most flexible option, but requires user to have set the controller\u0026rsquo;s y_ref, x_ref, and u_ref variables.\nControlOutputReference # inline const Vector \u0026amp; ControlOutputReference( const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_estimation [optional] whether to update state estimate (if false, effectively open-loop control) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.\nsys # inline const System \u0026amp; sys() const Kc # inline const Matrix \u0026amp; Kc() const Kc_inty # inline const Matrix \u0026amp; Kc_inty() const Kc_u # inline const Matrix \u0026amp; Kc_u() const g_design # inline const Vector \u0026amp; g_design() const u_ref # inline const Vector \u0026amp; u_ref() const x_ref # inline const Vector \u0026amp; x_ref() const y_ref # inline const Vector \u0026amp; y_ref() const control_type # inline size_t control_type() const tau_awu # inline data_t tau_awu() const u_lb # inline data_t u_lb() const u_ub # inline data_t u_ub() const set_sys # inline void set_sys( const System \u0026amp; sys ) set_g_design # inline void set_g_design( const Vector \u0026amp; g_design ) set_u_ref # inline void set_u_ref( const Vector \u0026amp; u_ref ) set_x_ref # inline void set_x_ref( const Vector \u0026amp; x_ref ) set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) Reimplemented by: lds::gaussian::Controller::set_y_ref, lds::gaussian::SwitchedController::set_y_ref, lds::poisson::Controller::set_y_ref, lds::poisson::SwitchedController::set_y_ref\nset_Kc # inline void set_Kc( const Matrix \u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const Matrix \u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const Matrix \u0026amp; Kc_u ) set_tau_awu # inline void set_tau_awu( data_t tau ) set_control_type # inline void set_control_type( size_t control_type ) Parameters:\ncontrol_type control type bit mask Template Parameters:\nSystem type derived from lds::System set_u_lb # inline void set_u_lb( data_t u_lb ) Parameters:\nu_lb control lower bound set_u_ub # inline void set_u_ub( data_t u_ub ) Parameters:\nu_ub control upper bound Reset # inline void Reset() Print # inline void Print() Protected Attribute Details # sys_ # System sys_; u_ # Vector u_; u_return_ # Vector u_return_; g_design_ # Vector g_design_; u_ref_ # Vector u_ref_; u_ref_prev_ # Vector u_ref_prev_; x_ref_ # Vector x_ref_; y_ref_ # Vector y_ref_; cx_ref_ # Vector cx_ref_; Kc_ # Matrix Kc_; Kc_u_ # Matrix Kc_u_; Kc_inty_ # Matrix Kc_inty_; du_ref_ # Vector du_ref_; dv_ref_ # Vector dv_ref_; v_ref_ # Vector v_ref_; dv_ # Vector dv_; v_ # Vector v_; int_e_ # Vector int_e_; int_e_awu_adjust_ # Vector int_e_awu_adjust_; u_sat_ # Vector u_sat_; do_control_prev_ # bool do_control_prev_ = false; do_lock_control_prev_ # bool do_lock_control_prev_ = false; u_saturated_ # bool u_saturated_ = false; u_lb_ # data_t u_lb_ {}; u_ub_ # data_t u_ub_ {}; tau_awu_ # data_t tau_awu_ {}; k_awu_ # data_t k_awu_ = 0; t_since_control_onset_ # data_t t_since_control_onset_ = 0; control_type_ # size_t control_type_ {}; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":30,"href":"/docs/api/classes/classlds_1_1_e_m/","title":"lds::EM","section":"Classes","content":" lds::EM # More\u0026hellip;\nInherited by lds::gaussian::FitEM, lds::poisson::FitEM\nPublic Functions # Name EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions # Name void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() virtual void MaximizeOutput() =0 virtual void MaximizeMeasurement() =0 void Smooth(bool force_common_initial)\nget smoothed estimates virtual void RecurseKe(Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t) =0\nrecursively update estimator gain Ke void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::EM; Public Function Details # EM # EM() =default EM # EM( size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nn_x number of states dt sample period u_train input training data z_train measurement training data EM # EM( const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nfit0 initial fit u_train input training data z_train measurement training data ~EM # virtual ~EM() =default Run # const Fit \u0026amp; Run( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2 ) Parameters:\ncalc_dynamics [optional] whether to calculate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law max_iter max number of iterations tol convergence tolerance (max fractional abs change) Return: Fit\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nx # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const y # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const sum_E_x_t_x_t # inline const Matrix \u0026amp; sum_E_x_t_x_t() const sum_E_xu_tm1_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const sum_E_xu_t_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const n_t_tot # inline size_t n_t_tot() theta # inline const Vector \u0026amp; theta() const Protected Function Details # Expectation # void Expectation( bool force_common_initial =false ) Parameters:\nforce_common_initial whether to force common initial condition for all trials Maximization # void Maximization( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false ) Parameters:\ncalc_dynamics [optional] whether to caclulate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law MaximizeDynamics # void MaximizeDynamics() MaximizeQ # void MaximizeQ() MaximizeInitial # void MaximizeInitial() MaximizeOutput # virtual void MaximizeOutput() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput\nMaximizeMeasurement # virtual void MaximizeMeasurement() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement\nSmooth # void Smooth( bool force_common_initial ) Parameters:\nforce_common_initial whether to force common initial conditions RecurseKe # virtual void RecurseKe( Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t ) =0 Parameters:\nKe estimator gain P_pre cov of predicted state est. P_post cov of postior sate est. t time Reimplemented by: lds::gaussian::FitEM::RecurseKe, lds::poisson::FitEM::RecurseKe\nReset # void Reset() InitVars # void InitVars() UpdateTheta # Vector UpdateTheta() Return: parameter list\nProtected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; x_ # std::vector\u0026lt; Matrix \u0026gt; x_; P_ # std::vector\u0026lt; Cube \u0026gt; P_; P_t_tm1_ # std::vector\u0026lt; Cube \u0026gt; P_t_tm1_; y_ # std::vector\u0026lt; Matrix \u0026gt; y_; diag_y_ # Matrix diag_y_; sum_E_x_t_x_t_ # Matrix sum_E_x_t_x_t_; sum_E_xu_tm1_xu_tm1_ # Matrix sum_E_xu_tm1_xu_tm1_; sum_E_xu_t_xu_tm1_ # Matrix sum_E_xu_t_xu_tm1_; fit_ # Fit fit_; theta_ # Vector theta_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":31,"href":"/docs/api/classes/classlds_1_1_fit/","title":"lds::Fit","section":"Classes","content":" lds::Fit # LDS Fit Type. #include \u0026lt;lds_fit.h\u0026gt;\nInherited by lds::gaussian::Fit, lds::poisson::Fit\nPublic Functions # Name Fit() =default\nConstructs a new Fit. Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias virtual const Matrix \u0026amp; R() const =0 void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance virtual void set_R(const Matrix \u0026amp; R) =0\nsets output noise covariance (if any) void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) =0\noutput function Protected Attributes # Name data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period ~Fit # virtual ~Fit() =default n_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const m # inline const Vector \u0026amp; m() const Q # inline const Matrix \u0026amp; Q() const x0 # inline const Vector \u0026amp; x0() const P0 # inline const Matrix \u0026amp; P0() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const R # virtual const Matrix \u0026amp; R() const =0 Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R\nset_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_g # inline void set_g( const Vector \u0026amp; g ) set_m # inline void set_m( const Vector \u0026amp; m ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # virtual void set_R( const Matrix \u0026amp; R ) =0 Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R\nset_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) f # inline View f( Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t ) Parameters:\nx state estimate (over time) u input (over time) t time index Return: view of updated state\nf # inline View f( Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t ) Parameters:\nx_pre predicted state est. x_post posterior state est. u input (over time) t time index Return: view of predicted state\nh # virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) =0 Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplemented by: lds::gaussian::Fit::h, lds::poisson::Fit::h\nProtected Attribute Details # dt_ # data_t dt_ {}; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; m_ # Vector m_; Q_ # Matrix Q_; C_ # Matrix C_; d_ # Vector d_; R_ # Matrix R_; x0_ # Vector x0_; P0_ # Matrix P0_; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":32,"href":"/docs/api/namespaces/namespacelds_1_1gaussian/","title":"lds::gaussian","section":"Namespaces","content":" lds::gaussian # Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. class lds::gaussian::Fit GLDS Fit Type. class lds::gaussian::FitEM GLDS E-M Fit Type. class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. class lds::gaussian::System Gaussian LDS Type. Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":33,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_controller/","title":"lds::gaussian::Controller","section":"Classes","content":" lds::gaussian::Controller # Gaussian-observation Controller Type. #include \u0026lt;lds_gaussian_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":34,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_fit/","title":"lds::gaussian::Fit","section":"Classes","content":" lds::gaussian::Fit # GLDS Fit Type. #include \u0026lt;lds_gaussian_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual const Matrix \u0026amp; R() const override\ngets measurement noise covariance virtual void set_R(const Matrix \u0026amp; R) override\nsets measurement noise covariance virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period R # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nh # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":35,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/","title":"lds::gaussian::FitEM","section":"Classes","content":" lds::gaussian::FitEM # GLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_gaussian_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::gaussian::FitEM; This type is used in the process of fitting GLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":36,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/","title":"lds::gaussian::FitSSID","section":"Classes","content":" lds::gaussian::FitSSID # Subspace Identification (SSID) for GLDS. #include \u0026lt;lds_gaussian_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":37,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/","title":"lds::gaussian::SwitchedController","section":"Classes","content":" lds::gaussian::SwitchedController # Gaussian-observation SwitchedController Type. #include \u0026lt;lds_gaussian_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":38,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_system/","title":"lds::gaussian::System","section":"Classes","content":" lds::gaussian::System # Gaussian LDS Type. #include \u0026lt;lds_gaussian_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)\nConstructs a new Gaussian System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. const Matrix \u0026amp; R() const\nGet output noise covariance. void set_Q(const Matrix \u0026amp; Q) void set_R(const Matrix \u0026amp; R)\nSet output noise covariance. void set_Ke(const Matrix \u0026amp; Ke)\nSet estimator gain. void set_Ke_m(const Matrix \u0026amp; Ke_m)\nSet disturbance estimator gain. void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively update estimator gain. Protected Attributes # Name Matrix R_ covariance of output noise bool do_recurse_Ke_ whether to recursively calculate estimator gain Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0 ) Parameters:\nn_u number of inputs (u) n_x number of states (x) n_y number of outputs (y) dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) r0 [optional] initial diagonal elements of output noise covariance (R) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nR # inline const Matrix \u0026amp; R() const set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # inline void set_R( const Matrix \u0026amp; R ) set_Ke # inline void set_Ke( const Matrix \u0026amp; Ke ) set_Ke_m # inline void set_Ke_m( const Matrix \u0026amp; Ke_m ) Print # void Print() Protected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nProtected Attribute Details # R_ # Matrix R_; do_recurse_Ke_ # bool do_recurse_Ke_ {}; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":39,"href":"/docs/api/namespaces/namespacelds_1_1poisson/","title":"lds::poisson","section":"Namespaces","content":" lds::poisson # Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. class lds::poisson::Fit PLDS Fit Type. class lds::poisson::FitEM PLDS E-M Fit Type. class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. class lds::poisson::System Poisson System type. Attributes # Name std::random_device rd random device for simulating poisson data std::mt19937 rng random number generator for simulating poisson data Attribute Details # rd # static std::random_device rd; rng # static std::mt19937 rng = std::mt19937( rd()); Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":40,"href":"/docs/api/classes/classlds_1_1poisson_1_1_controller/","title":"lds::poisson::Controller","section":"Classes","content":" lds::poisson::Controller # PLDS Controller Type. #include \u0026lt;lds_poisson_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":41,"href":"/docs/api/classes/classlds_1_1poisson_1_1_fit/","title":"lds::poisson::Fit","section":"Classes","content":" lds::poisson::Fit # PLDS Fit Type. #include \u0026lt;lds_poisson_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function virtual void set_R(const Matrix \u0026amp; R) override\nsets output noise covariance (if any) virtual const Matrix \u0026amp; R() const override Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # inline Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period h # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nR # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":42,"href":"/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/","title":"lds::poisson::FitEM","section":"Classes","content":" lds::poisson::FitEM # PLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_poisson_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::poisson::FitEM; This type is used in the process of fitting PLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":43,"href":"/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/","title":"lds::poisson::FitSSID","section":"Classes","content":" lds::poisson::FitSSID # Subspace Identification (SSID) for PLDS. #include \u0026lt;lds_poisson_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":44,"href":"/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/","title":"lds::poisson::SwitchedController","section":"Classes","content":" lds::poisson::SwitchedController # Poisson-observation SwitchedController Type. #include \u0026lt;lds_poisson_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":45,"href":"/docs/api/classes/classlds_1_1poisson_1_1_system/","title":"lds::poisson::System","section":"Classes","content":" lds::poisson::System # Poisson System type. #include \u0026lt;lds_poisson_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nConstructs a new Poisson System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively recalculate estimator gain (Ke) Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nProtected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nRecursively recalculate estimator gain (Ke).\nReferences:\nSmith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.\nEden UT, \u0026hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":46,"href":"/docs/api/classes/classlds_1_1_s_s_i_d/","title":"lds::SSID","section":"Classes","content":" lds::SSID # More\u0026hellip;\nInherited by lds::gaussian::FitSSID, lds::poisson::FitSSID\nPublic Functions # Name SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions # Name void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. virtual void DecomposeData() =0\nDecompose data to lower-triangular matrix (used in Solve) void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::SSID; Public Function Details # SSID # SSID() =default SSID # SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf) ) Parameters:\nn_x number of states n_h size of block-hankel data matrix dt sample period u_train input training data z_train measurement training data d output bias Run # std::tuple\u0026lt; Fit, Vector \u0026gt; Run( SSIDWt ssid_wt ) Parameters:\nssid_wt weight for singular value decomp Return: tuple (Fit, singular values)\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nProtected Function Details # CalcD # void CalcD( data_t t_silence =0.1, data_t thresh_silence =0.001 ) Parameters:\nt_silence threshold on period of time that qualifies as \u0026ldquo;silence\u0026rdquo; thresh_silence threshold on input amplitude u that qualifies as \u0026ldquo;silence\u0026rdquo; CreateHankelDataMat # void CreateHankelDataMat() Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.\nDecomposeData # virtual void DecomposeData() =0 Reimplemented by: lds::gaussian::FitSSID::DecomposeData, lds::poisson::FitSSID::DecomposeData\nCalcSVD # void CalcSVD( SSIDWt wt ) Parameters:\nssid_wt weight for SVD Solve # void Solve( data_t wt_dc ) Parameters:\nwt_dc weight placed on getting correct DC I/O gain RecomputeExtObs # void RecomputeExtObs() Protected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; D_ # Matrix D_; fit_ # Fit fit_; g_dc_ # Matrix g_dc_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_h_ # size_t n_h_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; L_ # Matrix L_; s_ # Vector s_; ext_obs_t_ # Matrix ext_obs_t_; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":47,"href":"/docs/api/classes/classlds_1_1_switched_controller/","title":"lds::SwitchedController","section":"Classes","content":" lds::SwitchedController # SwitchedController Type. More\u0026hellip;\n#include \u0026lt;lds_sctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nInherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController\nPublic Functions # Name SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes # Name std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::SwitchedController; Public Function Details # SwitchedController # SwitchedController() =default SwitchedController # inline SwitchedController( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask SwitchedController # inline SwitchedController( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Switch # inline void Switch( size_t idx, bool do_force_switch =false ) Parameters:\nidx index do_force_switch whether to force a system switch even if already there. set_Kc # inline void set_Kc( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc ) set_Kc # inline void set_Kc( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty ) set_Kc_inty # inline void set_Kc_inty( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u ) set_Kc_u # inline void set_Kc_u( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u ) set_g_design # inline void set_g_design( const UniformVectorList \u0026amp; g ) set_g_design # inline void set_g_design( UniformVectorList \u0026amp;\u0026amp; g ) Protected Attribute Details # systems_ # std::vector\u0026lt; System \u0026gt; systems_; n_sys_ # size_t n_sys_ {}; idx_ # size_t idx_ {}; Kc_list_ # UniformMatrixList Kc_list_; Kc_inty_list_ # UniformMatrixList Kc_inty_list_; Kc_u_list_ # UniformMatrixList Kc_u_list_; g_design_list_ # UniformVectorList g_design_list_; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":48,"href":"/docs/api/classes/classlds_1_1_system/","title":"lds::System","section":"Classes","content":" lds::System # Linear Dynamical System Type. #include \u0026lt;lds_sys.h\u0026gt;\nInherited by lds::gaussian::System, lds::poisson::System\nPublic Functions # Name System() =default\nConstructs a new System. System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nconstructs a new System virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) =0\nsimulates system (single time step) void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function virtual void h() =0\nsystem output function virtual Vector h_(Vector x) =0\nsystem output function (stateless) size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void RecurseKe() =0\nRecursively recalculate estimator gain (Ke) void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes # Name bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes # Name std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 diagonal elements for state estimate covariance q0 diagonal elements for process noise covariance ~System # inline virtual ~System() Filter # void Filter( const Vector \u0026amp; u_tm1, const Vector \u0026amp; z ) Parameters:\nu_tm1 input at t-minus-1 z_t current measurement Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.\nSimulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) =0 Parameters:\nu_tm1 input at time t-1 Return: simulated measurement at time t\nReimplemented by: lds::gaussian::System::Simulate, lds::poisson::System::Simulate\nf # inline void f( const Vector \u0026amp; u, bool do_add_noise =false ) Parameters:\nu input do_add_noise whether to add simulated process noise h # virtual void h() =0 Reimplemented by: lds::gaussian::System::h, lds::poisson::System::h\nh_ # virtual Vector h_( Vector x ) =0 Parameters:\nx_t state at time t Return: predicted state at time t + 1\nReimplemented by: lds::gaussian::System::h_, lds::poisson::System::h_\nn_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const x # inline const Vector \u0026amp; x() const P # inline const Matrix \u0026amp; P() const m # inline const Vector \u0026amp; m() const P_m # inline const Matrix \u0026amp; P_m() const cx # inline const Vector \u0026amp; cx() const y # inline const Vector \u0026amp; y() const x0 # inline const Vector \u0026amp; x0() const m0 # inline const Vector \u0026amp; m0() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const Ke # inline const Matrix \u0026amp; Ke() const Ke_m # inline const Matrix \u0026amp; Ke_m() const Q # inline const Matrix \u0026amp; Q() Q_m # inline const Matrix \u0026amp; Q_m() P0 # inline const Matrix \u0026amp; P0() P0_m # inline const Matrix \u0026amp; P0_m() set_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_m # inline void set_m( const Vector \u0026amp; m, bool do_force_assign =false ) set_g # inline void set_g( const Vector \u0026amp; g ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_Q_m # inline void set_Q_m( const Matrix \u0026amp; Q_m ) set_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_P0_m # inline void set_P0_m( const Matrix \u0026amp; P0_m ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) set_x # inline void set_x( const Vector \u0026amp; x ) Reset # void Reset() nstep_pred_block # std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block( UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1 ) Print # void Print() Protected Function Details # RecurseKe # virtual void RecurseKe() =0 Reimplemented by: lds::gaussian::System::RecurseKe, lds::poisson::System::RecurseKe\nInitVars # void InitVars( data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Public Attribute Details # do_adapt_m # bool do_adapt_m {}; Protected Attribute Details # n_x_ # std::size_t n_x_ {}; n_u_ # std::size_t n_u_ {}; n_y_ # std::size_t n_y_ {}; dt_ # data_t dt_ {}; x_ # Vector x_; P_ # Matrix P_; m_ # Vector m_; P_m_ # Matrix P_m_; cx_ # Vector cx_; y_ # Vector y_; z_ # Vector z_; x0_ # Vector x0_; P0_ # Matrix P0_; m0_ # Vector m0_; P0_m_ # Matrix P0_m_; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; Q_ # Matrix Q_; Q_m_ # Matrix Q_m_; C_ # Matrix C_; d_ # Vector d_; Ke_ # Matrix Ke_; Ke_m_ # Matrix Ke_m_; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":49,"href":"/docs/api/classes/classlds_1_1_uniform_matrix_list/","title":"lds::UniformMatrixList","section":"Classes","content":" lds::UniformMatrixList # More\u0026hellip;\nInherits from std::vector\u0026lt; Matrix \u0026gt;\nPublic Functions # Name UniformMatrixList() =default\nConstructs a new UniformMatrixList. UniformMatrixList(const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent. UniformMatrixList(std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent. UniformMatrixList(std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent. UniformMatrixList(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nConstructs a new UniformMatrixList (copy). UniformMatrixList(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nConstructs a new UniformMatrixList (move). ~UniformMatrixList() =default\nDestroys the object. const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim(size_t n =0) const\ngets dimensions of uniformly sized matrices size_t size()\nsize of container const Matrix \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Matrix \u0026amp; that, size_t n)\nswaps input matrix with n^th matrix of list UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nassigns the contents (copy) UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nassigns the contents (move) void append(const Matrix \u0026amp; mat)\nappends a matrix to the list Detailed Description # template \u0026lt;MatrixListFreeDim D =kMatFreeDimNone\u0026gt; class lds::UniformMatrixList; Public Function Details # UniformMatrixList # UniformMatrixList() =default UniformMatrixList # explicit UniformMatrixList( const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # explicit UniformMatrixList( std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList UniformMatrixList # UniformMatrixList( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList ~UniformMatrixList # ~UniformMatrixList() =default dim # inline const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim( size_t n =0 ) const Parameters:\nn [optional] index in list of matrices Return: dimensions\nsize # inline size_t size() at # inline const Matrix \u0026amp; at( size_t n ) Swap # inline void Swap( Matrix \u0026amp; that, size_t n ) Parameters:\nthat input matrix n index where the matrix is moved operator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\noperator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\nappend # void append( const Matrix \u0026amp; mat ) Parameters:\nmat input matrix Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":50,"href":"/docs/api/classes/classlds_1_1_uniform_system_list/","title":"lds::UniformSystemList","section":"Classes","content":" lds::UniformSystemList # More\u0026hellip;\nInherits from std::vector\u0026lt; System \u0026gt;\nPublic Functions # Name UniformSystemList() =default\nConstructs a new UniformSystemList. UniformSystemList(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by copying existing vector of System if dimensions consistent. UniformSystemList(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by moving existing vector of System if dimensions consistent. UniformSystemList(std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList from initializer_list of System if dimensions consistent. UniformSystemList(const UniformSystemList \u0026amp; that)\nConstructs a new UniformSystemList (copy). UniformSystemList(UniformSystemList \u0026amp;\u0026amp; that)\nConstructs a new UniformSystemList (move). ~UniformSystemList() =default\nDestroys the object. const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const\ngets dimensions of the uniformly sized systems size_t size()\nsize of container const System \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(System \u0026amp; that, size_t n)\nswaps input system with n^th system of list UniformSystemList \u0026amp; operator=(const UniformSystemList \u0026amp; that)\nassigns the contents (copy) UniformSystemList \u0026amp; operator=(UniformSystemList \u0026amp;\u0026amp; that)\nassigns the contents (move) Detailed Description # template \u0026lt;typename System \u0026gt; class lds::UniformSystemList; Public Function Details # UniformSystemList # UniformSystemList() =default UniformSystemList # explicit UniformSystemList( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # explicit UniformSystemList( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList UniformSystemList # UniformSystemList( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList ~UniformSystemList # ~UniformSystemList() =default dim # inline const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const size # inline size_t size() at # inline const System \u0026amp; at( size_t n ) Swap # inline void Swap( System \u0026amp; that, size_t n ) Parameters:\nthat input system n index where the system is moved operator= # inline UniformSystemList \u0026amp; operator=( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\noperator= # inline UniformSystemList \u0026amp; operator=( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":51,"href":"/docs/api/classes/classlds_1_1_uniform_vector_list/","title":"lds::UniformVectorList","section":"Classes","content":" lds::UniformVectorList # Inherits from std::vector\u0026lt; Vector \u0026gt;\nPublic Functions # Name UniformVectorList() =default\nConstructs a new UniformVectorList. UniformVectorList(const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent. UniformVectorList(std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent. UniformVectorList(std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0)\nConstructs a new UniformVectorList from initializer_list of Vector if dimensions consistent. UniformVectorList(const UniformVectorList \u0026amp; that)\nConstructs a new UniformVectorList (copy) UniformVectorList(UniformVectorList \u0026amp;\u0026amp; that)\nConstructs a new UniformVectorList (move) ~UniformVectorList() =default\nDestroys the object. size_t dim() const\ngets dimensions of the uniformly sized matrices size_t size()\nsize of container const Vector \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Vector \u0026amp; that, size_t n)\nswaps input matrix with n^th vector of list UniformVectorList \u0026amp; operator=(const UniformVectorList \u0026amp; that)\nassigns the contents (copy) UniformVectorList \u0026amp; operator=(UniformVectorList \u0026amp;\u0026amp; that)\nassigns the contents (move) Public Function Details # UniformVectorList # UniformVectorList() =default UniformVectorList # explicit UniformVectorList( const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dims dimension UniformVectorList # explicit UniformVectorList( std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList UniformVectorList # UniformVectorList( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList ~UniformVectorList # ~UniformVectorList() =default dim # inline size_t dim() const size # inline size_t size() at # inline const Vector \u0026amp; at( size_t n ) Swap # inline void Swap( Vector \u0026amp; that, size_t n ) Parameters:\nthat input vector n index where the vector is moved operator= # inline UniformVectorList \u0026amp; operator=( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\noperator= # inline UniformVectorList \u0026amp; operator=( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":52,"href":"/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/","title":"ldsCtrlEst_h","section":"Files","content":" ldsCtrlEst_h # Files # Name ldsCtrlEst_h/lds.h lds namespace ldsCtrlEst_h/lds_ctrl.h Controller. ldsCtrlEst_h/lds_fit.h LDS base fit type. ldsCtrlEst_h/lds_fit_em.h subspace identification ldsCtrlEst_h/lds_fit_ssid.h subspace identification ldsCtrlEst_h/lds_gaussian.h glds namespace ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller. ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type. ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type. ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type. ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type. ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type. ldsCtrlEst_h/lds_poisson.h plds namespace ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type. ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type. ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type. ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type. ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type. ldsCtrlEst_h/lds_poisson_sys.h PLDS base type. ldsCtrlEst_h/lds_sctrl.h SwitchedController type. ldsCtrlEst_h/lds_sys.h LDS base type. ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices. ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems. ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors. ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":53,"href":"/docs/api/files/lds__ctrl_8h/","title":"ldsCtrlEst_h/lds_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_ctrl.h # Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Controller Detailed Description # This file declares the type for control of a linear dynamical system (lds::Controller).\nSource code # //===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_CTRL_H #define LDSCTRLEST_LDS_CTRL_H // namespace #include \u0026#34;lds.h\u0026#34; // system type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class Controller { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); public: Controller() = default; Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); const Vector\u0026amp; Control(const Vector\u0026amp; z, bool do_control = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); const Vector\u0026amp; ControlOutputReference(const Vector\u0026amp; z, bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); // get methods: const System\u0026amp; sys() const { return sys_; }; const Matrix\u0026amp; Kc() const { return Kc_; }; const Matrix\u0026amp; Kc_inty() const { return Kc_inty_; }; const Matrix\u0026amp; Kc_u() const { return Kc_u_; }; const Vector\u0026amp; g_design() const { return g_design_; }; const Vector\u0026amp; u_ref() const { return u_ref_; }; const Vector\u0026amp; x_ref() const { return x_ref_; }; const Vector\u0026amp; y_ref() const { return y_ref_; }; size_t control_type() const { return control_type_; }; data_t tau_awu() const { return tau_awu_; }; data_t u_lb() const { return u_lb_; }; data_t u_ub() const { return u_ub_; }; // set methods void set_sys(const System\u0026amp; sys) { bool does_match = sys_.n_u() == sys.n_u(); does_match = does_match \u0026amp;\u0026amp; (sys_.n_x() == sys.n_x()); does_match = does_match \u0026amp;\u0026amp; (sys_.n_y() == sys.n_y()); if (does_match) { sys_ = sys; } else { throw std::runtime_error( \u0026#34;new system argument to `set_sys` does not match dimensionality of \u0026#34; \u0026#34;existing system\u0026#34;); } }; void set_g_design(const Vector\u0026amp; g_design) { Reassign(g_design_, g_design); }; void set_u_ref(const Vector\u0026amp; u_ref) { Reassign(u_ref_, u_ref); }; void set_x_ref(const Vector\u0026amp; x_ref) { Reassign(x_ref_, x_ref); cx_ref_ = sys_.C() * x_ref_; }; // y_ref needs to be handled differently depending on output fn. // (need to populate cx_ref_ too, which depends on output fn) virtual void set_y_ref(const Vector\u0026amp; y_ref) { Reassign(y_ref_, y_ref); }; void set_Kc(const Matrix\u0026amp; Kc) { Reassign(Kc_, Kc); }; void set_Kc_inty(const Matrix\u0026amp; Kc_inty) { Reassign(Kc_inty_, Kc_inty); }; void set_Kc_u(const Matrix\u0026amp; Kc_u) { Reassign(Kc_u_, Kc_u); }; void set_tau_awu(data_t tau) { tau_awu_ = tau; k_awu_ = sys_.dt() / tau_awu_; }; void set_control_type(size_t control_type); // There is no reason u_lb/ub should not be public, but making set methods // anyway. void set_u_lb(data_t u_lb) { u_lb_ = u_lb; }; void set_u_ub(data_t u_ub) { u_ub_ = u_ub; }; void Reset() { sys_.Reset(); u_ref_.zeros(); u_ref_prev_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); u_saturated_ = false; t_since_control_onset_ = 0.0; }; void Print() { sys_.Print(); std::cout \u0026lt;\u0026lt; \u0026#34;g_design : \u0026#34; \u0026lt;\u0026lt; g_design_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_lb : \u0026#34; \u0026lt;\u0026lt; u_lb_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_ub : \u0026#34; \u0026lt;\u0026lt; u_ub_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }; protected: System sys_; Vector u_; Vector u_return_; Vector g_design_; // reference signals Vector u_ref_; // create no set method for this: Vector u_ref_prev_; Vector x_ref_; Vector y_ref_; Vector cx_ref_; // Controller gains Matrix Kc_; Matrix Kc_u_; Matrix Kc_inty_; // control after g inversion // do not need set methods for these. Vector du_ref_; Vector dv_ref_; Vector v_ref_; Vector dv_; Vector v_; // integral error // do not need set method for this Vector int_e_; Vector int_e_awu_adjust_; Vector u_sat_; bool do_control_prev_ = false; bool do_lock_control_prev_ = false; // whether the g of system has become inverted from what you think it is // (gain_ref) bool u_saturated_ = false; // should be safe to have references here bc nothing needs to be done // (like reset vars) when it changes... data_t u_lb_{}; data_t u_ub_{}; data_t tau_awu_{}; data_t k_awu_ = 0; data_t t_since_control_onset_ = 0; size_t control_type_{}; private: void CalcControl(bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); void CalcSteadyStateSetPoint(); void AntiWindup(); void InitVars(size_t control_type); }; // Implement the above: template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(sys), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(std::move(sys)), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::set_control_type(size_t control_type) { if (control_type_ == control_type) { return; } // creating a blank slate... control_type_ = 0; Kc_inty_.zeros(0, 0); Kc_u_.zeros(0, 0); int_e_.zeros(0, 0); int_e_awu_adjust_.zeros(0, 0); // controller was designed to minimize integral error if (control_type \u0026amp; kControlTypeIntY) { Kc_inty_.zeros(sys_.n_u(), sys_.n_y()); int_e_.zeros(sys_.n_y()); int_e_awu_adjust_.zeros(sys_.n_u()); control_type_ = control_type_ | kControlTypeIntY; } // controller was designed to minimize deltaU // (i.e. state augmented with u) if (control_type \u0026amp; kControlTypeDeltaU) { Kc_u_.zeros(sys_.n_u(), sys_.n_u()); control_type_ = control_type_ | kControlTypeDeltaU; } // whether to adapt set point calculate with (re-estimated) process // disturbance (m) if (control_type \u0026amp; kControlTypeAdaptM) { if (sys_.do_adapt_m) // only if adapting m... { control_type_ = control_type_ | kControlTypeAdaptM; } } } // set_control_type template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::Control( const Vector\u0026amp; z, bool do_control, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement sys_.Filter(u_, z); bool do_estimation = true; // always have estimator on in this case // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::ControlOutputReference( const Vector\u0026amp; z, bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement if (do_estimation) { sys_.Filter(u_, z); } else { sys_.f(u_); } // calculate the set point // solves for u_ref and x_ref when output is at y_ref at steady state. if (do_control) { CalcSteadyStateSetPoint(); } // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcControl(bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { if (do_control \u0026amp;\u0026amp; do_estimation) { if (!do_control_prev_) { if (do_reset_at_control_onset) { Reset(); } t_since_control_onset_ = 0.0; } else { t_since_control_onset_ += sys_.dt(); } // enforce softstart on control vars. if (sigma_soft_start \u0026gt; 0) { // half-Gaussian soft-start scaling factor data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) / (2 * pow(sigma_soft_start, 2))); u_ref_ *= soft_start_sf; // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too // x_ref_ *= soft_start_sf; // cx_ref_ *= soft_start_sf; // y_ref_ *= soft_start_sf; } if (!do_lock_control) { // first do u -\u0026gt; v change of vars. (v = g.*u) // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver // control voltage u[=]V) v_ref_ = g_design_ % u_ref_; // Given FB, calc. the change in control if (control_type_ \u0026amp; kControlTypeDeltaU) { // if control designed to minimize not u but deltaU (i.e. state aug with // u): // TODO(mfbolus): Commented out for now. See note below. // du_ref_ = u_ref_ - u_ref_prev_; // dv_ref_ = g_design_ % du_ref_; // TODO(mfbolus): Assuming users want *smooth* control signals if using // kControlTypeDeltaU, it should be the case that dv_ref_ is --\u0026gt; 0. May // want to revisit, but I am going to force it to be zero unless a // situation arises that argues for keeping the above. dv_ref_.zeros(); dv_ = dv_ref_; // nominally-optimal. dv_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error dv_ -= Kc_u_ * (v_ - v_ref_); // penalty on amp u (rel to ref) if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if(!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error dv_ -= Kc_inty_ * int_e_; // control for integrated error } // update the control v_ += dv_; } else { v_ = v_ref_; // nominally-optimal. v_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if (!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error v_ -= Kc_inty_ * int_e_; // control for integrated error } } // convert back to control voltage u[=]V u_ = v_ / sys_.g(); } // else do nothing until lock is low } else { // if not control // feed through u_ref in open loop u_ = u_ref_ % g_design_ / sys_.g(); v_ = sys_.g() % u_; u_ref_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); } // ends do_control // enforce box constraints (and antiwindup) AntiWindup(); // add noise to input? // The value for u that is *returned* to user after addition of any noise, // while keeping controller/estimator blind to this addition. u_return_ = u_; if ((sigma_u_noise \u0026gt; 0.0) \u0026amp;\u0026amp; (do_control \u0026amp;\u0026amp; !do_lock_control)) { u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn); Limit(u_return_, u_lb_, u_ub_); }; // For next time step: u_ref_prev_ = u_ref_; do_control_prev_ = do_control; do_lock_control_prev_ = do_lock_control; } // CalcControl template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcSteadyStateSetPoint() { // Linearly-constrained least squares (ls). // // _reference: // Boyd \u0026amp; Vandenberghe (2018) Introduction to Applied Linear Algebra // Matrix a_ls = join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros)); Vector b_ls = cx_ref_; Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye), sys_.B() * arma::diagmat(sys_.g())); Vector d_ls = -sys_.m0(); if (control_type_ \u0026amp; kControlTypeAdaptM) { d_ls = -sys_.m(); // adapt setpoint calc with disturbance? } Matrix a_ls_t = a_ls.t(); // TODO(mfbolus): not sure why but causes seg // fault if I do not do this. Matrix phi_ls = join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()), join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros))); // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse: Matrix inv_phi = pinv(phi_ls); Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls); x_ref_ = xulam.subvec(0, sys_.n_x() - 1); u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1); cx_ref_ = sys_.C() * x_ref_; } // CalcSteadyStateSetPoint template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::AntiWindup() { u_saturated_ = false; u_sat_ = u_; // limit u and flag whether saturated for (size_t k = 0; k \u0026lt; u_.n_elem; k++) { if (u_[k] \u0026lt; u_lb_) { u_sat_[k] = u_lb_; u_saturated_ = true; } if (u_[k] \u0026gt; u_ub_) { u_sat_[k] = u_ub_; u_saturated_ = true; } } if ((control_type_ \u0026amp; kControlTypeIntY) \u0026amp;\u0026amp; (tau_awu_ \u0026lt; lds::kInf)) { // one-step back-calculation (calculate intE for u=u_sat) // (Astroem, Rundqwist 1989 warn against using this...) // int_e_awu_adjust_ = // solve(Kc_inty_, (u_ - u_sat_)); // pinv(Kc_inty) * (u-uSat); // gradual: see Astroem, Rundqwist 1989 // this is a fudge for doing MIMO gradual // n.b., went ahead and multiplied 1/T by dt so don\u0026#39;t have to do that here. int_e_awu_adjust_ = k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_); // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_); int_e_ += int_e_awu_adjust_; } // set u to saturated version u_ = u_sat_; } template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::InitVars(size_t control_type) { // initialize to default values u_ref_ = Vector(sys_.n_u(), fill::zeros); u_ref_prev_ = Vector(sys_.n_u(), fill::zeros); x_ref_ = Vector(sys_.n_x(), fill::zeros); y_ref_ = Vector(sys_.n_y(), fill::zeros); cx_ref_ = Vector(sys_.n_y(), fill::zeros); u_ = Vector(sys_.n_u(), fill::zeros); u_return_ = Vector(sys_.n_u(), fill::zeros); u_sat_ = Vector(sys_.n_u(), fill::zeros); // Might not need all these, so zero elements until later. Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros); Kc_u_ = Matrix(0, 0, fill::zeros); Kc_inty_ = Matrix(0, 0, fill::zeros); g_design_ = sys_.g(); // by default, same as model dv_ = Vector(sys_.n_u(), fill::zeros); v_ = Vector(sys_.n_u(), fill::zeros); du_ref_ = Vector(sys_.n_u(), fill::zeros); dv_ref_ = Vector(sys_.n_u(), fill::zeros); v_ref_ = Vector(sys_.n_u(), fill::zeros); int_e_ = Vector(0, fill::zeros); int_e_awu_adjust_ = Vector(0, fill::zeros); set_control_type(control_type); } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":54,"href":"/docs/api/files/lds__fit__em_8h/","title":"ldsCtrlEst_h/lds_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_em.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::EM Detailed Description # This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).\nSource code # //===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_EMAX_H #define LDSCTRLEST_LDS_EMAX_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class EM { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: EM() = default; EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); virtual ~EM() = default; const Fit\u0026amp; Run(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = true, bool calc_output = true, bool calc_measurement = true, size_t max_iter = 100, data_t tol = 1e-2); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); // auto tuple = std::make_tuple(u_, z_); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; x() const { return x_; }; const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; y() const { return y_; }; const Matrix\u0026amp; sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; }; const Matrix\u0026amp; sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; }; const Matrix\u0026amp; sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; }; size_t n_t_tot() { return n_t_tot_; } const Vector\u0026amp; theta() const { return theta_; }; protected: void Expectation(bool force_common_initial = false); void Maximization(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = false, bool calc_output = false, bool calc_measurement = false); void MaximizeDynamics(); void MaximizeQ(); void MaximizeInitial(); virtual void MaximizeOutput() = 0; virtual void MaximizeMeasurement() = 0; void Smooth(bool force_common_initial); virtual void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) = 0; void Reset(); void InitVars(); Vector UpdateTheta(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; std::vector\u0026lt;Matrix\u0026gt; x_; std::vector\u0026lt;Cube\u0026gt; P_; std::vector\u0026lt;Cube\u0026gt; P_t_tm1_; std::vector\u0026lt;Matrix\u0026gt; y_; Matrix diag_y_; // expectations calculated in E-step Matrix sum_E_x_t_x_t_; Matrix sum_E_xu_tm1_xu_tm1_; Matrix sum_E_xu_t_xu_tm1_; Fit fit_; Vector theta_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; }; template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; fit_ = Fit(n_u_, n_x, n_y_, dt); u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { // make sure fit dims match I/O data if (fit0.n_u() != u_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and input training data have inconsistent dimensions\u0026#34;); } if (fit0.n_y() != z_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and output training data have inconsistent dimensions\u0026#34;); } fit_ = fit0; u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::InitVars() { // check input/output data dimensions are consistent if (z_.size() != u_.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_.at(trial).n_cols != u_.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } n_u_ = fit_.n_u(); n_x_ = fit_.n_x(); n_y_ = fit_.n_y(); dt_ = fit_.dt(); x_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); P_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); P_t_tm1_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); y_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros); P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros); } diag_y_ = Matrix(n_y_, n_y_, fill::zeros); // covariances in expectation step sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); } template \u0026lt;typename Fit\u0026gt; const Fit\u0026amp; EM\u0026lt;Fit\u0026gt;::Run(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement, size_t max_iter, data_t tol) { Reset(); // to initial conditions size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_; Vector theta(n_params); Vector theta_new(n_params); data_t max_dtheta = 1; // if solving for initial conditions, allow them be varied. // otherwise, freeze at provided values. bool force_common_initial = !calc_init; // go until parameter convergence for (size_t l = 0; l \u0026lt; max_iter; l++) { theta_ = UpdateTheta(); std::cout \u0026lt;\u0026lt; \u0026#34;Iteration \u0026#34; \u0026lt;\u0026lt; l + 1 \u0026lt;\u0026lt; \u0026#34;/\u0026#34; \u0026lt;\u0026lt; max_iter \u0026lt;\u0026lt; \u0026#34; ...\\n\u0026#34;; Expectation(force_common_initial); Maximization(calc_dynamics, calc_Q, calc_init, calc_output, calc_measurement); // check convergence theta_new = UpdateTheta(); Vector dtheta = abs(theta_new - theta_) / abs(theta_); // some parameters could be zero... arma::uvec ubi_finite = find_finite(dtheta); max_dtheta = max(dtheta.elem(ubi_finite)); std::cout \u0026lt;\u0026lt; \u0026#34;max dtheta: \u0026#34; \u0026lt;\u0026lt; max_dtheta \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; if (max_dtheta \u0026lt; tol) { std::cout \u0026lt;\u0026lt; \u0026#34;Converged.\\n\u0026#34;; break; } std::cout \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } return fit_; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Smooth(bool force_common_initial) { Matrix k_e(n_x_, n_y_); // estimator gain Cube k_backfilt; // back-filtering gains // TODO(mfbolus): this loop could be made parallel for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { Matrix x_pre(n_x_, n_t_[trial], fill::zeros); Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros); Matrix x_post(n_x_, n_t_[trial], fill::zeros); Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros); if (force_common_initial) // forces all trials to have same initial // conditions. { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); } y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); // This *should not* be necessary but make sure P is symmetric. ForceSymPD(P_[trial].slice(0)); x_pre.col(0) = x_[trial].col(0); p_pre.slice(0) = P_[trial].slice(0); x_post.col(0) = x_[trial].col(0); p_post.slice(0) = P_[trial].slice(0); // filter for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // predict fit_.f(x_pre, x_post, u_.at(trial), t); fit_.h(y_[trial], x_pre, t); diag_y_.diag() = y_[trial].col(t); // TODO(mfbolus): change if parallel // update --\u0026gt; posterior estimation RecurseKe(k_e, p_pre, p_post, t); x_post.col(t) = x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t)); y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d(); } // backfilter -\u0026gt; Smoothed estimate // Reference: // Shumway et Stoffer (1982) ForceSymPD(p_post.slice(n_t_[trial] - 1)); k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1); P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 0; t--) { // TODO(mfmbolus): should not be necessary to force symm positive def ForceSymPD(p_pre.slice(t)); ForceSymPD(p_post.slice(t - 1)); ForceSymPD(P_[trial].slice(t)); k_backfilt.slice(t - 1) = p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t)); x_[trial].col(t - 1) = x_post.col(t - 1) + k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t)); P_[trial].slice(t - 1) = p_post.slice(t - 1) + k_backfilt.slice(t - 1) * (P_[trial].slice(t) - p_pre.slice(t)) * k_backfilt.slice(t - 1).t(); } // do the same for P_t_tm1 Matrix id(n_x_, n_x_, fill::eye); P_t_tm1_[trial].slice(n_t_[trial] - 1) = (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 1; t--) { P_t_tm1_[trial].slice(t - 1) = p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() + k_backfilt.slice(t - 1) * (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) * k_backfilt.slice(t - 2).t(); } // finally, get smoothed estimate of output for (size_t t = 0; t \u0026lt; n_t_[trial]; t++) { fit_.h(y_[trial], x_[trial], t); } // samps loop } // trial loop } // Smooth // template \u0026lt;typename Fit\u0026gt; // void EM\u0026lt;Fit\u0026gt;::RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) { // // predict covar // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q(); // // update Ke // Ke = P_pre.slice(t) * fit_.C().t() * // inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R()); // // update cov // // Reference: Ghahramani et Hinton (1996) // P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t); // // // n.b. for poisson : // // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + // fit_.Q(); // // // update cov // // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ * // // fit_.C()); // // // update Ke // // Ke = P_post.slice(t) * fit_.C(); // } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Expectation(bool force_common_initial) { // calculate the mean/cov of state needed for maximizing E[pr(z|theta)] Smooth(force_common_initial); // now get the various forms of sum(E[xx\u0026#39;]) needed // n.b. Going to start at t=1 rather than 0 bc most max terms need that. // so really \u0026#34;n_t_tot_\u0026#34; is (n_t_tot_-1) n_t_tot_ = 0; sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); Vector xu_tm1(n_x_ + n_u_, fill::zeros); Vector xu_t(n_x_ + n_u_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // ------------ sum_E_x_t_x_t ------------ sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t(); sum_E_x_t_x_t_ += P_[trial].slice(t); // ------------ sum_E_xu_tm1_xu_tm1 ------------ xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1)); sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t(); sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t - 1); // ------------ sum_E_xu_t_xu_tm1 ------------ xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t)); sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t(); sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_t_tm1_[trial].slice(t); n_t_tot_ += 1; } // time } // trial } // Expectation template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement) { if (calc_output) { MaximizeOutput(); } if (calc_measurement) { MaximizeMeasurement(); } if (calc_dynamics) { MaximizeDynamics(); } if (calc_Q) { MaximizeQ(); } if (calc_init) { MaximizeInitial(); } } // Maximization template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeDynamics() { // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) * inv_sympd(sum_E_xu_tm1_xu_tm1_); fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1)); std::cout \u0026lt;\u0026lt; \u0026#34;A_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.A()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.B()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeQ() { // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 * // inv_sympd(sum_E_xu_tm1_xu_tm1_) * // sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // this way is same as above iff dynamics were just updated: // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix ab = arma::join_horiz(fit_.A(), fit_.B()); // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // From scratch method: // Q is covariance of the error between state and dynamics-predicted state // (aka process noise) // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})\u0026#39;] // t-1 terms: View sum_e_x_tm1_x_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_u_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1); View sum_e_x_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); // t, t-1 terms: View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_x_t_u_tm1 = sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); Matrix q = sum_E_x_t_x_t_; q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t(); q -= sum_e_x_t_x_tm1 * fit_.A().t(); q -= fit_.A() * sum_e_x_t_x_tm1.t(); // input-related terms: q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t(); q -= sum_e_x_t_u_tm1 * fit_.B().t(); q -= fit_.B() * sum_e_x_t_u_tm1.t(); q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t(); q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t(); q /= n_t_tot_; fit_.set_Q(q); std::cout \u0026lt;\u0026lt; \u0026#34;Q_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.Q()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // std::cout \u0026lt;\u0026lt; \u0026#34;Q_new: \\n\u0026#34; \u0026lt;\u0026lt; fit_.Q() \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeInitial() { Vector x0 = fit_.x0(); x0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { x0 += x_[trial].col(0); } x0 /= z_.size(); std::cout \u0026lt;\u0026lt; \u0026#34;x0_new[0]: \u0026#34; \u0026lt;\u0026lt; x0[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // always recalc P0 even if the initial state is fixed (at zero, for // example) Matrix e_var(n_x_, n_x_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t(); } e_var /= z_.size(); // go ahead and subtract x0*x0\u0026#39; so don\u0026#39;t have to below. e_var -= x0 * x0.t(); // To get P0, going to get initial P_ per trial and average. // (which might be wrong, but need a single number) Matrix p0 = fit_.P0(); p0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { p0 += (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var; } p0 /= z_.size(); fit_.set_P0(p0); std::cout \u0026lt;\u0026lt; \u0026#34;P0_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.P0()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeOutput() { // solve for C+d: Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros); Vector x1(n_x_ + 1, fill::zeros); x1[n_x_] = 1.0; // augment with one to solve for bias Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { x1.subvec(0, n_x_ - 1) = x_[trial].col(t); sum_zx += z_.at(trial).col(t) * x1.t(); sum_e_x1_x1 += x1 * x1.t(); sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t); } } Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1); fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1)); fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_))); std::cout \u0026lt;\u0026lt; \u0026#34;C_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.C()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.d()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeMeasurement() { // Solve for measurement noise covar size_t n_t_tot = 0; // Ghahgramani, Hinton 1996: Matrix sum_zz(n_y_, n_y_, fill::zeros); Matrix sum_yz(n_y_, n_y_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t(); // Use Cnew: sum_yz += (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t(); n_t_tot += 1; } } fit_.set_R((sum_zz - sum_yz) / n_t_tot); std::cout \u0026lt;\u0026lt; \u0026#34;R_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.R()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Reset() { // reset to initial conditions for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); } } template \u0026lt;typename Fit\u0026gt; Vector EM\u0026lt;Fit\u0026gt;::UpdateTheta() { // TODO(mfbolus): This should include n_y_ more params for d. size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_; if (fit_.R().n_elem \u0026gt; 0) { n_params += n_y_ * n_y_; } Vector theta(n_params); size_t idx_start = 0; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B()); idx_start += n_x_ * n_u_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0()); idx_start += n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C()); idx_start += n_y_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d()); idx_start += n_y_; if (fit_.R().n_elem \u0026gt; 0) { theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R()); } return theta; } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":55,"href":"/docs/api/files/lds__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_ssid.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SSID Detailed Description # This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/docs/api/classes/classlds_1_1_s_s_i_d/)\u0026lt;Fit\u0026gt;).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_FIT_SSID_H #define LDSCTRLEST_LDS_FIT_SSID_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class SSID { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: SSID() = default; SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d = Vector(1).fill(-kInf)); std::tuple\u0026lt;Fit, Vector\u0026gt; Run(SSIDWt ssid_wt); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } protected: void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001); void CreateHankelDataMat(); virtual void DecomposeData() = 0; void CalcSVD(SSIDWt wt); void Solve(data_t wt_dc); void RecomputeExtObs(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; Matrix D_; Fit fit_; Matrix g_dc_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_h_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; Matrix L_; Vector s_; Matrix ext_obs_t_; }; template \u0026lt;typename Fit\u0026gt; SSID\u0026lt;Fit\u0026gt;::SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d) { // check input/output data dimensions are consistent if (z_train.size() != u_train.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_train.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_train.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } dt_ = dt; n_x_ = n_x; n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; n_h_ = n_h; // dimensionality check for eventual block-hankel data matrix size_t len = n_t_tot_ - 2 * n_h_ + 1; if (len \u0026lt; (2 * n_h_ * (n_u_ + n_y_))) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;Dataset problem! More rows than columns in block-hankel data \u0026#34; \u0026#34;matrix: 2*(n_u+n_y)*n_h \u0026gt; data-length! Need higher data-length or \u0026#34; \u0026#34;lower n_h.\u0026#34;; throw std::runtime_error(ss.str()); } fit_ = Fit(n_u_, n_x_, n_y_, dt_); u_ = std::move(u_train); z_ = std::move(z_train); if (!d.is_finite() || (d.n_rows != n_y_)) { // TODO(mfbolus): implement least-square solution for impulse response with // a second input of ones. Data-driven way of accounting for offset *not* // driven by an input. // // For now, calculate output bias (d) as the // output wherever the stimulus has not been on for some amount of time. // convolve u with rectangle and take all samples. This is a reasonable // approach, since often when autonomous systems are fit (i.e., systems with // no input), they will subtract off the mean of the output. This // essentially amounts to setting output bias to the mean of the output when // there is no stimulation. data_t t_silence = 0.1; data_t thresh_silence = 0.001; CalcD(t_silence, thresh_silence); } else { fit_.set_d(d); } } template \u0026lt;typename Fit\u0026gt; std::tuple\u0026lt;Fit, Vector\u0026gt; SSID\u0026lt;Fit\u0026gt;::Run(SSIDWt ssid_wt) { // the weight on minimizing dc I/O gain only works for gaussian, // and hopefully not necessary with appropriate dataset. data_t wt_dc = 0; // std::cout \u0026lt;\u0026lt; \u0026#34;creating hankel mat\\n\u0026#34;; CreateHankelDataMat(); // std::cout \u0026lt;\u0026lt; \u0026#34;decomposing data\\n\u0026#34;; DecomposeData(); // std::cout \u0026lt;\u0026lt; \u0026#34;calculating svd\\n\u0026#34;; CalcSVD(ssid_wt); // std::cout \u0026lt;\u0026lt; \u0026#34;solving for params\\n\u0026#34;; Solve(wt_dc); // std::cout \u0026lt;\u0026lt; \u0026#34;fin\\n\u0026#34;; return std::make_tuple(fit_, s_); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcD(data_t t_silence, data_t thresh_silence) { Vector d(z_.at(0).n_rows, fill::zeros); Vector win(static_cast\u0026lt;size_t\u0026gt;(t_silence / dt_), fill::ones); Vector sum_z_silence(n_y_, fill::zeros); size_t n_silence(0); for (size_t trial = 0; trial \u0026lt; u_.size(); trial++) { // find silent samples // start by convolving with Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0)); Vector u_conv = conv(sum_u, win, \u0026#34;same\u0026#34;); // get only the samples that are silent... arma::uvec ubi_silence = find(u_conv \u0026lt;= thresh_silence); if (ubi_silence.n_elem \u0026gt; 0) { sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1); n_silence += ubi_silence.n_elem; } } if (n_silence \u0026gt; 0) { d = sum_z_silence / n_silence; } fit_.set_d(d); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat() { // temporary copy of data Matrix z(n_y_, n_t_tot_, fill::zeros); Matrix u(n_u_, n_t_tot_, fill::zeros); size_t so_far(0); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial); u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial); so_far += n_t_.at(trial); } // remove output bias z.each_col() -= fit_.d(); // calculate I/O gain @ DC while data in convenient form g_dc_ = z * pinv(u); // std::cout \u0026lt;\u0026lt; \u0026#34;G0_data = \u0026#34; \u0026lt;\u0026lt; g_dc_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // create hankel data matrix size_t len = z.n_cols - 2 * n_h_ + 1; // data length in hankel mat // block-hankel data matrix D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros); // past input auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1); // future input auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1); // past output auto y_p = D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1); // future output auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, len - 1); size_t idx = 0; for (size_t k = 0; k \u0026lt; len; k++) { idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } } D_ /= sqrt(static_cast\u0026lt;data_t\u0026gt;(len)); } // template \u0026lt;typename Fit\u0026gt; // void SSID\u0026lt;Fit\u0026gt;::DecomposeData() { // // do LQ decomp instead of calculating covariance expensive way // // Note that \u0026#34;R\u0026#34; in van Overschee is lower-triangular (L), not \u0026#34;R\u0026#34; in QR // // decomp. Very confusing. // Matrix q_t; // lq(L_, q_t, D_); // // van Overschee zeros out the other elements. // L_ = trimatl(L_); // } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcSVD(SSIDWt wt) { // submats that will be needed: auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1); auto R_23_13 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1); auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_) - 1); auto R_44 = L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14); auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1); auto Luf = Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1); auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); // aka: R_f Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1); // from van Overschee subid.m: // Rf = R((2*m+l)*i+1:2*(m+l)*i,:); % Future outputs Matrix U; Matrix V; switch (wt) { case kSSIDNone: { // No weighting. (what van Overschee calls \u0026#34;N4SID\u0026#34;) Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14; arma::svd(U, s_, V, O_k_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDMOESP: { // MOESP weighting // This is what they use in the \u0026#34;robust\u0026#34; algorithm van Overschee, de Moor // 1996 Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); svd(U, s_, V, O_k_ortho_Uf_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDCVA: { // CVA weighting // See van Overschee\u0026#39;s matlab code (subid.m): // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); Matrix inv_w1; Matrix qt1; lq(inv_w1, qt1, R_56_16); // lq decomp of R_f (future output data) inv_w1 = trimatl(inv_w1); inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1); Matrix w_o_w = arma::solve( inv_w1, O_k_ortho_Uf_sans_Qt); // alternatively // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt svd(U, s_, V, w_o_w, \u0026#34;std\u0026#34;); U = inv_w1 * U; break; } } // Truncate to model order (heart of ssid method) auto s_hat = s_.subvec(0, n_x_ - 1); Matrix diag_sqrt_s = diagmat(sqrt(s_hat)); auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1); // get extended observability and controllability mats ext_obs_t_ = u_hat * diag_sqrt_s; // extended observability matrix } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::Solve(data_t wt_dc) { // required submats auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_56_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); // Solve for params using appropriate algorithm: // robust deterministic/stochastic algorithm in van Overschee 1996 // algorithm that the authors say \u0026#34;works\u0026#34; in practice. auto ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix // This is what textbook (1996) says: // // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15); // // HOWEVER, do not know why but have to fill the last place with zeros like // authors\u0026#39; matlab implementation (see `subid.m`) // Otherwise, get ridiculous covariances (although A,C estimates are close to // same...) Matrix Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Matrix S = Tl * pinv(Tr); // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure // stability. fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1)); Matrix ext_obs_t_p1 = join_vert( ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1), Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros)); fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1); // At this point, van Overschee \u0026amp; de Moor suggest re-calculating ext_obs_t_, // ext_obs_tm1 from (A, C) because it was just an approximation. This is RecomputeExtObs(); ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); S = Tl * pinv(Tr); Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_); Matrix Mcurly = pinv(ext_obs_tm1); Matrix Pcurly = Tl - Lcurly * R_56_15; Vector Pvec = vectorise(Pcurly); Matrix Qcurly = R_23_15; // Identify [D; B], assuming D=0 and ensuring DC gain is correct Matrix sum_QcurlyT_kron_Ncurly( (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_), fill::zeros); Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols, fill::eye); eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1, eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1; // van Overschee (1996) p. 126 Matrix N1_Tl = -Lcurly; N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) += join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly); N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) += Matrix(n_y_, n_y_, fill::eye); Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros); Matrix N_k; for (size_t k = 0; k \u0026lt; n_h_; k++) { auto Qcurly_k = Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1); Nk_Tl.zeros(); Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) = N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1); N_k = Nk_Tl * eye_ext_obs_tm1; sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k); } Matrix err_vec; if (wt_dc \u0026gt; 0) { // Constraints enforced by weighted least squares // // Reference: // // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly // Excited Industrial Systems. Conference in Decision and Control. // constraint 1: assume D=0 --\u0026gt; remove the components for Dvec (this is // actually a hard constraint in that it ignores D) Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly; sum_QcurlyT_kron_Ncurly = Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_); size_t kkk = 0; for (size_t k = 1; k \u0026lt; (n_u_ + 1); k++) { size_t start_idx = k * (n_y_ + n_x_) - n_x_; for (size_t kk = 0; kk \u0026lt; n_x_; kk++) { sum_QcurlyT_kron_Ncurly.col(kkk) = sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk); kkk++; } } // constraint 2: Make sure DC I/O gain is correct Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A()); Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_)); Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0); Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 = join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0); // WEIGHTED LS // Important in practice because I care a lot about at least getting the DC // gain correct. Put x weight on minimizing error at DC, relative to others Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye); // Make weight on minimizing DC error immense so at least that // should be nailed. size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows; size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows; size_t stop_row = w.n_rows - 1; size_t stop_col = w.n_cols - 1; // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale // weight with data length? w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc; Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * sum_QcurlyT_kron_Ncurly_b_to_g0) * sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec; fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_)); // Calculate residuals and their cov. // Because I\u0026#39;ve added constraints, I need to re-calculate the right term // with b_vec instead of how van Overschee do in final algorithm. err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec; } else { // default way: *no* constraint on G0 or D=0 Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec; // TODO(mfbolus) n.b., this gets thrown away... // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_); fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_)); err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec; } // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols); // TODO(mfbolus): Something is wrong with the error calculation above. // Use the way van overschee does it in `subid.m` // WARNING: this ignores any above constraints, so Q, R will be approximate... Matrix err = Tl - S * Tr; Matrix cov_err = err * err.t(); fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1)); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::RecomputeExtObs() { ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C(); for (size_t k = 2; k \u0026lt; (n_h_ + 1); k++) { ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) = ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1, ext_obs_t_.n_cols - 1) * fit_.A(); } } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":56,"href":"/docs/api/files/lds__fit_8h/","title":"ldsCtrlEst_h/lds_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_fit.h # LDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Fit LDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.\nSource code # //===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDS_FIT_HPP #define LDS_FIT_HPP // namespace #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); virtual ~Fit() = default; // get methods size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; Q() const { return Q_; }; const Vector\u0026amp; x0() const { return x0_; }; const Matrix\u0026amp; P0() const { return P0_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; // gets measurement noise virtual const Matrix\u0026amp; R() const = 0; // set methods (e.g., seeding initial fit values) void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_m(const Vector\u0026amp; m) { Reassign(m_, m); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); ForceSymPD(Q_); }; virtual void set_R(const Matrix\u0026amp; R) = 0; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); ForceSymPD(P0_); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; View f(Matrix\u0026amp; x, const Matrix\u0026amp; u, size_t t) { x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x.col(t); }; View f(Matrix\u0026amp; x_pre, const Matrix\u0026amp; x_post, const Matrix\u0026amp; u, size_t t) { x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x_pre.col(t); }; virtual View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) = 0; protected: data_t dt_{}; // Dynamics Matrix A_; Matrix B_; Vector g_; Vector m_; Matrix Q_; // Output Matrix C_; Vector d_; Matrix R_; // initial conditions Vector x0_; Matrix P0_; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; }; } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":57,"href":"/docs/api/files/lds__gaussian__ctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_ctrl.h # GLDS Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. Detailed Description # This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_gaussian_sys.h\u0026#34; // controller #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace gaussian { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); cx_ref_ = y_ref - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":58,"href":"/docs/api/files/lds__gaussian__fit__em_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_em.h # GLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitEM GLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":59,"href":"/docs/api/files/lds__gaussian__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_ssid.h # GLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; using SSID\u0026lt;Fit\u0026gt;::Run; private: using SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat; using SSID\u0026lt;Fit\u0026gt;::CalcSVD; using SSID\u0026lt;Fit\u0026gt;::Solve; void DecomposeData() override; void SolveVanOverschee(); }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":60,"href":"/docs/api/files/lds__gaussian__fit_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit.h # GLDS fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Fit GLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // fit type #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace gaussian { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); const Matrix\u0026amp; R() const override { return R_; }; void set_R(const Matrix\u0026amp; R) override { Reassign(R_, R); ForceSymPD(R_); }; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = C_ * x.col(t) + d_; return y.col(t); }; }; }; // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":61,"href":"/docs/api/files/lds__gaussian__sctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sctrl.h # GLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H // controller type #include \u0026#34;lds_gaussian_ctrl.h\u0026#34; // switched controller #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace gaussian { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); cx_ref_ = y_ref - sys_.d(); } // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; }; // SwitchedController } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":62,"href":"/docs/api/files/lds__gaussian__sys_8h/","title":"ldsCtrlEst_h/lds_gaussian_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sys.h # GLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::System Gaussian LDS Type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/docs/api/classes/classlds_1_1gaussian_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H #define LDSCTRLEST_LDS_GAUSSIAN_SYS_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; namespace lds { namespace gaussian { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0, data_t r0 = kDefaultR0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; // get methods const Matrix\u0026amp; R() const { return R_; }; // set methods void set_Q(const Matrix\u0026amp; Q) { lds::System::set_Q(Q); do_recurse_Ke_ = true; } void set_R(const Matrix\u0026amp; R) { Reassign(R_, R); do_recurse_Ke_ = true; }; void set_Ke(const Matrix\u0026amp; Ke) { Reassign(Ke_, Ke); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void set_Ke_m(const Matrix\u0026amp; Ke_m) { Reassign(Ke_m_, Ke_m); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void Print(); protected: void h() override { cx_ = C_ * x_; y_ = cx_ + d_; }; Vector h_(Vector x) override { return C_ * x + d_; }; void RecurseKe() override; // Gaussian-output-specific Matrix R_; bool do_recurse_Ke_{}; }; // System } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":63,"href":"/docs/api/files/lds__gaussian_8h/","title":"ldsCtrlEst_h/lds_gaussian.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian.h # glds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/docs/api/namespaces/namespacelds_1_1gaussian/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_H #define LDSCTRLEST_LDS_GAUSSIAN_H // namespace #include \u0026#34;lds.h\u0026#34; namespace lds { namespace gaussian { // insert any Gaussian-specific things here... } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":64,"href":"/docs/api/files/lds__poisson__ctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_ctrl.h # PLDS controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. Detailed Description # This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/docs/api/classes/classlds_1_1poisson_1_1_controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/docs/api/classes/classlds_1_1poisson_1_1_system/)), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_CTRL_H #define LDSCTRLEST_LDS_POISSON_CTRL_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system type #include \u0026#34;lds_poisson_sys.h\u0026#34; // control type #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace poisson { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); lds::Limit(y_ref_, kYRefLb, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; private: constexpr static const data_t kYRefLb = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":65,"href":"/docs/api/files/lds__poisson__fit__em_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_em.h # PLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitEM PLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\n[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H #define LDSCTRLEST_LDS_POISSON_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override{}; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; data_t NewtonSolveC(); void AnalyticalSolveD(); }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":66,"href":"/docs/api/files/lds__poisson__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_ssid.h # PLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. Detailed Description # This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/)).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H #define LDSCTRLEST_LDS_POISSON_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; private: void DecomposeData() override; void CalcCov(); void PoissonToGaussianMoments(); Matrix cov_; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":67,"href":"/docs/api/files/lds__poisson__fit_8h/","title":"ldsCtrlEst_h/lds_poisson_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit.h # PLDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Fit PLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_H #define LDSCTRLEST_LDS_POISSON_FIT_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // fit #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace poisson { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) : lds::Fit(n_u, n_x, n_y, dt){}; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = exp(C_ * x.col(t) + d_); return y.col(t); }; void set_R(const Matrix\u0026amp; R) override { std::cerr \u0026lt;\u0026lt; \u0026#34;WARNING: Cannot set R (R[0] = \u0026#34; \u0026lt;\u0026lt; R.at(0) \u0026lt;\u0026lt; \u0026#34;). No Gaussian measurement noise in Poisson observation model.\\n\u0026#34;; }; const Matrix\u0026amp; R() const override { return R_; }; }; }; // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":68,"href":"/docs/api/files/lds__poisson__sctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sctrl.h # PLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #include \u0026#34;lds_poisson_ctrl.h\u0026#34; #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace poisson { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); lds::Limit(y_ref_, kYRefLB, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; private: constexpr static data_t kYRefLB = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":69,"href":"/docs/api/files/lds__poisson__sys_8h/","title":"ldsCtrlEst_h/lds_poisson_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sys.h # PLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::System Poisson System type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/docs/api/classes/classlds_1_1poisson_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SYS_H #define LDSCTRLEST_LDS_POISSON_SYS_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; // needed for Poisson random number generation #include \u0026lt;random\u0026gt; namespace lds { namespace poisson { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; protected: void h() override { cx_ = C_ * x_; y_ = exp(cx_ + d_); diag_y_.diag() = y_; }; Vector h_(Vector x) override { return exp(C_ * x + d_); }; void RecurseKe() override; private: // Poisson-output-specific Matrix diag_y_; std::poisson_distribution\u0026lt;size_t\u0026gt; pd_; }; // System } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":70,"href":"/docs/api/files/lds__poisson_8h/","title":"ldsCtrlEst_h/lds_poisson.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson.h # plds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/docs/api/namespaces/namespacelds_1_1poisson/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_H #define LDSCTRLEST_LDS_POISSON_H #include \u0026#34;lds.h\u0026#34; namespace lds { namespace poisson { // TODO(mfbolus): Not sure if defining these as static here makes the most // sense. Is there a downside to letting multiple poisson System objects share a // common random number generator? static std::random_device rd; static std::mt19937 rng = std::mt19937( rd()); } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":71,"href":"/docs/api/files/lds__sctrl_8h/","title":"ldsCtrlEst_h/lds_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_sctrl.h # SwitchedController type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SwitchedController SwitchedController Type. Detailed Description # This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SCTRL_H #define LDSCTRLEST_LDS_SCTRL_H #include \u0026#34;lds_ctrl.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; #include \u0026#34;lds_uniform_vecs.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class SwitchedController : public Controller\u0026lt;System\u0026gt; { public: SwitchedController() = default; SwitchedController(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); SwitchedController(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); void Switch(size_t idx, bool do_force_switch = false); void set_Kc(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc) { Kc_list_ = Kc; Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc) { Kc_list_ = std::move(Kc); Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_inty) { Kc_inty_list_ = Kc_inty; Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_inty) { Kc_inty_list_ = std::move(Kc_inty); Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_u) { Kc_u_list_ = Kc_u; Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_u) { Kc_u_list_ = std::move(Kc_u); Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(const UniformVectorList\u0026amp; g) { g_design_list_ = g; g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(UniformVectorList\u0026amp;\u0026amp; g) { g_design_list_ = std::move(g); g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; protected: std::vector\u0026lt;System\u0026gt; systems_; size_t n_sys_{}; size_t idx_{}; // controller gains could be different for each UniformMatrixList\u0026lt;\u0026gt; Kc_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_inty_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_u_list_; // design-phase input gain could also be different UniformVectorList g_design_list_; // TODO(mfbolus): not sure why I need to do this. using Controller\u0026lt;System\u0026gt;::Kc_; using Controller\u0026lt;System\u0026gt;::Kc_inty_; using Controller\u0026lt;System\u0026gt;::Kc_u_; using Controller\u0026lt;System\u0026gt;::g_design_; using Controller\u0026lt;System\u0026gt;::sys_; // using Controller\u0026lt;System\u0026gt;::u_ref_; // using Controller\u0026lt;System\u0026gt;::x_ref_; // using Controller\u0026lt;System\u0026gt;::y_ref_; // using Controller\u0026lt;System\u0026gt;::control_type_; private: void InitVars(); using lds::Controller\u0026lt;System\u0026gt;::set_sys; // using Controller\u0026lt;System\u0026gt;::set_Kc; // using Controller\u0026lt;System\u0026gt;::set_Kc_inty; // using Controller\u0026lt;System\u0026gt;::set_Kc_u; // using Controller\u0026lt;System\u0026gt;::set_g_design; }; template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(systems.at(0), u_lb, u_ub, control_type), systems_(systems) { InitVars(); } template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(System(systems.at(0).n_u(), systems.at(0).n_x(), systems.at(0).n_y(), systems.at(0).dt()), u_lb, u_ub, control_type), systems_(std::move(systems)) { InitVars(); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::InitVars() { n_sys_ = systems_.size(); sys_ = systems_.at(0); Kc_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_)); Kc_inty_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); Kc_u_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); g_design_list_ = UniformVectorList(std::vector\u0026lt;Vector\u0026gt;(n_sys_, g_design_)); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::Switch(size_t idx, bool do_force_switch) { if ((idx == idx_) \u0026amp;\u0026amp; !do_force_switch) { return; // already there. } // put old up and get new one out systems_.at(idx_) = std::move(sys_); sys_ = std::move(systems_.at(idx)); // set the state of this system to that of the previous system // TODO(mfbolus): This will only work as intended if state matrix is the same. // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp. sys_.set_m(systems_.at(idx_).m(), true); sys_.set_x(systems_.at(idx_).x()); // swap controller gains Kc_list_.Swap(Kc_, idx_); Kc_list_.Swap(Kc_, idx); if (control_type_ \u0026amp; kControlTypeIntY) { Kc_inty_list_.Swap(Kc_inty_, idx_); Kc_inty_list_.Swap(Kc_inty_, idx); } if (control_type_ \u0026amp; kControlTypeDeltaU) { Kc_u_list_.Swap(Kc_u_, idx_); Kc_u_list_.Swap(Kc_u_, idx); } g_design_list_.Swap(g_design_, idx_); g_design_list_.Swap(g_design_, idx); idx_ = idx; } // Switch } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":72,"href":"/docs/api/files/lds__sys_8h/","title":"ldsCtrlEst_h/lds_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_sys.h # LDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::System Linear Dynamical System Type. Detailed Description # This file declares and partially defines the base type for linear dynamical systems ([lds::System](/docs/api/classes/classlds_1_1_system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SYS_H #define LDSCTRLEST_LDS_SYS_H #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class System { public: System() = default; System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); virtual ~System() {} void Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z); virtual const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) = 0; void f(const Vector\u0026amp; u, bool do_add_noise = false) { x_ = A_ * x_ + B_ * (g_ % u) + m_; if (do_add_noise) { x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_); } }; virtual void h() = 0; virtual Vector h_(Vector x) = 0; size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Vector\u0026amp; x() const { return x_; }; const Matrix\u0026amp; P() const { return P_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; P_m() const { return P_m_; }; const Vector\u0026amp; cx() const { return cx_; }; const Vector\u0026amp; y() const { return y_; }; const Vector\u0026amp; x0() const { return x0_; }; const Vector\u0026amp; m0() const { return m0_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; const Matrix\u0026amp; Ke() const { return Ke_; }; const Matrix\u0026amp; Ke_m() const { return Ke_m_; }; const Matrix\u0026amp; Q() { return Q_; }; const Matrix\u0026amp; Q_m() { return Q_m_; }; const Matrix\u0026amp; P0() { return P0_; }; const Matrix\u0026amp; P0_m() { return P0_m_; }; void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_m(const Vector\u0026amp; m, bool do_force_assign = false) { Reassign(m0_, m); if ((!do_adapt_m) || do_force_assign) { Reassign(m_, m); } }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); }; void set_Q_m(const Matrix\u0026amp; Q_m) { Reassign(Q_m_, Q_m); }; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); }; void set_P0_m(const Matrix\u0026amp; P0_m) { Reassign(P0_m_, P0_m); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; void set_x(const Vector\u0026amp; x) { Reassign(x_, x); h(); }; void Reset(); std::vector\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; nstep_pred_block( UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z, size_t n_pred = 1); void Print(); // safe to leave this public and non-const bool do_adapt_m{}; protected: virtual void RecurseKe() = 0; void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); std::size_t n_x_{}; std::size_t n_u_{}; std::size_t n_y_{}; data_t dt_{}; // Signals: Vector x_; Matrix P_; Vector m_; Matrix P_m_; Vector cx_; Vector y_; Vector z_; // Parameters: Vector x0_; Matrix P0_; Vector m0_; Matrix P0_m_; Matrix A_; Matrix B_; Vector g_; Matrix Q_; Matrix Q_m_; Matrix C_; Vector d_; Matrix Ke_; Matrix Ke_m_; }; // System } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":73,"href":"/docs/api/files/lds__uniform__mats_8h/","title":"ldsCtrlEst_h/lds_uniform_mats.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_mats.h # List of uniformly sized matrices. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformMatrixList Detailed Description # This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H #define LDSCTRLEST_LDS_UNIFORM_MATS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { template \u0026lt;MatrixListFreeDim D = kMatFreeDimNone\u0026gt; class UniformMatrixList : public std::vector\u0026lt;Matrix\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Matrix\u0026gt;::vector; // don\u0026#39;t allow push_back to be used since it doesn\u0026#39;t check dims using std::vector\u0026lt;Matrix\u0026gt;::push_back; public: using std::vector\u0026lt;Matrix\u0026gt;::operator=; using std::vector\u0026lt;Matrix\u0026gt;::operator[]; using std::vector\u0026lt;Matrix\u0026gt;::begin; using std::vector\u0026lt;Matrix\u0026gt;::end; using std::vector\u0026lt;Matrix\u0026gt;::size; using std::vector\u0026lt;Matrix\u0026gt;::at; UniformMatrixList() = default; explicit UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); explicit UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; ~UniformMatrixList() = default; const std::array\u0026lt;size_t, 2\u0026gt;\u0026amp; dim(size_t n = 0) const { return dim_.at(n); } size_t size() { return std::vector\u0026lt;Matrix\u0026gt;::size(); }; const Matrix\u0026amp; at(size_t n) { return std::vector\u0026lt;Matrix\u0026gt;::at(n); }; void Swap(Matrix\u0026amp; that, size_t n); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; void append(const Matrix\u0026amp; mat); private: void CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim); std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt; dim_; }; template \u0026lt;MatrixListFreeDim D\u0026gt; inline void UniformMatrixList\u0026lt;D\u0026gt;::Swap(Matrix\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.n_cols); } if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap // not moving, since it causes memory issues. // so this method isn\u0026#39;t a memory-saver as designed for now Matrix tmp = (*this)[n]; (*this)[n] = that; that = tmp; if (D == kMatFreeDim1) { this-\u0026gt;dim_[n][0] = (*this)[n].n_rows; } if (D == kMatFreeDim2) { this-\u0026gt;dim_[n][1] = (*this)[n].n_cols; } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::append(const Matrix\u0026amp; mat) { std::array\u0026lt;size_t, 2\u0026gt; dim({mat.n_rows, mat.n_cols}); CheckDimensions(dim); std::vector\u0026lt;Matrix\u0026gt;::push_back(mat); dim_.push_back(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) { // make sure dim_ vector is initialized if (dim_.empty()) { dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(that.size(), {0, 0}); } // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices\u0026#34;; throw std::runtime_error(ss.str()); } // if dimensions a not zero and do not match, skip move with error message. bool dims_nonzero = true; for (auto d : dim_) { if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; d[0] \u0026lt; 1) { dims_nonzero = false; break; } if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; d[1] \u0026lt; 1) { dims_nonzero = false; break; } } if (dims_nonzero) { bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); } if (!does_match) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrices of size \u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; dim_[k] = that.dim(k); } return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. bool dims_nonzero = true; for (auto d : dim_) { // if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; (d[0] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; (d[1] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // } // // if (dims_nonzero) { // bool does_match = true; // if (!(D == kMatFreeDim1)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); // } // // if (!(D == kMatFreeDim2)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); // } // // if (!does_match) { // this-\u0026gt;at(0).print(\u0026#34;this[0] = \u0026#34;); // that.at(0).print(\u0026#34;that[0] = \u0026#34;); // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformMatrixList element of size (\u0026#34; \u0026lt;\u0026lt; // that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols \u0026lt;\u0026lt; \u0026#34;) for an // element of size (\u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34;). // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Matrix\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(std::move(mats)) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) : vector(that) { (*this) = that; } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { std::array\u0026lt;size_t, 2\u0026gt; dim_k({this-\u0026gt;at(k).n_rows, this-\u0026gt;at(k).n_cols}); dim_.push_back(dim_k); } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim) { // change behavior based on free dim D if ((dim[0] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim1)) { dim[0] = this-\u0026gt;at(0).n_rows; } if ((dim[1] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim2)) { dim[1] = this-\u0026gt;at(0).n_cols; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Matrix\u0026amp; mat : *this) { if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_rows == dim[0]); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_cols == dim[1]); } if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(this-\u0026gt;size(), dim); for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { dim_[k][0] = (*this)[k].n_rows; dim_[k][1] = (*this)[k].n_cols; } } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":74,"href":"/docs/api/files/lds__uniform__systems_8h/","title":"ldsCtrlEst_h/lds_uniform_systems.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_systems.h # List of uniformly sized Systems. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformSystemList Detailed Description # This file provides a container for uniformly sized Systems.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector // namespace #include \u0026#34;lds.h\u0026#34; // System type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class UniformSystemList : public std::vector\u0026lt;System\u0026gt; { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;System\u0026gt;::vector; using std::vector\u0026lt;System\u0026gt;::operator=; using std::vector\u0026lt;System\u0026gt;::operator[]; using std::vector\u0026lt;System\u0026gt;::at; using std::vector\u0026lt;System\u0026gt;::begin; using std::vector\u0026lt;System\u0026gt;::end; using std::vector\u0026lt;System\u0026gt;::size; public: UniformSystemList() = default; explicit UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); explicit UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(const UniformSystemList\u0026amp; that); UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept; ~UniformSystemList() = default; const std::array\u0026lt;size_t, 3\u0026gt;\u0026amp; dim() const { return dim_; } size_t size() { return std::vector\u0026lt;System\u0026gt;::size(); }; const System\u0026amp; at(size_t n) { return std::vector\u0026lt;System\u0026gt;::at(n); }; void Swap(System\u0026amp; that, size_t n); UniformSystemList\u0026amp; operator=(const UniformSystemList\u0026amp; that); UniformSystemList\u0026amp; operator=(UniformSystemList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim); std::array\u0026lt;size_t, 3\u0026gt; dim_{}; }; template \u0026lt;typename System\u0026gt; inline void UniformSystemList\u0026lt;System\u0026gt;::Swap(System\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformSystemList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = (dim_[0] == that.n_u()) \u0026amp;\u0026amp; (dim_[1] == that.n_x()) \u0026amp;\u0026amp; (dim_[2] == that.n_y()); if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformSystemList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap System tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( const UniformSystemList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_[0] + dim_[1] + dim_[2]) { std::array\u0026lt;size_t, 3\u0026gt; other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign systems of size \u0026#34; \u0026lt;\u0026lt; dim_[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2] \u0026lt;\u0026lt; \u0026#34; with systems of size \u0026#34; \u0026lt;\u0026lt; other_dim[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other_dim[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2]; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( UniformSystemList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. if (dim_[0] + dim_[1] + dim_[2]) { // bool does_match = (dim_[0] == that.at(0).n_u()) \u0026amp;\u0026amp; // (dim_[1] == that.at(0).n_x()) \u0026amp;\u0026amp; // (dim_[2] == that.at(0).n_y()); // if (!does_match) { // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformSystemList element for an element of \u0026#34; // \u0026#34;different size. Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;System\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(std::move(systems)) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList( std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const UniformSystemList\u0026amp; that) : std::vector\u0026lt;System\u0026gt;(that) { (*this) = that; } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept : std::vector\u0026lt;System\u0026gt;(std::move(that)) { this-\u0026gt;dim_[0] = this-\u0026gt;at(0).n_u(); this-\u0026gt;dim_[1] = this-\u0026gt;at(0).n_x(); this-\u0026gt;dim_[2] = this-\u0026gt;at(0).n_y(); } template \u0026lt;typename System\u0026gt; void UniformSystemList\u0026lt;System\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim) { if (dim[0] + dim[1] + dim[2]) { dim_ = dim; } else { dim_[0] = this-\u0026gt;at(0).n_u(); dim_[1] = this-\u0026gt;at(0).n_x(); dim_[2] = this-\u0026gt;at(0).n_y(); } // make sure dimensiolaties are all uniform bool does_match(true); for (const System\u0026amp; sys : *this) { does_match = does_match \u0026amp;\u0026amp; (sys.n_u() == dim_[0]); does_match = does_match \u0026amp;\u0026amp; (sys.n_x() == dim_[1]); does_match = does_match \u0026amp;\u0026amp; (sys.n_y() == dim_[2]); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input systems are not uniform.\u0026#34;); } } } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":75,"href":"/docs/api/files/lds__uniform__vecs_8h/","title":"ldsCtrlEst_h/lds_uniform_vecs.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_vecs.h # List of uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformVectorList Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H #define LDSCTRLEST_LDS_UNIFORM_VECS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { class UniformVectorList : public std::vector\u0026lt;Vector\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Vector\u0026gt;::vector; using std::vector\u0026lt;Vector\u0026gt;::operator=; using std::vector\u0026lt;Vector\u0026gt;::operator[]; using std::vector\u0026lt;Vector\u0026gt;::at; using std::vector\u0026lt;Vector\u0026gt;::begin; using std::vector\u0026lt;Vector\u0026gt;::end; using std::vector\u0026lt;Vector\u0026gt;::size; public: UniformVectorList() = default; explicit UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim = 0); explicit UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim = 0); UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim = 0); UniformVectorList(const UniformVectorList\u0026amp; that); UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept; ~UniformVectorList() = default; size_t dim() const { return dim_; } size_t size() { return std::vector\u0026lt;Vector\u0026gt;::size(); }; const Vector\u0026amp; at(size_t n) { return std::vector\u0026lt;Vector\u0026gt;::at(n); }; void Swap(Vector\u0026amp; that, size_t n); UniformVectorList\u0026amp; operator=(const UniformVectorList\u0026amp; that); UniformVectorList\u0026amp; operator=(UniformVectorList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(size_t dim); size_t dim_{}; }; inline void UniformVectorList::Swap(Vector\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = dim_ == that.n_elem; if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap Vector tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( const UniformVectorList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_) { size_t other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ \u0026lt;\u0026lt; \u0026#34; with vectors of size \u0026#34; \u0026lt;\u0026lt; other_dim; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( UniformVectorList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors. Skipping.\\n\u0026#34;; // return (*this); // } // // if (dim_) { // size_t other_dim(that.dim()); // if (dim_ != other_dim) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ // \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; other_dim \u0026lt;\u0026lt; \u0026#34;. // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Vector\u0026gt;::operator=(std::move(that)); return (*this); } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":76,"href":"/docs/api/files/lds_8h/","title":"ldsCtrlEst_h/lds.h","section":"Files","content":" ldsCtrlEst_h/lds.h # lds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/docs/api/namespaces/namespacelds_1_1poisson/)) observations.\nSource code # //===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_H #define LDSCTRLEST_LDS_H // #ifndef LDSCTRLEST // #include \u0026lt;ldsCtrlEst\u0026gt; // #endif #include \u0026lt;armadillo\u0026gt; namespace lds { using data_t = double; // may change to float (but breaks mex functions) using Vector = arma::Col\u0026lt;data_t\u0026gt;; using Matrix = arma::Mat\u0026lt;data_t\u0026gt;; using Cube = arma::Cube\u0026lt;data_t\u0026gt;; using View = arma::subview\u0026lt;data_t\u0026gt;; namespace fill = arma::fill; static const std::size_t kControlTypeDeltaU = 0x1; static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); static const data_t kPi = arma::datum::pi; static const data_t kDefaultP0 = 1e-6; static const data_t kDefaultQ0 = 1e-6; static const data_t kDefaultR0 = 1e-2; enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA }; enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2 }; // TODO(mfbolus): for SwitchedController, may want systems to have differing // numbers of states. Use this enum as template parameter? // enum SystemListFreeDim { // kSysFreeDimNone, // kSysFreeDimX ///\u0026lt; allow state dim (x) of systems in list to be hetero // }; // place hard limits on contents of vecors/mats void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub); void Limit(Vector\u0026amp; x, data_t lb, data_t ub); void Limit(Matrix\u0026amp; x, data_t lb, data_t ub); // in-place assign that errs if there are dimension mismatches: void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); // TODO(mfbolus): this is a fudge, but for some reason, cov mats often going // numerically asymm. void ForceSymPD(Matrix\u0026amp; X); void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min = 0); void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X); Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B); inline void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Vector\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Matrix\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if (other.n_elem != some.n_elem) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vector of size \u0026#34; \u0026lt;\u0026lt; some.n_elem \u0026lt;\u0026lt; \u0026#34; with vector of size \u0026#34; \u0026lt;\u0026lt; other.n_elem \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } inline void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrix of size \u0026#34; \u0026lt;\u0026lt; some.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; some.n_cols \u0026lt;\u0026lt; \u0026#34; with matrix of size \u0026#34; \u0026lt;\u0026lt; other.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other.n_cols \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":77,"href":"/docs/api/files/mex__c__util_8h/","title":"ldsCtrlEst_h/mex_c_util.h","section":"Files","content":" ldsCtrlEst_h/mex_c_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) More\u0026hellip;\nNamespaces # Name armamexc arma/mex interface using Matlab C API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab/Octave\u0026rsquo;s C mex API.\nSource code # //===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXC_UTIL_H #define LDSCTRLEST_MEXC_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.h\u0026#34; // // If Matlab_FOUND, include matrix.h. // // (Octave does not need/have it.) // #ifdef Matlab_FOUND // #include \u0026#34;matrix.h\u0026#34; // #endif namespace armamexc { template \u0026lt;class T\u0026gt; inline auto m2T_scalar(const mxArray *matlab_scalar) -\u0026gt; T { if (mxGetData(matlab_scalar)) { return static_cast\u0026lt;T\u0026gt;(mxGetScalar(matlab_scalar)); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return 0; } template \u0026lt;class T\u0026gt; inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false, bool strict = true) -\u0026gt; arma::Mat\u0026lt;T\u0026gt; { if (mxGetData(matlab_mat)) { const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat); if (n_dim == 2) { return arma::Mat\u0026lt;T\u0026gt;(static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)), mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem, strict); } mexErrMsgTxt(\u0026#34;Number of dimensions must be 2.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } // TODO(mfbolus): make these templated. template \u0026lt;typename T\u0026gt; inline auto a2m_mat(arma::Mat\u0026lt;T\u0026gt; const \u0026amp;arma_mat) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_mat.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Mat.\u0026#34;); return nullptr; } template \u0026lt;typename T\u0026gt; inline auto a2m_vec(arma::Col\u0026lt;T\u0026gt; const \u0026amp;arma_vec) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_vec.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Col.\u0026#34;); return nullptr; } } // namespace armamexc #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":78,"href":"/docs/api/files/mex__cpp__util_8h/","title":"ldsCtrlEst_h/mex_cpp_util.h","section":"Files","content":" ldsCtrlEst_h/mex_cpp_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) More\u0026hellip;\nNamespaces # Name armamexcpp arma/mex interface using Matlab C++ API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab\u0026rsquo;s C++ mex API.\nSource code # //===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXCPP_UTIL_H #define LDSCTRLEST_MEXCPP_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.hpp\u0026#34; #include \u0026#34;mexAdapter.hpp\u0026#34; namespace armamexcpp { template \u0026lt;class T\u0026gt; std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; m2a_cellmat(matlab::data::CellArray\u0026amp; matlab_cell) { size_t n_cells = matlab_cell.getNumberOfElements(); std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; arma_mat(n_cells, arma::Mat\u0026lt;T\u0026gt;(1, 1, arma::fill::zeros)); for (size_t k = 0; k \u0026lt; n_cells; k++) { matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = matlab_cell[k]; auto dims = matlab_mat.getDimensions(); arma_mat[k] = arma::Mat\u0026lt;T\u0026gt;(matlab_mat.release().get(), dims[0], dims[1]); } return arma_mat; }; template \u0026lt;class T\u0026gt; std::vector\u0026lt;T\u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt;T\u0026gt;\u0026amp; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); T* ptr = matlab_array.release().get(); std::vector\u0026lt;T\u0026gt; vec(ptr, ptr + n_elem); return vec; }; template \u0026lt;class T\u0026gt; arma::Col\u0026lt;T\u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); // T* ptr = matlab_array.release().get(); // arma::Col\u0026lt;T\u0026gt; vec(ptr, n_elem); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? arma::Col\u0026lt;T\u0026gt; vec(n_elem, arma::fill::zeros); for (size_t k = 0; k \u0026lt; n_elem; k++) { vec[k] = matlab_array[k]; } return vec; }; template \u0026lt;class T\u0026gt; arma::Mat\u0026lt;T\u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { // ArrayDimensions == std::vector\u0026lt;size_t\u0026gt; auto dims = matlab_array.getDimensions(); // T* ptr = matlab_array.release().get(); // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false) // arma::Mat\u0026lt;T\u0026gt; mat(ptr, dims[0], dims[1]); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? // // armadillo and matlab both use column-major ordering, so this should work: size_t n_elem = dims[0] * dims[1]; arma::Mat\u0026lt;T\u0026gt; mat(dims[0], dims[1], arma::fill::zeros); size_t k(0); for (auto m: matlab_array) { mat[k] = m; k++; } return mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_mat(const arma::Mat\u0026lt;T\u0026gt;\u0026amp; arma_mat, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(), arma_mat.memptr() + arma_mat.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_vec(const arma::Col\u0026lt;T\u0026gt;\u0026amp; arma_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;({arma_vec.n_elem, 1}, arma_vec.memptr(), arma_vec.memptr() + arma_vec.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; s2m_vec(const std::vector\u0026lt;T\u0026gt;\u0026amp; std_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size()); return matlab_mat; }; } // namespace armamexcpp #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":79,"href":"/docs/terminology/model/","title":"Models","section":"LDS C+E Documentation","content":" Model Definitions # This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form: \\[\\mathbf{x}_{t\u0026#43;1} = f\\left( \\mathbf{x}_{t}, \\mathbf{v}_{t} \\right) = \\mathbf{A} \\mathbf{x}_{t} \u0026#43; \\mathbf{B} \\mathbf{v}_{t} \u0026#43; \\mathbf{m}_{t} \u0026#43; \\mathbf{w}_{t}\\] \\[\\mathbf{y}_{t} = h\\left( \\mathbf{x}_{t} \\right)\\] t : time index x : system state v = g%u : input (e.g., in physical units used for model fit) u : control signal sent to actuator (e.g., in Volts) y : system output m : process disturbance w ~ N(0, Q) : process noise/disturbance A : state matrix B : input coupling matrix g : input gain (e.g., for converting to control signal actuator voltage) n.b., assumes this conversion is linear Q : process noise covariance % : element-wise multiplication LDS with Gaussian Observations # For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.\n\\[\\mathbf{y}_{t} = \\mathbf{C} \\mathbf{x}_{t} \u0026#43; \\mathbf{d}\\] \\[\\mathbf{z}_{t} \\sim \\mathcal{N}\\left(\\mathbf{y}_{t} , \\mathbf{R} \\right)\\] z : measurement C : output matrix d : output bias R : measurement noise covariance LDS with Poisson Observations # For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.\n\\[y_{t}^{i} = \\exp \\left(\\mathbf{c}^i \\mathbf{x}_{t} \u0026#43; d^i\\right)\\] \\[z_{t}^i \\sim \\rm{Poisson} \\left(y_{t}^i \\right)\\] i : output index z : measurement (count data) c : i^th row of output matrix (C) d : output bias Model Predictive Control (MPC) # Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.\nIn the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.\n"},{"id":80,"href":"/docs/api/modules/","title":"Modules","section":"API Reference","content":" Modules # "},{"id":81,"href":"/docs/api/namespaces/","title":"Namespaces","section":"API Reference","content":" Namespaces # "},{"id":82,"href":"/docs/api/pages/","title":"Pages","section":"API Reference","content":" Pages # Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":83,"href":"/docs/tutorials/eg_plds_state_estimation/","title":"PLDS State Estimation","section":"LDS C+E Examples","content":" PLDS State Estimation Tutorial # This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[x_{t\u0026#43;1} = x_t \u0026#43; w_t\\] \\[y_{t} = \\exp\\left(x_t\\right)\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state Finally, assign the parameters using corresponding set-methods.\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); Creating the estimator # Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. To ensure robust estimates, adaptively re-estimate the process disturbance.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); Simulating estimation # In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.\n// Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.\nWith this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.\n"},{"id":84,"href":"/docs/tutorials/eg_switched_plds_control/","title":"PLDS Switched Control","section":"LDS C+E Examples","content":" PLDS Switched Control Tutorial # This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.\nIn the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating the simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); The system\u0026rsquo;s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 Initially, the system will be in \u0026ldquo;mode\u0026rdquo; 1, where B = b1.\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.\nCreating the controller # Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.\nSimilar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.\nMoreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.\nPutting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying systems: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nNow that the SwitchedController is instantiated, assign its parameters.\n// Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).\nThe control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.\n// Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system\u0026rsquo;s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.\nExample simulation result # Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller\u0026rsquo;s online estimates of the output and state are shown in purple.\nNote that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.\n"},{"id":85,"href":"/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/","title":"src","section":"Files","content":" src # Files # Name src/lds.cpp misc lds namespace functions src/lds_gaussian_sys.cpp GLDS base type. src/lds_poisson_sys.cpp PLDS base type. src/lds_sys.cpp LDS base type. src/lds_uniform_vecs.cpp Uniformly sized vectors. Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":86,"href":"/docs/api/files/lds__gaussian__sys_8cpp/","title":"src/lds_gaussian_sys.cpp","section":"Files","content":" src/lds_gaussian_sys.cpp # GLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_gaussian_sys.h\u0026gt; lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0, data_t r0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { R_.zeros(n_y, n_y); R_.diag().fill(r0); do_recurse_Ke_=true; }; // recursively estimate Ke void lds::gaussian::System::RecurseKe() { if (!do_recurse_Ke_) { return; } // predict covariance P_ = A_ * P_ * A_.t() + Q_; // calc Kalman gain Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_); // update covariance // Reference: Ghahramani et Hinton (1996) P_ = P_ - Ke_ * C_ * P_; if (do_adapt_m) { P_m_ += Q_m_; // A_m = I (i.e., random walk) Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_); P_m_ = P_m_ - Ke_m_ * C_ * P_m_; } } // Simulate const lds::Vector\u0026amp; lds::gaussian::System::Simulate(const Vector\u0026amp; u_tm1){ f(u_tm1, true);//simulate dynamics with noise added h();//output z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure return z_; } void lds::gaussian::System::Print() { lds::System::Print(); std::cout \u0026lt;\u0026lt; \u0026#34;R: \\n\u0026#34; \u0026lt;\u0026lt; R_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":87,"href":"/docs/api/files/lds__poisson__sys_8cpp/","title":"src/lds_poisson_sys.cpp","section":"Files","content":" src/lds_poisson_sys.cpp # PLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_poisson_sys.h\u0026gt; lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { diag_y_ = diagmat(y_); pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(0); }; // Correct: Given measurement (z) and current input (u), update estimate of the // state, covar, output. // // see Eden et al. 2004 void lds::poisson::System::RecurseKe() { // predict covariance P_ = A_ * P_ * A_.t() + Q_; // update cov P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_); Ke_ = P_ * C_.t(); if (do_adapt_m) { P_m_ += Q_m_; // predict (A_m = I) P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_); // update Ke_m_ = P_m_ * C_.t(); } } // Simulate Measurement: z ~ Poisson(y) const lds::Vector\u0026amp; lds::poisson::System::Simulate(const Vector\u0026amp; u_tm1) { f(u_tm1, true); // simulate dynamics with noise added h(); // output z_.zeros(); for (std::size_t k = 0; k \u0026lt; n_y_; k++) { // construct a Poisson distribution object with mean y[k] pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(y_[k]); // pull random sample from this distribution z_[k] = pd_(rng); } return z_; } // ******************* SYS_T ******************* Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":88,"href":"/docs/api/files/lds__sys_8cpp/","title":"src/lds_sys.cpp","section":"Files","content":" src/lds_sys.cpp # LDS base type. More\u0026hellip;\nDetailed Description # This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- lds_sys.cpp - LDS -------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_sys.h\u0026gt; #include \u0026lt;vector\u0026gt; lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) { InitVars(p0, q0); } void lds::System::InitVars(data_t p0, data_t q0) { // initial conditions. x0_ = Vector(n_x_, fill::zeros); // includes bias (nY) and g (nU) P0_ = p0 * Matrix(n_x_, n_x_, fill::eye); m0_ = x0_; P0_m_ = P0_; // signals x_ = x0_; P_ = P0_; m_ = m0_; P_m_ = P0_m_; y_ = Vector(n_y_, fill::zeros); cx_ = Vector(n_y_, fill::zeros); z_ = Vector(n_y_, fill::zeros); // By default, random walk where each state is independent // In this way, provides independent estimates of rate per channel of output. A_ = Matrix(n_x_, n_x_, fill::eye); B_ = Matrix(n_x_, n_u_, fill::zeros); g_ = Vector(n_u_, fill::ones); Q_ = q0 * Matrix(n_x_, n_x_, fill::eye); Q_m_ = Q_; C_ = Matrix(n_y_, n_x_, fill::eye); // each state will map to an output by d_ = Vector(n_y_, fill::zeros); Ke_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain. Ke_m_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain for m adaptation. do_adapt_m = false; } // Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state // and update estimate of the state, covar, output using Kalman filter void lds::System::Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z_t) { // predict mean f(u_tm1); // dynamics h(); // output // recursively calculate esimator gains (or just keep existing values) // (also predicts+updates estimate covariance) RecurseKe(); // update x_ += Ke_ * (z_t - y_); if (do_adapt_m) { m_ += Ke_m_ * (z_t - y_); // adaptively estimating disturbance } // With new state, estimate output. h(); // --\u0026gt; posterior } void lds::System::Reset() { // reset to initial conditions x_ = x0_; // mean P_ = P0_; // cov of state estimate m_ = m0_; // process disturbance P_m_ = P0_m_; // cov of disturbance estimate h(); } std::vector\u0026lt;lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt;\u0026gt; lds::System::nstep_pred_block(lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; u, lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; z, size_t n_pred) { lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_filt; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_pred; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; y_pred; for (size_t k = 0; k \u0026lt; u.size(); k++) { Reset(); size_t n_t = arma::size(u[k])[1]; Matrix x_filt_k(n_x_, n_t, fill::zeros); Matrix x_pred_k(n_x_, n_t - n_pred, fill::zeros); Matrix y_pred_k(n_y_, n_t - n_pred, fill::zeros); for (size_t t = 0; t \u0026lt; n_t - n_pred; t++) { Vector x_pred_ahead = x_; for (size_t t_u = t; t_u \u0026lt; t + n_pred; t_u++) { x_pred_ahead = A_ * x_pred_ahead + B_ * u[k].col(t_u); } x_pred_k.col(t) = x_pred_ahead; y_pred_k.col(t) = h_(x_pred_ahead); if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; // given previous measurment } for (size_t t = n_t - n_pred; t \u0026lt; n_t; t++) { if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; } x_filt.append(x_filt_k); x_pred.append(x_pred_k); y_pred.append(y_pred_k); } return {x_filt, x_pred, y_pred}; } void lds::System::Print() { std::cout \u0026lt;\u0026lt; \u0026#34;\\n ********** SYSTEM ********** \\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;x: \\n\u0026#34; \u0026lt;\u0026lt; x_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;P: \\n\u0026#34; \u0026lt;\u0026lt; P_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;A: \\n\u0026#34; \u0026lt;\u0026lt; A_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B: \\n\u0026#34; \u0026lt;\u0026lt; B_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;g: \\n\u0026#34; \u0026lt;\u0026lt; g_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;m: \\n\u0026#34; \u0026lt;\u0026lt; m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q: \\n\u0026#34; \u0026lt;\u0026lt; Q_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q_m: \\n\u0026#34; \u0026lt;\u0026lt; Q_m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d: \\n\u0026#34; \u0026lt;\u0026lt; d_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;C: \\n\u0026#34; \u0026lt;\u0026lt; C_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;y: \\n\u0026#34; \u0026lt;\u0026lt; y_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } //******************* SYS_T ******************* Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":89,"href":"/docs/api/files/lds__uniform__vecs_8cpp/","title":"src/lds_uniform_vecs.cpp","section":"Files","content":" src/lds_uniform_vecs.cpp # Uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_uniform_vecs.h\u0026gt; namespace lds { UniformVectorList::UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); } UniformVectorList::UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim) : vector(std::move(vecs)) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(const UniformVectorList\u0026amp; that) : vector(that) { (*this) = that; } UniformVectorList::UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { this-\u0026gt;dim_ = this-\u0026gt;at(0).n_elem; } void UniformVectorList::CheckDimensions(size_t dim) { if (dim) { dim_ = dim; } else { dim_ = this-\u0026gt;at(0).n_elem; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Vector\u0026amp; vec : *this) { does_match = does_match \u0026amp;\u0026amp; (vec.n_elem == dim_); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } } } // namespace lds Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":90,"href":"/docs/api/files/lds_8cpp/","title":"src/lds.cpp","section":"Files","content":" src/lds.cpp # misc lds namespace functions More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file implements miscellaneous lds namespace functions not bound to a class.\nSource code # //===-- lds.cpp - LDS -----------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds.h\u0026gt; // insert any necessary function definitions here. namespace lds { void ForceSymPD(Matrix\u0026amp; X) { if (X.is_sympd() || !X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; // for eigenval decomp bool did_succeed(true); Vector d; Matrix u; // see first method (which may not be ideal): // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/ size_t k(1); bool is_sympd = X.is_sympd(); Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye); while (!is_sympd) { if (k \u0026gt; 100) { did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); data_t min_eig = arma::min(d); std::cerr \u0026lt;\u0026lt; \u0026#34;After multiple iterations, min eigen val = \u0026#34; \u0026lt;\u0026lt; min_eig \u0026lt;\u0026lt; \u0026#34;.\\n\u0026#34;; throw std::runtime_error( \u0026#34;Failed to make matrix symmetric positive definite.\u0026#34;); return; } // Limit(d, arma::eps(0), kInf); // force to be positive... // Matrix d_diag = arma::diagmat(d); // X = u * d_diag * u.t(); did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymPD failed.\u0026#34;); } data_t min_eig = arma::min(d); X += id * abs(min_eig) + arma::datum::eps; // make sure symm: X = (X + X.t()) / 2; // double check eigenvals positive after symmetrizing: arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); min_eig = arma::min(d); is_sympd = min_eig \u0026gt; 0; k++; } } void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min) { if (!X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; bool did_succeed(true); Vector d; Matrix u; did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymMinEig failed.\u0026#34;); } Limit(d, eig_min + arma::eps(eig_min), kInf); // enforce lower bound Matrix d_diag = arma::diagmat(d); X = u * d_diag * u.t(); // double check symmetric X = (X + X.t()) / 2; } void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X) { bool did_succeed(true); did_succeed = arma::qr_econ(Qt, L, X.t()); if (!did_succeed) { throw std::runtime_error(\u0026#34;LQ decomposition failed.\u0026#34;); } arma::inplace_trans(L); arma::inplace_trans(Qt); } Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B) { // subtract out mean auto m_a = arma::mean(A, 1); Matrix a0 = A; a0.each_col() -= m_a; auto m_b = arma::mean(B, 1); Matrix b0 = B; b0.each_col() -= m_b; Matrix cov = a0 * b0.t() / a0.n_cols; return cov; } } // namespace lds Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":91,"href":"/docs/api/namespaces/namespacestd/","title":"std","section":"Namespaces","content":" std # Updated on 5 March 2025 at 16:35:01 EST\n"}]
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+[{"id":0,"href":"/docs/","title":"LDS C+E Documentation","section":"LDS Control \u0026 Estimation","content":" LDS Control \u0026amp; Estimation Documentation # "},{"id":1,"href":"/docs/tutorials/","title":"LDS C+E Examples","section":"LDS C+E Documentation","content":" Examples # "},{"id":2,"href":"/acknowledgements/","title":"Acknowledgements","section":"LDS Control \u0026 Estimation","content":" Acknowledgements # Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.\n"},{"id":3,"href":"/docs/getting-started/getting-started/","title":"Getting Started","section":"LDS C+E Documentation","content":" Getting Started # This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.\nldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.\nTested Configurations # Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the RelWithDebInfo build type in the CMake configure command (-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo):\nUbuntu 18.04 with GCC 7.5 compiler macOS 11 (Big Sur) with Apple Clang 12 compiler Windows 10 with Visual Studio 16.11 (2019 release) and Clang 12 compiler That being said, if you want to debug a build for a single platform, here are some things you can try:\nUse different compilers (or even different versions of a single compiler) Use different versions of vcpkg (which you can control by checking out a different commit in the vcpkg submodule) Mac Pre-requisities # Xcode Command Line Tools will get you clang, gcc, make, and git:\nxcode-select --install Homebrew is \u0026ldquo;The Missing Package Manager for macOS\u0026rdquo; which will make installing lots of things easy. Install like this:\n/bin/bash -c \u0026#34;$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\u0026#34; You can then use it to install CMake, gfortran, and pkg-config:\nbrew install cmake gfortran pkg-config Linux Pre-requisites # You\u0026rsquo;ll need Git, CMake, GCC, gfortran, etc.\nsudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential ninja-build Windows Installation # Look here for Windows-specific instructions.\nDownloading the Library # First, clone the repository along with submodules:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est\rgit submodule update --init Compilation + Installation # Now generate the cache and build using your IDE or from the command line as follows.\nmkdir build \u0026amp;\u0026amp; cd build\rcmake ..\rcmake --build . The first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.\nIf you want to use vcpkg set up somewhere besides this repo\u0026rsquo;s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE\u0026rsquo;s settings.\nYou can verify the build is working by running ctest from the build folder, which runs all the example scripts.\nOptions # This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\nLDSCTRLEST_BUILD_EXAMPLES : [default=ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake to configure/build the library.\nFor basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build cmake --build #build the project sudo make install #[optional] installs to default location (OS-specific) To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location cmake --build #build the project make install #install to /your/install/prefix To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\nLD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.\nPython bindings package ldsctrlest # With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython3_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn\u0026rsquo;t use an undesired version. That environment needs to have NumPy installed.\ncmake --build . --target python_modules The bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory (pip install pytest matplotlib first if you need to).\nSee python/ldsctrlest/README.md for usage details.\nAlso, beware that a single build will probably not work for both the standalone library and the Python package, since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.\nCommon issues # \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.\nexport CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix vcpkg fails on configuration Try running ./bootstrap-vcpkg from the vcpkg folder and try again. If that doesn\u0026rsquo;t work, try updating vcpkg to a newer version (in the source control tab, click on the commit hash by the vcpkg repo then select from the dropdown) and running boostsrap-vcpkg again. You can also try upgrading your system (e.g., apt update, apt upgrade).\nCould not find Python3 (missing: Python3_NumPy_INCLUDE_DIRS NumPy)\nMake sure NumPy is installed in the Python environment you specified. If CMake still can\u0026rsquo;t find it, you may need to tell CMake exactly where to find it by adding an argument to the configure command: -DPython3_NumPy_INCLUDE_DIR=.... You can find that location like this: python -c 'import numpy; print(numpy.get_include())'\n"},{"id":4,"href":"/docs/getting-started/windows/","title":"Windows","section":"LDS C+E Documentation","content":" Windows Installation # Windows Pre-requisites # Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:\nSet-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time iwr get.scoop.sh | Invoke-Expression Install Git and CMake if you don\u0026rsquo;t already have them:\nscoop install git cmake If that didn\u0026rsquo;t work, follow more detailed instructions here.\nThe easiest way to compile C++ project on Windows is with Visual Studio\u0026rsquo;s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on \u0026ldquo;Desktop development with C++.\u0026rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the \u0026ldquo;Installation details\u0026rdquo; sidebar under optional features.\nAnd the easiest way to use Visual Studio\u0026rsquo;s build tools is with VS Code, along with the CMake Tools extension. Install them and you should be ready to go.\nDownloading the Library # First, clone the repository, either from VS Code or the command line:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est You\u0026rsquo;ll need to initialize the submodules from the command line after the repo is cloned:\ngit submodule update --init Installation # When you open the folder in VS Code, you will like be prompted by the CMake Tools extension to configure the project. Make sure you select the kit (you\u0026rsquo;ll be prompted when you configure\u0026ndash;else there\u0026rsquo;s an icon in the bar on the bottom of the window or type Ctrl+Shift+P, then \u0026ldquo;cmake select kit\u0026rdquo;). Choose Clang [latest version] with GNU CLI ... amd64 assuming you are running a 64-bit OS. (MSVC may work okay too if you don\u0026rsquo;t need to build Python bindings.)\nFollow along with the \u0026ldquo;Getting Started\u0026rdquo; instructions, but where you see config options specified as -DLDSCTREST_BUILD_STATIC=OFF or -DPython3_ROOT_DIR=..., you will enter those in settings: open with Ctrl+,, click \u0026ldquo;workspace\u0026rdquo;, then search for \u0026ldquo;CMake: Configure Args\u0026rdquo; and enter each of your desired arguments as a separate item.\nTo configure, use Ctrl+Shift+P and search for the \u0026ldquo;CMake: Configure\u0026rdquo; command. To build, click the \u0026ldquo;Build\u0026rdquo; button on the bottom bar. Then click the \u0026ldquo;CTest\u0026rdquo; button to run the example scripts.\nConsiderations # Development on Windows has been more prone to bugs than on Unix systems, so if you encounter many problems, consider switching—WSL (Windows Subsystem for Linux) is a good option for Windows users who don\u0026rsquo;t want to work on a different machine.\nCompilation has been successfully tested in VS Code using the following kit, using the \u0026ldquo;RelWithDebInfo\u0026rdquo; config:\nClang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64) Troubleshooting # The build appears to work, but tests fail with code 0xc0000135 OR \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; Have you installed the library? In VS Code, use Shift+F7 to build a specific target, in this case INSTALL. If that doesn\u0026rsquo;t solve your problem, you will likely need to add the build or install folder to your PATH environment variable, which you can do using the settings GUI (search for \u0026ldquo;Edit the system environment variables\u0026rdquo;).\nOn Windows, \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted. Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":5,"href":"/issues-contributing/","title":"Issues Contributing","section":"LDS Control \u0026 Estimation","content":" Reporting Issues # If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.\nContributing # We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.\n"},{"id":6,"href":"/docs/api/namespaces/namespacearmamexc/","title":"armamexc","section":"Namespaces","content":" armamexc # arma/mex interface using Matlab C API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; T m2T_scalar(const mxArray * matlab_scalar)\nConvert Matlab mxArray to scalar of type T. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)\nConvert matlab matrix to armadillo. template \u0026lt;typename T \u0026gt; mxArray * a2m_mat(arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat)\nConvert armadillo to matlab matrix. template \u0026lt;typename T \u0026gt; mxArray * a2m_vec(arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec)\nConvert armadillo to matlab vector. Detailed Description # Utilities for arma/mex interface using Matlab C API\nFunction Details # m2T_scalar # template \u0026lt;class T \u0026gt; inline T m2T_scalar( const mxArray * matlab_scalar ) Parameters:\nmatlab_scalar matlab scalar Template Parameters:\nT type Return: scalar of type T\nm2a_mat # template \u0026lt;class T \u0026gt; inline arma::Mat\u0026lt; T \u0026gt; m2a_mat( const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true ) Parameters:\nmatlab_mat matlab matrix copy_aux_mem [optional] whether to copy auxiliary memory strict [optional] strictly enforce the above Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_mat( arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat ) Parameters:\narma_mat armadillo matrix Return: matlab matrix\na2m_vec # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_vec( arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec ) Parameters:\narma_vec armadillo vector Return: matlab vector\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":7,"href":"/docs/api/namespaces/namespacearmamexcpp/","title":"armamexcpp","section":"Namespaces","content":" armamexcpp # arma/mex interface using Matlab C++ API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat(matlab::data::CellArray \u0026amp; matlab_cell)\nConvert matlab cell array to vector of armadillo matrices. template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array)\nConvert matlab matrix to a vector of scalars. template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo vector. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat(const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec(const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab vector. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec(const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert vector of scalar T to matlab matrix. Detailed Description # utilities for arma/mex interface using Matlab C++ API\nFunction Details # m2a_cellmat # template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat( matlab::data::CellArray \u0026amp; matlab_cell ) Parameters:\nmatlab_cell matlab cell Template Parameters:\nT type Return: vector of armadillo matrices of type T\nm2s_vec # template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: vector of type T\nm2a_vec # template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: armadillo vector of type T\nm2a_mat # template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab matrix Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat( const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_mat arma matrix factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\na2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec( const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_vec armadillo vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\ns2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec( const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\nstd_vec standard vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":8,"href":"/docs/terminology/control-estimation/","title":"C\u0026E","section":"LDS C+E Documentation","content":" Control \u0026amp; Estimation # The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.\nState estimation # In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by \\[\\widehat{\\mathbf{x}}_{t|t-1} = \\mathbf{A}\\widehat{\\mathbf{x}}_{t-1|t-1} \u0026#43; \\mathbf{B} u_{t-1} \u0026#43; \\mathbf{m}_{t-1} \\;,\\] \\[\\widehat{\\mathbf{x}}_{t|t} = \\widehat{\\mathbf{x}}_{t|t-1} \u0026#43; \\mathbf{K}^{\\rm e}_t \\left(\\mathbf{z}_t - \\widehat{\\mathbf{y}}_{t|t-1}\\right)\\;,\\] where \\( \\hat{\\left(\\cdot\\right)}_{t|j} \\) indicates an estimate at time \\( t \\) given data up to time \\( j \\) inclusive, \\( \\mathbf{K}^{\\rm e} \\) is the estimator gain, and\n\\[ \\widehat{\\mathbf{y}}_{t|t-1} = h\\left( \\widehat{\\mathbf{x}}_{t|t-1} \\right) \\; .\\] In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.\nIn the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.\nAdaptive estimation of process disturbance # Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.\nWhen parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk \\[\\mathbf{m}_{t} = \\mathbf{m}_{t-1} \u0026#43; \\mathbf{w}^m_{t-1} \\;,\\] where \\( \\mathbf{w}^m \\sim \\mathcal{N}\\left(0, \\mathbf{Q}_m\\right)\\) . Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.\nControl # Given the estimated state, the controller updates the inputs to the system according to the following law: \\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] where \\( \\left( \\cdot \\right)^{\\rm ref} \\) correspond to reference/target signals and \\( \\mathbf{K}^c_x \\) is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.\nIf users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as\n\\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right) - \\mathbf{K}^c_{\\rm inty} \\sum_{j=1}^{t}\\left( \\widehat{\\mathbf{y}}_j - \\mathbf{y}^{\\rm ref}_j \\right) \\;.\\] An additional option available to users is a control law that updates the change in u,\n\\[\\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\left(\\mathbf{u}_{t-1} - \\mathbf{u}^{\\rm ref}_{t-1} \\right) - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] \\[\\mathbf{u}_{t} = \\mathbf{u}_{t-1} \u0026#43; \\Delta\\mathbf{u}_{t} \\; .\\] Notice that this takes the form of a first-order difference equation for updating control (i.e., \\( \\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\mathbf{u}_{t-1} \u0026#43; \\epsilon_{t-1} \\) ), effectively low-pass filtering the input depending on the characteristics of \\( \\mathbf{K}^c_u \\) . This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.\nIntegral action and the \\( \\Delta \\mathbf{u} \\) control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR\u0026rsquo;d to use in combination.\nCalculating reference state-control from output # In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce \\( \\mathbf{x}^{\\rm ref} \\) and \\( \\mathbf{u}^{\\rm ref} \\) . Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.\n"},{"id":9,"href":"/docs/api/classes/","title":"Classes","section":"LDS C+E Documentation","content":" Classes # lds::Controller\nlds::EM\nlds::Fit LDS Fit Type.\nlds::SSID\nlds::SwitchedController SwitchedController Type.\nlds::System Linear Dynamical System Type.\nlds::UniformMatrixList\nlds::UniformSystemList\nlds::UniformVectorList\nlds::gaussian::Controller Gaussian-observation Controller Type.\nlds::gaussian::Fit GLDS Fit Type.\nlds::gaussian::FitEM GLDS E-M Fit Type.\nlds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.\nlds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.\nlds::gaussian::System Gaussian LDS Type.\nlds::poisson::Controller PLDS Controller Type.\nlds::poisson::Fit PLDS Fit Type.\nlds::poisson::FitEM PLDS E-M Fit Type.\nlds::poisson::FitSSID Subspace Identification (SSID) for PLDS.\nlds::poisson::SwitchedController Poisson-observation SwitchedController Type.\nlds::poisson::System Poisson System type.\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":10,"href":"/docs/api/modules/group__control__masks/","title":"Control Mode Bit Masks","section":"Modules","content":" Control Mode Bit Masks # provides fill types for constructing new armadillo vectors, matrices More\u0026hellip; Attributes # Name const std::size_t kControlTypeDeltaU control designed to penalize change in input const std::size_t kControlTypeIntY control using integral action const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m Detailed Description # Control mode bit masks. These can be bit-wise OR\u0026rsquo;d to use in combination.\nAttribute Details # kControlTypeDeltaU # static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY # static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM # static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":11,"href":"/docs/api/modules/group__defaults/","title":"Defaults","section":"Modules","content":" Defaults # More\u0026hellip; Attributes # Name const data_t kDefaultP0 default state estimate covar const data_t kDefaultQ0 default process noise covar const data_t kDefaultR0 default output noise covar Detailed Description # Default values for common variables (e.g., default diagonal elements of covariances)\nAttribute Details # kDefaultP0 # static const data_t kDefaultP0 = 1e-6; kDefaultQ0 # static const data_t kDefaultQ0 = 1e-6; kDefaultR0 # static const data_t kDefaultR0 = 1e-2; Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":12,"href":"/docs/api/examples/eg_glds_ctrl_8cpp-example/","title":"eg_glds_ctrl.cpp","section":"Examples","content":" eg_glds_ctrl.cpp # Example GLDS Control ```cpp\n//===\u0026ndash; eg_glds_ctrl.cpp - Example GLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS Control ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\n// output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false;\n// Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err\n// setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]);\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; }\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:32:33 EST "},{"id":13,"href":"/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/","title":"eg_glds_du_plds_ctrl.cpp","section":"Examples","content":" eg_glds_du_plds_ctrl.cpp # Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u). ```cpp\n//===\u0026ndash; eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt);\n// to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU;\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_du_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:32:33 EST "},{"id":14,"href":"/docs/api/examples/eg_plds_ctrl_8cpp-example/","title":"eg_plds_ctrl.cpp","section":"Examples","content":" eg_plds_ctrl.cpp # Example PLDS Control ```cpp\n//===\u0026ndash; eg_plds_ctrl.cpp - Example PLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Control ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt);\n// Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err\n// Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\nsize_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\nVector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled\u0026hellip; lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system);\n// for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type);\n// set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty);\n// to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0;\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros);\n// set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y();\nx_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x();\nm_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:32:33 EST "},{"id":15,"href":"/docs/api/examples/eg_plds_est_8cpp-example/","title":"eg_plds_est.cpp","section":"Examples","content":" eg_plds_est.cpp # Example PLDS Estimation ```cpp\n//===\u0026ndash; eg_plds_est.cpp - Example PLDS Estimation \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0);\nint main() { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.\n// construct ground truth system\u0026hellip; lds::poisson::System system_true(n_u, n_x, n_y, dt);\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset();\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt);\n// Can copy parameters from another system object system_estimator = system_true;\n// wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est);\n// set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true;\n// set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;estimator:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; system_estimator.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));\n// create matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros);\n// states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\nMatrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simlation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1));\n// Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simlation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows;\nif ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026ldquo;Q must be n x n.\u0026rdquo;); }\nMatrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); }\nreturn x; }\n_Filename: eg_plds_est.cpp_ ------------------------------- Updated on 5 March 2025 at 16:32:33 EST "},{"id":16,"href":"/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/","title":"eg_plds_switched_ctrl.cpp","section":"Examples","content":" eg_plds_switched_ctrl.cpp # Example Switched PLDS Control ```cpp\n//===\u0026ndash; eg_plds_switched_ctrl.cpp - Example Switched PLDS Control \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026quot;;\n// whether to do switched control bool do_switch_ctrl = true;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt);\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices. data_t scale_sys_b = 2;\nMatrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));\ncontrolled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions\n// reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);\n// Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system);\n// set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys1:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys2:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x));\nswitched_controller.set_y_ref(y_ref0);\nstd::vectorlds::poisson::System systems_vec(3, lds::poisson::System()); lds::UniformSystemListlds::poisson::System systems(std::move(systems_vec));\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;switched_controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; switched_controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Fake measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros);\n// create Matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);\n// modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones);\n// set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } }\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace)); mode.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;mode\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_switched_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:32:33 EST "},{"id":17,"href":"/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/","title":"examples","section":"Files","content":" examples # Files # Name examples/eg_glds_ctrl.cpp examples/eg_glds_du_plds_ctrl.cpp examples/eg_plds_ctrl.cpp examples/eg_plds_est.cpp examples/eg_plds_switched_ctrl.cpp Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":18,"href":"/docs/api/examples/","title":"Examples","section":"LDS C+E Documentation","content":" Examples # eg_glds_ctrl.cpp Example GLDS Control.\neg_glds_du_plds_ctrl.cpp Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).\neg_plds_ctrl.cpp Example PLDS Control.\neg_plds_est.cpp Example PLDS Estimation.\neg_plds_switched_ctrl.cpp Example Switched PLDS Control.\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":19,"href":"/docs/api/files/eg__glds__ctrl_8cpp/","title":"examples/eg_glds_ctrl.cpp","section":"Files","content":" examples/eg_glds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":20,"href":"/docs/api/files/eg__glds__du__plds__ctrl_8cpp/","title":"examples/eg_glds_du_plds_ctrl.cpp","section":"Files","content":" examples/eg_glds_du_plds_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU; // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":21,"href":"/docs/api/files/eg__plds__ctrl_8cpp/","title":"examples/eg_plds_ctrl.cpp","section":"Files","content":" examples/eg_plds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err // Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled... lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type); // set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":22,"href":"/docs/api/files/eg__plds__est_8cpp/","title":"examples/eg_plds_est.cpp","section":"Files","content":" examples/eg_plds_est.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name Matrix random_walk(size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0) int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # random_walk # Matrix random_walk( size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0 ) main # int main() Source code # //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. // construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state // Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. // turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; } Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":23,"href":"/docs/api/files/eg__plds__switched__ctrl_8cpp/","title":"examples/eg_plds_switched_ctrl.cpp","section":"Files","content":" examples/eg_plds_switched_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Source code # //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 // simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions // reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":24,"href":"/docs/api/files/","title":"Files","section":"LDS C+E Documentation","content":" Files # examples/eg_glds_ctrl.cpp\nexamples/eg_glds_du_plds_ctrl.cpp\nexamples/eg_plds_ctrl.cpp\nexamples/eg_plds_est.cpp\nexamples/eg_plds_switched_ctrl.cpp\nldsCtrlEst_h/lds.h lds namespace\nldsCtrlEst_h/lds_ctrl.h Controller.\nldsCtrlEst_h/lds_fit.h LDS base fit type.\nldsCtrlEst_h/lds_fit_em.h subspace identification\nldsCtrlEst_h/lds_fit_ssid.h subspace identification\nldsCtrlEst_h/lds_gaussian.h glds namespace\nldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.\nldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.\nldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.\nldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.\nldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.\nldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.\nldsCtrlEst_h/lds_poisson.h plds namespace\nldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.\nldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.\nldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.\nldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.\nldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.\nldsCtrlEst_h/lds_poisson_sys.h PLDS base type.\nldsCtrlEst_h/lds_sctrl.h SwitchedController type.\nldsCtrlEst_h/lds_sys.h LDS base type.\nldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.\nldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.\nldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.\nldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)\nldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)\nsrc/lds.cpp misc lds namespace functions\nsrc/lds_gaussian_sys.cpp GLDS base type.\nsrc/lds_poisson_sys.cpp PLDS base type.\nsrc/lds_sys.cpp LDS base type.\nsrc/lds_uniform_vecs.cpp Uniformly sized vectors.\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":25,"href":"/docs/tutorials/eg_glds_control/","title":"GLDS Control","section":"LDS C+E Examples","content":" GLDS Control Tutorial # This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\] \\[\ry_{t} = x_t\r\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.\n/// Going to simulate a switching disturbance (m) acting on system size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_y).fill(m_low); Finally, assign the parameters using corresponding set-methods.\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); Creating the controller # Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.\nFor the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.\n// make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nWith the controller constructed, control variables may be set.\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.\nThe control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller\u0026rsquo;s online estimates of the output, state, and disturbance are given in purple.\n"},{"id":26,"href":"/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/","title":"include","section":"Files","content":" include # Directories # Name ldsCtrlEst_h Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":27,"href":"/docs/api/namespaces/namespacelds/","title":"lds","section":"Namespaces","content":" lds # Linear Dynamical Systems (LDS) namespace. Namespaces # Name lds::gaussian Linear Dynamical Systems with Gaussian observations. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::Controller class lds::EM class lds::Fit LDS Fit Type. class lds::SSID class lds::SwitchedController SwitchedController Type. class lds::System Linear Dynamical System Type. class lds::UniformMatrixList class lds::UniformSystemList class lds::UniformVectorList Types # Name enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}\nweighting options for SSID enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2} using double data_t using arma::Col\u0026lt; data_t \u0026gt; Vector using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Cube\u0026lt; data_t \u0026gt; Cube using arma::subview\u0026lt; data_t \u0026gt; View Functions # Name void Limit(std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub) void Limit(Vector \u0026amp; x, data_t lb, data_t ub) void Limit(Matrix \u0026amp; x, data_t lb, data_t ub) void Reassign(Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Vector in place void Reassign(Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Matrix in place void ForceSymPD(Matrix \u0026amp; X)\nforces matrix to be symmetric positive-definite void ForceSymMinEig(Matrix \u0026amp; X, data_t eig_min =0)\nforces matrix to be symmetric and have a minimum eigenvalue void lq(Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X)\nLQ decomposition. Matrix calcCov(const Matrix \u0026amp; A, const Matrix \u0026amp; B)\nCalculate covariance matrix. Attributes # Name const data_t kInf Some useful numbers. const data_t kPi Type Details # SSIDWt # Enumerator Value Description kSSIDNone None. kSSIDMOESP MOESP (AKA \u0026ldquo;robust method\u0026rdquo; in van Overschee 1996) kSSIDCVA CVA \u0026ldquo;Canonical Variate Analysis\u0026rdquo;. Weighting options for singular value decomposition performed during subspace identification (SSID)\nReference:\nvan Overschee, de Moor. 1996. Subspace Identification for Linear Systems.\nMatrixListFreeDim # Enumerator Value Description kMatFreeDimNone neither dim free to be hetero in mat list kMatFreeDim1 allow 1st dim of mats in list to be hetero kMatFreeDim2 allow 2nd dim of mats in list to be hetero data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nVector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Cube # using lds::Cube = arma::Cube\u0026lt;data_t\u0026gt;; View # using lds::View = arma::subview\u0026lt;data_t\u0026gt;; Function Details # Limit # inline void Limit( std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Vector \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Matrix \u0026amp; x, data_t lb, data_t ub ) Reassign # inline void Reassign( Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Vector other other Vector parenthetical optional description provided by caller to ease debugging Reassign # inline void Reassign( Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Matrix other other Matrix parenthetical optional description provided by caller to ease debugging ForceSymPD # void ForceSymPD( Matrix \u0026amp; X ) Parameters:\nX mutated matrix ForceSymMinEig # void ForceSymMinEig( Matrix \u0026amp; X, data_t eig_min =0 ) Parameters:\nX mutated matrix eig_min [optional] minimum eigen value lq # void lq( Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X ) Parameters:\nL lower triangle matrix Qt orthonormal matrix (transposed cf QR decomp) X matrix being decomposed calcCov # Matrix calcCov( const Matrix \u0026amp; A, const Matrix \u0026amp; B ) Parameters:\nA some matrix B some other matrix Return: covariance\nAttribute Details # kInf # static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); kPi # static const data_t kPi = arma::datum::pi; Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":28,"href":"/docs/api/classes/classlds_1_1_controller/","title":"lds::Controller","section":"Classes","content":" lds::Controller # More\u0026hellip;\nInherited by lds::SwitchedController\u0026lt; System \u0026gt;, lds::gaussian::Controller, lds::poisson::Controller\nPublic Functions # Name Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes # Name System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::Controller; Public Function Details # Controller # Controller() =default Controller # inline Controller( const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Controller # inline Controller( System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Control # inline const Vector \u0026amp; Control( const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step). This is the most flexible option, but requires user to have set the controller\u0026rsquo;s y_ref, x_ref, and u_ref variables.\nControlOutputReference # inline const Vector \u0026amp; ControlOutputReference( const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_estimation [optional] whether to update state estimate (if false, effectively open-loop control) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.\nsys # inline const System \u0026amp; sys() const Kc # inline const Matrix \u0026amp; Kc() const Kc_inty # inline const Matrix \u0026amp; Kc_inty() const Kc_u # inline const Matrix \u0026amp; Kc_u() const g_design # inline const Vector \u0026amp; g_design() const u_ref # inline const Vector \u0026amp; u_ref() const x_ref # inline const Vector \u0026amp; x_ref() const y_ref # inline const Vector \u0026amp; y_ref() const control_type # inline size_t control_type() const tau_awu # inline data_t tau_awu() const u_lb # inline data_t u_lb() const u_ub # inline data_t u_ub() const set_sys # inline void set_sys( const System \u0026amp; sys ) set_g_design # inline void set_g_design( const Vector \u0026amp; g_design ) set_u_ref # inline void set_u_ref( const Vector \u0026amp; u_ref ) set_x_ref # inline void set_x_ref( const Vector \u0026amp; x_ref ) set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) Reimplemented by: lds::gaussian::Controller::set_y_ref, lds::gaussian::SwitchedController::set_y_ref, lds::poisson::Controller::set_y_ref, lds::poisson::SwitchedController::set_y_ref\nset_Kc # inline void set_Kc( const Matrix \u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const Matrix \u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const Matrix \u0026amp; Kc_u ) set_tau_awu # inline void set_tau_awu( data_t tau ) set_control_type # inline void set_control_type( size_t control_type ) Parameters:\ncontrol_type control type bit mask Template Parameters:\nSystem type derived from lds::System set_u_lb # inline void set_u_lb( data_t u_lb ) Parameters:\nu_lb control lower bound set_u_ub # inline void set_u_ub( data_t u_ub ) Parameters:\nu_ub control upper bound Reset # inline void Reset() Print # inline void Print() Protected Attribute Details # sys_ # System sys_; u_ # Vector u_; u_return_ # Vector u_return_; g_design_ # Vector g_design_; u_ref_ # Vector u_ref_; u_ref_prev_ # Vector u_ref_prev_; x_ref_ # Vector x_ref_; y_ref_ # Vector y_ref_; cx_ref_ # Vector cx_ref_; Kc_ # Matrix Kc_; Kc_u_ # Matrix Kc_u_; Kc_inty_ # Matrix Kc_inty_; du_ref_ # Vector du_ref_; dv_ref_ # Vector dv_ref_; v_ref_ # Vector v_ref_; dv_ # Vector dv_; v_ # Vector v_; int_e_ # Vector int_e_; int_e_awu_adjust_ # Vector int_e_awu_adjust_; u_sat_ # Vector u_sat_; do_control_prev_ # bool do_control_prev_ = false; do_lock_control_prev_ # bool do_lock_control_prev_ = false; u_saturated_ # bool u_saturated_ = false; u_lb_ # data_t u_lb_ {}; u_ub_ # data_t u_ub_ {}; tau_awu_ # data_t tau_awu_ {}; k_awu_ # data_t k_awu_ = 0; t_since_control_onset_ # data_t t_since_control_onset_ = 0; control_type_ # size_t control_type_ {}; Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":29,"href":"/docs/api/classes/classlds_1_1_e_m/","title":"lds::EM","section":"Classes","content":" lds::EM # More\u0026hellip;\nInherited by lds::gaussian::FitEM, lds::poisson::FitEM\nPublic Functions # Name EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions # Name void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() virtual void MaximizeOutput() =0 virtual void MaximizeMeasurement() =0 void Smooth(bool force_common_initial)\nget smoothed estimates virtual void RecurseKe(Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t) =0\nrecursively update estimator gain Ke void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::EM; Public Function Details # EM # EM() =default EM # EM( size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nn_x number of states dt sample period u_train input training data z_train measurement training data EM # EM( const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nfit0 initial fit u_train input training data z_train measurement training data ~EM # virtual ~EM() =default Run # const Fit \u0026amp; Run( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2 ) Parameters:\ncalc_dynamics [optional] whether to calculate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law max_iter max number of iterations tol convergence tolerance (max fractional abs change) Return: Fit\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nx # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const y # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const sum_E_x_t_x_t # inline const Matrix \u0026amp; sum_E_x_t_x_t() const sum_E_xu_tm1_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const sum_E_xu_t_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const n_t_tot # inline size_t n_t_tot() theta # inline const Vector \u0026amp; theta() const Protected Function Details # Expectation # void Expectation( bool force_common_initial =false ) Parameters:\nforce_common_initial whether to force common initial condition for all trials Maximization # void Maximization( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false ) Parameters:\ncalc_dynamics [optional] whether to caclulate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law MaximizeDynamics # void MaximizeDynamics() MaximizeQ # void MaximizeQ() MaximizeInitial # void MaximizeInitial() MaximizeOutput # virtual void MaximizeOutput() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput\nMaximizeMeasurement # virtual void MaximizeMeasurement() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement\nSmooth # void Smooth( bool force_common_initial ) Parameters:\nforce_common_initial whether to force common initial conditions RecurseKe # virtual void RecurseKe( Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t ) =0 Parameters:\nKe estimator gain P_pre cov of predicted state est. P_post cov of postior sate est. t time Reimplemented by: lds::gaussian::FitEM::RecurseKe, lds::poisson::FitEM::RecurseKe\nReset # void Reset() InitVars # void InitVars() UpdateTheta # Vector UpdateTheta() Return: parameter list\nProtected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; x_ # std::vector\u0026lt; Matrix \u0026gt; x_; P_ # std::vector\u0026lt; Cube \u0026gt; P_; P_t_tm1_ # std::vector\u0026lt; Cube \u0026gt; P_t_tm1_; y_ # std::vector\u0026lt; Matrix \u0026gt; y_; diag_y_ # Matrix diag_y_; sum_E_x_t_x_t_ # Matrix sum_E_x_t_x_t_; sum_E_xu_tm1_xu_tm1_ # Matrix sum_E_xu_tm1_xu_tm1_; sum_E_xu_t_xu_tm1_ # Matrix sum_E_xu_t_xu_tm1_; fit_ # Fit fit_; theta_ # Vector theta_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":30,"href":"/docs/api/classes/classlds_1_1_fit/","title":"lds::Fit","section":"Classes","content":" lds::Fit # LDS Fit Type. #include \u0026lt;lds_fit.h\u0026gt;\nInherited by lds::gaussian::Fit, lds::poisson::Fit\nPublic Functions # Name Fit() =default\nConstructs a new Fit. Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias virtual const Matrix \u0026amp; R() const =0 void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance virtual void set_R(const Matrix \u0026amp; R) =0\nsets output noise covariance (if any) void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) =0\noutput function Protected Attributes # Name data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period ~Fit # virtual ~Fit() =default n_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const m # inline const Vector \u0026amp; m() const Q # inline const Matrix \u0026amp; Q() const x0 # inline const Vector \u0026amp; x0() const P0 # inline const Matrix \u0026amp; P0() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const R # virtual const Matrix \u0026amp; R() const =0 Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R\nset_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_g # inline void set_g( const Vector \u0026amp; g ) set_m # inline void set_m( const Vector \u0026amp; m ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # virtual void set_R( const Matrix \u0026amp; R ) =0 Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R\nset_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) f # inline View f( Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t ) Parameters:\nx state estimate (over time) u input (over time) t time index Return: view of updated state\nf # inline View f( Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t ) Parameters:\nx_pre predicted state est. x_post posterior state est. u input (over time) t time index Return: view of predicted state\nh # virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) =0 Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplemented by: lds::gaussian::Fit::h, lds::poisson::Fit::h\nProtected Attribute Details # dt_ # data_t dt_ {}; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; m_ # Vector m_; Q_ # Matrix Q_; C_ # Matrix C_; d_ # Vector d_; R_ # Matrix R_; x0_ # Vector x0_; P0_ # Matrix P0_; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":31,"href":"/docs/api/namespaces/namespacelds_1_1gaussian/","title":"lds::gaussian","section":"Namespaces","content":" lds::gaussian # Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. class lds::gaussian::Fit GLDS Fit Type. class lds::gaussian::FitEM GLDS E-M Fit Type. class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. class lds::gaussian::System Gaussian LDS Type. Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":32,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_controller/","title":"lds::gaussian::Controller","section":"Classes","content":" lds::gaussian::Controller # Gaussian-observation Controller Type. #include \u0026lt;lds_gaussian_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":33,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_fit/","title":"lds::gaussian::Fit","section":"Classes","content":" lds::gaussian::Fit # GLDS Fit Type. #include \u0026lt;lds_gaussian_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual const Matrix \u0026amp; R() const override\ngets measurement noise covariance virtual void set_R(const Matrix \u0026amp; R) override\nsets measurement noise covariance virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period R # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nh # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":34,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/","title":"lds::gaussian::FitEM","section":"Classes","content":" lds::gaussian::FitEM # GLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_gaussian_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::gaussian::FitEM; This type is used in the process of fitting GLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":35,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/","title":"lds::gaussian::FitSSID","section":"Classes","content":" lds::gaussian::FitSSID # Subspace Identification (SSID) for GLDS. #include \u0026lt;lds_gaussian_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":36,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/","title":"lds::gaussian::SwitchedController","section":"Classes","content":" lds::gaussian::SwitchedController # Gaussian-observation SwitchedController Type. #include \u0026lt;lds_gaussian_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":37,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_system/","title":"lds::gaussian::System","section":"Classes","content":" lds::gaussian::System # Gaussian LDS Type. #include \u0026lt;lds_gaussian_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)\nConstructs a new Gaussian System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. const Matrix \u0026amp; R() const\nGet output noise covariance. void set_Q(const Matrix \u0026amp; Q) void set_R(const Matrix \u0026amp; R)\nSet output noise covariance. void set_Ke(const Matrix \u0026amp; Ke)\nSet estimator gain. void set_Ke_m(const Matrix \u0026amp; Ke_m)\nSet disturbance estimator gain. void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively update estimator gain. Protected Attributes # Name Matrix R_ covariance of output noise bool do_recurse_Ke_ whether to recursively calculate estimator gain Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0 ) Parameters:\nn_u number of inputs (u) n_x number of states (x) n_y number of outputs (y) dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) r0 [optional] initial diagonal elements of output noise covariance (R) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nR # inline const Matrix \u0026amp; R() const set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # inline void set_R( const Matrix \u0026amp; R ) set_Ke # inline void set_Ke( const Matrix \u0026amp; Ke ) set_Ke_m # inline void set_Ke_m( const Matrix \u0026amp; Ke_m ) Print # void Print() Protected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nProtected Attribute Details # R_ # Matrix R_; do_recurse_Ke_ # bool do_recurse_Ke_ {}; Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":38,"href":"/docs/api/namespaces/namespacelds_1_1poisson/","title":"lds::poisson","section":"Namespaces","content":" lds::poisson # Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. class lds::poisson::Fit PLDS Fit Type. class lds::poisson::FitEM PLDS E-M Fit Type. class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. class lds::poisson::System Poisson System type. Attributes # Name std::random_device rd random device for simulating poisson data std::mt19937 rng random number generator for simulating poisson data Attribute Details # rd # static std::random_device rd; rng # static std::mt19937 rng = std::mt19937( rd()); Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":39,"href":"/docs/api/classes/classlds_1_1poisson_1_1_controller/","title":"lds::poisson::Controller","section":"Classes","content":" lds::poisson::Controller # PLDS Controller Type. #include \u0026lt;lds_poisson_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":40,"href":"/docs/api/classes/classlds_1_1poisson_1_1_fit/","title":"lds::poisson::Fit","section":"Classes","content":" lds::poisson::Fit # PLDS Fit Type. #include \u0026lt;lds_poisson_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function virtual void set_R(const Matrix \u0026amp; R) override\nsets output noise covariance (if any) virtual const Matrix \u0026amp; R() const override Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # inline Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period h # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nR # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":41,"href":"/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/","title":"lds::poisson::FitEM","section":"Classes","content":" lds::poisson::FitEM # PLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_poisson_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::poisson::FitEM; This type is used in the process of fitting PLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":42,"href":"/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/","title":"lds::poisson::FitSSID","section":"Classes","content":" lds::poisson::FitSSID # Subspace Identification (SSID) for PLDS. #include \u0026lt;lds_poisson_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":43,"href":"/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/","title":"lds::poisson::SwitchedController","section":"Classes","content":" lds::poisson::SwitchedController # Poisson-observation SwitchedController Type. #include \u0026lt;lds_poisson_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":44,"href":"/docs/api/classes/classlds_1_1poisson_1_1_system/","title":"lds::poisson::System","section":"Classes","content":" lds::poisson::System # Poisson System type. #include \u0026lt;lds_poisson_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nConstructs a new Poisson System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively recalculate estimator gain (Ke) Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nProtected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nRecursively recalculate estimator gain (Ke).\nReferences:\nSmith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.\nEden UT, \u0026hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":45,"href":"/docs/api/classes/classlds_1_1_s_s_i_d/","title":"lds::SSID","section":"Classes","content":" lds::SSID # More\u0026hellip;\nInherited by lds::gaussian::FitSSID, lds::poisson::FitSSID\nPublic Functions # Name SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions # Name void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. virtual void DecomposeData() =0\nDecompose data to lower-triangular matrix (used in Solve) void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::SSID; Public Function Details # SSID # SSID() =default SSID # SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf) ) Parameters:\nn_x number of states n_h size of block-hankel data matrix dt sample period u_train input training data z_train measurement training data d output bias Run # std::tuple\u0026lt; Fit, Vector \u0026gt; Run( SSIDWt ssid_wt ) Parameters:\nssid_wt weight for singular value decomp Return: tuple (Fit, singular values)\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nProtected Function Details # CalcD # void CalcD( data_t t_silence =0.1, data_t thresh_silence =0.001 ) Parameters:\nt_silence threshold on period of time that qualifies as \u0026ldquo;silence\u0026rdquo; thresh_silence threshold on input amplitude u that qualifies as \u0026ldquo;silence\u0026rdquo; CreateHankelDataMat # void CreateHankelDataMat() Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.\nDecomposeData # virtual void DecomposeData() =0 Reimplemented by: lds::gaussian::FitSSID::DecomposeData, lds::poisson::FitSSID::DecomposeData\nCalcSVD # void CalcSVD( SSIDWt wt ) Parameters:\nssid_wt weight for SVD Solve # void Solve( data_t wt_dc ) Parameters:\nwt_dc weight placed on getting correct DC I/O gain RecomputeExtObs # void RecomputeExtObs() Protected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; D_ # Matrix D_; fit_ # Fit fit_; g_dc_ # Matrix g_dc_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_h_ # size_t n_h_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; L_ # Matrix L_; s_ # Vector s_; ext_obs_t_ # Matrix ext_obs_t_; Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":46,"href":"/docs/api/classes/classlds_1_1_switched_controller/","title":"lds::SwitchedController","section":"Classes","content":" lds::SwitchedController # SwitchedController Type. More\u0026hellip;\n#include \u0026lt;lds_sctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nInherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController\nPublic Functions # Name SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes # Name std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::SwitchedController; Public Function Details # SwitchedController # SwitchedController() =default SwitchedController # inline SwitchedController( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask SwitchedController # inline SwitchedController( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Switch # inline void Switch( size_t idx, bool do_force_switch =false ) Parameters:\nidx index do_force_switch whether to force a system switch even if already there. set_Kc # inline void set_Kc( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc ) set_Kc # inline void set_Kc( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty ) set_Kc_inty # inline void set_Kc_inty( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u ) set_Kc_u # inline void set_Kc_u( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u ) set_g_design # inline void set_g_design( const UniformVectorList \u0026amp; g ) set_g_design # inline void set_g_design( UniformVectorList \u0026amp;\u0026amp; g ) Protected Attribute Details # systems_ # std::vector\u0026lt; System \u0026gt; systems_; n_sys_ # size_t n_sys_ {}; idx_ # size_t idx_ {}; Kc_list_ # UniformMatrixList Kc_list_; Kc_inty_list_ # UniformMatrixList Kc_inty_list_; Kc_u_list_ # UniformMatrixList Kc_u_list_; g_design_list_ # UniformVectorList g_design_list_; Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":47,"href":"/docs/api/classes/classlds_1_1_system/","title":"lds::System","section":"Classes","content":" lds::System # Linear Dynamical System Type. #include \u0026lt;lds_sys.h\u0026gt;\nInherited by lds::gaussian::System, lds::poisson::System\nPublic Functions # Name System() =default\nConstructs a new System. System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nconstructs a new System virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) =0\nsimulates system (single time step) void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function virtual void h() =0\nsystem output function virtual Vector h_(Vector x) =0\nsystem output function (stateless) size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void RecurseKe() =0\nRecursively recalculate estimator gain (Ke) void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes # Name bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes # Name std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 diagonal elements for state estimate covariance q0 diagonal elements for process noise covariance ~System # inline virtual ~System() Filter # void Filter( const Vector \u0026amp; u_tm1, const Vector \u0026amp; z ) Parameters:\nu_tm1 input at t-minus-1 z_t current measurement Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.\nSimulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) =0 Parameters:\nu_tm1 input at time t-1 Return: simulated measurement at time t\nReimplemented by: lds::gaussian::System::Simulate, lds::poisson::System::Simulate\nf # inline void f( const Vector \u0026amp; u, bool do_add_noise =false ) Parameters:\nu input do_add_noise whether to add simulated process noise h # virtual void h() =0 Reimplemented by: lds::gaussian::System::h, lds::poisson::System::h\nh_ # virtual Vector h_( Vector x ) =0 Parameters:\nx_t state at time t Return: predicted state at time t + 1\nReimplemented by: lds::gaussian::System::h_, lds::poisson::System::h_\nn_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const x # inline const Vector \u0026amp; x() const P # inline const Matrix \u0026amp; P() const m # inline const Vector \u0026amp; m() const P_m # inline const Matrix \u0026amp; P_m() const cx # inline const Vector \u0026amp; cx() const y # inline const Vector \u0026amp; y() const x0 # inline const Vector \u0026amp; x0() const m0 # inline const Vector \u0026amp; m0() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const Ke # inline const Matrix \u0026amp; Ke() const Ke_m # inline const Matrix \u0026amp; Ke_m() const Q # inline const Matrix \u0026amp; Q() Q_m # inline const Matrix \u0026amp; Q_m() P0 # inline const Matrix \u0026amp; P0() P0_m # inline const Matrix \u0026amp; P0_m() set_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_m # inline void set_m( const Vector \u0026amp; m, bool do_force_assign =false ) set_g # inline void set_g( const Vector \u0026amp; g ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_Q_m # inline void set_Q_m( const Matrix \u0026amp; Q_m ) set_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_P0_m # inline void set_P0_m( const Matrix \u0026amp; P0_m ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) set_x # inline void set_x( const Vector \u0026amp; x ) Reset # void Reset() nstep_pred_block # std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block( UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1 ) Print # void Print() Protected Function Details # RecurseKe # virtual void RecurseKe() =0 Reimplemented by: lds::gaussian::System::RecurseKe, lds::poisson::System::RecurseKe\nInitVars # void InitVars( data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Public Attribute Details # do_adapt_m # bool do_adapt_m {}; Protected Attribute Details # n_x_ # std::size_t n_x_ {}; n_u_ # std::size_t n_u_ {}; n_y_ # std::size_t n_y_ {}; dt_ # data_t dt_ {}; x_ # Vector x_; P_ # Matrix P_; m_ # Vector m_; P_m_ # Matrix P_m_; cx_ # Vector cx_; y_ # Vector y_; z_ # Vector z_; x0_ # Vector x0_; P0_ # Matrix P0_; m0_ # Vector m0_; P0_m_ # Matrix P0_m_; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; Q_ # Matrix Q_; Q_m_ # Matrix Q_m_; C_ # Matrix C_; d_ # Vector d_; Ke_ # Matrix Ke_; Ke_m_ # Matrix Ke_m_; Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":48,"href":"/docs/api/classes/classlds_1_1_uniform_matrix_list/","title":"lds::UniformMatrixList","section":"Classes","content":" lds::UniformMatrixList # More\u0026hellip;\nInherits from std::vector\u0026lt; Matrix \u0026gt;\nPublic Functions # Name UniformMatrixList() =default\nConstructs a new UniformMatrixList. UniformMatrixList(const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent. UniformMatrixList(std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent. UniformMatrixList(std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent. UniformMatrixList(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nConstructs a new UniformMatrixList (copy). UniformMatrixList(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nConstructs a new UniformMatrixList (move). ~UniformMatrixList() =default\nDestroys the object. const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim(size_t n =0) const\ngets dimensions of uniformly sized matrices size_t size()\nsize of container const Matrix \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Matrix \u0026amp; that, size_t n)\nswaps input matrix with n^th matrix of list UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nassigns the contents (copy) UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nassigns the contents (move) void append(const Matrix \u0026amp; mat)\nappends a matrix to the list Detailed Description # template \u0026lt;MatrixListFreeDim D =kMatFreeDimNone\u0026gt; class lds::UniformMatrixList; Public Function Details # UniformMatrixList # UniformMatrixList() =default UniformMatrixList # explicit UniformMatrixList( const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # explicit UniformMatrixList( std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList UniformMatrixList # UniformMatrixList( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList ~UniformMatrixList # ~UniformMatrixList() =default dim # inline const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim( size_t n =0 ) const Parameters:\nn [optional] index in list of matrices Return: dimensions\nsize # inline size_t size() at # inline const Matrix \u0026amp; at( size_t n ) Swap # inline void Swap( Matrix \u0026amp; that, size_t n ) Parameters:\nthat input matrix n index where the matrix is moved operator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\noperator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\nappend # void append( const Matrix \u0026amp; mat ) Parameters:\nmat input matrix Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":49,"href":"/docs/api/classes/classlds_1_1_uniform_system_list/","title":"lds::UniformSystemList","section":"Classes","content":" lds::UniformSystemList # More\u0026hellip;\nInherits from std::vector\u0026lt; System \u0026gt;\nPublic Functions # Name UniformSystemList() =default\nConstructs a new UniformSystemList. UniformSystemList(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by copying existing vector of System if dimensions consistent. UniformSystemList(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by moving existing vector of System if dimensions consistent. UniformSystemList(std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList from initializer_list of System if dimensions consistent. UniformSystemList(const UniformSystemList \u0026amp; that)\nConstructs a new UniformSystemList (copy). UniformSystemList(UniformSystemList \u0026amp;\u0026amp; that)\nConstructs a new UniformSystemList (move). ~UniformSystemList() =default\nDestroys the object. const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const\ngets dimensions of the uniformly sized systems size_t size()\nsize of container const System \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(System \u0026amp; that, size_t n)\nswaps input system with n^th system of list UniformSystemList \u0026amp; operator=(const UniformSystemList \u0026amp; that)\nassigns the contents (copy) UniformSystemList \u0026amp; operator=(UniformSystemList \u0026amp;\u0026amp; that)\nassigns the contents (move) Detailed Description # template \u0026lt;typename System \u0026gt; class lds::UniformSystemList; Public Function Details # UniformSystemList # UniformSystemList() =default UniformSystemList # explicit UniformSystemList( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # explicit UniformSystemList( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList UniformSystemList # UniformSystemList( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList ~UniformSystemList # ~UniformSystemList() =default dim # inline const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const size # inline size_t size() at # inline const System \u0026amp; at( size_t n ) Swap # inline void Swap( System \u0026amp; that, size_t n ) Parameters:\nthat input system n index where the system is moved operator= # inline UniformSystemList \u0026amp; operator=( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\noperator= # inline UniformSystemList \u0026amp; operator=( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":50,"href":"/docs/api/classes/classlds_1_1_uniform_vector_list/","title":"lds::UniformVectorList","section":"Classes","content":" lds::UniformVectorList # Inherits from std::vector\u0026lt; Vector \u0026gt;\nPublic Functions # Name UniformVectorList() =default\nConstructs a new UniformVectorList. UniformVectorList(const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent. UniformVectorList(std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent. UniformVectorList(std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0)\nConstructs a new UniformVectorList from initializer_list of Vector if dimensions consistent. UniformVectorList(const UniformVectorList \u0026amp; that)\nConstructs a new UniformVectorList (copy) UniformVectorList(UniformVectorList \u0026amp;\u0026amp; that)\nConstructs a new UniformVectorList (move) ~UniformVectorList() =default\nDestroys the object. size_t dim() const\ngets dimensions of the uniformly sized matrices size_t size()\nsize of container const Vector \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Vector \u0026amp; that, size_t n)\nswaps input matrix with n^th vector of list UniformVectorList \u0026amp; operator=(const UniformVectorList \u0026amp; that)\nassigns the contents (copy) UniformVectorList \u0026amp; operator=(UniformVectorList \u0026amp;\u0026amp; that)\nassigns the contents (move) Public Function Details # UniformVectorList # UniformVectorList() =default UniformVectorList # explicit UniformVectorList( const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dims dimension UniformVectorList # explicit UniformVectorList( std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList UniformVectorList # UniformVectorList( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList ~UniformVectorList # ~UniformVectorList() =default dim # inline size_t dim() const size # inline size_t size() at # inline const Vector \u0026amp; at( size_t n ) Swap # inline void Swap( Vector \u0026amp; that, size_t n ) Parameters:\nthat input vector n index where the vector is moved operator= # inline UniformVectorList \u0026amp; operator=( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\noperator= # inline UniformVectorList \u0026amp; operator=( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":51,"href":"/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/","title":"ldsCtrlEst_h","section":"Files","content":" ldsCtrlEst_h # Files # Name ldsCtrlEst_h/lds.h lds namespace ldsCtrlEst_h/lds_ctrl.h Controller. ldsCtrlEst_h/lds_fit.h LDS base fit type. ldsCtrlEst_h/lds_fit_em.h subspace identification ldsCtrlEst_h/lds_fit_ssid.h subspace identification ldsCtrlEst_h/lds_gaussian.h glds namespace ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller. ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type. ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type. ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type. ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type. ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type. ldsCtrlEst_h/lds_poisson.h plds namespace ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type. ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type. ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type. ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type. ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type. ldsCtrlEst_h/lds_poisson_sys.h PLDS base type. ldsCtrlEst_h/lds_sctrl.h SwitchedController type. ldsCtrlEst_h/lds_sys.h LDS base type. ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices. ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems. ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors. ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":52,"href":"/docs/api/files/lds__ctrl_8h/","title":"ldsCtrlEst_h/lds_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_ctrl.h # Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Controller Detailed Description # This file declares the type for control of a linear dynamical system (lds::Controller).\nSource code # //===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_CTRL_H #define LDSCTRLEST_LDS_CTRL_H // namespace #include \u0026#34;lds.h\u0026#34; // system type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class Controller { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); public: Controller() = default; Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); const Vector\u0026amp; Control(const Vector\u0026amp; z, bool do_control = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); const Vector\u0026amp; ControlOutputReference(const Vector\u0026amp; z, bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); // get methods: const System\u0026amp; sys() const { return sys_; }; const Matrix\u0026amp; Kc() const { return Kc_; }; const Matrix\u0026amp; Kc_inty() const { return Kc_inty_; }; const Matrix\u0026amp; Kc_u() const { return Kc_u_; }; const Vector\u0026amp; g_design() const { return g_design_; }; const Vector\u0026amp; u_ref() const { return u_ref_; }; const Vector\u0026amp; x_ref() const { return x_ref_; }; const Vector\u0026amp; y_ref() const { return y_ref_; }; size_t control_type() const { return control_type_; }; data_t tau_awu() const { return tau_awu_; }; data_t u_lb() const { return u_lb_; }; data_t u_ub() const { return u_ub_; }; // set methods void set_sys(const System\u0026amp; sys) { bool does_match = sys_.n_u() == sys.n_u(); does_match = does_match \u0026amp;\u0026amp; (sys_.n_x() == sys.n_x()); does_match = does_match \u0026amp;\u0026amp; (sys_.n_y() == sys.n_y()); if (does_match) { sys_ = sys; } else { throw std::runtime_error( \u0026#34;new system argument to `set_sys` does not match dimensionality of \u0026#34; \u0026#34;existing system\u0026#34;); } }; void set_g_design(const Vector\u0026amp; g_design) { Reassign(g_design_, g_design); }; void set_u_ref(const Vector\u0026amp; u_ref) { Reassign(u_ref_, u_ref); }; void set_x_ref(const Vector\u0026amp; x_ref) { Reassign(x_ref_, x_ref); cx_ref_ = sys_.C() * x_ref_; }; // y_ref needs to be handled differently depending on output fn. // (need to populate cx_ref_ too, which depends on output fn) virtual void set_y_ref(const Vector\u0026amp; y_ref) { Reassign(y_ref_, y_ref); }; void set_Kc(const Matrix\u0026amp; Kc) { Reassign(Kc_, Kc); }; void set_Kc_inty(const Matrix\u0026amp; Kc_inty) { Reassign(Kc_inty_, Kc_inty); }; void set_Kc_u(const Matrix\u0026amp; Kc_u) { Reassign(Kc_u_, Kc_u); }; void set_tau_awu(data_t tau) { tau_awu_ = tau; k_awu_ = sys_.dt() / tau_awu_; }; void set_control_type(size_t control_type); // There is no reason u_lb/ub should not be public, but making set methods // anyway. void set_u_lb(data_t u_lb) { u_lb_ = u_lb; }; void set_u_ub(data_t u_ub) { u_ub_ = u_ub; }; void Reset() { sys_.Reset(); u_ref_.zeros(); u_ref_prev_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); u_saturated_ = false; t_since_control_onset_ = 0.0; }; void Print() { sys_.Print(); std::cout \u0026lt;\u0026lt; \u0026#34;g_design : \u0026#34; \u0026lt;\u0026lt; g_design_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_lb : \u0026#34; \u0026lt;\u0026lt; u_lb_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_ub : \u0026#34; \u0026lt;\u0026lt; u_ub_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }; protected: System sys_; Vector u_; Vector u_return_; Vector g_design_; // reference signals Vector u_ref_; // create no set method for this: Vector u_ref_prev_; Vector x_ref_; Vector y_ref_; Vector cx_ref_; // Controller gains Matrix Kc_; Matrix Kc_u_; Matrix Kc_inty_; // control after g inversion // do not need set methods for these. Vector du_ref_; Vector dv_ref_; Vector v_ref_; Vector dv_; Vector v_; // integral error // do not need set method for this Vector int_e_; Vector int_e_awu_adjust_; Vector u_sat_; bool do_control_prev_ = false; bool do_lock_control_prev_ = false; // whether the g of system has become inverted from what you think it is // (gain_ref) bool u_saturated_ = false; // should be safe to have references here bc nothing needs to be done // (like reset vars) when it changes... data_t u_lb_{}; data_t u_ub_{}; data_t tau_awu_{}; data_t k_awu_ = 0; data_t t_since_control_onset_ = 0; size_t control_type_{}; private: void CalcControl(bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); void CalcSteadyStateSetPoint(); void AntiWindup(); void InitVars(size_t control_type); }; // Implement the above: template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(sys), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(std::move(sys)), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::set_control_type(size_t control_type) { if (control_type_ == control_type) { return; } // creating a blank slate... control_type_ = 0; Kc_inty_.zeros(0, 0); Kc_u_.zeros(0, 0); int_e_.zeros(0, 0); int_e_awu_adjust_.zeros(0, 0); // controller was designed to minimize integral error if (control_type \u0026amp; kControlTypeIntY) { Kc_inty_.zeros(sys_.n_u(), sys_.n_y()); int_e_.zeros(sys_.n_y()); int_e_awu_adjust_.zeros(sys_.n_u()); control_type_ = control_type_ | kControlTypeIntY; } // controller was designed to minimize deltaU // (i.e. state augmented with u) if (control_type \u0026amp; kControlTypeDeltaU) { Kc_u_.zeros(sys_.n_u(), sys_.n_u()); control_type_ = control_type_ | kControlTypeDeltaU; } // whether to adapt set point calculate with (re-estimated) process // disturbance (m) if (control_type \u0026amp; kControlTypeAdaptM) { if (sys_.do_adapt_m) // only if adapting m... { control_type_ = control_type_ | kControlTypeAdaptM; } } } // set_control_type template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::Control( const Vector\u0026amp; z, bool do_control, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement sys_.Filter(u_, z); bool do_estimation = true; // always have estimator on in this case // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::ControlOutputReference( const Vector\u0026amp; z, bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement if (do_estimation) { sys_.Filter(u_, z); } else { sys_.f(u_); } // calculate the set point // solves for u_ref and x_ref when output is at y_ref at steady state. if (do_control) { CalcSteadyStateSetPoint(); } // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcControl(bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { if (do_control \u0026amp;\u0026amp; do_estimation) { if (!do_control_prev_) { if (do_reset_at_control_onset) { Reset(); } t_since_control_onset_ = 0.0; } else { t_since_control_onset_ += sys_.dt(); } // enforce softstart on control vars. if (sigma_soft_start \u0026gt; 0) { // half-Gaussian soft-start scaling factor data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) / (2 * pow(sigma_soft_start, 2))); u_ref_ *= soft_start_sf; // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too // x_ref_ *= soft_start_sf; // cx_ref_ *= soft_start_sf; // y_ref_ *= soft_start_sf; } if (!do_lock_control) { // first do u -\u0026gt; v change of vars. (v = g.*u) // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver // control voltage u[=]V) v_ref_ = g_design_ % u_ref_; // Given FB, calc. the change in control if (control_type_ \u0026amp; kControlTypeDeltaU) { // if control designed to minimize not u but deltaU (i.e. state aug with // u): // TODO(mfbolus): Commented out for now. See note below. // du_ref_ = u_ref_ - u_ref_prev_; // dv_ref_ = g_design_ % du_ref_; // TODO(mfbolus): Assuming users want *smooth* control signals if using // kControlTypeDeltaU, it should be the case that dv_ref_ is --\u0026gt; 0. May // want to revisit, but I am going to force it to be zero unless a // situation arises that argues for keeping the above. dv_ref_.zeros(); dv_ = dv_ref_; // nominally-optimal. dv_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error dv_ -= Kc_u_ * (v_ - v_ref_); // penalty on amp u (rel to ref) if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if(!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error dv_ -= Kc_inty_ * int_e_; // control for integrated error } // update the control v_ += dv_; } else { v_ = v_ref_; // nominally-optimal. v_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if (!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error v_ -= Kc_inty_ * int_e_; // control for integrated error } } // convert back to control voltage u[=]V u_ = v_ / sys_.g(); } // else do nothing until lock is low } else { // if not control // feed through u_ref in open loop u_ = u_ref_ % g_design_ / sys_.g(); v_ = sys_.g() % u_; u_ref_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); } // ends do_control // enforce box constraints (and antiwindup) AntiWindup(); // add noise to input? // The value for u that is *returned* to user after addition of any noise, // while keeping controller/estimator blind to this addition. u_return_ = u_; if ((sigma_u_noise \u0026gt; 0.0) \u0026amp;\u0026amp; (do_control \u0026amp;\u0026amp; !do_lock_control)) { u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn); Limit(u_return_, u_lb_, u_ub_); }; // For next time step: u_ref_prev_ = u_ref_; do_control_prev_ = do_control; do_lock_control_prev_ = do_lock_control; } // CalcControl template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcSteadyStateSetPoint() { // Linearly-constrained least squares (ls). // // _reference: // Boyd \u0026amp; Vandenberghe (2018) Introduction to Applied Linear Algebra // Matrix a_ls = join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros)); Vector b_ls = cx_ref_; Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye), sys_.B() * arma::diagmat(sys_.g())); Vector d_ls = -sys_.m0(); if (control_type_ \u0026amp; kControlTypeAdaptM) { d_ls = -sys_.m(); // adapt setpoint calc with disturbance? } Matrix a_ls_t = a_ls.t(); // TODO(mfbolus): not sure why but causes seg // fault if I do not do this. Matrix phi_ls = join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()), join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros))); // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse: Matrix inv_phi = pinv(phi_ls); Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls); x_ref_ = xulam.subvec(0, sys_.n_x() - 1); u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1); cx_ref_ = sys_.C() * x_ref_; } // CalcSteadyStateSetPoint template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::AntiWindup() { u_saturated_ = false; u_sat_ = u_; // limit u and flag whether saturated for (size_t k = 0; k \u0026lt; u_.n_elem; k++) { if (u_[k] \u0026lt; u_lb_) { u_sat_[k] = u_lb_; u_saturated_ = true; } if (u_[k] \u0026gt; u_ub_) { u_sat_[k] = u_ub_; u_saturated_ = true; } } if ((control_type_ \u0026amp; kControlTypeIntY) \u0026amp;\u0026amp; (tau_awu_ \u0026lt; lds::kInf)) { // one-step back-calculation (calculate intE for u=u_sat) // (Astroem, Rundqwist 1989 warn against using this...) // int_e_awu_adjust_ = // solve(Kc_inty_, (u_ - u_sat_)); // pinv(Kc_inty) * (u-uSat); // gradual: see Astroem, Rundqwist 1989 // this is a fudge for doing MIMO gradual // n.b., went ahead and multiplied 1/T by dt so don\u0026#39;t have to do that here. int_e_awu_adjust_ = k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_); // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_); int_e_ += int_e_awu_adjust_; } // set u to saturated version u_ = u_sat_; } template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::InitVars(size_t control_type) { // initialize to default values u_ref_ = Vector(sys_.n_u(), fill::zeros); u_ref_prev_ = Vector(sys_.n_u(), fill::zeros); x_ref_ = Vector(sys_.n_x(), fill::zeros); y_ref_ = Vector(sys_.n_y(), fill::zeros); cx_ref_ = Vector(sys_.n_y(), fill::zeros); u_ = Vector(sys_.n_u(), fill::zeros); u_return_ = Vector(sys_.n_u(), fill::zeros); u_sat_ = Vector(sys_.n_u(), fill::zeros); // Might not need all these, so zero elements until later. Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros); Kc_u_ = Matrix(0, 0, fill::zeros); Kc_inty_ = Matrix(0, 0, fill::zeros); g_design_ = sys_.g(); // by default, same as model dv_ = Vector(sys_.n_u(), fill::zeros); v_ = Vector(sys_.n_u(), fill::zeros); du_ref_ = Vector(sys_.n_u(), fill::zeros); dv_ref_ = Vector(sys_.n_u(), fill::zeros); v_ref_ = Vector(sys_.n_u(), fill::zeros); int_e_ = Vector(0, fill::zeros); int_e_awu_adjust_ = Vector(0, fill::zeros); set_control_type(control_type); } } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":53,"href":"/docs/api/files/lds__fit__em_8h/","title":"ldsCtrlEst_h/lds_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_em.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::EM Detailed Description # This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).\nSource code # //===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_EMAX_H #define LDSCTRLEST_LDS_EMAX_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class EM { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: EM() = default; EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); virtual ~EM() = default; const Fit\u0026amp; Run(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = true, bool calc_output = true, bool calc_measurement = true, size_t max_iter = 100, data_t tol = 1e-2); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); // auto tuple = std::make_tuple(u_, z_); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; x() const { return x_; }; const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; y() const { return y_; }; const Matrix\u0026amp; sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; }; const Matrix\u0026amp; sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; }; const Matrix\u0026amp; sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; }; size_t n_t_tot() { return n_t_tot_; } const Vector\u0026amp; theta() const { return theta_; }; protected: void Expectation(bool force_common_initial = false); void Maximization(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = false, bool calc_output = false, bool calc_measurement = false); void MaximizeDynamics(); void MaximizeQ(); void MaximizeInitial(); virtual void MaximizeOutput() = 0; virtual void MaximizeMeasurement() = 0; void Smooth(bool force_common_initial); virtual void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) = 0; void Reset(); void InitVars(); Vector UpdateTheta(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; std::vector\u0026lt;Matrix\u0026gt; x_; std::vector\u0026lt;Cube\u0026gt; P_; std::vector\u0026lt;Cube\u0026gt; P_t_tm1_; std::vector\u0026lt;Matrix\u0026gt; y_; Matrix diag_y_; // expectations calculated in E-step Matrix sum_E_x_t_x_t_; Matrix sum_E_xu_tm1_xu_tm1_; Matrix sum_E_xu_t_xu_tm1_; Fit fit_; Vector theta_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; }; template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; fit_ = Fit(n_u_, n_x, n_y_, dt); u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { // make sure fit dims match I/O data if (fit0.n_u() != u_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and input training data have inconsistent dimensions\u0026#34;); } if (fit0.n_y() != z_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and output training data have inconsistent dimensions\u0026#34;); } fit_ = fit0; u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::InitVars() { // check input/output data dimensions are consistent if (z_.size() != u_.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_.at(trial).n_cols != u_.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } n_u_ = fit_.n_u(); n_x_ = fit_.n_x(); n_y_ = fit_.n_y(); dt_ = fit_.dt(); x_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); P_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); P_t_tm1_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); y_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros); P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros); } diag_y_ = Matrix(n_y_, n_y_, fill::zeros); // covariances in expectation step sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); } template \u0026lt;typename Fit\u0026gt; const Fit\u0026amp; EM\u0026lt;Fit\u0026gt;::Run(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement, size_t max_iter, data_t tol) { Reset(); // to initial conditions size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_; Vector theta(n_params); Vector theta_new(n_params); data_t max_dtheta = 1; // if solving for initial conditions, allow them be varied. // otherwise, freeze at provided values. bool force_common_initial = !calc_init; // go until parameter convergence for (size_t l = 0; l \u0026lt; max_iter; l++) { theta_ = UpdateTheta(); std::cout \u0026lt;\u0026lt; \u0026#34;Iteration \u0026#34; \u0026lt;\u0026lt; l + 1 \u0026lt;\u0026lt; \u0026#34;/\u0026#34; \u0026lt;\u0026lt; max_iter \u0026lt;\u0026lt; \u0026#34; ...\\n\u0026#34;; Expectation(force_common_initial); Maximization(calc_dynamics, calc_Q, calc_init, calc_output, calc_measurement); // check convergence theta_new = UpdateTheta(); Vector dtheta = abs(theta_new - theta_) / abs(theta_); // some parameters could be zero... arma::uvec ubi_finite = find_finite(dtheta); max_dtheta = max(dtheta.elem(ubi_finite)); std::cout \u0026lt;\u0026lt; \u0026#34;max dtheta: \u0026#34; \u0026lt;\u0026lt; max_dtheta \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; if (max_dtheta \u0026lt; tol) { std::cout \u0026lt;\u0026lt; \u0026#34;Converged.\\n\u0026#34;; break; } std::cout \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } return fit_; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Smooth(bool force_common_initial) { Matrix k_e(n_x_, n_y_); // estimator gain Cube k_backfilt; // back-filtering gains // TODO(mfbolus): this loop could be made parallel for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { Matrix x_pre(n_x_, n_t_[trial], fill::zeros); Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros); Matrix x_post(n_x_, n_t_[trial], fill::zeros); Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros); if (force_common_initial) // forces all trials to have same initial // conditions. { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); } y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); // This *should not* be necessary but make sure P is symmetric. ForceSymPD(P_[trial].slice(0)); x_pre.col(0) = x_[trial].col(0); p_pre.slice(0) = P_[trial].slice(0); x_post.col(0) = x_[trial].col(0); p_post.slice(0) = P_[trial].slice(0); // filter for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // predict fit_.f(x_pre, x_post, u_.at(trial), t); fit_.h(y_[trial], x_pre, t); diag_y_.diag() = y_[trial].col(t); // TODO(mfbolus): change if parallel // update --\u0026gt; posterior estimation RecurseKe(k_e, p_pre, p_post, t); x_post.col(t) = x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t)); y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d(); } // backfilter -\u0026gt; Smoothed estimate // Reference: // Shumway et Stoffer (1982) ForceSymPD(p_post.slice(n_t_[trial] - 1)); k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1); P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 0; t--) { // TODO(mfmbolus): should not be necessary to force symm positive def ForceSymPD(p_pre.slice(t)); ForceSymPD(p_post.slice(t - 1)); ForceSymPD(P_[trial].slice(t)); k_backfilt.slice(t - 1) = p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t)); x_[trial].col(t - 1) = x_post.col(t - 1) + k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t)); P_[trial].slice(t - 1) = p_post.slice(t - 1) + k_backfilt.slice(t - 1) * (P_[trial].slice(t) - p_pre.slice(t)) * k_backfilt.slice(t - 1).t(); } // do the same for P_t_tm1 Matrix id(n_x_, n_x_, fill::eye); P_t_tm1_[trial].slice(n_t_[trial] - 1) = (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 1; t--) { P_t_tm1_[trial].slice(t - 1) = p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() + k_backfilt.slice(t - 1) * (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) * k_backfilt.slice(t - 2).t(); } // finally, get smoothed estimate of output for (size_t t = 0; t \u0026lt; n_t_[trial]; t++) { fit_.h(y_[trial], x_[trial], t); } // samps loop } // trial loop } // Smooth // template \u0026lt;typename Fit\u0026gt; // void EM\u0026lt;Fit\u0026gt;::RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) { // // predict covar // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q(); // // update Ke // Ke = P_pre.slice(t) * fit_.C().t() * // inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R()); // // update cov // // Reference: Ghahramani et Hinton (1996) // P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t); // // // n.b. for poisson : // // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + // fit_.Q(); // // // update cov // // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ * // // fit_.C()); // // // update Ke // // Ke = P_post.slice(t) * fit_.C(); // } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Expectation(bool force_common_initial) { // calculate the mean/cov of state needed for maximizing E[pr(z|theta)] Smooth(force_common_initial); // now get the various forms of sum(E[xx\u0026#39;]) needed // n.b. Going to start at t=1 rather than 0 bc most max terms need that. // so really \u0026#34;n_t_tot_\u0026#34; is (n_t_tot_-1) n_t_tot_ = 0; sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); Vector xu_tm1(n_x_ + n_u_, fill::zeros); Vector xu_t(n_x_ + n_u_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // ------------ sum_E_x_t_x_t ------------ sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t(); sum_E_x_t_x_t_ += P_[trial].slice(t); // ------------ sum_E_xu_tm1_xu_tm1 ------------ xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1)); sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t(); sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t - 1); // ------------ sum_E_xu_t_xu_tm1 ------------ xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t)); sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t(); sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_t_tm1_[trial].slice(t); n_t_tot_ += 1; } // time } // trial } // Expectation template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement) { if (calc_output) { MaximizeOutput(); } if (calc_measurement) { MaximizeMeasurement(); } if (calc_dynamics) { MaximizeDynamics(); } if (calc_Q) { MaximizeQ(); } if (calc_init) { MaximizeInitial(); } } // Maximization template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeDynamics() { // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) * inv_sympd(sum_E_xu_tm1_xu_tm1_); fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1)); std::cout \u0026lt;\u0026lt; \u0026#34;A_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.A()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.B()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeQ() { // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 * // inv_sympd(sum_E_xu_tm1_xu_tm1_) * // sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // this way is same as above iff dynamics were just updated: // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix ab = arma::join_horiz(fit_.A(), fit_.B()); // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // From scratch method: // Q is covariance of the error between state and dynamics-predicted state // (aka process noise) // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})\u0026#39;] // t-1 terms: View sum_e_x_tm1_x_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_u_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1); View sum_e_x_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); // t, t-1 terms: View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_x_t_u_tm1 = sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); Matrix q = sum_E_x_t_x_t_; q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t(); q -= sum_e_x_t_x_tm1 * fit_.A().t(); q -= fit_.A() * sum_e_x_t_x_tm1.t(); // input-related terms: q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t(); q -= sum_e_x_t_u_tm1 * fit_.B().t(); q -= fit_.B() * sum_e_x_t_u_tm1.t(); q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t(); q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t(); q /= n_t_tot_; fit_.set_Q(q); std::cout \u0026lt;\u0026lt; \u0026#34;Q_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.Q()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // std::cout \u0026lt;\u0026lt; \u0026#34;Q_new: \\n\u0026#34; \u0026lt;\u0026lt; fit_.Q() \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeInitial() { Vector x0 = fit_.x0(); x0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { x0 += x_[trial].col(0); } x0 /= z_.size(); std::cout \u0026lt;\u0026lt; \u0026#34;x0_new[0]: \u0026#34; \u0026lt;\u0026lt; x0[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // always recalc P0 even if the initial state is fixed (at zero, for // example) Matrix e_var(n_x_, n_x_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t(); } e_var /= z_.size(); // go ahead and subtract x0*x0\u0026#39; so don\u0026#39;t have to below. e_var -= x0 * x0.t(); // To get P0, going to get initial P_ per trial and average. // (which might be wrong, but need a single number) Matrix p0 = fit_.P0(); p0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { p0 += (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var; } p0 /= z_.size(); fit_.set_P0(p0); std::cout \u0026lt;\u0026lt; \u0026#34;P0_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.P0()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeOutput() { // solve for C+d: Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros); Vector x1(n_x_ + 1, fill::zeros); x1[n_x_] = 1.0; // augment with one to solve for bias Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { x1.subvec(0, n_x_ - 1) = x_[trial].col(t); sum_zx += z_.at(trial).col(t) * x1.t(); sum_e_x1_x1 += x1 * x1.t(); sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t); } } Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1); fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1)); fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_))); std::cout \u0026lt;\u0026lt; \u0026#34;C_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.C()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.d()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeMeasurement() { // Solve for measurement noise covar size_t n_t_tot = 0; // Ghahgramani, Hinton 1996: Matrix sum_zz(n_y_, n_y_, fill::zeros); Matrix sum_yz(n_y_, n_y_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t(); // Use Cnew: sum_yz += (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t(); n_t_tot += 1; } } fit_.set_R((sum_zz - sum_yz) / n_t_tot); std::cout \u0026lt;\u0026lt; \u0026#34;R_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.R()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Reset() { // reset to initial conditions for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); } } template \u0026lt;typename Fit\u0026gt; Vector EM\u0026lt;Fit\u0026gt;::UpdateTheta() { // TODO(mfbolus): This should include n_y_ more params for d. size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_; if (fit_.R().n_elem \u0026gt; 0) { n_params += n_y_ * n_y_; } Vector theta(n_params); size_t idx_start = 0; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B()); idx_start += n_x_ * n_u_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0()); idx_start += n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C()); idx_start += n_y_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d()); idx_start += n_y_; if (fit_.R().n_elem \u0026gt; 0) { theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R()); } return theta; } } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":54,"href":"/docs/api/files/lds__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_ssid.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SSID Detailed Description # This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)\u0026lt;Fit\u0026gt;).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_FIT_SSID_H #define LDSCTRLEST_LDS_FIT_SSID_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class SSID { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: SSID() = default; SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d = Vector(1).fill(-kInf)); std::tuple\u0026lt;Fit, Vector\u0026gt; Run(SSIDWt ssid_wt); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } protected: void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001); void CreateHankelDataMat(); virtual void DecomposeData() = 0; void CalcSVD(SSIDWt wt); void Solve(data_t wt_dc); void RecomputeExtObs(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; Matrix D_; Fit fit_; Matrix g_dc_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_h_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; Matrix L_; Vector s_; Matrix ext_obs_t_; }; template \u0026lt;typename Fit\u0026gt; SSID\u0026lt;Fit\u0026gt;::SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d) { // check input/output data dimensions are consistent if (z_train.size() != u_train.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_train.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_train.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } dt_ = dt; n_x_ = n_x; n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; n_h_ = n_h; // dimensionality check for eventual block-hankel data matrix size_t len = n_t_tot_ - 2 * n_h_ + 1; if (len \u0026lt; (2 * n_h_ * (n_u_ + n_y_))) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;Dataset problem! More rows than columns in block-hankel data \u0026#34; \u0026#34;matrix: 2*(n_u+n_y)*n_h \u0026gt; data-length! Need higher data-length or \u0026#34; \u0026#34;lower n_h.\u0026#34;; throw std::runtime_error(ss.str()); } fit_ = Fit(n_u_, n_x_, n_y_, dt_); u_ = std::move(u_train); z_ = std::move(z_train); if (!d.is_finite() || (d.n_rows != n_y_)) { // TODO(mfbolus): implement least-square solution for impulse response with // a second input of ones. Data-driven way of accounting for offset *not* // driven by an input. // // For now, calculate output bias (d) as the // output wherever the stimulus has not been on for some amount of time. // convolve u with rectangle and take all samples. This is a reasonable // approach, since often when autonomous systems are fit (i.e., systems with // no input), they will subtract off the mean of the output. This // essentially amounts to setting output bias to the mean of the output when // there is no stimulation. data_t t_silence = 0.1; data_t thresh_silence = 0.001; CalcD(t_silence, thresh_silence); } else { fit_.set_d(d); } } template \u0026lt;typename Fit\u0026gt; std::tuple\u0026lt;Fit, Vector\u0026gt; SSID\u0026lt;Fit\u0026gt;::Run(SSIDWt ssid_wt) { // the weight on minimizing dc I/O gain only works for gaussian, // and hopefully not necessary with appropriate dataset. data_t wt_dc = 0; // std::cout \u0026lt;\u0026lt; \u0026#34;creating hankel mat\\n\u0026#34;; CreateHankelDataMat(); // std::cout \u0026lt;\u0026lt; \u0026#34;decomposing data\\n\u0026#34;; DecomposeData(); // std::cout \u0026lt;\u0026lt; \u0026#34;calculating svd\\n\u0026#34;; CalcSVD(ssid_wt); // std::cout \u0026lt;\u0026lt; \u0026#34;solving for params\\n\u0026#34;; Solve(wt_dc); // std::cout \u0026lt;\u0026lt; \u0026#34;fin\\n\u0026#34;; return std::make_tuple(fit_, s_); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcD(data_t t_silence, data_t thresh_silence) { Vector d(z_.at(0).n_rows, fill::zeros); Vector win(static_cast\u0026lt;size_t\u0026gt;(t_silence / dt_), fill::ones); Vector sum_z_silence(n_y_, fill::zeros); size_t n_silence(0); for (size_t trial = 0; trial \u0026lt; u_.size(); trial++) { // find silent samples // start by convolving with Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0)); Vector u_conv = conv(sum_u, win, \u0026#34;same\u0026#34;); // get only the samples that are silent... arma::uvec ubi_silence = find(u_conv \u0026lt;= thresh_silence); if (ubi_silence.n_elem \u0026gt; 0) { sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1); n_silence += ubi_silence.n_elem; } } if (n_silence \u0026gt; 0) { d = sum_z_silence / n_silence; } fit_.set_d(d); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat() { // temporary copy of data Matrix z(n_y_, n_t_tot_, fill::zeros); Matrix u(n_u_, n_t_tot_, fill::zeros); size_t so_far(0); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial); u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial); so_far += n_t_.at(trial); } // remove output bias z.each_col() -= fit_.d(); // calculate I/O gain @ DC while data in convenient form g_dc_ = z * pinv(u); // std::cout \u0026lt;\u0026lt; \u0026#34;G0_data = \u0026#34; \u0026lt;\u0026lt; g_dc_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // create hankel data matrix size_t len = z.n_cols - 2 * n_h_ + 1; // data length in hankel mat // block-hankel data matrix D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros); // past input auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1); // future input auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1); // past output auto y_p = D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1); // future output auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, len - 1); size_t idx = 0; for (size_t k = 0; k \u0026lt; len; k++) { idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } } D_ /= sqrt(static_cast\u0026lt;data_t\u0026gt;(len)); } // template \u0026lt;typename Fit\u0026gt; // void SSID\u0026lt;Fit\u0026gt;::DecomposeData() { // // do LQ decomp instead of calculating covariance expensive way // // Note that \u0026#34;R\u0026#34; in van Overschee is lower-triangular (L), not \u0026#34;R\u0026#34; in QR // // decomp. Very confusing. // Matrix q_t; // lq(L_, q_t, D_); // // van Overschee zeros out the other elements. // L_ = trimatl(L_); // } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcSVD(SSIDWt wt) { // submats that will be needed: auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1); auto R_23_13 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1); auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_) - 1); auto R_44 = L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14); auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1); auto Luf = Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1); auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); // aka: R_f Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1); // from van Overschee subid.m: // Rf = R((2*m+l)*i+1:2*(m+l)*i,:); % Future outputs Matrix U; Matrix V; switch (wt) { case kSSIDNone: { // No weighting. (what van Overschee calls \u0026#34;N4SID\u0026#34;) Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14; arma::svd(U, s_, V, O_k_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDMOESP: { // MOESP weighting // This is what they use in the \u0026#34;robust\u0026#34; algorithm van Overschee, de Moor // 1996 Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); svd(U, s_, V, O_k_ortho_Uf_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDCVA: { // CVA weighting // See van Overschee\u0026#39;s matlab code (subid.m): // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); Matrix inv_w1; Matrix qt1; lq(inv_w1, qt1, R_56_16); // lq decomp of R_f (future output data) inv_w1 = trimatl(inv_w1); inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1); Matrix w_o_w = arma::solve( inv_w1, O_k_ortho_Uf_sans_Qt); // alternatively // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt svd(U, s_, V, w_o_w, \u0026#34;std\u0026#34;); U = inv_w1 * U; break; } } // Truncate to model order (heart of ssid method) auto s_hat = s_.subvec(0, n_x_ - 1); Matrix diag_sqrt_s = diagmat(sqrt(s_hat)); auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1); // get extended observability and controllability mats ext_obs_t_ = u_hat * diag_sqrt_s; // extended observability matrix } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::Solve(data_t wt_dc) { // required submats auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_56_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); // Solve for params using appropriate algorithm: // robust deterministic/stochastic algorithm in van Overschee 1996 // algorithm that the authors say \u0026#34;works\u0026#34; in practice. auto ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix // This is what textbook (1996) says: // // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15); // // HOWEVER, do not know why but have to fill the last place with zeros like // authors\u0026#39; matlab implementation (see `subid.m`) // Otherwise, get ridiculous covariances (although A,C estimates are close to // same...) Matrix Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Matrix S = Tl * pinv(Tr); // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure // stability. fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1)); Matrix ext_obs_t_p1 = join_vert( ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1), Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros)); fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1); // At this point, van Overschee \u0026amp; de Moor suggest re-calculating ext_obs_t_, // ext_obs_tm1 from (A, C) because it was just an approximation. This is RecomputeExtObs(); ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); S = Tl * pinv(Tr); Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_); Matrix Mcurly = pinv(ext_obs_tm1); Matrix Pcurly = Tl - Lcurly * R_56_15; Vector Pvec = vectorise(Pcurly); Matrix Qcurly = R_23_15; // Identify [D; B], assuming D=0 and ensuring DC gain is correct Matrix sum_QcurlyT_kron_Ncurly( (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_), fill::zeros); Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols, fill::eye); eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1, eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1; // van Overschee (1996) p. 126 Matrix N1_Tl = -Lcurly; N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) += join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly); N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) += Matrix(n_y_, n_y_, fill::eye); Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros); Matrix N_k; for (size_t k = 0; k \u0026lt; n_h_; k++) { auto Qcurly_k = Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1); Nk_Tl.zeros(); Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) = N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1); N_k = Nk_Tl * eye_ext_obs_tm1; sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k); } Matrix err_vec; if (wt_dc \u0026gt; 0) { // Constraints enforced by weighted least squares // // Reference: // // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly // Excited Industrial Systems. Conference in Decision and Control. // constraint 1: assume D=0 --\u0026gt; remove the components for Dvec (this is // actually a hard constraint in that it ignores D) Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly; sum_QcurlyT_kron_Ncurly = Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_); size_t kkk = 0; for (size_t k = 1; k \u0026lt; (n_u_ + 1); k++) { size_t start_idx = k * (n_y_ + n_x_) - n_x_; for (size_t kk = 0; kk \u0026lt; n_x_; kk++) { sum_QcurlyT_kron_Ncurly.col(kkk) = sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk); kkk++; } } // constraint 2: Make sure DC I/O gain is correct Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A()); Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_)); Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0); Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 = join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0); // WEIGHTED LS // Important in practice because I care a lot about at least getting the DC // gain correct. Put x weight on minimizing error at DC, relative to others Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye); // Make weight on minimizing DC error immense so at least that // should be nailed. size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows; size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows; size_t stop_row = w.n_rows - 1; size_t stop_col = w.n_cols - 1; // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale // weight with data length? w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc; Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * sum_QcurlyT_kron_Ncurly_b_to_g0) * sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec; fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_)); // Calculate residuals and their cov. // Because I\u0026#39;ve added constraints, I need to re-calculate the right term // with b_vec instead of how van Overschee do in final algorithm. err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec; } else { // default way: *no* constraint on G0 or D=0 Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec; // TODO(mfbolus) n.b., this gets thrown away... // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_); fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_)); err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec; } // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols); // TODO(mfbolus): Something is wrong with the error calculation above. // Use the way van overschee does it in `subid.m` // WARNING: this ignores any above constraints, so Q, R will be approximate... Matrix err = Tl - S * Tr; Matrix cov_err = err * err.t(); fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1)); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::RecomputeExtObs() { ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C(); for (size_t k = 2; k \u0026lt; (n_h_ + 1); k++) { ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) = ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1, ext_obs_t_.n_cols - 1) * fit_.A(); } } } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":55,"href":"/docs/api/files/lds__fit_8h/","title":"ldsCtrlEst_h/lds_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_fit.h # LDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Fit LDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.\nSource code # //===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDS_FIT_HPP #define LDS_FIT_HPP // namespace #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); virtual ~Fit() = default; // get methods size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; Q() const { return Q_; }; const Vector\u0026amp; x0() const { return x0_; }; const Matrix\u0026amp; P0() const { return P0_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; // gets measurement noise virtual const Matrix\u0026amp; R() const = 0; // set methods (e.g., seeding initial fit values) void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_m(const Vector\u0026amp; m) { Reassign(m_, m); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); ForceSymPD(Q_); }; virtual void set_R(const Matrix\u0026amp; R) = 0; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); ForceSymPD(P0_); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; View f(Matrix\u0026amp; x, const Matrix\u0026amp; u, size_t t) { x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x.col(t); }; View f(Matrix\u0026amp; x_pre, const Matrix\u0026amp; x_post, const Matrix\u0026amp; u, size_t t) { x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x_pre.col(t); }; virtual View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) = 0; protected: data_t dt_{}; // Dynamics Matrix A_; Matrix B_; Vector g_; Vector m_; Matrix Q_; // Output Matrix C_; Vector d_; Matrix R_; // initial conditions Vector x0_; Matrix P0_; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; }; } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":56,"href":"/docs/api/files/lds__gaussian__ctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_ctrl.h # GLDS Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. Detailed Description # This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_gaussian_sys.h\u0026#34; // controller #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace gaussian { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); cx_ref_ = y_ref - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":57,"href":"/docs/api/files/lds__gaussian__fit__em_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_em.h # GLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitEM GLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":58,"href":"/docs/api/files/lds__gaussian__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_ssid.h # GLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; using SSID\u0026lt;Fit\u0026gt;::Run; private: using SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat; using SSID\u0026lt;Fit\u0026gt;::CalcSVD; using SSID\u0026lt;Fit\u0026gt;::Solve; void DecomposeData() override; void SolveVanOverschee(); }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":59,"href":"/docs/api/files/lds__gaussian__fit_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit.h # GLDS fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Fit GLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // fit type #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace gaussian { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); const Matrix\u0026amp; R() const override { return R_; }; void set_R(const Matrix\u0026amp; R) override { Reassign(R_, R); ForceSymPD(R_); }; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = C_ * x.col(t) + d_; return y.col(t); }; }; }; // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":60,"href":"/docs/api/files/lds__gaussian__sctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sctrl.h # GLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H // controller type #include \u0026#34;lds_gaussian_ctrl.h\u0026#34; // switched controller #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace gaussian { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); cx_ref_ = y_ref - sys_.d(); } // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; }; // SwitchedController } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":61,"href":"/docs/api/files/lds__gaussian__sys_8h/","title":"ldsCtrlEst_h/lds_gaussian_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sys.h # GLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::System Gaussian LDS Type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H #define LDSCTRLEST_LDS_GAUSSIAN_SYS_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; namespace lds { namespace gaussian { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0, data_t r0 = kDefaultR0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; // get methods const Matrix\u0026amp; R() const { return R_; }; // set methods void set_Q(const Matrix\u0026amp; Q) { lds::System::set_Q(Q); do_recurse_Ke_ = true; } void set_R(const Matrix\u0026amp; R) { Reassign(R_, R); do_recurse_Ke_ = true; }; void set_Ke(const Matrix\u0026amp; Ke) { Reassign(Ke_, Ke); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void set_Ke_m(const Matrix\u0026amp; Ke_m) { Reassign(Ke_m_, Ke_m); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void Print(); protected: void h() override { cx_ = C_ * x_; y_ = cx_ + d_; }; Vector h_(Vector x) override { return C_ * x + d_; }; void RecurseKe() override; // Gaussian-output-specific Matrix R_; bool do_recurse_Ke_{}; }; // System } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":62,"href":"/docs/api/files/lds__gaussian_8h/","title":"ldsCtrlEst_h/lds_gaussian.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian.h # glds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_H #define LDSCTRLEST_LDS_GAUSSIAN_H // namespace #include \u0026#34;lds.h\u0026#34; namespace lds { namespace gaussian { // insert any Gaussian-specific things here... } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":63,"href":"/docs/api/files/lds__poisson__ctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_ctrl.h # PLDS controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. Detailed Description # This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_CTRL_H #define LDSCTRLEST_LDS_POISSON_CTRL_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system type #include \u0026#34;lds_poisson_sys.h\u0026#34; // control type #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace poisson { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); lds::Limit(y_ref_, kYRefLb, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; private: constexpr static const data_t kYRefLb = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":64,"href":"/docs/api/files/lds__poisson__fit__em_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_em.h # PLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitEM PLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\n[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H #define LDSCTRLEST_LDS_POISSON_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override{}; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; data_t NewtonSolveC(); void AnalyticalSolveD(); }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":65,"href":"/docs/api/files/lds__poisson__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_ssid.h # PLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. Detailed Description # This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/)).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H #define LDSCTRLEST_LDS_POISSON_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; private: void DecomposeData() override; void CalcCov(); void PoissonToGaussianMoments(); Matrix cov_; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":66,"href":"/docs/api/files/lds__poisson__fit_8h/","title":"ldsCtrlEst_h/lds_poisson_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit.h # PLDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Fit PLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_H #define LDSCTRLEST_LDS_POISSON_FIT_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // fit #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace poisson { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) : lds::Fit(n_u, n_x, n_y, dt){}; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = exp(C_ * x.col(t) + d_); return y.col(t); }; void set_R(const Matrix\u0026amp; R) override { std::cerr \u0026lt;\u0026lt; \u0026#34;WARNING: Cannot set R (R[0] = \u0026#34; \u0026lt;\u0026lt; R.at(0) \u0026lt;\u0026lt; \u0026#34;). No Gaussian measurement noise in Poisson observation model.\\n\u0026#34;; }; const Matrix\u0026amp; R() const override { return R_; }; }; }; // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":67,"href":"/docs/api/files/lds__poisson__sctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sctrl.h # PLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #include \u0026#34;lds_poisson_ctrl.h\u0026#34; #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace poisson { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); lds::Limit(y_ref_, kYRefLB, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; private: constexpr static data_t kYRefLB = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":68,"href":"/docs/api/files/lds__poisson__sys_8h/","title":"ldsCtrlEst_h/lds_poisson_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sys.h # PLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::System Poisson System type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SYS_H #define LDSCTRLEST_LDS_POISSON_SYS_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; // needed for Poisson random number generation #include \u0026lt;random\u0026gt; namespace lds { namespace poisson { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; protected: void h() override { cx_ = C_ * x_; y_ = exp(cx_ + d_); diag_y_.diag() = y_; }; Vector h_(Vector x) override { return exp(C_ * x + d_); }; void RecurseKe() override; private: // Poisson-output-specific Matrix diag_y_; std::poisson_distribution\u0026lt;size_t\u0026gt; pd_; }; // System } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":69,"href":"/docs/api/files/lds__poisson_8h/","title":"ldsCtrlEst_h/lds_poisson.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson.h # plds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_H #define LDSCTRLEST_LDS_POISSON_H #include \u0026#34;lds.h\u0026#34; namespace lds { namespace poisson { // TODO(mfbolus): Not sure if defining these as static here makes the most // sense. Is there a downside to letting multiple poisson System objects share a // common random number generator? static std::random_device rd; static std::mt19937 rng = std::mt19937( rd()); } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":70,"href":"/docs/api/files/lds__sctrl_8h/","title":"ldsCtrlEst_h/lds_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_sctrl.h # SwitchedController type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SwitchedController SwitchedController Type. Detailed Description # This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SCTRL_H #define LDSCTRLEST_LDS_SCTRL_H #include \u0026#34;lds_ctrl.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; #include \u0026#34;lds_uniform_vecs.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class SwitchedController : public Controller\u0026lt;System\u0026gt; { public: SwitchedController() = default; SwitchedController(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); SwitchedController(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); void Switch(size_t idx, bool do_force_switch = false); void set_Kc(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc) { Kc_list_ = Kc; Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc) { Kc_list_ = std::move(Kc); Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_inty) { Kc_inty_list_ = Kc_inty; Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_inty) { Kc_inty_list_ = std::move(Kc_inty); Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_u) { Kc_u_list_ = Kc_u; Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_u) { Kc_u_list_ = std::move(Kc_u); Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(const UniformVectorList\u0026amp; g) { g_design_list_ = g; g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(UniformVectorList\u0026amp;\u0026amp; g) { g_design_list_ = std::move(g); g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; protected: std::vector\u0026lt;System\u0026gt; systems_; size_t n_sys_{}; size_t idx_{}; // controller gains could be different for each UniformMatrixList\u0026lt;\u0026gt; Kc_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_inty_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_u_list_; // design-phase input gain could also be different UniformVectorList g_design_list_; // TODO(mfbolus): not sure why I need to do this. using Controller\u0026lt;System\u0026gt;::Kc_; using Controller\u0026lt;System\u0026gt;::Kc_inty_; using Controller\u0026lt;System\u0026gt;::Kc_u_; using Controller\u0026lt;System\u0026gt;::g_design_; using Controller\u0026lt;System\u0026gt;::sys_; // using Controller\u0026lt;System\u0026gt;::u_ref_; // using Controller\u0026lt;System\u0026gt;::x_ref_; // using Controller\u0026lt;System\u0026gt;::y_ref_; // using Controller\u0026lt;System\u0026gt;::control_type_; private: void InitVars(); using lds::Controller\u0026lt;System\u0026gt;::set_sys; // using Controller\u0026lt;System\u0026gt;::set_Kc; // using Controller\u0026lt;System\u0026gt;::set_Kc_inty; // using Controller\u0026lt;System\u0026gt;::set_Kc_u; // using Controller\u0026lt;System\u0026gt;::set_g_design; }; template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(systems.at(0), u_lb, u_ub, control_type), systems_(systems) { InitVars(); } template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(System(systems.at(0).n_u(), systems.at(0).n_x(), systems.at(0).n_y(), systems.at(0).dt()), u_lb, u_ub, control_type), systems_(std::move(systems)) { InitVars(); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::InitVars() { n_sys_ = systems_.size(); sys_ = systems_.at(0); Kc_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_)); Kc_inty_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); Kc_u_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); g_design_list_ = UniformVectorList(std::vector\u0026lt;Vector\u0026gt;(n_sys_, g_design_)); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::Switch(size_t idx, bool do_force_switch) { if ((idx == idx_) \u0026amp;\u0026amp; !do_force_switch) { return; // already there. } // put old up and get new one out systems_.at(idx_) = std::move(sys_); sys_ = std::move(systems_.at(idx)); // set the state of this system to that of the previous system // TODO(mfbolus): This will only work as intended if state matrix is the same. // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp. sys_.set_m(systems_.at(idx_).m(), true); sys_.set_x(systems_.at(idx_).x()); // swap controller gains Kc_list_.Swap(Kc_, idx_); Kc_list_.Swap(Kc_, idx); if (control_type_ \u0026amp; kControlTypeIntY) { Kc_inty_list_.Swap(Kc_inty_, idx_); Kc_inty_list_.Swap(Kc_inty_, idx); } if (control_type_ \u0026amp; kControlTypeDeltaU) { Kc_u_list_.Swap(Kc_u_, idx_); Kc_u_list_.Swap(Kc_u_, idx); } g_design_list_.Swap(g_design_, idx_); g_design_list_.Swap(g_design_, idx); idx_ = idx; } // Switch } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":71,"href":"/docs/api/files/lds__sys_8h/","title":"ldsCtrlEst_h/lds_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_sys.h # LDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::System Linear Dynamical System Type. Detailed Description # This file declares and partially defines the base type for linear dynamical systems ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SYS_H #define LDSCTRLEST_LDS_SYS_H #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class System { public: System() = default; System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); virtual ~System() {} void Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z); virtual const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) = 0; void f(const Vector\u0026amp; u, bool do_add_noise = false) { x_ = A_ * x_ + B_ * (g_ % u) + m_; if (do_add_noise) { x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_); } }; virtual void h() = 0; virtual Vector h_(Vector x) = 0; size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Vector\u0026amp; x() const { return x_; }; const Matrix\u0026amp; P() const { return P_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; P_m() const { return P_m_; }; const Vector\u0026amp; cx() const { return cx_; }; const Vector\u0026amp; y() const { return y_; }; const Vector\u0026amp; x0() const { return x0_; }; const Vector\u0026amp; m0() const { return m0_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; const Matrix\u0026amp; Ke() const { return Ke_; }; const Matrix\u0026amp; Ke_m() const { return Ke_m_; }; const Matrix\u0026amp; Q() { return Q_; }; const Matrix\u0026amp; Q_m() { return Q_m_; }; const Matrix\u0026amp; P0() { return P0_; }; const Matrix\u0026amp; P0_m() { return P0_m_; }; void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_m(const Vector\u0026amp; m, bool do_force_assign = false) { Reassign(m0_, m); if ((!do_adapt_m) || do_force_assign) { Reassign(m_, m); } }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); }; void set_Q_m(const Matrix\u0026amp; Q_m) { Reassign(Q_m_, Q_m); }; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); }; void set_P0_m(const Matrix\u0026amp; P0_m) { Reassign(P0_m_, P0_m); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; void set_x(const Vector\u0026amp; x) { Reassign(x_, x); h(); }; void Reset(); std::vector\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; nstep_pred_block( UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z, size_t n_pred = 1); void Print(); // safe to leave this public and non-const bool do_adapt_m{}; protected: virtual void RecurseKe() = 0; void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); std::size_t n_x_{}; std::size_t n_u_{}; std::size_t n_y_{}; data_t dt_{}; // Signals: Vector x_; Matrix P_; Vector m_; Matrix P_m_; Vector cx_; Vector y_; Vector z_; // Parameters: Vector x0_; Matrix P0_; Vector m0_; Matrix P0_m_; Matrix A_; Matrix B_; Vector g_; Matrix Q_; Matrix Q_m_; Matrix C_; Vector d_; Matrix Ke_; Matrix Ke_m_; }; // System } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":72,"href":"/docs/api/files/lds__uniform__mats_8h/","title":"ldsCtrlEst_h/lds_uniform_mats.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_mats.h # List of uniformly sized matrices. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformMatrixList Detailed Description # This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H #define LDSCTRLEST_LDS_UNIFORM_MATS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { template \u0026lt;MatrixListFreeDim D = kMatFreeDimNone\u0026gt; class UniformMatrixList : public std::vector\u0026lt;Matrix\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Matrix\u0026gt;::vector; // don\u0026#39;t allow push_back to be used since it doesn\u0026#39;t check dims using std::vector\u0026lt;Matrix\u0026gt;::push_back; public: using std::vector\u0026lt;Matrix\u0026gt;::operator=; using std::vector\u0026lt;Matrix\u0026gt;::operator[]; using std::vector\u0026lt;Matrix\u0026gt;::begin; using std::vector\u0026lt;Matrix\u0026gt;::end; using std::vector\u0026lt;Matrix\u0026gt;::size; using std::vector\u0026lt;Matrix\u0026gt;::at; UniformMatrixList() = default; explicit UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); explicit UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; ~UniformMatrixList() = default; const std::array\u0026lt;size_t, 2\u0026gt;\u0026amp; dim(size_t n = 0) const { return dim_.at(n); } size_t size() { return std::vector\u0026lt;Matrix\u0026gt;::size(); }; const Matrix\u0026amp; at(size_t n) { return std::vector\u0026lt;Matrix\u0026gt;::at(n); }; void Swap(Matrix\u0026amp; that, size_t n); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; void append(const Matrix\u0026amp; mat); private: void CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim); std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt; dim_; }; template \u0026lt;MatrixListFreeDim D\u0026gt; inline void UniformMatrixList\u0026lt;D\u0026gt;::Swap(Matrix\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.n_cols); } if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap // not moving, since it causes memory issues. // so this method isn\u0026#39;t a memory-saver as designed for now Matrix tmp = (*this)[n]; (*this)[n] = that; that = tmp; if (D == kMatFreeDim1) { this-\u0026gt;dim_[n][0] = (*this)[n].n_rows; } if (D == kMatFreeDim2) { this-\u0026gt;dim_[n][1] = (*this)[n].n_cols; } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::append(const Matrix\u0026amp; mat) { std::array\u0026lt;size_t, 2\u0026gt; dim({mat.n_rows, mat.n_cols}); CheckDimensions(dim); std::vector\u0026lt;Matrix\u0026gt;::push_back(mat); dim_.push_back(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) { // make sure dim_ vector is initialized if (dim_.empty()) { dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(that.size(), {0, 0}); } // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices\u0026#34;; throw std::runtime_error(ss.str()); } // if dimensions a not zero and do not match, skip move with error message. bool dims_nonzero = true; for (auto d : dim_) { if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; d[0] \u0026lt; 1) { dims_nonzero = false; break; } if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; d[1] \u0026lt; 1) { dims_nonzero = false; break; } } if (dims_nonzero) { bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); } if (!does_match) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrices of size \u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; dim_[k] = that.dim(k); } return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. bool dims_nonzero = true; for (auto d : dim_) { // if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; (d[0] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; (d[1] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // } // // if (dims_nonzero) { // bool does_match = true; // if (!(D == kMatFreeDim1)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); // } // // if (!(D == kMatFreeDim2)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); // } // // if (!does_match) { // this-\u0026gt;at(0).print(\u0026#34;this[0] = \u0026#34;); // that.at(0).print(\u0026#34;that[0] = \u0026#34;); // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformMatrixList element of size (\u0026#34; \u0026lt;\u0026lt; // that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols \u0026lt;\u0026lt; \u0026#34;) for an // element of size (\u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34;). // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Matrix\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(std::move(mats)) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) : vector(that) { (*this) = that; } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { std::array\u0026lt;size_t, 2\u0026gt; dim_k({this-\u0026gt;at(k).n_rows, this-\u0026gt;at(k).n_cols}); dim_.push_back(dim_k); } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim) { // change behavior based on free dim D if ((dim[0] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim1)) { dim[0] = this-\u0026gt;at(0).n_rows; } if ((dim[1] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim2)) { dim[1] = this-\u0026gt;at(0).n_cols; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Matrix\u0026amp; mat : *this) { if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_rows == dim[0]); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_cols == dim[1]); } if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(this-\u0026gt;size(), dim); for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { dim_[k][0] = (*this)[k].n_rows; dim_[k][1] = (*this)[k].n_cols; } } } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":73,"href":"/docs/api/files/lds__uniform__systems_8h/","title":"ldsCtrlEst_h/lds_uniform_systems.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_systems.h # List of uniformly sized Systems. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformSystemList Detailed Description # This file provides a container for uniformly sized Systems.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector // namespace #include \u0026#34;lds.h\u0026#34; // System type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class UniformSystemList : public std::vector\u0026lt;System\u0026gt; { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;System\u0026gt;::vector; using std::vector\u0026lt;System\u0026gt;::operator=; using std::vector\u0026lt;System\u0026gt;::operator[]; using std::vector\u0026lt;System\u0026gt;::at; using std::vector\u0026lt;System\u0026gt;::begin; using std::vector\u0026lt;System\u0026gt;::end; using std::vector\u0026lt;System\u0026gt;::size; public: UniformSystemList() = default; explicit UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); explicit UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(const UniformSystemList\u0026amp; that); UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept; ~UniformSystemList() = default; const std::array\u0026lt;size_t, 3\u0026gt;\u0026amp; dim() const { return dim_; } size_t size() { return std::vector\u0026lt;System\u0026gt;::size(); }; const System\u0026amp; at(size_t n) { return std::vector\u0026lt;System\u0026gt;::at(n); }; void Swap(System\u0026amp; that, size_t n); UniformSystemList\u0026amp; operator=(const UniformSystemList\u0026amp; that); UniformSystemList\u0026amp; operator=(UniformSystemList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim); std::array\u0026lt;size_t, 3\u0026gt; dim_{}; }; template \u0026lt;typename System\u0026gt; inline void UniformSystemList\u0026lt;System\u0026gt;::Swap(System\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformSystemList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = (dim_[0] == that.n_u()) \u0026amp;\u0026amp; (dim_[1] == that.n_x()) \u0026amp;\u0026amp; (dim_[2] == that.n_y()); if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformSystemList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap System tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( const UniformSystemList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_[0] + dim_[1] + dim_[2]) { std::array\u0026lt;size_t, 3\u0026gt; other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign systems of size \u0026#34; \u0026lt;\u0026lt; dim_[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2] \u0026lt;\u0026lt; \u0026#34; with systems of size \u0026#34; \u0026lt;\u0026lt; other_dim[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other_dim[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2]; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( UniformSystemList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. if (dim_[0] + dim_[1] + dim_[2]) { // bool does_match = (dim_[0] == that.at(0).n_u()) \u0026amp;\u0026amp; // (dim_[1] == that.at(0).n_x()) \u0026amp;\u0026amp; // (dim_[2] == that.at(0).n_y()); // if (!does_match) { // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformSystemList element for an element of \u0026#34; // \u0026#34;different size. Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;System\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(std::move(systems)) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList( std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const UniformSystemList\u0026amp; that) : std::vector\u0026lt;System\u0026gt;(that) { (*this) = that; } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept : std::vector\u0026lt;System\u0026gt;(std::move(that)) { this-\u0026gt;dim_[0] = this-\u0026gt;at(0).n_u(); this-\u0026gt;dim_[1] = this-\u0026gt;at(0).n_x(); this-\u0026gt;dim_[2] = this-\u0026gt;at(0).n_y(); } template \u0026lt;typename System\u0026gt; void UniformSystemList\u0026lt;System\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim) { if (dim[0] + dim[1] + dim[2]) { dim_ = dim; } else { dim_[0] = this-\u0026gt;at(0).n_u(); dim_[1] = this-\u0026gt;at(0).n_x(); dim_[2] = this-\u0026gt;at(0).n_y(); } // make sure dimensiolaties are all uniform bool does_match(true); for (const System\u0026amp; sys : *this) { does_match = does_match \u0026amp;\u0026amp; (sys.n_u() == dim_[0]); does_match = does_match \u0026amp;\u0026amp; (sys.n_x() == dim_[1]); does_match = does_match \u0026amp;\u0026amp; (sys.n_y() == dim_[2]); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input systems are not uniform.\u0026#34;); } } } } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":74,"href":"/docs/api/files/lds__uniform__vecs_8h/","title":"ldsCtrlEst_h/lds_uniform_vecs.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_vecs.h # List of uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformVectorList Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H #define LDSCTRLEST_LDS_UNIFORM_VECS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { class UniformVectorList : public std::vector\u0026lt;Vector\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Vector\u0026gt;::vector; using std::vector\u0026lt;Vector\u0026gt;::operator=; using std::vector\u0026lt;Vector\u0026gt;::operator[]; using std::vector\u0026lt;Vector\u0026gt;::at; using std::vector\u0026lt;Vector\u0026gt;::begin; using std::vector\u0026lt;Vector\u0026gt;::end; using std::vector\u0026lt;Vector\u0026gt;::size; public: UniformVectorList() = default; explicit UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim = 0); explicit UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim = 0); UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim = 0); UniformVectorList(const UniformVectorList\u0026amp; that); UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept; ~UniformVectorList() = default; size_t dim() const { return dim_; } size_t size() { return std::vector\u0026lt;Vector\u0026gt;::size(); }; const Vector\u0026amp; at(size_t n) { return std::vector\u0026lt;Vector\u0026gt;::at(n); }; void Swap(Vector\u0026amp; that, size_t n); UniformVectorList\u0026amp; operator=(const UniformVectorList\u0026amp; that); UniformVectorList\u0026amp; operator=(UniformVectorList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(size_t dim); size_t dim_{}; }; inline void UniformVectorList::Swap(Vector\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = dim_ == that.n_elem; if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap Vector tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( const UniformVectorList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_) { size_t other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ \u0026lt;\u0026lt; \u0026#34; with vectors of size \u0026#34; \u0026lt;\u0026lt; other_dim; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( UniformVectorList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors. Skipping.\\n\u0026#34;; // return (*this); // } // // if (dim_) { // size_t other_dim(that.dim()); // if (dim_ != other_dim) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ // \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; other_dim \u0026lt;\u0026lt; \u0026#34;. // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Vector\u0026gt;::operator=(std::move(that)); return (*this); } } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":75,"href":"/docs/api/files/lds_8h/","title":"ldsCtrlEst_h/lds.h","section":"Files","content":" ldsCtrlEst_h/lds.h # lds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)) observations.\nSource code # //===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_H #define LDSCTRLEST_LDS_H // #ifndef LDSCTRLEST // #include \u0026lt;ldsCtrlEst\u0026gt; // #endif #include \u0026lt;armadillo\u0026gt; namespace lds { using data_t = double; // may change to float (but breaks mex functions) using Vector = arma::Col\u0026lt;data_t\u0026gt;; using Matrix = arma::Mat\u0026lt;data_t\u0026gt;; using Cube = arma::Cube\u0026lt;data_t\u0026gt;; using View = arma::subview\u0026lt;data_t\u0026gt;; namespace fill = arma::fill; static const std::size_t kControlTypeDeltaU = 0x1; static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); static const data_t kPi = arma::datum::pi; static const data_t kDefaultP0 = 1e-6; static const data_t kDefaultQ0 = 1e-6; static const data_t kDefaultR0 = 1e-2; enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA }; enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2 }; // TODO(mfbolus): for SwitchedController, may want systems to have differing // numbers of states. Use this enum as template parameter? // enum SystemListFreeDim { // kSysFreeDimNone, // kSysFreeDimX ///\u0026lt; allow state dim (x) of systems in list to be hetero // }; // place hard limits on contents of vecors/mats void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub); void Limit(Vector\u0026amp; x, data_t lb, data_t ub); void Limit(Matrix\u0026amp; x, data_t lb, data_t ub); // in-place assign that errs if there are dimension mismatches: void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); // TODO(mfbolus): this is a fudge, but for some reason, cov mats often going // numerically asymm. void ForceSymPD(Matrix\u0026amp; X); void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min = 0); void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X); Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B); inline void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Vector\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Matrix\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if (other.n_elem != some.n_elem) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vector of size \u0026#34; \u0026lt;\u0026lt; some.n_elem \u0026lt;\u0026lt; \u0026#34; with vector of size \u0026#34; \u0026lt;\u0026lt; other.n_elem \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } inline void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrix of size \u0026#34; \u0026lt;\u0026lt; some.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; some.n_cols \u0026lt;\u0026lt; \u0026#34; with matrix of size \u0026#34; \u0026lt;\u0026lt; other.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other.n_cols \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } } // namespace lds #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":76,"href":"/docs/api/files/mex__c__util_8h/","title":"ldsCtrlEst_h/mex_c_util.h","section":"Files","content":" ldsCtrlEst_h/mex_c_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) More\u0026hellip;\nNamespaces # Name armamexc arma/mex interface using Matlab C API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab/Octave\u0026rsquo;s C mex API.\nSource code # //===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXC_UTIL_H #define LDSCTRLEST_MEXC_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.h\u0026#34; // // If Matlab_FOUND, include matrix.h. // // (Octave does not need/have it.) // #ifdef Matlab_FOUND // #include \u0026#34;matrix.h\u0026#34; // #endif namespace armamexc { template \u0026lt;class T\u0026gt; inline auto m2T_scalar(const mxArray *matlab_scalar) -\u0026gt; T { if (mxGetData(matlab_scalar)) { return static_cast\u0026lt;T\u0026gt;(mxGetScalar(matlab_scalar)); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return 0; } template \u0026lt;class T\u0026gt; inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false, bool strict = true) -\u0026gt; arma::Mat\u0026lt;T\u0026gt; { if (mxGetData(matlab_mat)) { const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat); if (n_dim == 2) { return arma::Mat\u0026lt;T\u0026gt;(static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)), mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem, strict); } mexErrMsgTxt(\u0026#34;Number of dimensions must be 2.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } // TODO(mfbolus): make these templated. template \u0026lt;typename T\u0026gt; inline auto a2m_mat(arma::Mat\u0026lt;T\u0026gt; const \u0026amp;arma_mat) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_mat.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Mat.\u0026#34;); return nullptr; } template \u0026lt;typename T\u0026gt; inline auto a2m_vec(arma::Col\u0026lt;T\u0026gt; const \u0026amp;arma_vec) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_vec.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Col.\u0026#34;); return nullptr; } } // namespace armamexc #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":77,"href":"/docs/api/files/mex__cpp__util_8h/","title":"ldsCtrlEst_h/mex_cpp_util.h","section":"Files","content":" ldsCtrlEst_h/mex_cpp_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) More\u0026hellip;\nNamespaces # Name armamexcpp arma/mex interface using Matlab C++ API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab\u0026rsquo;s C++ mex API.\nSource code # //===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXCPP_UTIL_H #define LDSCTRLEST_MEXCPP_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.hpp\u0026#34; #include \u0026#34;mexAdapter.hpp\u0026#34; namespace armamexcpp { template \u0026lt;class T\u0026gt; std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; m2a_cellmat(matlab::data::CellArray\u0026amp; matlab_cell) { size_t n_cells = matlab_cell.getNumberOfElements(); std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; arma_mat(n_cells, arma::Mat\u0026lt;T\u0026gt;(1, 1, arma::fill::zeros)); for (size_t k = 0; k \u0026lt; n_cells; k++) { matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = matlab_cell[k]; auto dims = matlab_mat.getDimensions(); arma_mat[k] = arma::Mat\u0026lt;T\u0026gt;(matlab_mat.release().get(), dims[0], dims[1]); } return arma_mat; }; template \u0026lt;class T\u0026gt; std::vector\u0026lt;T\u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt;T\u0026gt;\u0026amp; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); T* ptr = matlab_array.release().get(); std::vector\u0026lt;T\u0026gt; vec(ptr, ptr + n_elem); return vec; }; template \u0026lt;class T\u0026gt; arma::Col\u0026lt;T\u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); // T* ptr = matlab_array.release().get(); // arma::Col\u0026lt;T\u0026gt; vec(ptr, n_elem); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? arma::Col\u0026lt;T\u0026gt; vec(n_elem, arma::fill::zeros); for (size_t k = 0; k \u0026lt; n_elem; k++) { vec[k] = matlab_array[k]; } return vec; }; template \u0026lt;class T\u0026gt; arma::Mat\u0026lt;T\u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { // ArrayDimensions == std::vector\u0026lt;size_t\u0026gt; auto dims = matlab_array.getDimensions(); // T* ptr = matlab_array.release().get(); // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false) // arma::Mat\u0026lt;T\u0026gt; mat(ptr, dims[0], dims[1]); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? // // armadillo and matlab both use column-major ordering, so this should work: size_t n_elem = dims[0] * dims[1]; arma::Mat\u0026lt;T\u0026gt; mat(dims[0], dims[1], arma::fill::zeros); size_t k(0); for (auto m: matlab_array) { mat[k] = m; k++; } return mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_mat(const arma::Mat\u0026lt;T\u0026gt;\u0026amp; arma_mat, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(), arma_mat.memptr() + arma_mat.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_vec(const arma::Col\u0026lt;T\u0026gt;\u0026amp; arma_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;({arma_vec.n_elem, 1}, arma_vec.memptr(), arma_vec.memptr() + arma_vec.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; s2m_vec(const std::vector\u0026lt;T\u0026gt;\u0026amp; std_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size()); return matlab_mat; }; } // namespace armamexcpp #endif Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":78,"href":"/docs/terminology/model/","title":"Models","section":"LDS C+E Documentation","content":" Model Definitions # This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form: \\[\\mathbf{x}_{t\u0026#43;1} = f\\left( \\mathbf{x}_{t}, \\mathbf{v}_{t} \\right) = \\mathbf{A} \\mathbf{x}_{t} \u0026#43; \\mathbf{B} \\mathbf{v}_{t} \u0026#43; \\mathbf{m}_{t} \u0026#43; \\mathbf{w}_{t}\\] \\[\\mathbf{y}_{t} = h\\left( \\mathbf{x}_{t} \\right)\\] t : time index x : system state v = g%u : input (e.g., in physical units used for model fit) u : control signal sent to actuator (e.g., in Volts) y : system output m : process disturbance w ~ N(0, Q) : process noise/disturbance A : state matrix B : input coupling matrix g : input gain (e.g., for converting to control signal actuator voltage) n.b., assumes this conversion is linear Q : process noise covariance % : element-wise multiplication LDS with Gaussian Observations # For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.\n\\[\\mathbf{y}_{t} = \\mathbf{C} \\mathbf{x}_{t} \u0026#43; \\mathbf{d}\\] \\[\\mathbf{z}_{t} \\sim \\mathcal{N}\\left(\\mathbf{y}_{t} , \\mathbf{R} \\right)\\] z : measurement C : output matrix d : output bias R : measurement noise covariance LDS with Poisson Observations # For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.\n\\[y_{t}^{i} = \\exp \\left(\\mathbf{c}^i \\mathbf{x}_{t} \u0026#43; d^i\\right)\\] \\[z_{t}^i \\sim \\rm{Poisson} \\left(y_{t}^i \\right)\\] i : output index z : measurement (count data) c : i^th row of output matrix (C) d : output bias Model Predictive Control (MPC) # Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.\nIn the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.\n"},{"id":79,"href":"/docs/api/modules/","title":"Modules","section":"LDS C+E Documentation","content":" Modules # Control Mode Bit Masks provides fill types for constructing new armadillo vectors, matrices\nDefaults\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":80,"href":"/docs/api/namespaces/","title":"Namespaces","section":"LDS C+E Documentation","content":" Namespaces # armamexc arma/mex interface using Matlab C API\narmamexcpp arma/mex interface using Matlab C++ API\nlds::gaussian Linear Dynamical Systems with Gaussian observations.\nlds::poisson Linear Dynamical Systems with Poisson observations.\nstd\nUpdated on 5 March 2025 at 16:32:33 EST\n"},{"id":81,"href":"/docs/api/pages/","title":"Pages","section":"LDS C+E Documentation","content":" Pages # Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":82,"href":"/docs/tutorials/eg_plds_state_estimation/","title":"PLDS State Estimation","section":"LDS C+E Examples","content":" PLDS State Estimation Tutorial # This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[x_{t\u0026#43;1} = x_t \u0026#43; w_t\\] \\[y_{t} = \\exp\\left(x_t\\right)\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state Finally, assign the parameters using corresponding set-methods.\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); Creating the estimator # Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. To ensure robust estimates, adaptively re-estimate the process disturbance.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); Simulating estimation # In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.\n// Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.\nWith this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.\n"},{"id":83,"href":"/docs/tutorials/eg_switched_plds_control/","title":"PLDS Switched Control","section":"LDS C+E Examples","content":" PLDS Switched Control Tutorial # This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.\nIn the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating the simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); The system\u0026rsquo;s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 Initially, the system will be in \u0026ldquo;mode\u0026rdquo; 1, where B = b1.\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.\nCreating the controller # Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.\nSimilar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.\nMoreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.\nPutting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying systems: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nNow that the SwitchedController is instantiated, assign its parameters.\n// Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).\nThe control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.\n// Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system\u0026rsquo;s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.\nExample simulation result # Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller\u0026rsquo;s online estimates of the output and state are shown in purple.\nNote that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.\n"},{"id":84,"href":"/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/","title":"src","section":"Files","content":" src # Files # Name src/lds.cpp misc lds namespace functions src/lds_gaussian_sys.cpp GLDS base type. src/lds_poisson_sys.cpp PLDS base type. src/lds_sys.cpp LDS base type. src/lds_uniform_vecs.cpp Uniformly sized vectors. Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":85,"href":"/docs/api/files/lds__gaussian__sys_8cpp/","title":"src/lds_gaussian_sys.cpp","section":"Files","content":" src/lds_gaussian_sys.cpp # GLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_gaussian_sys.h\u0026gt; lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0, data_t r0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { R_.zeros(n_y, n_y); R_.diag().fill(r0); do_recurse_Ke_=true; }; // recursively estimate Ke void lds::gaussian::System::RecurseKe() { if (!do_recurse_Ke_) { return; } // predict covariance P_ = A_ * P_ * A_.t() + Q_; // calc Kalman gain Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_); // update covariance // Reference: Ghahramani et Hinton (1996) P_ = P_ - Ke_ * C_ * P_; if (do_adapt_m) { P_m_ += Q_m_; // A_m = I (i.e., random walk) Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_); P_m_ = P_m_ - Ke_m_ * C_ * P_m_; } } // Simulate const lds::Vector\u0026amp; lds::gaussian::System::Simulate(const Vector\u0026amp; u_tm1){ f(u_tm1, true);//simulate dynamics with noise added h();//output z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure return z_; } void lds::gaussian::System::Print() { lds::System::Print(); std::cout \u0026lt;\u0026lt; \u0026#34;R: \\n\u0026#34; \u0026lt;\u0026lt; R_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":86,"href":"/docs/api/files/lds__poisson__sys_8cpp/","title":"src/lds_poisson_sys.cpp","section":"Files","content":" src/lds_poisson_sys.cpp # PLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_poisson_sys.h\u0026gt; lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { diag_y_ = diagmat(y_); pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(0); }; // Correct: Given measurement (z) and current input (u), update estimate of the // state, covar, output. // // see Eden et al. 2004 void lds::poisson::System::RecurseKe() { // predict covariance P_ = A_ * P_ * A_.t() + Q_; // update cov P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_); Ke_ = P_ * C_.t(); if (do_adapt_m) { P_m_ += Q_m_; // predict (A_m = I) P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_); // update Ke_m_ = P_m_ * C_.t(); } } // Simulate Measurement: z ~ Poisson(y) const lds::Vector\u0026amp; lds::poisson::System::Simulate(const Vector\u0026amp; u_tm1) { f(u_tm1, true); // simulate dynamics with noise added h(); // output z_.zeros(); for (std::size_t k = 0; k \u0026lt; n_y_; k++) { // construct a Poisson distribution object with mean y[k] pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(y_[k]); // pull random sample from this distribution z_[k] = pd_(rng); } return z_; } // ******************* SYS_T ******************* Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":87,"href":"/docs/api/files/lds__sys_8cpp/","title":"src/lds_sys.cpp","section":"Files","content":" src/lds_sys.cpp # LDS base type. More\u0026hellip;\nDetailed Description # This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- lds_sys.cpp - LDS -------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_sys.h\u0026gt; #include \u0026lt;vector\u0026gt; lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) { InitVars(p0, q0); } void lds::System::InitVars(data_t p0, data_t q0) { // initial conditions. x0_ = Vector(n_x_, fill::zeros); // includes bias (nY) and g (nU) P0_ = p0 * Matrix(n_x_, n_x_, fill::eye); m0_ = x0_; P0_m_ = P0_; // signals x_ = x0_; P_ = P0_; m_ = m0_; P_m_ = P0_m_; y_ = Vector(n_y_, fill::zeros); cx_ = Vector(n_y_, fill::zeros); z_ = Vector(n_y_, fill::zeros); // By default, random walk where each state is independent // In this way, provides independent estimates of rate per channel of output. A_ = Matrix(n_x_, n_x_, fill::eye); B_ = Matrix(n_x_, n_u_, fill::zeros); g_ = Vector(n_u_, fill::ones); Q_ = q0 * Matrix(n_x_, n_x_, fill::eye); Q_m_ = Q_; C_ = Matrix(n_y_, n_x_, fill::eye); // each state will map to an output by d_ = Vector(n_y_, fill::zeros); Ke_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain. Ke_m_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain for m adaptation. do_adapt_m = false; } // Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state // and update estimate of the state, covar, output using Kalman filter void lds::System::Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z_t) { // predict mean f(u_tm1); // dynamics h(); // output // recursively calculate esimator gains (or just keep existing values) // (also predicts+updates estimate covariance) RecurseKe(); // update x_ += Ke_ * (z_t - y_); if (do_adapt_m) { m_ += Ke_m_ * (z_t - y_); // adaptively estimating disturbance } // With new state, estimate output. h(); // --\u0026gt; posterior } void lds::System::Reset() { // reset to initial conditions x_ = x0_; // mean P_ = P0_; // cov of state estimate m_ = m0_; // process disturbance P_m_ = P0_m_; // cov of disturbance estimate h(); } std::vector\u0026lt;lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt;\u0026gt; lds::System::nstep_pred_block(lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; u, lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; z, size_t n_pred) { lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_filt; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_pred; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; y_pred; for (size_t k = 0; k \u0026lt; u.size(); k++) { Reset(); size_t n_t = arma::size(u[k])[1]; Matrix x_filt_k(n_x_, n_t, fill::zeros); Matrix x_pred_k(n_x_, n_t - n_pred, fill::zeros); Matrix y_pred_k(n_y_, n_t - n_pred, fill::zeros); for (size_t t = 0; t \u0026lt; n_t - n_pred; t++) { Vector x_pred_ahead = x_; for (size_t t_u = t; t_u \u0026lt; t + n_pred; t_u++) { x_pred_ahead = A_ * x_pred_ahead + B_ * u[k].col(t_u); } x_pred_k.col(t) = x_pred_ahead; y_pred_k.col(t) = h_(x_pred_ahead); if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; // given previous measurment } for (size_t t = n_t - n_pred; t \u0026lt; n_t; t++) { if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; } x_filt.append(x_filt_k); x_pred.append(x_pred_k); y_pred.append(y_pred_k); } return {x_filt, x_pred, y_pred}; } void lds::System::Print() { std::cout \u0026lt;\u0026lt; \u0026#34;\\n ********** SYSTEM ********** \\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;x: \\n\u0026#34; \u0026lt;\u0026lt; x_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;P: \\n\u0026#34; \u0026lt;\u0026lt; P_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;A: \\n\u0026#34; \u0026lt;\u0026lt; A_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B: \\n\u0026#34; \u0026lt;\u0026lt; B_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;g: \\n\u0026#34; \u0026lt;\u0026lt; g_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;m: \\n\u0026#34; \u0026lt;\u0026lt; m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q: \\n\u0026#34; \u0026lt;\u0026lt; Q_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q_m: \\n\u0026#34; \u0026lt;\u0026lt; Q_m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d: \\n\u0026#34; \u0026lt;\u0026lt; d_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;C: \\n\u0026#34; \u0026lt;\u0026lt; C_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;y: \\n\u0026#34; \u0026lt;\u0026lt; y_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } //******************* SYS_T ******************* Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":88,"href":"/docs/api/files/lds__uniform__vecs_8cpp/","title":"src/lds_uniform_vecs.cpp","section":"Files","content":" src/lds_uniform_vecs.cpp # Uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_uniform_vecs.h\u0026gt; namespace lds { UniformVectorList::UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); } UniformVectorList::UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim) : vector(std::move(vecs)) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(const UniformVectorList\u0026amp; that) : vector(that) { (*this) = that; } UniformVectorList::UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { this-\u0026gt;dim_ = this-\u0026gt;at(0).n_elem; } void UniformVectorList::CheckDimensions(size_t dim) { if (dim) { dim_ = dim; } else { dim_ = this-\u0026gt;at(0).n_elem; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Vector\u0026amp; vec : *this) { does_match = does_match \u0026amp;\u0026amp; (vec.n_elem == dim_); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } } } // namespace lds Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":89,"href":"/docs/api/files/lds_8cpp/","title":"src/lds.cpp","section":"Files","content":" src/lds.cpp # misc lds namespace functions More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file implements miscellaneous lds namespace functions not bound to a class.\nSource code # //===-- lds.cpp - LDS -----------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds.h\u0026gt; // insert any necessary function definitions here. namespace lds { void ForceSymPD(Matrix\u0026amp; X) { if (X.is_sympd() || !X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; // for eigenval decomp bool did_succeed(true); Vector d; Matrix u; // see first method (which may not be ideal): // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/ size_t k(1); bool is_sympd = X.is_sympd(); Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye); while (!is_sympd) { if (k \u0026gt; 100) { did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); data_t min_eig = arma::min(d); std::cerr \u0026lt;\u0026lt; \u0026#34;After multiple iterations, min eigen val = \u0026#34; \u0026lt;\u0026lt; min_eig \u0026lt;\u0026lt; \u0026#34;.\\n\u0026#34;; throw std::runtime_error( \u0026#34;Failed to make matrix symmetric positive definite.\u0026#34;); return; } // Limit(d, arma::eps(0), kInf); // force to be positive... // Matrix d_diag = arma::diagmat(d); // X = u * d_diag * u.t(); did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymPD failed.\u0026#34;); } data_t min_eig = arma::min(d); X += id * abs(min_eig) + arma::datum::eps; // make sure symm: X = (X + X.t()) / 2; // double check eigenvals positive after symmetrizing: arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); min_eig = arma::min(d); is_sympd = min_eig \u0026gt; 0; k++; } } void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min) { if (!X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; bool did_succeed(true); Vector d; Matrix u; did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymMinEig failed.\u0026#34;); } Limit(d, eig_min + arma::eps(eig_min), kInf); // enforce lower bound Matrix d_diag = arma::diagmat(d); X = u * d_diag * u.t(); // double check symmetric X = (X + X.t()) / 2; } void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X) { bool did_succeed(true); did_succeed = arma::qr_econ(Qt, L, X.t()); if (!did_succeed) { throw std::runtime_error(\u0026#34;LQ decomposition failed.\u0026#34;); } arma::inplace_trans(L); arma::inplace_trans(Qt); } Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B) { // subtract out mean auto m_a = arma::mean(A, 1); Matrix a0 = A; a0.each_col() -= m_a; auto m_b = arma::mean(B, 1); Matrix b0 = B; b0.each_col() -= m_b; Matrix cov = a0 * b0.t() / a0.n_cols; return cov; } } // namespace lds Updated on 5 March 2025 at 16:32:33 EST\n"},{"id":90,"href":"/docs/api/namespaces/namespacestd/","title":"std","section":"Namespaces","content":" std # Updated on 5 March 2025 at 16:32:33 EST\n"}]
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+[{"id":0,"href":"/lds-ctrl-est/docs/","title":"LDS C+E Documentation","section":"LDS Control \u0026 Estimation","content":" LDS Control \u0026amp; Estimation Documentation # "},{"id":1,"href":"/lds-ctrl-est/docs/tutorials/","title":"LDS C+E Examples","section":"LDS C+E Documentation","content":" Examples # "},{"id":2,"href":"/lds-ctrl-est/acknowledgements/","title":"Acknowledgements","section":"LDS Control \u0026 Estimation","content":" Acknowledgements # Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.\n"},{"id":3,"href":"/lds-ctrl-est/docs/getting-started/getting-started/","title":"Getting Started","section":"LDS C+E Documentation","content":" Getting Started # This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.\nldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.\nTested Configurations # Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the RelWithDebInfo build type in the CMake configure command (-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo):\nUbuntu 18.04 with GCC 7.5 compiler macOS 11 (Big Sur) with Apple Clang 12 compiler Windows 10 with Visual Studio 16.11 (2019 release) and Clang 12 compiler That being said, if you want to debug a build for a single platform, here are some things you can try:\nUse different compilers (or even different versions of a single compiler) Use different versions of vcpkg (which you can control by checking out a different commit in the vcpkg submodule) Mac Pre-requisities # Xcode Command Line Tools will get you clang, gcc, make, and git:\nxcode-select --install Homebrew is \u0026ldquo;The Missing Package Manager for macOS\u0026rdquo; which will make installing lots of things easy. Install like this:\n/bin/bash -c \u0026#34;$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\u0026#34; You can then use it to install CMake, gfortran, and pkg-config:\nbrew install cmake gfortran pkg-config Linux Pre-requisites # You\u0026rsquo;ll need Git, CMake, GCC, gfortran, etc.\nsudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential ninja-build Windows Installation # Look here for Windows-specific instructions.\nDownloading the Library # First, clone the repository along with submodules:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est\rgit submodule update --init Compilation + Installation # Now generate the cache and build using your IDE or from the command line as follows.\nmkdir build \u0026amp;\u0026amp; cd build\rcmake ..\rcmake --build . The first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.\nIf you want to use vcpkg set up somewhere besides this repo\u0026rsquo;s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE\u0026rsquo;s settings.\nYou can verify the build is working by running ctest from the build folder, which runs all the example scripts.\nOptions # This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\nLDSCTRLEST_BUILD_EXAMPLES : [default=ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake to configure/build the library.\nFor basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build cmake --build #build the project sudo make install #[optional] installs to default location (OS-specific) To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location cmake --build #build the project make install #install to /your/install/prefix To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\nLD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.\nPython bindings package ldsctrlest # With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython3_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn\u0026rsquo;t use an undesired version. That environment needs to have NumPy installed.\ncmake --build . --target python_modules The bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory (pip install pytest matplotlib first if you need to).\nSee python/ldsctrlest/README.md for usage details.\nAlso, beware that a single build will probably not work for both the standalone library and the Python package, since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.\nCommon issues # \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.\nexport CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix vcpkg fails on configuration Try running ./bootstrap-vcpkg from the vcpkg folder and try again. If that doesn\u0026rsquo;t work, try updating vcpkg to a newer version (in the source control tab, click on the commit hash by the vcpkg repo then select from the dropdown) and running boostsrap-vcpkg again. You can also try upgrading your system (e.g., apt update, apt upgrade).\nCould not find Python3 (missing: Python3_NumPy_INCLUDE_DIRS NumPy)\nMake sure NumPy is installed in the Python environment you specified. If CMake still can\u0026rsquo;t find it, you may need to tell CMake exactly where to find it by adding an argument to the configure command: -DPython3_NumPy_INCLUDE_DIR=.... You can find that location like this: python -c 'import numpy; print(numpy.get_include())'\n"},{"id":4,"href":"/lds-ctrl-est/docs/getting-started/windows/","title":"Windows","section":"LDS C+E Documentation","content":" Windows Installation # Windows Pre-requisites # Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:\nSet-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time iwr get.scoop.sh | Invoke-Expression Install Git and CMake if you don\u0026rsquo;t already have them:\nscoop install git cmake If that didn\u0026rsquo;t work, follow more detailed instructions here.\nThe easiest way to compile C++ project on Windows is with Visual Studio\u0026rsquo;s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on \u0026ldquo;Desktop development with C++.\u0026rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the \u0026ldquo;Installation details\u0026rdquo; sidebar under optional features.\nAnd the easiest way to use Visual Studio\u0026rsquo;s build tools is with VS Code, along with the CMake Tools extension. Install them and you should be ready to go.\nDownloading the Library # First, clone the repository, either from VS Code or the command line:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est You\u0026rsquo;ll need to initialize the submodules from the command line after the repo is cloned:\ngit submodule update --init Installation # When you open the folder in VS Code, you will like be prompted by the CMake Tools extension to configure the project. Make sure you select the kit (you\u0026rsquo;ll be prompted when you configure\u0026ndash;else there\u0026rsquo;s an icon in the bar on the bottom of the window or type Ctrl+Shift+P, then \u0026ldquo;cmake select kit\u0026rdquo;). Choose Clang [latest version] with GNU CLI ... amd64 assuming you are running a 64-bit OS. (MSVC may work okay too if you don\u0026rsquo;t need to build Python bindings.)\nFollow along with the \u0026ldquo;Getting Started\u0026rdquo; instructions, but where you see config options specified as -DLDSCTREST_BUILD_STATIC=OFF or -DPython3_ROOT_DIR=..., you will enter those in settings: open with Ctrl+,, click \u0026ldquo;workspace\u0026rdquo;, then search for \u0026ldquo;CMake: Configure Args\u0026rdquo; and enter each of your desired arguments as a separate item.\nTo configure, use Ctrl+Shift+P and search for the \u0026ldquo;CMake: Configure\u0026rdquo; command. To build, click the \u0026ldquo;Build\u0026rdquo; button on the bottom bar. Then click the \u0026ldquo;CTest\u0026rdquo; button to run the example scripts.\nConsiderations # Development on Windows has been more prone to bugs than on Unix systems, so if you encounter many problems, consider switching—WSL (Windows Subsystem for Linux) is a good option for Windows users who don\u0026rsquo;t want to work on a different machine.\nCompilation has been successfully tested in VS Code using the following kit, using the \u0026ldquo;RelWithDebInfo\u0026rdquo; config:\nClang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64) Troubleshooting # The build appears to work, but tests fail with code 0xc0000135 OR \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; Have you installed the library? In VS Code, use Shift+F7 to build a specific target, in this case INSTALL. If that doesn\u0026rsquo;t solve your problem, you will likely need to add the build or install folder to your PATH environment variable, which you can do using the settings GUI (search for \u0026ldquo;Edit the system environment variables\u0026rdquo;).\nOn Windows, \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted. Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":5,"href":"/lds-ctrl-est/issues-contributing/","title":"Issues Contributing","section":"LDS Control \u0026 Estimation","content":" Reporting Issues # If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.\nContributing # We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.\n"},{"id":6,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/","title":"armamexc","section":"Namespaces","content":" armamexc # arma/mex interface using Matlab C API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; T m2T_scalar(const mxArray * matlab_scalar)\nConvert Matlab mxArray to scalar of type T. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)\nConvert matlab matrix to armadillo. template \u0026lt;typename T \u0026gt; mxArray * a2m_mat(arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat)\nConvert armadillo to matlab matrix. template \u0026lt;typename T \u0026gt; mxArray * a2m_vec(arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec)\nConvert armadillo to matlab vector. Detailed Description # Utilities for arma/mex interface using Matlab C API\nFunction Details # m2T_scalar # template \u0026lt;class T \u0026gt; inline T m2T_scalar( const mxArray * matlab_scalar ) Parameters:\nmatlab_scalar matlab scalar Template Parameters:\nT type Return: scalar of type T\nm2a_mat # template \u0026lt;class T \u0026gt; inline arma::Mat\u0026lt; T \u0026gt; m2a_mat( const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true ) Parameters:\nmatlab_mat matlab matrix copy_aux_mem [optional] whether to copy auxiliary memory strict [optional] strictly enforce the above Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_mat( arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat ) Parameters:\narma_mat armadillo matrix Return: matlab matrix\na2m_vec # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_vec( arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec ) Parameters:\narma_vec armadillo vector Return: matlab vector\nUpdated on 5 March 2025 at 16:20:07 EST\n"},{"id":7,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/","title":"armamexcpp","section":"Namespaces","content":" armamexcpp # arma/mex interface using Matlab C++ API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat(matlab::data::CellArray \u0026amp; matlab_cell)\nConvert matlab cell array to vector of armadillo matrices. template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array)\nConvert matlab matrix to a vector of scalars. template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo vector. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat(const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec(const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab vector. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec(const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert vector of scalar T to matlab matrix. Detailed Description # utilities for arma/mex interface using Matlab C++ API\nFunction Details # m2a_cellmat # template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat( matlab::data::CellArray \u0026amp; matlab_cell ) Parameters:\nmatlab_cell matlab cell Template Parameters:\nT type Return: vector of armadillo matrices of type T\nm2s_vec # template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: vector of type T\nm2a_vec # template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: armadillo vector of type T\nm2a_mat # template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab matrix Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat( const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_mat arma matrix factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\na2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec( const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_vec armadillo vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\ns2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec( const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\nstd_vec standard vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\nUpdated on 5 March 2025 at 16:20:07 EST\n"},{"id":8,"href":"/lds-ctrl-est/docs/terminology/control-estimation/","title":"C\u0026E","section":"LDS C+E Documentation","content":" Control \u0026amp; Estimation # The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.\nState estimation # In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by \\[\\widehat{\\mathbf{x}}_{t|t-1} = \\mathbf{A}\\widehat{\\mathbf{x}}_{t-1|t-1} \u0026#43; \\mathbf{B} u_{t-1} \u0026#43; \\mathbf{m}_{t-1} \\;,\\] \\[\\widehat{\\mathbf{x}}_{t|t} = \\widehat{\\mathbf{x}}_{t|t-1} \u0026#43; \\mathbf{K}^{\\rm e}_t \\left(\\mathbf{z}_t - \\widehat{\\mathbf{y}}_{t|t-1}\\right)\\;,\\] where \\( \\hat{\\left(\\cdot\\right)}_{t|j} \\) indicates an estimate at time \\( t \\) given data up to time \\( j \\) inclusive, \\( \\mathbf{K}^{\\rm e} \\) is the estimator gain, and\n\\[ \\widehat{\\mathbf{y}}_{t|t-1} = h\\left( \\widehat{\\mathbf{x}}_{t|t-1} \\right) \\; .\\] In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.\nIn the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.\nAdaptive estimation of process disturbance # Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.\nWhen parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk \\[\\mathbf{m}_{t} = \\mathbf{m}_{t-1} \u0026#43; \\mathbf{w}^m_{t-1} \\;,\\] where \\( \\mathbf{w}^m \\sim \\mathcal{N}\\left(0, \\mathbf{Q}_m\\right)\\) . Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.\nControl # Given the estimated state, the controller updates the inputs to the system according to the following law: \\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] where \\( \\left( \\cdot \\right)^{\\rm ref} \\) correspond to reference/target signals and \\( \\mathbf{K}^c_x \\) is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.\nIf users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as\n\\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right) - \\mathbf{K}^c_{\\rm inty} \\sum_{j=1}^{t}\\left( \\widehat{\\mathbf{y}}_j - \\mathbf{y}^{\\rm ref}_j \\right) \\;.\\] An additional option available to users is a control law that updates the change in u,\n\\[\\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\left(\\mathbf{u}_{t-1} - \\mathbf{u}^{\\rm ref}_{t-1} \\right) - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] \\[\\mathbf{u}_{t} = \\mathbf{u}_{t-1} \u0026#43; \\Delta\\mathbf{u}_{t} \\; .\\] Notice that this takes the form of a first-order difference equation for updating control (i.e., \\( \\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\mathbf{u}_{t-1} \u0026#43; \\epsilon_{t-1} \\) ), effectively low-pass filtering the input depending on the characteristics of \\( \\mathbf{K}^c_u \\) . This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.\nIntegral action and the \\( \\Delta \\mathbf{u} \\) control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR\u0026rsquo;d to use in combination.\nCalculating reference state-control from output # In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce \\( \\mathbf{x}^{\\rm ref} \\) and \\( \\mathbf{u}^{\\rm ref} \\) . Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.\n"},{"id":9,"href":"/lds-ctrl-est/docs/api/classes/","title":"Classes","section":"LDS C+E Documentation","content":" Classes # lds::Controller\nlds::EM\nlds::Fit LDS Fit Type.\nlds::SSID\nlds::SwitchedController SwitchedController Type.\nlds::System Linear Dynamical System Type.\nlds::UniformMatrixList\nlds::UniformSystemList\nlds::UniformVectorList\nlds::gaussian::Controller Gaussian-observation Controller Type.\nlds::gaussian::Fit GLDS Fit Type.\nlds::gaussian::FitEM GLDS E-M Fit Type.\nlds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.\nlds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.\nlds::gaussian::System Gaussian LDS Type.\nlds::poisson::Controller PLDS Controller Type.\nlds::poisson::Fit PLDS Fit Type.\nlds::poisson::FitEM PLDS E-M Fit Type.\nlds::poisson::FitSSID Subspace Identification (SSID) for PLDS.\nlds::poisson::SwitchedController Poisson-observation SwitchedController Type.\nlds::poisson::System Poisson System type.\nUpdated on 5 March 2025 at 16:20:07 EST\n"},{"id":10,"href":"/lds-ctrl-est/docs/api/modules/group__control__masks/","title":"Control Mode Bit Masks","section":"Modules","content":" Control Mode Bit Masks # provides fill types for constructing new armadillo vectors, matrices More\u0026hellip; Attributes # Name const std::size_t kControlTypeDeltaU control designed to penalize change in input const std::size_t kControlTypeIntY control using integral action const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m Detailed Description # Control mode bit masks. These can be bit-wise OR\u0026rsquo;d to use in combination.\nAttribute Details # kControlTypeDeltaU # static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY # static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM # static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\nUpdated on 5 March 2025 at 16:20:07 EST\n"},{"id":11,"href":"/lds-ctrl-est/docs/api/modules/group__defaults/","title":"Defaults","section":"Modules","content":" Defaults # More\u0026hellip; Attributes # Name const data_t kDefaultP0 default state estimate covar const data_t kDefaultQ0 default process noise covar const data_t kDefaultR0 default output noise covar Detailed Description # Default values for common variables (e.g., default diagonal elements of covariances)\nAttribute Details # kDefaultP0 # static const data_t kDefaultP0 = 1e-6; kDefaultQ0 # static const data_t kDefaultQ0 = 1e-6; kDefaultR0 # static const data_t kDefaultR0 = 1e-2; Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":12,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/","title":"eg_glds_ctrl.cpp","section":"Examples","content":" eg_glds_ctrl.cpp # Example GLDS Control ```cpp\n//===\u0026ndash; eg_glds_ctrl.cpp - Example GLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS Control ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\n// output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false;\n// Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err\n// setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]);\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; }\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:20:07 EST "},{"id":13,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/","title":"eg_glds_du_plds_ctrl.cpp","section":"Examples","content":" eg_glds_du_plds_ctrl.cpp # Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u). ```cpp\n//===\u0026ndash; eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt);\n// to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU;\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_du_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:20:07 EST "},{"id":14,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/","title":"eg_plds_ctrl.cpp","section":"Examples","content":" eg_plds_ctrl.cpp # Example PLDS Control ```cpp\n//===\u0026ndash; eg_plds_ctrl.cpp - Example PLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Control ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt);\n// Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err\n// Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\nsize_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\nVector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled\u0026hellip; lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system);\n// for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type);\n// set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty);\n// to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0;\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros);\n// set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y();\nx_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x();\nm_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:20:07 EST "},{"id":15,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/","title":"eg_plds_est.cpp","section":"Examples","content":" eg_plds_est.cpp # Example PLDS Estimation ```cpp\n//===\u0026ndash; eg_plds_est.cpp - Example PLDS Estimation \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0);\nint main() { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.\n// construct ground truth system\u0026hellip; lds::poisson::System system_true(n_u, n_x, n_y, dt);\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset();\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt);\n// Can copy parameters from another system object system_estimator = system_true;\n// wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est);\n// set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true;\n// set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;estimator:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; system_estimator.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));\n// create matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros);\n// states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\nMatrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simlation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1));\n// Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simlation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows;\nif ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026ldquo;Q must be n x n.\u0026rdquo;); }\nMatrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); }\nreturn x; }\n_Filename: eg_plds_est.cpp_ ------------------------------- Updated on 5 March 2025 at 16:20:07 EST "},{"id":16,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/","title":"eg_plds_switched_ctrl.cpp","section":"Examples","content":" eg_plds_switched_ctrl.cpp # Example Switched PLDS Control ```cpp\n//===\u0026ndash; eg_plds_switched_ctrl.cpp - Example Switched PLDS Control \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026quot;;\n// whether to do switched control bool do_switch_ctrl = true;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt);\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices. data_t scale_sys_b = 2;\nMatrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));\ncontrolled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions\n// reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);\n// Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system);\n// set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys1:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys2:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x));\nswitched_controller.set_y_ref(y_ref0);\nstd::vectorlds::poisson::System systems_vec(3, lds::poisson::System()); lds::UniformSystemListlds::poisson::System systems(std::move(systems_vec));\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;switched_controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; switched_controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Fake measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros);\n// create Matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);\n// modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones);\n// set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } }\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace)); mode.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;mode\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_switched_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:20:07 EST "},{"id":17,"href":"/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/","title":"examples","section":"Files","content":" examples # Files # Name examples/eg_glds_ctrl.cpp examples/eg_glds_du_plds_ctrl.cpp examples/eg_plds_ctrl.cpp examples/eg_plds_est.cpp examples/eg_plds_switched_ctrl.cpp Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":18,"href":"/lds-ctrl-est/docs/api/examples/","title":"Examples","section":"LDS C+E Documentation","content":" Examples # eg_glds_ctrl.cpp Example GLDS Control.\neg_glds_du_plds_ctrl.cpp Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).\neg_plds_ctrl.cpp Example PLDS Control.\neg_plds_est.cpp Example PLDS Estimation.\neg_plds_switched_ctrl.cpp Example Switched PLDS Control.\nUpdated on 5 March 2025 at 16:20:07 EST\n"},{"id":19,"href":"/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/","title":"examples/eg_glds_ctrl.cpp","section":"Files","content":" examples/eg_glds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":20,"href":"/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/","title":"examples/eg_glds_du_plds_ctrl.cpp","section":"Files","content":" examples/eg_glds_du_plds_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU; // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":21,"href":"/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/","title":"examples/eg_plds_ctrl.cpp","section":"Files","content":" examples/eg_plds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err // Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled... lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type); // set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":22,"href":"/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/","title":"examples/eg_plds_est.cpp","section":"Files","content":" examples/eg_plds_est.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name Matrix random_walk(size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0) int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # random_walk # Matrix random_walk( size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0 ) main # int main() Source code # //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. // construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state // Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. // turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; } Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":23,"href":"/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/","title":"examples/eg_plds_switched_ctrl.cpp","section":"Files","content":" examples/eg_plds_switched_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Source code # //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 // simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions // reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":24,"href":"/lds-ctrl-est/docs/api/files/","title":"Files","section":"LDS C+E Documentation","content":" Files # examples/eg_glds_ctrl.cpp\nexamples/eg_glds_du_plds_ctrl.cpp\nexamples/eg_plds_ctrl.cpp\nexamples/eg_plds_est.cpp\nexamples/eg_plds_switched_ctrl.cpp\nldsCtrlEst_h/lds.h lds namespace\nldsCtrlEst_h/lds_ctrl.h Controller.\nldsCtrlEst_h/lds_fit.h LDS base fit type.\nldsCtrlEst_h/lds_fit_em.h subspace identification\nldsCtrlEst_h/lds_fit_ssid.h subspace identification\nldsCtrlEst_h/lds_gaussian.h glds namespace\nldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.\nldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.\nldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.\nldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.\nldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.\nldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.\nldsCtrlEst_h/lds_poisson.h plds namespace\nldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.\nldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.\nldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.\nldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.\nldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.\nldsCtrlEst_h/lds_poisson_sys.h PLDS base type.\nldsCtrlEst_h/lds_sctrl.h SwitchedController type.\nldsCtrlEst_h/lds_sys.h LDS base type.\nldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.\nldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.\nldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.\nldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)\nldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)\nsrc/lds.cpp misc lds namespace functions\nsrc/lds_gaussian_sys.cpp GLDS base type.\nsrc/lds_poisson_sys.cpp PLDS base type.\nsrc/lds_sys.cpp LDS base type.\nsrc/lds_uniform_vecs.cpp Uniformly sized vectors.\nUpdated on 5 March 2025 at 16:20:07 EST\n"},{"id":25,"href":"/lds-ctrl-est/docs/tutorials/eg_glds_control/","title":"GLDS Control","section":"LDS C+E Examples","content":" GLDS Control Tutorial # This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\] \\[\ry_{t} = x_t\r\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.\n/// Going to simulate a switching disturbance (m) acting on system size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_y).fill(m_low); Finally, assign the parameters using corresponding set-methods.\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); Creating the controller # Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.\nFor the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.\n// make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nWith the controller constructed, control variables may be set.\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.\nThe control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller\u0026rsquo;s online estimates of the output, state, and disturbance are given in purple.\n"},{"id":26,"href":"/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/","title":"include","section":"Files","content":" include # Directories # Name ldsCtrlEst_h Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":27,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds/","title":"lds","section":"Namespaces","content":" lds # Linear Dynamical Systems (LDS) namespace. Namespaces # Name lds::gaussian Linear Dynamical Systems with Gaussian observations. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::Controller class lds::EM class lds::Fit LDS Fit Type. class lds::SSID class lds::SwitchedController SwitchedController Type. class lds::System Linear Dynamical System Type. class lds::UniformMatrixList class lds::UniformSystemList class lds::UniformVectorList Types # Name enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}\nweighting options for SSID enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2} using double data_t using arma::Col\u0026lt; data_t \u0026gt; Vector using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Cube\u0026lt; data_t \u0026gt; Cube using arma::subview\u0026lt; data_t \u0026gt; View Functions # Name void Limit(std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub) void Limit(Vector \u0026amp; x, data_t lb, data_t ub) void Limit(Matrix \u0026amp; x, data_t lb, data_t ub) void Reassign(Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Vector in place void Reassign(Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Matrix in place void ForceSymPD(Matrix \u0026amp; X)\nforces matrix to be symmetric positive-definite void ForceSymMinEig(Matrix \u0026amp; X, data_t eig_min =0)\nforces matrix to be symmetric and have a minimum eigenvalue void lq(Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X)\nLQ decomposition. Matrix calcCov(const Matrix \u0026amp; A, const Matrix \u0026amp; B)\nCalculate covariance matrix. Attributes # Name const data_t kInf Some useful numbers. const data_t kPi Type Details # SSIDWt # Enumerator Value Description kSSIDNone None. kSSIDMOESP MOESP (AKA \u0026ldquo;robust method\u0026rdquo; in van Overschee 1996) kSSIDCVA CVA \u0026ldquo;Canonical Variate Analysis\u0026rdquo;. Weighting options for singular value decomposition performed during subspace identification (SSID)\nReference:\nvan Overschee, de Moor. 1996. Subspace Identification for Linear Systems.\nMatrixListFreeDim # Enumerator Value Description kMatFreeDimNone neither dim free to be hetero in mat list kMatFreeDim1 allow 1st dim of mats in list to be hetero kMatFreeDim2 allow 2nd dim of mats in list to be hetero data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nVector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Cube # using lds::Cube = arma::Cube\u0026lt;data_t\u0026gt;; View # using lds::View = arma::subview\u0026lt;data_t\u0026gt;; Function Details # Limit # inline void Limit( std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Vector \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Matrix \u0026amp; x, data_t lb, data_t ub ) Reassign # inline void Reassign( Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Vector other other Vector parenthetical optional description provided by caller to ease debugging Reassign # inline void Reassign( Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Matrix other other Matrix parenthetical optional description provided by caller to ease debugging ForceSymPD # void ForceSymPD( Matrix \u0026amp; X ) Parameters:\nX mutated matrix ForceSymMinEig # void ForceSymMinEig( Matrix \u0026amp; X, data_t eig_min =0 ) Parameters:\nX mutated matrix eig_min [optional] minimum eigen value lq # void lq( Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X ) Parameters:\nL lower triangle matrix Qt orthonormal matrix (transposed cf QR decomp) X matrix being decomposed calcCov # Matrix calcCov( const Matrix \u0026amp; A, const Matrix \u0026amp; B ) Parameters:\nA some matrix B some other matrix Return: covariance\nAttribute Details # kInf # static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); kPi # static const data_t kPi = arma::datum::pi; Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":28,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/","title":"lds::Controller","section":"Classes","content":" lds::Controller # More\u0026hellip;\nInherited by lds::SwitchedController\u0026lt; System \u0026gt;, lds::gaussian::Controller, lds::poisson::Controller\nPublic Functions # Name Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes # Name System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::Controller; Public Function Details # Controller # Controller() =default Controller # inline Controller( const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Controller # inline Controller( System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Control # inline const Vector \u0026amp; Control( const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step). This is the most flexible option, but requires user to have set the controller\u0026rsquo;s y_ref, x_ref, and u_ref variables.\nControlOutputReference # inline const Vector \u0026amp; ControlOutputReference( const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_estimation [optional] whether to update state estimate (if false, effectively open-loop control) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.\nsys # inline const System \u0026amp; sys() const Kc # inline const Matrix \u0026amp; Kc() const Kc_inty # inline const Matrix \u0026amp; Kc_inty() const Kc_u # inline const Matrix \u0026amp; Kc_u() const g_design # inline const Vector \u0026amp; g_design() const u_ref # inline const Vector \u0026amp; u_ref() const x_ref # inline const Vector \u0026amp; x_ref() const y_ref # inline const Vector \u0026amp; y_ref() const control_type # inline size_t control_type() const tau_awu # inline data_t tau_awu() const u_lb # inline data_t u_lb() const u_ub # inline data_t u_ub() const set_sys # inline void set_sys( const System \u0026amp; sys ) set_g_design # inline void set_g_design( const Vector \u0026amp; g_design ) set_u_ref # inline void set_u_ref( const Vector \u0026amp; u_ref ) set_x_ref # inline void set_x_ref( const Vector \u0026amp; x_ref ) set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) Reimplemented by: lds::gaussian::Controller::set_y_ref, lds::gaussian::SwitchedController::set_y_ref, lds::poisson::Controller::set_y_ref, lds::poisson::SwitchedController::set_y_ref\nset_Kc # inline void set_Kc( const Matrix \u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const Matrix \u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const Matrix \u0026amp; Kc_u ) set_tau_awu # inline void set_tau_awu( data_t tau ) set_control_type # inline void set_control_type( size_t control_type ) Parameters:\ncontrol_type control type bit mask Template Parameters:\nSystem type derived from lds::System set_u_lb # inline void set_u_lb( data_t u_lb ) Parameters:\nu_lb control lower bound set_u_ub # inline void set_u_ub( data_t u_ub ) Parameters:\nu_ub control upper bound Reset # inline void Reset() Print # inline void Print() Protected Attribute Details # sys_ # System sys_; u_ # Vector u_; u_return_ # Vector u_return_; g_design_ # Vector g_design_; u_ref_ # Vector u_ref_; u_ref_prev_ # Vector u_ref_prev_; x_ref_ # Vector x_ref_; y_ref_ # Vector y_ref_; cx_ref_ # Vector cx_ref_; Kc_ # Matrix Kc_; Kc_u_ # Matrix Kc_u_; Kc_inty_ # Matrix Kc_inty_; du_ref_ # Vector du_ref_; dv_ref_ # Vector dv_ref_; v_ref_ # Vector v_ref_; dv_ # Vector dv_; v_ # Vector v_; int_e_ # Vector int_e_; int_e_awu_adjust_ # Vector int_e_awu_adjust_; u_sat_ # Vector u_sat_; do_control_prev_ # bool do_control_prev_ = false; do_lock_control_prev_ # bool do_lock_control_prev_ = false; u_saturated_ # bool u_saturated_ = false; u_lb_ # data_t u_lb_ {}; u_ub_ # data_t u_ub_ {}; tau_awu_ # data_t tau_awu_ {}; k_awu_ # data_t k_awu_ = 0; t_since_control_onset_ # data_t t_since_control_onset_ = 0; control_type_ # size_t control_type_ {}; Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":29,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/","title":"lds::EM","section":"Classes","content":" lds::EM # More\u0026hellip;\nInherited by lds::gaussian::FitEM, lds::poisson::FitEM\nPublic Functions # Name EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions # Name void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() virtual void MaximizeOutput() =0 virtual void MaximizeMeasurement() =0 void Smooth(bool force_common_initial)\nget smoothed estimates virtual void RecurseKe(Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t) =0\nrecursively update estimator gain Ke void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::EM; Public Function Details # EM # EM() =default EM # EM( size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nn_x number of states dt sample period u_train input training data z_train measurement training data EM # EM( const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nfit0 initial fit u_train input training data z_train measurement training data ~EM # virtual ~EM() =default Run # const Fit \u0026amp; Run( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2 ) Parameters:\ncalc_dynamics [optional] whether to calculate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law max_iter max number of iterations tol convergence tolerance (max fractional abs change) Return: Fit\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nx # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const y # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const sum_E_x_t_x_t # inline const Matrix \u0026amp; sum_E_x_t_x_t() const sum_E_xu_tm1_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const sum_E_xu_t_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const n_t_tot # inline size_t n_t_tot() theta # inline const Vector \u0026amp; theta() const Protected Function Details # Expectation # void Expectation( bool force_common_initial =false ) Parameters:\nforce_common_initial whether to force common initial condition for all trials Maximization # void Maximization( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false ) Parameters:\ncalc_dynamics [optional] whether to caclulate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law MaximizeDynamics # void MaximizeDynamics() MaximizeQ # void MaximizeQ() MaximizeInitial # void MaximizeInitial() MaximizeOutput # virtual void MaximizeOutput() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput\nMaximizeMeasurement # virtual void MaximizeMeasurement() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement\nSmooth # void Smooth( bool force_common_initial ) Parameters:\nforce_common_initial whether to force common initial conditions RecurseKe # virtual void RecurseKe( Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t ) =0 Parameters:\nKe estimator gain P_pre cov of predicted state est. P_post cov of postior sate est. t time Reimplemented by: lds::gaussian::FitEM::RecurseKe, lds::poisson::FitEM::RecurseKe\nReset # void Reset() InitVars # void InitVars() UpdateTheta # Vector UpdateTheta() Return: parameter list\nProtected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; x_ # std::vector\u0026lt; Matrix \u0026gt; x_; P_ # std::vector\u0026lt; Cube \u0026gt; P_; P_t_tm1_ # std::vector\u0026lt; Cube \u0026gt; P_t_tm1_; y_ # std::vector\u0026lt; Matrix \u0026gt; y_; diag_y_ # Matrix diag_y_; sum_E_x_t_x_t_ # Matrix sum_E_x_t_x_t_; sum_E_xu_tm1_xu_tm1_ # Matrix sum_E_xu_tm1_xu_tm1_; sum_E_xu_t_xu_tm1_ # Matrix sum_E_xu_t_xu_tm1_; fit_ # Fit fit_; theta_ # Vector theta_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":30,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/","title":"lds::Fit","section":"Classes","content":" lds::Fit # LDS Fit Type. #include \u0026lt;lds_fit.h\u0026gt;\nInherited by lds::gaussian::Fit, lds::poisson::Fit\nPublic Functions # Name Fit() =default\nConstructs a new Fit. Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias virtual const Matrix \u0026amp; R() const =0 void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance virtual void set_R(const Matrix \u0026amp; R) =0\nsets output noise covariance (if any) void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) =0\noutput function Protected Attributes # Name data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period ~Fit # virtual ~Fit() =default n_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const m # inline const Vector \u0026amp; m() const Q # inline const Matrix \u0026amp; Q() const x0 # inline const Vector \u0026amp; x0() const P0 # inline const Matrix \u0026amp; P0() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const R # virtual const Matrix \u0026amp; R() const =0 Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R\nset_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_g # inline void set_g( const Vector \u0026amp; g ) set_m # inline void set_m( const Vector \u0026amp; m ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # virtual void set_R( const Matrix \u0026amp; R ) =0 Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R\nset_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) f # inline View f( Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t ) Parameters:\nx state estimate (over time) u input (over time) t time index Return: view of updated state\nf # inline View f( Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t ) Parameters:\nx_pre predicted state est. x_post posterior state est. u input (over time) t time index Return: view of predicted state\nh # virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) =0 Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplemented by: lds::gaussian::Fit::h, lds::poisson::Fit::h\nProtected Attribute Details # dt_ # data_t dt_ {}; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; m_ # Vector m_; Q_ # Matrix Q_; C_ # Matrix C_; d_ # Vector d_; R_ # Matrix R_; x0_ # Vector x0_; P0_ # Matrix P0_; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":31,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/","title":"lds::gaussian","section":"Namespaces","content":" lds::gaussian # Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. class lds::gaussian::Fit GLDS Fit Type. class lds::gaussian::FitEM GLDS E-M Fit Type. class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. class lds::gaussian::System Gaussian LDS Type. Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":32,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/","title":"lds::gaussian::Controller","section":"Classes","content":" lds::gaussian::Controller # Gaussian-observation Controller Type. #include \u0026lt;lds_gaussian_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:20:07 EST\n"},{"id":33,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/","title":"lds::gaussian::Fit","section":"Classes","content":" lds::gaussian::Fit # GLDS Fit Type. #include \u0026lt;lds_gaussian_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual const Matrix \u0026amp; R() const override\ngets measurement noise covariance virtual void set_R(const Matrix \u0026amp; R) override\nsets measurement noise covariance virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period R # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nh # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nUpdated on 5 March 2025 at 16:20:07 EST\n"},{"id":34,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/","title":"lds::gaussian::FitEM","section":"Classes","content":" lds::gaussian::FitEM # GLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_gaussian_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::gaussian::FitEM; This type is used in the process of fitting GLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":35,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/","title":"lds::gaussian::FitSSID","section":"Classes","content":" lds::gaussian::FitSSID # Subspace Identification (SSID) for GLDS. #include \u0026lt;lds_gaussian_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":36,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/","title":"lds::gaussian::SwitchedController","section":"Classes","content":" lds::gaussian::SwitchedController # Gaussian-observation SwitchedController Type. #include \u0026lt;lds_gaussian_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:20:07 EST\n"},{"id":37,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/","title":"lds::gaussian::System","section":"Classes","content":" lds::gaussian::System # Gaussian LDS Type. #include \u0026lt;lds_gaussian_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)\nConstructs a new Gaussian System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. const Matrix \u0026amp; R() const\nGet output noise covariance. void set_Q(const Matrix \u0026amp; Q) void set_R(const Matrix \u0026amp; R)\nSet output noise covariance. void set_Ke(const Matrix \u0026amp; Ke)\nSet estimator gain. void set_Ke_m(const Matrix \u0026amp; Ke_m)\nSet disturbance estimator gain. void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively update estimator gain. Protected Attributes # Name Matrix R_ covariance of output noise bool do_recurse_Ke_ whether to recursively calculate estimator gain Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0 ) Parameters:\nn_u number of inputs (u) n_x number of states (x) n_y number of outputs (y) dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) r0 [optional] initial diagonal elements of output noise covariance (R) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nR # inline const Matrix \u0026amp; R() const set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # inline void set_R( const Matrix \u0026amp; R ) set_Ke # inline void set_Ke( const Matrix \u0026amp; Ke ) set_Ke_m # inline void set_Ke_m( const Matrix \u0026amp; Ke_m ) Print # void Print() Protected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nProtected Attribute Details # R_ # Matrix R_; do_recurse_Ke_ # bool do_recurse_Ke_ {}; Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":38,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/","title":"lds::poisson","section":"Namespaces","content":" lds::poisson # Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. class lds::poisson::Fit PLDS Fit Type. class lds::poisson::FitEM PLDS E-M Fit Type. class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. class lds::poisson::System Poisson System type. Attributes # Name std::random_device rd random device for simulating poisson data std::mt19937 rng random number generator for simulating poisson data Attribute Details # rd # static std::random_device rd; rng # static std::mt19937 rng = std::mt19937( rd()); Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":39,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/","title":"lds::poisson::Controller","section":"Classes","content":" lds::poisson::Controller # PLDS Controller Type. #include \u0026lt;lds_poisson_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:20:07 EST\n"},{"id":40,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/","title":"lds::poisson::Fit","section":"Classes","content":" lds::poisson::Fit # PLDS Fit Type. #include \u0026lt;lds_poisson_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function virtual void set_R(const Matrix \u0026amp; R) override\nsets output noise covariance (if any) virtual const Matrix \u0026amp; R() const override Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # inline Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period h # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nR # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nUpdated on 5 March 2025 at 16:20:07 EST\n"},{"id":41,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/","title":"lds::poisson::FitEM","section":"Classes","content":" lds::poisson::FitEM # PLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_poisson_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::poisson::FitEM; This type is used in the process of fitting PLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":42,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/","title":"lds::poisson::FitSSID","section":"Classes","content":" lds::poisson::FitSSID # Subspace Identification (SSID) for PLDS. #include \u0026lt;lds_poisson_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":43,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/","title":"lds::poisson::SwitchedController","section":"Classes","content":" lds::poisson::SwitchedController # Poisson-observation SwitchedController Type. #include \u0026lt;lds_poisson_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:20:07 EST\n"},{"id":44,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/","title":"lds::poisson::System","section":"Classes","content":" lds::poisson::System # Poisson System type. #include \u0026lt;lds_poisson_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nConstructs a new Poisson System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively recalculate estimator gain (Ke) Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nProtected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nRecursively recalculate estimator gain (Ke).\nReferences:\nSmith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.\nEden UT, \u0026hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.\nUpdated on 5 March 2025 at 16:20:07 EST\n"},{"id":45,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/","title":"lds::SSID","section":"Classes","content":" lds::SSID # More\u0026hellip;\nInherited by lds::gaussian::FitSSID, lds::poisson::FitSSID\nPublic Functions # Name SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions # Name void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. virtual void DecomposeData() =0\nDecompose data to lower-triangular matrix (used in Solve) void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::SSID; Public Function Details # SSID # SSID() =default SSID # SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf) ) Parameters:\nn_x number of states n_h size of block-hankel data matrix dt sample period u_train input training data z_train measurement training data d output bias Run # std::tuple\u0026lt; Fit, Vector \u0026gt; Run( SSIDWt ssid_wt ) Parameters:\nssid_wt weight for singular value decomp Return: tuple (Fit, singular values)\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nProtected Function Details # CalcD # void CalcD( data_t t_silence =0.1, data_t thresh_silence =0.001 ) Parameters:\nt_silence threshold on period of time that qualifies as \u0026ldquo;silence\u0026rdquo; thresh_silence threshold on input amplitude u that qualifies as \u0026ldquo;silence\u0026rdquo; CreateHankelDataMat # void CreateHankelDataMat() Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.\nDecomposeData # virtual void DecomposeData() =0 Reimplemented by: lds::gaussian::FitSSID::DecomposeData, lds::poisson::FitSSID::DecomposeData\nCalcSVD # void CalcSVD( SSIDWt wt ) Parameters:\nssid_wt weight for SVD Solve # void Solve( data_t wt_dc ) Parameters:\nwt_dc weight placed on getting correct DC I/O gain RecomputeExtObs # void RecomputeExtObs() Protected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; D_ # Matrix D_; fit_ # Fit fit_; g_dc_ # Matrix g_dc_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_h_ # size_t n_h_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; L_ # Matrix L_; s_ # Vector s_; ext_obs_t_ # Matrix ext_obs_t_; Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":46,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/","title":"lds::SwitchedController","section":"Classes","content":" lds::SwitchedController # SwitchedController Type. More\u0026hellip;\n#include \u0026lt;lds_sctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nInherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController\nPublic Functions # Name SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes # Name std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::SwitchedController; Public Function Details # SwitchedController # SwitchedController() =default SwitchedController # inline SwitchedController( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask SwitchedController # inline SwitchedController( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Switch # inline void Switch( size_t idx, bool do_force_switch =false ) Parameters:\nidx index do_force_switch whether to force a system switch even if already there. set_Kc # inline void set_Kc( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc ) set_Kc # inline void set_Kc( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty ) set_Kc_inty # inline void set_Kc_inty( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u ) set_Kc_u # inline void set_Kc_u( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u ) set_g_design # inline void set_g_design( const UniformVectorList \u0026amp; g ) set_g_design # inline void set_g_design( UniformVectorList \u0026amp;\u0026amp; g ) Protected Attribute Details # systems_ # std::vector\u0026lt; System \u0026gt; systems_; n_sys_ # size_t n_sys_ {}; idx_ # size_t idx_ {}; Kc_list_ # UniformMatrixList Kc_list_; Kc_inty_list_ # UniformMatrixList Kc_inty_list_; Kc_u_list_ # UniformMatrixList Kc_u_list_; g_design_list_ # UniformVectorList g_design_list_; Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":47,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_system/","title":"lds::System","section":"Classes","content":" lds::System # Linear Dynamical System Type. #include \u0026lt;lds_sys.h\u0026gt;\nInherited by lds::gaussian::System, lds::poisson::System\nPublic Functions # Name System() =default\nConstructs a new System. System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nconstructs a new System virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) =0\nsimulates system (single time step) void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function virtual void h() =0\nsystem output function virtual Vector h_(Vector x) =0\nsystem output function (stateless) size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void RecurseKe() =0\nRecursively recalculate estimator gain (Ke) void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes # Name bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes # Name std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 diagonal elements for state estimate covariance q0 diagonal elements for process noise covariance ~System # inline virtual ~System() Filter # void Filter( const Vector \u0026amp; u_tm1, const Vector \u0026amp; z ) Parameters:\nu_tm1 input at t-minus-1 z_t current measurement Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.\nSimulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) =0 Parameters:\nu_tm1 input at time t-1 Return: simulated measurement at time t\nReimplemented by: lds::gaussian::System::Simulate, lds::poisson::System::Simulate\nf # inline void f( const Vector \u0026amp; u, bool do_add_noise =false ) Parameters:\nu input do_add_noise whether to add simulated process noise h # virtual void h() =0 Reimplemented by: lds::gaussian::System::h, lds::poisson::System::h\nh_ # virtual Vector h_( Vector x ) =0 Parameters:\nx_t state at time t Return: predicted state at time t + 1\nReimplemented by: lds::gaussian::System::h_, lds::poisson::System::h_\nn_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const x # inline const Vector \u0026amp; x() const P # inline const Matrix \u0026amp; P() const m # inline const Vector \u0026amp; m() const P_m # inline const Matrix \u0026amp; P_m() const cx # inline const Vector \u0026amp; cx() const y # inline const Vector \u0026amp; y() const x0 # inline const Vector \u0026amp; x0() const m0 # inline const Vector \u0026amp; m0() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const Ke # inline const Matrix \u0026amp; Ke() const Ke_m # inline const Matrix \u0026amp; Ke_m() const Q # inline const Matrix \u0026amp; Q() Q_m # inline const Matrix \u0026amp; Q_m() P0 # inline const Matrix \u0026amp; P0() P0_m # inline const Matrix \u0026amp; P0_m() set_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_m # inline void set_m( const Vector \u0026amp; m, bool do_force_assign =false ) set_g # inline void set_g( const Vector \u0026amp; g ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_Q_m # inline void set_Q_m( const Matrix \u0026amp; Q_m ) set_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_P0_m # inline void set_P0_m( const Matrix \u0026amp; P0_m ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) set_x # inline void set_x( const Vector \u0026amp; x ) Reset # void Reset() nstep_pred_block # std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block( UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1 ) Print # void Print() Protected Function Details # RecurseKe # virtual void RecurseKe() =0 Reimplemented by: lds::gaussian::System::RecurseKe, lds::poisson::System::RecurseKe\nInitVars # void InitVars( data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Public Attribute Details # do_adapt_m # bool do_adapt_m {}; Protected Attribute Details # n_x_ # std::size_t n_x_ {}; n_u_ # std::size_t n_u_ {}; n_y_ # std::size_t n_y_ {}; dt_ # data_t dt_ {}; x_ # Vector x_; P_ # Matrix P_; m_ # Vector m_; P_m_ # Matrix P_m_; cx_ # Vector cx_; y_ # Vector y_; z_ # Vector z_; x0_ # Vector x0_; P0_ # Matrix P0_; m0_ # Vector m0_; P0_m_ # Matrix P0_m_; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; Q_ # Matrix Q_; Q_m_ # Matrix Q_m_; C_ # Matrix C_; d_ # Vector d_; Ke_ # Matrix Ke_; Ke_m_ # Matrix Ke_m_; Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":48,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/","title":"lds::UniformMatrixList","section":"Classes","content":" lds::UniformMatrixList # More\u0026hellip;\nInherits from std::vector\u0026lt; Matrix \u0026gt;\nPublic Functions # Name UniformMatrixList() =default\nConstructs a new UniformMatrixList. UniformMatrixList(const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent. UniformMatrixList(std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent. UniformMatrixList(std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent. UniformMatrixList(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nConstructs a new UniformMatrixList (copy). UniformMatrixList(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nConstructs a new UniformMatrixList (move). ~UniformMatrixList() =default\nDestroys the object. const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim(size_t n =0) const\ngets dimensions of uniformly sized matrices size_t size()\nsize of container const Matrix \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Matrix \u0026amp; that, size_t n)\nswaps input matrix with n^th matrix of list UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nassigns the contents (copy) UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nassigns the contents (move) void append(const Matrix \u0026amp; mat)\nappends a matrix to the list Detailed Description # template \u0026lt;MatrixListFreeDim D =kMatFreeDimNone\u0026gt; class lds::UniformMatrixList; Public Function Details # UniformMatrixList # UniformMatrixList() =default UniformMatrixList # explicit UniformMatrixList( const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # explicit UniformMatrixList( std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList UniformMatrixList # UniformMatrixList( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList ~UniformMatrixList # ~UniformMatrixList() =default dim # inline const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim( size_t n =0 ) const Parameters:\nn [optional] index in list of matrices Return: dimensions\nsize # inline size_t size() at # inline const Matrix \u0026amp; at( size_t n ) Swap # inline void Swap( Matrix \u0026amp; that, size_t n ) Parameters:\nthat input matrix n index where the matrix is moved operator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\noperator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\nappend # void append( const Matrix \u0026amp; mat ) Parameters:\nmat input matrix Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":49,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/","title":"lds::UniformSystemList","section":"Classes","content":" lds::UniformSystemList # More\u0026hellip;\nInherits from std::vector\u0026lt; System \u0026gt;\nPublic Functions # Name UniformSystemList() =default\nConstructs a new UniformSystemList. UniformSystemList(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by copying existing vector of System if dimensions consistent. UniformSystemList(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by moving existing vector of System if dimensions consistent. UniformSystemList(std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList from initializer_list of System if dimensions consistent. UniformSystemList(const UniformSystemList \u0026amp; that)\nConstructs a new UniformSystemList (copy). UniformSystemList(UniformSystemList \u0026amp;\u0026amp; that)\nConstructs a new UniformSystemList (move). ~UniformSystemList() =default\nDestroys the object. const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const\ngets dimensions of the uniformly sized systems size_t size()\nsize of container const System \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(System \u0026amp; that, size_t n)\nswaps input system with n^th system of list UniformSystemList \u0026amp; operator=(const UniformSystemList \u0026amp; that)\nassigns the contents (copy) UniformSystemList \u0026amp; operator=(UniformSystemList \u0026amp;\u0026amp; that)\nassigns the contents (move) Detailed Description # template \u0026lt;typename System \u0026gt; class lds::UniformSystemList; Public Function Details # UniformSystemList # UniformSystemList() =default UniformSystemList # explicit UniformSystemList( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # explicit UniformSystemList( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList UniformSystemList # UniformSystemList( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList ~UniformSystemList # ~UniformSystemList() =default dim # inline const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const size # inline size_t size() at # inline const System \u0026amp; at( size_t n ) Swap # inline void Swap( System \u0026amp; that, size_t n ) Parameters:\nthat input system n index where the system is moved operator= # inline UniformSystemList \u0026amp; operator=( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\noperator= # inline UniformSystemList \u0026amp; operator=( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\nUpdated on 5 March 2025 at 16:20:07 EST\n"},{"id":50,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/","title":"lds::UniformVectorList","section":"Classes","content":" lds::UniformVectorList # Inherits from std::vector\u0026lt; Vector \u0026gt;\nPublic Functions # Name UniformVectorList() =default\nConstructs a new UniformVectorList. UniformVectorList(const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent. UniformVectorList(std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent. UniformVectorList(std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0)\nConstructs a new UniformVectorList from initializer_list of Vector if dimensions consistent. UniformVectorList(const UniformVectorList \u0026amp; that)\nConstructs a new UniformVectorList (copy) UniformVectorList(UniformVectorList \u0026amp;\u0026amp; that)\nConstructs a new UniformVectorList (move) ~UniformVectorList() =default\nDestroys the object. size_t dim() const\ngets dimensions of the uniformly sized matrices size_t size()\nsize of container const Vector \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Vector \u0026amp; that, size_t n)\nswaps input matrix with n^th vector of list UniformVectorList \u0026amp; operator=(const UniformVectorList \u0026amp; that)\nassigns the contents (copy) UniformVectorList \u0026amp; operator=(UniformVectorList \u0026amp;\u0026amp; that)\nassigns the contents (move) Public Function Details # UniformVectorList # UniformVectorList() =default UniformVectorList # explicit UniformVectorList( const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dims dimension UniformVectorList # explicit UniformVectorList( std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList UniformVectorList # UniformVectorList( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList ~UniformVectorList # ~UniformVectorList() =default dim # inline size_t dim() const size # inline size_t size() at # inline const Vector \u0026amp; at( size_t n ) Swap # inline void Swap( Vector \u0026amp; that, size_t n ) Parameters:\nthat input vector n index where the vector is moved operator= # inline UniformVectorList \u0026amp; operator=( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\noperator= # inline UniformVectorList \u0026amp; operator=( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\nUpdated on 5 March 2025 at 16:20:07 EST\n"},{"id":51,"href":"/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/","title":"ldsCtrlEst_h","section":"Files","content":" ldsCtrlEst_h # Files # Name ldsCtrlEst_h/lds.h lds namespace ldsCtrlEst_h/lds_ctrl.h Controller. ldsCtrlEst_h/lds_fit.h LDS base fit type. ldsCtrlEst_h/lds_fit_em.h subspace identification ldsCtrlEst_h/lds_fit_ssid.h subspace identification ldsCtrlEst_h/lds_gaussian.h glds namespace ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller. ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type. ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type. ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type. ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type. ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type. ldsCtrlEst_h/lds_poisson.h plds namespace ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type. ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type. ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type. ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type. ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type. ldsCtrlEst_h/lds_poisson_sys.h PLDS base type. ldsCtrlEst_h/lds_sctrl.h SwitchedController type. ldsCtrlEst_h/lds_sys.h LDS base type. ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices. ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems. ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors. ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":52,"href":"/lds-ctrl-est/docs/api/files/lds__ctrl_8h/","title":"ldsCtrlEst_h/lds_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_ctrl.h # Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Controller Detailed Description # This file declares the type for control of a linear dynamical system (lds::Controller).\nSource code # //===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_CTRL_H #define LDSCTRLEST_LDS_CTRL_H // namespace #include \u0026#34;lds.h\u0026#34; // system type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class Controller { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); public: Controller() = default; Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); const Vector\u0026amp; Control(const Vector\u0026amp; z, bool do_control = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); const Vector\u0026amp; ControlOutputReference(const Vector\u0026amp; z, bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); // get methods: const System\u0026amp; sys() const { return sys_; }; const Matrix\u0026amp; Kc() const { return Kc_; }; const Matrix\u0026amp; Kc_inty() const { return Kc_inty_; }; const Matrix\u0026amp; Kc_u() const { return Kc_u_; }; const Vector\u0026amp; g_design() const { return g_design_; }; const Vector\u0026amp; u_ref() const { return u_ref_; }; const Vector\u0026amp; x_ref() const { return x_ref_; }; const Vector\u0026amp; y_ref() const { return y_ref_; }; size_t control_type() const { return control_type_; }; data_t tau_awu() const { return tau_awu_; }; data_t u_lb() const { return u_lb_; }; data_t u_ub() const { return u_ub_; }; // set methods void set_sys(const System\u0026amp; sys) { bool does_match = sys_.n_u() == sys.n_u(); does_match = does_match \u0026amp;\u0026amp; (sys_.n_x() == sys.n_x()); does_match = does_match \u0026amp;\u0026amp; (sys_.n_y() == sys.n_y()); if (does_match) { sys_ = sys; } else { throw std::runtime_error( \u0026#34;new system argument to `set_sys` does not match dimensionality of \u0026#34; \u0026#34;existing system\u0026#34;); } }; void set_g_design(const Vector\u0026amp; g_design) { Reassign(g_design_, g_design); }; void set_u_ref(const Vector\u0026amp; u_ref) { Reassign(u_ref_, u_ref); }; void set_x_ref(const Vector\u0026amp; x_ref) { Reassign(x_ref_, x_ref); cx_ref_ = sys_.C() * x_ref_; }; // y_ref needs to be handled differently depending on output fn. // (need to populate cx_ref_ too, which depends on output fn) virtual void set_y_ref(const Vector\u0026amp; y_ref) { Reassign(y_ref_, y_ref); }; void set_Kc(const Matrix\u0026amp; Kc) { Reassign(Kc_, Kc); }; void set_Kc_inty(const Matrix\u0026amp; Kc_inty) { Reassign(Kc_inty_, Kc_inty); }; void set_Kc_u(const Matrix\u0026amp; Kc_u) { Reassign(Kc_u_, Kc_u); }; void set_tau_awu(data_t tau) { tau_awu_ = tau; k_awu_ = sys_.dt() / tau_awu_; }; void set_control_type(size_t control_type); // There is no reason u_lb/ub should not be public, but making set methods // anyway. void set_u_lb(data_t u_lb) { u_lb_ = u_lb; }; void set_u_ub(data_t u_ub) { u_ub_ = u_ub; }; void Reset() { sys_.Reset(); u_ref_.zeros(); u_ref_prev_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); u_saturated_ = false; t_since_control_onset_ = 0.0; }; void Print() { sys_.Print(); std::cout \u0026lt;\u0026lt; \u0026#34;g_design : \u0026#34; \u0026lt;\u0026lt; g_design_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_lb : \u0026#34; \u0026lt;\u0026lt; u_lb_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_ub : \u0026#34; \u0026lt;\u0026lt; u_ub_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }; protected: System sys_; Vector u_; Vector u_return_; Vector g_design_; // reference signals Vector u_ref_; // create no set method for this: Vector u_ref_prev_; Vector x_ref_; Vector y_ref_; Vector cx_ref_; // Controller gains Matrix Kc_; Matrix Kc_u_; Matrix Kc_inty_; // control after g inversion // do not need set methods for these. Vector du_ref_; Vector dv_ref_; Vector v_ref_; Vector dv_; Vector v_; // integral error // do not need set method for this Vector int_e_; Vector int_e_awu_adjust_; Vector u_sat_; bool do_control_prev_ = false; bool do_lock_control_prev_ = false; // whether the g of system has become inverted from what you think it is // (gain_ref) bool u_saturated_ = false; // should be safe to have references here bc nothing needs to be done // (like reset vars) when it changes... data_t u_lb_{}; data_t u_ub_{}; data_t tau_awu_{}; data_t k_awu_ = 0; data_t t_since_control_onset_ = 0; size_t control_type_{}; private: void CalcControl(bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); void CalcSteadyStateSetPoint(); void AntiWindup(); void InitVars(size_t control_type); }; // Implement the above: template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(sys), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(std::move(sys)), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::set_control_type(size_t control_type) { if (control_type_ == control_type) { return; } // creating a blank slate... control_type_ = 0; Kc_inty_.zeros(0, 0); Kc_u_.zeros(0, 0); int_e_.zeros(0, 0); int_e_awu_adjust_.zeros(0, 0); // controller was designed to minimize integral error if (control_type \u0026amp; kControlTypeIntY) { Kc_inty_.zeros(sys_.n_u(), sys_.n_y()); int_e_.zeros(sys_.n_y()); int_e_awu_adjust_.zeros(sys_.n_u()); control_type_ = control_type_ | kControlTypeIntY; } // controller was designed to minimize deltaU // (i.e. state augmented with u) if (control_type \u0026amp; kControlTypeDeltaU) { Kc_u_.zeros(sys_.n_u(), sys_.n_u()); control_type_ = control_type_ | kControlTypeDeltaU; } // whether to adapt set point calculate with (re-estimated) process // disturbance (m) if (control_type \u0026amp; kControlTypeAdaptM) { if (sys_.do_adapt_m) // only if adapting m... { control_type_ = control_type_ | kControlTypeAdaptM; } } } // set_control_type template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::Control( const Vector\u0026amp; z, bool do_control, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement sys_.Filter(u_, z); bool do_estimation = true; // always have estimator on in this case // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::ControlOutputReference( const Vector\u0026amp; z, bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement if (do_estimation) { sys_.Filter(u_, z); } else { sys_.f(u_); } // calculate the set point // solves for u_ref and x_ref when output is at y_ref at steady state. if (do_control) { CalcSteadyStateSetPoint(); } // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcControl(bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { if (do_control \u0026amp;\u0026amp; do_estimation) { if (!do_control_prev_) { if (do_reset_at_control_onset) { Reset(); } t_since_control_onset_ = 0.0; } else { t_since_control_onset_ += sys_.dt(); } // enforce softstart on control vars. if (sigma_soft_start \u0026gt; 0) { // half-Gaussian soft-start scaling factor data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) / (2 * pow(sigma_soft_start, 2))); u_ref_ *= soft_start_sf; // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too // x_ref_ *= soft_start_sf; // cx_ref_ *= soft_start_sf; // y_ref_ *= soft_start_sf; } if (!do_lock_control) { // first do u -\u0026gt; v change of vars. (v = g.*u) // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver // control voltage u[=]V) v_ref_ = g_design_ % u_ref_; // Given FB, calc. the change in control if (control_type_ \u0026amp; kControlTypeDeltaU) { // if control designed to minimize not u but deltaU (i.e. state aug with // u): // TODO(mfbolus): Commented out for now. See note below. // du_ref_ = u_ref_ - u_ref_prev_; // dv_ref_ = g_design_ % du_ref_; // TODO(mfbolus): Assuming users want *smooth* control signals if using // kControlTypeDeltaU, it should be the case that dv_ref_ is --\u0026gt; 0. May // want to revisit, but I am going to force it to be zero unless a // situation arises that argues for keeping the above. dv_ref_.zeros(); dv_ = dv_ref_; // nominally-optimal. dv_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error dv_ -= Kc_u_ * (v_ - v_ref_); // penalty on amp u (rel to ref) if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if(!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error dv_ -= Kc_inty_ * int_e_; // control for integrated error } // update the control v_ += dv_; } else { v_ = v_ref_; // nominally-optimal. v_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if (!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error v_ -= Kc_inty_ * int_e_; // control for integrated error } } // convert back to control voltage u[=]V u_ = v_ / sys_.g(); } // else do nothing until lock is low } else { // if not control // feed through u_ref in open loop u_ = u_ref_ % g_design_ / sys_.g(); v_ = sys_.g() % u_; u_ref_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); } // ends do_control // enforce box constraints (and antiwindup) AntiWindup(); // add noise to input? // The value for u that is *returned* to user after addition of any noise, // while keeping controller/estimator blind to this addition. u_return_ = u_; if ((sigma_u_noise \u0026gt; 0.0) \u0026amp;\u0026amp; (do_control \u0026amp;\u0026amp; !do_lock_control)) { u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn); Limit(u_return_, u_lb_, u_ub_); }; // For next time step: u_ref_prev_ = u_ref_; do_control_prev_ = do_control; do_lock_control_prev_ = do_lock_control; } // CalcControl template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcSteadyStateSetPoint() { // Linearly-constrained least squares (ls). // // _reference: // Boyd \u0026amp; Vandenberghe (2018) Introduction to Applied Linear Algebra // Matrix a_ls = join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros)); Vector b_ls = cx_ref_; Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye), sys_.B() * arma::diagmat(sys_.g())); Vector d_ls = -sys_.m0(); if (control_type_ \u0026amp; kControlTypeAdaptM) { d_ls = -sys_.m(); // adapt setpoint calc with disturbance? } Matrix a_ls_t = a_ls.t(); // TODO(mfbolus): not sure why but causes seg // fault if I do not do this. Matrix phi_ls = join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()), join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros))); // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse: Matrix inv_phi = pinv(phi_ls); Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls); x_ref_ = xulam.subvec(0, sys_.n_x() - 1); u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1); cx_ref_ = sys_.C() * x_ref_; } // CalcSteadyStateSetPoint template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::AntiWindup() { u_saturated_ = false; u_sat_ = u_; // limit u and flag whether saturated for (size_t k = 0; k \u0026lt; u_.n_elem; k++) { if (u_[k] \u0026lt; u_lb_) { u_sat_[k] = u_lb_; u_saturated_ = true; } if (u_[k] \u0026gt; u_ub_) { u_sat_[k] = u_ub_; u_saturated_ = true; } } if ((control_type_ \u0026amp; kControlTypeIntY) \u0026amp;\u0026amp; (tau_awu_ \u0026lt; lds::kInf)) { // one-step back-calculation (calculate intE for u=u_sat) // (Astroem, Rundqwist 1989 warn against using this...) // int_e_awu_adjust_ = // solve(Kc_inty_, (u_ - u_sat_)); // pinv(Kc_inty) * (u-uSat); // gradual: see Astroem, Rundqwist 1989 // this is a fudge for doing MIMO gradual // n.b., went ahead and multiplied 1/T by dt so don\u0026#39;t have to do that here. int_e_awu_adjust_ = k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_); // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_); int_e_ += int_e_awu_adjust_; } // set u to saturated version u_ = u_sat_; } template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::InitVars(size_t control_type) { // initialize to default values u_ref_ = Vector(sys_.n_u(), fill::zeros); u_ref_prev_ = Vector(sys_.n_u(), fill::zeros); x_ref_ = Vector(sys_.n_x(), fill::zeros); y_ref_ = Vector(sys_.n_y(), fill::zeros); cx_ref_ = Vector(sys_.n_y(), fill::zeros); u_ = Vector(sys_.n_u(), fill::zeros); u_return_ = Vector(sys_.n_u(), fill::zeros); u_sat_ = Vector(sys_.n_u(), fill::zeros); // Might not need all these, so zero elements until later. Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros); Kc_u_ = Matrix(0, 0, fill::zeros); Kc_inty_ = Matrix(0, 0, fill::zeros); g_design_ = sys_.g(); // by default, same as model dv_ = Vector(sys_.n_u(), fill::zeros); v_ = Vector(sys_.n_u(), fill::zeros); du_ref_ = Vector(sys_.n_u(), fill::zeros); dv_ref_ = Vector(sys_.n_u(), fill::zeros); v_ref_ = Vector(sys_.n_u(), fill::zeros); int_e_ = Vector(0, fill::zeros); int_e_awu_adjust_ = Vector(0, fill::zeros); set_control_type(control_type); } } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":53,"href":"/lds-ctrl-est/docs/api/files/lds__fit__em_8h/","title":"ldsCtrlEst_h/lds_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_em.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::EM Detailed Description # This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).\nSource code # //===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_EMAX_H #define LDSCTRLEST_LDS_EMAX_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class EM { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: EM() = default; EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); virtual ~EM() = default; const Fit\u0026amp; Run(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = true, bool calc_output = true, bool calc_measurement = true, size_t max_iter = 100, data_t tol = 1e-2); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); // auto tuple = std::make_tuple(u_, z_); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; x() const { return x_; }; const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; y() const { return y_; }; const Matrix\u0026amp; sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; }; const Matrix\u0026amp; sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; }; const Matrix\u0026amp; sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; }; size_t n_t_tot() { return n_t_tot_; } const Vector\u0026amp; theta() const { return theta_; }; protected: void Expectation(bool force_common_initial = false); void Maximization(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = false, bool calc_output = false, bool calc_measurement = false); void MaximizeDynamics(); void MaximizeQ(); void MaximizeInitial(); virtual void MaximizeOutput() = 0; virtual void MaximizeMeasurement() = 0; void Smooth(bool force_common_initial); virtual void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) = 0; void Reset(); void InitVars(); Vector UpdateTheta(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; std::vector\u0026lt;Matrix\u0026gt; x_; std::vector\u0026lt;Cube\u0026gt; P_; std::vector\u0026lt;Cube\u0026gt; P_t_tm1_; std::vector\u0026lt;Matrix\u0026gt; y_; Matrix diag_y_; // expectations calculated in E-step Matrix sum_E_x_t_x_t_; Matrix sum_E_xu_tm1_xu_tm1_; Matrix sum_E_xu_t_xu_tm1_; Fit fit_; Vector theta_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; }; template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; fit_ = Fit(n_u_, n_x, n_y_, dt); u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { // make sure fit dims match I/O data if (fit0.n_u() != u_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and input training data have inconsistent dimensions\u0026#34;); } if (fit0.n_y() != z_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and output training data have inconsistent dimensions\u0026#34;); } fit_ = fit0; u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::InitVars() { // check input/output data dimensions are consistent if (z_.size() != u_.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_.at(trial).n_cols != u_.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } n_u_ = fit_.n_u(); n_x_ = fit_.n_x(); n_y_ = fit_.n_y(); dt_ = fit_.dt(); x_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); P_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); P_t_tm1_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); y_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros); P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros); } diag_y_ = Matrix(n_y_, n_y_, fill::zeros); // covariances in expectation step sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); } template \u0026lt;typename Fit\u0026gt; const Fit\u0026amp; EM\u0026lt;Fit\u0026gt;::Run(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement, size_t max_iter, data_t tol) { Reset(); // to initial conditions size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_; Vector theta(n_params); Vector theta_new(n_params); data_t max_dtheta = 1; // if solving for initial conditions, allow them be varied. // otherwise, freeze at provided values. bool force_common_initial = !calc_init; // go until parameter convergence for (size_t l = 0; l \u0026lt; max_iter; l++) { theta_ = UpdateTheta(); std::cout \u0026lt;\u0026lt; \u0026#34;Iteration \u0026#34; \u0026lt;\u0026lt; l + 1 \u0026lt;\u0026lt; \u0026#34;/\u0026#34; \u0026lt;\u0026lt; max_iter \u0026lt;\u0026lt; \u0026#34; ...\\n\u0026#34;; Expectation(force_common_initial); Maximization(calc_dynamics, calc_Q, calc_init, calc_output, calc_measurement); // check convergence theta_new = UpdateTheta(); Vector dtheta = abs(theta_new - theta_) / abs(theta_); // some parameters could be zero... arma::uvec ubi_finite = find_finite(dtheta); max_dtheta = max(dtheta.elem(ubi_finite)); std::cout \u0026lt;\u0026lt; \u0026#34;max dtheta: \u0026#34; \u0026lt;\u0026lt; max_dtheta \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; if (max_dtheta \u0026lt; tol) { std::cout \u0026lt;\u0026lt; \u0026#34;Converged.\\n\u0026#34;; break; } std::cout \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } return fit_; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Smooth(bool force_common_initial) { Matrix k_e(n_x_, n_y_); // estimator gain Cube k_backfilt; // back-filtering gains // TODO(mfbolus): this loop could be made parallel for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { Matrix x_pre(n_x_, n_t_[trial], fill::zeros); Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros); Matrix x_post(n_x_, n_t_[trial], fill::zeros); Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros); if (force_common_initial) // forces all trials to have same initial // conditions. { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); } y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); // This *should not* be necessary but make sure P is symmetric. ForceSymPD(P_[trial].slice(0)); x_pre.col(0) = x_[trial].col(0); p_pre.slice(0) = P_[trial].slice(0); x_post.col(0) = x_[trial].col(0); p_post.slice(0) = P_[trial].slice(0); // filter for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // predict fit_.f(x_pre, x_post, u_.at(trial), t); fit_.h(y_[trial], x_pre, t); diag_y_.diag() = y_[trial].col(t); // TODO(mfbolus): change if parallel // update --\u0026gt; posterior estimation RecurseKe(k_e, p_pre, p_post, t); x_post.col(t) = x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t)); y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d(); } // backfilter -\u0026gt; Smoothed estimate // Reference: // Shumway et Stoffer (1982) ForceSymPD(p_post.slice(n_t_[trial] - 1)); k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1); P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 0; t--) { // TODO(mfmbolus): should not be necessary to force symm positive def ForceSymPD(p_pre.slice(t)); ForceSymPD(p_post.slice(t - 1)); ForceSymPD(P_[trial].slice(t)); k_backfilt.slice(t - 1) = p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t)); x_[trial].col(t - 1) = x_post.col(t - 1) + k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t)); P_[trial].slice(t - 1) = p_post.slice(t - 1) + k_backfilt.slice(t - 1) * (P_[trial].slice(t) - p_pre.slice(t)) * k_backfilt.slice(t - 1).t(); } // do the same for P_t_tm1 Matrix id(n_x_, n_x_, fill::eye); P_t_tm1_[trial].slice(n_t_[trial] - 1) = (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 1; t--) { P_t_tm1_[trial].slice(t - 1) = p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() + k_backfilt.slice(t - 1) * (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) * k_backfilt.slice(t - 2).t(); } // finally, get smoothed estimate of output for (size_t t = 0; t \u0026lt; n_t_[trial]; t++) { fit_.h(y_[trial], x_[trial], t); } // samps loop } // trial loop } // Smooth // template \u0026lt;typename Fit\u0026gt; // void EM\u0026lt;Fit\u0026gt;::RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) { // // predict covar // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q(); // // update Ke // Ke = P_pre.slice(t) * fit_.C().t() * // inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R()); // // update cov // // Reference: Ghahramani et Hinton (1996) // P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t); // // // n.b. for poisson : // // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + // fit_.Q(); // // // update cov // // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ * // // fit_.C()); // // // update Ke // // Ke = P_post.slice(t) * fit_.C(); // } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Expectation(bool force_common_initial) { // calculate the mean/cov of state needed for maximizing E[pr(z|theta)] Smooth(force_common_initial); // now get the various forms of sum(E[xx\u0026#39;]) needed // n.b. Going to start at t=1 rather than 0 bc most max terms need that. // so really \u0026#34;n_t_tot_\u0026#34; is (n_t_tot_-1) n_t_tot_ = 0; sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); Vector xu_tm1(n_x_ + n_u_, fill::zeros); Vector xu_t(n_x_ + n_u_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // ------------ sum_E_x_t_x_t ------------ sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t(); sum_E_x_t_x_t_ += P_[trial].slice(t); // ------------ sum_E_xu_tm1_xu_tm1 ------------ xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1)); sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t(); sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t - 1); // ------------ sum_E_xu_t_xu_tm1 ------------ xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t)); sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t(); sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_t_tm1_[trial].slice(t); n_t_tot_ += 1; } // time } // trial } // Expectation template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement) { if (calc_output) { MaximizeOutput(); } if (calc_measurement) { MaximizeMeasurement(); } if (calc_dynamics) { MaximizeDynamics(); } if (calc_Q) { MaximizeQ(); } if (calc_init) { MaximizeInitial(); } } // Maximization template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeDynamics() { // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) * inv_sympd(sum_E_xu_tm1_xu_tm1_); fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1)); std::cout \u0026lt;\u0026lt; \u0026#34;A_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.A()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.B()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeQ() { // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 * // inv_sympd(sum_E_xu_tm1_xu_tm1_) * // sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // this way is same as above iff dynamics were just updated: // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix ab = arma::join_horiz(fit_.A(), fit_.B()); // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // From scratch method: // Q is covariance of the error between state and dynamics-predicted state // (aka process noise) // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})\u0026#39;] // t-1 terms: View sum_e_x_tm1_x_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_u_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1); View sum_e_x_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); // t, t-1 terms: View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_x_t_u_tm1 = sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); Matrix q = sum_E_x_t_x_t_; q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t(); q -= sum_e_x_t_x_tm1 * fit_.A().t(); q -= fit_.A() * sum_e_x_t_x_tm1.t(); // input-related terms: q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t(); q -= sum_e_x_t_u_tm1 * fit_.B().t(); q -= fit_.B() * sum_e_x_t_u_tm1.t(); q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t(); q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t(); q /= n_t_tot_; fit_.set_Q(q); std::cout \u0026lt;\u0026lt; \u0026#34;Q_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.Q()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // std::cout \u0026lt;\u0026lt; \u0026#34;Q_new: \\n\u0026#34; \u0026lt;\u0026lt; fit_.Q() \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeInitial() { Vector x0 = fit_.x0(); x0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { x0 += x_[trial].col(0); } x0 /= z_.size(); std::cout \u0026lt;\u0026lt; \u0026#34;x0_new[0]: \u0026#34; \u0026lt;\u0026lt; x0[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // always recalc P0 even if the initial state is fixed (at zero, for // example) Matrix e_var(n_x_, n_x_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t(); } e_var /= z_.size(); // go ahead and subtract x0*x0\u0026#39; so don\u0026#39;t have to below. e_var -= x0 * x0.t(); // To get P0, going to get initial P_ per trial and average. // (which might be wrong, but need a single number) Matrix p0 = fit_.P0(); p0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { p0 += (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var; } p0 /= z_.size(); fit_.set_P0(p0); std::cout \u0026lt;\u0026lt; \u0026#34;P0_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.P0()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeOutput() { // solve for C+d: Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros); Vector x1(n_x_ + 1, fill::zeros); x1[n_x_] = 1.0; // augment with one to solve for bias Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { x1.subvec(0, n_x_ - 1) = x_[trial].col(t); sum_zx += z_.at(trial).col(t) * x1.t(); sum_e_x1_x1 += x1 * x1.t(); sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t); } } Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1); fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1)); fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_))); std::cout \u0026lt;\u0026lt; \u0026#34;C_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.C()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.d()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeMeasurement() { // Solve for measurement noise covar size_t n_t_tot = 0; // Ghahgramani, Hinton 1996: Matrix sum_zz(n_y_, n_y_, fill::zeros); Matrix sum_yz(n_y_, n_y_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t(); // Use Cnew: sum_yz += (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t(); n_t_tot += 1; } } fit_.set_R((sum_zz - sum_yz) / n_t_tot); std::cout \u0026lt;\u0026lt; \u0026#34;R_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.R()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Reset() { // reset to initial conditions for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); } } template \u0026lt;typename Fit\u0026gt; Vector EM\u0026lt;Fit\u0026gt;::UpdateTheta() { // TODO(mfbolus): This should include n_y_ more params for d. size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_; if (fit_.R().n_elem \u0026gt; 0) { n_params += n_y_ * n_y_; } Vector theta(n_params); size_t idx_start = 0; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B()); idx_start += n_x_ * n_u_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0()); idx_start += n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C()); idx_start += n_y_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d()); idx_start += n_y_; if (fit_.R().n_elem \u0026gt; 0) { theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R()); } return theta; } } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":54,"href":"/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_ssid.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SSID Detailed Description # This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)\u0026lt;Fit\u0026gt;).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_FIT_SSID_H #define LDSCTRLEST_LDS_FIT_SSID_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class SSID { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: SSID() = default; SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d = Vector(1).fill(-kInf)); std::tuple\u0026lt;Fit, Vector\u0026gt; Run(SSIDWt ssid_wt); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } protected: void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001); void CreateHankelDataMat(); virtual void DecomposeData() = 0; void CalcSVD(SSIDWt wt); void Solve(data_t wt_dc); void RecomputeExtObs(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; Matrix D_; Fit fit_; Matrix g_dc_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_h_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; Matrix L_; Vector s_; Matrix ext_obs_t_; }; template \u0026lt;typename Fit\u0026gt; SSID\u0026lt;Fit\u0026gt;::SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d) { // check input/output data dimensions are consistent if (z_train.size() != u_train.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_train.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_train.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } dt_ = dt; n_x_ = n_x; n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; n_h_ = n_h; // dimensionality check for eventual block-hankel data matrix size_t len = n_t_tot_ - 2 * n_h_ + 1; if (len \u0026lt; (2 * n_h_ * (n_u_ + n_y_))) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;Dataset problem! More rows than columns in block-hankel data \u0026#34; \u0026#34;matrix: 2*(n_u+n_y)*n_h \u0026gt; data-length! Need higher data-length or \u0026#34; \u0026#34;lower n_h.\u0026#34;; throw std::runtime_error(ss.str()); } fit_ = Fit(n_u_, n_x_, n_y_, dt_); u_ = std::move(u_train); z_ = std::move(z_train); if (!d.is_finite() || (d.n_rows != n_y_)) { // TODO(mfbolus): implement least-square solution for impulse response with // a second input of ones. Data-driven way of accounting for offset *not* // driven by an input. // // For now, calculate output bias (d) as the // output wherever the stimulus has not been on for some amount of time. // convolve u with rectangle and take all samples. This is a reasonable // approach, since often when autonomous systems are fit (i.e., systems with // no input), they will subtract off the mean of the output. This // essentially amounts to setting output bias to the mean of the output when // there is no stimulation. data_t t_silence = 0.1; data_t thresh_silence = 0.001; CalcD(t_silence, thresh_silence); } else { fit_.set_d(d); } } template \u0026lt;typename Fit\u0026gt; std::tuple\u0026lt;Fit, Vector\u0026gt; SSID\u0026lt;Fit\u0026gt;::Run(SSIDWt ssid_wt) { // the weight on minimizing dc I/O gain only works for gaussian, // and hopefully not necessary with appropriate dataset. data_t wt_dc = 0; // std::cout \u0026lt;\u0026lt; \u0026#34;creating hankel mat\\n\u0026#34;; CreateHankelDataMat(); // std::cout \u0026lt;\u0026lt; \u0026#34;decomposing data\\n\u0026#34;; DecomposeData(); // std::cout \u0026lt;\u0026lt; \u0026#34;calculating svd\\n\u0026#34;; CalcSVD(ssid_wt); // std::cout \u0026lt;\u0026lt; \u0026#34;solving for params\\n\u0026#34;; Solve(wt_dc); // std::cout \u0026lt;\u0026lt; \u0026#34;fin\\n\u0026#34;; return std::make_tuple(fit_, s_); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcD(data_t t_silence, data_t thresh_silence) { Vector d(z_.at(0).n_rows, fill::zeros); Vector win(static_cast\u0026lt;size_t\u0026gt;(t_silence / dt_), fill::ones); Vector sum_z_silence(n_y_, fill::zeros); size_t n_silence(0); for (size_t trial = 0; trial \u0026lt; u_.size(); trial++) { // find silent samples // start by convolving with Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0)); Vector u_conv = conv(sum_u, win, \u0026#34;same\u0026#34;); // get only the samples that are silent... arma::uvec ubi_silence = find(u_conv \u0026lt;= thresh_silence); if (ubi_silence.n_elem \u0026gt; 0) { sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1); n_silence += ubi_silence.n_elem; } } if (n_silence \u0026gt; 0) { d = sum_z_silence / n_silence; } fit_.set_d(d); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat() { // temporary copy of data Matrix z(n_y_, n_t_tot_, fill::zeros); Matrix u(n_u_, n_t_tot_, fill::zeros); size_t so_far(0); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial); u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial); so_far += n_t_.at(trial); } // remove output bias z.each_col() -= fit_.d(); // calculate I/O gain @ DC while data in convenient form g_dc_ = z * pinv(u); // std::cout \u0026lt;\u0026lt; \u0026#34;G0_data = \u0026#34; \u0026lt;\u0026lt; g_dc_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // create hankel data matrix size_t len = z.n_cols - 2 * n_h_ + 1; // data length in hankel mat // block-hankel data matrix D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros); // past input auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1); // future input auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1); // past output auto y_p = D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1); // future output auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, len - 1); size_t idx = 0; for (size_t k = 0; k \u0026lt; len; k++) { idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } } D_ /= sqrt(static_cast\u0026lt;data_t\u0026gt;(len)); } // template \u0026lt;typename Fit\u0026gt; // void SSID\u0026lt;Fit\u0026gt;::DecomposeData() { // // do LQ decomp instead of calculating covariance expensive way // // Note that \u0026#34;R\u0026#34; in van Overschee is lower-triangular (L), not \u0026#34;R\u0026#34; in QR // // decomp. Very confusing. // Matrix q_t; // lq(L_, q_t, D_); // // van Overschee zeros out the other elements. // L_ = trimatl(L_); // } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcSVD(SSIDWt wt) { // submats that will be needed: auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1); auto R_23_13 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1); auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_) - 1); auto R_44 = L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14); auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1); auto Luf = Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1); auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); // aka: R_f Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1); // from van Overschee subid.m: // Rf = R((2*m+l)*i+1:2*(m+l)*i,:); % Future outputs Matrix U; Matrix V; switch (wt) { case kSSIDNone: { // No weighting. (what van Overschee calls \u0026#34;N4SID\u0026#34;) Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14; arma::svd(U, s_, V, O_k_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDMOESP: { // MOESP weighting // This is what they use in the \u0026#34;robust\u0026#34; algorithm van Overschee, de Moor // 1996 Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); svd(U, s_, V, O_k_ortho_Uf_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDCVA: { // CVA weighting // See van Overschee\u0026#39;s matlab code (subid.m): // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); Matrix inv_w1; Matrix qt1; lq(inv_w1, qt1, R_56_16); // lq decomp of R_f (future output data) inv_w1 = trimatl(inv_w1); inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1); Matrix w_o_w = arma::solve( inv_w1, O_k_ortho_Uf_sans_Qt); // alternatively // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt svd(U, s_, V, w_o_w, \u0026#34;std\u0026#34;); U = inv_w1 * U; break; } } // Truncate to model order (heart of ssid method) auto s_hat = s_.subvec(0, n_x_ - 1); Matrix diag_sqrt_s = diagmat(sqrt(s_hat)); auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1); // get extended observability and controllability mats ext_obs_t_ = u_hat * diag_sqrt_s; // extended observability matrix } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::Solve(data_t wt_dc) { // required submats auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_56_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); // Solve for params using appropriate algorithm: // robust deterministic/stochastic algorithm in van Overschee 1996 // algorithm that the authors say \u0026#34;works\u0026#34; in practice. auto ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix // This is what textbook (1996) says: // // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15); // // HOWEVER, do not know why but have to fill the last place with zeros like // authors\u0026#39; matlab implementation (see `subid.m`) // Otherwise, get ridiculous covariances (although A,C estimates are close to // same...) Matrix Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Matrix S = Tl * pinv(Tr); // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure // stability. fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1)); Matrix ext_obs_t_p1 = join_vert( ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1), Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros)); fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1); // At this point, van Overschee \u0026amp; de Moor suggest re-calculating ext_obs_t_, // ext_obs_tm1 from (A, C) because it was just an approximation. This is RecomputeExtObs(); ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); S = Tl * pinv(Tr); Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_); Matrix Mcurly = pinv(ext_obs_tm1); Matrix Pcurly = Tl - Lcurly * R_56_15; Vector Pvec = vectorise(Pcurly); Matrix Qcurly = R_23_15; // Identify [D; B], assuming D=0 and ensuring DC gain is correct Matrix sum_QcurlyT_kron_Ncurly( (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_), fill::zeros); Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols, fill::eye); eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1, eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1; // van Overschee (1996) p. 126 Matrix N1_Tl = -Lcurly; N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) += join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly); N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) += Matrix(n_y_, n_y_, fill::eye); Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros); Matrix N_k; for (size_t k = 0; k \u0026lt; n_h_; k++) { auto Qcurly_k = Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1); Nk_Tl.zeros(); Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) = N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1); N_k = Nk_Tl * eye_ext_obs_tm1; sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k); } Matrix err_vec; if (wt_dc \u0026gt; 0) { // Constraints enforced by weighted least squares // // Reference: // // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly // Excited Industrial Systems. Conference in Decision and Control. // constraint 1: assume D=0 --\u0026gt; remove the components for Dvec (this is // actually a hard constraint in that it ignores D) Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly; sum_QcurlyT_kron_Ncurly = Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_); size_t kkk = 0; for (size_t k = 1; k \u0026lt; (n_u_ + 1); k++) { size_t start_idx = k * (n_y_ + n_x_) - n_x_; for (size_t kk = 0; kk \u0026lt; n_x_; kk++) { sum_QcurlyT_kron_Ncurly.col(kkk) = sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk); kkk++; } } // constraint 2: Make sure DC I/O gain is correct Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A()); Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_)); Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0); Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 = join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0); // WEIGHTED LS // Important in practice because I care a lot about at least getting the DC // gain correct. Put x weight on minimizing error at DC, relative to others Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye); // Make weight on minimizing DC error immense so at least that // should be nailed. size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows; size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows; size_t stop_row = w.n_rows - 1; size_t stop_col = w.n_cols - 1; // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale // weight with data length? w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc; Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * sum_QcurlyT_kron_Ncurly_b_to_g0) * sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec; fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_)); // Calculate residuals and their cov. // Because I\u0026#39;ve added constraints, I need to re-calculate the right term // with b_vec instead of how van Overschee do in final algorithm. err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec; } else { // default way: *no* constraint on G0 or D=0 Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec; // TODO(mfbolus) n.b., this gets thrown away... // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_); fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_)); err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec; } // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols); // TODO(mfbolus): Something is wrong with the error calculation above. // Use the way van overschee does it in `subid.m` // WARNING: this ignores any above constraints, so Q, R will be approximate... Matrix err = Tl - S * Tr; Matrix cov_err = err * err.t(); fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1)); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::RecomputeExtObs() { ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C(); for (size_t k = 2; k \u0026lt; (n_h_ + 1); k++) { ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) = ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1, ext_obs_t_.n_cols - 1) * fit_.A(); } } } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":55,"href":"/lds-ctrl-est/docs/api/files/lds__fit_8h/","title":"ldsCtrlEst_h/lds_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_fit.h # LDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Fit LDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.\nSource code # //===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDS_FIT_HPP #define LDS_FIT_HPP // namespace #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); virtual ~Fit() = default; // get methods size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; Q() const { return Q_; }; const Vector\u0026amp; x0() const { return x0_; }; const Matrix\u0026amp; P0() const { return P0_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; // gets measurement noise virtual const Matrix\u0026amp; R() const = 0; // set methods (e.g., seeding initial fit values) void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_m(const Vector\u0026amp; m) { Reassign(m_, m); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); ForceSymPD(Q_); }; virtual void set_R(const Matrix\u0026amp; R) = 0; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); ForceSymPD(P0_); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; View f(Matrix\u0026amp; x, const Matrix\u0026amp; u, size_t t) { x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x.col(t); }; View f(Matrix\u0026amp; x_pre, const Matrix\u0026amp; x_post, const Matrix\u0026amp; u, size_t t) { x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x_pre.col(t); }; virtual View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) = 0; protected: data_t dt_{}; // Dynamics Matrix A_; Matrix B_; Vector g_; Vector m_; Matrix Q_; // Output Matrix C_; Vector d_; Matrix R_; // initial conditions Vector x0_; Matrix P0_; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; }; } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":56,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_ctrl.h # GLDS Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. Detailed Description # This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_gaussian_sys.h\u0026#34; // controller #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace gaussian { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); cx_ref_ = y_ref - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":57,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_em.h # GLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitEM GLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":58,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_ssid.h # GLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; using SSID\u0026lt;Fit\u0026gt;::Run; private: using SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat; using SSID\u0026lt;Fit\u0026gt;::CalcSVD; using SSID\u0026lt;Fit\u0026gt;::Solve; void DecomposeData() override; void SolveVanOverschee(); }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":59,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit.h # GLDS fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Fit GLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // fit type #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace gaussian { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); const Matrix\u0026amp; R() const override { return R_; }; void set_R(const Matrix\u0026amp; R) override { Reassign(R_, R); ForceSymPD(R_); }; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = C_ * x.col(t) + d_; return y.col(t); }; }; }; // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":60,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sctrl.h # GLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H // controller type #include \u0026#34;lds_gaussian_ctrl.h\u0026#34; // switched controller #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace gaussian { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); cx_ref_ = y_ref - sys_.d(); } // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; }; // SwitchedController } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":61,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/","title":"ldsCtrlEst_h/lds_gaussian_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sys.h # GLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::System Gaussian LDS Type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H #define LDSCTRLEST_LDS_GAUSSIAN_SYS_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; namespace lds { namespace gaussian { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0, data_t r0 = kDefaultR0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; // get methods const Matrix\u0026amp; R() const { return R_; }; // set methods void set_Q(const Matrix\u0026amp; Q) { lds::System::set_Q(Q); do_recurse_Ke_ = true; } void set_R(const Matrix\u0026amp; R) { Reassign(R_, R); do_recurse_Ke_ = true; }; void set_Ke(const Matrix\u0026amp; Ke) { Reassign(Ke_, Ke); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void set_Ke_m(const Matrix\u0026amp; Ke_m) { Reassign(Ke_m_, Ke_m); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void Print(); protected: void h() override { cx_ = C_ * x_; y_ = cx_ + d_; }; Vector h_(Vector x) override { return C_ * x + d_; }; void RecurseKe() override; // Gaussian-output-specific Matrix R_; bool do_recurse_Ke_{}; }; // System } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":62,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian_8h/","title":"ldsCtrlEst_h/lds_gaussian.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian.h # glds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_H #define LDSCTRLEST_LDS_GAUSSIAN_H // namespace #include \u0026#34;lds.h\u0026#34; namespace lds { namespace gaussian { // insert any Gaussian-specific things here... } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":63,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_ctrl.h # PLDS controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. Detailed Description # This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_CTRL_H #define LDSCTRLEST_LDS_POISSON_CTRL_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system type #include \u0026#34;lds_poisson_sys.h\u0026#34; // control type #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace poisson { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); lds::Limit(y_ref_, kYRefLb, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; private: constexpr static const data_t kYRefLb = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":64,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_em.h # PLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitEM PLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\n[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H #define LDSCTRLEST_LDS_POISSON_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override{}; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; data_t NewtonSolveC(); void AnalyticalSolveD(); }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":65,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_ssid.h # PLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. Detailed Description # This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/)).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H #define LDSCTRLEST_LDS_POISSON_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; private: void DecomposeData() override; void CalcCov(); void PoissonToGaussianMoments(); Matrix cov_; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":66,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/","title":"ldsCtrlEst_h/lds_poisson_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit.h # PLDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Fit PLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_H #define LDSCTRLEST_LDS_POISSON_FIT_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // fit #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace poisson { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) : lds::Fit(n_u, n_x, n_y, dt){}; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = exp(C_ * x.col(t) + d_); return y.col(t); }; void set_R(const Matrix\u0026amp; R) override { std::cerr \u0026lt;\u0026lt; \u0026#34;WARNING: Cannot set R (R[0] = \u0026#34; \u0026lt;\u0026lt; R.at(0) \u0026lt;\u0026lt; \u0026#34;). No Gaussian measurement noise in Poisson observation model.\\n\u0026#34;; }; const Matrix\u0026amp; R() const override { return R_; }; }; }; // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":67,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sctrl.h # PLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #include \u0026#34;lds_poisson_ctrl.h\u0026#34; #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace poisson { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); lds::Limit(y_ref_, kYRefLB, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; private: constexpr static data_t kYRefLB = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":68,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/","title":"ldsCtrlEst_h/lds_poisson_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sys.h # PLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::System Poisson System type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SYS_H #define LDSCTRLEST_LDS_POISSON_SYS_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; // needed for Poisson random number generation #include \u0026lt;random\u0026gt; namespace lds { namespace poisson { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; protected: void h() override { cx_ = C_ * x_; y_ = exp(cx_ + d_); diag_y_.diag() = y_; }; Vector h_(Vector x) override { return exp(C_ * x + d_); }; void RecurseKe() override; private: // Poisson-output-specific Matrix diag_y_; std::poisson_distribution\u0026lt;size_t\u0026gt; pd_; }; // System } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":69,"href":"/lds-ctrl-est/docs/api/files/lds__poisson_8h/","title":"ldsCtrlEst_h/lds_poisson.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson.h # plds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_H #define LDSCTRLEST_LDS_POISSON_H #include \u0026#34;lds.h\u0026#34; namespace lds { namespace poisson { // TODO(mfbolus): Not sure if defining these as static here makes the most // sense. Is there a downside to letting multiple poisson System objects share a // common random number generator? static std::random_device rd; static std::mt19937 rng = std::mt19937( rd()); } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":70,"href":"/lds-ctrl-est/docs/api/files/lds__sctrl_8h/","title":"ldsCtrlEst_h/lds_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_sctrl.h # SwitchedController type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SwitchedController SwitchedController Type. Detailed Description # This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SCTRL_H #define LDSCTRLEST_LDS_SCTRL_H #include \u0026#34;lds_ctrl.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; #include \u0026#34;lds_uniform_vecs.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class SwitchedController : public Controller\u0026lt;System\u0026gt; { public: SwitchedController() = default; SwitchedController(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); SwitchedController(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); void Switch(size_t idx, bool do_force_switch = false); void set_Kc(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc) { Kc_list_ = Kc; Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc) { Kc_list_ = std::move(Kc); Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_inty) { Kc_inty_list_ = Kc_inty; Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_inty) { Kc_inty_list_ = std::move(Kc_inty); Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_u) { Kc_u_list_ = Kc_u; Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_u) { Kc_u_list_ = std::move(Kc_u); Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(const UniformVectorList\u0026amp; g) { g_design_list_ = g; g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(UniformVectorList\u0026amp;\u0026amp; g) { g_design_list_ = std::move(g); g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; protected: std::vector\u0026lt;System\u0026gt; systems_; size_t n_sys_{}; size_t idx_{}; // controller gains could be different for each UniformMatrixList\u0026lt;\u0026gt; Kc_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_inty_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_u_list_; // design-phase input gain could also be different UniformVectorList g_design_list_; // TODO(mfbolus): not sure why I need to do this. using Controller\u0026lt;System\u0026gt;::Kc_; using Controller\u0026lt;System\u0026gt;::Kc_inty_; using Controller\u0026lt;System\u0026gt;::Kc_u_; using Controller\u0026lt;System\u0026gt;::g_design_; using Controller\u0026lt;System\u0026gt;::sys_; // using Controller\u0026lt;System\u0026gt;::u_ref_; // using Controller\u0026lt;System\u0026gt;::x_ref_; // using Controller\u0026lt;System\u0026gt;::y_ref_; // using Controller\u0026lt;System\u0026gt;::control_type_; private: void InitVars(); using lds::Controller\u0026lt;System\u0026gt;::set_sys; // using Controller\u0026lt;System\u0026gt;::set_Kc; // using Controller\u0026lt;System\u0026gt;::set_Kc_inty; // using Controller\u0026lt;System\u0026gt;::set_Kc_u; // using Controller\u0026lt;System\u0026gt;::set_g_design; }; template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(systems.at(0), u_lb, u_ub, control_type), systems_(systems) { InitVars(); } template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(System(systems.at(0).n_u(), systems.at(0).n_x(), systems.at(0).n_y(), systems.at(0).dt()), u_lb, u_ub, control_type), systems_(std::move(systems)) { InitVars(); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::InitVars() { n_sys_ = systems_.size(); sys_ = systems_.at(0); Kc_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_)); Kc_inty_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); Kc_u_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); g_design_list_ = UniformVectorList(std::vector\u0026lt;Vector\u0026gt;(n_sys_, g_design_)); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::Switch(size_t idx, bool do_force_switch) { if ((idx == idx_) \u0026amp;\u0026amp; !do_force_switch) { return; // already there. } // put old up and get new one out systems_.at(idx_) = std::move(sys_); sys_ = std::move(systems_.at(idx)); // set the state of this system to that of the previous system // TODO(mfbolus): This will only work as intended if state matrix is the same. // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp. sys_.set_m(systems_.at(idx_).m(), true); sys_.set_x(systems_.at(idx_).x()); // swap controller gains Kc_list_.Swap(Kc_, idx_); Kc_list_.Swap(Kc_, idx); if (control_type_ \u0026amp; kControlTypeIntY) { Kc_inty_list_.Swap(Kc_inty_, idx_); Kc_inty_list_.Swap(Kc_inty_, idx); } if (control_type_ \u0026amp; kControlTypeDeltaU) { Kc_u_list_.Swap(Kc_u_, idx_); Kc_u_list_.Swap(Kc_u_, idx); } g_design_list_.Swap(g_design_, idx_); g_design_list_.Swap(g_design_, idx); idx_ = idx; } // Switch } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":71,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8h/","title":"ldsCtrlEst_h/lds_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_sys.h # LDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::System Linear Dynamical System Type. Detailed Description # This file declares and partially defines the base type for linear dynamical systems ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SYS_H #define LDSCTRLEST_LDS_SYS_H #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class System { public: System() = default; System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); virtual ~System() {} void Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z); virtual const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) = 0; void f(const Vector\u0026amp; u, bool do_add_noise = false) { x_ = A_ * x_ + B_ * (g_ % u) + m_; if (do_add_noise) { x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_); } }; virtual void h() = 0; virtual Vector h_(Vector x) = 0; size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Vector\u0026amp; x() const { return x_; }; const Matrix\u0026amp; P() const { return P_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; P_m() const { return P_m_; }; const Vector\u0026amp; cx() const { return cx_; }; const Vector\u0026amp; y() const { return y_; }; const Vector\u0026amp; x0() const { return x0_; }; const Vector\u0026amp; m0() const { return m0_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; const Matrix\u0026amp; Ke() const { return Ke_; }; const Matrix\u0026amp; Ke_m() const { return Ke_m_; }; const Matrix\u0026amp; Q() { return Q_; }; const Matrix\u0026amp; Q_m() { return Q_m_; }; const Matrix\u0026amp; P0() { return P0_; }; const Matrix\u0026amp; P0_m() { return P0_m_; }; void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_m(const Vector\u0026amp; m, bool do_force_assign = false) { Reassign(m0_, m); if ((!do_adapt_m) || do_force_assign) { Reassign(m_, m); } }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); }; void set_Q_m(const Matrix\u0026amp; Q_m) { Reassign(Q_m_, Q_m); }; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); }; void set_P0_m(const Matrix\u0026amp; P0_m) { Reassign(P0_m_, P0_m); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; void set_x(const Vector\u0026amp; x) { Reassign(x_, x); h(); }; void Reset(); std::vector\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; nstep_pred_block( UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z, size_t n_pred = 1); void Print(); // safe to leave this public and non-const bool do_adapt_m{}; protected: virtual void RecurseKe() = 0; void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); std::size_t n_x_{}; std::size_t n_u_{}; std::size_t n_y_{}; data_t dt_{}; // Signals: Vector x_; Matrix P_; Vector m_; Matrix P_m_; Vector cx_; Vector y_; Vector z_; // Parameters: Vector x0_; Matrix P0_; Vector m0_; Matrix P0_m_; Matrix A_; Matrix B_; Vector g_; Matrix Q_; Matrix Q_m_; Matrix C_; Vector d_; Matrix Ke_; Matrix Ke_m_; }; // System } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":72,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/","title":"ldsCtrlEst_h/lds_uniform_mats.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_mats.h # List of uniformly sized matrices. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformMatrixList Detailed Description # This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H #define LDSCTRLEST_LDS_UNIFORM_MATS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { template \u0026lt;MatrixListFreeDim D = kMatFreeDimNone\u0026gt; class UniformMatrixList : public std::vector\u0026lt;Matrix\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Matrix\u0026gt;::vector; // don\u0026#39;t allow push_back to be used since it doesn\u0026#39;t check dims using std::vector\u0026lt;Matrix\u0026gt;::push_back; public: using std::vector\u0026lt;Matrix\u0026gt;::operator=; using std::vector\u0026lt;Matrix\u0026gt;::operator[]; using std::vector\u0026lt;Matrix\u0026gt;::begin; using std::vector\u0026lt;Matrix\u0026gt;::end; using std::vector\u0026lt;Matrix\u0026gt;::size; using std::vector\u0026lt;Matrix\u0026gt;::at; UniformMatrixList() = default; explicit UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); explicit UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; ~UniformMatrixList() = default; const std::array\u0026lt;size_t, 2\u0026gt;\u0026amp; dim(size_t n = 0) const { return dim_.at(n); } size_t size() { return std::vector\u0026lt;Matrix\u0026gt;::size(); }; const Matrix\u0026amp; at(size_t n) { return std::vector\u0026lt;Matrix\u0026gt;::at(n); }; void Swap(Matrix\u0026amp; that, size_t n); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; void append(const Matrix\u0026amp; mat); private: void CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim); std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt; dim_; }; template \u0026lt;MatrixListFreeDim D\u0026gt; inline void UniformMatrixList\u0026lt;D\u0026gt;::Swap(Matrix\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.n_cols); } if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap // not moving, since it causes memory issues. // so this method isn\u0026#39;t a memory-saver as designed for now Matrix tmp = (*this)[n]; (*this)[n] = that; that = tmp; if (D == kMatFreeDim1) { this-\u0026gt;dim_[n][0] = (*this)[n].n_rows; } if (D == kMatFreeDim2) { this-\u0026gt;dim_[n][1] = (*this)[n].n_cols; } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::append(const Matrix\u0026amp; mat) { std::array\u0026lt;size_t, 2\u0026gt; dim({mat.n_rows, mat.n_cols}); CheckDimensions(dim); std::vector\u0026lt;Matrix\u0026gt;::push_back(mat); dim_.push_back(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) { // make sure dim_ vector is initialized if (dim_.empty()) { dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(that.size(), {0, 0}); } // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices\u0026#34;; throw std::runtime_error(ss.str()); } // if dimensions a not zero and do not match, skip move with error message. bool dims_nonzero = true; for (auto d : dim_) { if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; d[0] \u0026lt; 1) { dims_nonzero = false; break; } if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; d[1] \u0026lt; 1) { dims_nonzero = false; break; } } if (dims_nonzero) { bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); } if (!does_match) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrices of size \u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; dim_[k] = that.dim(k); } return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. bool dims_nonzero = true; for (auto d : dim_) { // if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; (d[0] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; (d[1] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // } // // if (dims_nonzero) { // bool does_match = true; // if (!(D == kMatFreeDim1)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); // } // // if (!(D == kMatFreeDim2)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); // } // // if (!does_match) { // this-\u0026gt;at(0).print(\u0026#34;this[0] = \u0026#34;); // that.at(0).print(\u0026#34;that[0] = \u0026#34;); // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformMatrixList element of size (\u0026#34; \u0026lt;\u0026lt; // that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols \u0026lt;\u0026lt; \u0026#34;) for an // element of size (\u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34;). // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Matrix\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(std::move(mats)) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) : vector(that) { (*this) = that; } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { std::array\u0026lt;size_t, 2\u0026gt; dim_k({this-\u0026gt;at(k).n_rows, this-\u0026gt;at(k).n_cols}); dim_.push_back(dim_k); } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim) { // change behavior based on free dim D if ((dim[0] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim1)) { dim[0] = this-\u0026gt;at(0).n_rows; } if ((dim[1] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim2)) { dim[1] = this-\u0026gt;at(0).n_cols; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Matrix\u0026amp; mat : *this) { if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_rows == dim[0]); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_cols == dim[1]); } if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(this-\u0026gt;size(), dim); for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { dim_[k][0] = (*this)[k].n_rows; dim_[k][1] = (*this)[k].n_cols; } } } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":73,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/","title":"ldsCtrlEst_h/lds_uniform_systems.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_systems.h # List of uniformly sized Systems. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformSystemList Detailed Description # This file provides a container for uniformly sized Systems.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector // namespace #include \u0026#34;lds.h\u0026#34; // System type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class UniformSystemList : public std::vector\u0026lt;System\u0026gt; { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;System\u0026gt;::vector; using std::vector\u0026lt;System\u0026gt;::operator=; using std::vector\u0026lt;System\u0026gt;::operator[]; using std::vector\u0026lt;System\u0026gt;::at; using std::vector\u0026lt;System\u0026gt;::begin; using std::vector\u0026lt;System\u0026gt;::end; using std::vector\u0026lt;System\u0026gt;::size; public: UniformSystemList() = default; explicit UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); explicit UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(const UniformSystemList\u0026amp; that); UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept; ~UniformSystemList() = default; const std::array\u0026lt;size_t, 3\u0026gt;\u0026amp; dim() const { return dim_; } size_t size() { return std::vector\u0026lt;System\u0026gt;::size(); }; const System\u0026amp; at(size_t n) { return std::vector\u0026lt;System\u0026gt;::at(n); }; void Swap(System\u0026amp; that, size_t n); UniformSystemList\u0026amp; operator=(const UniformSystemList\u0026amp; that); UniformSystemList\u0026amp; operator=(UniformSystemList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim); std::array\u0026lt;size_t, 3\u0026gt; dim_{}; }; template \u0026lt;typename System\u0026gt; inline void UniformSystemList\u0026lt;System\u0026gt;::Swap(System\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformSystemList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = (dim_[0] == that.n_u()) \u0026amp;\u0026amp; (dim_[1] == that.n_x()) \u0026amp;\u0026amp; (dim_[2] == that.n_y()); if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformSystemList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap System tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( const UniformSystemList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_[0] + dim_[1] + dim_[2]) { std::array\u0026lt;size_t, 3\u0026gt; other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign systems of size \u0026#34; \u0026lt;\u0026lt; dim_[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2] \u0026lt;\u0026lt; \u0026#34; with systems of size \u0026#34; \u0026lt;\u0026lt; other_dim[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other_dim[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2]; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( UniformSystemList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. if (dim_[0] + dim_[1] + dim_[2]) { // bool does_match = (dim_[0] == that.at(0).n_u()) \u0026amp;\u0026amp; // (dim_[1] == that.at(0).n_x()) \u0026amp;\u0026amp; // (dim_[2] == that.at(0).n_y()); // if (!does_match) { // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformSystemList element for an element of \u0026#34; // \u0026#34;different size. Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;System\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(std::move(systems)) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList( std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const UniformSystemList\u0026amp; that) : std::vector\u0026lt;System\u0026gt;(that) { (*this) = that; } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept : std::vector\u0026lt;System\u0026gt;(std::move(that)) { this-\u0026gt;dim_[0] = this-\u0026gt;at(0).n_u(); this-\u0026gt;dim_[1] = this-\u0026gt;at(0).n_x(); this-\u0026gt;dim_[2] = this-\u0026gt;at(0).n_y(); } template \u0026lt;typename System\u0026gt; void UniformSystemList\u0026lt;System\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim) { if (dim[0] + dim[1] + dim[2]) { dim_ = dim; } else { dim_[0] = this-\u0026gt;at(0).n_u(); dim_[1] = this-\u0026gt;at(0).n_x(); dim_[2] = this-\u0026gt;at(0).n_y(); } // make sure dimensiolaties are all uniform bool does_match(true); for (const System\u0026amp; sys : *this) { does_match = does_match \u0026amp;\u0026amp; (sys.n_u() == dim_[0]); does_match = does_match \u0026amp;\u0026amp; (sys.n_x() == dim_[1]); does_match = does_match \u0026amp;\u0026amp; (sys.n_y() == dim_[2]); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input systems are not uniform.\u0026#34;); } } } } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":74,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/","title":"ldsCtrlEst_h/lds_uniform_vecs.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_vecs.h # List of uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformVectorList Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H #define LDSCTRLEST_LDS_UNIFORM_VECS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { class UniformVectorList : public std::vector\u0026lt;Vector\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Vector\u0026gt;::vector; using std::vector\u0026lt;Vector\u0026gt;::operator=; using std::vector\u0026lt;Vector\u0026gt;::operator[]; using std::vector\u0026lt;Vector\u0026gt;::at; using std::vector\u0026lt;Vector\u0026gt;::begin; using std::vector\u0026lt;Vector\u0026gt;::end; using std::vector\u0026lt;Vector\u0026gt;::size; public: UniformVectorList() = default; explicit UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim = 0); explicit UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim = 0); UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim = 0); UniformVectorList(const UniformVectorList\u0026amp; that); UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept; ~UniformVectorList() = default; size_t dim() const { return dim_; } size_t size() { return std::vector\u0026lt;Vector\u0026gt;::size(); }; const Vector\u0026amp; at(size_t n) { return std::vector\u0026lt;Vector\u0026gt;::at(n); }; void Swap(Vector\u0026amp; that, size_t n); UniformVectorList\u0026amp; operator=(const UniformVectorList\u0026amp; that); UniformVectorList\u0026amp; operator=(UniformVectorList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(size_t dim); size_t dim_{}; }; inline void UniformVectorList::Swap(Vector\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = dim_ == that.n_elem; if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap Vector tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( const UniformVectorList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_) { size_t other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ \u0026lt;\u0026lt; \u0026#34; with vectors of size \u0026#34; \u0026lt;\u0026lt; other_dim; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( UniformVectorList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors. Skipping.\\n\u0026#34;; // return (*this); // } // // if (dim_) { // size_t other_dim(that.dim()); // if (dim_ != other_dim) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ // \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; other_dim \u0026lt;\u0026lt; \u0026#34;. // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Vector\u0026gt;::operator=(std::move(that)); return (*this); } } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":75,"href":"/lds-ctrl-est/docs/api/files/lds_8h/","title":"ldsCtrlEst_h/lds.h","section":"Files","content":" ldsCtrlEst_h/lds.h # lds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)) observations.\nSource code # //===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_H #define LDSCTRLEST_LDS_H // #ifndef LDSCTRLEST // #include \u0026lt;ldsCtrlEst\u0026gt; // #endif #include \u0026lt;armadillo\u0026gt; namespace lds { using data_t = double; // may change to float (but breaks mex functions) using Vector = arma::Col\u0026lt;data_t\u0026gt;; using Matrix = arma::Mat\u0026lt;data_t\u0026gt;; using Cube = arma::Cube\u0026lt;data_t\u0026gt;; using View = arma::subview\u0026lt;data_t\u0026gt;; namespace fill = arma::fill; static const std::size_t kControlTypeDeltaU = 0x1; static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); static const data_t kPi = arma::datum::pi; static const data_t kDefaultP0 = 1e-6; static const data_t kDefaultQ0 = 1e-6; static const data_t kDefaultR0 = 1e-2; enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA }; enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2 }; // TODO(mfbolus): for SwitchedController, may want systems to have differing // numbers of states. Use this enum as template parameter? // enum SystemListFreeDim { // kSysFreeDimNone, // kSysFreeDimX ///\u0026lt; allow state dim (x) of systems in list to be hetero // }; // place hard limits on contents of vecors/mats void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub); void Limit(Vector\u0026amp; x, data_t lb, data_t ub); void Limit(Matrix\u0026amp; x, data_t lb, data_t ub); // in-place assign that errs if there are dimension mismatches: void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); // TODO(mfbolus): this is a fudge, but for some reason, cov mats often going // numerically asymm. void ForceSymPD(Matrix\u0026amp; X); void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min = 0); void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X); Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B); inline void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Vector\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Matrix\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if (other.n_elem != some.n_elem) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vector of size \u0026#34; \u0026lt;\u0026lt; some.n_elem \u0026lt;\u0026lt; \u0026#34; with vector of size \u0026#34; \u0026lt;\u0026lt; other.n_elem \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } inline void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrix of size \u0026#34; \u0026lt;\u0026lt; some.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; some.n_cols \u0026lt;\u0026lt; \u0026#34; with matrix of size \u0026#34; \u0026lt;\u0026lt; other.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other.n_cols \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } } // namespace lds #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":76,"href":"/lds-ctrl-est/docs/api/files/mex__c__util_8h/","title":"ldsCtrlEst_h/mex_c_util.h","section":"Files","content":" ldsCtrlEst_h/mex_c_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) More\u0026hellip;\nNamespaces # Name armamexc arma/mex interface using Matlab C API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab/Octave\u0026rsquo;s C mex API.\nSource code # //===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXC_UTIL_H #define LDSCTRLEST_MEXC_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.h\u0026#34; // // If Matlab_FOUND, include matrix.h. // // (Octave does not need/have it.) // #ifdef Matlab_FOUND // #include \u0026#34;matrix.h\u0026#34; // #endif namespace armamexc { template \u0026lt;class T\u0026gt; inline auto m2T_scalar(const mxArray *matlab_scalar) -\u0026gt; T { if (mxGetData(matlab_scalar)) { return static_cast\u0026lt;T\u0026gt;(mxGetScalar(matlab_scalar)); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return 0; } template \u0026lt;class T\u0026gt; inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false, bool strict = true) -\u0026gt; arma::Mat\u0026lt;T\u0026gt; { if (mxGetData(matlab_mat)) { const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat); if (n_dim == 2) { return arma::Mat\u0026lt;T\u0026gt;(static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)), mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem, strict); } mexErrMsgTxt(\u0026#34;Number of dimensions must be 2.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } // TODO(mfbolus): make these templated. template \u0026lt;typename T\u0026gt; inline auto a2m_mat(arma::Mat\u0026lt;T\u0026gt; const \u0026amp;arma_mat) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_mat.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Mat.\u0026#34;); return nullptr; } template \u0026lt;typename T\u0026gt; inline auto a2m_vec(arma::Col\u0026lt;T\u0026gt; const \u0026amp;arma_vec) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_vec.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Col.\u0026#34;); return nullptr; } } // namespace armamexc #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":77,"href":"/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/","title":"ldsCtrlEst_h/mex_cpp_util.h","section":"Files","content":" ldsCtrlEst_h/mex_cpp_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) More\u0026hellip;\nNamespaces # Name armamexcpp arma/mex interface using Matlab C++ API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab\u0026rsquo;s C++ mex API.\nSource code # //===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXCPP_UTIL_H #define LDSCTRLEST_MEXCPP_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.hpp\u0026#34; #include \u0026#34;mexAdapter.hpp\u0026#34; namespace armamexcpp { template \u0026lt;class T\u0026gt; std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; m2a_cellmat(matlab::data::CellArray\u0026amp; matlab_cell) { size_t n_cells = matlab_cell.getNumberOfElements(); std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; arma_mat(n_cells, arma::Mat\u0026lt;T\u0026gt;(1, 1, arma::fill::zeros)); for (size_t k = 0; k \u0026lt; n_cells; k++) { matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = matlab_cell[k]; auto dims = matlab_mat.getDimensions(); arma_mat[k] = arma::Mat\u0026lt;T\u0026gt;(matlab_mat.release().get(), dims[0], dims[1]); } return arma_mat; }; template \u0026lt;class T\u0026gt; std::vector\u0026lt;T\u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt;T\u0026gt;\u0026amp; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); T* ptr = matlab_array.release().get(); std::vector\u0026lt;T\u0026gt; vec(ptr, ptr + n_elem); return vec; }; template \u0026lt;class T\u0026gt; arma::Col\u0026lt;T\u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); // T* ptr = matlab_array.release().get(); // arma::Col\u0026lt;T\u0026gt; vec(ptr, n_elem); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? arma::Col\u0026lt;T\u0026gt; vec(n_elem, arma::fill::zeros); for (size_t k = 0; k \u0026lt; n_elem; k++) { vec[k] = matlab_array[k]; } return vec; }; template \u0026lt;class T\u0026gt; arma::Mat\u0026lt;T\u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { // ArrayDimensions == std::vector\u0026lt;size_t\u0026gt; auto dims = matlab_array.getDimensions(); // T* ptr = matlab_array.release().get(); // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false) // arma::Mat\u0026lt;T\u0026gt; mat(ptr, dims[0], dims[1]); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? // // armadillo and matlab both use column-major ordering, so this should work: size_t n_elem = dims[0] * dims[1]; arma::Mat\u0026lt;T\u0026gt; mat(dims[0], dims[1], arma::fill::zeros); size_t k(0); for (auto m: matlab_array) { mat[k] = m; k++; } return mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_mat(const arma::Mat\u0026lt;T\u0026gt;\u0026amp; arma_mat, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(), arma_mat.memptr() + arma_mat.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_vec(const arma::Col\u0026lt;T\u0026gt;\u0026amp; arma_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;({arma_vec.n_elem, 1}, arma_vec.memptr(), arma_vec.memptr() + arma_vec.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; s2m_vec(const std::vector\u0026lt;T\u0026gt;\u0026amp; std_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size()); return matlab_mat; }; } // namespace armamexcpp #endif Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":78,"href":"/lds-ctrl-est/docs/terminology/model/","title":"Models","section":"LDS C+E Documentation","content":" Model Definitions # This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form: \\[\\mathbf{x}_{t\u0026#43;1} = f\\left( \\mathbf{x}_{t}, \\mathbf{v}_{t} \\right) = \\mathbf{A} \\mathbf{x}_{t} \u0026#43; \\mathbf{B} \\mathbf{v}_{t} \u0026#43; \\mathbf{m}_{t} \u0026#43; \\mathbf{w}_{t}\\] \\[\\mathbf{y}_{t} = h\\left( \\mathbf{x}_{t} \\right)\\] t : time index x : system state v = g%u : input (e.g., in physical units used for model fit) u : control signal sent to actuator (e.g., in Volts) y : system output m : process disturbance w ~ N(0, Q) : process noise/disturbance A : state matrix B : input coupling matrix g : input gain (e.g., for converting to control signal actuator voltage) n.b., assumes this conversion is linear Q : process noise covariance % : element-wise multiplication LDS with Gaussian Observations # For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.\n\\[\\mathbf{y}_{t} = \\mathbf{C} \\mathbf{x}_{t} \u0026#43; \\mathbf{d}\\] \\[\\mathbf{z}_{t} \\sim \\mathcal{N}\\left(\\mathbf{y}_{t} , \\mathbf{R} \\right)\\] z : measurement C : output matrix d : output bias R : measurement noise covariance LDS with Poisson Observations # For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.\n\\[y_{t}^{i} = \\exp \\left(\\mathbf{c}^i \\mathbf{x}_{t} \u0026#43; d^i\\right)\\] \\[z_{t}^i \\sim \\rm{Poisson} \\left(y_{t}^i \\right)\\] i : output index z : measurement (count data) c : i^th row of output matrix (C) d : output bias Model Predictive Control (MPC) # Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.\nIn the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.\n"},{"id":79,"href":"/lds-ctrl-est/docs/api/modules/","title":"Modules","section":"LDS C+E Documentation","content":" Modules # Control Mode Bit Masks provides fill types for constructing new armadillo vectors, matrices\nDefaults\nUpdated on 5 March 2025 at 16:20:07 EST\n"},{"id":80,"href":"/lds-ctrl-est/docs/api/namespaces/","title":"Namespaces","section":"LDS C+E Documentation","content":" Namespaces # armamexc arma/mex interface using Matlab C API\narmamexcpp arma/mex interface using Matlab C++ API\nlds::gaussian Linear Dynamical Systems with Gaussian observations.\nlds::poisson Linear Dynamical Systems with Poisson observations.\nstd\nUpdated on 5 March 2025 at 16:20:07 EST\n"},{"id":81,"href":"/lds-ctrl-est/docs/api/pages/","title":"Pages","section":"LDS C+E Documentation","content":" Pages # Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":82,"href":"/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/","title":"PLDS State Estimation","section":"LDS C+E Examples","content":" PLDS State Estimation Tutorial # This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[x_{t\u0026#43;1} = x_t \u0026#43; w_t\\] \\[y_{t} = \\exp\\left(x_t\\right)\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state Finally, assign the parameters using corresponding set-methods.\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); Creating the estimator # Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. To ensure robust estimates, adaptively re-estimate the process disturbance.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); Simulating estimation # In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.\n// Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.\nWith this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.\n"},{"id":83,"href":"/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/","title":"PLDS Switched Control","section":"LDS C+E Examples","content":" PLDS Switched Control Tutorial # This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.\nIn the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating the simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); The system\u0026rsquo;s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 Initially, the system will be in \u0026ldquo;mode\u0026rdquo; 1, where B = b1.\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.\nCreating the controller # Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.\nSimilar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.\nMoreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.\nPutting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying systems: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nNow that the SwitchedController is instantiated, assign its parameters.\n// Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).\nThe control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.\n// Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system\u0026rsquo;s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.\nExample simulation result # Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller\u0026rsquo;s online estimates of the output and state are shown in purple.\nNote that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.\n"},{"id":84,"href":"/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/","title":"src","section":"Files","content":" src # Files # Name src/lds.cpp misc lds namespace functions src/lds_gaussian_sys.cpp GLDS base type. src/lds_poisson_sys.cpp PLDS base type. src/lds_sys.cpp LDS base type. src/lds_uniform_vecs.cpp Uniformly sized vectors. Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":85,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/","title":"src/lds_gaussian_sys.cpp","section":"Files","content":" src/lds_gaussian_sys.cpp # GLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_gaussian_sys.h\u0026gt; lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0, data_t r0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { R_.zeros(n_y, n_y); R_.diag().fill(r0); do_recurse_Ke_=true; }; // recursively estimate Ke void lds::gaussian::System::RecurseKe() { if (!do_recurse_Ke_) { return; } // predict covariance P_ = A_ * P_ * A_.t() + Q_; // calc Kalman gain Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_); // update covariance // Reference: Ghahramani et Hinton (1996) P_ = P_ - Ke_ * C_ * P_; if (do_adapt_m) { P_m_ += Q_m_; // A_m = I (i.e., random walk) Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_); P_m_ = P_m_ - Ke_m_ * C_ * P_m_; } } // Simulate const lds::Vector\u0026amp; lds::gaussian::System::Simulate(const Vector\u0026amp; u_tm1){ f(u_tm1, true);//simulate dynamics with noise added h();//output z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure return z_; } void lds::gaussian::System::Print() { lds::System::Print(); std::cout \u0026lt;\u0026lt; \u0026#34;R: \\n\u0026#34; \u0026lt;\u0026lt; R_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":86,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/","title":"src/lds_poisson_sys.cpp","section":"Files","content":" src/lds_poisson_sys.cpp # PLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_poisson_sys.h\u0026gt; lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { diag_y_ = diagmat(y_); pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(0); }; // Correct: Given measurement (z) and current input (u), update estimate of the // state, covar, output. // // see Eden et al. 2004 void lds::poisson::System::RecurseKe() { // predict covariance P_ = A_ * P_ * A_.t() + Q_; // update cov P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_); Ke_ = P_ * C_.t(); if (do_adapt_m) { P_m_ += Q_m_; // predict (A_m = I) P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_); // update Ke_m_ = P_m_ * C_.t(); } } // Simulate Measurement: z ~ Poisson(y) const lds::Vector\u0026amp; lds::poisson::System::Simulate(const Vector\u0026amp; u_tm1) { f(u_tm1, true); // simulate dynamics with noise added h(); // output z_.zeros(); for (std::size_t k = 0; k \u0026lt; n_y_; k++) { // construct a Poisson distribution object with mean y[k] pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(y_[k]); // pull random sample from this distribution z_[k] = pd_(rng); } return z_; } // ******************* SYS_T ******************* Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":87,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8cpp/","title":"src/lds_sys.cpp","section":"Files","content":" src/lds_sys.cpp # LDS base type. More\u0026hellip;\nDetailed Description # This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- lds_sys.cpp - LDS -------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_sys.h\u0026gt; #include \u0026lt;vector\u0026gt; lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) { InitVars(p0, q0); } void lds::System::InitVars(data_t p0, data_t q0) { // initial conditions. x0_ = Vector(n_x_, fill::zeros); // includes bias (nY) and g (nU) P0_ = p0 * Matrix(n_x_, n_x_, fill::eye); m0_ = x0_; P0_m_ = P0_; // signals x_ = x0_; P_ = P0_; m_ = m0_; P_m_ = P0_m_; y_ = Vector(n_y_, fill::zeros); cx_ = Vector(n_y_, fill::zeros); z_ = Vector(n_y_, fill::zeros); // By default, random walk where each state is independent // In this way, provides independent estimates of rate per channel of output. A_ = Matrix(n_x_, n_x_, fill::eye); B_ = Matrix(n_x_, n_u_, fill::zeros); g_ = Vector(n_u_, fill::ones); Q_ = q0 * Matrix(n_x_, n_x_, fill::eye); Q_m_ = Q_; C_ = Matrix(n_y_, n_x_, fill::eye); // each state will map to an output by d_ = Vector(n_y_, fill::zeros); Ke_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain. Ke_m_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain for m adaptation. do_adapt_m = false; } // Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state // and update estimate of the state, covar, output using Kalman filter void lds::System::Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z_t) { // predict mean f(u_tm1); // dynamics h(); // output // recursively calculate esimator gains (or just keep existing values) // (also predicts+updates estimate covariance) RecurseKe(); // update x_ += Ke_ * (z_t - y_); if (do_adapt_m) { m_ += Ke_m_ * (z_t - y_); // adaptively estimating disturbance } // With new state, estimate output. h(); // --\u0026gt; posterior } void lds::System::Reset() { // reset to initial conditions x_ = x0_; // mean P_ = P0_; // cov of state estimate m_ = m0_; // process disturbance P_m_ = P0_m_; // cov of disturbance estimate h(); } std::vector\u0026lt;lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt;\u0026gt; lds::System::nstep_pred_block(lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; u, lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; z, size_t n_pred) { lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_filt; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_pred; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; y_pred; for (size_t k = 0; k \u0026lt; u.size(); k++) { Reset(); size_t n_t = arma::size(u[k])[1]; Matrix x_filt_k(n_x_, n_t, fill::zeros); Matrix x_pred_k(n_x_, n_t - n_pred, fill::zeros); Matrix y_pred_k(n_y_, n_t - n_pred, fill::zeros); for (size_t t = 0; t \u0026lt; n_t - n_pred; t++) { Vector x_pred_ahead = x_; for (size_t t_u = t; t_u \u0026lt; t + n_pred; t_u++) { x_pred_ahead = A_ * x_pred_ahead + B_ * u[k].col(t_u); } x_pred_k.col(t) = x_pred_ahead; y_pred_k.col(t) = h_(x_pred_ahead); if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; // given previous measurment } for (size_t t = n_t - n_pred; t \u0026lt; n_t; t++) { if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; } x_filt.append(x_filt_k); x_pred.append(x_pred_k); y_pred.append(y_pred_k); } return {x_filt, x_pred, y_pred}; } void lds::System::Print() { std::cout \u0026lt;\u0026lt; \u0026#34;\\n ********** SYSTEM ********** \\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;x: \\n\u0026#34; \u0026lt;\u0026lt; x_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;P: \\n\u0026#34; \u0026lt;\u0026lt; P_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;A: \\n\u0026#34; \u0026lt;\u0026lt; A_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B: \\n\u0026#34; \u0026lt;\u0026lt; B_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;g: \\n\u0026#34; \u0026lt;\u0026lt; g_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;m: \\n\u0026#34; \u0026lt;\u0026lt; m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q: \\n\u0026#34; \u0026lt;\u0026lt; Q_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q_m: \\n\u0026#34; \u0026lt;\u0026lt; Q_m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d: \\n\u0026#34; \u0026lt;\u0026lt; d_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;C: \\n\u0026#34; \u0026lt;\u0026lt; C_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;y: \\n\u0026#34; \u0026lt;\u0026lt; y_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } //******************* SYS_T ******************* Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":88,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/","title":"src/lds_uniform_vecs.cpp","section":"Files","content":" src/lds_uniform_vecs.cpp # Uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_uniform_vecs.h\u0026gt; namespace lds { UniformVectorList::UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); } UniformVectorList::UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim) : vector(std::move(vecs)) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(const UniformVectorList\u0026amp; that) : vector(that) { (*this) = that; } UniformVectorList::UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { this-\u0026gt;dim_ = this-\u0026gt;at(0).n_elem; } void UniformVectorList::CheckDimensions(size_t dim) { if (dim) { dim_ = dim; } else { dim_ = this-\u0026gt;at(0).n_elem; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Vector\u0026amp; vec : *this) { does_match = does_match \u0026amp;\u0026amp; (vec.n_elem == dim_); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } } } // namespace lds Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":89,"href":"/lds-ctrl-est/docs/api/files/lds_8cpp/","title":"src/lds.cpp","section":"Files","content":" src/lds.cpp # misc lds namespace functions More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file implements miscellaneous lds namespace functions not bound to a class.\nSource code # //===-- lds.cpp - LDS -----------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds.h\u0026gt; // insert any necessary function definitions here. namespace lds { void ForceSymPD(Matrix\u0026amp; X) { if (X.is_sympd() || !X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; // for eigenval decomp bool did_succeed(true); Vector d; Matrix u; // see first method (which may not be ideal): // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/ size_t k(1); bool is_sympd = X.is_sympd(); Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye); while (!is_sympd) { if (k \u0026gt; 100) { did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); data_t min_eig = arma::min(d); std::cerr \u0026lt;\u0026lt; \u0026#34;After multiple iterations, min eigen val = \u0026#34; \u0026lt;\u0026lt; min_eig \u0026lt;\u0026lt; \u0026#34;.\\n\u0026#34;; throw std::runtime_error( \u0026#34;Failed to make matrix symmetric positive definite.\u0026#34;); return; } // Limit(d, arma::eps(0), kInf); // force to be positive... // Matrix d_diag = arma::diagmat(d); // X = u * d_diag * u.t(); did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymPD failed.\u0026#34;); } data_t min_eig = arma::min(d); X += id * abs(min_eig) + arma::datum::eps; // make sure symm: X = (X + X.t()) / 2; // double check eigenvals positive after symmetrizing: arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); min_eig = arma::min(d); is_sympd = min_eig \u0026gt; 0; k++; } } void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min) { if (!X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; bool did_succeed(true); Vector d; Matrix u; did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymMinEig failed.\u0026#34;); } Limit(d, eig_min + arma::eps(eig_min), kInf); // enforce lower bound Matrix d_diag = arma::diagmat(d); X = u * d_diag * u.t(); // double check symmetric X = (X + X.t()) / 2; } void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X) { bool did_succeed(true); did_succeed = arma::qr_econ(Qt, L, X.t()); if (!did_succeed) { throw std::runtime_error(\u0026#34;LQ decomposition failed.\u0026#34;); } arma::inplace_trans(L); arma::inplace_trans(Qt); } Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B) { // subtract out mean auto m_a = arma::mean(A, 1); Matrix a0 = A; a0.each_col() -= m_a; auto m_b = arma::mean(B, 1); Matrix b0 = B; b0.each_col() -= m_b; Matrix cov = a0 * b0.t() / a0.n_cols; return cov; } } // namespace lds Updated on 5 March 2025 at 16:20:07 EST\n"},{"id":90,"href":"/lds-ctrl-est/docs/api/namespaces/namespacestd/","title":"std","section":"Namespaces","content":" std # Updated on 5 March 2025 at 16:20:07 EST\n"}]
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+[{"id":0,"href":"/lds-ctrl-est/docs/","title":"LDS C+E Documentation","section":"LDS Control \u0026 Estimation","content":" LDS Control \u0026amp; Estimation Documentation # "},{"id":1,"href":"/lds-ctrl-est/docs/tutorials/","title":"LDS C+E Examples","section":"LDS C+E Documentation","content":" Examples # "},{"id":2,"href":"/lds-ctrl-est/acknowledgements/","title":"Acknowledgements","section":"LDS Control \u0026 Estimation","content":" Acknowledgements # Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.\n"},{"id":3,"href":"/lds-ctrl-est/docs/getting-started/getting-started/","title":"Getting Started","section":"LDS C+E Documentation","content":" Getting Started # This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.\nldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.\nTested Configurations # Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the RelWithDebInfo build type in the CMake configure command (-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo):\nUbuntu 18.04 with GCC 7.5 compiler macOS 11 (Big Sur) with Apple Clang 12 compiler Windows 10 with Visual Studio 16.11 (2019 release) and Clang 12 compiler That being said, if you want to debug a build for a single platform, here are some things you can try:\nUse different compilers (or even different versions of a single compiler) Use different versions of vcpkg (which you can control by checking out a different commit in the vcpkg submodule) Mac Pre-requisities # Xcode Command Line Tools will get you clang, gcc, make, and git:\nxcode-select --install Homebrew is \u0026ldquo;The Missing Package Manager for macOS\u0026rdquo; which will make installing lots of things easy. Install like this:\n/bin/bash -c \u0026#34;$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\u0026#34; You can then use it to install CMake, gfortran, and pkg-config:\nbrew install cmake gfortran pkg-config Linux Pre-requisites # You\u0026rsquo;ll need Git, CMake, GCC, gfortran, etc.\nsudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential ninja-build Windows Installation # Look here for Windows-specific instructions.\nDownloading the Library # First, clone the repository along with submodules:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est\rgit submodule update --init Compilation + Installation # Now generate the cache and build using your IDE or from the command line as follows.\nmkdir build \u0026amp;\u0026amp; cd build\rcmake ..\rcmake --build . The first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.\nIf you want to use vcpkg set up somewhere besides this repo\u0026rsquo;s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE\u0026rsquo;s settings.\nYou can verify the build is working by running ctest from the build folder, which runs all the example scripts.\nOptions # This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\nLDSCTRLEST_BUILD_EXAMPLES : [default=ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake to configure/build the library.\nFor basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build cmake --build #build the project sudo make install #[optional] installs to default location (OS-specific) To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location cmake --build #build the project make install #install to /your/install/prefix To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\nLD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.\nPython bindings package ldsctrlest # With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython3_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn\u0026rsquo;t use an undesired version. That environment needs to have NumPy installed.\ncmake --build . --target python_modules The bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory (pip install pytest matplotlib first if you need to).\nSee python/ldsctrlest/README.md for usage details.\nAlso, beware that a single build will probably not work for both the standalone library and the Python package, since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.\nCommon issues # \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.\nexport CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix vcpkg fails on configuration Try running ./bootstrap-vcpkg from the vcpkg folder and try again. If that doesn\u0026rsquo;t work, try updating vcpkg to a newer version (in the source control tab, click on the commit hash by the vcpkg repo then select from the dropdown) and running boostsrap-vcpkg again. You can also try upgrading your system (e.g., apt update, apt upgrade).\nCould not find Python3 (missing: Python3_NumPy_INCLUDE_DIRS NumPy)\nMake sure NumPy is installed in the Python environment you specified. If CMake still can\u0026rsquo;t find it, you may need to tell CMake exactly where to find it by adding an argument to the configure command: -DPython3_NumPy_INCLUDE_DIR=.... You can find that location like this: python -c 'import numpy; print(numpy.get_include())'\n"},{"id":4,"href":"/lds-ctrl-est/docs/getting-started/windows/","title":"Windows","section":"LDS C+E Documentation","content":" Windows Installation # Windows Pre-requisites # Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:\nSet-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time iwr get.scoop.sh | Invoke-Expression Install Git and CMake if you don\u0026rsquo;t already have them:\nscoop install git cmake If that didn\u0026rsquo;t work, follow more detailed instructions here.\nThe easiest way to compile C++ project on Windows is with Visual Studio\u0026rsquo;s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on \u0026ldquo;Desktop development with C++.\u0026rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the \u0026ldquo;Installation details\u0026rdquo; sidebar under optional features.\nAnd the easiest way to use Visual Studio\u0026rsquo;s build tools is with VS Code, along with the CMake Tools extension. Install them and you should be ready to go.\nDownloading the Library # First, clone the repository, either from VS Code or the command line:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est You\u0026rsquo;ll need to initialize the submodules from the command line after the repo is cloned:\ngit submodule update --init Installation # When you open the folder in VS Code, you will like be prompted by the CMake Tools extension to configure the project. Make sure you select the kit (you\u0026rsquo;ll be prompted when you configure\u0026ndash;else there\u0026rsquo;s an icon in the bar on the bottom of the window or type Ctrl+Shift+P, then \u0026ldquo;cmake select kit\u0026rdquo;). Choose Clang [latest version] with GNU CLI ... amd64 assuming you are running a 64-bit OS. (MSVC may work okay too if you don\u0026rsquo;t need to build Python bindings.)\nFollow along with the \u0026ldquo;Getting Started\u0026rdquo; instructions, but where you see config options specified as -DLDSCTREST_BUILD_STATIC=OFF or -DPython3_ROOT_DIR=..., you will enter those in settings: open with Ctrl+,, click \u0026ldquo;workspace\u0026rdquo;, then search for \u0026ldquo;CMake: Configure Args\u0026rdquo; and enter each of your desired arguments as a separate item.\nTo configure, use Ctrl+Shift+P and search for the \u0026ldquo;CMake: Configure\u0026rdquo; command. To build, click the \u0026ldquo;Build\u0026rdquo; button on the bottom bar. Then click the \u0026ldquo;CTest\u0026rdquo; button to run the example scripts.\nConsiderations # Development on Windows has been more prone to bugs than on Unix systems, so if you encounter many problems, consider switching—WSL (Windows Subsystem for Linux) is a good option for Windows users who don\u0026rsquo;t want to work on a different machine.\nCompilation has been successfully tested in VS Code using the following kit, using the \u0026ldquo;RelWithDebInfo\u0026rdquo; config:\nClang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64) Troubleshooting # The build appears to work, but tests fail with code 0xc0000135 OR \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; Have you installed the library? In VS Code, use Shift+F7 to build a specific target, in this case INSTALL. If that doesn\u0026rsquo;t solve your problem, you will likely need to add the build or install folder to your PATH environment variable, which you can do using the settings GUI (search for \u0026ldquo;Edit the system environment variables\u0026rdquo;).\nOn Windows, \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted. Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":5,"href":"/lds-ctrl-est/issues-contributing/","title":"Issues Contributing","section":"LDS Control \u0026 Estimation","content":" Reporting Issues # If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.\nContributing # We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.\n"},{"id":6,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/","title":"armamexc","section":"Namespaces","content":" armamexc # arma/mex interface using Matlab C API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; T m2T_scalar(const mxArray * matlab_scalar)\nConvert Matlab mxArray to scalar of type T. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)\nConvert matlab matrix to armadillo. template \u0026lt;typename T \u0026gt; mxArray * a2m_mat(arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat)\nConvert armadillo to matlab matrix. template \u0026lt;typename T \u0026gt; mxArray * a2m_vec(arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec)\nConvert armadillo to matlab vector. Detailed Description # Utilities for arma/mex interface using Matlab C API\nFunction Details # m2T_scalar # template \u0026lt;class T \u0026gt; inline T m2T_scalar( const mxArray * matlab_scalar ) Parameters:\nmatlab_scalar matlab scalar Template Parameters:\nT type Return: scalar of type T\nm2a_mat # template \u0026lt;class T \u0026gt; inline arma::Mat\u0026lt; T \u0026gt; m2a_mat( const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true ) Parameters:\nmatlab_mat matlab matrix copy_aux_mem [optional] whether to copy auxiliary memory strict [optional] strictly enforce the above Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_mat( arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat ) Parameters:\narma_mat armadillo matrix Return: matlab matrix\na2m_vec # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_vec( arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec ) Parameters:\narma_vec armadillo vector Return: matlab vector\nUpdated on 5 March 2025 at 21:11:27 EST\n"},{"id":7,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/","title":"armamexcpp","section":"Namespaces","content":" armamexcpp # arma/mex interface using Matlab C++ API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat(matlab::data::CellArray \u0026amp; matlab_cell)\nConvert matlab cell array to vector of armadillo matrices. template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array)\nConvert matlab matrix to a vector of scalars. template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo vector. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat(const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec(const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab vector. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec(const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert vector of scalar T to matlab matrix. Detailed Description # utilities for arma/mex interface using Matlab C++ API\nFunction Details # m2a_cellmat # template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat( matlab::data::CellArray \u0026amp; matlab_cell ) Parameters:\nmatlab_cell matlab cell Template Parameters:\nT type Return: vector of armadillo matrices of type T\nm2s_vec # template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: vector of type T\nm2a_vec # template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: armadillo vector of type T\nm2a_mat # template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab matrix Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat( const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_mat arma matrix factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\na2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec( const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_vec armadillo vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\ns2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec( const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\nstd_vec standard vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\nUpdated on 5 March 2025 at 21:11:27 EST\n"},{"id":8,"href":"/lds-ctrl-est/docs/terminology/control-estimation/","title":"C\u0026E","section":"LDS C+E Documentation","content":" Control \u0026amp; Estimation # The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.\nState estimation # In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by \\[\\widehat{\\mathbf{x}}_{t|t-1} = \\mathbf{A}\\widehat{\\mathbf{x}}_{t-1|t-1} \u0026#43; \\mathbf{B} u_{t-1} \u0026#43; \\mathbf{m}_{t-1} \\;,\\] \\[\\widehat{\\mathbf{x}}_{t|t} = \\widehat{\\mathbf{x}}_{t|t-1} \u0026#43; \\mathbf{K}^{\\rm e}_t \\left(\\mathbf{z}_t - \\widehat{\\mathbf{y}}_{t|t-1}\\right)\\;,\\] where \\( \\hat{\\left(\\cdot\\right)}_{t|j} \\) indicates an estimate at time \\( t \\) given data up to time \\( j \\) inclusive, \\( \\mathbf{K}^{\\rm e} \\) is the estimator gain, and\n\\[ \\widehat{\\mathbf{y}}_{t|t-1} = h\\left( \\widehat{\\mathbf{x}}_{t|t-1} \\right) \\; .\\] In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.\nIn the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.\nAdaptive estimation of process disturbance # Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.\nWhen parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk \\[\\mathbf{m}_{t} = \\mathbf{m}_{t-1} \u0026#43; \\mathbf{w}^m_{t-1} \\;,\\] where \\( \\mathbf{w}^m \\sim \\mathcal{N}\\left(0, \\mathbf{Q}_m\\right)\\) . Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.\nControl # Given the estimated state, the controller updates the inputs to the system according to the following law: \\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] where \\( \\left( \\cdot \\right)^{\\rm ref} \\) correspond to reference/target signals and \\( \\mathbf{K}^c_x \\) is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.\nIf users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as\n\\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right) - \\mathbf{K}^c_{\\rm inty} \\sum_{j=1}^{t}\\left( \\widehat{\\mathbf{y}}_j - \\mathbf{y}^{\\rm ref}_j \\right) \\;.\\] An additional option available to users is a control law that updates the change in u,\n\\[\\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\left(\\mathbf{u}_{t-1} - \\mathbf{u}^{\\rm ref}_{t-1} \\right) - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] \\[\\mathbf{u}_{t} = \\mathbf{u}_{t-1} \u0026#43; \\Delta\\mathbf{u}_{t} \\; .\\] Notice that this takes the form of a first-order difference equation for updating control (i.e., \\( \\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\mathbf{u}_{t-1} \u0026#43; \\epsilon_{t-1} \\) ), effectively low-pass filtering the input depending on the characteristics of \\( \\mathbf{K}^c_u \\) . This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.\nIntegral action and the \\( \\Delta \\mathbf{u} \\) control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR\u0026rsquo;d to use in combination.\nCalculating reference state-control from output # In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce \\( \\mathbf{x}^{\\rm ref} \\) and \\( \\mathbf{u}^{\\rm ref} \\) . Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.\n"},{"id":9,"href":"/lds-ctrl-est/docs/api/classes/","title":"Classes","section":"LDS C+E Documentation","content":" Classes # lds::Controller\nlds::EM\nlds::Fit LDS Fit Type.\nlds::SSID\nlds::SwitchedController SwitchedController Type.\nlds::System Linear Dynamical System Type.\nlds::UniformMatrixList\nlds::UniformSystemList\nlds::UniformVectorList\nlds::gaussian::Controller Gaussian-observation Controller Type.\nlds::gaussian::Fit GLDS Fit Type.\nlds::gaussian::FitEM GLDS E-M Fit Type.\nlds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.\nlds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.\nlds::gaussian::System Gaussian LDS Type.\nlds::poisson::Controller PLDS Controller Type.\nlds::poisson::Fit PLDS Fit Type.\nlds::poisson::FitEM PLDS E-M Fit Type.\nlds::poisson::FitSSID Subspace Identification (SSID) for PLDS.\nlds::poisson::SwitchedController Poisson-observation SwitchedController Type.\nlds::poisson::System Poisson System type.\nUpdated on 5 March 2025 at 21:11:28 EST\n"},{"id":10,"href":"/lds-ctrl-est/docs/api/modules/group__control__masks/","title":"Control Mode Bit Masks","section":"Modules","content":" Control Mode Bit Masks # provides fill types for constructing new armadillo vectors, matrices More\u0026hellip; Attributes # Name const std::size_t kControlTypeDeltaU control designed to penalize change in input const std::size_t kControlTypeIntY control using integral action const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m Detailed Description # Control mode bit masks. These can be bit-wise OR\u0026rsquo;d to use in combination.\nAttribute Details # kControlTypeDeltaU # static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY # static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM # static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\nUpdated on 5 March 2025 at 21:11:28 EST\n"},{"id":11,"href":"/lds-ctrl-est/docs/api/modules/group__defaults/","title":"Defaults","section":"Modules","content":" Defaults # More\u0026hellip; Attributes # Name const data_t kDefaultP0 default state estimate covar const data_t kDefaultQ0 default process noise covar const data_t kDefaultR0 default output noise covar Detailed Description # Default values for common variables (e.g., default diagonal elements of covariances)\nAttribute Details # kDefaultP0 # static const data_t kDefaultP0 = 1e-6; kDefaultQ0 # static const data_t kDefaultQ0 = 1e-6; kDefaultR0 # static const data_t kDefaultR0 = 1e-2; Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":12,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/","title":"eg_glds_ctrl.cpp","section":"Examples","content":" eg_glds_ctrl.cpp # Example GLDS Control ```cpp\n//===\u0026ndash; eg_glds_ctrl.cpp - Example GLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS Control ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\n// output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false;\n// Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err\n// setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]);\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; }\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 21:11:28 EST "},{"id":13,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/","title":"eg_glds_du_plds_ctrl.cpp","section":"Examples","content":" eg_glds_du_plds_ctrl.cpp # Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u). ```cpp\n//===\u0026ndash; eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt);\n// to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU;\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_du_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 21:11:28 EST "},{"id":14,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/","title":"eg_plds_ctrl.cpp","section":"Examples","content":" eg_plds_ctrl.cpp # Example PLDS Control ```cpp\n//===\u0026ndash; eg_plds_ctrl.cpp - Example PLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Control ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt);\n// Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err\n// Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\nsize_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\nVector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled\u0026hellip; lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system);\n// for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type);\n// set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty);\n// to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0;\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros);\n// set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y();\nx_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x();\nm_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 21:11:28 EST "},{"id":15,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/","title":"eg_plds_est.cpp","section":"Examples","content":" eg_plds_est.cpp # Example PLDS Estimation ```cpp\n//===\u0026ndash; eg_plds_est.cpp - Example PLDS Estimation \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0);\nint main() { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.\n// construct ground truth system\u0026hellip; lds::poisson::System system_true(n_u, n_x, n_y, dt);\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset();\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt);\n// Can copy parameters from another system object system_estimator = system_true;\n// wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est);\n// set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true;\n// set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;estimator:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; system_estimator.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));\n// create matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros);\n// states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\nMatrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simlation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1));\n// Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simlation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows;\nif ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026ldquo;Q must be n x n.\u0026rdquo;); }\nMatrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); }\nreturn x; }\n_Filename: eg_plds_est.cpp_ ------------------------------- Updated on 5 March 2025 at 21:11:28 EST "},{"id":16,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/","title":"eg_plds_switched_ctrl.cpp","section":"Examples","content":" eg_plds_switched_ctrl.cpp # Example Switched PLDS Control ```cpp\n//===\u0026ndash; eg_plds_switched_ctrl.cpp - Example Switched PLDS Control \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026quot;;\n// whether to do switched control bool do_switch_ctrl = true;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt);\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices. data_t scale_sys_b = 2;\nMatrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));\ncontrolled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions\n// reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);\n// Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system);\n// set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys1:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys2:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x));\nswitched_controller.set_y_ref(y_ref0);\nstd::vectorlds::poisson::System systems_vec(3, lds::poisson::System()); lds::UniformSystemListlds::poisson::System systems(std::move(systems_vec));\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;switched_controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; switched_controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Fake measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros);\n// create Matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);\n// modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones);\n// set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } }\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace)); mode.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;mode\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_switched_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 21:11:28 EST "},{"id":17,"href":"/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/","title":"examples","section":"Files","content":" examples # Files # Name examples/eg_glds_ctrl.cpp examples/eg_glds_du_plds_ctrl.cpp examples/eg_plds_ctrl.cpp examples/eg_plds_est.cpp examples/eg_plds_switched_ctrl.cpp Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":18,"href":"/lds-ctrl-est/docs/api/examples/","title":"Examples","section":"LDS C+E Documentation","content":" Examples # eg_glds_ctrl.cpp Example GLDS Control.\neg_glds_du_plds_ctrl.cpp Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).\neg_plds_ctrl.cpp Example PLDS Control.\neg_plds_est.cpp Example PLDS Estimation.\neg_plds_switched_ctrl.cpp Example Switched PLDS Control.\nUpdated on 5 March 2025 at 21:11:28 EST\n"},{"id":19,"href":"/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/","title":"examples/eg_glds_ctrl.cpp","section":"Files","content":" examples/eg_glds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":20,"href":"/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/","title":"examples/eg_glds_du_plds_ctrl.cpp","section":"Files","content":" examples/eg_glds_du_plds_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU; // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":21,"href":"/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/","title":"examples/eg_plds_ctrl.cpp","section":"Files","content":" examples/eg_plds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err // Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled... lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type); // set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":22,"href":"/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/","title":"examples/eg_plds_est.cpp","section":"Files","content":" examples/eg_plds_est.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name Matrix random_walk(size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0) int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # random_walk # Matrix random_walk( size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0 ) main # int main() Source code # //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. // construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state // Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. // turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; } Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":23,"href":"/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/","title":"examples/eg_plds_switched_ctrl.cpp","section":"Files","content":" examples/eg_plds_switched_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Source code # //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 // simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions // reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":24,"href":"/lds-ctrl-est/docs/api/files/","title":"Files","section":"LDS C+E Documentation","content":" Files # examples/eg_glds_ctrl.cpp\nexamples/eg_glds_du_plds_ctrl.cpp\nexamples/eg_plds_ctrl.cpp\nexamples/eg_plds_est.cpp\nexamples/eg_plds_switched_ctrl.cpp\nldsCtrlEst_h/lds.h lds namespace\nldsCtrlEst_h/lds_ctrl.h Controller.\nldsCtrlEst_h/lds_fit.h LDS base fit type.\nldsCtrlEst_h/lds_fit_em.h subspace identification\nldsCtrlEst_h/lds_fit_ssid.h subspace identification\nldsCtrlEst_h/lds_gaussian.h glds namespace\nldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.\nldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.\nldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.\nldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.\nldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.\nldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.\nldsCtrlEst_h/lds_poisson.h plds namespace\nldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.\nldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.\nldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.\nldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.\nldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.\nldsCtrlEst_h/lds_poisson_sys.h PLDS base type.\nldsCtrlEst_h/lds_sctrl.h SwitchedController type.\nldsCtrlEst_h/lds_sys.h LDS base type.\nldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.\nldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.\nldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.\nldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)\nldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)\nsrc/lds.cpp misc lds namespace functions\nsrc/lds_gaussian_sys.cpp GLDS base type.\nsrc/lds_poisson_sys.cpp PLDS base type.\nsrc/lds_sys.cpp LDS base type.\nsrc/lds_uniform_vecs.cpp Uniformly sized vectors.\nUpdated on 5 March 2025 at 21:11:28 EST\n"},{"id":25,"href":"/lds-ctrl-est/docs/tutorials/eg_glds_control/","title":"GLDS Control","section":"LDS C+E Examples","content":" GLDS Control Tutorial # This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\] \\[\ry_{t} = x_t\r\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.\n/// Going to simulate a switching disturbance (m) acting on system size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_y).fill(m_low); Finally, assign the parameters using corresponding set-methods.\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); Creating the controller # Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.\nFor the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.\n// make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nWith the controller constructed, control variables may be set.\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.\nThe control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller\u0026rsquo;s online estimates of the output, state, and disturbance are given in purple.\n"},{"id":26,"href":"/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/","title":"include","section":"Files","content":" include # Directories # Name ldsCtrlEst_h Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":27,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds/","title":"lds","section":"Namespaces","content":" lds # Linear Dynamical Systems (LDS) namespace. Namespaces # Name lds::gaussian Linear Dynamical Systems with Gaussian observations. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::Controller class lds::EM class lds::Fit LDS Fit Type. class lds::SSID class lds::SwitchedController SwitchedController Type. class lds::System Linear Dynamical System Type. class lds::UniformMatrixList class lds::UniformSystemList class lds::UniformVectorList Types # Name enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}\nweighting options for SSID enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2} using double data_t using arma::Col\u0026lt; data_t \u0026gt; Vector using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Cube\u0026lt; data_t \u0026gt; Cube using arma::subview\u0026lt; data_t \u0026gt; View Functions # Name void Limit(std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub) void Limit(Vector \u0026amp; x, data_t lb, data_t ub) void Limit(Matrix \u0026amp; x, data_t lb, data_t ub) void Reassign(Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Vector in place void Reassign(Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Matrix in place void ForceSymPD(Matrix \u0026amp; X)\nforces matrix to be symmetric positive-definite void ForceSymMinEig(Matrix \u0026amp; X, data_t eig_min =0)\nforces matrix to be symmetric and have a minimum eigenvalue void lq(Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X)\nLQ decomposition. Matrix calcCov(const Matrix \u0026amp; A, const Matrix \u0026amp; B)\nCalculate covariance matrix. Attributes # Name const data_t kInf Some useful numbers. const data_t kPi Type Details # SSIDWt # Enumerator Value Description kSSIDNone None. kSSIDMOESP MOESP (AKA \u0026ldquo;robust method\u0026rdquo; in van Overschee 1996) kSSIDCVA CVA \u0026ldquo;Canonical Variate Analysis\u0026rdquo;. Weighting options for singular value decomposition performed during subspace identification (SSID)\nReference:\nvan Overschee, de Moor. 1996. Subspace Identification for Linear Systems.\nMatrixListFreeDim # Enumerator Value Description kMatFreeDimNone neither dim free to be hetero in mat list kMatFreeDim1 allow 1st dim of mats in list to be hetero kMatFreeDim2 allow 2nd dim of mats in list to be hetero data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nVector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Cube # using lds::Cube = arma::Cube\u0026lt;data_t\u0026gt;; View # using lds::View = arma::subview\u0026lt;data_t\u0026gt;; Function Details # Limit # inline void Limit( std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Vector \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Matrix \u0026amp; x, data_t lb, data_t ub ) Reassign # inline void Reassign( Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Vector other other Vector parenthetical optional description provided by caller to ease debugging Reassign # inline void Reassign( Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Matrix other other Matrix parenthetical optional description provided by caller to ease debugging ForceSymPD # void ForceSymPD( Matrix \u0026amp; X ) Parameters:\nX mutated matrix ForceSymMinEig # void ForceSymMinEig( Matrix \u0026amp; X, data_t eig_min =0 ) Parameters:\nX mutated matrix eig_min [optional] minimum eigen value lq # void lq( Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X ) Parameters:\nL lower triangle matrix Qt orthonormal matrix (transposed cf QR decomp) X matrix being decomposed calcCov # Matrix calcCov( const Matrix \u0026amp; A, const Matrix \u0026amp; B ) Parameters:\nA some matrix B some other matrix Return: covariance\nAttribute Details # kInf # static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); kPi # static const data_t kPi = arma::datum::pi; Updated on 5 March 2025 at 21:11:27 EST\n"},{"id":28,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/","title":"lds::Controller","section":"Classes","content":" lds::Controller # More\u0026hellip;\nInherited by lds::SwitchedController\u0026lt; System \u0026gt;, lds::gaussian::Controller, lds::poisson::Controller\nPublic Functions # Name Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes # Name System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::Controller; Public Function Details # Controller # Controller() =default Controller # inline Controller( const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Controller # inline Controller( System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Control # inline const Vector \u0026amp; Control( const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step). This is the most flexible option, but requires user to have set the controller\u0026rsquo;s y_ref, x_ref, and u_ref variables.\nControlOutputReference # inline const Vector \u0026amp; ControlOutputReference( const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_estimation [optional] whether to update state estimate (if false, effectively open-loop control) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.\nsys # inline const System \u0026amp; sys() const Kc # inline const Matrix \u0026amp; Kc() const Kc_inty # inline const Matrix \u0026amp; Kc_inty() const Kc_u # inline const Matrix \u0026amp; Kc_u() const g_design # inline const Vector \u0026amp; g_design() const u_ref # inline const Vector \u0026amp; u_ref() const x_ref # inline const Vector \u0026amp; x_ref() const y_ref # inline const Vector \u0026amp; y_ref() const control_type # inline size_t control_type() const tau_awu # inline data_t tau_awu() const u_lb # inline data_t u_lb() const u_ub # inline data_t u_ub() const set_sys # inline void set_sys( const System \u0026amp; sys ) set_g_design # inline void set_g_design( const Vector \u0026amp; g_design ) set_u_ref # inline void set_u_ref( const Vector \u0026amp; u_ref ) set_x_ref # inline void set_x_ref( const Vector \u0026amp; x_ref ) set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) Reimplemented by: lds::gaussian::Controller::set_y_ref, lds::gaussian::SwitchedController::set_y_ref, lds::poisson::Controller::set_y_ref, lds::poisson::SwitchedController::set_y_ref\nset_Kc # inline void set_Kc( const Matrix \u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const Matrix \u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const Matrix \u0026amp; Kc_u ) set_tau_awu # inline void set_tau_awu( data_t tau ) set_control_type # inline void set_control_type( size_t control_type ) Parameters:\ncontrol_type control type bit mask Template Parameters:\nSystem type derived from lds::System set_u_lb # inline void set_u_lb( data_t u_lb ) Parameters:\nu_lb control lower bound set_u_ub # inline void set_u_ub( data_t u_ub ) Parameters:\nu_ub control upper bound Reset # inline void Reset() Print # inline void Print() Protected Attribute Details # sys_ # System sys_; u_ # Vector u_; u_return_ # Vector u_return_; g_design_ # Vector g_design_; u_ref_ # Vector u_ref_; u_ref_prev_ # Vector u_ref_prev_; x_ref_ # Vector x_ref_; y_ref_ # Vector y_ref_; cx_ref_ # Vector cx_ref_; Kc_ # Matrix Kc_; Kc_u_ # Matrix Kc_u_; Kc_inty_ # Matrix Kc_inty_; du_ref_ # Vector du_ref_; dv_ref_ # Vector dv_ref_; v_ref_ # Vector v_ref_; dv_ # Vector dv_; v_ # Vector v_; int_e_ # Vector int_e_; int_e_awu_adjust_ # Vector int_e_awu_adjust_; u_sat_ # Vector u_sat_; do_control_prev_ # bool do_control_prev_ = false; do_lock_control_prev_ # bool do_lock_control_prev_ = false; u_saturated_ # bool u_saturated_ = false; u_lb_ # data_t u_lb_ {}; u_ub_ # data_t u_ub_ {}; tau_awu_ # data_t tau_awu_ {}; k_awu_ # data_t k_awu_ = 0; t_since_control_onset_ # data_t t_since_control_onset_ = 0; control_type_ # size_t control_type_ {}; Updated on 5 March 2025 at 21:11:27 EST\n"},{"id":29,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/","title":"lds::EM","section":"Classes","content":" lds::EM # More\u0026hellip;\nInherited by lds::gaussian::FitEM, lds::poisson::FitEM\nPublic Functions # Name EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions # Name void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() virtual void MaximizeOutput() =0 virtual void MaximizeMeasurement() =0 void Smooth(bool force_common_initial)\nget smoothed estimates virtual void RecurseKe(Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t) =0\nrecursively update estimator gain Ke void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::EM; Public Function Details # EM # EM() =default EM # EM( size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nn_x number of states dt sample period u_train input training data z_train measurement training data EM # EM( const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nfit0 initial fit u_train input training data z_train measurement training data ~EM # virtual ~EM() =default Run # const Fit \u0026amp; Run( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2 ) Parameters:\ncalc_dynamics [optional] whether to calculate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law max_iter max number of iterations tol convergence tolerance (max fractional abs change) Return: Fit\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nx # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const y # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const sum_E_x_t_x_t # inline const Matrix \u0026amp; sum_E_x_t_x_t() const sum_E_xu_tm1_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const sum_E_xu_t_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const n_t_tot # inline size_t n_t_tot() theta # inline const Vector \u0026amp; theta() const Protected Function Details # Expectation # void Expectation( bool force_common_initial =false ) Parameters:\nforce_common_initial whether to force common initial condition for all trials Maximization # void Maximization( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false ) Parameters:\ncalc_dynamics [optional] whether to caclulate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law MaximizeDynamics # void MaximizeDynamics() MaximizeQ # void MaximizeQ() MaximizeInitial # void MaximizeInitial() MaximizeOutput # virtual void MaximizeOutput() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput\nMaximizeMeasurement # virtual void MaximizeMeasurement() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement\nSmooth # void Smooth( bool force_common_initial ) Parameters:\nforce_common_initial whether to force common initial conditions RecurseKe # virtual void RecurseKe( Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t ) =0 Parameters:\nKe estimator gain P_pre cov of predicted state est. P_post cov of postior sate est. t time Reimplemented by: lds::gaussian::FitEM::RecurseKe, lds::poisson::FitEM::RecurseKe\nReset # void Reset() InitVars # void InitVars() UpdateTheta # Vector UpdateTheta() Return: parameter list\nProtected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; x_ # std::vector\u0026lt; Matrix \u0026gt; x_; P_ # std::vector\u0026lt; Cube \u0026gt; P_; P_t_tm1_ # std::vector\u0026lt; Cube \u0026gt; P_t_tm1_; y_ # std::vector\u0026lt; Matrix \u0026gt; y_; diag_y_ # Matrix diag_y_; sum_E_x_t_x_t_ # Matrix sum_E_x_t_x_t_; sum_E_xu_tm1_xu_tm1_ # Matrix sum_E_xu_tm1_xu_tm1_; sum_E_xu_t_xu_tm1_ # Matrix sum_E_xu_t_xu_tm1_; fit_ # Fit fit_; theta_ # Vector theta_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; Updated on 5 March 2025 at 21:11:27 EST\n"},{"id":30,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/","title":"lds::Fit","section":"Classes","content":" lds::Fit # LDS Fit Type. #include \u0026lt;lds_fit.h\u0026gt;\nInherited by lds::gaussian::Fit, lds::poisson::Fit\nPublic Functions # Name Fit() =default\nConstructs a new Fit. Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias virtual const Matrix \u0026amp; R() const =0 void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance virtual void set_R(const Matrix \u0026amp; R) =0\nsets output noise covariance (if any) void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) =0\noutput function Protected Attributes # Name data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period ~Fit # virtual ~Fit() =default n_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const m # inline const Vector \u0026amp; m() const Q # inline const Matrix \u0026amp; Q() const x0 # inline const Vector \u0026amp; x0() const P0 # inline const Matrix \u0026amp; P0() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const R # virtual const Matrix \u0026amp; R() const =0 Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R\nset_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_g # inline void set_g( const Vector \u0026amp; g ) set_m # inline void set_m( const Vector \u0026amp; m ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # virtual void set_R( const Matrix \u0026amp; R ) =0 Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R\nset_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) f # inline View f( Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t ) Parameters:\nx state estimate (over time) u input (over time) t time index Return: view of updated state\nf # inline View f( Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t ) Parameters:\nx_pre predicted state est. x_post posterior state est. u input (over time) t time index Return: view of predicted state\nh # virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) =0 Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplemented by: lds::gaussian::Fit::h, lds::poisson::Fit::h\nProtected Attribute Details # dt_ # data_t dt_ {}; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; m_ # Vector m_; Q_ # Matrix Q_; C_ # Matrix C_; d_ # Vector d_; R_ # Matrix R_; x0_ # Vector x0_; P0_ # Matrix P0_; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; Updated on 5 March 2025 at 21:11:27 EST\n"},{"id":31,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/","title":"lds::gaussian","section":"Namespaces","content":" lds::gaussian # Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. class lds::gaussian::Fit GLDS Fit Type. class lds::gaussian::FitEM GLDS E-M Fit Type. class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. class lds::gaussian::System Gaussian LDS Type. Updated on 5 March 2025 at 21:11:27 EST\n"},{"id":32,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/","title":"lds::gaussian::Controller","section":"Classes","content":" lds::gaussian::Controller # Gaussian-observation Controller Type. #include \u0026lt;lds_gaussian_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 21:11:27 EST\n"},{"id":33,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/","title":"lds::gaussian::Fit","section":"Classes","content":" lds::gaussian::Fit # GLDS Fit Type. #include \u0026lt;lds_gaussian_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual const Matrix \u0026amp; R() const override\ngets measurement noise covariance virtual void set_R(const Matrix \u0026amp; R) override\nsets measurement noise covariance virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period R # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nh # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nUpdated on 5 March 2025 at 21:11:27 EST\n"},{"id":34,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/","title":"lds::gaussian::FitEM","section":"Classes","content":" lds::gaussian::FitEM # GLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_gaussian_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::gaussian::FitEM; This type is used in the process of fitting GLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 21:11:27 EST\n"},{"id":35,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/","title":"lds::gaussian::FitSSID","section":"Classes","content":" lds::gaussian::FitSSID # Subspace Identification (SSID) for GLDS. #include \u0026lt;lds_gaussian_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 21:11:27 EST\n"},{"id":36,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/","title":"lds::gaussian::SwitchedController","section":"Classes","content":" lds::gaussian::SwitchedController # Gaussian-observation SwitchedController Type. #include \u0026lt;lds_gaussian_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 21:11:27 EST\n"},{"id":37,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/","title":"lds::gaussian::System","section":"Classes","content":" lds::gaussian::System # Gaussian LDS Type. #include \u0026lt;lds_gaussian_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)\nConstructs a new Gaussian System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. const Matrix \u0026amp; R() const\nGet output noise covariance. void set_Q(const Matrix \u0026amp; Q) void set_R(const Matrix \u0026amp; R)\nSet output noise covariance. void set_Ke(const Matrix \u0026amp; Ke)\nSet estimator gain. void set_Ke_m(const Matrix \u0026amp; Ke_m)\nSet disturbance estimator gain. void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively update estimator gain. Protected Attributes # Name Matrix R_ covariance of output noise bool do_recurse_Ke_ whether to recursively calculate estimator gain Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0 ) Parameters:\nn_u number of inputs (u) n_x number of states (x) n_y number of outputs (y) dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) r0 [optional] initial diagonal elements of output noise covariance (R) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nR # inline const Matrix \u0026amp; R() const set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # inline void set_R( const Matrix \u0026amp; R ) set_Ke # inline void set_Ke( const Matrix \u0026amp; Ke ) set_Ke_m # inline void set_Ke_m( const Matrix \u0026amp; Ke_m ) Print # void Print() Protected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nProtected Attribute Details # R_ # Matrix R_; do_recurse_Ke_ # bool do_recurse_Ke_ {}; Updated on 5 March 2025 at 21:11:27 EST\n"},{"id":38,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/","title":"lds::poisson","section":"Namespaces","content":" lds::poisson # Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. class lds::poisson::Fit PLDS Fit Type. class lds::poisson::FitEM PLDS E-M Fit Type. class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. class lds::poisson::System Poisson System type. Attributes # Name std::random_device rd random device for simulating poisson data std::mt19937 rng random number generator for simulating poisson data Attribute Details # rd # static std::random_device rd; rng # static std::mt19937 rng = std::mt19937( rd()); Updated on 5 March 2025 at 21:11:27 EST\n"},{"id":39,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/","title":"lds::poisson::Controller","section":"Classes","content":" lds::poisson::Controller # PLDS Controller Type. #include \u0026lt;lds_poisson_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 21:11:27 EST\n"},{"id":40,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/","title":"lds::poisson::Fit","section":"Classes","content":" lds::poisson::Fit # PLDS Fit Type. #include \u0026lt;lds_poisson_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function virtual void set_R(const Matrix \u0026amp; R) override\nsets output noise covariance (if any) virtual const Matrix \u0026amp; R() const override Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # inline Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period h # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nR # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nUpdated on 5 March 2025 at 21:11:27 EST\n"},{"id":41,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/","title":"lds::poisson::FitEM","section":"Classes","content":" lds::poisson::FitEM # PLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_poisson_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::poisson::FitEM; This type is used in the process of fitting PLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":42,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/","title":"lds::poisson::FitSSID","section":"Classes","content":" lds::poisson::FitSSID # Subspace Identification (SSID) for PLDS. #include \u0026lt;lds_poisson_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":43,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/","title":"lds::poisson::SwitchedController","section":"Classes","content":" lds::poisson::SwitchedController # Poisson-observation SwitchedController Type. #include \u0026lt;lds_poisson_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 21:11:28 EST\n"},{"id":44,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/","title":"lds::poisson::System","section":"Classes","content":" lds::poisson::System # Poisson System type. #include \u0026lt;lds_poisson_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nConstructs a new Poisson System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively recalculate estimator gain (Ke) Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nProtected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nRecursively recalculate estimator gain (Ke).\nReferences:\nSmith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.\nEden UT, \u0026hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.\nUpdated on 5 March 2025 at 21:11:28 EST\n"},{"id":45,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/","title":"lds::SSID","section":"Classes","content":" lds::SSID # More\u0026hellip;\nInherited by lds::gaussian::FitSSID, lds::poisson::FitSSID\nPublic Functions # Name SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions # Name void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. virtual void DecomposeData() =0\nDecompose data to lower-triangular matrix (used in Solve) void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::SSID; Public Function Details # SSID # SSID() =default SSID # SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf) ) Parameters:\nn_x number of states n_h size of block-hankel data matrix dt sample period u_train input training data z_train measurement training data d output bias Run # std::tuple\u0026lt; Fit, Vector \u0026gt; Run( SSIDWt ssid_wt ) Parameters:\nssid_wt weight for singular value decomp Return: tuple (Fit, singular values)\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nProtected Function Details # CalcD # void CalcD( data_t t_silence =0.1, data_t thresh_silence =0.001 ) Parameters:\nt_silence threshold on period of time that qualifies as \u0026ldquo;silence\u0026rdquo; thresh_silence threshold on input amplitude u that qualifies as \u0026ldquo;silence\u0026rdquo; CreateHankelDataMat # void CreateHankelDataMat() Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.\nDecomposeData # virtual void DecomposeData() =0 Reimplemented by: lds::gaussian::FitSSID::DecomposeData, lds::poisson::FitSSID::DecomposeData\nCalcSVD # void CalcSVD( SSIDWt wt ) Parameters:\nssid_wt weight for SVD Solve # void Solve( data_t wt_dc ) Parameters:\nwt_dc weight placed on getting correct DC I/O gain RecomputeExtObs # void RecomputeExtObs() Protected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; D_ # Matrix D_; fit_ # Fit fit_; g_dc_ # Matrix g_dc_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_h_ # size_t n_h_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; L_ # Matrix L_; s_ # Vector s_; ext_obs_t_ # Matrix ext_obs_t_; Updated on 5 March 2025 at 21:11:27 EST\n"},{"id":46,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/","title":"lds::SwitchedController","section":"Classes","content":" lds::SwitchedController # SwitchedController Type. More\u0026hellip;\n#include \u0026lt;lds_sctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nInherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController\nPublic Functions # Name SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes # Name std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::SwitchedController; Public Function Details # SwitchedController # SwitchedController() =default SwitchedController # inline SwitchedController( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask SwitchedController # inline SwitchedController( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Switch # inline void Switch( size_t idx, bool do_force_switch =false ) Parameters:\nidx index do_force_switch whether to force a system switch even if already there. set_Kc # inline void set_Kc( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc ) set_Kc # inline void set_Kc( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty ) set_Kc_inty # inline void set_Kc_inty( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u ) set_Kc_u # inline void set_Kc_u( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u ) set_g_design # inline void set_g_design( const UniformVectorList \u0026amp; g ) set_g_design # inline void set_g_design( UniformVectorList \u0026amp;\u0026amp; g ) Protected Attribute Details # systems_ # std::vector\u0026lt; System \u0026gt; systems_; n_sys_ # size_t n_sys_ {}; idx_ # size_t idx_ {}; Kc_list_ # UniformMatrixList Kc_list_; Kc_inty_list_ # UniformMatrixList Kc_inty_list_; Kc_u_list_ # UniformMatrixList Kc_u_list_; g_design_list_ # UniformVectorList g_design_list_; Updated on 5 March 2025 at 21:11:27 EST\n"},{"id":47,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_system/","title":"lds::System","section":"Classes","content":" lds::System # Linear Dynamical System Type. #include \u0026lt;lds_sys.h\u0026gt;\nInherited by lds::gaussian::System, lds::poisson::System\nPublic Functions # Name System() =default\nConstructs a new System. System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nconstructs a new System virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) =0\nsimulates system (single time step) void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function virtual void h() =0\nsystem output function virtual Vector h_(Vector x) =0\nsystem output function (stateless) size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void RecurseKe() =0\nRecursively recalculate estimator gain (Ke) void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes # Name bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes # Name std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 diagonal elements for state estimate covariance q0 diagonal elements for process noise covariance ~System # inline virtual ~System() Filter # void Filter( const Vector \u0026amp; u_tm1, const Vector \u0026amp; z ) Parameters:\nu_tm1 input at t-minus-1 z_t current measurement Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.\nSimulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) =0 Parameters:\nu_tm1 input at time t-1 Return: simulated measurement at time t\nReimplemented by: lds::gaussian::System::Simulate, lds::poisson::System::Simulate\nf # inline void f( const Vector \u0026amp; u, bool do_add_noise =false ) Parameters:\nu input do_add_noise whether to add simulated process noise h # virtual void h() =0 Reimplemented by: lds::gaussian::System::h, lds::poisson::System::h\nh_ # virtual Vector h_( Vector x ) =0 Parameters:\nx_t state at time t Return: predicted state at time t + 1\nReimplemented by: lds::gaussian::System::h_, lds::poisson::System::h_\nn_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const x # inline const Vector \u0026amp; x() const P # inline const Matrix \u0026amp; P() const m # inline const Vector \u0026amp; m() const P_m # inline const Matrix \u0026amp; P_m() const cx # inline const Vector \u0026amp; cx() const y # inline const Vector \u0026amp; y() const x0 # inline const Vector \u0026amp; x0() const m0 # inline const Vector \u0026amp; m0() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const Ke # inline const Matrix \u0026amp; Ke() const Ke_m # inline const Matrix \u0026amp; Ke_m() const Q # inline const Matrix \u0026amp; Q() Q_m # inline const Matrix \u0026amp; Q_m() P0 # inline const Matrix \u0026amp; P0() P0_m # inline const Matrix \u0026amp; P0_m() set_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_m # inline void set_m( const Vector \u0026amp; m, bool do_force_assign =false ) set_g # inline void set_g( const Vector \u0026amp; g ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_Q_m # inline void set_Q_m( const Matrix \u0026amp; Q_m ) set_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_P0_m # inline void set_P0_m( const Matrix \u0026amp; P0_m ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) set_x # inline void set_x( const Vector \u0026amp; x ) Reset # void Reset() nstep_pred_block # std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block( UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1 ) Print # void Print() Protected Function Details # RecurseKe # virtual void RecurseKe() =0 Reimplemented by: lds::gaussian::System::RecurseKe, lds::poisson::System::RecurseKe\nInitVars # void InitVars( data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Public Attribute Details # do_adapt_m # bool do_adapt_m {}; Protected Attribute Details # n_x_ # std::size_t n_x_ {}; n_u_ # std::size_t n_u_ {}; n_y_ # std::size_t n_y_ {}; dt_ # data_t dt_ {}; x_ # Vector x_; P_ # Matrix P_; m_ # Vector m_; P_m_ # Matrix P_m_; cx_ # Vector cx_; y_ # Vector y_; z_ # Vector z_; x0_ # Vector x0_; P0_ # Matrix P0_; m0_ # Vector m0_; P0_m_ # Matrix P0_m_; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; Q_ # Matrix Q_; Q_m_ # Matrix Q_m_; C_ # Matrix C_; d_ # Vector d_; Ke_ # Matrix Ke_; Ke_m_ # Matrix Ke_m_; Updated on 5 March 2025 at 21:11:27 EST\n"},{"id":48,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/","title":"lds::UniformMatrixList","section":"Classes","content":" lds::UniformMatrixList # More\u0026hellip;\nInherits from std::vector\u0026lt; Matrix \u0026gt;\nPublic Functions # Name UniformMatrixList() =default\nConstructs a new UniformMatrixList. UniformMatrixList(const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent. UniformMatrixList(std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent. UniformMatrixList(std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent. UniformMatrixList(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nConstructs a new UniformMatrixList (copy). UniformMatrixList(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nConstructs a new UniformMatrixList (move). ~UniformMatrixList() =default\nDestroys the object. const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim(size_t n =0) const\ngets dimensions of uniformly sized matrices size_t size()\nsize of container const Matrix \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Matrix \u0026amp; that, size_t n)\nswaps input matrix with n^th matrix of list UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nassigns the contents (copy) UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nassigns the contents (move) void append(const Matrix \u0026amp; mat)\nappends a matrix to the list Detailed Description # template \u0026lt;MatrixListFreeDim D =kMatFreeDimNone\u0026gt; class lds::UniformMatrixList; Public Function Details # UniformMatrixList # UniformMatrixList() =default UniformMatrixList # explicit UniformMatrixList( const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # explicit UniformMatrixList( std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList UniformMatrixList # UniformMatrixList( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList ~UniformMatrixList # ~UniformMatrixList() =default dim # inline const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim( size_t n =0 ) const Parameters:\nn [optional] index in list of matrices Return: dimensions\nsize # inline size_t size() at # inline const Matrix \u0026amp; at( size_t n ) Swap # inline void Swap( Matrix \u0026amp; that, size_t n ) Parameters:\nthat input matrix n index where the matrix is moved operator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\noperator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\nappend # void append( const Matrix \u0026amp; mat ) Parameters:\nmat input matrix Updated on 5 March 2025 at 21:11:27 EST\n"},{"id":49,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/","title":"lds::UniformSystemList","section":"Classes","content":" lds::UniformSystemList # More\u0026hellip;\nInherits from std::vector\u0026lt; System \u0026gt;\nPublic Functions # Name UniformSystemList() =default\nConstructs a new UniformSystemList. UniformSystemList(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by copying existing vector of System if dimensions consistent. UniformSystemList(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by moving existing vector of System if dimensions consistent. UniformSystemList(std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList from initializer_list of System if dimensions consistent. UniformSystemList(const UniformSystemList \u0026amp; that)\nConstructs a new UniformSystemList (copy). UniformSystemList(UniformSystemList \u0026amp;\u0026amp; that)\nConstructs a new UniformSystemList (move). ~UniformSystemList() =default\nDestroys the object. const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const\ngets dimensions of the uniformly sized systems size_t size()\nsize of container const System \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(System \u0026amp; that, size_t n)\nswaps input system with n^th system of list UniformSystemList \u0026amp; operator=(const UniformSystemList \u0026amp; that)\nassigns the contents (copy) UniformSystemList \u0026amp; operator=(UniformSystemList \u0026amp;\u0026amp; that)\nassigns the contents (move) Detailed Description # template \u0026lt;typename System \u0026gt; class lds::UniformSystemList; Public Function Details # UniformSystemList # UniformSystemList() =default UniformSystemList # explicit UniformSystemList( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # explicit UniformSystemList( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList UniformSystemList # UniformSystemList( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList ~UniformSystemList # ~UniformSystemList() =default dim # inline const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const size # inline size_t size() at # inline const System \u0026amp; at( size_t n ) Swap # inline void Swap( System \u0026amp; that, size_t n ) Parameters:\nthat input system n index where the system is moved operator= # inline UniformSystemList \u0026amp; operator=( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\noperator= # inline UniformSystemList \u0026amp; operator=( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\nUpdated on 5 March 2025 at 21:11:27 EST\n"},{"id":50,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/","title":"lds::UniformVectorList","section":"Classes","content":" lds::UniformVectorList # Inherits from std::vector\u0026lt; Vector \u0026gt;\nPublic Functions # Name UniformVectorList() =default\nConstructs a new UniformVectorList. UniformVectorList(const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent. UniformVectorList(std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent. UniformVectorList(std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0)\nConstructs a new UniformVectorList from initializer_list of Vector if dimensions consistent. UniformVectorList(const UniformVectorList \u0026amp; that)\nConstructs a new UniformVectorList (copy) UniformVectorList(UniformVectorList \u0026amp;\u0026amp; that)\nConstructs a new UniformVectorList (move) ~UniformVectorList() =default\nDestroys the object. size_t dim() const\ngets dimensions of the uniformly sized matrices size_t size()\nsize of container const Vector \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Vector \u0026amp; that, size_t n)\nswaps input matrix with n^th vector of list UniformVectorList \u0026amp; operator=(const UniformVectorList \u0026amp; that)\nassigns the contents (copy) UniformVectorList \u0026amp; operator=(UniformVectorList \u0026amp;\u0026amp; that)\nassigns the contents (move) Public Function Details # UniformVectorList # UniformVectorList() =default UniformVectorList # explicit UniformVectorList( const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dims dimension UniformVectorList # explicit UniformVectorList( std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList UniformVectorList # UniformVectorList( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList ~UniformVectorList # ~UniformVectorList() =default dim # inline size_t dim() const size # inline size_t size() at # inline const Vector \u0026amp; at( size_t n ) Swap # inline void Swap( Vector \u0026amp; that, size_t n ) Parameters:\nthat input vector n index where the vector is moved operator= # inline UniformVectorList \u0026amp; operator=( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\noperator= # inline UniformVectorList \u0026amp; operator=( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\nUpdated on 5 March 2025 at 21:11:27 EST\n"},{"id":51,"href":"/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/","title":"ldsCtrlEst_h","section":"Files","content":" ldsCtrlEst_h # Files # Name ldsCtrlEst_h/lds.h lds namespace ldsCtrlEst_h/lds_ctrl.h Controller. ldsCtrlEst_h/lds_fit.h LDS base fit type. ldsCtrlEst_h/lds_fit_em.h subspace identification ldsCtrlEst_h/lds_fit_ssid.h subspace identification ldsCtrlEst_h/lds_gaussian.h glds namespace ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller. ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type. ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type. ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type. ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type. ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type. ldsCtrlEst_h/lds_poisson.h plds namespace ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type. ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type. ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type. ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type. ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type. ldsCtrlEst_h/lds_poisson_sys.h PLDS base type. ldsCtrlEst_h/lds_sctrl.h SwitchedController type. ldsCtrlEst_h/lds_sys.h LDS base type. ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices. ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems. ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors. ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":52,"href":"/lds-ctrl-est/docs/api/files/lds__ctrl_8h/","title":"ldsCtrlEst_h/lds_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_ctrl.h # Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Controller Detailed Description # This file declares the type for control of a linear dynamical system (lds::Controller).\nSource code # //===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_CTRL_H #define LDSCTRLEST_LDS_CTRL_H // namespace #include \u0026#34;lds.h\u0026#34; // system type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class Controller { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); public: Controller() = default; Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); const Vector\u0026amp; Control(const Vector\u0026amp; z, bool do_control = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); const Vector\u0026amp; ControlOutputReference(const Vector\u0026amp; z, bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); // get methods: const System\u0026amp; sys() const { return sys_; }; const Matrix\u0026amp; Kc() const { return Kc_; }; const Matrix\u0026amp; Kc_inty() const { return Kc_inty_; }; const Matrix\u0026amp; Kc_u() const { return Kc_u_; }; const Vector\u0026amp; g_design() const { return g_design_; }; const Vector\u0026amp; u_ref() const { return u_ref_; }; const Vector\u0026amp; x_ref() const { return x_ref_; }; const Vector\u0026amp; y_ref() const { return y_ref_; }; size_t control_type() const { return control_type_; }; data_t tau_awu() const { return tau_awu_; }; data_t u_lb() const { return u_lb_; }; data_t u_ub() const { return u_ub_; }; // set methods void set_sys(const System\u0026amp; sys) { bool does_match = sys_.n_u() == sys.n_u(); does_match = does_match \u0026amp;\u0026amp; (sys_.n_x() == sys.n_x()); does_match = does_match \u0026amp;\u0026amp; (sys_.n_y() == sys.n_y()); if (does_match) { sys_ = sys; } else { throw std::runtime_error( \u0026#34;new system argument to `set_sys` does not match dimensionality of \u0026#34; \u0026#34;existing system\u0026#34;); } }; void set_g_design(const Vector\u0026amp; g_design) { Reassign(g_design_, g_design); }; void set_u_ref(const Vector\u0026amp; u_ref) { Reassign(u_ref_, u_ref); }; void set_x_ref(const Vector\u0026amp; x_ref) { Reassign(x_ref_, x_ref); cx_ref_ = sys_.C() * x_ref_; }; // y_ref needs to be handled differently depending on output fn. // (need to populate cx_ref_ too, which depends on output fn) virtual void set_y_ref(const Vector\u0026amp; y_ref) { Reassign(y_ref_, y_ref); }; void set_Kc(const Matrix\u0026amp; Kc) { Reassign(Kc_, Kc); }; void set_Kc_inty(const Matrix\u0026amp; Kc_inty) { Reassign(Kc_inty_, Kc_inty); }; void set_Kc_u(const Matrix\u0026amp; Kc_u) { Reassign(Kc_u_, Kc_u); }; void set_tau_awu(data_t tau) { tau_awu_ = tau; k_awu_ = sys_.dt() / tau_awu_; }; void set_control_type(size_t control_type); // There is no reason u_lb/ub should not be public, but making set methods // anyway. void set_u_lb(data_t u_lb) { u_lb_ = u_lb; }; void set_u_ub(data_t u_ub) { u_ub_ = u_ub; }; void Reset() { sys_.Reset(); u_ref_.zeros(); u_ref_prev_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); u_saturated_ = false; t_since_control_onset_ = 0.0; }; void Print() { sys_.Print(); std::cout \u0026lt;\u0026lt; \u0026#34;g_design : \u0026#34; \u0026lt;\u0026lt; g_design_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_lb : \u0026#34; \u0026lt;\u0026lt; u_lb_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_ub : \u0026#34; \u0026lt;\u0026lt; u_ub_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }; protected: System sys_; Vector u_; Vector u_return_; Vector g_design_; // reference signals Vector u_ref_; // create no set method for this: Vector u_ref_prev_; Vector x_ref_; Vector y_ref_; Vector cx_ref_; // Controller gains Matrix Kc_; Matrix Kc_u_; Matrix Kc_inty_; // control after g inversion // do not need set methods for these. Vector du_ref_; Vector dv_ref_; Vector v_ref_; Vector dv_; Vector v_; // integral error // do not need set method for this Vector int_e_; Vector int_e_awu_adjust_; Vector u_sat_; bool do_control_prev_ = false; bool do_lock_control_prev_ = false; // whether the g of system has become inverted from what you think it is // (gain_ref) bool u_saturated_ = false; // should be safe to have references here bc nothing needs to be done // (like reset vars) when it changes... data_t u_lb_{}; data_t u_ub_{}; data_t tau_awu_{}; data_t k_awu_ = 0; data_t t_since_control_onset_ = 0; size_t control_type_{}; private: void CalcControl(bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); void CalcSteadyStateSetPoint(); void AntiWindup(); void InitVars(size_t control_type); }; // Implement the above: template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(sys), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(std::move(sys)), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::set_control_type(size_t control_type) { if (control_type_ == control_type) { return; } // creating a blank slate... control_type_ = 0; Kc_inty_.zeros(0, 0); Kc_u_.zeros(0, 0); int_e_.zeros(0, 0); int_e_awu_adjust_.zeros(0, 0); // controller was designed to minimize integral error if (control_type \u0026amp; kControlTypeIntY) { Kc_inty_.zeros(sys_.n_u(), sys_.n_y()); int_e_.zeros(sys_.n_y()); int_e_awu_adjust_.zeros(sys_.n_u()); control_type_ = control_type_ | kControlTypeIntY; } // controller was designed to minimize deltaU // (i.e. state augmented with u) if (control_type \u0026amp; kControlTypeDeltaU) { Kc_u_.zeros(sys_.n_u(), sys_.n_u()); control_type_ = control_type_ | kControlTypeDeltaU; } // whether to adapt set point calculate with (re-estimated) process // disturbance (m) if (control_type \u0026amp; kControlTypeAdaptM) { if (sys_.do_adapt_m) // only if adapting m... { control_type_ = control_type_ | kControlTypeAdaptM; } } } // set_control_type template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::Control( const Vector\u0026amp; z, bool do_control, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement sys_.Filter(u_, z); bool do_estimation = true; // always have estimator on in this case // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::ControlOutputReference( const Vector\u0026amp; z, bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement if (do_estimation) { sys_.Filter(u_, z); } else { sys_.f(u_); } // calculate the set point // solves for u_ref and x_ref when output is at y_ref at steady state. if (do_control) { CalcSteadyStateSetPoint(); } // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcControl(bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { if (do_control \u0026amp;\u0026amp; do_estimation) { if (!do_control_prev_) { if (do_reset_at_control_onset) { Reset(); } t_since_control_onset_ = 0.0; } else { t_since_control_onset_ += sys_.dt(); } // enforce softstart on control vars. if (sigma_soft_start \u0026gt; 0) { // half-Gaussian soft-start scaling factor data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) / (2 * pow(sigma_soft_start, 2))); u_ref_ *= soft_start_sf; // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too // x_ref_ *= soft_start_sf; // cx_ref_ *= soft_start_sf; // y_ref_ *= soft_start_sf; } if (!do_lock_control) { // first do u -\u0026gt; v change of vars. (v = g.*u) // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver // control voltage u[=]V) v_ref_ = g_design_ % u_ref_; // Given FB, calc. the change in control if (control_type_ \u0026amp; kControlTypeDeltaU) { // if control designed to minimize not u but deltaU (i.e. state aug with // u): // TODO(mfbolus): Commented out for now. See note below. // du_ref_ = u_ref_ - u_ref_prev_; // dv_ref_ = g_design_ % du_ref_; // TODO(mfbolus): Assuming users want *smooth* control signals if using // kControlTypeDeltaU, it should be the case that dv_ref_ is --\u0026gt; 0. May // want to revisit, but I am going to force it to be zero unless a // situation arises that argues for keeping the above. dv_ref_.zeros(); dv_ = dv_ref_; // nominally-optimal. dv_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error dv_ -= Kc_u_ * (v_ - v_ref_); // penalty on amp u (rel to ref) if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if(!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error dv_ -= Kc_inty_ * int_e_; // control for integrated error } // update the control v_ += dv_; } else { v_ = v_ref_; // nominally-optimal. v_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if (!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error v_ -= Kc_inty_ * int_e_; // control for integrated error } } // convert back to control voltage u[=]V u_ = v_ / sys_.g(); } // else do nothing until lock is low } else { // if not control // feed through u_ref in open loop u_ = u_ref_ % g_design_ / sys_.g(); v_ = sys_.g() % u_; u_ref_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); } // ends do_control // enforce box constraints (and antiwindup) AntiWindup(); // add noise to input? // The value for u that is *returned* to user after addition of any noise, // while keeping controller/estimator blind to this addition. u_return_ = u_; if ((sigma_u_noise \u0026gt; 0.0) \u0026amp;\u0026amp; (do_control \u0026amp;\u0026amp; !do_lock_control)) { u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn); Limit(u_return_, u_lb_, u_ub_); }; // For next time step: u_ref_prev_ = u_ref_; do_control_prev_ = do_control; do_lock_control_prev_ = do_lock_control; } // CalcControl template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcSteadyStateSetPoint() { // Linearly-constrained least squares (ls). // // _reference: // Boyd \u0026amp; Vandenberghe (2018) Introduction to Applied Linear Algebra // Matrix a_ls = join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros)); Vector b_ls = cx_ref_; Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye), sys_.B() * arma::diagmat(sys_.g())); Vector d_ls = -sys_.m0(); if (control_type_ \u0026amp; kControlTypeAdaptM) { d_ls = -sys_.m(); // adapt setpoint calc with disturbance? } Matrix a_ls_t = a_ls.t(); // TODO(mfbolus): not sure why but causes seg // fault if I do not do this. Matrix phi_ls = join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()), join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros))); // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse: Matrix inv_phi = pinv(phi_ls); Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls); x_ref_ = xulam.subvec(0, sys_.n_x() - 1); u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1); cx_ref_ = sys_.C() * x_ref_; } // CalcSteadyStateSetPoint template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::AntiWindup() { u_saturated_ = false; u_sat_ = u_; // limit u and flag whether saturated for (size_t k = 0; k \u0026lt; u_.n_elem; k++) { if (u_[k] \u0026lt; u_lb_) { u_sat_[k] = u_lb_; u_saturated_ = true; } if (u_[k] \u0026gt; u_ub_) { u_sat_[k] = u_ub_; u_saturated_ = true; } } if ((control_type_ \u0026amp; kControlTypeIntY) \u0026amp;\u0026amp; (tau_awu_ \u0026lt; lds::kInf)) { // one-step back-calculation (calculate intE for u=u_sat) // (Astroem, Rundqwist 1989 warn against using this...) // int_e_awu_adjust_ = // solve(Kc_inty_, (u_ - u_sat_)); // pinv(Kc_inty) * (u-uSat); // gradual: see Astroem, Rundqwist 1989 // this is a fudge for doing MIMO gradual // n.b., went ahead and multiplied 1/T by dt so don\u0026#39;t have to do that here. int_e_awu_adjust_ = k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_); // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_); int_e_ += int_e_awu_adjust_; } // set u to saturated version u_ = u_sat_; } template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::InitVars(size_t control_type) { // initialize to default values u_ref_ = Vector(sys_.n_u(), fill::zeros); u_ref_prev_ = Vector(sys_.n_u(), fill::zeros); x_ref_ = Vector(sys_.n_x(), fill::zeros); y_ref_ = Vector(sys_.n_y(), fill::zeros); cx_ref_ = Vector(sys_.n_y(), fill::zeros); u_ = Vector(sys_.n_u(), fill::zeros); u_return_ = Vector(sys_.n_u(), fill::zeros); u_sat_ = Vector(sys_.n_u(), fill::zeros); // Might not need all these, so zero elements until later. Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros); Kc_u_ = Matrix(0, 0, fill::zeros); Kc_inty_ = Matrix(0, 0, fill::zeros); g_design_ = sys_.g(); // by default, same as model dv_ = Vector(sys_.n_u(), fill::zeros); v_ = Vector(sys_.n_u(), fill::zeros); du_ref_ = Vector(sys_.n_u(), fill::zeros); dv_ref_ = Vector(sys_.n_u(), fill::zeros); v_ref_ = Vector(sys_.n_u(), fill::zeros); int_e_ = Vector(0, fill::zeros); int_e_awu_adjust_ = Vector(0, fill::zeros); set_control_type(control_type); } } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":53,"href":"/lds-ctrl-est/docs/api/files/lds__fit__em_8h/","title":"ldsCtrlEst_h/lds_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_em.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::EM Detailed Description # This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).\nSource code # //===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_EMAX_H #define LDSCTRLEST_LDS_EMAX_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class EM { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: EM() = default; EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); virtual ~EM() = default; const Fit\u0026amp; Run(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = true, bool calc_output = true, bool calc_measurement = true, size_t max_iter = 100, data_t tol = 1e-2); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); // auto tuple = std::make_tuple(u_, z_); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; x() const { return x_; }; const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; y() const { return y_; }; const Matrix\u0026amp; sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; }; const Matrix\u0026amp; sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; }; const Matrix\u0026amp; sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; }; size_t n_t_tot() { return n_t_tot_; } const Vector\u0026amp; theta() const { return theta_; }; protected: void Expectation(bool force_common_initial = false); void Maximization(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = false, bool calc_output = false, bool calc_measurement = false); void MaximizeDynamics(); void MaximizeQ(); void MaximizeInitial(); virtual void MaximizeOutput() = 0; virtual void MaximizeMeasurement() = 0; void Smooth(bool force_common_initial); virtual void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) = 0; void Reset(); void InitVars(); Vector UpdateTheta(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; std::vector\u0026lt;Matrix\u0026gt; x_; std::vector\u0026lt;Cube\u0026gt; P_; std::vector\u0026lt;Cube\u0026gt; P_t_tm1_; std::vector\u0026lt;Matrix\u0026gt; y_; Matrix diag_y_; // expectations calculated in E-step Matrix sum_E_x_t_x_t_; Matrix sum_E_xu_tm1_xu_tm1_; Matrix sum_E_xu_t_xu_tm1_; Fit fit_; Vector theta_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; }; template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; fit_ = Fit(n_u_, n_x, n_y_, dt); u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { // make sure fit dims match I/O data if (fit0.n_u() != u_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and input training data have inconsistent dimensions\u0026#34;); } if (fit0.n_y() != z_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and output training data have inconsistent dimensions\u0026#34;); } fit_ = fit0; u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::InitVars() { // check input/output data dimensions are consistent if (z_.size() != u_.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_.at(trial).n_cols != u_.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } n_u_ = fit_.n_u(); n_x_ = fit_.n_x(); n_y_ = fit_.n_y(); dt_ = fit_.dt(); x_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); P_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); P_t_tm1_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); y_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros); P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros); } diag_y_ = Matrix(n_y_, n_y_, fill::zeros); // covariances in expectation step sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); } template \u0026lt;typename Fit\u0026gt; const Fit\u0026amp; EM\u0026lt;Fit\u0026gt;::Run(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement, size_t max_iter, data_t tol) { Reset(); // to initial conditions size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_; Vector theta(n_params); Vector theta_new(n_params); data_t max_dtheta = 1; // if solving for initial conditions, allow them be varied. // otherwise, freeze at provided values. bool force_common_initial = !calc_init; // go until parameter convergence for (size_t l = 0; l \u0026lt; max_iter; l++) { theta_ = UpdateTheta(); std::cout \u0026lt;\u0026lt; \u0026#34;Iteration \u0026#34; \u0026lt;\u0026lt; l + 1 \u0026lt;\u0026lt; \u0026#34;/\u0026#34; \u0026lt;\u0026lt; max_iter \u0026lt;\u0026lt; \u0026#34; ...\\n\u0026#34;; Expectation(force_common_initial); Maximization(calc_dynamics, calc_Q, calc_init, calc_output, calc_measurement); // check convergence theta_new = UpdateTheta(); Vector dtheta = abs(theta_new - theta_) / abs(theta_); // some parameters could be zero... arma::uvec ubi_finite = find_finite(dtheta); max_dtheta = max(dtheta.elem(ubi_finite)); std::cout \u0026lt;\u0026lt; \u0026#34;max dtheta: \u0026#34; \u0026lt;\u0026lt; max_dtheta \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; if (max_dtheta \u0026lt; tol) { std::cout \u0026lt;\u0026lt; \u0026#34;Converged.\\n\u0026#34;; break; } std::cout \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } return fit_; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Smooth(bool force_common_initial) { Matrix k_e(n_x_, n_y_); // estimator gain Cube k_backfilt; // back-filtering gains // TODO(mfbolus): this loop could be made parallel for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { Matrix x_pre(n_x_, n_t_[trial], fill::zeros); Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros); Matrix x_post(n_x_, n_t_[trial], fill::zeros); Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros); if (force_common_initial) // forces all trials to have same initial // conditions. { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); } y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); // This *should not* be necessary but make sure P is symmetric. ForceSymPD(P_[trial].slice(0)); x_pre.col(0) = x_[trial].col(0); p_pre.slice(0) = P_[trial].slice(0); x_post.col(0) = x_[trial].col(0); p_post.slice(0) = P_[trial].slice(0); // filter for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // predict fit_.f(x_pre, x_post, u_.at(trial), t); fit_.h(y_[trial], x_pre, t); diag_y_.diag() = y_[trial].col(t); // TODO(mfbolus): change if parallel // update --\u0026gt; posterior estimation RecurseKe(k_e, p_pre, p_post, t); x_post.col(t) = x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t)); y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d(); } // backfilter -\u0026gt; Smoothed estimate // Reference: // Shumway et Stoffer (1982) ForceSymPD(p_post.slice(n_t_[trial] - 1)); k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1); P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 0; t--) { // TODO(mfmbolus): should not be necessary to force symm positive def ForceSymPD(p_pre.slice(t)); ForceSymPD(p_post.slice(t - 1)); ForceSymPD(P_[trial].slice(t)); k_backfilt.slice(t - 1) = p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t)); x_[trial].col(t - 1) = x_post.col(t - 1) + k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t)); P_[trial].slice(t - 1) = p_post.slice(t - 1) + k_backfilt.slice(t - 1) * (P_[trial].slice(t) - p_pre.slice(t)) * k_backfilt.slice(t - 1).t(); } // do the same for P_t_tm1 Matrix id(n_x_, n_x_, fill::eye); P_t_tm1_[trial].slice(n_t_[trial] - 1) = (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 1; t--) { P_t_tm1_[trial].slice(t - 1) = p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() + k_backfilt.slice(t - 1) * (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) * k_backfilt.slice(t - 2).t(); } // finally, get smoothed estimate of output for (size_t t = 0; t \u0026lt; n_t_[trial]; t++) { fit_.h(y_[trial], x_[trial], t); } // samps loop } // trial loop } // Smooth // template \u0026lt;typename Fit\u0026gt; // void EM\u0026lt;Fit\u0026gt;::RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) { // // predict covar // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q(); // // update Ke // Ke = P_pre.slice(t) * fit_.C().t() * // inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R()); // // update cov // // Reference: Ghahramani et Hinton (1996) // P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t); // // // n.b. for poisson : // // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + // fit_.Q(); // // // update cov // // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ * // // fit_.C()); // // // update Ke // // Ke = P_post.slice(t) * fit_.C(); // } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Expectation(bool force_common_initial) { // calculate the mean/cov of state needed for maximizing E[pr(z|theta)] Smooth(force_common_initial); // now get the various forms of sum(E[xx\u0026#39;]) needed // n.b. Going to start at t=1 rather than 0 bc most max terms need that. // so really \u0026#34;n_t_tot_\u0026#34; is (n_t_tot_-1) n_t_tot_ = 0; sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); Vector xu_tm1(n_x_ + n_u_, fill::zeros); Vector xu_t(n_x_ + n_u_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // ------------ sum_E_x_t_x_t ------------ sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t(); sum_E_x_t_x_t_ += P_[trial].slice(t); // ------------ sum_E_xu_tm1_xu_tm1 ------------ xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1)); sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t(); sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t - 1); // ------------ sum_E_xu_t_xu_tm1 ------------ xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t)); sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t(); sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_t_tm1_[trial].slice(t); n_t_tot_ += 1; } // time } // trial } // Expectation template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement) { if (calc_output) { MaximizeOutput(); } if (calc_measurement) { MaximizeMeasurement(); } if (calc_dynamics) { MaximizeDynamics(); } if (calc_Q) { MaximizeQ(); } if (calc_init) { MaximizeInitial(); } } // Maximization template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeDynamics() { // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) * inv_sympd(sum_E_xu_tm1_xu_tm1_); fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1)); std::cout \u0026lt;\u0026lt; \u0026#34;A_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.A()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.B()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeQ() { // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 * // inv_sympd(sum_E_xu_tm1_xu_tm1_) * // sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // this way is same as above iff dynamics were just updated: // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix ab = arma::join_horiz(fit_.A(), fit_.B()); // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // From scratch method: // Q is covariance of the error between state and dynamics-predicted state // (aka process noise) // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})\u0026#39;] // t-1 terms: View sum_e_x_tm1_x_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_u_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1); View sum_e_x_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); // t, t-1 terms: View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_x_t_u_tm1 = sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); Matrix q = sum_E_x_t_x_t_; q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t(); q -= sum_e_x_t_x_tm1 * fit_.A().t(); q -= fit_.A() * sum_e_x_t_x_tm1.t(); // input-related terms: q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t(); q -= sum_e_x_t_u_tm1 * fit_.B().t(); q -= fit_.B() * sum_e_x_t_u_tm1.t(); q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t(); q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t(); q /= n_t_tot_; fit_.set_Q(q); std::cout \u0026lt;\u0026lt; \u0026#34;Q_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.Q()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // std::cout \u0026lt;\u0026lt; \u0026#34;Q_new: \\n\u0026#34; \u0026lt;\u0026lt; fit_.Q() \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeInitial() { Vector x0 = fit_.x0(); x0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { x0 += x_[trial].col(0); } x0 /= z_.size(); std::cout \u0026lt;\u0026lt; \u0026#34;x0_new[0]: \u0026#34; \u0026lt;\u0026lt; x0[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // always recalc P0 even if the initial state is fixed (at zero, for // example) Matrix e_var(n_x_, n_x_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t(); } e_var /= z_.size(); // go ahead and subtract x0*x0\u0026#39; so don\u0026#39;t have to below. e_var -= x0 * x0.t(); // To get P0, going to get initial P_ per trial and average. // (which might be wrong, but need a single number) Matrix p0 = fit_.P0(); p0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { p0 += (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var; } p0 /= z_.size(); fit_.set_P0(p0); std::cout \u0026lt;\u0026lt; \u0026#34;P0_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.P0()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeOutput() { // solve for C+d: Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros); Vector x1(n_x_ + 1, fill::zeros); x1[n_x_] = 1.0; // augment with one to solve for bias Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { x1.subvec(0, n_x_ - 1) = x_[trial].col(t); sum_zx += z_.at(trial).col(t) * x1.t(); sum_e_x1_x1 += x1 * x1.t(); sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t); } } Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1); fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1)); fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_))); std::cout \u0026lt;\u0026lt; \u0026#34;C_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.C()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.d()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeMeasurement() { // Solve for measurement noise covar size_t n_t_tot = 0; // Ghahgramani, Hinton 1996: Matrix sum_zz(n_y_, n_y_, fill::zeros); Matrix sum_yz(n_y_, n_y_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t(); // Use Cnew: sum_yz += (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t(); n_t_tot += 1; } } fit_.set_R((sum_zz - sum_yz) / n_t_tot); std::cout \u0026lt;\u0026lt; \u0026#34;R_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.R()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Reset() { // reset to initial conditions for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); } } template \u0026lt;typename Fit\u0026gt; Vector EM\u0026lt;Fit\u0026gt;::UpdateTheta() { // TODO(mfbolus): This should include n_y_ more params for d. size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_; if (fit_.R().n_elem \u0026gt; 0) { n_params += n_y_ * n_y_; } Vector theta(n_params); size_t idx_start = 0; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B()); idx_start += n_x_ * n_u_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0()); idx_start += n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C()); idx_start += n_y_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d()); idx_start += n_y_; if (fit_.R().n_elem \u0026gt; 0) { theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R()); } return theta; } } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":54,"href":"/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_ssid.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SSID Detailed Description # This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)\u0026lt;Fit\u0026gt;).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_FIT_SSID_H #define LDSCTRLEST_LDS_FIT_SSID_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class SSID { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: SSID() = default; SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d = Vector(1).fill(-kInf)); std::tuple\u0026lt;Fit, Vector\u0026gt; Run(SSIDWt ssid_wt); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } protected: void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001); void CreateHankelDataMat(); virtual void DecomposeData() = 0; void CalcSVD(SSIDWt wt); void Solve(data_t wt_dc); void RecomputeExtObs(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; Matrix D_; Fit fit_; Matrix g_dc_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_h_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; Matrix L_; Vector s_; Matrix ext_obs_t_; }; template \u0026lt;typename Fit\u0026gt; SSID\u0026lt;Fit\u0026gt;::SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d) { // check input/output data dimensions are consistent if (z_train.size() != u_train.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_train.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_train.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } dt_ = dt; n_x_ = n_x; n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; n_h_ = n_h; // dimensionality check for eventual block-hankel data matrix size_t len = n_t_tot_ - 2 * n_h_ + 1; if (len \u0026lt; (2 * n_h_ * (n_u_ + n_y_))) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;Dataset problem! More rows than columns in block-hankel data \u0026#34; \u0026#34;matrix: 2*(n_u+n_y)*n_h \u0026gt; data-length! Need higher data-length or \u0026#34; \u0026#34;lower n_h.\u0026#34;; throw std::runtime_error(ss.str()); } fit_ = Fit(n_u_, n_x_, n_y_, dt_); u_ = std::move(u_train); z_ = std::move(z_train); if (!d.is_finite() || (d.n_rows != n_y_)) { // TODO(mfbolus): implement least-square solution for impulse response with // a second input of ones. Data-driven way of accounting for offset *not* // driven by an input. // // For now, calculate output bias (d) as the // output wherever the stimulus has not been on for some amount of time. // convolve u with rectangle and take all samples. This is a reasonable // approach, since often when autonomous systems are fit (i.e., systems with // no input), they will subtract off the mean of the output. This // essentially amounts to setting output bias to the mean of the output when // there is no stimulation. data_t t_silence = 0.1; data_t thresh_silence = 0.001; CalcD(t_silence, thresh_silence); } else { fit_.set_d(d); } } template \u0026lt;typename Fit\u0026gt; std::tuple\u0026lt;Fit, Vector\u0026gt; SSID\u0026lt;Fit\u0026gt;::Run(SSIDWt ssid_wt) { // the weight on minimizing dc I/O gain only works for gaussian, // and hopefully not necessary with appropriate dataset. data_t wt_dc = 0; // std::cout \u0026lt;\u0026lt; \u0026#34;creating hankel mat\\n\u0026#34;; CreateHankelDataMat(); // std::cout \u0026lt;\u0026lt; \u0026#34;decomposing data\\n\u0026#34;; DecomposeData(); // std::cout \u0026lt;\u0026lt; \u0026#34;calculating svd\\n\u0026#34;; CalcSVD(ssid_wt); // std::cout \u0026lt;\u0026lt; \u0026#34;solving for params\\n\u0026#34;; Solve(wt_dc); // std::cout \u0026lt;\u0026lt; \u0026#34;fin\\n\u0026#34;; return std::make_tuple(fit_, s_); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcD(data_t t_silence, data_t thresh_silence) { Vector d(z_.at(0).n_rows, fill::zeros); Vector win(static_cast\u0026lt;size_t\u0026gt;(t_silence / dt_), fill::ones); Vector sum_z_silence(n_y_, fill::zeros); size_t n_silence(0); for (size_t trial = 0; trial \u0026lt; u_.size(); trial++) { // find silent samples // start by convolving with Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0)); Vector u_conv = conv(sum_u, win, \u0026#34;same\u0026#34;); // get only the samples that are silent... arma::uvec ubi_silence = find(u_conv \u0026lt;= thresh_silence); if (ubi_silence.n_elem \u0026gt; 0) { sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1); n_silence += ubi_silence.n_elem; } } if (n_silence \u0026gt; 0) { d = sum_z_silence / n_silence; } fit_.set_d(d); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat() { // temporary copy of data Matrix z(n_y_, n_t_tot_, fill::zeros); Matrix u(n_u_, n_t_tot_, fill::zeros); size_t so_far(0); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial); u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial); so_far += n_t_.at(trial); } // remove output bias z.each_col() -= fit_.d(); // calculate I/O gain @ DC while data in convenient form g_dc_ = z * pinv(u); // std::cout \u0026lt;\u0026lt; \u0026#34;G0_data = \u0026#34; \u0026lt;\u0026lt; g_dc_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // create hankel data matrix size_t len = z.n_cols - 2 * n_h_ + 1; // data length in hankel mat // block-hankel data matrix D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros); // past input auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1); // future input auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1); // past output auto y_p = D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1); // future output auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, len - 1); size_t idx = 0; for (size_t k = 0; k \u0026lt; len; k++) { idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } } D_ /= sqrt(static_cast\u0026lt;data_t\u0026gt;(len)); } // template \u0026lt;typename Fit\u0026gt; // void SSID\u0026lt;Fit\u0026gt;::DecomposeData() { // // do LQ decomp instead of calculating covariance expensive way // // Note that \u0026#34;R\u0026#34; in van Overschee is lower-triangular (L), not \u0026#34;R\u0026#34; in QR // // decomp. Very confusing. // Matrix q_t; // lq(L_, q_t, D_); // // van Overschee zeros out the other elements. // L_ = trimatl(L_); // } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcSVD(SSIDWt wt) { // submats that will be needed: auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1); auto R_23_13 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1); auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_) - 1); auto R_44 = L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14); auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1); auto Luf = Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1); auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); // aka: R_f Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1); // from van Overschee subid.m: // Rf = R((2*m+l)*i+1:2*(m+l)*i,:); % Future outputs Matrix U; Matrix V; switch (wt) { case kSSIDNone: { // No weighting. (what van Overschee calls \u0026#34;N4SID\u0026#34;) Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14; arma::svd(U, s_, V, O_k_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDMOESP: { // MOESP weighting // This is what they use in the \u0026#34;robust\u0026#34; algorithm van Overschee, de Moor // 1996 Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); svd(U, s_, V, O_k_ortho_Uf_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDCVA: { // CVA weighting // See van Overschee\u0026#39;s matlab code (subid.m): // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); Matrix inv_w1; Matrix qt1; lq(inv_w1, qt1, R_56_16); // lq decomp of R_f (future output data) inv_w1 = trimatl(inv_w1); inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1); Matrix w_o_w = arma::solve( inv_w1, O_k_ortho_Uf_sans_Qt); // alternatively // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt svd(U, s_, V, w_o_w, \u0026#34;std\u0026#34;); U = inv_w1 * U; break; } } // Truncate to model order (heart of ssid method) auto s_hat = s_.subvec(0, n_x_ - 1); Matrix diag_sqrt_s = diagmat(sqrt(s_hat)); auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1); // get extended observability and controllability mats ext_obs_t_ = u_hat * diag_sqrt_s; // extended observability matrix } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::Solve(data_t wt_dc) { // required submats auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_56_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); // Solve for params using appropriate algorithm: // robust deterministic/stochastic algorithm in van Overschee 1996 // algorithm that the authors say \u0026#34;works\u0026#34; in practice. auto ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix // This is what textbook (1996) says: // // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15); // // HOWEVER, do not know why but have to fill the last place with zeros like // authors\u0026#39; matlab implementation (see `subid.m`) // Otherwise, get ridiculous covariances (although A,C estimates are close to // same...) Matrix Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Matrix S = Tl * pinv(Tr); // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure // stability. fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1)); Matrix ext_obs_t_p1 = join_vert( ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1), Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros)); fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1); // At this point, van Overschee \u0026amp; de Moor suggest re-calculating ext_obs_t_, // ext_obs_tm1 from (A, C) because it was just an approximation. This is RecomputeExtObs(); ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); S = Tl * pinv(Tr); Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_); Matrix Mcurly = pinv(ext_obs_tm1); Matrix Pcurly = Tl - Lcurly * R_56_15; Vector Pvec = vectorise(Pcurly); Matrix Qcurly = R_23_15; // Identify [D; B], assuming D=0 and ensuring DC gain is correct Matrix sum_QcurlyT_kron_Ncurly( (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_), fill::zeros); Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols, fill::eye); eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1, eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1; // van Overschee (1996) p. 126 Matrix N1_Tl = -Lcurly; N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) += join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly); N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) += Matrix(n_y_, n_y_, fill::eye); Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros); Matrix N_k; for (size_t k = 0; k \u0026lt; n_h_; k++) { auto Qcurly_k = Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1); Nk_Tl.zeros(); Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) = N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1); N_k = Nk_Tl * eye_ext_obs_tm1; sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k); } Matrix err_vec; if (wt_dc \u0026gt; 0) { // Constraints enforced by weighted least squares // // Reference: // // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly // Excited Industrial Systems. Conference in Decision and Control. // constraint 1: assume D=0 --\u0026gt; remove the components for Dvec (this is // actually a hard constraint in that it ignores D) Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly; sum_QcurlyT_kron_Ncurly = Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_); size_t kkk = 0; for (size_t k = 1; k \u0026lt; (n_u_ + 1); k++) { size_t start_idx = k * (n_y_ + n_x_) - n_x_; for (size_t kk = 0; kk \u0026lt; n_x_; kk++) { sum_QcurlyT_kron_Ncurly.col(kkk) = sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk); kkk++; } } // constraint 2: Make sure DC I/O gain is correct Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A()); Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_)); Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0); Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 = join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0); // WEIGHTED LS // Important in practice because I care a lot about at least getting the DC // gain correct. Put x weight on minimizing error at DC, relative to others Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye); // Make weight on minimizing DC error immense so at least that // should be nailed. size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows; size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows; size_t stop_row = w.n_rows - 1; size_t stop_col = w.n_cols - 1; // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale // weight with data length? w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc; Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * sum_QcurlyT_kron_Ncurly_b_to_g0) * sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec; fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_)); // Calculate residuals and their cov. // Because I\u0026#39;ve added constraints, I need to re-calculate the right term // with b_vec instead of how van Overschee do in final algorithm. err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec; } else { // default way: *no* constraint on G0 or D=0 Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec; // TODO(mfbolus) n.b., this gets thrown away... // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_); fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_)); err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec; } // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols); // TODO(mfbolus): Something is wrong with the error calculation above. // Use the way van overschee does it in `subid.m` // WARNING: this ignores any above constraints, so Q, R will be approximate... Matrix err = Tl - S * Tr; Matrix cov_err = err * err.t(); fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1)); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::RecomputeExtObs() { ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C(); for (size_t k = 2; k \u0026lt; (n_h_ + 1); k++) { ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) = ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1, ext_obs_t_.n_cols - 1) * fit_.A(); } } } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":55,"href":"/lds-ctrl-est/docs/api/files/lds__fit_8h/","title":"ldsCtrlEst_h/lds_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_fit.h # LDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Fit LDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.\nSource code # //===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDS_FIT_HPP #define LDS_FIT_HPP // namespace #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); virtual ~Fit() = default; // get methods size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; Q() const { return Q_; }; const Vector\u0026amp; x0() const { return x0_; }; const Matrix\u0026amp; P0() const { return P0_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; // gets measurement noise virtual const Matrix\u0026amp; R() const = 0; // set methods (e.g., seeding initial fit values) void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_m(const Vector\u0026amp; m) { Reassign(m_, m); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); ForceSymPD(Q_); }; virtual void set_R(const Matrix\u0026amp; R) = 0; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); ForceSymPD(P0_); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; View f(Matrix\u0026amp; x, const Matrix\u0026amp; u, size_t t) { x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x.col(t); }; View f(Matrix\u0026amp; x_pre, const Matrix\u0026amp; x_post, const Matrix\u0026amp; u, size_t t) { x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x_pre.col(t); }; virtual View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) = 0; protected: data_t dt_{}; // Dynamics Matrix A_; Matrix B_; Vector g_; Vector m_; Matrix Q_; // Output Matrix C_; Vector d_; Matrix R_; // initial conditions Vector x0_; Matrix P0_; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; }; } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":56,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_ctrl.h # GLDS Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. Detailed Description # This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_gaussian_sys.h\u0026#34; // controller #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace gaussian { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); cx_ref_ = y_ref - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":57,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_em.h # GLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitEM GLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":58,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_ssid.h # GLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; using SSID\u0026lt;Fit\u0026gt;::Run; private: using SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat; using SSID\u0026lt;Fit\u0026gt;::CalcSVD; using SSID\u0026lt;Fit\u0026gt;::Solve; void DecomposeData() override; void SolveVanOverschee(); }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":59,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit.h # GLDS fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Fit GLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // fit type #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace gaussian { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); const Matrix\u0026amp; R() const override { return R_; }; void set_R(const Matrix\u0026amp; R) override { Reassign(R_, R); ForceSymPD(R_); }; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = C_ * x.col(t) + d_; return y.col(t); }; }; }; // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":60,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sctrl.h # GLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H // controller type #include \u0026#34;lds_gaussian_ctrl.h\u0026#34; // switched controller #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace gaussian { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); cx_ref_ = y_ref - sys_.d(); } // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; }; // SwitchedController } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":61,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/","title":"ldsCtrlEst_h/lds_gaussian_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sys.h # GLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::System Gaussian LDS Type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H #define LDSCTRLEST_LDS_GAUSSIAN_SYS_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; namespace lds { namespace gaussian { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0, data_t r0 = kDefaultR0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; // get methods const Matrix\u0026amp; R() const { return R_; }; // set methods void set_Q(const Matrix\u0026amp; Q) { lds::System::set_Q(Q); do_recurse_Ke_ = true; } void set_R(const Matrix\u0026amp; R) { Reassign(R_, R); do_recurse_Ke_ = true; }; void set_Ke(const Matrix\u0026amp; Ke) { Reassign(Ke_, Ke); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void set_Ke_m(const Matrix\u0026amp; Ke_m) { Reassign(Ke_m_, Ke_m); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void Print(); protected: void h() override { cx_ = C_ * x_; y_ = cx_ + d_; }; Vector h_(Vector x) override { return C_ * x + d_; }; void RecurseKe() override; // Gaussian-output-specific Matrix R_; bool do_recurse_Ke_{}; }; // System } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":62,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian_8h/","title":"ldsCtrlEst_h/lds_gaussian.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian.h # glds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_H #define LDSCTRLEST_LDS_GAUSSIAN_H // namespace #include \u0026#34;lds.h\u0026#34; namespace lds { namespace gaussian { // insert any Gaussian-specific things here... } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":63,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_ctrl.h # PLDS controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. Detailed Description # This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_CTRL_H #define LDSCTRLEST_LDS_POISSON_CTRL_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system type #include \u0026#34;lds_poisson_sys.h\u0026#34; // control type #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace poisson { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); lds::Limit(y_ref_, kYRefLb, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; private: constexpr static const data_t kYRefLb = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":64,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_em.h # PLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitEM PLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\n[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H #define LDSCTRLEST_LDS_POISSON_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override{}; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; data_t NewtonSolveC(); void AnalyticalSolveD(); }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":65,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_ssid.h # PLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. Detailed Description # This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/)).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H #define LDSCTRLEST_LDS_POISSON_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; private: void DecomposeData() override; void CalcCov(); void PoissonToGaussianMoments(); Matrix cov_; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":66,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/","title":"ldsCtrlEst_h/lds_poisson_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit.h # PLDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Fit PLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_H #define LDSCTRLEST_LDS_POISSON_FIT_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // fit #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace poisson { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) : lds::Fit(n_u, n_x, n_y, dt){}; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = exp(C_ * x.col(t) + d_); return y.col(t); }; void set_R(const Matrix\u0026amp; R) override { std::cerr \u0026lt;\u0026lt; \u0026#34;WARNING: Cannot set R (R[0] = \u0026#34; \u0026lt;\u0026lt; R.at(0) \u0026lt;\u0026lt; \u0026#34;). No Gaussian measurement noise in Poisson observation model.\\n\u0026#34;; }; const Matrix\u0026amp; R() const override { return R_; }; }; }; // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":67,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sctrl.h # PLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #include \u0026#34;lds_poisson_ctrl.h\u0026#34; #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace poisson { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); lds::Limit(y_ref_, kYRefLB, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; private: constexpr static data_t kYRefLB = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":68,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/","title":"ldsCtrlEst_h/lds_poisson_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sys.h # PLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::System Poisson System type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SYS_H #define LDSCTRLEST_LDS_POISSON_SYS_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; // needed for Poisson random number generation #include \u0026lt;random\u0026gt; namespace lds { namespace poisson { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; protected: void h() override { cx_ = C_ * x_; y_ = exp(cx_ + d_); diag_y_.diag() = y_; }; Vector h_(Vector x) override { return exp(C_ * x + d_); }; void RecurseKe() override; private: // Poisson-output-specific Matrix diag_y_; std::poisson_distribution\u0026lt;size_t\u0026gt; pd_; }; // System } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":69,"href":"/lds-ctrl-est/docs/api/files/lds__poisson_8h/","title":"ldsCtrlEst_h/lds_poisson.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson.h # plds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_H #define LDSCTRLEST_LDS_POISSON_H #include \u0026#34;lds.h\u0026#34; namespace lds { namespace poisson { // TODO(mfbolus): Not sure if defining these as static here makes the most // sense. Is there a downside to letting multiple poisson System objects share a // common random number generator? static std::random_device rd; static std::mt19937 rng = std::mt19937( rd()); } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":70,"href":"/lds-ctrl-est/docs/api/files/lds__sctrl_8h/","title":"ldsCtrlEst_h/lds_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_sctrl.h # SwitchedController type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SwitchedController SwitchedController Type. Detailed Description # This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SCTRL_H #define LDSCTRLEST_LDS_SCTRL_H #include \u0026#34;lds_ctrl.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; #include \u0026#34;lds_uniform_vecs.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class SwitchedController : public Controller\u0026lt;System\u0026gt; { public: SwitchedController() = default; SwitchedController(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); SwitchedController(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); void Switch(size_t idx, bool do_force_switch = false); void set_Kc(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc) { Kc_list_ = Kc; Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc) { Kc_list_ = std::move(Kc); Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_inty) { Kc_inty_list_ = Kc_inty; Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_inty) { Kc_inty_list_ = std::move(Kc_inty); Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_u) { Kc_u_list_ = Kc_u; Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_u) { Kc_u_list_ = std::move(Kc_u); Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(const UniformVectorList\u0026amp; g) { g_design_list_ = g; g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(UniformVectorList\u0026amp;\u0026amp; g) { g_design_list_ = std::move(g); g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; protected: std::vector\u0026lt;System\u0026gt; systems_; size_t n_sys_{}; size_t idx_{}; // controller gains could be different for each UniformMatrixList\u0026lt;\u0026gt; Kc_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_inty_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_u_list_; // design-phase input gain could also be different UniformVectorList g_design_list_; // TODO(mfbolus): not sure why I need to do this. using Controller\u0026lt;System\u0026gt;::Kc_; using Controller\u0026lt;System\u0026gt;::Kc_inty_; using Controller\u0026lt;System\u0026gt;::Kc_u_; using Controller\u0026lt;System\u0026gt;::g_design_; using Controller\u0026lt;System\u0026gt;::sys_; // using Controller\u0026lt;System\u0026gt;::u_ref_; // using Controller\u0026lt;System\u0026gt;::x_ref_; // using Controller\u0026lt;System\u0026gt;::y_ref_; // using Controller\u0026lt;System\u0026gt;::control_type_; private: void InitVars(); using lds::Controller\u0026lt;System\u0026gt;::set_sys; // using Controller\u0026lt;System\u0026gt;::set_Kc; // using Controller\u0026lt;System\u0026gt;::set_Kc_inty; // using Controller\u0026lt;System\u0026gt;::set_Kc_u; // using Controller\u0026lt;System\u0026gt;::set_g_design; }; template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(systems.at(0), u_lb, u_ub, control_type), systems_(systems) { InitVars(); } template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(System(systems.at(0).n_u(), systems.at(0).n_x(), systems.at(0).n_y(), systems.at(0).dt()), u_lb, u_ub, control_type), systems_(std::move(systems)) { InitVars(); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::InitVars() { n_sys_ = systems_.size(); sys_ = systems_.at(0); Kc_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_)); Kc_inty_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); Kc_u_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); g_design_list_ = UniformVectorList(std::vector\u0026lt;Vector\u0026gt;(n_sys_, g_design_)); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::Switch(size_t idx, bool do_force_switch) { if ((idx == idx_) \u0026amp;\u0026amp; !do_force_switch) { return; // already there. } // put old up and get new one out systems_.at(idx_) = std::move(sys_); sys_ = std::move(systems_.at(idx)); // set the state of this system to that of the previous system // TODO(mfbolus): This will only work as intended if state matrix is the same. // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp. sys_.set_m(systems_.at(idx_).m(), true); sys_.set_x(systems_.at(idx_).x()); // swap controller gains Kc_list_.Swap(Kc_, idx_); Kc_list_.Swap(Kc_, idx); if (control_type_ \u0026amp; kControlTypeIntY) { Kc_inty_list_.Swap(Kc_inty_, idx_); Kc_inty_list_.Swap(Kc_inty_, idx); } if (control_type_ \u0026amp; kControlTypeDeltaU) { Kc_u_list_.Swap(Kc_u_, idx_); Kc_u_list_.Swap(Kc_u_, idx); } g_design_list_.Swap(g_design_, idx_); g_design_list_.Swap(g_design_, idx); idx_ = idx; } // Switch } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":71,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8h/","title":"ldsCtrlEst_h/lds_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_sys.h # LDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::System Linear Dynamical System Type. Detailed Description # This file declares and partially defines the base type for linear dynamical systems ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SYS_H #define LDSCTRLEST_LDS_SYS_H #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class System { public: System() = default; System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); virtual ~System() {} void Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z); virtual const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) = 0; void f(const Vector\u0026amp; u, bool do_add_noise = false) { x_ = A_ * x_ + B_ * (g_ % u) + m_; if (do_add_noise) { x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_); } }; virtual void h() = 0; virtual Vector h_(Vector x) = 0; size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Vector\u0026amp; x() const { return x_; }; const Matrix\u0026amp; P() const { return P_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; P_m() const { return P_m_; }; const Vector\u0026amp; cx() const { return cx_; }; const Vector\u0026amp; y() const { return y_; }; const Vector\u0026amp; x0() const { return x0_; }; const Vector\u0026amp; m0() const { return m0_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; const Matrix\u0026amp; Ke() const { return Ke_; }; const Matrix\u0026amp; Ke_m() const { return Ke_m_; }; const Matrix\u0026amp; Q() { return Q_; }; const Matrix\u0026amp; Q_m() { return Q_m_; }; const Matrix\u0026amp; P0() { return P0_; }; const Matrix\u0026amp; P0_m() { return P0_m_; }; void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_m(const Vector\u0026amp; m, bool do_force_assign = false) { Reassign(m0_, m); if ((!do_adapt_m) || do_force_assign) { Reassign(m_, m); } }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); }; void set_Q_m(const Matrix\u0026amp; Q_m) { Reassign(Q_m_, Q_m); }; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); }; void set_P0_m(const Matrix\u0026amp; P0_m) { Reassign(P0_m_, P0_m); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; void set_x(const Vector\u0026amp; x) { Reassign(x_, x); h(); }; void Reset(); std::vector\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; nstep_pred_block( UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z, size_t n_pred = 1); void Print(); // safe to leave this public and non-const bool do_adapt_m{}; protected: virtual void RecurseKe() = 0; void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); std::size_t n_x_{}; std::size_t n_u_{}; std::size_t n_y_{}; data_t dt_{}; // Signals: Vector x_; Matrix P_; Vector m_; Matrix P_m_; Vector cx_; Vector y_; Vector z_; // Parameters: Vector x0_; Matrix P0_; Vector m0_; Matrix P0_m_; Matrix A_; Matrix B_; Vector g_; Matrix Q_; Matrix Q_m_; Matrix C_; Vector d_; Matrix Ke_; Matrix Ke_m_; }; // System } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":72,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/","title":"ldsCtrlEst_h/lds_uniform_mats.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_mats.h # List of uniformly sized matrices. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformMatrixList Detailed Description # This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H #define LDSCTRLEST_LDS_UNIFORM_MATS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { template \u0026lt;MatrixListFreeDim D = kMatFreeDimNone\u0026gt; class UniformMatrixList : public std::vector\u0026lt;Matrix\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Matrix\u0026gt;::vector; // don\u0026#39;t allow push_back to be used since it doesn\u0026#39;t check dims using std::vector\u0026lt;Matrix\u0026gt;::push_back; public: using std::vector\u0026lt;Matrix\u0026gt;::operator=; using std::vector\u0026lt;Matrix\u0026gt;::operator[]; using std::vector\u0026lt;Matrix\u0026gt;::begin; using std::vector\u0026lt;Matrix\u0026gt;::end; using std::vector\u0026lt;Matrix\u0026gt;::size; using std::vector\u0026lt;Matrix\u0026gt;::at; UniformMatrixList() = default; explicit UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); explicit UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; ~UniformMatrixList() = default; const std::array\u0026lt;size_t, 2\u0026gt;\u0026amp; dim(size_t n = 0) const { return dim_.at(n); } size_t size() { return std::vector\u0026lt;Matrix\u0026gt;::size(); }; const Matrix\u0026amp; at(size_t n) { return std::vector\u0026lt;Matrix\u0026gt;::at(n); }; void Swap(Matrix\u0026amp; that, size_t n); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; void append(const Matrix\u0026amp; mat); private: void CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim); std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt; dim_; }; template \u0026lt;MatrixListFreeDim D\u0026gt; inline void UniformMatrixList\u0026lt;D\u0026gt;::Swap(Matrix\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.n_cols); } if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap // not moving, since it causes memory issues. // so this method isn\u0026#39;t a memory-saver as designed for now Matrix tmp = (*this)[n]; (*this)[n] = that; that = tmp; if (D == kMatFreeDim1) { this-\u0026gt;dim_[n][0] = (*this)[n].n_rows; } if (D == kMatFreeDim2) { this-\u0026gt;dim_[n][1] = (*this)[n].n_cols; } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::append(const Matrix\u0026amp; mat) { std::array\u0026lt;size_t, 2\u0026gt; dim({mat.n_rows, mat.n_cols}); CheckDimensions(dim); std::vector\u0026lt;Matrix\u0026gt;::push_back(mat); dim_.push_back(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) { // make sure dim_ vector is initialized if (dim_.empty()) { dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(that.size(), {0, 0}); } // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices\u0026#34;; throw std::runtime_error(ss.str()); } // if dimensions a not zero and do not match, skip move with error message. bool dims_nonzero = true; for (auto d : dim_) { if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; d[0] \u0026lt; 1) { dims_nonzero = false; break; } if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; d[1] \u0026lt; 1) { dims_nonzero = false; break; } } if (dims_nonzero) { bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); } if (!does_match) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrices of size \u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; dim_[k] = that.dim(k); } return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. bool dims_nonzero = true; for (auto d : dim_) { // if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; (d[0] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; (d[1] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // } // // if (dims_nonzero) { // bool does_match = true; // if (!(D == kMatFreeDim1)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); // } // // if (!(D == kMatFreeDim2)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); // } // // if (!does_match) { // this-\u0026gt;at(0).print(\u0026#34;this[0] = \u0026#34;); // that.at(0).print(\u0026#34;that[0] = \u0026#34;); // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformMatrixList element of size (\u0026#34; \u0026lt;\u0026lt; // that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols \u0026lt;\u0026lt; \u0026#34;) for an // element of size (\u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34;). // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Matrix\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(std::move(mats)) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) : vector(that) { (*this) = that; } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { std::array\u0026lt;size_t, 2\u0026gt; dim_k({this-\u0026gt;at(k).n_rows, this-\u0026gt;at(k).n_cols}); dim_.push_back(dim_k); } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim) { // change behavior based on free dim D if ((dim[0] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim1)) { dim[0] = this-\u0026gt;at(0).n_rows; } if ((dim[1] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim2)) { dim[1] = this-\u0026gt;at(0).n_cols; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Matrix\u0026amp; mat : *this) { if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_rows == dim[0]); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_cols == dim[1]); } if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(this-\u0026gt;size(), dim); for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { dim_[k][0] = (*this)[k].n_rows; dim_[k][1] = (*this)[k].n_cols; } } } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":73,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/","title":"ldsCtrlEst_h/lds_uniform_systems.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_systems.h # List of uniformly sized Systems. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformSystemList Detailed Description # This file provides a container for uniformly sized Systems.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector // namespace #include \u0026#34;lds.h\u0026#34; // System type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class UniformSystemList : public std::vector\u0026lt;System\u0026gt; { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;System\u0026gt;::vector; using std::vector\u0026lt;System\u0026gt;::operator=; using std::vector\u0026lt;System\u0026gt;::operator[]; using std::vector\u0026lt;System\u0026gt;::at; using std::vector\u0026lt;System\u0026gt;::begin; using std::vector\u0026lt;System\u0026gt;::end; using std::vector\u0026lt;System\u0026gt;::size; public: UniformSystemList() = default; explicit UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); explicit UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(const UniformSystemList\u0026amp; that); UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept; ~UniformSystemList() = default; const std::array\u0026lt;size_t, 3\u0026gt;\u0026amp; dim() const { return dim_; } size_t size() { return std::vector\u0026lt;System\u0026gt;::size(); }; const System\u0026amp; at(size_t n) { return std::vector\u0026lt;System\u0026gt;::at(n); }; void Swap(System\u0026amp; that, size_t n); UniformSystemList\u0026amp; operator=(const UniformSystemList\u0026amp; that); UniformSystemList\u0026amp; operator=(UniformSystemList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim); std::array\u0026lt;size_t, 3\u0026gt; dim_{}; }; template \u0026lt;typename System\u0026gt; inline void UniformSystemList\u0026lt;System\u0026gt;::Swap(System\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformSystemList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = (dim_[0] == that.n_u()) \u0026amp;\u0026amp; (dim_[1] == that.n_x()) \u0026amp;\u0026amp; (dim_[2] == that.n_y()); if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformSystemList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap System tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( const UniformSystemList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_[0] + dim_[1] + dim_[2]) { std::array\u0026lt;size_t, 3\u0026gt; other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign systems of size \u0026#34; \u0026lt;\u0026lt; dim_[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2] \u0026lt;\u0026lt; \u0026#34; with systems of size \u0026#34; \u0026lt;\u0026lt; other_dim[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other_dim[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2]; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( UniformSystemList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. if (dim_[0] + dim_[1] + dim_[2]) { // bool does_match = (dim_[0] == that.at(0).n_u()) \u0026amp;\u0026amp; // (dim_[1] == that.at(0).n_x()) \u0026amp;\u0026amp; // (dim_[2] == that.at(0).n_y()); // if (!does_match) { // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformSystemList element for an element of \u0026#34; // \u0026#34;different size. Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;System\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(std::move(systems)) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList( std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const UniformSystemList\u0026amp; that) : std::vector\u0026lt;System\u0026gt;(that) { (*this) = that; } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept : std::vector\u0026lt;System\u0026gt;(std::move(that)) { this-\u0026gt;dim_[0] = this-\u0026gt;at(0).n_u(); this-\u0026gt;dim_[1] = this-\u0026gt;at(0).n_x(); this-\u0026gt;dim_[2] = this-\u0026gt;at(0).n_y(); } template \u0026lt;typename System\u0026gt; void UniformSystemList\u0026lt;System\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim) { if (dim[0] + dim[1] + dim[2]) { dim_ = dim; } else { dim_[0] = this-\u0026gt;at(0).n_u(); dim_[1] = this-\u0026gt;at(0).n_x(); dim_[2] = this-\u0026gt;at(0).n_y(); } // make sure dimensiolaties are all uniform bool does_match(true); for (const System\u0026amp; sys : *this) { does_match = does_match \u0026amp;\u0026amp; (sys.n_u() == dim_[0]); does_match = does_match \u0026amp;\u0026amp; (sys.n_x() == dim_[1]); does_match = does_match \u0026amp;\u0026amp; (sys.n_y() == dim_[2]); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input systems are not uniform.\u0026#34;); } } } } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":74,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/","title":"ldsCtrlEst_h/lds_uniform_vecs.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_vecs.h # List of uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformVectorList Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H #define LDSCTRLEST_LDS_UNIFORM_VECS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { class UniformVectorList : public std::vector\u0026lt;Vector\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Vector\u0026gt;::vector; using std::vector\u0026lt;Vector\u0026gt;::operator=; using std::vector\u0026lt;Vector\u0026gt;::operator[]; using std::vector\u0026lt;Vector\u0026gt;::at; using std::vector\u0026lt;Vector\u0026gt;::begin; using std::vector\u0026lt;Vector\u0026gt;::end; using std::vector\u0026lt;Vector\u0026gt;::size; public: UniformVectorList() = default; explicit UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim = 0); explicit UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim = 0); UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim = 0); UniformVectorList(const UniformVectorList\u0026amp; that); UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept; ~UniformVectorList() = default; size_t dim() const { return dim_; } size_t size() { return std::vector\u0026lt;Vector\u0026gt;::size(); }; const Vector\u0026amp; at(size_t n) { return std::vector\u0026lt;Vector\u0026gt;::at(n); }; void Swap(Vector\u0026amp; that, size_t n); UniformVectorList\u0026amp; operator=(const UniformVectorList\u0026amp; that); UniformVectorList\u0026amp; operator=(UniformVectorList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(size_t dim); size_t dim_{}; }; inline void UniformVectorList::Swap(Vector\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = dim_ == that.n_elem; if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap Vector tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( const UniformVectorList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_) { size_t other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ \u0026lt;\u0026lt; \u0026#34; with vectors of size \u0026#34; \u0026lt;\u0026lt; other_dim; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( UniformVectorList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors. Skipping.\\n\u0026#34;; // return (*this); // } // // if (dim_) { // size_t other_dim(that.dim()); // if (dim_ != other_dim) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ // \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; other_dim \u0026lt;\u0026lt; \u0026#34;. // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Vector\u0026gt;::operator=(std::move(that)); return (*this); } } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":75,"href":"/lds-ctrl-est/docs/api/files/lds_8h/","title":"ldsCtrlEst_h/lds.h","section":"Files","content":" ldsCtrlEst_h/lds.h # lds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)) observations.\nSource code # //===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_H #define LDSCTRLEST_LDS_H // #ifndef LDSCTRLEST // #include \u0026lt;ldsCtrlEst\u0026gt; // #endif #include \u0026lt;armadillo\u0026gt; namespace lds { using data_t = double; // may change to float (but breaks mex functions) using Vector = arma::Col\u0026lt;data_t\u0026gt;; using Matrix = arma::Mat\u0026lt;data_t\u0026gt;; using Cube = arma::Cube\u0026lt;data_t\u0026gt;; using View = arma::subview\u0026lt;data_t\u0026gt;; namespace fill = arma::fill; static const std::size_t kControlTypeDeltaU = 0x1; static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); static const data_t kPi = arma::datum::pi; static const data_t kDefaultP0 = 1e-6; static const data_t kDefaultQ0 = 1e-6; static const data_t kDefaultR0 = 1e-2; enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA }; enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2 }; // TODO(mfbolus): for SwitchedController, may want systems to have differing // numbers of states. Use this enum as template parameter? // enum SystemListFreeDim { // kSysFreeDimNone, // kSysFreeDimX ///\u0026lt; allow state dim (x) of systems in list to be hetero // }; // place hard limits on contents of vecors/mats void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub); void Limit(Vector\u0026amp; x, data_t lb, data_t ub); void Limit(Matrix\u0026amp; x, data_t lb, data_t ub); // in-place assign that errs if there are dimension mismatches: void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); // TODO(mfbolus): this is a fudge, but for some reason, cov mats often going // numerically asymm. void ForceSymPD(Matrix\u0026amp; X); void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min = 0); void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X); Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B); inline void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Vector\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Matrix\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if (other.n_elem != some.n_elem) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vector of size \u0026#34; \u0026lt;\u0026lt; some.n_elem \u0026lt;\u0026lt; \u0026#34; with vector of size \u0026#34; \u0026lt;\u0026lt; other.n_elem \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } inline void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrix of size \u0026#34; \u0026lt;\u0026lt; some.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; some.n_cols \u0026lt;\u0026lt; \u0026#34; with matrix of size \u0026#34; \u0026lt;\u0026lt; other.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other.n_cols \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } } // namespace lds #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":76,"href":"/lds-ctrl-est/docs/api/files/mex__c__util_8h/","title":"ldsCtrlEst_h/mex_c_util.h","section":"Files","content":" ldsCtrlEst_h/mex_c_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) More\u0026hellip;\nNamespaces # Name armamexc arma/mex interface using Matlab C API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab/Octave\u0026rsquo;s C mex API.\nSource code # //===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXC_UTIL_H #define LDSCTRLEST_MEXC_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.h\u0026#34; // // If Matlab_FOUND, include matrix.h. // // (Octave does not need/have it.) // #ifdef Matlab_FOUND // #include \u0026#34;matrix.h\u0026#34; // #endif namespace armamexc { template \u0026lt;class T\u0026gt; inline auto m2T_scalar(const mxArray *matlab_scalar) -\u0026gt; T { if (mxGetData(matlab_scalar)) { return static_cast\u0026lt;T\u0026gt;(mxGetScalar(matlab_scalar)); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return 0; } template \u0026lt;class T\u0026gt; inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false, bool strict = true) -\u0026gt; arma::Mat\u0026lt;T\u0026gt; { if (mxGetData(matlab_mat)) { const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat); if (n_dim == 2) { return arma::Mat\u0026lt;T\u0026gt;(static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)), mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem, strict); } mexErrMsgTxt(\u0026#34;Number of dimensions must be 2.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } // TODO(mfbolus): make these templated. template \u0026lt;typename T\u0026gt; inline auto a2m_mat(arma::Mat\u0026lt;T\u0026gt; const \u0026amp;arma_mat) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_mat.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Mat.\u0026#34;); return nullptr; } template \u0026lt;typename T\u0026gt; inline auto a2m_vec(arma::Col\u0026lt;T\u0026gt; const \u0026amp;arma_vec) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_vec.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Col.\u0026#34;); return nullptr; } } // namespace armamexc #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":77,"href":"/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/","title":"ldsCtrlEst_h/mex_cpp_util.h","section":"Files","content":" ldsCtrlEst_h/mex_cpp_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) More\u0026hellip;\nNamespaces # Name armamexcpp arma/mex interface using Matlab C++ API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab\u0026rsquo;s C++ mex API.\nSource code # //===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXCPP_UTIL_H #define LDSCTRLEST_MEXCPP_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.hpp\u0026#34; #include \u0026#34;mexAdapter.hpp\u0026#34; namespace armamexcpp { template \u0026lt;class T\u0026gt; std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; m2a_cellmat(matlab::data::CellArray\u0026amp; matlab_cell) { size_t n_cells = matlab_cell.getNumberOfElements(); std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; arma_mat(n_cells, arma::Mat\u0026lt;T\u0026gt;(1, 1, arma::fill::zeros)); for (size_t k = 0; k \u0026lt; n_cells; k++) { matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = matlab_cell[k]; auto dims = matlab_mat.getDimensions(); arma_mat[k] = arma::Mat\u0026lt;T\u0026gt;(matlab_mat.release().get(), dims[0], dims[1]); } return arma_mat; }; template \u0026lt;class T\u0026gt; std::vector\u0026lt;T\u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt;T\u0026gt;\u0026amp; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); T* ptr = matlab_array.release().get(); std::vector\u0026lt;T\u0026gt; vec(ptr, ptr + n_elem); return vec; }; template \u0026lt;class T\u0026gt; arma::Col\u0026lt;T\u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); // T* ptr = matlab_array.release().get(); // arma::Col\u0026lt;T\u0026gt; vec(ptr, n_elem); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? arma::Col\u0026lt;T\u0026gt; vec(n_elem, arma::fill::zeros); for (size_t k = 0; k \u0026lt; n_elem; k++) { vec[k] = matlab_array[k]; } return vec; }; template \u0026lt;class T\u0026gt; arma::Mat\u0026lt;T\u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { // ArrayDimensions == std::vector\u0026lt;size_t\u0026gt; auto dims = matlab_array.getDimensions(); // T* ptr = matlab_array.release().get(); // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false) // arma::Mat\u0026lt;T\u0026gt; mat(ptr, dims[0], dims[1]); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? // // armadillo and matlab both use column-major ordering, so this should work: size_t n_elem = dims[0] * dims[1]; arma::Mat\u0026lt;T\u0026gt; mat(dims[0], dims[1], arma::fill::zeros); size_t k(0); for (auto m: matlab_array) { mat[k] = m; k++; } return mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_mat(const arma::Mat\u0026lt;T\u0026gt;\u0026amp; arma_mat, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(), arma_mat.memptr() + arma_mat.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_vec(const arma::Col\u0026lt;T\u0026gt;\u0026amp; arma_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;({arma_vec.n_elem, 1}, arma_vec.memptr(), arma_vec.memptr() + arma_vec.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; s2m_vec(const std::vector\u0026lt;T\u0026gt;\u0026amp; std_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size()); return matlab_mat; }; } // namespace armamexcpp #endif Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":78,"href":"/lds-ctrl-est/docs/terminology/model/","title":"Models","section":"LDS C+E Documentation","content":" Model Definitions # This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form: \\[\\mathbf{x}_{t\u0026#43;1} = f\\left( \\mathbf{x}_{t}, \\mathbf{v}_{t} \\right) = \\mathbf{A} \\mathbf{x}_{t} \u0026#43; \\mathbf{B} \\mathbf{v}_{t} \u0026#43; \\mathbf{m}_{t} \u0026#43; \\mathbf{w}_{t}\\] \\[\\mathbf{y}_{t} = h\\left( \\mathbf{x}_{t} \\right)\\] t : time index x : system state v = g%u : input (e.g., in physical units used for model fit) u : control signal sent to actuator (e.g., in Volts) y : system output m : process disturbance w ~ N(0, Q) : process noise/disturbance A : state matrix B : input coupling matrix g : input gain (e.g., for converting to control signal actuator voltage) n.b., assumes this conversion is linear Q : process noise covariance % : element-wise multiplication LDS with Gaussian Observations # For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.\n\\[\\mathbf{y}_{t} = \\mathbf{C} \\mathbf{x}_{t} \u0026#43; \\mathbf{d}\\] \\[\\mathbf{z}_{t} \\sim \\mathcal{N}\\left(\\mathbf{y}_{t} , \\mathbf{R} \\right)\\] z : measurement C : output matrix d : output bias R : measurement noise covariance LDS with Poisson Observations # For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.\n\\[y_{t}^{i} = \\exp \\left(\\mathbf{c}^i \\mathbf{x}_{t} \u0026#43; d^i\\right)\\] \\[z_{t}^i \\sim \\rm{Poisson} \\left(y_{t}^i \\right)\\] i : output index z : measurement (count data) c : i^th row of output matrix (C) d : output bias Model Predictive Control (MPC) # Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.\nIn the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.\n"},{"id":79,"href":"/lds-ctrl-est/docs/api/modules/","title":"Modules","section":"LDS C+E Documentation","content":" Modules # Control Mode Bit Masks provides fill types for constructing new armadillo vectors, matrices\nDefaults\nUpdated on 5 March 2025 at 21:11:28 EST\n"},{"id":80,"href":"/lds-ctrl-est/docs/api/namespaces/","title":"Namespaces","section":"LDS C+E Documentation","content":" Namespaces # armamexc arma/mex interface using Matlab C API\narmamexcpp arma/mex interface using Matlab C++ API\nlds::gaussian Linear Dynamical Systems with Gaussian observations.\nlds::poisson Linear Dynamical Systems with Poisson observations.\nstd\nUpdated on 5 March 2025 at 21:11:28 EST\n"},{"id":81,"href":"/lds-ctrl-est/docs/api/pages/","title":"Pages","section":"LDS C+E Documentation","content":" Pages # Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":82,"href":"/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/","title":"PLDS State Estimation","section":"LDS C+E Examples","content":" PLDS State Estimation Tutorial # This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[x_{t\u0026#43;1} = x_t \u0026#43; w_t\\] \\[y_{t} = \\exp\\left(x_t\\right)\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state Finally, assign the parameters using corresponding set-methods.\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); Creating the estimator # Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. To ensure robust estimates, adaptively re-estimate the process disturbance.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); Simulating estimation # In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.\n// Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.\nWith this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.\n"},{"id":83,"href":"/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/","title":"PLDS Switched Control","section":"LDS C+E Examples","content":" PLDS Switched Control Tutorial # This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.\nIn the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating the simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); The system\u0026rsquo;s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 Initially, the system will be in \u0026ldquo;mode\u0026rdquo; 1, where B = b1.\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.\nCreating the controller # Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.\nSimilar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.\nMoreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.\nPutting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying systems: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nNow that the SwitchedController is instantiated, assign its parameters.\n// Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).\nThe control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.\n// Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system\u0026rsquo;s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.\nExample simulation result # Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller\u0026rsquo;s online estimates of the output and state are shown in purple.\nNote that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.\n"},{"id":84,"href":"/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/","title":"src","section":"Files","content":" src # Files # Name src/lds.cpp misc lds namespace functions src/lds_gaussian_sys.cpp GLDS base type. src/lds_poisson_sys.cpp PLDS base type. src/lds_sys.cpp LDS base type. src/lds_uniform_vecs.cpp Uniformly sized vectors. Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":85,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/","title":"src/lds_gaussian_sys.cpp","section":"Files","content":" src/lds_gaussian_sys.cpp # GLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_gaussian_sys.h\u0026gt; lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0, data_t r0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { R_.zeros(n_y, n_y); R_.diag().fill(r0); do_recurse_Ke_=true; }; // recursively estimate Ke void lds::gaussian::System::RecurseKe() { if (!do_recurse_Ke_) { return; } // predict covariance P_ = A_ * P_ * A_.t() + Q_; // calc Kalman gain Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_); // update covariance // Reference: Ghahramani et Hinton (1996) P_ = P_ - Ke_ * C_ * P_; if (do_adapt_m) { P_m_ += Q_m_; // A_m = I (i.e., random walk) Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_); P_m_ = P_m_ - Ke_m_ * C_ * P_m_; } } // Simulate const lds::Vector\u0026amp; lds::gaussian::System::Simulate(const Vector\u0026amp; u_tm1){ f(u_tm1, true);//simulate dynamics with noise added h();//output z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure return z_; } void lds::gaussian::System::Print() { lds::System::Print(); std::cout \u0026lt;\u0026lt; \u0026#34;R: \\n\u0026#34; \u0026lt;\u0026lt; R_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":86,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/","title":"src/lds_poisson_sys.cpp","section":"Files","content":" src/lds_poisson_sys.cpp # PLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_poisson_sys.h\u0026gt; lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { diag_y_ = diagmat(y_); pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(0); }; // Correct: Given measurement (z) and current input (u), update estimate of the // state, covar, output. // // see Eden et al. 2004 void lds::poisson::System::RecurseKe() { // predict covariance P_ = A_ * P_ * A_.t() + Q_; // update cov P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_); Ke_ = P_ * C_.t(); if (do_adapt_m) { P_m_ += Q_m_; // predict (A_m = I) P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_); // update Ke_m_ = P_m_ * C_.t(); } } // Simulate Measurement: z ~ Poisson(y) const lds::Vector\u0026amp; lds::poisson::System::Simulate(const Vector\u0026amp; u_tm1) { f(u_tm1, true); // simulate dynamics with noise added h(); // output z_.zeros(); for (std::size_t k = 0; k \u0026lt; n_y_; k++) { // construct a Poisson distribution object with mean y[k] pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(y_[k]); // pull random sample from this distribution z_[k] = pd_(rng); } return z_; } // ******************* SYS_T ******************* Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":87,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8cpp/","title":"src/lds_sys.cpp","section":"Files","content":" src/lds_sys.cpp # LDS base type. More\u0026hellip;\nDetailed Description # This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- lds_sys.cpp - LDS -------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_sys.h\u0026gt; #include \u0026lt;vector\u0026gt; lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) { InitVars(p0, q0); } void lds::System::InitVars(data_t p0, data_t q0) { // initial conditions. x0_ = Vector(n_x_, fill::zeros); // includes bias (nY) and g (nU) P0_ = p0 * Matrix(n_x_, n_x_, fill::eye); m0_ = x0_; P0_m_ = P0_; // signals x_ = x0_; P_ = P0_; m_ = m0_; P_m_ = P0_m_; y_ = Vector(n_y_, fill::zeros); cx_ = Vector(n_y_, fill::zeros); z_ = Vector(n_y_, fill::zeros); // By default, random walk where each state is independent // In this way, provides independent estimates of rate per channel of output. A_ = Matrix(n_x_, n_x_, fill::eye); B_ = Matrix(n_x_, n_u_, fill::zeros); g_ = Vector(n_u_, fill::ones); Q_ = q0 * Matrix(n_x_, n_x_, fill::eye); Q_m_ = Q_; C_ = Matrix(n_y_, n_x_, fill::eye); // each state will map to an output by d_ = Vector(n_y_, fill::zeros); Ke_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain. Ke_m_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain for m adaptation. do_adapt_m = false; } // Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state // and update estimate of the state, covar, output using Kalman filter void lds::System::Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z_t) { // predict mean f(u_tm1); // dynamics h(); // output // recursively calculate esimator gains (or just keep existing values) // (also predicts+updates estimate covariance) RecurseKe(); // update x_ += Ke_ * (z_t - y_); if (do_adapt_m) { m_ += Ke_m_ * (z_t - y_); // adaptively estimating disturbance } // With new state, estimate output. h(); // --\u0026gt; posterior } void lds::System::Reset() { // reset to initial conditions x_ = x0_; // mean P_ = P0_; // cov of state estimate m_ = m0_; // process disturbance P_m_ = P0_m_; // cov of disturbance estimate h(); } std::vector\u0026lt;lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt;\u0026gt; lds::System::nstep_pred_block(lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; u, lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; z, size_t n_pred) { lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_filt; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_pred; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; y_pred; for (size_t k = 0; k \u0026lt; u.size(); k++) { Reset(); size_t n_t = arma::size(u[k])[1]; Matrix x_filt_k(n_x_, n_t, fill::zeros); Matrix x_pred_k(n_x_, n_t - n_pred, fill::zeros); Matrix y_pred_k(n_y_, n_t - n_pred, fill::zeros); for (size_t t = 0; t \u0026lt; n_t - n_pred; t++) { Vector x_pred_ahead = x_; for (size_t t_u = t; t_u \u0026lt; t + n_pred; t_u++) { x_pred_ahead = A_ * x_pred_ahead + B_ * u[k].col(t_u); } x_pred_k.col(t) = x_pred_ahead; y_pred_k.col(t) = h_(x_pred_ahead); if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; // given previous measurment } for (size_t t = n_t - n_pred; t \u0026lt; n_t; t++) { if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; } x_filt.append(x_filt_k); x_pred.append(x_pred_k); y_pred.append(y_pred_k); } return {x_filt, x_pred, y_pred}; } void lds::System::Print() { std::cout \u0026lt;\u0026lt; \u0026#34;\\n ********** SYSTEM ********** \\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;x: \\n\u0026#34; \u0026lt;\u0026lt; x_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;P: \\n\u0026#34; \u0026lt;\u0026lt; P_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;A: \\n\u0026#34; \u0026lt;\u0026lt; A_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B: \\n\u0026#34; \u0026lt;\u0026lt; B_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;g: \\n\u0026#34; \u0026lt;\u0026lt; g_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;m: \\n\u0026#34; \u0026lt;\u0026lt; m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q: \\n\u0026#34; \u0026lt;\u0026lt; Q_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q_m: \\n\u0026#34; \u0026lt;\u0026lt; Q_m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d: \\n\u0026#34; \u0026lt;\u0026lt; d_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;C: \\n\u0026#34; \u0026lt;\u0026lt; C_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;y: \\n\u0026#34; \u0026lt;\u0026lt; y_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } //******************* SYS_T ******************* Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":88,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/","title":"src/lds_uniform_vecs.cpp","section":"Files","content":" src/lds_uniform_vecs.cpp # Uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_uniform_vecs.h\u0026gt; namespace lds { UniformVectorList::UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); } UniformVectorList::UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim) : vector(std::move(vecs)) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(const UniformVectorList\u0026amp; that) : vector(that) { (*this) = that; } UniformVectorList::UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { this-\u0026gt;dim_ = this-\u0026gt;at(0).n_elem; } void UniformVectorList::CheckDimensions(size_t dim) { if (dim) { dim_ = dim; } else { dim_ = this-\u0026gt;at(0).n_elem; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Vector\u0026amp; vec : *this) { does_match = does_match \u0026amp;\u0026amp; (vec.n_elem == dim_); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } } } // namespace lds Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":89,"href":"/lds-ctrl-est/docs/api/files/lds_8cpp/","title":"src/lds.cpp","section":"Files","content":" src/lds.cpp # misc lds namespace functions More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file implements miscellaneous lds namespace functions not bound to a class.\nSource code # //===-- lds.cpp - LDS -----------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds.h\u0026gt; // insert any necessary function definitions here. namespace lds { void ForceSymPD(Matrix\u0026amp; X) { if (X.is_sympd() || !X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; // for eigenval decomp bool did_succeed(true); Vector d; Matrix u; // see first method (which may not be ideal): // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/ size_t k(1); bool is_sympd = X.is_sympd(); Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye); while (!is_sympd) { if (k \u0026gt; 100) { did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); data_t min_eig = arma::min(d); std::cerr \u0026lt;\u0026lt; \u0026#34;After multiple iterations, min eigen val = \u0026#34; \u0026lt;\u0026lt; min_eig \u0026lt;\u0026lt; \u0026#34;.\\n\u0026#34;; throw std::runtime_error( \u0026#34;Failed to make matrix symmetric positive definite.\u0026#34;); return; } // Limit(d, arma::eps(0), kInf); // force to be positive... // Matrix d_diag = arma::diagmat(d); // X = u * d_diag * u.t(); did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymPD failed.\u0026#34;); } data_t min_eig = arma::min(d); X += id * abs(min_eig) + arma::datum::eps; // make sure symm: X = (X + X.t()) / 2; // double check eigenvals positive after symmetrizing: arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); min_eig = arma::min(d); is_sympd = min_eig \u0026gt; 0; k++; } } void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min) { if (!X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; bool did_succeed(true); Vector d; Matrix u; did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymMinEig failed.\u0026#34;); } Limit(d, eig_min + arma::eps(eig_min), kInf); // enforce lower bound Matrix d_diag = arma::diagmat(d); X = u * d_diag * u.t(); // double check symmetric X = (X + X.t()) / 2; } void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X) { bool did_succeed(true); did_succeed = arma::qr_econ(Qt, L, X.t()); if (!did_succeed) { throw std::runtime_error(\u0026#34;LQ decomposition failed.\u0026#34;); } arma::inplace_trans(L); arma::inplace_trans(Qt); } Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B) { // subtract out mean auto m_a = arma::mean(A, 1); Matrix a0 = A; a0.each_col() -= m_a; auto m_b = arma::mean(B, 1); Matrix b0 = B; b0.each_col() -= m_b; Matrix cov = a0 * b0.t() / a0.n_cols; return cov; } } // namespace lds Updated on 5 March 2025 at 21:11:28 EST\n"},{"id":90,"href":"/lds-ctrl-est/docs/api/namespaces/namespacestd/","title":"std","section":"Namespaces","content":" std # Updated on 5 March 2025 at 21:11:28 EST\n"}]
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+[{"id":0,"href":"/lds-ctrl-est/docs/","title":"LDS C+E Documentation","section":"LDS Control \u0026 Estimation","content":" LDS Control \u0026amp; Estimation Documentation # "},{"id":1,"href":"/lds-ctrl-est/docs/tutorials/","title":"LDS C+E Examples","section":"LDS C+E Documentation","content":" Examples # "},{"id":2,"href":"/lds-ctrl-est/acknowledgements/","title":"Acknowledgements","section":"LDS Control \u0026 Estimation","content":" Acknowledgements # Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.\n"},{"id":3,"href":"/lds-ctrl-est/docs/getting-started/getting-started/","title":"Getting Started","section":"LDS C+E Documentation","content":" Getting Started # This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.\nldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.\nTested Configurations # Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the RelWithDebInfo build type in the CMake configure command (-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo):\nUbuntu 18.04 with GCC 7.5 compiler macOS 11 (Big Sur) with Apple Clang 12 compiler Windows 10 with Visual Studio 16.11 (2019 release) and Clang 12 compiler That being said, if you want to debug a build for a single platform, here are some things you can try:\nUse different compilers (or even different versions of a single compiler) Use different versions of vcpkg (which you can control by checking out a different commit in the vcpkg submodule) Mac Pre-requisities # Xcode Command Line Tools will get you clang, gcc, make, and git:\nxcode-select --install Homebrew is \u0026ldquo;The Missing Package Manager for macOS\u0026rdquo; which will make installing lots of things easy. Install like this:\n/bin/bash -c \u0026#34;$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\u0026#34; You can then use it to install CMake, gfortran, and pkg-config:\nbrew install cmake gfortran pkg-config Linux Pre-requisites # You\u0026rsquo;ll need Git, CMake, GCC, gfortran, etc.\nsudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential ninja-build Windows Installation # Look here for Windows-specific instructions.\nDownloading the Library # First, clone the repository along with submodules:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est\rgit submodule update --init Compilation + Installation # Now generate the cache and build using your IDE or from the command line as follows.\nmkdir build \u0026amp;\u0026amp; cd build\rcmake ..\rcmake --build . The first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.\nIf you want to use vcpkg set up somewhere besides this repo\u0026rsquo;s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE\u0026rsquo;s settings.\nYou can verify the build is working by running ctest from the build folder, which runs all the example scripts.\nOptions # This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\nLDSCTRLEST_BUILD_EXAMPLES : [default=ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake to configure/build the library.\nFor basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build cmake --build #build the project sudo make install #[optional] installs to default location (OS-specific) To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location cmake --build #build the project make install #install to /your/install/prefix To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\nLD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.\nPython bindings package ldsctrlest # With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython3_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn\u0026rsquo;t use an undesired version. That environment needs to have NumPy installed.\ncmake --build . --target python_modules The bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory (pip install pytest matplotlib first if you need to).\nSee python/ldsctrlest/README.md for usage details.\nAlso, beware that a single build will probably not work for both the standalone library and the Python package, since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.\nCommon issues # \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.\nexport CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix vcpkg fails on configuration Try running ./bootstrap-vcpkg from the vcpkg folder and try again. If that doesn\u0026rsquo;t work, try updating vcpkg to a newer version (in the source control tab, click on the commit hash by the vcpkg repo then select from the dropdown) and running boostsrap-vcpkg again. You can also try upgrading your system (e.g., apt update, apt upgrade).\nCould not find Python3 (missing: Python3_NumPy_INCLUDE_DIRS NumPy)\nMake sure NumPy is installed in the Python environment you specified. If CMake still can\u0026rsquo;t find it, you may need to tell CMake exactly where to find it by adding an argument to the configure command: -DPython3_NumPy_INCLUDE_DIR=.... You can find that location like this: python -c 'import numpy; print(numpy.get_include())'\n"},{"id":4,"href":"/lds-ctrl-est/docs/getting-started/windows/","title":"Windows","section":"LDS C+E Documentation","content":" Windows Installation # Windows Pre-requisites # Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:\nSet-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time iwr get.scoop.sh | Invoke-Expression Install Git and CMake if you don\u0026rsquo;t already have them:\nscoop install git cmake If that didn\u0026rsquo;t work, follow more detailed instructions here.\nThe easiest way to compile C++ project on Windows is with Visual Studio\u0026rsquo;s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on \u0026ldquo;Desktop development with C++.\u0026rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the \u0026ldquo;Installation details\u0026rdquo; sidebar under optional features.\nAnd the easiest way to use Visual Studio\u0026rsquo;s build tools is with VS Code, along with the CMake Tools extension. Install them and you should be ready to go.\nDownloading the Library # First, clone the repository, either from VS Code or the command line:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est You\u0026rsquo;ll need to initialize the submodules from the command line after the repo is cloned:\ngit submodule update --init Installation # When you open the folder in VS Code, you will like be prompted by the CMake Tools extension to configure the project. Make sure you select the kit (you\u0026rsquo;ll be prompted when you configure\u0026ndash;else there\u0026rsquo;s an icon in the bar on the bottom of the window or type Ctrl+Shift+P, then \u0026ldquo;cmake select kit\u0026rdquo;). Choose Clang [latest version] with GNU CLI ... amd64 assuming you are running a 64-bit OS. (MSVC may work okay too if you don\u0026rsquo;t need to build Python bindings.)\nFollow along with the \u0026ldquo;Getting Started\u0026rdquo; instructions, but where you see config options specified as -DLDSCTREST_BUILD_STATIC=OFF or -DPython3_ROOT_DIR=..., you will enter those in settings: open with Ctrl+,, click \u0026ldquo;workspace\u0026rdquo;, then search for \u0026ldquo;CMake: Configure Args\u0026rdquo; and enter each of your desired arguments as a separate item.\nTo configure, use Ctrl+Shift+P and search for the \u0026ldquo;CMake: Configure\u0026rdquo; command. To build, click the \u0026ldquo;Build\u0026rdquo; button on the bottom bar. Then click the \u0026ldquo;CTest\u0026rdquo; button to run the example scripts.\nConsiderations # Development on Windows has been more prone to bugs than on Unix systems, so if you encounter many problems, consider switching—WSL (Windows Subsystem for Linux) is a good option for Windows users who don\u0026rsquo;t want to work on a different machine.\nCompilation has been successfully tested in VS Code using the following kit, using the \u0026ldquo;RelWithDebInfo\u0026rdquo; config:\nClang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64) Troubleshooting # The build appears to work, but tests fail with code 0xc0000135 OR \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; Have you installed the library? In VS Code, use Shift+F7 to build a specific target, in this case INSTALL. If that doesn\u0026rsquo;t solve your problem, you will likely need to add the build or install folder to your PATH environment variable, which you can do using the settings GUI (search for \u0026ldquo;Edit the system environment variables\u0026rdquo;).\nOn Windows, \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted. Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":5,"href":"/lds-ctrl-est/issues-contributing/","title":"Issues Contributing","section":"LDS Control \u0026 Estimation","content":" Reporting Issues # If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.\nContributing # We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.\n"},{"id":6,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/","title":"armamexc","section":"Namespaces","content":" armamexc # arma/mex interface using Matlab C API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; T m2T_scalar(const mxArray * matlab_scalar)\nConvert Matlab mxArray to scalar of type T. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)\nConvert matlab matrix to armadillo. template \u0026lt;typename T \u0026gt; mxArray * a2m_mat(arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat)\nConvert armadillo to matlab matrix. template \u0026lt;typename T \u0026gt; mxArray * a2m_vec(arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec)\nConvert armadillo to matlab vector. Detailed Description # Utilities for arma/mex interface using Matlab C API\nFunction Details # m2T_scalar # template \u0026lt;class T \u0026gt; inline T m2T_scalar( const mxArray * matlab_scalar ) Parameters:\nmatlab_scalar matlab scalar Template Parameters:\nT type Return: scalar of type T\nm2a_mat # template \u0026lt;class T \u0026gt; inline arma::Mat\u0026lt; T \u0026gt; m2a_mat( const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true ) Parameters:\nmatlab_mat matlab matrix copy_aux_mem [optional] whether to copy auxiliary memory strict [optional] strictly enforce the above Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_mat( arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat ) Parameters:\narma_mat armadillo matrix Return: matlab matrix\na2m_vec # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_vec( arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec ) Parameters:\narma_vec armadillo vector Return: matlab vector\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":7,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/","title":"armamexcpp","section":"Namespaces","content":" armamexcpp # arma/mex interface using Matlab C++ API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat(matlab::data::CellArray \u0026amp; matlab_cell)\nConvert matlab cell array to vector of armadillo matrices. template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array)\nConvert matlab matrix to a vector of scalars. template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo vector. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat(const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec(const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab vector. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec(const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert vector of scalar T to matlab matrix. Detailed Description # utilities for arma/mex interface using Matlab C++ API\nFunction Details # m2a_cellmat # template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat( matlab::data::CellArray \u0026amp; matlab_cell ) Parameters:\nmatlab_cell matlab cell Template Parameters:\nT type Return: vector of armadillo matrices of type T\nm2s_vec # template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: vector of type T\nm2a_vec # template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: armadillo vector of type T\nm2a_mat # template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab matrix Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat( const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_mat arma matrix factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\na2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec( const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_vec armadillo vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\ns2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec( const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\nstd_vec standard vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":8,"href":"/lds-ctrl-est/docs/terminology/control-estimation/","title":"C\u0026E","section":"LDS C+E Documentation","content":" Control \u0026amp; Estimation # The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.\nState estimation # In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by \\[\\widehat{\\mathbf{x}}_{t|t-1} = \\mathbf{A}\\widehat{\\mathbf{x}}_{t-1|t-1} \u0026#43; \\mathbf{B} u_{t-1} \u0026#43; \\mathbf{m}_{t-1} \\;,\\] \\[\\widehat{\\mathbf{x}}_{t|t} = \\widehat{\\mathbf{x}}_{t|t-1} \u0026#43; \\mathbf{K}^{\\rm e}_t \\left(\\mathbf{z}_t - \\widehat{\\mathbf{y}}_{t|t-1}\\right)\\;,\\] where \\( \\hat{\\left(\\cdot\\right)}_{t|j} \\) indicates an estimate at time \\( t \\) given data up to time \\( j \\) inclusive, \\( \\mathbf{K}^{\\rm e} \\) is the estimator gain, and\n\\[ \\widehat{\\mathbf{y}}_{t|t-1} = h\\left( \\widehat{\\mathbf{x}}_{t|t-1} \\right) \\; .\\] In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.\nIn the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.\nAdaptive estimation of process disturbance # Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.\nWhen parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk \\[\\mathbf{m}_{t} = \\mathbf{m}_{t-1} \u0026#43; \\mathbf{w}^m_{t-1} \\;,\\] where \\( \\mathbf{w}^m \\sim \\mathcal{N}\\left(0, \\mathbf{Q}_m\\right)\\) . Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.\nControl # Given the estimated state, the controller updates the inputs to the system according to the following law: \\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] where \\( \\left( \\cdot \\right)^{\\rm ref} \\) correspond to reference/target signals and \\( \\mathbf{K}^c_x \\) is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.\nIf users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as\n\\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right) - \\mathbf{K}^c_{\\rm inty} \\sum_{j=1}^{t}\\left( \\widehat{\\mathbf{y}}_j - \\mathbf{y}^{\\rm ref}_j \\right) \\;.\\] An additional option available to users is a control law that updates the change in u,\n\\[\\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\left(\\mathbf{u}_{t-1} - \\mathbf{u}^{\\rm ref}_{t-1} \\right) - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] \\[\\mathbf{u}_{t} = \\mathbf{u}_{t-1} \u0026#43; \\Delta\\mathbf{u}_{t} \\; .\\] Notice that this takes the form of a first-order difference equation for updating control (i.e., \\( \\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\mathbf{u}_{t-1} \u0026#43; \\epsilon_{t-1} \\) ), effectively low-pass filtering the input depending on the characteristics of \\( \\mathbf{K}^c_u \\) . This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.\nIntegral action and the \\( \\Delta \\mathbf{u} \\) control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR\u0026rsquo;d to use in combination.\nCalculating reference state-control from output # In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce \\( \\mathbf{x}^{\\rm ref} \\) and \\( \\mathbf{u}^{\\rm ref} \\) . Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.\n"},{"id":9,"href":"/lds-ctrl-est/docs/api/classes/","title":"Classes","section":"LDS C+E Documentation","content":" Classes # "},{"id":10,"href":"/lds-ctrl-est/docs/api/modules/group__control__masks/","title":"Control Mode Bit Masks","section":"Modules","content":" Control Mode Bit Masks # provides fill types for constructing new armadillo vectors, matrices More\u0026hellip; Attributes # Name const std::size_t kControlTypeDeltaU control designed to penalize change in input const std::size_t kControlTypeIntY control using integral action const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m Detailed Description # Control mode bit masks. These can be bit-wise OR\u0026rsquo;d to use in combination.\nAttribute Details # kControlTypeDeltaU # static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY # static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM # static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":11,"href":"/lds-ctrl-est/docs/api/modules/group__defaults/","title":"Defaults","section":"Modules","content":" Defaults # More\u0026hellip; Attributes # Name const data_t kDefaultP0 default state estimate covar const data_t kDefaultQ0 default process noise covar const data_t kDefaultR0 default output noise covar Detailed Description # Default values for common variables (e.g., default diagonal elements of covariances)\nAttribute Details # kDefaultP0 # static const data_t kDefaultP0 = 1e-6; kDefaultQ0 # static const data_t kDefaultQ0 = 1e-6; kDefaultR0 # static const data_t kDefaultR0 = 1e-2; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":12,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/","title":"eg_glds_ctrl.cpp","section":"Examples","content":" eg_glds_ctrl.cpp # Example GLDS Control ```cpp\n//===\u0026ndash; eg_glds_ctrl.cpp - Example GLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS Control ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\n// output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false;\n// Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err\n// setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]);\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; }\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:35:01 EST "},{"id":13,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/","title":"eg_glds_du_plds_ctrl.cpp","section":"Examples","content":" eg_glds_du_plds_ctrl.cpp # Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u). ```cpp\n//===\u0026ndash; eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt);\n// to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU;\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_du_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:35:01 EST "},{"id":14,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/","title":"eg_plds_ctrl.cpp","section":"Examples","content":" eg_plds_ctrl.cpp # Example PLDS Control ```cpp\n//===\u0026ndash; eg_plds_ctrl.cpp - Example PLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Control ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt);\n// Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err\n// Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\nsize_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\nVector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled\u0026hellip; lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system);\n// for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type);\n// set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty);\n// to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0;\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros);\n// set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y();\nx_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x();\nm_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:35:01 EST "},{"id":15,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/","title":"eg_plds_est.cpp","section":"Examples","content":" eg_plds_est.cpp # Example PLDS Estimation ```cpp\n//===\u0026ndash; eg_plds_est.cpp - Example PLDS Estimation \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0);\nint main() { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.\n// construct ground truth system\u0026hellip; lds::poisson::System system_true(n_u, n_x, n_y, dt);\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset();\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt);\n// Can copy parameters from another system object system_estimator = system_true;\n// wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est);\n// set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true;\n// set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;estimator:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; system_estimator.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));\n// create matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros);\n// states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\nMatrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simlation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1));\n// Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simlation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows;\nif ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026ldquo;Q must be n x n.\u0026rdquo;); }\nMatrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); }\nreturn x; }\n_Filename: eg_plds_est.cpp_ ------------------------------- Updated on 5 March 2025 at 16:35:01 EST "},{"id":16,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/","title":"eg_plds_switched_ctrl.cpp","section":"Examples","content":" eg_plds_switched_ctrl.cpp # Example Switched PLDS Control ```cpp\n//===\u0026ndash; eg_plds_switched_ctrl.cpp - Example Switched PLDS Control \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026quot;;\n// whether to do switched control bool do_switch_ctrl = true;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt);\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices. data_t scale_sys_b = 2;\nMatrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));\ncontrolled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions\n// reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);\n// Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system);\n// set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys1:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys2:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x));\nswitched_controller.set_y_ref(y_ref0);\nstd::vectorlds::poisson::System systems_vec(3, lds::poisson::System()); lds::UniformSystemListlds::poisson::System systems(std::move(systems_vec));\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;switched_controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; switched_controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Fake measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros);\n// create Matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);\n// modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones);\n// set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } }\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace)); mode.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;mode\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_switched_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:35:01 EST "},{"id":17,"href":"/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/","title":"examples","section":"Files","content":" examples # Files # Name examples/eg_glds_ctrl.cpp examples/eg_glds_du_plds_ctrl.cpp examples/eg_plds_ctrl.cpp examples/eg_plds_est.cpp examples/eg_plds_switched_ctrl.cpp Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":18,"href":"/lds-ctrl-est/docs/api/examples/","title":"Examples","section":"LDS C+E Documentation","content":" Examples # "},{"id":19,"href":"/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/","title":"examples/eg_glds_ctrl.cpp","section":"Files","content":" examples/eg_glds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":20,"href":"/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/","title":"examples/eg_glds_du_plds_ctrl.cpp","section":"Files","content":" examples/eg_glds_du_plds_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU; // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":21,"href":"/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/","title":"examples/eg_plds_ctrl.cpp","section":"Files","content":" examples/eg_plds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err // Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled... lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type); // set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":22,"href":"/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/","title":"examples/eg_plds_est.cpp","section":"Files","content":" examples/eg_plds_est.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name Matrix random_walk(size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0) int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # random_walk # Matrix random_walk( size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0 ) main # int main() Source code # //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. // construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state // Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. // turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":23,"href":"/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/","title":"examples/eg_plds_switched_ctrl.cpp","section":"Files","content":" examples/eg_plds_switched_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Source code # //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 // simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions // reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":24,"href":"/lds-ctrl-est/docs/api/files/","title":"Files","section":"LDS C+E Documentation","content":" Files # "},{"id":25,"href":"/lds-ctrl-est/docs/tutorials/eg_glds_control/","title":"GLDS Control","section":"LDS C+E Examples","content":" GLDS Control Tutorial # This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\] \\[\ry_{t} = x_t\r\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.\n/// Going to simulate a switching disturbance (m) acting on system size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_y).fill(m_low); Finally, assign the parameters using corresponding set-methods.\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); Creating the controller # Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.\nFor the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.\n// make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nWith the controller constructed, control variables may be set.\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.\nThe control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller\u0026rsquo;s online estimates of the output, state, and disturbance are given in purple.\n"},{"id":26,"href":"/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/","title":"include","section":"Files","content":" include # Directories # Name ldsCtrlEst_h Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":27,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds/","title":"lds","section":"Namespaces","content":" lds # Linear Dynamical Systems (LDS) namespace. Namespaces # Name lds::gaussian Linear Dynamical Systems with Gaussian observations. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::Controller class lds::EM class lds::Fit LDS Fit Type. class lds::SSID class lds::SwitchedController SwitchedController Type. class lds::System Linear Dynamical System Type. class lds::UniformMatrixList class lds::UniformSystemList class lds::UniformVectorList Types # Name enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}\nweighting options for SSID enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2} using double data_t using arma::Col\u0026lt; data_t \u0026gt; Vector using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Cube\u0026lt; data_t \u0026gt; Cube using arma::subview\u0026lt; data_t \u0026gt; View Functions # Name void Limit(std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub) void Limit(Vector \u0026amp; x, data_t lb, data_t ub) void Limit(Matrix \u0026amp; x, data_t lb, data_t ub) void Reassign(Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Vector in place void Reassign(Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Matrix in place void ForceSymPD(Matrix \u0026amp; X)\nforces matrix to be symmetric positive-definite void ForceSymMinEig(Matrix \u0026amp; X, data_t eig_min =0)\nforces matrix to be symmetric and have a minimum eigenvalue void lq(Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X)\nLQ decomposition. Matrix calcCov(const Matrix \u0026amp; A, const Matrix \u0026amp; B)\nCalculate covariance matrix. Attributes # Name const data_t kInf Some useful numbers. const data_t kPi Type Details # SSIDWt # Enumerator Value Description kSSIDNone None. kSSIDMOESP MOESP (AKA \u0026ldquo;robust method\u0026rdquo; in van Overschee 1996) kSSIDCVA CVA \u0026ldquo;Canonical Variate Analysis\u0026rdquo;. Weighting options for singular value decomposition performed during subspace identification (SSID)\nReference:\nvan Overschee, de Moor. 1996. Subspace Identification for Linear Systems.\nMatrixListFreeDim # Enumerator Value Description kMatFreeDimNone neither dim free to be hetero in mat list kMatFreeDim1 allow 1st dim of mats in list to be hetero kMatFreeDim2 allow 2nd dim of mats in list to be hetero data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nVector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Cube # using lds::Cube = arma::Cube\u0026lt;data_t\u0026gt;; View # using lds::View = arma::subview\u0026lt;data_t\u0026gt;; Function Details # Limit # inline void Limit( std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Vector \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Matrix \u0026amp; x, data_t lb, data_t ub ) Reassign # inline void Reassign( Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Vector other other Vector parenthetical optional description provided by caller to ease debugging Reassign # inline void Reassign( Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Matrix other other Matrix parenthetical optional description provided by caller to ease debugging ForceSymPD # void ForceSymPD( Matrix \u0026amp; X ) Parameters:\nX mutated matrix ForceSymMinEig # void ForceSymMinEig( Matrix \u0026amp; X, data_t eig_min =0 ) Parameters:\nX mutated matrix eig_min [optional] minimum eigen value lq # void lq( Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X ) Parameters:\nL lower triangle matrix Qt orthonormal matrix (transposed cf QR decomp) X matrix being decomposed calcCov # Matrix calcCov( const Matrix \u0026amp; A, const Matrix \u0026amp; B ) Parameters:\nA some matrix B some other matrix Return: covariance\nAttribute Details # kInf # static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); kPi # static const data_t kPi = arma::datum::pi; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":28,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/","title":"lds::Controller","section":"Classes","content":" lds::Controller # More\u0026hellip;\nInherited by lds::SwitchedController\u0026lt; System \u0026gt;, lds::gaussian::Controller, lds::poisson::Controller\nPublic Functions # Name Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes # Name System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::Controller; Public Function Details # Controller # Controller() =default Controller # inline Controller( const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Controller # inline Controller( System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Control # inline const Vector \u0026amp; Control( const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step). This is the most flexible option, but requires user to have set the controller\u0026rsquo;s y_ref, x_ref, and u_ref variables.\nControlOutputReference # inline const Vector \u0026amp; ControlOutputReference( const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_estimation [optional] whether to update state estimate (if false, effectively open-loop control) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.\nsys # inline const System \u0026amp; sys() const Kc # inline const Matrix \u0026amp; Kc() const Kc_inty # inline const Matrix \u0026amp; Kc_inty() const Kc_u # inline const Matrix \u0026amp; Kc_u() const g_design # inline const Vector \u0026amp; g_design() const u_ref # inline const Vector \u0026amp; u_ref() const x_ref # inline const Vector \u0026amp; x_ref() const y_ref # inline const Vector \u0026amp; y_ref() const control_type # inline size_t control_type() const tau_awu # inline data_t tau_awu() const u_lb # inline data_t u_lb() const u_ub # inline data_t u_ub() const set_sys # inline void set_sys( const System \u0026amp; sys ) set_g_design # inline void set_g_design( const Vector \u0026amp; g_design ) set_u_ref # inline void set_u_ref( const Vector \u0026amp; u_ref ) set_x_ref # inline void set_x_ref( const Vector \u0026amp; x_ref ) set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) Reimplemented by: lds::gaussian::Controller::set_y_ref, lds::gaussian::SwitchedController::set_y_ref, lds::poisson::Controller::set_y_ref, lds::poisson::SwitchedController::set_y_ref\nset_Kc # inline void set_Kc( const Matrix \u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const Matrix \u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const Matrix \u0026amp; Kc_u ) set_tau_awu # inline void set_tau_awu( data_t tau ) set_control_type # inline void set_control_type( size_t control_type ) Parameters:\ncontrol_type control type bit mask Template Parameters:\nSystem type derived from lds::System set_u_lb # inline void set_u_lb( data_t u_lb ) Parameters:\nu_lb control lower bound set_u_ub # inline void set_u_ub( data_t u_ub ) Parameters:\nu_ub control upper bound Reset # inline void Reset() Print # inline void Print() Protected Attribute Details # sys_ # System sys_; u_ # Vector u_; u_return_ # Vector u_return_; g_design_ # Vector g_design_; u_ref_ # Vector u_ref_; u_ref_prev_ # Vector u_ref_prev_; x_ref_ # Vector x_ref_; y_ref_ # Vector y_ref_; cx_ref_ # Vector cx_ref_; Kc_ # Matrix Kc_; Kc_u_ # Matrix Kc_u_; Kc_inty_ # Matrix Kc_inty_; du_ref_ # Vector du_ref_; dv_ref_ # Vector dv_ref_; v_ref_ # Vector v_ref_; dv_ # Vector dv_; v_ # Vector v_; int_e_ # Vector int_e_; int_e_awu_adjust_ # Vector int_e_awu_adjust_; u_sat_ # Vector u_sat_; do_control_prev_ # bool do_control_prev_ = false; do_lock_control_prev_ # bool do_lock_control_prev_ = false; u_saturated_ # bool u_saturated_ = false; u_lb_ # data_t u_lb_ {}; u_ub_ # data_t u_ub_ {}; tau_awu_ # data_t tau_awu_ {}; k_awu_ # data_t k_awu_ = 0; t_since_control_onset_ # data_t t_since_control_onset_ = 0; control_type_ # size_t control_type_ {}; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":29,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/","title":"lds::EM","section":"Classes","content":" lds::EM # More\u0026hellip;\nInherited by lds::gaussian::FitEM, lds::poisson::FitEM\nPublic Functions # Name EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions # Name void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() virtual void MaximizeOutput() =0 virtual void MaximizeMeasurement() =0 void Smooth(bool force_common_initial)\nget smoothed estimates virtual void RecurseKe(Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t) =0\nrecursively update estimator gain Ke void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::EM; Public Function Details # EM # EM() =default EM # EM( size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nn_x number of states dt sample period u_train input training data z_train measurement training data EM # EM( const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nfit0 initial fit u_train input training data z_train measurement training data ~EM # virtual ~EM() =default Run # const Fit \u0026amp; Run( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2 ) Parameters:\ncalc_dynamics [optional] whether to calculate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law max_iter max number of iterations tol convergence tolerance (max fractional abs change) Return: Fit\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nx # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const y # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const sum_E_x_t_x_t # inline const Matrix \u0026amp; sum_E_x_t_x_t() const sum_E_xu_tm1_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const sum_E_xu_t_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const n_t_tot # inline size_t n_t_tot() theta # inline const Vector \u0026amp; theta() const Protected Function Details # Expectation # void Expectation( bool force_common_initial =false ) Parameters:\nforce_common_initial whether to force common initial condition for all trials Maximization # void Maximization( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false ) Parameters:\ncalc_dynamics [optional] whether to caclulate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law MaximizeDynamics # void MaximizeDynamics() MaximizeQ # void MaximizeQ() MaximizeInitial # void MaximizeInitial() MaximizeOutput # virtual void MaximizeOutput() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput\nMaximizeMeasurement # virtual void MaximizeMeasurement() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement\nSmooth # void Smooth( bool force_common_initial ) Parameters:\nforce_common_initial whether to force common initial conditions RecurseKe # virtual void RecurseKe( Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t ) =0 Parameters:\nKe estimator gain P_pre cov of predicted state est. P_post cov of postior sate est. t time Reimplemented by: lds::gaussian::FitEM::RecurseKe, lds::poisson::FitEM::RecurseKe\nReset # void Reset() InitVars # void InitVars() UpdateTheta # Vector UpdateTheta() Return: parameter list\nProtected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; x_ # std::vector\u0026lt; Matrix \u0026gt; x_; P_ # std::vector\u0026lt; Cube \u0026gt; P_; P_t_tm1_ # std::vector\u0026lt; Cube \u0026gt; P_t_tm1_; y_ # std::vector\u0026lt; Matrix \u0026gt; y_; diag_y_ # Matrix diag_y_; sum_E_x_t_x_t_ # Matrix sum_E_x_t_x_t_; sum_E_xu_tm1_xu_tm1_ # Matrix sum_E_xu_tm1_xu_tm1_; sum_E_xu_t_xu_tm1_ # Matrix sum_E_xu_t_xu_tm1_; fit_ # Fit fit_; theta_ # Vector theta_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":30,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/","title":"lds::Fit","section":"Classes","content":" lds::Fit # LDS Fit Type. #include \u0026lt;lds_fit.h\u0026gt;\nInherited by lds::gaussian::Fit, lds::poisson::Fit\nPublic Functions # Name Fit() =default\nConstructs a new Fit. Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias virtual const Matrix \u0026amp; R() const =0 void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance virtual void set_R(const Matrix \u0026amp; R) =0\nsets output noise covariance (if any) void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) =0\noutput function Protected Attributes # Name data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period ~Fit # virtual ~Fit() =default n_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const m # inline const Vector \u0026amp; m() const Q # inline const Matrix \u0026amp; Q() const x0 # inline const Vector \u0026amp; x0() const P0 # inline const Matrix \u0026amp; P0() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const R # virtual const Matrix \u0026amp; R() const =0 Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R\nset_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_g # inline void set_g( const Vector \u0026amp; g ) set_m # inline void set_m( const Vector \u0026amp; m ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # virtual void set_R( const Matrix \u0026amp; R ) =0 Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R\nset_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) f # inline View f( Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t ) Parameters:\nx state estimate (over time) u input (over time) t time index Return: view of updated state\nf # inline View f( Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t ) Parameters:\nx_pre predicted state est. x_post posterior state est. u input (over time) t time index Return: view of predicted state\nh # virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) =0 Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplemented by: lds::gaussian::Fit::h, lds::poisson::Fit::h\nProtected Attribute Details # dt_ # data_t dt_ {}; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; m_ # Vector m_; Q_ # Matrix Q_; C_ # Matrix C_; d_ # Vector d_; R_ # Matrix R_; x0_ # Vector x0_; P0_ # Matrix P0_; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":31,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/","title":"lds::gaussian","section":"Namespaces","content":" lds::gaussian # Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. class lds::gaussian::Fit GLDS Fit Type. class lds::gaussian::FitEM GLDS E-M Fit Type. class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. class lds::gaussian::System Gaussian LDS Type. Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":32,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/","title":"lds::gaussian::Controller","section":"Classes","content":" lds::gaussian::Controller # Gaussian-observation Controller Type. #include \u0026lt;lds_gaussian_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":33,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/","title":"lds::gaussian::Fit","section":"Classes","content":" lds::gaussian::Fit # GLDS Fit Type. #include \u0026lt;lds_gaussian_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual const Matrix \u0026amp; R() const override\ngets measurement noise covariance virtual void set_R(const Matrix \u0026amp; R) override\nsets measurement noise covariance virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period R # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nh # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":34,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/","title":"lds::gaussian::FitEM","section":"Classes","content":" lds::gaussian::FitEM # GLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_gaussian_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::gaussian::FitEM; This type is used in the process of fitting GLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":35,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/","title":"lds::gaussian::FitSSID","section":"Classes","content":" lds::gaussian::FitSSID # Subspace Identification (SSID) for GLDS. #include \u0026lt;lds_gaussian_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":36,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/","title":"lds::gaussian::SwitchedController","section":"Classes","content":" lds::gaussian::SwitchedController # Gaussian-observation SwitchedController Type. #include \u0026lt;lds_gaussian_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":37,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/","title":"lds::gaussian::System","section":"Classes","content":" lds::gaussian::System # Gaussian LDS Type. #include \u0026lt;lds_gaussian_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)\nConstructs a new Gaussian System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. const Matrix \u0026amp; R() const\nGet output noise covariance. void set_Q(const Matrix \u0026amp; Q) void set_R(const Matrix \u0026amp; R)\nSet output noise covariance. void set_Ke(const Matrix \u0026amp; Ke)\nSet estimator gain. void set_Ke_m(const Matrix \u0026amp; Ke_m)\nSet disturbance estimator gain. void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively update estimator gain. Protected Attributes # Name Matrix R_ covariance of output noise bool do_recurse_Ke_ whether to recursively calculate estimator gain Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0 ) Parameters:\nn_u number of inputs (u) n_x number of states (x) n_y number of outputs (y) dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) r0 [optional] initial diagonal elements of output noise covariance (R) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nR # inline const Matrix \u0026amp; R() const set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # inline void set_R( const Matrix \u0026amp; R ) set_Ke # inline void set_Ke( const Matrix \u0026amp; Ke ) set_Ke_m # inline void set_Ke_m( const Matrix \u0026amp; Ke_m ) Print # void Print() Protected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nProtected Attribute Details # R_ # Matrix R_; do_recurse_Ke_ # bool do_recurse_Ke_ {}; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":38,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/","title":"lds::poisson","section":"Namespaces","content":" lds::poisson # Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. class lds::poisson::Fit PLDS Fit Type. class lds::poisson::FitEM PLDS E-M Fit Type. class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. class lds::poisson::System Poisson System type. Attributes # Name std::random_device rd random device for simulating poisson data std::mt19937 rng random number generator for simulating poisson data Attribute Details # rd # static std::random_device rd; rng # static std::mt19937 rng = std::mt19937( rd()); Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":39,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/","title":"lds::poisson::Controller","section":"Classes","content":" lds::poisson::Controller # PLDS Controller Type. #include \u0026lt;lds_poisson_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":40,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/","title":"lds::poisson::Fit","section":"Classes","content":" lds::poisson::Fit # PLDS Fit Type. #include \u0026lt;lds_poisson_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function virtual void set_R(const Matrix \u0026amp; R) override\nsets output noise covariance (if any) virtual const Matrix \u0026amp; R() const override Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # inline Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period h # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nR # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":41,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/","title":"lds::poisson::FitEM","section":"Classes","content":" lds::poisson::FitEM # PLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_poisson_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::poisson::FitEM; This type is used in the process of fitting PLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":42,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/","title":"lds::poisson::FitSSID","section":"Classes","content":" lds::poisson::FitSSID # Subspace Identification (SSID) for PLDS. #include \u0026lt;lds_poisson_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":43,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/","title":"lds::poisson::SwitchedController","section":"Classes","content":" lds::poisson::SwitchedController # Poisson-observation SwitchedController Type. #include \u0026lt;lds_poisson_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":44,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/","title":"lds::poisson::System","section":"Classes","content":" lds::poisson::System # Poisson System type. #include \u0026lt;lds_poisson_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nConstructs a new Poisson System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively recalculate estimator gain (Ke) Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nProtected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nRecursively recalculate estimator gain (Ke).\nReferences:\nSmith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.\nEden UT, \u0026hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":45,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/","title":"lds::SSID","section":"Classes","content":" lds::SSID # More\u0026hellip;\nInherited by lds::gaussian::FitSSID, lds::poisson::FitSSID\nPublic Functions # Name SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions # Name void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. virtual void DecomposeData() =0\nDecompose data to lower-triangular matrix (used in Solve) void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::SSID; Public Function Details # SSID # SSID() =default SSID # SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf) ) Parameters:\nn_x number of states n_h size of block-hankel data matrix dt sample period u_train input training data z_train measurement training data d output bias Run # std::tuple\u0026lt; Fit, Vector \u0026gt; Run( SSIDWt ssid_wt ) Parameters:\nssid_wt weight for singular value decomp Return: tuple (Fit, singular values)\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nProtected Function Details # CalcD # void CalcD( data_t t_silence =0.1, data_t thresh_silence =0.001 ) Parameters:\nt_silence threshold on period of time that qualifies as \u0026ldquo;silence\u0026rdquo; thresh_silence threshold on input amplitude u that qualifies as \u0026ldquo;silence\u0026rdquo; CreateHankelDataMat # void CreateHankelDataMat() Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.\nDecomposeData # virtual void DecomposeData() =0 Reimplemented by: lds::gaussian::FitSSID::DecomposeData, lds::poisson::FitSSID::DecomposeData\nCalcSVD # void CalcSVD( SSIDWt wt ) Parameters:\nssid_wt weight for SVD Solve # void Solve( data_t wt_dc ) Parameters:\nwt_dc weight placed on getting correct DC I/O gain RecomputeExtObs # void RecomputeExtObs() Protected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; D_ # Matrix D_; fit_ # Fit fit_; g_dc_ # Matrix g_dc_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_h_ # size_t n_h_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; L_ # Matrix L_; s_ # Vector s_; ext_obs_t_ # Matrix ext_obs_t_; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":46,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/","title":"lds::SwitchedController","section":"Classes","content":" lds::SwitchedController # SwitchedController Type. More\u0026hellip;\n#include \u0026lt;lds_sctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nInherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController\nPublic Functions # Name SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes # Name std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::SwitchedController; Public Function Details # SwitchedController # SwitchedController() =default SwitchedController # inline SwitchedController( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask SwitchedController # inline SwitchedController( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Switch # inline void Switch( size_t idx, bool do_force_switch =false ) Parameters:\nidx index do_force_switch whether to force a system switch even if already there. set_Kc # inline void set_Kc( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc ) set_Kc # inline void set_Kc( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty ) set_Kc_inty # inline void set_Kc_inty( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u ) set_Kc_u # inline void set_Kc_u( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u ) set_g_design # inline void set_g_design( const UniformVectorList \u0026amp; g ) set_g_design # inline void set_g_design( UniformVectorList \u0026amp;\u0026amp; g ) Protected Attribute Details # systems_ # std::vector\u0026lt; System \u0026gt; systems_; n_sys_ # size_t n_sys_ {}; idx_ # size_t idx_ {}; Kc_list_ # UniformMatrixList Kc_list_; Kc_inty_list_ # UniformMatrixList Kc_inty_list_; Kc_u_list_ # UniformMatrixList Kc_u_list_; g_design_list_ # UniformVectorList g_design_list_; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":47,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_system/","title":"lds::System","section":"Classes","content":" lds::System # Linear Dynamical System Type. #include \u0026lt;lds_sys.h\u0026gt;\nInherited by lds::gaussian::System, lds::poisson::System\nPublic Functions # Name System() =default\nConstructs a new System. System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nconstructs a new System virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) =0\nsimulates system (single time step) void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function virtual void h() =0\nsystem output function virtual Vector h_(Vector x) =0\nsystem output function (stateless) size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void RecurseKe() =0\nRecursively recalculate estimator gain (Ke) void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes # Name bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes # Name std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 diagonal elements for state estimate covariance q0 diagonal elements for process noise covariance ~System # inline virtual ~System() Filter # void Filter( const Vector \u0026amp; u_tm1, const Vector \u0026amp; z ) Parameters:\nu_tm1 input at t-minus-1 z_t current measurement Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.\nSimulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) =0 Parameters:\nu_tm1 input at time t-1 Return: simulated measurement at time t\nReimplemented by: lds::gaussian::System::Simulate, lds::poisson::System::Simulate\nf # inline void f( const Vector \u0026amp; u, bool do_add_noise =false ) Parameters:\nu input do_add_noise whether to add simulated process noise h # virtual void h() =0 Reimplemented by: lds::gaussian::System::h, lds::poisson::System::h\nh_ # virtual Vector h_( Vector x ) =0 Parameters:\nx_t state at time t Return: predicted state at time t + 1\nReimplemented by: lds::gaussian::System::h_, lds::poisson::System::h_\nn_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const x # inline const Vector \u0026amp; x() const P # inline const Matrix \u0026amp; P() const m # inline const Vector \u0026amp; m() const P_m # inline const Matrix \u0026amp; P_m() const cx # inline const Vector \u0026amp; cx() const y # inline const Vector \u0026amp; y() const x0 # inline const Vector \u0026amp; x0() const m0 # inline const Vector \u0026amp; m0() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const Ke # inline const Matrix \u0026amp; Ke() const Ke_m # inline const Matrix \u0026amp; Ke_m() const Q # inline const Matrix \u0026amp; Q() Q_m # inline const Matrix \u0026amp; Q_m() P0 # inline const Matrix \u0026amp; P0() P0_m # inline const Matrix \u0026amp; P0_m() set_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_m # inline void set_m( const Vector \u0026amp; m, bool do_force_assign =false ) set_g # inline void set_g( const Vector \u0026amp; g ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_Q_m # inline void set_Q_m( const Matrix \u0026amp; Q_m ) set_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_P0_m # inline void set_P0_m( const Matrix \u0026amp; P0_m ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) set_x # inline void set_x( const Vector \u0026amp; x ) Reset # void Reset() nstep_pred_block # std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block( UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1 ) Print # void Print() Protected Function Details # RecurseKe # virtual void RecurseKe() =0 Reimplemented by: lds::gaussian::System::RecurseKe, lds::poisson::System::RecurseKe\nInitVars # void InitVars( data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Public Attribute Details # do_adapt_m # bool do_adapt_m {}; Protected Attribute Details # n_x_ # std::size_t n_x_ {}; n_u_ # std::size_t n_u_ {}; n_y_ # std::size_t n_y_ {}; dt_ # data_t dt_ {}; x_ # Vector x_; P_ # Matrix P_; m_ # Vector m_; P_m_ # Matrix P_m_; cx_ # Vector cx_; y_ # Vector y_; z_ # Vector z_; x0_ # Vector x0_; P0_ # Matrix P0_; m0_ # Vector m0_; P0_m_ # Matrix P0_m_; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; Q_ # Matrix Q_; Q_m_ # Matrix Q_m_; C_ # Matrix C_; d_ # Vector d_; Ke_ # Matrix Ke_; Ke_m_ # Matrix Ke_m_; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":48,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/","title":"lds::UniformMatrixList","section":"Classes","content":" lds::UniformMatrixList # More\u0026hellip;\nInherits from std::vector\u0026lt; Matrix \u0026gt;\nPublic Functions # Name UniformMatrixList() =default\nConstructs a new UniformMatrixList. UniformMatrixList(const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent. UniformMatrixList(std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent. UniformMatrixList(std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent. UniformMatrixList(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nConstructs a new UniformMatrixList (copy). UniformMatrixList(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nConstructs a new UniformMatrixList (move). ~UniformMatrixList() =default\nDestroys the object. const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim(size_t n =0) const\ngets dimensions of uniformly sized matrices size_t size()\nsize of container const Matrix \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Matrix \u0026amp; that, size_t n)\nswaps input matrix with n^th matrix of list UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nassigns the contents (copy) UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nassigns the contents (move) void append(const Matrix \u0026amp; mat)\nappends a matrix to the list Detailed Description # template \u0026lt;MatrixListFreeDim D =kMatFreeDimNone\u0026gt; class lds::UniformMatrixList; Public Function Details # UniformMatrixList # UniformMatrixList() =default UniformMatrixList # explicit UniformMatrixList( const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # explicit UniformMatrixList( std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList UniformMatrixList # UniformMatrixList( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList ~UniformMatrixList # ~UniformMatrixList() =default dim # inline const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim( size_t n =0 ) const Parameters:\nn [optional] index in list of matrices Return: dimensions\nsize # inline size_t size() at # inline const Matrix \u0026amp; at( size_t n ) Swap # inline void Swap( Matrix \u0026amp; that, size_t n ) Parameters:\nthat input matrix n index where the matrix is moved operator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\noperator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\nappend # void append( const Matrix \u0026amp; mat ) Parameters:\nmat input matrix Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":49,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/","title":"lds::UniformSystemList","section":"Classes","content":" lds::UniformSystemList # More\u0026hellip;\nInherits from std::vector\u0026lt; System \u0026gt;\nPublic Functions # Name UniformSystemList() =default\nConstructs a new UniformSystemList. UniformSystemList(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by copying existing vector of System if dimensions consistent. UniformSystemList(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by moving existing vector of System if dimensions consistent. UniformSystemList(std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList from initializer_list of System if dimensions consistent. UniformSystemList(const UniformSystemList \u0026amp; that)\nConstructs a new UniformSystemList (copy). UniformSystemList(UniformSystemList \u0026amp;\u0026amp; that)\nConstructs a new UniformSystemList (move). ~UniformSystemList() =default\nDestroys the object. const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const\ngets dimensions of the uniformly sized systems size_t size()\nsize of container const System \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(System \u0026amp; that, size_t n)\nswaps input system with n^th system of list UniformSystemList \u0026amp; operator=(const UniformSystemList \u0026amp; that)\nassigns the contents (copy) UniformSystemList \u0026amp; operator=(UniformSystemList \u0026amp;\u0026amp; that)\nassigns the contents (move) Detailed Description # template \u0026lt;typename System \u0026gt; class lds::UniformSystemList; Public Function Details # UniformSystemList # UniformSystemList() =default UniformSystemList # explicit UniformSystemList( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # explicit UniformSystemList( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList UniformSystemList # UniformSystemList( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList ~UniformSystemList # ~UniformSystemList() =default dim # inline const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const size # inline size_t size() at # inline const System \u0026amp; at( size_t n ) Swap # inline void Swap( System \u0026amp; that, size_t n ) Parameters:\nthat input system n index where the system is moved operator= # inline UniformSystemList \u0026amp; operator=( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\noperator= # inline UniformSystemList \u0026amp; operator=( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":50,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/","title":"lds::UniformVectorList","section":"Classes","content":" lds::UniformVectorList # Inherits from std::vector\u0026lt; Vector \u0026gt;\nPublic Functions # Name UniformVectorList() =default\nConstructs a new UniformVectorList. UniformVectorList(const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent. UniformVectorList(std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent. UniformVectorList(std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0)\nConstructs a new UniformVectorList from initializer_list of Vector if dimensions consistent. UniformVectorList(const UniformVectorList \u0026amp; that)\nConstructs a new UniformVectorList (copy) UniformVectorList(UniformVectorList \u0026amp;\u0026amp; that)\nConstructs a new UniformVectorList (move) ~UniformVectorList() =default\nDestroys the object. size_t dim() const\ngets dimensions of the uniformly sized matrices size_t size()\nsize of container const Vector \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Vector \u0026amp; that, size_t n)\nswaps input matrix with n^th vector of list UniformVectorList \u0026amp; operator=(const UniformVectorList \u0026amp; that)\nassigns the contents (copy) UniformVectorList \u0026amp; operator=(UniformVectorList \u0026amp;\u0026amp; that)\nassigns the contents (move) Public Function Details # UniformVectorList # UniformVectorList() =default UniformVectorList # explicit UniformVectorList( const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dims dimension UniformVectorList # explicit UniformVectorList( std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList UniformVectorList # UniformVectorList( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList ~UniformVectorList # ~UniformVectorList() =default dim # inline size_t dim() const size # inline size_t size() at # inline const Vector \u0026amp; at( size_t n ) Swap # inline void Swap( Vector \u0026amp; that, size_t n ) Parameters:\nthat input vector n index where the vector is moved operator= # inline UniformVectorList \u0026amp; operator=( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\noperator= # inline UniformVectorList \u0026amp; operator=( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":51,"href":"/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/","title":"ldsCtrlEst_h","section":"Files","content":" ldsCtrlEst_h # Files # Name ldsCtrlEst_h/lds.h lds namespace ldsCtrlEst_h/lds_ctrl.h Controller. ldsCtrlEst_h/lds_fit.h LDS base fit type. ldsCtrlEst_h/lds_fit_em.h subspace identification ldsCtrlEst_h/lds_fit_ssid.h subspace identification ldsCtrlEst_h/lds_gaussian.h glds namespace ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller. ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type. ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type. ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type. ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type. ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type. ldsCtrlEst_h/lds_poisson.h plds namespace ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type. ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type. ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type. ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type. ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type. ldsCtrlEst_h/lds_poisson_sys.h PLDS base type. ldsCtrlEst_h/lds_sctrl.h SwitchedController type. ldsCtrlEst_h/lds_sys.h LDS base type. ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices. ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems. ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors. ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":52,"href":"/lds-ctrl-est/docs/api/files/lds__ctrl_8h/","title":"ldsCtrlEst_h/lds_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_ctrl.h # Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Controller Detailed Description # This file declares the type for control of a linear dynamical system (lds::Controller).\nSource code # //===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_CTRL_H #define LDSCTRLEST_LDS_CTRL_H // namespace #include \u0026#34;lds.h\u0026#34; // system type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class Controller { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); public: Controller() = default; Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); const Vector\u0026amp; Control(const Vector\u0026amp; z, bool do_control = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); const Vector\u0026amp; ControlOutputReference(const Vector\u0026amp; z, bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); // get methods: const System\u0026amp; sys() const { return sys_; }; const Matrix\u0026amp; Kc() const { return Kc_; }; const Matrix\u0026amp; Kc_inty() const { return Kc_inty_; }; const Matrix\u0026amp; Kc_u() const { return Kc_u_; }; const Vector\u0026amp; g_design() const { return g_design_; }; const Vector\u0026amp; u_ref() const { return u_ref_; }; const Vector\u0026amp; x_ref() const { return x_ref_; }; const Vector\u0026amp; y_ref() const { return y_ref_; }; size_t control_type() const { return control_type_; }; data_t tau_awu() const { return tau_awu_; }; data_t u_lb() const { return u_lb_; }; data_t u_ub() const { return u_ub_; }; // set methods void set_sys(const System\u0026amp; sys) { bool does_match = sys_.n_u() == sys.n_u(); does_match = does_match \u0026amp;\u0026amp; (sys_.n_x() == sys.n_x()); does_match = does_match \u0026amp;\u0026amp; (sys_.n_y() == sys.n_y()); if (does_match) { sys_ = sys; } else { throw std::runtime_error( \u0026#34;new system argument to `set_sys` does not match dimensionality of \u0026#34; \u0026#34;existing system\u0026#34;); } }; void set_g_design(const Vector\u0026amp; g_design) { Reassign(g_design_, g_design); }; void set_u_ref(const Vector\u0026amp; u_ref) { Reassign(u_ref_, u_ref); }; void set_x_ref(const Vector\u0026amp; x_ref) { Reassign(x_ref_, x_ref); cx_ref_ = sys_.C() * x_ref_; }; // y_ref needs to be handled differently depending on output fn. // (need to populate cx_ref_ too, which depends on output fn) virtual void set_y_ref(const Vector\u0026amp; y_ref) { Reassign(y_ref_, y_ref); }; void set_Kc(const Matrix\u0026amp; Kc) { Reassign(Kc_, Kc); }; void set_Kc_inty(const Matrix\u0026amp; Kc_inty) { Reassign(Kc_inty_, Kc_inty); }; void set_Kc_u(const Matrix\u0026amp; Kc_u) { Reassign(Kc_u_, Kc_u); }; void set_tau_awu(data_t tau) { tau_awu_ = tau; k_awu_ = sys_.dt() / tau_awu_; }; void set_control_type(size_t control_type); // There is no reason u_lb/ub should not be public, but making set methods // anyway. void set_u_lb(data_t u_lb) { u_lb_ = u_lb; }; void set_u_ub(data_t u_ub) { u_ub_ = u_ub; }; void Reset() { sys_.Reset(); u_ref_.zeros(); u_ref_prev_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); u_saturated_ = false; t_since_control_onset_ = 0.0; }; void Print() { sys_.Print(); std::cout \u0026lt;\u0026lt; \u0026#34;g_design : \u0026#34; \u0026lt;\u0026lt; g_design_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_lb : \u0026#34; \u0026lt;\u0026lt; u_lb_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_ub : \u0026#34; \u0026lt;\u0026lt; u_ub_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }; protected: System sys_; Vector u_; Vector u_return_; Vector g_design_; // reference signals Vector u_ref_; // create no set method for this: Vector u_ref_prev_; Vector x_ref_; Vector y_ref_; Vector cx_ref_; // Controller gains Matrix Kc_; Matrix Kc_u_; Matrix Kc_inty_; // control after g inversion // do not need set methods for these. Vector du_ref_; Vector dv_ref_; Vector v_ref_; Vector dv_; Vector v_; // integral error // do not need set method for this Vector int_e_; Vector int_e_awu_adjust_; Vector u_sat_; bool do_control_prev_ = false; bool do_lock_control_prev_ = false; // whether the g of system has become inverted from what you think it is // (gain_ref) bool u_saturated_ = false; // should be safe to have references here bc nothing needs to be done // (like reset vars) when it changes... data_t u_lb_{}; data_t u_ub_{}; data_t tau_awu_{}; data_t k_awu_ = 0; data_t t_since_control_onset_ = 0; size_t control_type_{}; private: void CalcControl(bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); void CalcSteadyStateSetPoint(); void AntiWindup(); void InitVars(size_t control_type); }; // Implement the above: template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(sys), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(std::move(sys)), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::set_control_type(size_t control_type) { if (control_type_ == control_type) { return; } // creating a blank slate... control_type_ = 0; Kc_inty_.zeros(0, 0); Kc_u_.zeros(0, 0); int_e_.zeros(0, 0); int_e_awu_adjust_.zeros(0, 0); // controller was designed to minimize integral error if (control_type \u0026amp; kControlTypeIntY) { Kc_inty_.zeros(sys_.n_u(), sys_.n_y()); int_e_.zeros(sys_.n_y()); int_e_awu_adjust_.zeros(sys_.n_u()); control_type_ = control_type_ | kControlTypeIntY; } // controller was designed to minimize deltaU // (i.e. state augmented with u) if (control_type \u0026amp; kControlTypeDeltaU) { Kc_u_.zeros(sys_.n_u(), sys_.n_u()); control_type_ = control_type_ | kControlTypeDeltaU; } // whether to adapt set point calculate with (re-estimated) process // disturbance (m) if (control_type \u0026amp; kControlTypeAdaptM) { if (sys_.do_adapt_m) // only if adapting m... { control_type_ = control_type_ | kControlTypeAdaptM; } } } // set_control_type template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::Control( const Vector\u0026amp; z, bool do_control, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement sys_.Filter(u_, z); bool do_estimation = true; // always have estimator on in this case // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::ControlOutputReference( const Vector\u0026amp; z, bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement if (do_estimation) { sys_.Filter(u_, z); } else { sys_.f(u_); } // calculate the set point // solves for u_ref and x_ref when output is at y_ref at steady state. if (do_control) { CalcSteadyStateSetPoint(); } // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcControl(bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { if (do_control \u0026amp;\u0026amp; do_estimation) { if (!do_control_prev_) { if (do_reset_at_control_onset) { Reset(); } t_since_control_onset_ = 0.0; } else { t_since_control_onset_ += sys_.dt(); } // enforce softstart on control vars. if (sigma_soft_start \u0026gt; 0) { // half-Gaussian soft-start scaling factor data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) / (2 * pow(sigma_soft_start, 2))); u_ref_ *= soft_start_sf; // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too // x_ref_ *= soft_start_sf; // cx_ref_ *= soft_start_sf; // y_ref_ *= soft_start_sf; } if (!do_lock_control) { // first do u -\u0026gt; v change of vars. (v = g.*u) // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver // control voltage u[=]V) v_ref_ = g_design_ % u_ref_; // Given FB, calc. the change in control if (control_type_ \u0026amp; kControlTypeDeltaU) { // if control designed to minimize not u but deltaU (i.e. state aug with // u): // TODO(mfbolus): Commented out for now. See note below. // du_ref_ = u_ref_ - u_ref_prev_; // dv_ref_ = g_design_ % du_ref_; // TODO(mfbolus): Assuming users want *smooth* control signals if using // kControlTypeDeltaU, it should be the case that dv_ref_ is --\u0026gt; 0. May // want to revisit, but I am going to force it to be zero unless a // situation arises that argues for keeping the above. dv_ref_.zeros(); dv_ = dv_ref_; // nominally-optimal. dv_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error dv_ -= Kc_u_ * (v_ - v_ref_); // penalty on amp u (rel to ref) if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if(!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error dv_ -= Kc_inty_ * int_e_; // control for integrated error } // update the control v_ += dv_; } else { v_ = v_ref_; // nominally-optimal. v_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if (!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error v_ -= Kc_inty_ * int_e_; // control for integrated error } } // convert back to control voltage u[=]V u_ = v_ / sys_.g(); } // else do nothing until lock is low } else { // if not control // feed through u_ref in open loop u_ = u_ref_ % g_design_ / sys_.g(); v_ = sys_.g() % u_; u_ref_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); } // ends do_control // enforce box constraints (and antiwindup) AntiWindup(); // add noise to input? // The value for u that is *returned* to user after addition of any noise, // while keeping controller/estimator blind to this addition. u_return_ = u_; if ((sigma_u_noise \u0026gt; 0.0) \u0026amp;\u0026amp; (do_control \u0026amp;\u0026amp; !do_lock_control)) { u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn); Limit(u_return_, u_lb_, u_ub_); }; // For next time step: u_ref_prev_ = u_ref_; do_control_prev_ = do_control; do_lock_control_prev_ = do_lock_control; } // CalcControl template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcSteadyStateSetPoint() { // Linearly-constrained least squares (ls). // // _reference: // Boyd \u0026amp; Vandenberghe (2018) Introduction to Applied Linear Algebra // Matrix a_ls = join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros)); Vector b_ls = cx_ref_; Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye), sys_.B() * arma::diagmat(sys_.g())); Vector d_ls = -sys_.m0(); if (control_type_ \u0026amp; kControlTypeAdaptM) { d_ls = -sys_.m(); // adapt setpoint calc with disturbance? } Matrix a_ls_t = a_ls.t(); // TODO(mfbolus): not sure why but causes seg // fault if I do not do this. Matrix phi_ls = join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()), join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros))); // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse: Matrix inv_phi = pinv(phi_ls); Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls); x_ref_ = xulam.subvec(0, sys_.n_x() - 1); u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1); cx_ref_ = sys_.C() * x_ref_; } // CalcSteadyStateSetPoint template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::AntiWindup() { u_saturated_ = false; u_sat_ = u_; // limit u and flag whether saturated for (size_t k = 0; k \u0026lt; u_.n_elem; k++) { if (u_[k] \u0026lt; u_lb_) { u_sat_[k] = u_lb_; u_saturated_ = true; } if (u_[k] \u0026gt; u_ub_) { u_sat_[k] = u_ub_; u_saturated_ = true; } } if ((control_type_ \u0026amp; kControlTypeIntY) \u0026amp;\u0026amp; (tau_awu_ \u0026lt; lds::kInf)) { // one-step back-calculation (calculate intE for u=u_sat) // (Astroem, Rundqwist 1989 warn against using this...) // int_e_awu_adjust_ = // solve(Kc_inty_, (u_ - u_sat_)); // pinv(Kc_inty) * (u-uSat); // gradual: see Astroem, Rundqwist 1989 // this is a fudge for doing MIMO gradual // n.b., went ahead and multiplied 1/T by dt so don\u0026#39;t have to do that here. int_e_awu_adjust_ = k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_); // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_); int_e_ += int_e_awu_adjust_; } // set u to saturated version u_ = u_sat_; } template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::InitVars(size_t control_type) { // initialize to default values u_ref_ = Vector(sys_.n_u(), fill::zeros); u_ref_prev_ = Vector(sys_.n_u(), fill::zeros); x_ref_ = Vector(sys_.n_x(), fill::zeros); y_ref_ = Vector(sys_.n_y(), fill::zeros); cx_ref_ = Vector(sys_.n_y(), fill::zeros); u_ = Vector(sys_.n_u(), fill::zeros); u_return_ = Vector(sys_.n_u(), fill::zeros); u_sat_ = Vector(sys_.n_u(), fill::zeros); // Might not need all these, so zero elements until later. Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros); Kc_u_ = Matrix(0, 0, fill::zeros); Kc_inty_ = Matrix(0, 0, fill::zeros); g_design_ = sys_.g(); // by default, same as model dv_ = Vector(sys_.n_u(), fill::zeros); v_ = Vector(sys_.n_u(), fill::zeros); du_ref_ = Vector(sys_.n_u(), fill::zeros); dv_ref_ = Vector(sys_.n_u(), fill::zeros); v_ref_ = Vector(sys_.n_u(), fill::zeros); int_e_ = Vector(0, fill::zeros); int_e_awu_adjust_ = Vector(0, fill::zeros); set_control_type(control_type); } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":53,"href":"/lds-ctrl-est/docs/api/files/lds__fit__em_8h/","title":"ldsCtrlEst_h/lds_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_em.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::EM Detailed Description # This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).\nSource code # //===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_EMAX_H #define LDSCTRLEST_LDS_EMAX_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class EM { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: EM() = default; EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); virtual ~EM() = default; const Fit\u0026amp; Run(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = true, bool calc_output = true, bool calc_measurement = true, size_t max_iter = 100, data_t tol = 1e-2); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); // auto tuple = std::make_tuple(u_, z_); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; x() const { return x_; }; const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; y() const { return y_; }; const Matrix\u0026amp; sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; }; const Matrix\u0026amp; sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; }; const Matrix\u0026amp; sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; }; size_t n_t_tot() { return n_t_tot_; } const Vector\u0026amp; theta() const { return theta_; }; protected: void Expectation(bool force_common_initial = false); void Maximization(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = false, bool calc_output = false, bool calc_measurement = false); void MaximizeDynamics(); void MaximizeQ(); void MaximizeInitial(); virtual void MaximizeOutput() = 0; virtual void MaximizeMeasurement() = 0; void Smooth(bool force_common_initial); virtual void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) = 0; void Reset(); void InitVars(); Vector UpdateTheta(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; std::vector\u0026lt;Matrix\u0026gt; x_; std::vector\u0026lt;Cube\u0026gt; P_; std::vector\u0026lt;Cube\u0026gt; P_t_tm1_; std::vector\u0026lt;Matrix\u0026gt; y_; Matrix diag_y_; // expectations calculated in E-step Matrix sum_E_x_t_x_t_; Matrix sum_E_xu_tm1_xu_tm1_; Matrix sum_E_xu_t_xu_tm1_; Fit fit_; Vector theta_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; }; template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; fit_ = Fit(n_u_, n_x, n_y_, dt); u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { // make sure fit dims match I/O data if (fit0.n_u() != u_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and input training data have inconsistent dimensions\u0026#34;); } if (fit0.n_y() != z_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and output training data have inconsistent dimensions\u0026#34;); } fit_ = fit0; u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::InitVars() { // check input/output data dimensions are consistent if (z_.size() != u_.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_.at(trial).n_cols != u_.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } n_u_ = fit_.n_u(); n_x_ = fit_.n_x(); n_y_ = fit_.n_y(); dt_ = fit_.dt(); x_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); P_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); P_t_tm1_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); y_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros); P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros); } diag_y_ = Matrix(n_y_, n_y_, fill::zeros); // covariances in expectation step sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); } template \u0026lt;typename Fit\u0026gt; const Fit\u0026amp; EM\u0026lt;Fit\u0026gt;::Run(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement, size_t max_iter, data_t tol) { Reset(); // to initial conditions size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_; Vector theta(n_params); Vector theta_new(n_params); data_t max_dtheta = 1; // if solving for initial conditions, allow them be varied. // otherwise, freeze at provided values. bool force_common_initial = !calc_init; // go until parameter convergence for (size_t l = 0; l \u0026lt; max_iter; l++) { theta_ = UpdateTheta(); std::cout \u0026lt;\u0026lt; \u0026#34;Iteration \u0026#34; \u0026lt;\u0026lt; l + 1 \u0026lt;\u0026lt; \u0026#34;/\u0026#34; \u0026lt;\u0026lt; max_iter \u0026lt;\u0026lt; \u0026#34; ...\\n\u0026#34;; Expectation(force_common_initial); Maximization(calc_dynamics, calc_Q, calc_init, calc_output, calc_measurement); // check convergence theta_new = UpdateTheta(); Vector dtheta = abs(theta_new - theta_) / abs(theta_); // some parameters could be zero... arma::uvec ubi_finite = find_finite(dtheta); max_dtheta = max(dtheta.elem(ubi_finite)); std::cout \u0026lt;\u0026lt; \u0026#34;max dtheta: \u0026#34; \u0026lt;\u0026lt; max_dtheta \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; if (max_dtheta \u0026lt; tol) { std::cout \u0026lt;\u0026lt; \u0026#34;Converged.\\n\u0026#34;; break; } std::cout \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } return fit_; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Smooth(bool force_common_initial) { Matrix k_e(n_x_, n_y_); // estimator gain Cube k_backfilt; // back-filtering gains // TODO(mfbolus): this loop could be made parallel for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { Matrix x_pre(n_x_, n_t_[trial], fill::zeros); Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros); Matrix x_post(n_x_, n_t_[trial], fill::zeros); Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros); if (force_common_initial) // forces all trials to have same initial // conditions. { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); } y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); // This *should not* be necessary but make sure P is symmetric. ForceSymPD(P_[trial].slice(0)); x_pre.col(0) = x_[trial].col(0); p_pre.slice(0) = P_[trial].slice(0); x_post.col(0) = x_[trial].col(0); p_post.slice(0) = P_[trial].slice(0); // filter for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // predict fit_.f(x_pre, x_post, u_.at(trial), t); fit_.h(y_[trial], x_pre, t); diag_y_.diag() = y_[trial].col(t); // TODO(mfbolus): change if parallel // update --\u0026gt; posterior estimation RecurseKe(k_e, p_pre, p_post, t); x_post.col(t) = x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t)); y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d(); } // backfilter -\u0026gt; Smoothed estimate // Reference: // Shumway et Stoffer (1982) ForceSymPD(p_post.slice(n_t_[trial] - 1)); k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1); P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 0; t--) { // TODO(mfmbolus): should not be necessary to force symm positive def ForceSymPD(p_pre.slice(t)); ForceSymPD(p_post.slice(t - 1)); ForceSymPD(P_[trial].slice(t)); k_backfilt.slice(t - 1) = p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t)); x_[trial].col(t - 1) = x_post.col(t - 1) + k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t)); P_[trial].slice(t - 1) = p_post.slice(t - 1) + k_backfilt.slice(t - 1) * (P_[trial].slice(t) - p_pre.slice(t)) * k_backfilt.slice(t - 1).t(); } // do the same for P_t_tm1 Matrix id(n_x_, n_x_, fill::eye); P_t_tm1_[trial].slice(n_t_[trial] - 1) = (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 1; t--) { P_t_tm1_[trial].slice(t - 1) = p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() + k_backfilt.slice(t - 1) * (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) * k_backfilt.slice(t - 2).t(); } // finally, get smoothed estimate of output for (size_t t = 0; t \u0026lt; n_t_[trial]; t++) { fit_.h(y_[trial], x_[trial], t); } // samps loop } // trial loop } // Smooth // template \u0026lt;typename Fit\u0026gt; // void EM\u0026lt;Fit\u0026gt;::RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) { // // predict covar // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q(); // // update Ke // Ke = P_pre.slice(t) * fit_.C().t() * // inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R()); // // update cov // // Reference: Ghahramani et Hinton (1996) // P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t); // // // n.b. for poisson : // // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + // fit_.Q(); // // // update cov // // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ * // // fit_.C()); // // // update Ke // // Ke = P_post.slice(t) * fit_.C(); // } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Expectation(bool force_common_initial) { // calculate the mean/cov of state needed for maximizing E[pr(z|theta)] Smooth(force_common_initial); // now get the various forms of sum(E[xx\u0026#39;]) needed // n.b. Going to start at t=1 rather than 0 bc most max terms need that. // so really \u0026#34;n_t_tot_\u0026#34; is (n_t_tot_-1) n_t_tot_ = 0; sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); Vector xu_tm1(n_x_ + n_u_, fill::zeros); Vector xu_t(n_x_ + n_u_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // ------------ sum_E_x_t_x_t ------------ sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t(); sum_E_x_t_x_t_ += P_[trial].slice(t); // ------------ sum_E_xu_tm1_xu_tm1 ------------ xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1)); sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t(); sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t - 1); // ------------ sum_E_xu_t_xu_tm1 ------------ xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t)); sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t(); sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_t_tm1_[trial].slice(t); n_t_tot_ += 1; } // time } // trial } // Expectation template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement) { if (calc_output) { MaximizeOutput(); } if (calc_measurement) { MaximizeMeasurement(); } if (calc_dynamics) { MaximizeDynamics(); } if (calc_Q) { MaximizeQ(); } if (calc_init) { MaximizeInitial(); } } // Maximization template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeDynamics() { // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) * inv_sympd(sum_E_xu_tm1_xu_tm1_); fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1)); std::cout \u0026lt;\u0026lt; \u0026#34;A_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.A()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.B()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeQ() { // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 * // inv_sympd(sum_E_xu_tm1_xu_tm1_) * // sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // this way is same as above iff dynamics were just updated: // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix ab = arma::join_horiz(fit_.A(), fit_.B()); // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // From scratch method: // Q is covariance of the error between state and dynamics-predicted state // (aka process noise) // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})\u0026#39;] // t-1 terms: View sum_e_x_tm1_x_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_u_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1); View sum_e_x_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); // t, t-1 terms: View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_x_t_u_tm1 = sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); Matrix q = sum_E_x_t_x_t_; q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t(); q -= sum_e_x_t_x_tm1 * fit_.A().t(); q -= fit_.A() * sum_e_x_t_x_tm1.t(); // input-related terms: q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t(); q -= sum_e_x_t_u_tm1 * fit_.B().t(); q -= fit_.B() * sum_e_x_t_u_tm1.t(); q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t(); q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t(); q /= n_t_tot_; fit_.set_Q(q); std::cout \u0026lt;\u0026lt; \u0026#34;Q_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.Q()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // std::cout \u0026lt;\u0026lt; \u0026#34;Q_new: \\n\u0026#34; \u0026lt;\u0026lt; fit_.Q() \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeInitial() { Vector x0 = fit_.x0(); x0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { x0 += x_[trial].col(0); } x0 /= z_.size(); std::cout \u0026lt;\u0026lt; \u0026#34;x0_new[0]: \u0026#34; \u0026lt;\u0026lt; x0[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // always recalc P0 even if the initial state is fixed (at zero, for // example) Matrix e_var(n_x_, n_x_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t(); } e_var /= z_.size(); // go ahead and subtract x0*x0\u0026#39; so don\u0026#39;t have to below. e_var -= x0 * x0.t(); // To get P0, going to get initial P_ per trial and average. // (which might be wrong, but need a single number) Matrix p0 = fit_.P0(); p0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { p0 += (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var; } p0 /= z_.size(); fit_.set_P0(p0); std::cout \u0026lt;\u0026lt; \u0026#34;P0_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.P0()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeOutput() { // solve for C+d: Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros); Vector x1(n_x_ + 1, fill::zeros); x1[n_x_] = 1.0; // augment with one to solve for bias Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { x1.subvec(0, n_x_ - 1) = x_[trial].col(t); sum_zx += z_.at(trial).col(t) * x1.t(); sum_e_x1_x1 += x1 * x1.t(); sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t); } } Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1); fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1)); fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_))); std::cout \u0026lt;\u0026lt; \u0026#34;C_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.C()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.d()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeMeasurement() { // Solve for measurement noise covar size_t n_t_tot = 0; // Ghahgramani, Hinton 1996: Matrix sum_zz(n_y_, n_y_, fill::zeros); Matrix sum_yz(n_y_, n_y_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t(); // Use Cnew: sum_yz += (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t(); n_t_tot += 1; } } fit_.set_R((sum_zz - sum_yz) / n_t_tot); std::cout \u0026lt;\u0026lt; \u0026#34;R_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.R()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Reset() { // reset to initial conditions for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); } } template \u0026lt;typename Fit\u0026gt; Vector EM\u0026lt;Fit\u0026gt;::UpdateTheta() { // TODO(mfbolus): This should include n_y_ more params for d. size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_; if (fit_.R().n_elem \u0026gt; 0) { n_params += n_y_ * n_y_; } Vector theta(n_params); size_t idx_start = 0; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B()); idx_start += n_x_ * n_u_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0()); idx_start += n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C()); idx_start += n_y_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d()); idx_start += n_y_; if (fit_.R().n_elem \u0026gt; 0) { theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R()); } return theta; } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":54,"href":"/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_ssid.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SSID Detailed Description # This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/docs/api/classes/classlds_1_1_s_s_i_d/)\u0026lt;Fit\u0026gt;).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_FIT_SSID_H #define LDSCTRLEST_LDS_FIT_SSID_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class SSID { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: SSID() = default; SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d = Vector(1).fill(-kInf)); std::tuple\u0026lt;Fit, Vector\u0026gt; Run(SSIDWt ssid_wt); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } protected: void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001); void CreateHankelDataMat(); virtual void DecomposeData() = 0; void CalcSVD(SSIDWt wt); void Solve(data_t wt_dc); void RecomputeExtObs(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; Matrix D_; Fit fit_; Matrix g_dc_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_h_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; Matrix L_; Vector s_; Matrix ext_obs_t_; }; template \u0026lt;typename Fit\u0026gt; SSID\u0026lt;Fit\u0026gt;::SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d) { // check input/output data dimensions are consistent if (z_train.size() != u_train.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_train.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_train.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } dt_ = dt; n_x_ = n_x; n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; n_h_ = n_h; // dimensionality check for eventual block-hankel data matrix size_t len = n_t_tot_ - 2 * n_h_ + 1; if (len \u0026lt; (2 * n_h_ * (n_u_ + n_y_))) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;Dataset problem! More rows than columns in block-hankel data \u0026#34; \u0026#34;matrix: 2*(n_u+n_y)*n_h \u0026gt; data-length! Need higher data-length or \u0026#34; \u0026#34;lower n_h.\u0026#34;; throw std::runtime_error(ss.str()); } fit_ = Fit(n_u_, n_x_, n_y_, dt_); u_ = std::move(u_train); z_ = std::move(z_train); if (!d.is_finite() || (d.n_rows != n_y_)) { // TODO(mfbolus): implement least-square solution for impulse response with // a second input of ones. Data-driven way of accounting for offset *not* // driven by an input. // // For now, calculate output bias (d) as the // output wherever the stimulus has not been on for some amount of time. // convolve u with rectangle and take all samples. This is a reasonable // approach, since often when autonomous systems are fit (i.e., systems with // no input), they will subtract off the mean of the output. This // essentially amounts to setting output bias to the mean of the output when // there is no stimulation. data_t t_silence = 0.1; data_t thresh_silence = 0.001; CalcD(t_silence, thresh_silence); } else { fit_.set_d(d); } } template \u0026lt;typename Fit\u0026gt; std::tuple\u0026lt;Fit, Vector\u0026gt; SSID\u0026lt;Fit\u0026gt;::Run(SSIDWt ssid_wt) { // the weight on minimizing dc I/O gain only works for gaussian, // and hopefully not necessary with appropriate dataset. data_t wt_dc = 0; // std::cout \u0026lt;\u0026lt; \u0026#34;creating hankel mat\\n\u0026#34;; CreateHankelDataMat(); // std::cout \u0026lt;\u0026lt; \u0026#34;decomposing data\\n\u0026#34;; DecomposeData(); // std::cout \u0026lt;\u0026lt; \u0026#34;calculating svd\\n\u0026#34;; CalcSVD(ssid_wt); // std::cout \u0026lt;\u0026lt; \u0026#34;solving for params\\n\u0026#34;; Solve(wt_dc); // std::cout \u0026lt;\u0026lt; \u0026#34;fin\\n\u0026#34;; return std::make_tuple(fit_, s_); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcD(data_t t_silence, data_t thresh_silence) { Vector d(z_.at(0).n_rows, fill::zeros); Vector win(static_cast\u0026lt;size_t\u0026gt;(t_silence / dt_), fill::ones); Vector sum_z_silence(n_y_, fill::zeros); size_t n_silence(0); for (size_t trial = 0; trial \u0026lt; u_.size(); trial++) { // find silent samples // start by convolving with Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0)); Vector u_conv = conv(sum_u, win, \u0026#34;same\u0026#34;); // get only the samples that are silent... arma::uvec ubi_silence = find(u_conv \u0026lt;= thresh_silence); if (ubi_silence.n_elem \u0026gt; 0) { sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1); n_silence += ubi_silence.n_elem; } } if (n_silence \u0026gt; 0) { d = sum_z_silence / n_silence; } fit_.set_d(d); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat() { // temporary copy of data Matrix z(n_y_, n_t_tot_, fill::zeros); Matrix u(n_u_, n_t_tot_, fill::zeros); size_t so_far(0); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial); u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial); so_far += n_t_.at(trial); } // remove output bias z.each_col() -= fit_.d(); // calculate I/O gain @ DC while data in convenient form g_dc_ = z * pinv(u); // std::cout \u0026lt;\u0026lt; \u0026#34;G0_data = \u0026#34; \u0026lt;\u0026lt; g_dc_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // create hankel data matrix size_t len = z.n_cols - 2 * n_h_ + 1; // data length in hankel mat // block-hankel data matrix D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros); // past input auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1); // future input auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1); // past output auto y_p = D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1); // future output auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, len - 1); size_t idx = 0; for (size_t k = 0; k \u0026lt; len; k++) { idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } } D_ /= sqrt(static_cast\u0026lt;data_t\u0026gt;(len)); } // template \u0026lt;typename Fit\u0026gt; // void SSID\u0026lt;Fit\u0026gt;::DecomposeData() { // // do LQ decomp instead of calculating covariance expensive way // // Note that \u0026#34;R\u0026#34; in van Overschee is lower-triangular (L), not \u0026#34;R\u0026#34; in QR // // decomp. Very confusing. // Matrix q_t; // lq(L_, q_t, D_); // // van Overschee zeros out the other elements. // L_ = trimatl(L_); // } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcSVD(SSIDWt wt) { // submats that will be needed: auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1); auto R_23_13 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1); auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_) - 1); auto R_44 = L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14); auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1); auto Luf = Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1); auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); // aka: R_f Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1); // from van Overschee subid.m: // Rf = R((2*m+l)*i+1:2*(m+l)*i,:); % Future outputs Matrix U; Matrix V; switch (wt) { case kSSIDNone: { // No weighting. (what van Overschee calls \u0026#34;N4SID\u0026#34;) Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14; arma::svd(U, s_, V, O_k_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDMOESP: { // MOESP weighting // This is what they use in the \u0026#34;robust\u0026#34; algorithm van Overschee, de Moor // 1996 Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); svd(U, s_, V, O_k_ortho_Uf_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDCVA: { // CVA weighting // See van Overschee\u0026#39;s matlab code (subid.m): // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); Matrix inv_w1; Matrix qt1; lq(inv_w1, qt1, R_56_16); // lq decomp of R_f (future output data) inv_w1 = trimatl(inv_w1); inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1); Matrix w_o_w = arma::solve( inv_w1, O_k_ortho_Uf_sans_Qt); // alternatively // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt svd(U, s_, V, w_o_w, \u0026#34;std\u0026#34;); U = inv_w1 * U; break; } } // Truncate to model order (heart of ssid method) auto s_hat = s_.subvec(0, n_x_ - 1); Matrix diag_sqrt_s = diagmat(sqrt(s_hat)); auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1); // get extended observability and controllability mats ext_obs_t_ = u_hat * diag_sqrt_s; // extended observability matrix } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::Solve(data_t wt_dc) { // required submats auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_56_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); // Solve for params using appropriate algorithm: // robust deterministic/stochastic algorithm in van Overschee 1996 // algorithm that the authors say \u0026#34;works\u0026#34; in practice. auto ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix // This is what textbook (1996) says: // // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15); // // HOWEVER, do not know why but have to fill the last place with zeros like // authors\u0026#39; matlab implementation (see `subid.m`) // Otherwise, get ridiculous covariances (although A,C estimates are close to // same...) Matrix Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Matrix S = Tl * pinv(Tr); // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure // stability. fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1)); Matrix ext_obs_t_p1 = join_vert( ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1), Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros)); fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1); // At this point, van Overschee \u0026amp; de Moor suggest re-calculating ext_obs_t_, // ext_obs_tm1 from (A, C) because it was just an approximation. This is RecomputeExtObs(); ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); S = Tl * pinv(Tr); Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_); Matrix Mcurly = pinv(ext_obs_tm1); Matrix Pcurly = Tl - Lcurly * R_56_15; Vector Pvec = vectorise(Pcurly); Matrix Qcurly = R_23_15; // Identify [D; B], assuming D=0 and ensuring DC gain is correct Matrix sum_QcurlyT_kron_Ncurly( (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_), fill::zeros); Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols, fill::eye); eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1, eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1; // van Overschee (1996) p. 126 Matrix N1_Tl = -Lcurly; N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) += join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly); N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) += Matrix(n_y_, n_y_, fill::eye); Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros); Matrix N_k; for (size_t k = 0; k \u0026lt; n_h_; k++) { auto Qcurly_k = Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1); Nk_Tl.zeros(); Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) = N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1); N_k = Nk_Tl * eye_ext_obs_tm1; sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k); } Matrix err_vec; if (wt_dc \u0026gt; 0) { // Constraints enforced by weighted least squares // // Reference: // // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly // Excited Industrial Systems. Conference in Decision and Control. // constraint 1: assume D=0 --\u0026gt; remove the components for Dvec (this is // actually a hard constraint in that it ignores D) Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly; sum_QcurlyT_kron_Ncurly = Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_); size_t kkk = 0; for (size_t k = 1; k \u0026lt; (n_u_ + 1); k++) { size_t start_idx = k * (n_y_ + n_x_) - n_x_; for (size_t kk = 0; kk \u0026lt; n_x_; kk++) { sum_QcurlyT_kron_Ncurly.col(kkk) = sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk); kkk++; } } // constraint 2: Make sure DC I/O gain is correct Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A()); Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_)); Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0); Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 = join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0); // WEIGHTED LS // Important in practice because I care a lot about at least getting the DC // gain correct. Put x weight on minimizing error at DC, relative to others Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye); // Make weight on minimizing DC error immense so at least that // should be nailed. size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows; size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows; size_t stop_row = w.n_rows - 1; size_t stop_col = w.n_cols - 1; // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale // weight with data length? w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc; Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * sum_QcurlyT_kron_Ncurly_b_to_g0) * sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec; fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_)); // Calculate residuals and their cov. // Because I\u0026#39;ve added constraints, I need to re-calculate the right term // with b_vec instead of how van Overschee do in final algorithm. err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec; } else { // default way: *no* constraint on G0 or D=0 Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec; // TODO(mfbolus) n.b., this gets thrown away... // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_); fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_)); err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec; } // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols); // TODO(mfbolus): Something is wrong with the error calculation above. // Use the way van overschee does it in `subid.m` // WARNING: this ignores any above constraints, so Q, R will be approximate... Matrix err = Tl - S * Tr; Matrix cov_err = err * err.t(); fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1)); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::RecomputeExtObs() { ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C(); for (size_t k = 2; k \u0026lt; (n_h_ + 1); k++) { ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) = ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1, ext_obs_t_.n_cols - 1) * fit_.A(); } } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":55,"href":"/lds-ctrl-est/docs/api/files/lds__fit_8h/","title":"ldsCtrlEst_h/lds_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_fit.h # LDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Fit LDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.\nSource code # //===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDS_FIT_HPP #define LDS_FIT_HPP // namespace #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); virtual ~Fit() = default; // get methods size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; Q() const { return Q_; }; const Vector\u0026amp; x0() const { return x0_; }; const Matrix\u0026amp; P0() const { return P0_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; // gets measurement noise virtual const Matrix\u0026amp; R() const = 0; // set methods (e.g., seeding initial fit values) void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_m(const Vector\u0026amp; m) { Reassign(m_, m); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); ForceSymPD(Q_); }; virtual void set_R(const Matrix\u0026amp; R) = 0; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); ForceSymPD(P0_); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; View f(Matrix\u0026amp; x, const Matrix\u0026amp; u, size_t t) { x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x.col(t); }; View f(Matrix\u0026amp; x_pre, const Matrix\u0026amp; x_post, const Matrix\u0026amp; u, size_t t) { x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x_pre.col(t); }; virtual View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) = 0; protected: data_t dt_{}; // Dynamics Matrix A_; Matrix B_; Vector g_; Vector m_; Matrix Q_; // Output Matrix C_; Vector d_; Matrix R_; // initial conditions Vector x0_; Matrix P0_; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; }; } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":56,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_ctrl.h # GLDS Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. Detailed Description # This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_gaussian_sys.h\u0026#34; // controller #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace gaussian { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); cx_ref_ = y_ref - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":57,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_em.h # GLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitEM GLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":58,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_ssid.h # GLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; using SSID\u0026lt;Fit\u0026gt;::Run; private: using SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat; using SSID\u0026lt;Fit\u0026gt;::CalcSVD; using SSID\u0026lt;Fit\u0026gt;::Solve; void DecomposeData() override; void SolveVanOverschee(); }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":59,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit.h # GLDS fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Fit GLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // fit type #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace gaussian { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); const Matrix\u0026amp; R() const override { return R_; }; void set_R(const Matrix\u0026amp; R) override { Reassign(R_, R); ForceSymPD(R_); }; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = C_ * x.col(t) + d_; return y.col(t); }; }; }; // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":60,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sctrl.h # GLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H // controller type #include \u0026#34;lds_gaussian_ctrl.h\u0026#34; // switched controller #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace gaussian { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); cx_ref_ = y_ref - sys_.d(); } // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; }; // SwitchedController } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":61,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/","title":"ldsCtrlEst_h/lds_gaussian_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sys.h # GLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::System Gaussian LDS Type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/docs/api/classes/classlds_1_1gaussian_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H #define LDSCTRLEST_LDS_GAUSSIAN_SYS_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; namespace lds { namespace gaussian { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0, data_t r0 = kDefaultR0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; // get methods const Matrix\u0026amp; R() const { return R_; }; // set methods void set_Q(const Matrix\u0026amp; Q) { lds::System::set_Q(Q); do_recurse_Ke_ = true; } void set_R(const Matrix\u0026amp; R) { Reassign(R_, R); do_recurse_Ke_ = true; }; void set_Ke(const Matrix\u0026amp; Ke) { Reassign(Ke_, Ke); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void set_Ke_m(const Matrix\u0026amp; Ke_m) { Reassign(Ke_m_, Ke_m); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void Print(); protected: void h() override { cx_ = C_ * x_; y_ = cx_ + d_; }; Vector h_(Vector x) override { return C_ * x + d_; }; void RecurseKe() override; // Gaussian-output-specific Matrix R_; bool do_recurse_Ke_{}; }; // System } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":62,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian_8h/","title":"ldsCtrlEst_h/lds_gaussian.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian.h # glds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/docs/api/namespaces/namespacelds_1_1gaussian/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_H #define LDSCTRLEST_LDS_GAUSSIAN_H // namespace #include \u0026#34;lds.h\u0026#34; namespace lds { namespace gaussian { // insert any Gaussian-specific things here... } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":63,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_ctrl.h # PLDS controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. Detailed Description # This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/docs/api/classes/classlds_1_1poisson_1_1_controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/docs/api/classes/classlds_1_1poisson_1_1_system/)), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_CTRL_H #define LDSCTRLEST_LDS_POISSON_CTRL_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system type #include \u0026#34;lds_poisson_sys.h\u0026#34; // control type #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace poisson { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); lds::Limit(y_ref_, kYRefLb, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; private: constexpr static const data_t kYRefLb = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":64,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_em.h # PLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitEM PLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\n[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H #define LDSCTRLEST_LDS_POISSON_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override{}; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; data_t NewtonSolveC(); void AnalyticalSolveD(); }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":65,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_ssid.h # PLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. Detailed Description # This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/)).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H #define LDSCTRLEST_LDS_POISSON_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; private: void DecomposeData() override; void CalcCov(); void PoissonToGaussianMoments(); Matrix cov_; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":66,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/","title":"ldsCtrlEst_h/lds_poisson_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit.h # PLDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Fit PLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_H #define LDSCTRLEST_LDS_POISSON_FIT_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // fit #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace poisson { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) : lds::Fit(n_u, n_x, n_y, dt){}; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = exp(C_ * x.col(t) + d_); return y.col(t); }; void set_R(const Matrix\u0026amp; R) override { std::cerr \u0026lt;\u0026lt; \u0026#34;WARNING: Cannot set R (R[0] = \u0026#34; \u0026lt;\u0026lt; R.at(0) \u0026lt;\u0026lt; \u0026#34;). No Gaussian measurement noise in Poisson observation model.\\n\u0026#34;; }; const Matrix\u0026amp; R() const override { return R_; }; }; }; // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":67,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sctrl.h # PLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #include \u0026#34;lds_poisson_ctrl.h\u0026#34; #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace poisson { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); lds::Limit(y_ref_, kYRefLB, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; private: constexpr static data_t kYRefLB = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":68,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/","title":"ldsCtrlEst_h/lds_poisson_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sys.h # PLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::System Poisson System type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/docs/api/classes/classlds_1_1poisson_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SYS_H #define LDSCTRLEST_LDS_POISSON_SYS_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; // needed for Poisson random number generation #include \u0026lt;random\u0026gt; namespace lds { namespace poisson { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; protected: void h() override { cx_ = C_ * x_; y_ = exp(cx_ + d_); diag_y_.diag() = y_; }; Vector h_(Vector x) override { return exp(C_ * x + d_); }; void RecurseKe() override; private: // Poisson-output-specific Matrix diag_y_; std::poisson_distribution\u0026lt;size_t\u0026gt; pd_; }; // System } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":69,"href":"/lds-ctrl-est/docs/api/files/lds__poisson_8h/","title":"ldsCtrlEst_h/lds_poisson.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson.h # plds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/docs/api/namespaces/namespacelds_1_1poisson/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_H #define LDSCTRLEST_LDS_POISSON_H #include \u0026#34;lds.h\u0026#34; namespace lds { namespace poisson { // TODO(mfbolus): Not sure if defining these as static here makes the most // sense. Is there a downside to letting multiple poisson System objects share a // common random number generator? static std::random_device rd; static std::mt19937 rng = std::mt19937( rd()); } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":70,"href":"/lds-ctrl-est/docs/api/files/lds__sctrl_8h/","title":"ldsCtrlEst_h/lds_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_sctrl.h # SwitchedController type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SwitchedController SwitchedController Type. Detailed Description # This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SCTRL_H #define LDSCTRLEST_LDS_SCTRL_H #include \u0026#34;lds_ctrl.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; #include \u0026#34;lds_uniform_vecs.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class SwitchedController : public Controller\u0026lt;System\u0026gt; { public: SwitchedController() = default; SwitchedController(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); SwitchedController(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); void Switch(size_t idx, bool do_force_switch = false); void set_Kc(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc) { Kc_list_ = Kc; Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc) { Kc_list_ = std::move(Kc); Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_inty) { Kc_inty_list_ = Kc_inty; Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_inty) { Kc_inty_list_ = std::move(Kc_inty); Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_u) { Kc_u_list_ = Kc_u; Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_u) { Kc_u_list_ = std::move(Kc_u); Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(const UniformVectorList\u0026amp; g) { g_design_list_ = g; g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(UniformVectorList\u0026amp;\u0026amp; g) { g_design_list_ = std::move(g); g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; protected: std::vector\u0026lt;System\u0026gt; systems_; size_t n_sys_{}; size_t idx_{}; // controller gains could be different for each UniformMatrixList\u0026lt;\u0026gt; Kc_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_inty_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_u_list_; // design-phase input gain could also be different UniformVectorList g_design_list_; // TODO(mfbolus): not sure why I need to do this. using Controller\u0026lt;System\u0026gt;::Kc_; using Controller\u0026lt;System\u0026gt;::Kc_inty_; using Controller\u0026lt;System\u0026gt;::Kc_u_; using Controller\u0026lt;System\u0026gt;::g_design_; using Controller\u0026lt;System\u0026gt;::sys_; // using Controller\u0026lt;System\u0026gt;::u_ref_; // using Controller\u0026lt;System\u0026gt;::x_ref_; // using Controller\u0026lt;System\u0026gt;::y_ref_; // using Controller\u0026lt;System\u0026gt;::control_type_; private: void InitVars(); using lds::Controller\u0026lt;System\u0026gt;::set_sys; // using Controller\u0026lt;System\u0026gt;::set_Kc; // using Controller\u0026lt;System\u0026gt;::set_Kc_inty; // using Controller\u0026lt;System\u0026gt;::set_Kc_u; // using Controller\u0026lt;System\u0026gt;::set_g_design; }; template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(systems.at(0), u_lb, u_ub, control_type), systems_(systems) { InitVars(); } template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(System(systems.at(0).n_u(), systems.at(0).n_x(), systems.at(0).n_y(), systems.at(0).dt()), u_lb, u_ub, control_type), systems_(std::move(systems)) { InitVars(); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::InitVars() { n_sys_ = systems_.size(); sys_ = systems_.at(0); Kc_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_)); Kc_inty_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); Kc_u_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); g_design_list_ = UniformVectorList(std::vector\u0026lt;Vector\u0026gt;(n_sys_, g_design_)); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::Switch(size_t idx, bool do_force_switch) { if ((idx == idx_) \u0026amp;\u0026amp; !do_force_switch) { return; // already there. } // put old up and get new one out systems_.at(idx_) = std::move(sys_); sys_ = std::move(systems_.at(idx)); // set the state of this system to that of the previous system // TODO(mfbolus): This will only work as intended if state matrix is the same. // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp. sys_.set_m(systems_.at(idx_).m(), true); sys_.set_x(systems_.at(idx_).x()); // swap controller gains Kc_list_.Swap(Kc_, idx_); Kc_list_.Swap(Kc_, idx); if (control_type_ \u0026amp; kControlTypeIntY) { Kc_inty_list_.Swap(Kc_inty_, idx_); Kc_inty_list_.Swap(Kc_inty_, idx); } if (control_type_ \u0026amp; kControlTypeDeltaU) { Kc_u_list_.Swap(Kc_u_, idx_); Kc_u_list_.Swap(Kc_u_, idx); } g_design_list_.Swap(g_design_, idx_); g_design_list_.Swap(g_design_, idx); idx_ = idx; } // Switch } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":71,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8h/","title":"ldsCtrlEst_h/lds_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_sys.h # LDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::System Linear Dynamical System Type. Detailed Description # This file declares and partially defines the base type for linear dynamical systems ([lds::System](/docs/api/classes/classlds_1_1_system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SYS_H #define LDSCTRLEST_LDS_SYS_H #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class System { public: System() = default; System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); virtual ~System() {} void Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z); virtual const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) = 0; void f(const Vector\u0026amp; u, bool do_add_noise = false) { x_ = A_ * x_ + B_ * (g_ % u) + m_; if (do_add_noise) { x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_); } }; virtual void h() = 0; virtual Vector h_(Vector x) = 0; size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Vector\u0026amp; x() const { return x_; }; const Matrix\u0026amp; P() const { return P_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; P_m() const { return P_m_; }; const Vector\u0026amp; cx() const { return cx_; }; const Vector\u0026amp; y() const { return y_; }; const Vector\u0026amp; x0() const { return x0_; }; const Vector\u0026amp; m0() const { return m0_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; const Matrix\u0026amp; Ke() const { return Ke_; }; const Matrix\u0026amp; Ke_m() const { return Ke_m_; }; const Matrix\u0026amp; Q() { return Q_; }; const Matrix\u0026amp; Q_m() { return Q_m_; }; const Matrix\u0026amp; P0() { return P0_; }; const Matrix\u0026amp; P0_m() { return P0_m_; }; void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_m(const Vector\u0026amp; m, bool do_force_assign = false) { Reassign(m0_, m); if ((!do_adapt_m) || do_force_assign) { Reassign(m_, m); } }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); }; void set_Q_m(const Matrix\u0026amp; Q_m) { Reassign(Q_m_, Q_m); }; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); }; void set_P0_m(const Matrix\u0026amp; P0_m) { Reassign(P0_m_, P0_m); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; void set_x(const Vector\u0026amp; x) { Reassign(x_, x); h(); }; void Reset(); std::vector\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; nstep_pred_block( UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z, size_t n_pred = 1); void Print(); // safe to leave this public and non-const bool do_adapt_m{}; protected: virtual void RecurseKe() = 0; void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); std::size_t n_x_{}; std::size_t n_u_{}; std::size_t n_y_{}; data_t dt_{}; // Signals: Vector x_; Matrix P_; Vector m_; Matrix P_m_; Vector cx_; Vector y_; Vector z_; // Parameters: Vector x0_; Matrix P0_; Vector m0_; Matrix P0_m_; Matrix A_; Matrix B_; Vector g_; Matrix Q_; Matrix Q_m_; Matrix C_; Vector d_; Matrix Ke_; Matrix Ke_m_; }; // System } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":72,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/","title":"ldsCtrlEst_h/lds_uniform_mats.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_mats.h # List of uniformly sized matrices. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformMatrixList Detailed Description # This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H #define LDSCTRLEST_LDS_UNIFORM_MATS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { template \u0026lt;MatrixListFreeDim D = kMatFreeDimNone\u0026gt; class UniformMatrixList : public std::vector\u0026lt;Matrix\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Matrix\u0026gt;::vector; // don\u0026#39;t allow push_back to be used since it doesn\u0026#39;t check dims using std::vector\u0026lt;Matrix\u0026gt;::push_back; public: using std::vector\u0026lt;Matrix\u0026gt;::operator=; using std::vector\u0026lt;Matrix\u0026gt;::operator[]; using std::vector\u0026lt;Matrix\u0026gt;::begin; using std::vector\u0026lt;Matrix\u0026gt;::end; using std::vector\u0026lt;Matrix\u0026gt;::size; using std::vector\u0026lt;Matrix\u0026gt;::at; UniformMatrixList() = default; explicit UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); explicit UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; ~UniformMatrixList() = default; const std::array\u0026lt;size_t, 2\u0026gt;\u0026amp; dim(size_t n = 0) const { return dim_.at(n); } size_t size() { return std::vector\u0026lt;Matrix\u0026gt;::size(); }; const Matrix\u0026amp; at(size_t n) { return std::vector\u0026lt;Matrix\u0026gt;::at(n); }; void Swap(Matrix\u0026amp; that, size_t n); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; void append(const Matrix\u0026amp; mat); private: void CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim); std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt; dim_; }; template \u0026lt;MatrixListFreeDim D\u0026gt; inline void UniformMatrixList\u0026lt;D\u0026gt;::Swap(Matrix\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.n_cols); } if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap // not moving, since it causes memory issues. // so this method isn\u0026#39;t a memory-saver as designed for now Matrix tmp = (*this)[n]; (*this)[n] = that; that = tmp; if (D == kMatFreeDim1) { this-\u0026gt;dim_[n][0] = (*this)[n].n_rows; } if (D == kMatFreeDim2) { this-\u0026gt;dim_[n][1] = (*this)[n].n_cols; } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::append(const Matrix\u0026amp; mat) { std::array\u0026lt;size_t, 2\u0026gt; dim({mat.n_rows, mat.n_cols}); CheckDimensions(dim); std::vector\u0026lt;Matrix\u0026gt;::push_back(mat); dim_.push_back(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) { // make sure dim_ vector is initialized if (dim_.empty()) { dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(that.size(), {0, 0}); } // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices\u0026#34;; throw std::runtime_error(ss.str()); } // if dimensions a not zero and do not match, skip move with error message. bool dims_nonzero = true; for (auto d : dim_) { if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; d[0] \u0026lt; 1) { dims_nonzero = false; break; } if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; d[1] \u0026lt; 1) { dims_nonzero = false; break; } } if (dims_nonzero) { bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); } if (!does_match) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrices of size \u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; dim_[k] = that.dim(k); } return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. bool dims_nonzero = true; for (auto d : dim_) { // if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; (d[0] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; (d[1] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // } // // if (dims_nonzero) { // bool does_match = true; // if (!(D == kMatFreeDim1)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); // } // // if (!(D == kMatFreeDim2)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); // } // // if (!does_match) { // this-\u0026gt;at(0).print(\u0026#34;this[0] = \u0026#34;); // that.at(0).print(\u0026#34;that[0] = \u0026#34;); // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformMatrixList element of size (\u0026#34; \u0026lt;\u0026lt; // that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols \u0026lt;\u0026lt; \u0026#34;) for an // element of size (\u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34;). // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Matrix\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(std::move(mats)) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) : vector(that) { (*this) = that; } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { std::array\u0026lt;size_t, 2\u0026gt; dim_k({this-\u0026gt;at(k).n_rows, this-\u0026gt;at(k).n_cols}); dim_.push_back(dim_k); } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim) { // change behavior based on free dim D if ((dim[0] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim1)) { dim[0] = this-\u0026gt;at(0).n_rows; } if ((dim[1] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim2)) { dim[1] = this-\u0026gt;at(0).n_cols; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Matrix\u0026amp; mat : *this) { if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_rows == dim[0]); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_cols == dim[1]); } if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(this-\u0026gt;size(), dim); for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { dim_[k][0] = (*this)[k].n_rows; dim_[k][1] = (*this)[k].n_cols; } } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":73,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/","title":"ldsCtrlEst_h/lds_uniform_systems.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_systems.h # List of uniformly sized Systems. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformSystemList Detailed Description # This file provides a container for uniformly sized Systems.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector // namespace #include \u0026#34;lds.h\u0026#34; // System type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class UniformSystemList : public std::vector\u0026lt;System\u0026gt; { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;System\u0026gt;::vector; using std::vector\u0026lt;System\u0026gt;::operator=; using std::vector\u0026lt;System\u0026gt;::operator[]; using std::vector\u0026lt;System\u0026gt;::at; using std::vector\u0026lt;System\u0026gt;::begin; using std::vector\u0026lt;System\u0026gt;::end; using std::vector\u0026lt;System\u0026gt;::size; public: UniformSystemList() = default; explicit UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); explicit UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(const UniformSystemList\u0026amp; that); UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept; ~UniformSystemList() = default; const std::array\u0026lt;size_t, 3\u0026gt;\u0026amp; dim() const { return dim_; } size_t size() { return std::vector\u0026lt;System\u0026gt;::size(); }; const System\u0026amp; at(size_t n) { return std::vector\u0026lt;System\u0026gt;::at(n); }; void Swap(System\u0026amp; that, size_t n); UniformSystemList\u0026amp; operator=(const UniformSystemList\u0026amp; that); UniformSystemList\u0026amp; operator=(UniformSystemList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim); std::array\u0026lt;size_t, 3\u0026gt; dim_{}; }; template \u0026lt;typename System\u0026gt; inline void UniformSystemList\u0026lt;System\u0026gt;::Swap(System\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformSystemList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = (dim_[0] == that.n_u()) \u0026amp;\u0026amp; (dim_[1] == that.n_x()) \u0026amp;\u0026amp; (dim_[2] == that.n_y()); if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformSystemList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap System tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( const UniformSystemList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_[0] + dim_[1] + dim_[2]) { std::array\u0026lt;size_t, 3\u0026gt; other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign systems of size \u0026#34; \u0026lt;\u0026lt; dim_[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2] \u0026lt;\u0026lt; \u0026#34; with systems of size \u0026#34; \u0026lt;\u0026lt; other_dim[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other_dim[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2]; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( UniformSystemList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. if (dim_[0] + dim_[1] + dim_[2]) { // bool does_match = (dim_[0] == that.at(0).n_u()) \u0026amp;\u0026amp; // (dim_[1] == that.at(0).n_x()) \u0026amp;\u0026amp; // (dim_[2] == that.at(0).n_y()); // if (!does_match) { // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformSystemList element for an element of \u0026#34; // \u0026#34;different size. Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;System\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(std::move(systems)) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList( std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const UniformSystemList\u0026amp; that) : std::vector\u0026lt;System\u0026gt;(that) { (*this) = that; } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept : std::vector\u0026lt;System\u0026gt;(std::move(that)) { this-\u0026gt;dim_[0] = this-\u0026gt;at(0).n_u(); this-\u0026gt;dim_[1] = this-\u0026gt;at(0).n_x(); this-\u0026gt;dim_[2] = this-\u0026gt;at(0).n_y(); } template \u0026lt;typename System\u0026gt; void UniformSystemList\u0026lt;System\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim) { if (dim[0] + dim[1] + dim[2]) { dim_ = dim; } else { dim_[0] = this-\u0026gt;at(0).n_u(); dim_[1] = this-\u0026gt;at(0).n_x(); dim_[2] = this-\u0026gt;at(0).n_y(); } // make sure dimensiolaties are all uniform bool does_match(true); for (const System\u0026amp; sys : *this) { does_match = does_match \u0026amp;\u0026amp; (sys.n_u() == dim_[0]); does_match = does_match \u0026amp;\u0026amp; (sys.n_x() == dim_[1]); does_match = does_match \u0026amp;\u0026amp; (sys.n_y() == dim_[2]); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input systems are not uniform.\u0026#34;); } } } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":74,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/","title":"ldsCtrlEst_h/lds_uniform_vecs.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_vecs.h # List of uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformVectorList Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H #define LDSCTRLEST_LDS_UNIFORM_VECS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { class UniformVectorList : public std::vector\u0026lt;Vector\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Vector\u0026gt;::vector; using std::vector\u0026lt;Vector\u0026gt;::operator=; using std::vector\u0026lt;Vector\u0026gt;::operator[]; using std::vector\u0026lt;Vector\u0026gt;::at; using std::vector\u0026lt;Vector\u0026gt;::begin; using std::vector\u0026lt;Vector\u0026gt;::end; using std::vector\u0026lt;Vector\u0026gt;::size; public: UniformVectorList() = default; explicit UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim = 0); explicit UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim = 0); UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim = 0); UniformVectorList(const UniformVectorList\u0026amp; that); UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept; ~UniformVectorList() = default; size_t dim() const { return dim_; } size_t size() { return std::vector\u0026lt;Vector\u0026gt;::size(); }; const Vector\u0026amp; at(size_t n) { return std::vector\u0026lt;Vector\u0026gt;::at(n); }; void Swap(Vector\u0026amp; that, size_t n); UniformVectorList\u0026amp; operator=(const UniformVectorList\u0026amp; that); UniformVectorList\u0026amp; operator=(UniformVectorList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(size_t dim); size_t dim_{}; }; inline void UniformVectorList::Swap(Vector\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = dim_ == that.n_elem; if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap Vector tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( const UniformVectorList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_) { size_t other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ \u0026lt;\u0026lt; \u0026#34; with vectors of size \u0026#34; \u0026lt;\u0026lt; other_dim; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( UniformVectorList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors. Skipping.\\n\u0026#34;; // return (*this); // } // // if (dim_) { // size_t other_dim(that.dim()); // if (dim_ != other_dim) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ // \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; other_dim \u0026lt;\u0026lt; \u0026#34;. // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Vector\u0026gt;::operator=(std::move(that)); return (*this); } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":75,"href":"/lds-ctrl-est/docs/api/files/lds_8h/","title":"ldsCtrlEst_h/lds.h","section":"Files","content":" ldsCtrlEst_h/lds.h # lds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/docs/api/namespaces/namespacelds_1_1poisson/)) observations.\nSource code # //===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_H #define LDSCTRLEST_LDS_H // #ifndef LDSCTRLEST // #include \u0026lt;ldsCtrlEst\u0026gt; // #endif #include \u0026lt;armadillo\u0026gt; namespace lds { using data_t = double; // may change to float (but breaks mex functions) using Vector = arma::Col\u0026lt;data_t\u0026gt;; using Matrix = arma::Mat\u0026lt;data_t\u0026gt;; using Cube = arma::Cube\u0026lt;data_t\u0026gt;; using View = arma::subview\u0026lt;data_t\u0026gt;; namespace fill = arma::fill; static const std::size_t kControlTypeDeltaU = 0x1; static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); static const data_t kPi = arma::datum::pi; static const data_t kDefaultP0 = 1e-6; static const data_t kDefaultQ0 = 1e-6; static const data_t kDefaultR0 = 1e-2; enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA }; enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2 }; // TODO(mfbolus): for SwitchedController, may want systems to have differing // numbers of states. Use this enum as template parameter? // enum SystemListFreeDim { // kSysFreeDimNone, // kSysFreeDimX ///\u0026lt; allow state dim (x) of systems in list to be hetero // }; // place hard limits on contents of vecors/mats void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub); void Limit(Vector\u0026amp; x, data_t lb, data_t ub); void Limit(Matrix\u0026amp; x, data_t lb, data_t ub); // in-place assign that errs if there are dimension mismatches: void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); // TODO(mfbolus): this is a fudge, but for some reason, cov mats often going // numerically asymm. void ForceSymPD(Matrix\u0026amp; X); void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min = 0); void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X); Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B); inline void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Vector\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Matrix\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if (other.n_elem != some.n_elem) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vector of size \u0026#34; \u0026lt;\u0026lt; some.n_elem \u0026lt;\u0026lt; \u0026#34; with vector of size \u0026#34; \u0026lt;\u0026lt; other.n_elem \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } inline void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrix of size \u0026#34; \u0026lt;\u0026lt; some.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; some.n_cols \u0026lt;\u0026lt; \u0026#34; with matrix of size \u0026#34; \u0026lt;\u0026lt; other.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other.n_cols \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":76,"href":"/lds-ctrl-est/docs/api/files/mex__c__util_8h/","title":"ldsCtrlEst_h/mex_c_util.h","section":"Files","content":" ldsCtrlEst_h/mex_c_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) More\u0026hellip;\nNamespaces # Name armamexc arma/mex interface using Matlab C API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab/Octave\u0026rsquo;s C mex API.\nSource code # //===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXC_UTIL_H #define LDSCTRLEST_MEXC_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.h\u0026#34; // // If Matlab_FOUND, include matrix.h. // // (Octave does not need/have it.) // #ifdef Matlab_FOUND // #include \u0026#34;matrix.h\u0026#34; // #endif namespace armamexc { template \u0026lt;class T\u0026gt; inline auto m2T_scalar(const mxArray *matlab_scalar) -\u0026gt; T { if (mxGetData(matlab_scalar)) { return static_cast\u0026lt;T\u0026gt;(mxGetScalar(matlab_scalar)); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return 0; } template \u0026lt;class T\u0026gt; inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false, bool strict = true) -\u0026gt; arma::Mat\u0026lt;T\u0026gt; { if (mxGetData(matlab_mat)) { const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat); if (n_dim == 2) { return arma::Mat\u0026lt;T\u0026gt;(static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)), mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem, strict); } mexErrMsgTxt(\u0026#34;Number of dimensions must be 2.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } // TODO(mfbolus): make these templated. template \u0026lt;typename T\u0026gt; inline auto a2m_mat(arma::Mat\u0026lt;T\u0026gt; const \u0026amp;arma_mat) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_mat.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Mat.\u0026#34;); return nullptr; } template \u0026lt;typename T\u0026gt; inline auto a2m_vec(arma::Col\u0026lt;T\u0026gt; const \u0026amp;arma_vec) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_vec.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Col.\u0026#34;); return nullptr; } } // namespace armamexc #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":77,"href":"/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/","title":"ldsCtrlEst_h/mex_cpp_util.h","section":"Files","content":" ldsCtrlEst_h/mex_cpp_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) More\u0026hellip;\nNamespaces # Name armamexcpp arma/mex interface using Matlab C++ API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab\u0026rsquo;s C++ mex API.\nSource code # //===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXCPP_UTIL_H #define LDSCTRLEST_MEXCPP_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.hpp\u0026#34; #include \u0026#34;mexAdapter.hpp\u0026#34; namespace armamexcpp { template \u0026lt;class T\u0026gt; std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; m2a_cellmat(matlab::data::CellArray\u0026amp; matlab_cell) { size_t n_cells = matlab_cell.getNumberOfElements(); std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; arma_mat(n_cells, arma::Mat\u0026lt;T\u0026gt;(1, 1, arma::fill::zeros)); for (size_t k = 0; k \u0026lt; n_cells; k++) { matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = matlab_cell[k]; auto dims = matlab_mat.getDimensions(); arma_mat[k] = arma::Mat\u0026lt;T\u0026gt;(matlab_mat.release().get(), dims[0], dims[1]); } return arma_mat; }; template \u0026lt;class T\u0026gt; std::vector\u0026lt;T\u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt;T\u0026gt;\u0026amp; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); T* ptr = matlab_array.release().get(); std::vector\u0026lt;T\u0026gt; vec(ptr, ptr + n_elem); return vec; }; template \u0026lt;class T\u0026gt; arma::Col\u0026lt;T\u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); // T* ptr = matlab_array.release().get(); // arma::Col\u0026lt;T\u0026gt; vec(ptr, n_elem); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? arma::Col\u0026lt;T\u0026gt; vec(n_elem, arma::fill::zeros); for (size_t k = 0; k \u0026lt; n_elem; k++) { vec[k] = matlab_array[k]; } return vec; }; template \u0026lt;class T\u0026gt; arma::Mat\u0026lt;T\u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { // ArrayDimensions == std::vector\u0026lt;size_t\u0026gt; auto dims = matlab_array.getDimensions(); // T* ptr = matlab_array.release().get(); // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false) // arma::Mat\u0026lt;T\u0026gt; mat(ptr, dims[0], dims[1]); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? // // armadillo and matlab both use column-major ordering, so this should work: size_t n_elem = dims[0] * dims[1]; arma::Mat\u0026lt;T\u0026gt; mat(dims[0], dims[1], arma::fill::zeros); size_t k(0); for (auto m: matlab_array) { mat[k] = m; k++; } return mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_mat(const arma::Mat\u0026lt;T\u0026gt;\u0026amp; arma_mat, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(), arma_mat.memptr() + arma_mat.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_vec(const arma::Col\u0026lt;T\u0026gt;\u0026amp; arma_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;({arma_vec.n_elem, 1}, arma_vec.memptr(), arma_vec.memptr() + arma_vec.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; s2m_vec(const std::vector\u0026lt;T\u0026gt;\u0026amp; std_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size()); return matlab_mat; }; } // namespace armamexcpp #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":78,"href":"/lds-ctrl-est/docs/terminology/model/","title":"Models","section":"LDS C+E Documentation","content":" Model Definitions # This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form: \\[\\mathbf{x}_{t\u0026#43;1} = f\\left( \\mathbf{x}_{t}, \\mathbf{v}_{t} \\right) = \\mathbf{A} \\mathbf{x}_{t} \u0026#43; \\mathbf{B} \\mathbf{v}_{t} \u0026#43; \\mathbf{m}_{t} \u0026#43; \\mathbf{w}_{t}\\] \\[\\mathbf{y}_{t} = h\\left( \\mathbf{x}_{t} \\right)\\] t : time index x : system state v = g%u : input (e.g., in physical units used for model fit) u : control signal sent to actuator (e.g., in Volts) y : system output m : process disturbance w ~ N(0, Q) : process noise/disturbance A : state matrix B : input coupling matrix g : input gain (e.g., for converting to control signal actuator voltage) n.b., assumes this conversion is linear Q : process noise covariance % : element-wise multiplication LDS with Gaussian Observations # For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.\n\\[\\mathbf{y}_{t} = \\mathbf{C} \\mathbf{x}_{t} \u0026#43; \\mathbf{d}\\] \\[\\mathbf{z}_{t} \\sim \\mathcal{N}\\left(\\mathbf{y}_{t} , \\mathbf{R} \\right)\\] z : measurement C : output matrix d : output bias R : measurement noise covariance LDS with Poisson Observations # For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.\n\\[y_{t}^{i} = \\exp \\left(\\mathbf{c}^i \\mathbf{x}_{t} \u0026#43; d^i\\right)\\] \\[z_{t}^i \\sim \\rm{Poisson} \\left(y_{t}^i \\right)\\] i : output index z : measurement (count data) c : i^th row of output matrix (C) d : output bias Model Predictive Control (MPC) # Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.\nIn the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.\n"},{"id":79,"href":"/lds-ctrl-est/docs/api/modules/","title":"Modules","section":"LDS C+E Documentation","content":" Modules # "},{"id":80,"href":"/lds-ctrl-est/docs/api/namespaces/","title":"Namespaces","section":"LDS C+E Documentation","content":" Namespaces # "},{"id":81,"href":"/lds-ctrl-est/docs/api/pages/","title":"Pages","section":"LDS C+E Documentation","content":" Pages # Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":82,"href":"/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/","title":"PLDS State Estimation","section":"LDS C+E Examples","content":" PLDS State Estimation Tutorial # This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[x_{t\u0026#43;1} = x_t \u0026#43; w_t\\] \\[y_{t} = \\exp\\left(x_t\\right)\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state Finally, assign the parameters using corresponding set-methods.\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); Creating the estimator # Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. To ensure robust estimates, adaptively re-estimate the process disturbance.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); Simulating estimation # In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.\n// Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.\nWith this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.\n"},{"id":83,"href":"/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/","title":"PLDS Switched Control","section":"LDS C+E Examples","content":" PLDS Switched Control Tutorial # This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.\nIn the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating the simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); The system\u0026rsquo;s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 Initially, the system will be in \u0026ldquo;mode\u0026rdquo; 1, where B = b1.\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.\nCreating the controller # Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.\nSimilar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.\nMoreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.\nPutting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying systems: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nNow that the SwitchedController is instantiated, assign its parameters.\n// Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).\nThe control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.\n// Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system\u0026rsquo;s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.\nExample simulation result # Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller\u0026rsquo;s online estimates of the output and state are shown in purple.\nNote that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.\n"},{"id":84,"href":"/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/","title":"src","section":"Files","content":" src # Files # Name src/lds.cpp misc lds namespace functions src/lds_gaussian_sys.cpp GLDS base type. src/lds_poisson_sys.cpp PLDS base type. src/lds_sys.cpp LDS base type. src/lds_uniform_vecs.cpp Uniformly sized vectors. Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":85,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/","title":"src/lds_gaussian_sys.cpp","section":"Files","content":" src/lds_gaussian_sys.cpp # GLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_gaussian_sys.h\u0026gt; lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0, data_t r0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { R_.zeros(n_y, n_y); R_.diag().fill(r0); do_recurse_Ke_=true; }; // recursively estimate Ke void lds::gaussian::System::RecurseKe() { if (!do_recurse_Ke_) { return; } // predict covariance P_ = A_ * P_ * A_.t() + Q_; // calc Kalman gain Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_); // update covariance // Reference: Ghahramani et Hinton (1996) P_ = P_ - Ke_ * C_ * P_; if (do_adapt_m) { P_m_ += Q_m_; // A_m = I (i.e., random walk) Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_); P_m_ = P_m_ - Ke_m_ * C_ * P_m_; } } // Simulate const lds::Vector\u0026amp; lds::gaussian::System::Simulate(const Vector\u0026amp; u_tm1){ f(u_tm1, true);//simulate dynamics with noise added h();//output z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure return z_; } void lds::gaussian::System::Print() { lds::System::Print(); std::cout \u0026lt;\u0026lt; \u0026#34;R: \\n\u0026#34; \u0026lt;\u0026lt; R_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":86,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/","title":"src/lds_poisson_sys.cpp","section":"Files","content":" src/lds_poisson_sys.cpp # PLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_poisson_sys.h\u0026gt; lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { diag_y_ = diagmat(y_); pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(0); }; // Correct: Given measurement (z) and current input (u), update estimate of the // state, covar, output. // // see Eden et al. 2004 void lds::poisson::System::RecurseKe() { // predict covariance P_ = A_ * P_ * A_.t() + Q_; // update cov P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_); Ke_ = P_ * C_.t(); if (do_adapt_m) { P_m_ += Q_m_; // predict (A_m = I) P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_); // update Ke_m_ = P_m_ * C_.t(); } } // Simulate Measurement: z ~ Poisson(y) const lds::Vector\u0026amp; lds::poisson::System::Simulate(const Vector\u0026amp; u_tm1) { f(u_tm1, true); // simulate dynamics with noise added h(); // output z_.zeros(); for (std::size_t k = 0; k \u0026lt; n_y_; k++) { // construct a Poisson distribution object with mean y[k] pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(y_[k]); // pull random sample from this distribution z_[k] = pd_(rng); } return z_; } // ******************* SYS_T ******************* Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":87,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8cpp/","title":"src/lds_sys.cpp","section":"Files","content":" src/lds_sys.cpp # LDS base type. More\u0026hellip;\nDetailed Description # This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- lds_sys.cpp - LDS -------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_sys.h\u0026gt; #include \u0026lt;vector\u0026gt; lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) { InitVars(p0, q0); } void lds::System::InitVars(data_t p0, data_t q0) { // initial conditions. x0_ = Vector(n_x_, fill::zeros); // includes bias (nY) and g (nU) P0_ = p0 * Matrix(n_x_, n_x_, fill::eye); m0_ = x0_; P0_m_ = P0_; // signals x_ = x0_; P_ = P0_; m_ = m0_; P_m_ = P0_m_; y_ = Vector(n_y_, fill::zeros); cx_ = Vector(n_y_, fill::zeros); z_ = Vector(n_y_, fill::zeros); // By default, random walk where each state is independent // In this way, provides independent estimates of rate per channel of output. A_ = Matrix(n_x_, n_x_, fill::eye); B_ = Matrix(n_x_, n_u_, fill::zeros); g_ = Vector(n_u_, fill::ones); Q_ = q0 * Matrix(n_x_, n_x_, fill::eye); Q_m_ = Q_; C_ = Matrix(n_y_, n_x_, fill::eye); // each state will map to an output by d_ = Vector(n_y_, fill::zeros); Ke_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain. Ke_m_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain for m adaptation. do_adapt_m = false; } // Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state // and update estimate of the state, covar, output using Kalman filter void lds::System::Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z_t) { // predict mean f(u_tm1); // dynamics h(); // output // recursively calculate esimator gains (or just keep existing values) // (also predicts+updates estimate covariance) RecurseKe(); // update x_ += Ke_ * (z_t - y_); if (do_adapt_m) { m_ += Ke_m_ * (z_t - y_); // adaptively estimating disturbance } // With new state, estimate output. h(); // --\u0026gt; posterior } void lds::System::Reset() { // reset to initial conditions x_ = x0_; // mean P_ = P0_; // cov of state estimate m_ = m0_; // process disturbance P_m_ = P0_m_; // cov of disturbance estimate h(); } std::vector\u0026lt;lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt;\u0026gt; lds::System::nstep_pred_block(lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; u, lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; z, size_t n_pred) { lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_filt; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_pred; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; y_pred; for (size_t k = 0; k \u0026lt; u.size(); k++) { Reset(); size_t n_t = arma::size(u[k])[1]; Matrix x_filt_k(n_x_, n_t, fill::zeros); Matrix x_pred_k(n_x_, n_t - n_pred, fill::zeros); Matrix y_pred_k(n_y_, n_t - n_pred, fill::zeros); for (size_t t = 0; t \u0026lt; n_t - n_pred; t++) { Vector x_pred_ahead = x_; for (size_t t_u = t; t_u \u0026lt; t + n_pred; t_u++) { x_pred_ahead = A_ * x_pred_ahead + B_ * u[k].col(t_u); } x_pred_k.col(t) = x_pred_ahead; y_pred_k.col(t) = h_(x_pred_ahead); if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; // given previous measurment } for (size_t t = n_t - n_pred; t \u0026lt; n_t; t++) { if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; } x_filt.append(x_filt_k); x_pred.append(x_pred_k); y_pred.append(y_pred_k); } return {x_filt, x_pred, y_pred}; } void lds::System::Print() { std::cout \u0026lt;\u0026lt; \u0026#34;\\n ********** SYSTEM ********** \\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;x: \\n\u0026#34; \u0026lt;\u0026lt; x_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;P: \\n\u0026#34; \u0026lt;\u0026lt; P_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;A: \\n\u0026#34; \u0026lt;\u0026lt; A_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B: \\n\u0026#34; \u0026lt;\u0026lt; B_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;g: \\n\u0026#34; \u0026lt;\u0026lt; g_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;m: \\n\u0026#34; \u0026lt;\u0026lt; m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q: \\n\u0026#34; \u0026lt;\u0026lt; Q_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q_m: \\n\u0026#34; \u0026lt;\u0026lt; Q_m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d: \\n\u0026#34; \u0026lt;\u0026lt; d_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;C: \\n\u0026#34; \u0026lt;\u0026lt; C_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;y: \\n\u0026#34; \u0026lt;\u0026lt; y_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } //******************* SYS_T ******************* Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":88,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/","title":"src/lds_uniform_vecs.cpp","section":"Files","content":" src/lds_uniform_vecs.cpp # Uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_uniform_vecs.h\u0026gt; namespace lds { UniformVectorList::UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); } UniformVectorList::UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim) : vector(std::move(vecs)) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(const UniformVectorList\u0026amp; that) : vector(that) { (*this) = that; } UniformVectorList::UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { this-\u0026gt;dim_ = this-\u0026gt;at(0).n_elem; } void UniformVectorList::CheckDimensions(size_t dim) { if (dim) { dim_ = dim; } else { dim_ = this-\u0026gt;at(0).n_elem; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Vector\u0026amp; vec : *this) { does_match = does_match \u0026amp;\u0026amp; (vec.n_elem == dim_); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } } } // namespace lds Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":89,"href":"/lds-ctrl-est/docs/api/files/lds_8cpp/","title":"src/lds.cpp","section":"Files","content":" src/lds.cpp # misc lds namespace functions More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file implements miscellaneous lds namespace functions not bound to a class.\nSource code # //===-- lds.cpp - LDS -----------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds.h\u0026gt; // insert any necessary function definitions here. namespace lds { void ForceSymPD(Matrix\u0026amp; X) { if (X.is_sympd() || !X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; // for eigenval decomp bool did_succeed(true); Vector d; Matrix u; // see first method (which may not be ideal): // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/ size_t k(1); bool is_sympd = X.is_sympd(); Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye); while (!is_sympd) { if (k \u0026gt; 100) { did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); data_t min_eig = arma::min(d); std::cerr \u0026lt;\u0026lt; \u0026#34;After multiple iterations, min eigen val = \u0026#34; \u0026lt;\u0026lt; min_eig \u0026lt;\u0026lt; \u0026#34;.\\n\u0026#34;; throw std::runtime_error( \u0026#34;Failed to make matrix symmetric positive definite.\u0026#34;); return; } // Limit(d, arma::eps(0), kInf); // force to be positive... // Matrix d_diag = arma::diagmat(d); // X = u * d_diag * u.t(); did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymPD failed.\u0026#34;); } data_t min_eig = arma::min(d); X += id * abs(min_eig) + arma::datum::eps; // make sure symm: X = (X + X.t()) / 2; // double check eigenvals positive after symmetrizing: arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); min_eig = arma::min(d); is_sympd = min_eig \u0026gt; 0; k++; } } void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min) { if (!X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; bool did_succeed(true); Vector d; Matrix u; did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymMinEig failed.\u0026#34;); } Limit(d, eig_min + arma::eps(eig_min), kInf); // enforce lower bound Matrix d_diag = arma::diagmat(d); X = u * d_diag * u.t(); // double check symmetric X = (X + X.t()) / 2; } void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X) { bool did_succeed(true); did_succeed = arma::qr_econ(Qt, L, X.t()); if (!did_succeed) { throw std::runtime_error(\u0026#34;LQ decomposition failed.\u0026#34;); } arma::inplace_trans(L); arma::inplace_trans(Qt); } Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B) { // subtract out mean auto m_a = arma::mean(A, 1); Matrix a0 = A; a0.each_col() -= m_a; auto m_b = arma::mean(B, 1); Matrix b0 = B; b0.each_col() -= m_b; Matrix cov = a0 * b0.t() / a0.n_cols; return cov; } } // namespace lds Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":90,"href":"/lds-ctrl-est/docs/api/namespaces/namespacestd/","title":"std","section":"Namespaces","content":" std # Updated on 5 March 2025 at 16:35:01 EST\n"}]
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+[{"id":0,"href":"/lds-ctrl-est/docs/","title":"LDS C+E Documentation","section":"LDS Control \u0026 Estimation","content":" LDS Control \u0026amp; Estimation Documentation # "},{"id":1,"href":"/lds-ctrl-est/docs/tutorials/","title":"LDS C+E Examples","section":"LDS C+E Documentation","content":" Examples # "},{"id":2,"href":"/lds-ctrl-est/acknowledgements/","title":"Acknowledgements","section":"LDS Control \u0026 Estimation","content":" Acknowledgements # Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.\n"},{"id":3,"href":"/lds-ctrl-est/docs/getting-started/getting-started/","title":"Getting Started","section":"LDS C+E Documentation","content":" Getting Started # This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.\nldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.\nTested Configurations # Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the RelWithDebInfo build type in the CMake configure command (-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo):\nUbuntu 18.04 with GCC 7.5 compiler macOS 11 (Big Sur) with Apple Clang 12 compiler Windows 10 with Visual Studio 16.11 (2019 release) and Clang 12 compiler That being said, if you want to debug a build for a single platform, here are some things you can try:\nUse different compilers (or even different versions of a single compiler) Use different versions of vcpkg (which you can control by checking out a different commit in the vcpkg submodule) Mac Pre-requisities # Xcode Command Line Tools will get you clang, gcc, make, and git:\nxcode-select --install Homebrew is \u0026ldquo;The Missing Package Manager for macOS\u0026rdquo; which will make installing lots of things easy. Install like this:\n/bin/bash -c \u0026#34;$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\u0026#34; You can then use it to install CMake, gfortran, and pkg-config:\nbrew install cmake gfortran pkg-config Linux Pre-requisites # You\u0026rsquo;ll need Git, CMake, GCC, gfortran, etc.\nsudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential ninja-build Windows Installation # Look here for Windows-specific instructions.\nDownloading the Library # First, clone the repository along with submodules:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est\rgit submodule update --init Compilation + Installation # Now generate the cache and build using your IDE or from the command line as follows.\nmkdir build \u0026amp;\u0026amp; cd build\rcmake ..\rcmake --build . The first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.\nIf you want to use vcpkg set up somewhere besides this repo\u0026rsquo;s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE\u0026rsquo;s settings.\nYou can verify the build is working by running ctest from the build folder, which runs all the example scripts.\nOptions # This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\nLDSCTRLEST_BUILD_EXAMPLES : [default=ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake to configure/build the library.\nFor basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build cmake --build #build the project sudo make install #[optional] installs to default location (OS-specific) To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location cmake --build #build the project make install #install to /your/install/prefix To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\nLD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.\nPython bindings package ldsctrlest # With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython3_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn\u0026rsquo;t use an undesired version. That environment needs to have NumPy installed.\ncmake --build . --target python_modules The bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory (pip install pytest matplotlib first if you need to).\nSee python/ldsctrlest/README.md for usage details.\nAlso, beware that a single build will probably not work for both the standalone library and the Python package, since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.\nCommon issues # \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.\nexport CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix vcpkg fails on configuration Try running ./bootstrap-vcpkg from the vcpkg folder and try again. If that doesn\u0026rsquo;t work, try updating vcpkg to a newer version (in the source control tab, click on the commit hash by the vcpkg repo then select from the dropdown) and running boostsrap-vcpkg again. You can also try upgrading your system (e.g., apt update, apt upgrade).\nCould not find Python3 (missing: Python3_NumPy_INCLUDE_DIRS NumPy)\nMake sure NumPy is installed in the Python environment you specified. If CMake still can\u0026rsquo;t find it, you may need to tell CMake exactly where to find it by adding an argument to the configure command: -DPython3_NumPy_INCLUDE_DIR=.... You can find that location like this: python -c 'import numpy; print(numpy.get_include())'\n"},{"id":4,"href":"/lds-ctrl-est/docs/getting-started/windows/","title":"Windows","section":"LDS C+E Documentation","content":" Windows Installation # Windows Pre-requisites # Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:\nSet-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time iwr get.scoop.sh | Invoke-Expression Install Git and CMake if you don\u0026rsquo;t already have them:\nscoop install git cmake If that didn\u0026rsquo;t work, follow more detailed instructions here.\nThe easiest way to compile C++ project on Windows is with Visual Studio\u0026rsquo;s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on \u0026ldquo;Desktop development with C++.\u0026rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the \u0026ldquo;Installation details\u0026rdquo; sidebar under optional features.\nAnd the easiest way to use Visual Studio\u0026rsquo;s build tools is with VS Code, along with the CMake Tools extension. Install them and you should be ready to go.\nDownloading the Library # First, clone the repository, either from VS Code or the command line:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est You\u0026rsquo;ll need to initialize the submodules from the command line after the repo is cloned:\ngit submodule update --init Installation # When you open the folder in VS Code, you will like be prompted by the CMake Tools extension to configure the project. Make sure you select the kit (you\u0026rsquo;ll be prompted when you configure\u0026ndash;else there\u0026rsquo;s an icon in the bar on the bottom of the window or type Ctrl+Shift+P, then \u0026ldquo;cmake select kit\u0026rdquo;). Choose Clang [latest version] with GNU CLI ... amd64 assuming you are running a 64-bit OS. (MSVC may work okay too if you don\u0026rsquo;t need to build Python bindings.)\nFollow along with the \u0026ldquo;Getting Started\u0026rdquo; instructions, but where you see config options specified as -DLDSCTREST_BUILD_STATIC=OFF or -DPython3_ROOT_DIR=..., you will enter those in settings: open with Ctrl+,, click \u0026ldquo;workspace\u0026rdquo;, then search for \u0026ldquo;CMake: Configure Args\u0026rdquo; and enter each of your desired arguments as a separate item.\nTo configure, use Ctrl+Shift+P and search for the \u0026ldquo;CMake: Configure\u0026rdquo; command. To build, click the \u0026ldquo;Build\u0026rdquo; button on the bottom bar. Then click the \u0026ldquo;CTest\u0026rdquo; button to run the example scripts.\nConsiderations # Development on Windows has been more prone to bugs than on Unix systems, so if you encounter many problems, consider switching—WSL (Windows Subsystem for Linux) is a good option for Windows users who don\u0026rsquo;t want to work on a different machine.\nCompilation has been successfully tested in VS Code using the following kit, using the \u0026ldquo;RelWithDebInfo\u0026rdquo; config:\nClang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64) Troubleshooting # The build appears to work, but tests fail with code 0xc0000135 OR \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; Have you installed the library? In VS Code, use Shift+F7 to build a specific target, in this case INSTALL. If that doesn\u0026rsquo;t solve your problem, you will likely need to add the build or install folder to your PATH environment variable, which you can do using the settings GUI (search for \u0026ldquo;Edit the system environment variables\u0026rdquo;).\nOn Windows, \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted. Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":5,"href":"/lds-ctrl-est/issues-contributing/","title":"Issues Contributing","section":"LDS Control \u0026 Estimation","content":" Reporting Issues # If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.\nContributing # We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.\n"},{"id":6,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/","title":"armamexc","section":"Namespaces","content":" armamexc # arma/mex interface using Matlab C API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; T m2T_scalar(const mxArray * matlab_scalar)\nConvert Matlab mxArray to scalar of type T. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)\nConvert matlab matrix to armadillo. template \u0026lt;typename T \u0026gt; mxArray * a2m_mat(arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat)\nConvert armadillo to matlab matrix. template \u0026lt;typename T \u0026gt; mxArray * a2m_vec(arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec)\nConvert armadillo to matlab vector. Detailed Description # Utilities for arma/mex interface using Matlab C API\nFunction Details # m2T_scalar # template \u0026lt;class T \u0026gt; inline T m2T_scalar( const mxArray * matlab_scalar ) Parameters:\nmatlab_scalar matlab scalar Template Parameters:\nT type Return: scalar of type T\nm2a_mat # template \u0026lt;class T \u0026gt; inline arma::Mat\u0026lt; T \u0026gt; m2a_mat( const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true ) Parameters:\nmatlab_mat matlab matrix copy_aux_mem [optional] whether to copy auxiliary memory strict [optional] strictly enforce the above Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_mat( arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat ) Parameters:\narma_mat armadillo matrix Return: matlab matrix\na2m_vec # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_vec( arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec ) Parameters:\narma_vec armadillo vector Return: matlab vector\nUpdated on 5 March 2025 at 16:42:06 EST\n"},{"id":7,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/","title":"armamexcpp","section":"Namespaces","content":" armamexcpp # arma/mex interface using Matlab C++ API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat(matlab::data::CellArray \u0026amp; matlab_cell)\nConvert matlab cell array to vector of armadillo matrices. template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array)\nConvert matlab matrix to a vector of scalars. template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo vector. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat(const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec(const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab vector. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec(const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert vector of scalar T to matlab matrix. Detailed Description # utilities for arma/mex interface using Matlab C++ API\nFunction Details # m2a_cellmat # template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat( matlab::data::CellArray \u0026amp; matlab_cell ) Parameters:\nmatlab_cell matlab cell Template Parameters:\nT type Return: vector of armadillo matrices of type T\nm2s_vec # template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: vector of type T\nm2a_vec # template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: armadillo vector of type T\nm2a_mat # template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab matrix Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat( const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_mat arma matrix factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\na2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec( const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_vec armadillo vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\ns2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec( const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\nstd_vec standard vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\nUpdated on 5 March 2025 at 16:42:06 EST\n"},{"id":8,"href":"/lds-ctrl-est/docs/terminology/control-estimation/","title":"C\u0026E","section":"LDS C+E Documentation","content":" Control \u0026amp; Estimation # The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.\nState estimation # In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by \\[\\widehat{\\mathbf{x}}_{t|t-1} = \\mathbf{A}\\widehat{\\mathbf{x}}_{t-1|t-1} \u0026#43; \\mathbf{B} u_{t-1} \u0026#43; \\mathbf{m}_{t-1} \\;,\\] \\[\\widehat{\\mathbf{x}}_{t|t} = \\widehat{\\mathbf{x}}_{t|t-1} \u0026#43; \\mathbf{K}^{\\rm e}_t \\left(\\mathbf{z}_t - \\widehat{\\mathbf{y}}_{t|t-1}\\right)\\;,\\] where \\( \\hat{\\left(\\cdot\\right)}_{t|j} \\) indicates an estimate at time \\( t \\) given data up to time \\( j \\) inclusive, \\( \\mathbf{K}^{\\rm e} \\) is the estimator gain, and\n\\[ \\widehat{\\mathbf{y}}_{t|t-1} = h\\left( \\widehat{\\mathbf{x}}_{t|t-1} \\right) \\; .\\] In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.\nIn the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.\nAdaptive estimation of process disturbance # Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.\nWhen parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk \\[\\mathbf{m}_{t} = \\mathbf{m}_{t-1} \u0026#43; \\mathbf{w}^m_{t-1} \\;,\\] where \\( \\mathbf{w}^m \\sim \\mathcal{N}\\left(0, \\mathbf{Q}_m\\right)\\) . Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.\nControl # Given the estimated state, the controller updates the inputs to the system according to the following law: \\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] where \\( \\left( \\cdot \\right)^{\\rm ref} \\) correspond to reference/target signals and \\( \\mathbf{K}^c_x \\) is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.\nIf users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as\n\\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right) - \\mathbf{K}^c_{\\rm inty} \\sum_{j=1}^{t}\\left( \\widehat{\\mathbf{y}}_j - \\mathbf{y}^{\\rm ref}_j \\right) \\;.\\] An additional option available to users is a control law that updates the change in u,\n\\[\\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\left(\\mathbf{u}_{t-1} - \\mathbf{u}^{\\rm ref}_{t-1} \\right) - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] \\[\\mathbf{u}_{t} = \\mathbf{u}_{t-1} \u0026#43; \\Delta\\mathbf{u}_{t} \\; .\\] Notice that this takes the form of a first-order difference equation for updating control (i.e., \\( \\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\mathbf{u}_{t-1} \u0026#43; \\epsilon_{t-1} \\) ), effectively low-pass filtering the input depending on the characteristics of \\( \\mathbf{K}^c_u \\) . This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.\nIntegral action and the \\( \\Delta \\mathbf{u} \\) control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR\u0026rsquo;d to use in combination.\nCalculating reference state-control from output # In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce \\( \\mathbf{x}^{\\rm ref} \\) and \\( \\mathbf{u}^{\\rm ref} \\) . Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.\n"},{"id":9,"href":"/lds-ctrl-est/docs/api/classes/","title":"Classes","section":"LDS C+E Documentation","content":" Classes # lds::Controller\nlds::EM\nlds::Fit LDS Fit Type.\nlds::SSID\nlds::SwitchedController SwitchedController Type.\nlds::System Linear Dynamical System Type.\nlds::UniformMatrixList\nlds::UniformSystemList\nlds::UniformVectorList\nlds::gaussian::Controller Gaussian-observation Controller Type.\nlds::gaussian::Fit GLDS Fit Type.\nlds::gaussian::FitEM GLDS E-M Fit Type.\nlds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.\nlds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.\nlds::gaussian::System Gaussian LDS Type.\nlds::poisson::Controller PLDS Controller Type.\nlds::poisson::Fit PLDS Fit Type.\nlds::poisson::FitEM PLDS E-M Fit Type.\nlds::poisson::FitSSID Subspace Identification (SSID) for PLDS.\nlds::poisson::SwitchedController Poisson-observation SwitchedController Type.\nlds::poisson::System Poisson System type.\nUpdated on 5 March 2025 at 16:42:06 EST\n"},{"id":10,"href":"/lds-ctrl-est/docs/api/modules/group__control__masks/","title":"Control Mode Bit Masks","section":"Modules","content":" Control Mode Bit Masks # provides fill types for constructing new armadillo vectors, matrices More\u0026hellip; Attributes # Name const std::size_t kControlTypeDeltaU control designed to penalize change in input const std::size_t kControlTypeIntY control using integral action const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m Detailed Description # Control mode bit masks. These can be bit-wise OR\u0026rsquo;d to use in combination.\nAttribute Details # kControlTypeDeltaU # static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY # static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM # static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\nUpdated on 5 March 2025 at 16:42:06 EST\n"},{"id":11,"href":"/lds-ctrl-est/docs/api/modules/group__defaults/","title":"Defaults","section":"Modules","content":" Defaults # More\u0026hellip; Attributes # Name const data_t kDefaultP0 default state estimate covar const data_t kDefaultQ0 default process noise covar const data_t kDefaultR0 default output noise covar Detailed Description # Default values for common variables (e.g., default diagonal elements of covariances)\nAttribute Details # kDefaultP0 # static const data_t kDefaultP0 = 1e-6; kDefaultQ0 # static const data_t kDefaultQ0 = 1e-6; kDefaultR0 # static const data_t kDefaultR0 = 1e-2; Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":12,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/","title":"eg_glds_ctrl.cpp","section":"Examples","content":" eg_glds_ctrl.cpp # Example GLDS Control ```cpp\n//===\u0026ndash; eg_glds_ctrl.cpp - Example GLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS Control ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\n// output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false;\n// Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err\n// setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]);\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; }\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:42:06 EST "},{"id":13,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/","title":"eg_glds_du_plds_ctrl.cpp","section":"Examples","content":" eg_glds_du_plds_ctrl.cpp # Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u). ```cpp\n//===\u0026ndash; eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt);\n// to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU;\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_du_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:42:06 EST "},{"id":14,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/","title":"eg_plds_ctrl.cpp","section":"Examples","content":" eg_plds_ctrl.cpp # Example PLDS Control ```cpp\n//===\u0026ndash; eg_plds_ctrl.cpp - Example PLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Control ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt);\n// Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err\n// Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\nsize_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\nVector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled\u0026hellip; lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system);\n// for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type);\n// set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty);\n// to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0;\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros);\n// set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y();\nx_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x();\nm_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:42:06 EST "},{"id":15,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/","title":"eg_plds_est.cpp","section":"Examples","content":" eg_plds_est.cpp # Example PLDS Estimation ```cpp\n//===\u0026ndash; eg_plds_est.cpp - Example PLDS Estimation \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0);\nint main() { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.\n// construct ground truth system\u0026hellip; lds::poisson::System system_true(n_u, n_x, n_y, dt);\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset();\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt);\n// Can copy parameters from another system object system_estimator = system_true;\n// wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est);\n// set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true;\n// set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;estimator:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; system_estimator.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));\n// create matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros);\n// states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\nMatrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simlation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1));\n// Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simlation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows;\nif ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026ldquo;Q must be n x n.\u0026rdquo;); }\nMatrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); }\nreturn x; }\n_Filename: eg_plds_est.cpp_ ------------------------------- Updated on 5 March 2025 at 16:42:06 EST "},{"id":16,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/","title":"eg_plds_switched_ctrl.cpp","section":"Examples","content":" eg_plds_switched_ctrl.cpp # Example Switched PLDS Control ```cpp\n//===\u0026ndash; eg_plds_switched_ctrl.cpp - Example Switched PLDS Control \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026quot;;\n// whether to do switched control bool do_switch_ctrl = true;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt);\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices. data_t scale_sys_b = 2;\nMatrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));\ncontrolled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions\n// reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);\n// Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system);\n// set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys1:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys2:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x));\nswitched_controller.set_y_ref(y_ref0);\nstd::vectorlds::poisson::System systems_vec(3, lds::poisson::System()); lds::UniformSystemListlds::poisson::System systems(std::move(systems_vec));\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;switched_controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; switched_controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Fake measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros);\n// create Matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);\n// modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones);\n// set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } }\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace)); mode.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;mode\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_switched_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:42:06 EST "},{"id":17,"href":"/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/","title":"examples","section":"Files","content":" examples # Files # Name examples/eg_glds_ctrl.cpp examples/eg_glds_du_plds_ctrl.cpp examples/eg_plds_ctrl.cpp examples/eg_plds_est.cpp examples/eg_plds_switched_ctrl.cpp Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":18,"href":"/lds-ctrl-est/docs/api/examples/","title":"Examples","section":"LDS C+E Documentation","content":" Examples # eg_glds_ctrl.cpp Example GLDS Control.\neg_glds_du_plds_ctrl.cpp Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).\neg_plds_ctrl.cpp Example PLDS Control.\neg_plds_est.cpp Example PLDS Estimation.\neg_plds_switched_ctrl.cpp Example Switched PLDS Control.\nUpdated on 5 March 2025 at 16:42:06 EST\n"},{"id":19,"href":"/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/","title":"examples/eg_glds_ctrl.cpp","section":"Files","content":" examples/eg_glds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":20,"href":"/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/","title":"examples/eg_glds_du_plds_ctrl.cpp","section":"Files","content":" examples/eg_glds_du_plds_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU; // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":21,"href":"/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/","title":"examples/eg_plds_ctrl.cpp","section":"Files","content":" examples/eg_plds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err // Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled... lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type); // set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":22,"href":"/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/","title":"examples/eg_plds_est.cpp","section":"Files","content":" examples/eg_plds_est.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name Matrix random_walk(size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0) int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # random_walk # Matrix random_walk( size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0 ) main # int main() Source code # //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. // construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state // Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. // turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; } Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":23,"href":"/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/","title":"examples/eg_plds_switched_ctrl.cpp","section":"Files","content":" examples/eg_plds_switched_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Source code # //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 // simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions // reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":24,"href":"/lds-ctrl-est/docs/api/files/","title":"Files","section":"LDS C+E Documentation","content":" Files # examples/eg_glds_ctrl.cpp\nexamples/eg_glds_du_plds_ctrl.cpp\nexamples/eg_plds_ctrl.cpp\nexamples/eg_plds_est.cpp\nexamples/eg_plds_switched_ctrl.cpp\nldsCtrlEst_h/lds.h lds namespace\nldsCtrlEst_h/lds_ctrl.h Controller.\nldsCtrlEst_h/lds_fit.h LDS base fit type.\nldsCtrlEst_h/lds_fit_em.h subspace identification\nldsCtrlEst_h/lds_fit_ssid.h subspace identification\nldsCtrlEst_h/lds_gaussian.h glds namespace\nldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.\nldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.\nldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.\nldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.\nldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.\nldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.\nldsCtrlEst_h/lds_poisson.h plds namespace\nldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.\nldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.\nldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.\nldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.\nldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.\nldsCtrlEst_h/lds_poisson_sys.h PLDS base type.\nldsCtrlEst_h/lds_sctrl.h SwitchedController type.\nldsCtrlEst_h/lds_sys.h LDS base type.\nldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.\nldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.\nldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.\nldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)\nldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)\nsrc/lds.cpp misc lds namespace functions\nsrc/lds_gaussian_sys.cpp GLDS base type.\nsrc/lds_poisson_sys.cpp PLDS base type.\nsrc/lds_sys.cpp LDS base type.\nsrc/lds_uniform_vecs.cpp Uniformly sized vectors.\nUpdated on 5 March 2025 at 16:42:06 EST\n"},{"id":25,"href":"/lds-ctrl-est/docs/tutorials/eg_glds_control/","title":"GLDS Control","section":"LDS C+E Examples","content":" GLDS Control Tutorial # This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\] \\[\ry_{t} = x_t\r\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.\n/// Going to simulate a switching disturbance (m) acting on system size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_y).fill(m_low); Finally, assign the parameters using corresponding set-methods.\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); Creating the controller # Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.\nFor the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.\n// make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nWith the controller constructed, control variables may be set.\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.\nThe control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller\u0026rsquo;s online estimates of the output, state, and disturbance are given in purple.\n"},{"id":26,"href":"/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/","title":"include","section":"Files","content":" include # Directories # Name ldsCtrlEst_h Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":27,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds/","title":"lds","section":"Namespaces","content":" lds # Linear Dynamical Systems (LDS) namespace. Namespaces # Name lds::gaussian Linear Dynamical Systems with Gaussian observations. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::Controller class lds::EM class lds::Fit LDS Fit Type. class lds::SSID class lds::SwitchedController SwitchedController Type. class lds::System Linear Dynamical System Type. class lds::UniformMatrixList class lds::UniformSystemList class lds::UniformVectorList Types # Name enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}\nweighting options for SSID enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2} using double data_t using arma::Col\u0026lt; data_t \u0026gt; Vector using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Cube\u0026lt; data_t \u0026gt; Cube using arma::subview\u0026lt; data_t \u0026gt; View Functions # Name void Limit(std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub) void Limit(Vector \u0026amp; x, data_t lb, data_t ub) void Limit(Matrix \u0026amp; x, data_t lb, data_t ub) void Reassign(Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Vector in place void Reassign(Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Matrix in place void ForceSymPD(Matrix \u0026amp; X)\nforces matrix to be symmetric positive-definite void ForceSymMinEig(Matrix \u0026amp; X, data_t eig_min =0)\nforces matrix to be symmetric and have a minimum eigenvalue void lq(Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X)\nLQ decomposition. Matrix calcCov(const Matrix \u0026amp; A, const Matrix \u0026amp; B)\nCalculate covariance matrix. Attributes # Name const data_t kInf Some useful numbers. const data_t kPi Type Details # SSIDWt # Enumerator Value Description kSSIDNone None. kSSIDMOESP MOESP (AKA \u0026ldquo;robust method\u0026rdquo; in van Overschee 1996) kSSIDCVA CVA \u0026ldquo;Canonical Variate Analysis\u0026rdquo;. Weighting options for singular value decomposition performed during subspace identification (SSID)\nReference:\nvan Overschee, de Moor. 1996. Subspace Identification for Linear Systems.\nMatrixListFreeDim # Enumerator Value Description kMatFreeDimNone neither dim free to be hetero in mat list kMatFreeDim1 allow 1st dim of mats in list to be hetero kMatFreeDim2 allow 2nd dim of mats in list to be hetero data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nVector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Cube # using lds::Cube = arma::Cube\u0026lt;data_t\u0026gt;; View # using lds::View = arma::subview\u0026lt;data_t\u0026gt;; Function Details # Limit # inline void Limit( std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Vector \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Matrix \u0026amp; x, data_t lb, data_t ub ) Reassign # inline void Reassign( Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Vector other other Vector parenthetical optional description provided by caller to ease debugging Reassign # inline void Reassign( Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Matrix other other Matrix parenthetical optional description provided by caller to ease debugging ForceSymPD # void ForceSymPD( Matrix \u0026amp; X ) Parameters:\nX mutated matrix ForceSymMinEig # void ForceSymMinEig( Matrix \u0026amp; X, data_t eig_min =0 ) Parameters:\nX mutated matrix eig_min [optional] minimum eigen value lq # void lq( Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X ) Parameters:\nL lower triangle matrix Qt orthonormal matrix (transposed cf QR decomp) X matrix being decomposed calcCov # Matrix calcCov( const Matrix \u0026amp; A, const Matrix \u0026amp; B ) Parameters:\nA some matrix B some other matrix Return: covariance\nAttribute Details # kInf # static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); kPi # static const data_t kPi = arma::datum::pi; Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":28,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/","title":"lds::Controller","section":"Classes","content":" lds::Controller # More\u0026hellip;\nInherited by lds::SwitchedController\u0026lt; System \u0026gt;, lds::gaussian::Controller, lds::poisson::Controller\nPublic Functions # Name Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes # Name System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::Controller; Public Function Details # Controller # Controller() =default Controller # inline Controller( const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Controller # inline Controller( System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Control # inline const Vector \u0026amp; Control( const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step). This is the most flexible option, but requires user to have set the controller\u0026rsquo;s y_ref, x_ref, and u_ref variables.\nControlOutputReference # inline const Vector \u0026amp; ControlOutputReference( const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_estimation [optional] whether to update state estimate (if false, effectively open-loop control) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.\nsys # inline const System \u0026amp; sys() const Kc # inline const Matrix \u0026amp; Kc() const Kc_inty # inline const Matrix \u0026amp; Kc_inty() const Kc_u # inline const Matrix \u0026amp; Kc_u() const g_design # inline const Vector \u0026amp; g_design() const u_ref # inline const Vector \u0026amp; u_ref() const x_ref # inline const Vector \u0026amp; x_ref() const y_ref # inline const Vector \u0026amp; y_ref() const control_type # inline size_t control_type() const tau_awu # inline data_t tau_awu() const u_lb # inline data_t u_lb() const u_ub # inline data_t u_ub() const set_sys # inline void set_sys( const System \u0026amp; sys ) set_g_design # inline void set_g_design( const Vector \u0026amp; g_design ) set_u_ref # inline void set_u_ref( const Vector \u0026amp; u_ref ) set_x_ref # inline void set_x_ref( const Vector \u0026amp; x_ref ) set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) Reimplemented by: lds::gaussian::Controller::set_y_ref, lds::gaussian::SwitchedController::set_y_ref, lds::poisson::Controller::set_y_ref, lds::poisson::SwitchedController::set_y_ref\nset_Kc # inline void set_Kc( const Matrix \u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const Matrix \u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const Matrix \u0026amp; Kc_u ) set_tau_awu # inline void set_tau_awu( data_t tau ) set_control_type # inline void set_control_type( size_t control_type ) Parameters:\ncontrol_type control type bit mask Template Parameters:\nSystem type derived from lds::System set_u_lb # inline void set_u_lb( data_t u_lb ) Parameters:\nu_lb control lower bound set_u_ub # inline void set_u_ub( data_t u_ub ) Parameters:\nu_ub control upper bound Reset # inline void Reset() Print # inline void Print() Protected Attribute Details # sys_ # System sys_; u_ # Vector u_; u_return_ # Vector u_return_; g_design_ # Vector g_design_; u_ref_ # Vector u_ref_; u_ref_prev_ # Vector u_ref_prev_; x_ref_ # Vector x_ref_; y_ref_ # Vector y_ref_; cx_ref_ # Vector cx_ref_; Kc_ # Matrix Kc_; Kc_u_ # Matrix Kc_u_; Kc_inty_ # Matrix Kc_inty_; du_ref_ # Vector du_ref_; dv_ref_ # Vector dv_ref_; v_ref_ # Vector v_ref_; dv_ # Vector dv_; v_ # Vector v_; int_e_ # Vector int_e_; int_e_awu_adjust_ # Vector int_e_awu_adjust_; u_sat_ # Vector u_sat_; do_control_prev_ # bool do_control_prev_ = false; do_lock_control_prev_ # bool do_lock_control_prev_ = false; u_saturated_ # bool u_saturated_ = false; u_lb_ # data_t u_lb_ {}; u_ub_ # data_t u_ub_ {}; tau_awu_ # data_t tau_awu_ {}; k_awu_ # data_t k_awu_ = 0; t_since_control_onset_ # data_t t_since_control_onset_ = 0; control_type_ # size_t control_type_ {}; Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":29,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/","title":"lds::EM","section":"Classes","content":" lds::EM # More\u0026hellip;\nInherited by lds::gaussian::FitEM, lds::poisson::FitEM\nPublic Functions # Name EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions # Name void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() virtual void MaximizeOutput() =0 virtual void MaximizeMeasurement() =0 void Smooth(bool force_common_initial)\nget smoothed estimates virtual void RecurseKe(Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t) =0\nrecursively update estimator gain Ke void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::EM; Public Function Details # EM # EM() =default EM # EM( size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nn_x number of states dt sample period u_train input training data z_train measurement training data EM # EM( const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nfit0 initial fit u_train input training data z_train measurement training data ~EM # virtual ~EM() =default Run # const Fit \u0026amp; Run( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2 ) Parameters:\ncalc_dynamics [optional] whether to calculate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law max_iter max number of iterations tol convergence tolerance (max fractional abs change) Return: Fit\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nx # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const y # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const sum_E_x_t_x_t # inline const Matrix \u0026amp; sum_E_x_t_x_t() const sum_E_xu_tm1_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const sum_E_xu_t_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const n_t_tot # inline size_t n_t_tot() theta # inline const Vector \u0026amp; theta() const Protected Function Details # Expectation # void Expectation( bool force_common_initial =false ) Parameters:\nforce_common_initial whether to force common initial condition for all trials Maximization # void Maximization( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false ) Parameters:\ncalc_dynamics [optional] whether to caclulate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law MaximizeDynamics # void MaximizeDynamics() MaximizeQ # void MaximizeQ() MaximizeInitial # void MaximizeInitial() MaximizeOutput # virtual void MaximizeOutput() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput\nMaximizeMeasurement # virtual void MaximizeMeasurement() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement\nSmooth # void Smooth( bool force_common_initial ) Parameters:\nforce_common_initial whether to force common initial conditions RecurseKe # virtual void RecurseKe( Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t ) =0 Parameters:\nKe estimator gain P_pre cov of predicted state est. P_post cov of postior sate est. t time Reimplemented by: lds::gaussian::FitEM::RecurseKe, lds::poisson::FitEM::RecurseKe\nReset # void Reset() InitVars # void InitVars() UpdateTheta # Vector UpdateTheta() Return: parameter list\nProtected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; x_ # std::vector\u0026lt; Matrix \u0026gt; x_; P_ # std::vector\u0026lt; Cube \u0026gt; P_; P_t_tm1_ # std::vector\u0026lt; Cube \u0026gt; P_t_tm1_; y_ # std::vector\u0026lt; Matrix \u0026gt; y_; diag_y_ # Matrix diag_y_; sum_E_x_t_x_t_ # Matrix sum_E_x_t_x_t_; sum_E_xu_tm1_xu_tm1_ # Matrix sum_E_xu_tm1_xu_tm1_; sum_E_xu_t_xu_tm1_ # Matrix sum_E_xu_t_xu_tm1_; fit_ # Fit fit_; theta_ # Vector theta_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":30,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/","title":"lds::Fit","section":"Classes","content":" lds::Fit # LDS Fit Type. #include \u0026lt;lds_fit.h\u0026gt;\nInherited by lds::gaussian::Fit, lds::poisson::Fit\nPublic Functions # Name Fit() =default\nConstructs a new Fit. Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias virtual const Matrix \u0026amp; R() const =0 void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance virtual void set_R(const Matrix \u0026amp; R) =0\nsets output noise covariance (if any) void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) =0\noutput function Protected Attributes # Name data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period ~Fit # virtual ~Fit() =default n_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const m # inline const Vector \u0026amp; m() const Q # inline const Matrix \u0026amp; Q() const x0 # inline const Vector \u0026amp; x0() const P0 # inline const Matrix \u0026amp; P0() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const R # virtual const Matrix \u0026amp; R() const =0 Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R\nset_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_g # inline void set_g( const Vector \u0026amp; g ) set_m # inline void set_m( const Vector \u0026amp; m ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # virtual void set_R( const Matrix \u0026amp; R ) =0 Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R\nset_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) f # inline View f( Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t ) Parameters:\nx state estimate (over time) u input (over time) t time index Return: view of updated state\nf # inline View f( Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t ) Parameters:\nx_pre predicted state est. x_post posterior state est. u input (over time) t time index Return: view of predicted state\nh # virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) =0 Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplemented by: lds::gaussian::Fit::h, lds::poisson::Fit::h\nProtected Attribute Details # dt_ # data_t dt_ {}; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; m_ # Vector m_; Q_ # Matrix Q_; C_ # Matrix C_; d_ # Vector d_; R_ # Matrix R_; x0_ # Vector x0_; P0_ # Matrix P0_; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":31,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/","title":"lds::gaussian","section":"Namespaces","content":" lds::gaussian # Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. class lds::gaussian::Fit GLDS Fit Type. class lds::gaussian::FitEM GLDS E-M Fit Type. class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. class lds::gaussian::System Gaussian LDS Type. Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":32,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/","title":"lds::gaussian::Controller","section":"Classes","content":" lds::gaussian::Controller # Gaussian-observation Controller Type. #include \u0026lt;lds_gaussian_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:42:06 EST\n"},{"id":33,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/","title":"lds::gaussian::Fit","section":"Classes","content":" lds::gaussian::Fit # GLDS Fit Type. #include \u0026lt;lds_gaussian_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual const Matrix \u0026amp; R() const override\ngets measurement noise covariance virtual void set_R(const Matrix \u0026amp; R) override\nsets measurement noise covariance virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period R # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nh # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nUpdated on 5 March 2025 at 16:42:06 EST\n"},{"id":34,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/","title":"lds::gaussian::FitEM","section":"Classes","content":" lds::gaussian::FitEM # GLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_gaussian_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::gaussian::FitEM; This type is used in the process of fitting GLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":35,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/","title":"lds::gaussian::FitSSID","section":"Classes","content":" lds::gaussian::FitSSID # Subspace Identification (SSID) for GLDS. #include \u0026lt;lds_gaussian_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":36,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/","title":"lds::gaussian::SwitchedController","section":"Classes","content":" lds::gaussian::SwitchedController # Gaussian-observation SwitchedController Type. #include \u0026lt;lds_gaussian_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:42:06 EST\n"},{"id":37,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/","title":"lds::gaussian::System","section":"Classes","content":" lds::gaussian::System # Gaussian LDS Type. #include \u0026lt;lds_gaussian_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)\nConstructs a new Gaussian System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. const Matrix \u0026amp; R() const\nGet output noise covariance. void set_Q(const Matrix \u0026amp; Q) void set_R(const Matrix \u0026amp; R)\nSet output noise covariance. void set_Ke(const Matrix \u0026amp; Ke)\nSet estimator gain. void set_Ke_m(const Matrix \u0026amp; Ke_m)\nSet disturbance estimator gain. void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively update estimator gain. Protected Attributes # Name Matrix R_ covariance of output noise bool do_recurse_Ke_ whether to recursively calculate estimator gain Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0 ) Parameters:\nn_u number of inputs (u) n_x number of states (x) n_y number of outputs (y) dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) r0 [optional] initial diagonal elements of output noise covariance (R) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nR # inline const Matrix \u0026amp; R() const set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # inline void set_R( const Matrix \u0026amp; R ) set_Ke # inline void set_Ke( const Matrix \u0026amp; Ke ) set_Ke_m # inline void set_Ke_m( const Matrix \u0026amp; Ke_m ) Print # void Print() Protected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nProtected Attribute Details # R_ # Matrix R_; do_recurse_Ke_ # bool do_recurse_Ke_ {}; Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":38,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/","title":"lds::poisson","section":"Namespaces","content":" lds::poisson # Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. class lds::poisson::Fit PLDS Fit Type. class lds::poisson::FitEM PLDS E-M Fit Type. class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. class lds::poisson::System Poisson System type. Attributes # Name std::random_device rd random device for simulating poisson data std::mt19937 rng random number generator for simulating poisson data Attribute Details # rd # static std::random_device rd; rng # static std::mt19937 rng = std::mt19937( rd()); Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":39,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/","title":"lds::poisson::Controller","section":"Classes","content":" lds::poisson::Controller # PLDS Controller Type. #include \u0026lt;lds_poisson_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:42:06 EST\n"},{"id":40,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/","title":"lds::poisson::Fit","section":"Classes","content":" lds::poisson::Fit # PLDS Fit Type. #include \u0026lt;lds_poisson_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function virtual void set_R(const Matrix \u0026amp; R) override\nsets output noise covariance (if any) virtual const Matrix \u0026amp; R() const override Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # inline Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period h # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nR # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nUpdated on 5 March 2025 at 16:42:06 EST\n"},{"id":41,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/","title":"lds::poisson::FitEM","section":"Classes","content":" lds::poisson::FitEM # PLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_poisson_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::poisson::FitEM; This type is used in the process of fitting PLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":42,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/","title":"lds::poisson::FitSSID","section":"Classes","content":" lds::poisson::FitSSID # Subspace Identification (SSID) for PLDS. #include \u0026lt;lds_poisson_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":43,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/","title":"lds::poisson::SwitchedController","section":"Classes","content":" lds::poisson::SwitchedController # Poisson-observation SwitchedController Type. #include \u0026lt;lds_poisson_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:42:06 EST\n"},{"id":44,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/","title":"lds::poisson::System","section":"Classes","content":" lds::poisson::System # Poisson System type. #include \u0026lt;lds_poisson_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nConstructs a new Poisson System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively recalculate estimator gain (Ke) Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nProtected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nRecursively recalculate estimator gain (Ke).\nReferences:\nSmith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.\nEden UT, \u0026hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.\nUpdated on 5 March 2025 at 16:42:06 EST\n"},{"id":45,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/","title":"lds::SSID","section":"Classes","content":" lds::SSID # More\u0026hellip;\nInherited by lds::gaussian::FitSSID, lds::poisson::FitSSID\nPublic Functions # Name SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions # Name void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. virtual void DecomposeData() =0\nDecompose data to lower-triangular matrix (used in Solve) void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::SSID; Public Function Details # SSID # SSID() =default SSID # SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf) ) Parameters:\nn_x number of states n_h size of block-hankel data matrix dt sample period u_train input training data z_train measurement training data d output bias Run # std::tuple\u0026lt; Fit, Vector \u0026gt; Run( SSIDWt ssid_wt ) Parameters:\nssid_wt weight for singular value decomp Return: tuple (Fit, singular values)\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nProtected Function Details # CalcD # void CalcD( data_t t_silence =0.1, data_t thresh_silence =0.001 ) Parameters:\nt_silence threshold on period of time that qualifies as \u0026ldquo;silence\u0026rdquo; thresh_silence threshold on input amplitude u that qualifies as \u0026ldquo;silence\u0026rdquo; CreateHankelDataMat # void CreateHankelDataMat() Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.\nDecomposeData # virtual void DecomposeData() =0 Reimplemented by: lds::gaussian::FitSSID::DecomposeData, lds::poisson::FitSSID::DecomposeData\nCalcSVD # void CalcSVD( SSIDWt wt ) Parameters:\nssid_wt weight for SVD Solve # void Solve( data_t wt_dc ) Parameters:\nwt_dc weight placed on getting correct DC I/O gain RecomputeExtObs # void RecomputeExtObs() Protected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; D_ # Matrix D_; fit_ # Fit fit_; g_dc_ # Matrix g_dc_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_h_ # size_t n_h_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; L_ # Matrix L_; s_ # Vector s_; ext_obs_t_ # Matrix ext_obs_t_; Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":46,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/","title":"lds::SwitchedController","section":"Classes","content":" lds::SwitchedController # SwitchedController Type. More\u0026hellip;\n#include \u0026lt;lds_sctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nInherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController\nPublic Functions # Name SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes # Name std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::SwitchedController; Public Function Details # SwitchedController # SwitchedController() =default SwitchedController # inline SwitchedController( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask SwitchedController # inline SwitchedController( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Switch # inline void Switch( size_t idx, bool do_force_switch =false ) Parameters:\nidx index do_force_switch whether to force a system switch even if already there. set_Kc # inline void set_Kc( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc ) set_Kc # inline void set_Kc( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty ) set_Kc_inty # inline void set_Kc_inty( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u ) set_Kc_u # inline void set_Kc_u( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u ) set_g_design # inline void set_g_design( const UniformVectorList \u0026amp; g ) set_g_design # inline void set_g_design( UniformVectorList \u0026amp;\u0026amp; g ) Protected Attribute Details # systems_ # std::vector\u0026lt; System \u0026gt; systems_; n_sys_ # size_t n_sys_ {}; idx_ # size_t idx_ {}; Kc_list_ # UniformMatrixList Kc_list_; Kc_inty_list_ # UniformMatrixList Kc_inty_list_; Kc_u_list_ # UniformMatrixList Kc_u_list_; g_design_list_ # UniformVectorList g_design_list_; Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":47,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_system/","title":"lds::System","section":"Classes","content":" lds::System # Linear Dynamical System Type. #include \u0026lt;lds_sys.h\u0026gt;\nInherited by lds::gaussian::System, lds::poisson::System\nPublic Functions # Name System() =default\nConstructs a new System. System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nconstructs a new System virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) =0\nsimulates system (single time step) void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function virtual void h() =0\nsystem output function virtual Vector h_(Vector x) =0\nsystem output function (stateless) size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void RecurseKe() =0\nRecursively recalculate estimator gain (Ke) void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes # Name bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes # Name std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 diagonal elements for state estimate covariance q0 diagonal elements for process noise covariance ~System # inline virtual ~System() Filter # void Filter( const Vector \u0026amp; u_tm1, const Vector \u0026amp; z ) Parameters:\nu_tm1 input at t-minus-1 z_t current measurement Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.\nSimulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) =0 Parameters:\nu_tm1 input at time t-1 Return: simulated measurement at time t\nReimplemented by: lds::gaussian::System::Simulate, lds::poisson::System::Simulate\nf # inline void f( const Vector \u0026amp; u, bool do_add_noise =false ) Parameters:\nu input do_add_noise whether to add simulated process noise h # virtual void h() =0 Reimplemented by: lds::gaussian::System::h, lds::poisson::System::h\nh_ # virtual Vector h_( Vector x ) =0 Parameters:\nx_t state at time t Return: predicted state at time t + 1\nReimplemented by: lds::gaussian::System::h_, lds::poisson::System::h_\nn_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const x # inline const Vector \u0026amp; x() const P # inline const Matrix \u0026amp; P() const m # inline const Vector \u0026amp; m() const P_m # inline const Matrix \u0026amp; P_m() const cx # inline const Vector \u0026amp; cx() const y # inline const Vector \u0026amp; y() const x0 # inline const Vector \u0026amp; x0() const m0 # inline const Vector \u0026amp; m0() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const Ke # inline const Matrix \u0026amp; Ke() const Ke_m # inline const Matrix \u0026amp; Ke_m() const Q # inline const Matrix \u0026amp; Q() Q_m # inline const Matrix \u0026amp; Q_m() P0 # inline const Matrix \u0026amp; P0() P0_m # inline const Matrix \u0026amp; P0_m() set_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_m # inline void set_m( const Vector \u0026amp; m, bool do_force_assign =false ) set_g # inline void set_g( const Vector \u0026amp; g ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_Q_m # inline void set_Q_m( const Matrix \u0026amp; Q_m ) set_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_P0_m # inline void set_P0_m( const Matrix \u0026amp; P0_m ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) set_x # inline void set_x( const Vector \u0026amp; x ) Reset # void Reset() nstep_pred_block # std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block( UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1 ) Print # void Print() Protected Function Details # RecurseKe # virtual void RecurseKe() =0 Reimplemented by: lds::gaussian::System::RecurseKe, lds::poisson::System::RecurseKe\nInitVars # void InitVars( data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Public Attribute Details # do_adapt_m # bool do_adapt_m {}; Protected Attribute Details # n_x_ # std::size_t n_x_ {}; n_u_ # std::size_t n_u_ {}; n_y_ # std::size_t n_y_ {}; dt_ # data_t dt_ {}; x_ # Vector x_; P_ # Matrix P_; m_ # Vector m_; P_m_ # Matrix P_m_; cx_ # Vector cx_; y_ # Vector y_; z_ # Vector z_; x0_ # Vector x0_; P0_ # Matrix P0_; m0_ # Vector m0_; P0_m_ # Matrix P0_m_; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; Q_ # Matrix Q_; Q_m_ # Matrix Q_m_; C_ # Matrix C_; d_ # Vector d_; Ke_ # Matrix Ke_; Ke_m_ # Matrix Ke_m_; Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":48,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/","title":"lds::UniformMatrixList","section":"Classes","content":" lds::UniformMatrixList # More\u0026hellip;\nInherits from std::vector\u0026lt; Matrix \u0026gt;\nPublic Functions # Name UniformMatrixList() =default\nConstructs a new UniformMatrixList. UniformMatrixList(const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent. UniformMatrixList(std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent. UniformMatrixList(std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent. UniformMatrixList(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nConstructs a new UniformMatrixList (copy). UniformMatrixList(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nConstructs a new UniformMatrixList (move). ~UniformMatrixList() =default\nDestroys the object. const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim(size_t n =0) const\ngets dimensions of uniformly sized matrices size_t size()\nsize of container const Matrix \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Matrix \u0026amp; that, size_t n)\nswaps input matrix with n^th matrix of list UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nassigns the contents (copy) UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nassigns the contents (move) void append(const Matrix \u0026amp; mat)\nappends a matrix to the list Detailed Description # template \u0026lt;MatrixListFreeDim D =kMatFreeDimNone\u0026gt; class lds::UniformMatrixList; Public Function Details # UniformMatrixList # UniformMatrixList() =default UniformMatrixList # explicit UniformMatrixList( const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # explicit UniformMatrixList( std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList UniformMatrixList # UniformMatrixList( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList ~UniformMatrixList # ~UniformMatrixList() =default dim # inline const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim( size_t n =0 ) const Parameters:\nn [optional] index in list of matrices Return: dimensions\nsize # inline size_t size() at # inline const Matrix \u0026amp; at( size_t n ) Swap # inline void Swap( Matrix \u0026amp; that, size_t n ) Parameters:\nthat input matrix n index where the matrix is moved operator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\noperator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\nappend # void append( const Matrix \u0026amp; mat ) Parameters:\nmat input matrix Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":49,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/","title":"lds::UniformSystemList","section":"Classes","content":" lds::UniformSystemList # More\u0026hellip;\nInherits from std::vector\u0026lt; System \u0026gt;\nPublic Functions # Name UniformSystemList() =default\nConstructs a new UniformSystemList. UniformSystemList(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by copying existing vector of System if dimensions consistent. UniformSystemList(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by moving existing vector of System if dimensions consistent. UniformSystemList(std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList from initializer_list of System if dimensions consistent. UniformSystemList(const UniformSystemList \u0026amp; that)\nConstructs a new UniformSystemList (copy). UniformSystemList(UniformSystemList \u0026amp;\u0026amp; that)\nConstructs a new UniformSystemList (move). ~UniformSystemList() =default\nDestroys the object. const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const\ngets dimensions of the uniformly sized systems size_t size()\nsize of container const System \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(System \u0026amp; that, size_t n)\nswaps input system with n^th system of list UniformSystemList \u0026amp; operator=(const UniformSystemList \u0026amp; that)\nassigns the contents (copy) UniformSystemList \u0026amp; operator=(UniformSystemList \u0026amp;\u0026amp; that)\nassigns the contents (move) Detailed Description # template \u0026lt;typename System \u0026gt; class lds::UniformSystemList; Public Function Details # UniformSystemList # UniformSystemList() =default UniformSystemList # explicit UniformSystemList( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # explicit UniformSystemList( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList UniformSystemList # UniformSystemList( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList ~UniformSystemList # ~UniformSystemList() =default dim # inline const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const size # inline size_t size() at # inline const System \u0026amp; at( size_t n ) Swap # inline void Swap( System \u0026amp; that, size_t n ) Parameters:\nthat input system n index where the system is moved operator= # inline UniformSystemList \u0026amp; operator=( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\noperator= # inline UniformSystemList \u0026amp; operator=( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\nUpdated on 5 March 2025 at 16:42:06 EST\n"},{"id":50,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/","title":"lds::UniformVectorList","section":"Classes","content":" lds::UniformVectorList # Inherits from std::vector\u0026lt; Vector \u0026gt;\nPublic Functions # Name UniformVectorList() =default\nConstructs a new UniformVectorList. UniformVectorList(const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent. UniformVectorList(std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent. UniformVectorList(std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0)\nConstructs a new UniformVectorList from initializer_list of Vector if dimensions consistent. UniformVectorList(const UniformVectorList \u0026amp; that)\nConstructs a new UniformVectorList (copy) UniformVectorList(UniformVectorList \u0026amp;\u0026amp; that)\nConstructs a new UniformVectorList (move) ~UniformVectorList() =default\nDestroys the object. size_t dim() const\ngets dimensions of the uniformly sized matrices size_t size()\nsize of container const Vector \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Vector \u0026amp; that, size_t n)\nswaps input matrix with n^th vector of list UniformVectorList \u0026amp; operator=(const UniformVectorList \u0026amp; that)\nassigns the contents (copy) UniformVectorList \u0026amp; operator=(UniformVectorList \u0026amp;\u0026amp; that)\nassigns the contents (move) Public Function Details # UniformVectorList # UniformVectorList() =default UniformVectorList # explicit UniformVectorList( const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dims dimension UniformVectorList # explicit UniformVectorList( std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList UniformVectorList # UniformVectorList( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList ~UniformVectorList # ~UniformVectorList() =default dim # inline size_t dim() const size # inline size_t size() at # inline const Vector \u0026amp; at( size_t n ) Swap # inline void Swap( Vector \u0026amp; that, size_t n ) Parameters:\nthat input vector n index where the vector is moved operator= # inline UniformVectorList \u0026amp; operator=( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\noperator= # inline UniformVectorList \u0026amp; operator=( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\nUpdated on 5 March 2025 at 16:42:06 EST\n"},{"id":51,"href":"/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/","title":"ldsCtrlEst_h","section":"Files","content":" ldsCtrlEst_h # Files # Name ldsCtrlEst_h/lds.h lds namespace ldsCtrlEst_h/lds_ctrl.h Controller. ldsCtrlEst_h/lds_fit.h LDS base fit type. ldsCtrlEst_h/lds_fit_em.h subspace identification ldsCtrlEst_h/lds_fit_ssid.h subspace identification ldsCtrlEst_h/lds_gaussian.h glds namespace ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller. ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type. ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type. ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type. ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type. ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type. ldsCtrlEst_h/lds_poisson.h plds namespace ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type. ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type. ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type. ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type. ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type. ldsCtrlEst_h/lds_poisson_sys.h PLDS base type. ldsCtrlEst_h/lds_sctrl.h SwitchedController type. ldsCtrlEst_h/lds_sys.h LDS base type. ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices. ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems. ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors. ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":52,"href":"/lds-ctrl-est/docs/api/files/lds__ctrl_8h/","title":"ldsCtrlEst_h/lds_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_ctrl.h # Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Controller Detailed Description # This file declares the type for control of a linear dynamical system (lds::Controller).\nSource code # //===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_CTRL_H #define LDSCTRLEST_LDS_CTRL_H // namespace #include \u0026#34;lds.h\u0026#34; // system type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class Controller { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); public: Controller() = default; Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); const Vector\u0026amp; Control(const Vector\u0026amp; z, bool do_control = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); const Vector\u0026amp; ControlOutputReference(const Vector\u0026amp; z, bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); // get methods: const System\u0026amp; sys() const { return sys_; }; const Matrix\u0026amp; Kc() const { return Kc_; }; const Matrix\u0026amp; Kc_inty() const { return Kc_inty_; }; const Matrix\u0026amp; Kc_u() const { return Kc_u_; }; const Vector\u0026amp; g_design() const { return g_design_; }; const Vector\u0026amp; u_ref() const { return u_ref_; }; const Vector\u0026amp; x_ref() const { return x_ref_; }; const Vector\u0026amp; y_ref() const { return y_ref_; }; size_t control_type() const { return control_type_; }; data_t tau_awu() const { return tau_awu_; }; data_t u_lb() const { return u_lb_; }; data_t u_ub() const { return u_ub_; }; // set methods void set_sys(const System\u0026amp; sys) { bool does_match = sys_.n_u() == sys.n_u(); does_match = does_match \u0026amp;\u0026amp; (sys_.n_x() == sys.n_x()); does_match = does_match \u0026amp;\u0026amp; (sys_.n_y() == sys.n_y()); if (does_match) { sys_ = sys; } else { throw std::runtime_error( \u0026#34;new system argument to `set_sys` does not match dimensionality of \u0026#34; \u0026#34;existing system\u0026#34;); } }; void set_g_design(const Vector\u0026amp; g_design) { Reassign(g_design_, g_design); }; void set_u_ref(const Vector\u0026amp; u_ref) { Reassign(u_ref_, u_ref); }; void set_x_ref(const Vector\u0026amp; x_ref) { Reassign(x_ref_, x_ref); cx_ref_ = sys_.C() * x_ref_; }; // y_ref needs to be handled differently depending on output fn. // (need to populate cx_ref_ too, which depends on output fn) virtual void set_y_ref(const Vector\u0026amp; y_ref) { Reassign(y_ref_, y_ref); }; void set_Kc(const Matrix\u0026amp; Kc) { Reassign(Kc_, Kc); }; void set_Kc_inty(const Matrix\u0026amp; Kc_inty) { Reassign(Kc_inty_, Kc_inty); }; void set_Kc_u(const Matrix\u0026amp; Kc_u) { Reassign(Kc_u_, Kc_u); }; void set_tau_awu(data_t tau) { tau_awu_ = tau; k_awu_ = sys_.dt() / tau_awu_; }; void set_control_type(size_t control_type); // There is no reason u_lb/ub should not be public, but making set methods // anyway. void set_u_lb(data_t u_lb) { u_lb_ = u_lb; }; void set_u_ub(data_t u_ub) { u_ub_ = u_ub; }; void Reset() { sys_.Reset(); u_ref_.zeros(); u_ref_prev_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); u_saturated_ = false; t_since_control_onset_ = 0.0; }; void Print() { sys_.Print(); std::cout \u0026lt;\u0026lt; \u0026#34;g_design : \u0026#34; \u0026lt;\u0026lt; g_design_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_lb : \u0026#34; \u0026lt;\u0026lt; u_lb_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_ub : \u0026#34; \u0026lt;\u0026lt; u_ub_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }; protected: System sys_; Vector u_; Vector u_return_; Vector g_design_; // reference signals Vector u_ref_; // create no set method for this: Vector u_ref_prev_; Vector x_ref_; Vector y_ref_; Vector cx_ref_; // Controller gains Matrix Kc_; Matrix Kc_u_; Matrix Kc_inty_; // control after g inversion // do not need set methods for these. Vector du_ref_; Vector dv_ref_; Vector v_ref_; Vector dv_; Vector v_; // integral error // do not need set method for this Vector int_e_; Vector int_e_awu_adjust_; Vector u_sat_; bool do_control_prev_ = false; bool do_lock_control_prev_ = false; // whether the g of system has become inverted from what you think it is // (gain_ref) bool u_saturated_ = false; // should be safe to have references here bc nothing needs to be done // (like reset vars) when it changes... data_t u_lb_{}; data_t u_ub_{}; data_t tau_awu_{}; data_t k_awu_ = 0; data_t t_since_control_onset_ = 0; size_t control_type_{}; private: void CalcControl(bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); void CalcSteadyStateSetPoint(); void AntiWindup(); void InitVars(size_t control_type); }; // Implement the above: template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(sys), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(std::move(sys)), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::set_control_type(size_t control_type) { if (control_type_ == control_type) { return; } // creating a blank slate... control_type_ = 0; Kc_inty_.zeros(0, 0); Kc_u_.zeros(0, 0); int_e_.zeros(0, 0); int_e_awu_adjust_.zeros(0, 0); // controller was designed to minimize integral error if (control_type \u0026amp; kControlTypeIntY) { Kc_inty_.zeros(sys_.n_u(), sys_.n_y()); int_e_.zeros(sys_.n_y()); int_e_awu_adjust_.zeros(sys_.n_u()); control_type_ = control_type_ | kControlTypeIntY; } // controller was designed to minimize deltaU // (i.e. state augmented with u) if (control_type \u0026amp; kControlTypeDeltaU) { Kc_u_.zeros(sys_.n_u(), sys_.n_u()); control_type_ = control_type_ | kControlTypeDeltaU; } // whether to adapt set point calculate with (re-estimated) process // disturbance (m) if (control_type \u0026amp; kControlTypeAdaptM) { if (sys_.do_adapt_m) // only if adapting m... { control_type_ = control_type_ | kControlTypeAdaptM; } } } // set_control_type template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::Control( const Vector\u0026amp; z, bool do_control, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement sys_.Filter(u_, z); bool do_estimation = true; // always have estimator on in this case // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::ControlOutputReference( const Vector\u0026amp; z, bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement if (do_estimation) { sys_.Filter(u_, z); } else { sys_.f(u_); } // calculate the set point // solves for u_ref and x_ref when output is at y_ref at steady state. if (do_control) { CalcSteadyStateSetPoint(); } // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcControl(bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { if (do_control \u0026amp;\u0026amp; do_estimation) { if (!do_control_prev_) { if (do_reset_at_control_onset) { Reset(); } t_since_control_onset_ = 0.0; } else { t_since_control_onset_ += sys_.dt(); } // enforce softstart on control vars. if (sigma_soft_start \u0026gt; 0) { // half-Gaussian soft-start scaling factor data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) / (2 * pow(sigma_soft_start, 2))); u_ref_ *= soft_start_sf; // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too // x_ref_ *= soft_start_sf; // cx_ref_ *= soft_start_sf; // y_ref_ *= soft_start_sf; } if (!do_lock_control) { // first do u -\u0026gt; v change of vars. (v = g.*u) // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver // control voltage u[=]V) v_ref_ = g_design_ % u_ref_; // Given FB, calc. the change in control if (control_type_ \u0026amp; kControlTypeDeltaU) { // if control designed to minimize not u but deltaU (i.e. state aug with // u): // TODO(mfbolus): Commented out for now. See note below. // du_ref_ = u_ref_ - u_ref_prev_; // dv_ref_ = g_design_ % du_ref_; // TODO(mfbolus): Assuming users want *smooth* control signals if using // kControlTypeDeltaU, it should be the case that dv_ref_ is --\u0026gt; 0. May // want to revisit, but I am going to force it to be zero unless a // situation arises that argues for keeping the above. dv_ref_.zeros(); dv_ = dv_ref_; // nominally-optimal. dv_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error dv_ -= Kc_u_ * (v_ - v_ref_); // penalty on amp u (rel to ref) if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if(!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error dv_ -= Kc_inty_ * int_e_; // control for integrated error } // update the control v_ += dv_; } else { v_ = v_ref_; // nominally-optimal. v_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if (!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error v_ -= Kc_inty_ * int_e_; // control for integrated error } } // convert back to control voltage u[=]V u_ = v_ / sys_.g(); } // else do nothing until lock is low } else { // if not control // feed through u_ref in open loop u_ = u_ref_ % g_design_ / sys_.g(); v_ = sys_.g() % u_; u_ref_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); } // ends do_control // enforce box constraints (and antiwindup) AntiWindup(); // add noise to input? // The value for u that is *returned* to user after addition of any noise, // while keeping controller/estimator blind to this addition. u_return_ = u_; if ((sigma_u_noise \u0026gt; 0.0) \u0026amp;\u0026amp; (do_control \u0026amp;\u0026amp; !do_lock_control)) { u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn); Limit(u_return_, u_lb_, u_ub_); }; // For next time step: u_ref_prev_ = u_ref_; do_control_prev_ = do_control; do_lock_control_prev_ = do_lock_control; } // CalcControl template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcSteadyStateSetPoint() { // Linearly-constrained least squares (ls). // // _reference: // Boyd \u0026amp; Vandenberghe (2018) Introduction to Applied Linear Algebra // Matrix a_ls = join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros)); Vector b_ls = cx_ref_; Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye), sys_.B() * arma::diagmat(sys_.g())); Vector d_ls = -sys_.m0(); if (control_type_ \u0026amp; kControlTypeAdaptM) { d_ls = -sys_.m(); // adapt setpoint calc with disturbance? } Matrix a_ls_t = a_ls.t(); // TODO(mfbolus): not sure why but causes seg // fault if I do not do this. Matrix phi_ls = join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()), join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros))); // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse: Matrix inv_phi = pinv(phi_ls); Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls); x_ref_ = xulam.subvec(0, sys_.n_x() - 1); u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1); cx_ref_ = sys_.C() * x_ref_; } // CalcSteadyStateSetPoint template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::AntiWindup() { u_saturated_ = false; u_sat_ = u_; // limit u and flag whether saturated for (size_t k = 0; k \u0026lt; u_.n_elem; k++) { if (u_[k] \u0026lt; u_lb_) { u_sat_[k] = u_lb_; u_saturated_ = true; } if (u_[k] \u0026gt; u_ub_) { u_sat_[k] = u_ub_; u_saturated_ = true; } } if ((control_type_ \u0026amp; kControlTypeIntY) \u0026amp;\u0026amp; (tau_awu_ \u0026lt; lds::kInf)) { // one-step back-calculation (calculate intE for u=u_sat) // (Astroem, Rundqwist 1989 warn against using this...) // int_e_awu_adjust_ = // solve(Kc_inty_, (u_ - u_sat_)); // pinv(Kc_inty) * (u-uSat); // gradual: see Astroem, Rundqwist 1989 // this is a fudge for doing MIMO gradual // n.b., went ahead and multiplied 1/T by dt so don\u0026#39;t have to do that here. int_e_awu_adjust_ = k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_); // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_); int_e_ += int_e_awu_adjust_; } // set u to saturated version u_ = u_sat_; } template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::InitVars(size_t control_type) { // initialize to default values u_ref_ = Vector(sys_.n_u(), fill::zeros); u_ref_prev_ = Vector(sys_.n_u(), fill::zeros); x_ref_ = Vector(sys_.n_x(), fill::zeros); y_ref_ = Vector(sys_.n_y(), fill::zeros); cx_ref_ = Vector(sys_.n_y(), fill::zeros); u_ = Vector(sys_.n_u(), fill::zeros); u_return_ = Vector(sys_.n_u(), fill::zeros); u_sat_ = Vector(sys_.n_u(), fill::zeros); // Might not need all these, so zero elements until later. Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros); Kc_u_ = Matrix(0, 0, fill::zeros); Kc_inty_ = Matrix(0, 0, fill::zeros); g_design_ = sys_.g(); // by default, same as model dv_ = Vector(sys_.n_u(), fill::zeros); v_ = Vector(sys_.n_u(), fill::zeros); du_ref_ = Vector(sys_.n_u(), fill::zeros); dv_ref_ = Vector(sys_.n_u(), fill::zeros); v_ref_ = Vector(sys_.n_u(), fill::zeros); int_e_ = Vector(0, fill::zeros); int_e_awu_adjust_ = Vector(0, fill::zeros); set_control_type(control_type); } } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":53,"href":"/lds-ctrl-est/docs/api/files/lds__fit__em_8h/","title":"ldsCtrlEst_h/lds_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_em.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::EM Detailed Description # This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).\nSource code # //===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_EMAX_H #define LDSCTRLEST_LDS_EMAX_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class EM { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: EM() = default; EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); virtual ~EM() = default; const Fit\u0026amp; Run(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = true, bool calc_output = true, bool calc_measurement = true, size_t max_iter = 100, data_t tol = 1e-2); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); // auto tuple = std::make_tuple(u_, z_); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; x() const { return x_; }; const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; y() const { return y_; }; const Matrix\u0026amp; sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; }; const Matrix\u0026amp; sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; }; const Matrix\u0026amp; sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; }; size_t n_t_tot() { return n_t_tot_; } const Vector\u0026amp; theta() const { return theta_; }; protected: void Expectation(bool force_common_initial = false); void Maximization(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = false, bool calc_output = false, bool calc_measurement = false); void MaximizeDynamics(); void MaximizeQ(); void MaximizeInitial(); virtual void MaximizeOutput() = 0; virtual void MaximizeMeasurement() = 0; void Smooth(bool force_common_initial); virtual void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) = 0; void Reset(); void InitVars(); Vector UpdateTheta(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; std::vector\u0026lt;Matrix\u0026gt; x_; std::vector\u0026lt;Cube\u0026gt; P_; std::vector\u0026lt;Cube\u0026gt; P_t_tm1_; std::vector\u0026lt;Matrix\u0026gt; y_; Matrix diag_y_; // expectations calculated in E-step Matrix sum_E_x_t_x_t_; Matrix sum_E_xu_tm1_xu_tm1_; Matrix sum_E_xu_t_xu_tm1_; Fit fit_; Vector theta_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; }; template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; fit_ = Fit(n_u_, n_x, n_y_, dt); u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { // make sure fit dims match I/O data if (fit0.n_u() != u_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and input training data have inconsistent dimensions\u0026#34;); } if (fit0.n_y() != z_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and output training data have inconsistent dimensions\u0026#34;); } fit_ = fit0; u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::InitVars() { // check input/output data dimensions are consistent if (z_.size() != u_.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_.at(trial).n_cols != u_.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } n_u_ = fit_.n_u(); n_x_ = fit_.n_x(); n_y_ = fit_.n_y(); dt_ = fit_.dt(); x_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); P_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); P_t_tm1_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); y_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros); P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros); } diag_y_ = Matrix(n_y_, n_y_, fill::zeros); // covariances in expectation step sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); } template \u0026lt;typename Fit\u0026gt; const Fit\u0026amp; EM\u0026lt;Fit\u0026gt;::Run(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement, size_t max_iter, data_t tol) { Reset(); // to initial conditions size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_; Vector theta(n_params); Vector theta_new(n_params); data_t max_dtheta = 1; // if solving for initial conditions, allow them be varied. // otherwise, freeze at provided values. bool force_common_initial = !calc_init; // go until parameter convergence for (size_t l = 0; l \u0026lt; max_iter; l++) { theta_ = UpdateTheta(); std::cout \u0026lt;\u0026lt; \u0026#34;Iteration \u0026#34; \u0026lt;\u0026lt; l + 1 \u0026lt;\u0026lt; \u0026#34;/\u0026#34; \u0026lt;\u0026lt; max_iter \u0026lt;\u0026lt; \u0026#34; ...\\n\u0026#34;; Expectation(force_common_initial); Maximization(calc_dynamics, calc_Q, calc_init, calc_output, calc_measurement); // check convergence theta_new = UpdateTheta(); Vector dtheta = abs(theta_new - theta_) / abs(theta_); // some parameters could be zero... arma::uvec ubi_finite = find_finite(dtheta); max_dtheta = max(dtheta.elem(ubi_finite)); std::cout \u0026lt;\u0026lt; \u0026#34;max dtheta: \u0026#34; \u0026lt;\u0026lt; max_dtheta \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; if (max_dtheta \u0026lt; tol) { std::cout \u0026lt;\u0026lt; \u0026#34;Converged.\\n\u0026#34;; break; } std::cout \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } return fit_; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Smooth(bool force_common_initial) { Matrix k_e(n_x_, n_y_); // estimator gain Cube k_backfilt; // back-filtering gains // TODO(mfbolus): this loop could be made parallel for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { Matrix x_pre(n_x_, n_t_[trial], fill::zeros); Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros); Matrix x_post(n_x_, n_t_[trial], fill::zeros); Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros); if (force_common_initial) // forces all trials to have same initial // conditions. { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); } y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); // This *should not* be necessary but make sure P is symmetric. ForceSymPD(P_[trial].slice(0)); x_pre.col(0) = x_[trial].col(0); p_pre.slice(0) = P_[trial].slice(0); x_post.col(0) = x_[trial].col(0); p_post.slice(0) = P_[trial].slice(0); // filter for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // predict fit_.f(x_pre, x_post, u_.at(trial), t); fit_.h(y_[trial], x_pre, t); diag_y_.diag() = y_[trial].col(t); // TODO(mfbolus): change if parallel // update --\u0026gt; posterior estimation RecurseKe(k_e, p_pre, p_post, t); x_post.col(t) = x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t)); y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d(); } // backfilter -\u0026gt; Smoothed estimate // Reference: // Shumway et Stoffer (1982) ForceSymPD(p_post.slice(n_t_[trial] - 1)); k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1); P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 0; t--) { // TODO(mfmbolus): should not be necessary to force symm positive def ForceSymPD(p_pre.slice(t)); ForceSymPD(p_post.slice(t - 1)); ForceSymPD(P_[trial].slice(t)); k_backfilt.slice(t - 1) = p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t)); x_[trial].col(t - 1) = x_post.col(t - 1) + k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t)); P_[trial].slice(t - 1) = p_post.slice(t - 1) + k_backfilt.slice(t - 1) * (P_[trial].slice(t) - p_pre.slice(t)) * k_backfilt.slice(t - 1).t(); } // do the same for P_t_tm1 Matrix id(n_x_, n_x_, fill::eye); P_t_tm1_[trial].slice(n_t_[trial] - 1) = (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 1; t--) { P_t_tm1_[trial].slice(t - 1) = p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() + k_backfilt.slice(t - 1) * (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) * k_backfilt.slice(t - 2).t(); } // finally, get smoothed estimate of output for (size_t t = 0; t \u0026lt; n_t_[trial]; t++) { fit_.h(y_[trial], x_[trial], t); } // samps loop } // trial loop } // Smooth // template \u0026lt;typename Fit\u0026gt; // void EM\u0026lt;Fit\u0026gt;::RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) { // // predict covar // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q(); // // update Ke // Ke = P_pre.slice(t) * fit_.C().t() * // inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R()); // // update cov // // Reference: Ghahramani et Hinton (1996) // P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t); // // // n.b. for poisson : // // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + // fit_.Q(); // // // update cov // // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ * // // fit_.C()); // // // update Ke // // Ke = P_post.slice(t) * fit_.C(); // } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Expectation(bool force_common_initial) { // calculate the mean/cov of state needed for maximizing E[pr(z|theta)] Smooth(force_common_initial); // now get the various forms of sum(E[xx\u0026#39;]) needed // n.b. Going to start at t=1 rather than 0 bc most max terms need that. // so really \u0026#34;n_t_tot_\u0026#34; is (n_t_tot_-1) n_t_tot_ = 0; sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); Vector xu_tm1(n_x_ + n_u_, fill::zeros); Vector xu_t(n_x_ + n_u_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // ------------ sum_E_x_t_x_t ------------ sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t(); sum_E_x_t_x_t_ += P_[trial].slice(t); // ------------ sum_E_xu_tm1_xu_tm1 ------------ xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1)); sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t(); sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t - 1); // ------------ sum_E_xu_t_xu_tm1 ------------ xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t)); sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t(); sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_t_tm1_[trial].slice(t); n_t_tot_ += 1; } // time } // trial } // Expectation template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement) { if (calc_output) { MaximizeOutput(); } if (calc_measurement) { MaximizeMeasurement(); } if (calc_dynamics) { MaximizeDynamics(); } if (calc_Q) { MaximizeQ(); } if (calc_init) { MaximizeInitial(); } } // Maximization template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeDynamics() { // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) * inv_sympd(sum_E_xu_tm1_xu_tm1_); fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1)); std::cout \u0026lt;\u0026lt; \u0026#34;A_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.A()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.B()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeQ() { // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 * // inv_sympd(sum_E_xu_tm1_xu_tm1_) * // sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // this way is same as above iff dynamics were just updated: // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix ab = arma::join_horiz(fit_.A(), fit_.B()); // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // From scratch method: // Q is covariance of the error between state and dynamics-predicted state // (aka process noise) // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})\u0026#39;] // t-1 terms: View sum_e_x_tm1_x_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_u_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1); View sum_e_x_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); // t, t-1 terms: View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_x_t_u_tm1 = sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); Matrix q = sum_E_x_t_x_t_; q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t(); q -= sum_e_x_t_x_tm1 * fit_.A().t(); q -= fit_.A() * sum_e_x_t_x_tm1.t(); // input-related terms: q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t(); q -= sum_e_x_t_u_tm1 * fit_.B().t(); q -= fit_.B() * sum_e_x_t_u_tm1.t(); q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t(); q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t(); q /= n_t_tot_; fit_.set_Q(q); std::cout \u0026lt;\u0026lt; \u0026#34;Q_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.Q()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // std::cout \u0026lt;\u0026lt; \u0026#34;Q_new: \\n\u0026#34; \u0026lt;\u0026lt; fit_.Q() \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeInitial() { Vector x0 = fit_.x0(); x0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { x0 += x_[trial].col(0); } x0 /= z_.size(); std::cout \u0026lt;\u0026lt; \u0026#34;x0_new[0]: \u0026#34; \u0026lt;\u0026lt; x0[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // always recalc P0 even if the initial state is fixed (at zero, for // example) Matrix e_var(n_x_, n_x_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t(); } e_var /= z_.size(); // go ahead and subtract x0*x0\u0026#39; so don\u0026#39;t have to below. e_var -= x0 * x0.t(); // To get P0, going to get initial P_ per trial and average. // (which might be wrong, but need a single number) Matrix p0 = fit_.P0(); p0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { p0 += (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var; } p0 /= z_.size(); fit_.set_P0(p0); std::cout \u0026lt;\u0026lt; \u0026#34;P0_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.P0()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeOutput() { // solve for C+d: Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros); Vector x1(n_x_ + 1, fill::zeros); x1[n_x_] = 1.0; // augment with one to solve for bias Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { x1.subvec(0, n_x_ - 1) = x_[trial].col(t); sum_zx += z_.at(trial).col(t) * x1.t(); sum_e_x1_x1 += x1 * x1.t(); sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t); } } Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1); fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1)); fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_))); std::cout \u0026lt;\u0026lt; \u0026#34;C_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.C()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.d()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeMeasurement() { // Solve for measurement noise covar size_t n_t_tot = 0; // Ghahgramani, Hinton 1996: Matrix sum_zz(n_y_, n_y_, fill::zeros); Matrix sum_yz(n_y_, n_y_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t(); // Use Cnew: sum_yz += (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t(); n_t_tot += 1; } } fit_.set_R((sum_zz - sum_yz) / n_t_tot); std::cout \u0026lt;\u0026lt; \u0026#34;R_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.R()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Reset() { // reset to initial conditions for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); } } template \u0026lt;typename Fit\u0026gt; Vector EM\u0026lt;Fit\u0026gt;::UpdateTheta() { // TODO(mfbolus): This should include n_y_ more params for d. size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_; if (fit_.R().n_elem \u0026gt; 0) { n_params += n_y_ * n_y_; } Vector theta(n_params); size_t idx_start = 0; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B()); idx_start += n_x_ * n_u_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0()); idx_start += n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C()); idx_start += n_y_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d()); idx_start += n_y_; if (fit_.R().n_elem \u0026gt; 0) { theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R()); } return theta; } } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":54,"href":"/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_ssid.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SSID Detailed Description # This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)\u0026lt;Fit\u0026gt;).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_FIT_SSID_H #define LDSCTRLEST_LDS_FIT_SSID_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class SSID { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: SSID() = default; SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d = Vector(1).fill(-kInf)); std::tuple\u0026lt;Fit, Vector\u0026gt; Run(SSIDWt ssid_wt); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } protected: void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001); void CreateHankelDataMat(); virtual void DecomposeData() = 0; void CalcSVD(SSIDWt wt); void Solve(data_t wt_dc); void RecomputeExtObs(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; Matrix D_; Fit fit_; Matrix g_dc_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_h_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; Matrix L_; Vector s_; Matrix ext_obs_t_; }; template \u0026lt;typename Fit\u0026gt; SSID\u0026lt;Fit\u0026gt;::SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d) { // check input/output data dimensions are consistent if (z_train.size() != u_train.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_train.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_train.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } dt_ = dt; n_x_ = n_x; n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; n_h_ = n_h; // dimensionality check for eventual block-hankel data matrix size_t len = n_t_tot_ - 2 * n_h_ + 1; if (len \u0026lt; (2 * n_h_ * (n_u_ + n_y_))) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;Dataset problem! More rows than columns in block-hankel data \u0026#34; \u0026#34;matrix: 2*(n_u+n_y)*n_h \u0026gt; data-length! Need higher data-length or \u0026#34; \u0026#34;lower n_h.\u0026#34;; throw std::runtime_error(ss.str()); } fit_ = Fit(n_u_, n_x_, n_y_, dt_); u_ = std::move(u_train); z_ = std::move(z_train); if (!d.is_finite() || (d.n_rows != n_y_)) { // TODO(mfbolus): implement least-square solution for impulse response with // a second input of ones. Data-driven way of accounting for offset *not* // driven by an input. // // For now, calculate output bias (d) as the // output wherever the stimulus has not been on for some amount of time. // convolve u with rectangle and take all samples. This is a reasonable // approach, since often when autonomous systems are fit (i.e., systems with // no input), they will subtract off the mean of the output. This // essentially amounts to setting output bias to the mean of the output when // there is no stimulation. data_t t_silence = 0.1; data_t thresh_silence = 0.001; CalcD(t_silence, thresh_silence); } else { fit_.set_d(d); } } template \u0026lt;typename Fit\u0026gt; std::tuple\u0026lt;Fit, Vector\u0026gt; SSID\u0026lt;Fit\u0026gt;::Run(SSIDWt ssid_wt) { // the weight on minimizing dc I/O gain only works for gaussian, // and hopefully not necessary with appropriate dataset. data_t wt_dc = 0; // std::cout \u0026lt;\u0026lt; \u0026#34;creating hankel mat\\n\u0026#34;; CreateHankelDataMat(); // std::cout \u0026lt;\u0026lt; \u0026#34;decomposing data\\n\u0026#34;; DecomposeData(); // std::cout \u0026lt;\u0026lt; \u0026#34;calculating svd\\n\u0026#34;; CalcSVD(ssid_wt); // std::cout \u0026lt;\u0026lt; \u0026#34;solving for params\\n\u0026#34;; Solve(wt_dc); // std::cout \u0026lt;\u0026lt; \u0026#34;fin\\n\u0026#34;; return std::make_tuple(fit_, s_); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcD(data_t t_silence, data_t thresh_silence) { Vector d(z_.at(0).n_rows, fill::zeros); Vector win(static_cast\u0026lt;size_t\u0026gt;(t_silence / dt_), fill::ones); Vector sum_z_silence(n_y_, fill::zeros); size_t n_silence(0); for (size_t trial = 0; trial \u0026lt; u_.size(); trial++) { // find silent samples // start by convolving with Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0)); Vector u_conv = conv(sum_u, win, \u0026#34;same\u0026#34;); // get only the samples that are silent... arma::uvec ubi_silence = find(u_conv \u0026lt;= thresh_silence); if (ubi_silence.n_elem \u0026gt; 0) { sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1); n_silence += ubi_silence.n_elem; } } if (n_silence \u0026gt; 0) { d = sum_z_silence / n_silence; } fit_.set_d(d); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat() { // temporary copy of data Matrix z(n_y_, n_t_tot_, fill::zeros); Matrix u(n_u_, n_t_tot_, fill::zeros); size_t so_far(0); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial); u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial); so_far += n_t_.at(trial); } // remove output bias z.each_col() -= fit_.d(); // calculate I/O gain @ DC while data in convenient form g_dc_ = z * pinv(u); // std::cout \u0026lt;\u0026lt; \u0026#34;G0_data = \u0026#34; \u0026lt;\u0026lt; g_dc_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // create hankel data matrix size_t len = z.n_cols - 2 * n_h_ + 1; // data length in hankel mat // block-hankel data matrix D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros); // past input auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1); // future input auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1); // past output auto y_p = D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1); // future output auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, len - 1); size_t idx = 0; for (size_t k = 0; k \u0026lt; len; k++) { idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } } D_ /= sqrt(static_cast\u0026lt;data_t\u0026gt;(len)); } // template \u0026lt;typename Fit\u0026gt; // void SSID\u0026lt;Fit\u0026gt;::DecomposeData() { // // do LQ decomp instead of calculating covariance expensive way // // Note that \u0026#34;R\u0026#34; in van Overschee is lower-triangular (L), not \u0026#34;R\u0026#34; in QR // // decomp. Very confusing. // Matrix q_t; // lq(L_, q_t, D_); // // van Overschee zeros out the other elements. // L_ = trimatl(L_); // } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcSVD(SSIDWt wt) { // submats that will be needed: auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1); auto R_23_13 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1); auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_) - 1); auto R_44 = L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14); auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1); auto Luf = Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1); auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); // aka: R_f Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1); // from van Overschee subid.m: // Rf = R((2*m+l)*i+1:2*(m+l)*i,:); % Future outputs Matrix U; Matrix V; switch (wt) { case kSSIDNone: { // No weighting. (what van Overschee calls \u0026#34;N4SID\u0026#34;) Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14; arma::svd(U, s_, V, O_k_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDMOESP: { // MOESP weighting // This is what they use in the \u0026#34;robust\u0026#34; algorithm van Overschee, de Moor // 1996 Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); svd(U, s_, V, O_k_ortho_Uf_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDCVA: { // CVA weighting // See van Overschee\u0026#39;s matlab code (subid.m): // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); Matrix inv_w1; Matrix qt1; lq(inv_w1, qt1, R_56_16); // lq decomp of R_f (future output data) inv_w1 = trimatl(inv_w1); inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1); Matrix w_o_w = arma::solve( inv_w1, O_k_ortho_Uf_sans_Qt); // alternatively // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt svd(U, s_, V, w_o_w, \u0026#34;std\u0026#34;); U = inv_w1 * U; break; } } // Truncate to model order (heart of ssid method) auto s_hat = s_.subvec(0, n_x_ - 1); Matrix diag_sqrt_s = diagmat(sqrt(s_hat)); auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1); // get extended observability and controllability mats ext_obs_t_ = u_hat * diag_sqrt_s; // extended observability matrix } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::Solve(data_t wt_dc) { // required submats auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_56_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); // Solve for params using appropriate algorithm: // robust deterministic/stochastic algorithm in van Overschee 1996 // algorithm that the authors say \u0026#34;works\u0026#34; in practice. auto ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix // This is what textbook (1996) says: // // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15); // // HOWEVER, do not know why but have to fill the last place with zeros like // authors\u0026#39; matlab implementation (see `subid.m`) // Otherwise, get ridiculous covariances (although A,C estimates are close to // same...) Matrix Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Matrix S = Tl * pinv(Tr); // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure // stability. fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1)); Matrix ext_obs_t_p1 = join_vert( ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1), Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros)); fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1); // At this point, van Overschee \u0026amp; de Moor suggest re-calculating ext_obs_t_, // ext_obs_tm1 from (A, C) because it was just an approximation. This is RecomputeExtObs(); ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); S = Tl * pinv(Tr); Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_); Matrix Mcurly = pinv(ext_obs_tm1); Matrix Pcurly = Tl - Lcurly * R_56_15; Vector Pvec = vectorise(Pcurly); Matrix Qcurly = R_23_15; // Identify [D; B], assuming D=0 and ensuring DC gain is correct Matrix sum_QcurlyT_kron_Ncurly( (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_), fill::zeros); Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols, fill::eye); eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1, eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1; // van Overschee (1996) p. 126 Matrix N1_Tl = -Lcurly; N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) += join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly); N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) += Matrix(n_y_, n_y_, fill::eye); Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros); Matrix N_k; for (size_t k = 0; k \u0026lt; n_h_; k++) { auto Qcurly_k = Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1); Nk_Tl.zeros(); Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) = N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1); N_k = Nk_Tl * eye_ext_obs_tm1; sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k); } Matrix err_vec; if (wt_dc \u0026gt; 0) { // Constraints enforced by weighted least squares // // Reference: // // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly // Excited Industrial Systems. Conference in Decision and Control. // constraint 1: assume D=0 --\u0026gt; remove the components for Dvec (this is // actually a hard constraint in that it ignores D) Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly; sum_QcurlyT_kron_Ncurly = Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_); size_t kkk = 0; for (size_t k = 1; k \u0026lt; (n_u_ + 1); k++) { size_t start_idx = k * (n_y_ + n_x_) - n_x_; for (size_t kk = 0; kk \u0026lt; n_x_; kk++) { sum_QcurlyT_kron_Ncurly.col(kkk) = sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk); kkk++; } } // constraint 2: Make sure DC I/O gain is correct Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A()); Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_)); Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0); Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 = join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0); // WEIGHTED LS // Important in practice because I care a lot about at least getting the DC // gain correct. Put x weight on minimizing error at DC, relative to others Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye); // Make weight on minimizing DC error immense so at least that // should be nailed. size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows; size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows; size_t stop_row = w.n_rows - 1; size_t stop_col = w.n_cols - 1; // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale // weight with data length? w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc; Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * sum_QcurlyT_kron_Ncurly_b_to_g0) * sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec; fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_)); // Calculate residuals and their cov. // Because I\u0026#39;ve added constraints, I need to re-calculate the right term // with b_vec instead of how van Overschee do in final algorithm. err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec; } else { // default way: *no* constraint on G0 or D=0 Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec; // TODO(mfbolus) n.b., this gets thrown away... // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_); fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_)); err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec; } // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols); // TODO(mfbolus): Something is wrong with the error calculation above. // Use the way van overschee does it in `subid.m` // WARNING: this ignores any above constraints, so Q, R will be approximate... Matrix err = Tl - S * Tr; Matrix cov_err = err * err.t(); fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1)); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::RecomputeExtObs() { ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C(); for (size_t k = 2; k \u0026lt; (n_h_ + 1); k++) { ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) = ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1, ext_obs_t_.n_cols - 1) * fit_.A(); } } } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":55,"href":"/lds-ctrl-est/docs/api/files/lds__fit_8h/","title":"ldsCtrlEst_h/lds_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_fit.h # LDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Fit LDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.\nSource code # //===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDS_FIT_HPP #define LDS_FIT_HPP // namespace #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); virtual ~Fit() = default; // get methods size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; Q() const { return Q_; }; const Vector\u0026amp; x0() const { return x0_; }; const Matrix\u0026amp; P0() const { return P0_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; // gets measurement noise virtual const Matrix\u0026amp; R() const = 0; // set methods (e.g., seeding initial fit values) void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_m(const Vector\u0026amp; m) { Reassign(m_, m); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); ForceSymPD(Q_); }; virtual void set_R(const Matrix\u0026amp; R) = 0; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); ForceSymPD(P0_); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; View f(Matrix\u0026amp; x, const Matrix\u0026amp; u, size_t t) { x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x.col(t); }; View f(Matrix\u0026amp; x_pre, const Matrix\u0026amp; x_post, const Matrix\u0026amp; u, size_t t) { x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x_pre.col(t); }; virtual View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) = 0; protected: data_t dt_{}; // Dynamics Matrix A_; Matrix B_; Vector g_; Vector m_; Matrix Q_; // Output Matrix C_; Vector d_; Matrix R_; // initial conditions Vector x0_; Matrix P0_; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; }; } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":56,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_ctrl.h # GLDS Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. Detailed Description # This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_gaussian_sys.h\u0026#34; // controller #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace gaussian { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); cx_ref_ = y_ref - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":57,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_em.h # GLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitEM GLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":58,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_ssid.h # GLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; using SSID\u0026lt;Fit\u0026gt;::Run; private: using SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat; using SSID\u0026lt;Fit\u0026gt;::CalcSVD; using SSID\u0026lt;Fit\u0026gt;::Solve; void DecomposeData() override; void SolveVanOverschee(); }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":59,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit.h # GLDS fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Fit GLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // fit type #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace gaussian { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); const Matrix\u0026amp; R() const override { return R_; }; void set_R(const Matrix\u0026amp; R) override { Reassign(R_, R); ForceSymPD(R_); }; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = C_ * x.col(t) + d_; return y.col(t); }; }; }; // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":60,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sctrl.h # GLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H // controller type #include \u0026#34;lds_gaussian_ctrl.h\u0026#34; // switched controller #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace gaussian { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); cx_ref_ = y_ref - sys_.d(); } // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; }; // SwitchedController } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":61,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/","title":"ldsCtrlEst_h/lds_gaussian_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sys.h # GLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::System Gaussian LDS Type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H #define LDSCTRLEST_LDS_GAUSSIAN_SYS_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; namespace lds { namespace gaussian { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0, data_t r0 = kDefaultR0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; // get methods const Matrix\u0026amp; R() const { return R_; }; // set methods void set_Q(const Matrix\u0026amp; Q) { lds::System::set_Q(Q); do_recurse_Ke_ = true; } void set_R(const Matrix\u0026amp; R) { Reassign(R_, R); do_recurse_Ke_ = true; }; void set_Ke(const Matrix\u0026amp; Ke) { Reassign(Ke_, Ke); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void set_Ke_m(const Matrix\u0026amp; Ke_m) { Reassign(Ke_m_, Ke_m); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void Print(); protected: void h() override { cx_ = C_ * x_; y_ = cx_ + d_; }; Vector h_(Vector x) override { return C_ * x + d_; }; void RecurseKe() override; // Gaussian-output-specific Matrix R_; bool do_recurse_Ke_{}; }; // System } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":62,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian_8h/","title":"ldsCtrlEst_h/lds_gaussian.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian.h # glds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_H #define LDSCTRLEST_LDS_GAUSSIAN_H // namespace #include \u0026#34;lds.h\u0026#34; namespace lds { namespace gaussian { // insert any Gaussian-specific things here... } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":63,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_ctrl.h # PLDS controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. Detailed Description # This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_CTRL_H #define LDSCTRLEST_LDS_POISSON_CTRL_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system type #include \u0026#34;lds_poisson_sys.h\u0026#34; // control type #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace poisson { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); lds::Limit(y_ref_, kYRefLb, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; private: constexpr static const data_t kYRefLb = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":64,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_em.h # PLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitEM PLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\n[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H #define LDSCTRLEST_LDS_POISSON_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override{}; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; data_t NewtonSolveC(); void AnalyticalSolveD(); }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":65,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_ssid.h # PLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. Detailed Description # This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/)).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H #define LDSCTRLEST_LDS_POISSON_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; private: void DecomposeData() override; void CalcCov(); void PoissonToGaussianMoments(); Matrix cov_; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":66,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/","title":"ldsCtrlEst_h/lds_poisson_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit.h # PLDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Fit PLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_H #define LDSCTRLEST_LDS_POISSON_FIT_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // fit #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace poisson { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) : lds::Fit(n_u, n_x, n_y, dt){}; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = exp(C_ * x.col(t) + d_); return y.col(t); }; void set_R(const Matrix\u0026amp; R) override { std::cerr \u0026lt;\u0026lt; \u0026#34;WARNING: Cannot set R (R[0] = \u0026#34; \u0026lt;\u0026lt; R.at(0) \u0026lt;\u0026lt; \u0026#34;). No Gaussian measurement noise in Poisson observation model.\\n\u0026#34;; }; const Matrix\u0026amp; R() const override { return R_; }; }; }; // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":67,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sctrl.h # PLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #include \u0026#34;lds_poisson_ctrl.h\u0026#34; #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace poisson { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); lds::Limit(y_ref_, kYRefLB, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; private: constexpr static data_t kYRefLB = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":68,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/","title":"ldsCtrlEst_h/lds_poisson_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sys.h # PLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::System Poisson System type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SYS_H #define LDSCTRLEST_LDS_POISSON_SYS_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; // needed for Poisson random number generation #include \u0026lt;random\u0026gt; namespace lds { namespace poisson { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; protected: void h() override { cx_ = C_ * x_; y_ = exp(cx_ + d_); diag_y_.diag() = y_; }; Vector h_(Vector x) override { return exp(C_ * x + d_); }; void RecurseKe() override; private: // Poisson-output-specific Matrix diag_y_; std::poisson_distribution\u0026lt;size_t\u0026gt; pd_; }; // System } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":69,"href":"/lds-ctrl-est/docs/api/files/lds__poisson_8h/","title":"ldsCtrlEst_h/lds_poisson.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson.h # plds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_H #define LDSCTRLEST_LDS_POISSON_H #include \u0026#34;lds.h\u0026#34; namespace lds { namespace poisson { // TODO(mfbolus): Not sure if defining these as static here makes the most // sense. Is there a downside to letting multiple poisson System objects share a // common random number generator? static std::random_device rd; static std::mt19937 rng = std::mt19937( rd()); } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":70,"href":"/lds-ctrl-est/docs/api/files/lds__sctrl_8h/","title":"ldsCtrlEst_h/lds_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_sctrl.h # SwitchedController type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SwitchedController SwitchedController Type. Detailed Description # This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SCTRL_H #define LDSCTRLEST_LDS_SCTRL_H #include \u0026#34;lds_ctrl.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; #include \u0026#34;lds_uniform_vecs.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class SwitchedController : public Controller\u0026lt;System\u0026gt; { public: SwitchedController() = default; SwitchedController(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); SwitchedController(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); void Switch(size_t idx, bool do_force_switch = false); void set_Kc(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc) { Kc_list_ = Kc; Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc) { Kc_list_ = std::move(Kc); Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_inty) { Kc_inty_list_ = Kc_inty; Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_inty) { Kc_inty_list_ = std::move(Kc_inty); Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_u) { Kc_u_list_ = Kc_u; Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_u) { Kc_u_list_ = std::move(Kc_u); Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(const UniformVectorList\u0026amp; g) { g_design_list_ = g; g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(UniformVectorList\u0026amp;\u0026amp; g) { g_design_list_ = std::move(g); g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; protected: std::vector\u0026lt;System\u0026gt; systems_; size_t n_sys_{}; size_t idx_{}; // controller gains could be different for each UniformMatrixList\u0026lt;\u0026gt; Kc_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_inty_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_u_list_; // design-phase input gain could also be different UniformVectorList g_design_list_; // TODO(mfbolus): not sure why I need to do this. using Controller\u0026lt;System\u0026gt;::Kc_; using Controller\u0026lt;System\u0026gt;::Kc_inty_; using Controller\u0026lt;System\u0026gt;::Kc_u_; using Controller\u0026lt;System\u0026gt;::g_design_; using Controller\u0026lt;System\u0026gt;::sys_; // using Controller\u0026lt;System\u0026gt;::u_ref_; // using Controller\u0026lt;System\u0026gt;::x_ref_; // using Controller\u0026lt;System\u0026gt;::y_ref_; // using Controller\u0026lt;System\u0026gt;::control_type_; private: void InitVars(); using lds::Controller\u0026lt;System\u0026gt;::set_sys; // using Controller\u0026lt;System\u0026gt;::set_Kc; // using Controller\u0026lt;System\u0026gt;::set_Kc_inty; // using Controller\u0026lt;System\u0026gt;::set_Kc_u; // using Controller\u0026lt;System\u0026gt;::set_g_design; }; template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(systems.at(0), u_lb, u_ub, control_type), systems_(systems) { InitVars(); } template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(System(systems.at(0).n_u(), systems.at(0).n_x(), systems.at(0).n_y(), systems.at(0).dt()), u_lb, u_ub, control_type), systems_(std::move(systems)) { InitVars(); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::InitVars() { n_sys_ = systems_.size(); sys_ = systems_.at(0); Kc_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_)); Kc_inty_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); Kc_u_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); g_design_list_ = UniformVectorList(std::vector\u0026lt;Vector\u0026gt;(n_sys_, g_design_)); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::Switch(size_t idx, bool do_force_switch) { if ((idx == idx_) \u0026amp;\u0026amp; !do_force_switch) { return; // already there. } // put old up and get new one out systems_.at(idx_) = std::move(sys_); sys_ = std::move(systems_.at(idx)); // set the state of this system to that of the previous system // TODO(mfbolus): This will only work as intended if state matrix is the same. // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp. sys_.set_m(systems_.at(idx_).m(), true); sys_.set_x(systems_.at(idx_).x()); // swap controller gains Kc_list_.Swap(Kc_, idx_); Kc_list_.Swap(Kc_, idx); if (control_type_ \u0026amp; kControlTypeIntY) { Kc_inty_list_.Swap(Kc_inty_, idx_); Kc_inty_list_.Swap(Kc_inty_, idx); } if (control_type_ \u0026amp; kControlTypeDeltaU) { Kc_u_list_.Swap(Kc_u_, idx_); Kc_u_list_.Swap(Kc_u_, idx); } g_design_list_.Swap(g_design_, idx_); g_design_list_.Swap(g_design_, idx); idx_ = idx; } // Switch } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":71,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8h/","title":"ldsCtrlEst_h/lds_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_sys.h # LDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::System Linear Dynamical System Type. Detailed Description # This file declares and partially defines the base type for linear dynamical systems ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SYS_H #define LDSCTRLEST_LDS_SYS_H #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class System { public: System() = default; System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); virtual ~System() {} void Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z); virtual const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) = 0; void f(const Vector\u0026amp; u, bool do_add_noise = false) { x_ = A_ * x_ + B_ * (g_ % u) + m_; if (do_add_noise) { x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_); } }; virtual void h() = 0; virtual Vector h_(Vector x) = 0; size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Vector\u0026amp; x() const { return x_; }; const Matrix\u0026amp; P() const { return P_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; P_m() const { return P_m_; }; const Vector\u0026amp; cx() const { return cx_; }; const Vector\u0026amp; y() const { return y_; }; const Vector\u0026amp; x0() const { return x0_; }; const Vector\u0026amp; m0() const { return m0_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; const Matrix\u0026amp; Ke() const { return Ke_; }; const Matrix\u0026amp; Ke_m() const { return Ke_m_; }; const Matrix\u0026amp; Q() { return Q_; }; const Matrix\u0026amp; Q_m() { return Q_m_; }; const Matrix\u0026amp; P0() { return P0_; }; const Matrix\u0026amp; P0_m() { return P0_m_; }; void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_m(const Vector\u0026amp; m, bool do_force_assign = false) { Reassign(m0_, m); if ((!do_adapt_m) || do_force_assign) { Reassign(m_, m); } }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); }; void set_Q_m(const Matrix\u0026amp; Q_m) { Reassign(Q_m_, Q_m); }; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); }; void set_P0_m(const Matrix\u0026amp; P0_m) { Reassign(P0_m_, P0_m); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; void set_x(const Vector\u0026amp; x) { Reassign(x_, x); h(); }; void Reset(); std::vector\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; nstep_pred_block( UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z, size_t n_pred = 1); void Print(); // safe to leave this public and non-const bool do_adapt_m{}; protected: virtual void RecurseKe() = 0; void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); std::size_t n_x_{}; std::size_t n_u_{}; std::size_t n_y_{}; data_t dt_{}; // Signals: Vector x_; Matrix P_; Vector m_; Matrix P_m_; Vector cx_; Vector y_; Vector z_; // Parameters: Vector x0_; Matrix P0_; Vector m0_; Matrix P0_m_; Matrix A_; Matrix B_; Vector g_; Matrix Q_; Matrix Q_m_; Matrix C_; Vector d_; Matrix Ke_; Matrix Ke_m_; }; // System } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":72,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/","title":"ldsCtrlEst_h/lds_uniform_mats.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_mats.h # List of uniformly sized matrices. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformMatrixList Detailed Description # This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H #define LDSCTRLEST_LDS_UNIFORM_MATS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { template \u0026lt;MatrixListFreeDim D = kMatFreeDimNone\u0026gt; class UniformMatrixList : public std::vector\u0026lt;Matrix\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Matrix\u0026gt;::vector; // don\u0026#39;t allow push_back to be used since it doesn\u0026#39;t check dims using std::vector\u0026lt;Matrix\u0026gt;::push_back; public: using std::vector\u0026lt;Matrix\u0026gt;::operator=; using std::vector\u0026lt;Matrix\u0026gt;::operator[]; using std::vector\u0026lt;Matrix\u0026gt;::begin; using std::vector\u0026lt;Matrix\u0026gt;::end; using std::vector\u0026lt;Matrix\u0026gt;::size; using std::vector\u0026lt;Matrix\u0026gt;::at; UniformMatrixList() = default; explicit UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); explicit UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; ~UniformMatrixList() = default; const std::array\u0026lt;size_t, 2\u0026gt;\u0026amp; dim(size_t n = 0) const { return dim_.at(n); } size_t size() { return std::vector\u0026lt;Matrix\u0026gt;::size(); }; const Matrix\u0026amp; at(size_t n) { return std::vector\u0026lt;Matrix\u0026gt;::at(n); }; void Swap(Matrix\u0026amp; that, size_t n); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; void append(const Matrix\u0026amp; mat); private: void CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim); std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt; dim_; }; template \u0026lt;MatrixListFreeDim D\u0026gt; inline void UniformMatrixList\u0026lt;D\u0026gt;::Swap(Matrix\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.n_cols); } if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap // not moving, since it causes memory issues. // so this method isn\u0026#39;t a memory-saver as designed for now Matrix tmp = (*this)[n]; (*this)[n] = that; that = tmp; if (D == kMatFreeDim1) { this-\u0026gt;dim_[n][0] = (*this)[n].n_rows; } if (D == kMatFreeDim2) { this-\u0026gt;dim_[n][1] = (*this)[n].n_cols; } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::append(const Matrix\u0026amp; mat) { std::array\u0026lt;size_t, 2\u0026gt; dim({mat.n_rows, mat.n_cols}); CheckDimensions(dim); std::vector\u0026lt;Matrix\u0026gt;::push_back(mat); dim_.push_back(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) { // make sure dim_ vector is initialized if (dim_.empty()) { dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(that.size(), {0, 0}); } // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices\u0026#34;; throw std::runtime_error(ss.str()); } // if dimensions a not zero and do not match, skip move with error message. bool dims_nonzero = true; for (auto d : dim_) { if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; d[0] \u0026lt; 1) { dims_nonzero = false; break; } if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; d[1] \u0026lt; 1) { dims_nonzero = false; break; } } if (dims_nonzero) { bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); } if (!does_match) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrices of size \u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; dim_[k] = that.dim(k); } return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. bool dims_nonzero = true; for (auto d : dim_) { // if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; (d[0] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; (d[1] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // } // // if (dims_nonzero) { // bool does_match = true; // if (!(D == kMatFreeDim1)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); // } // // if (!(D == kMatFreeDim2)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); // } // // if (!does_match) { // this-\u0026gt;at(0).print(\u0026#34;this[0] = \u0026#34;); // that.at(0).print(\u0026#34;that[0] = \u0026#34;); // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformMatrixList element of size (\u0026#34; \u0026lt;\u0026lt; // that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols \u0026lt;\u0026lt; \u0026#34;) for an // element of size (\u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34;). // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Matrix\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(std::move(mats)) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) : vector(that) { (*this) = that; } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { std::array\u0026lt;size_t, 2\u0026gt; dim_k({this-\u0026gt;at(k).n_rows, this-\u0026gt;at(k).n_cols}); dim_.push_back(dim_k); } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim) { // change behavior based on free dim D if ((dim[0] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim1)) { dim[0] = this-\u0026gt;at(0).n_rows; } if ((dim[1] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim2)) { dim[1] = this-\u0026gt;at(0).n_cols; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Matrix\u0026amp; mat : *this) { if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_rows == dim[0]); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_cols == dim[1]); } if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(this-\u0026gt;size(), dim); for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { dim_[k][0] = (*this)[k].n_rows; dim_[k][1] = (*this)[k].n_cols; } } } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":73,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/","title":"ldsCtrlEst_h/lds_uniform_systems.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_systems.h # List of uniformly sized Systems. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformSystemList Detailed Description # This file provides a container for uniformly sized Systems.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector // namespace #include \u0026#34;lds.h\u0026#34; // System type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class UniformSystemList : public std::vector\u0026lt;System\u0026gt; { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;System\u0026gt;::vector; using std::vector\u0026lt;System\u0026gt;::operator=; using std::vector\u0026lt;System\u0026gt;::operator[]; using std::vector\u0026lt;System\u0026gt;::at; using std::vector\u0026lt;System\u0026gt;::begin; using std::vector\u0026lt;System\u0026gt;::end; using std::vector\u0026lt;System\u0026gt;::size; public: UniformSystemList() = default; explicit UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); explicit UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(const UniformSystemList\u0026amp; that); UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept; ~UniformSystemList() = default; const std::array\u0026lt;size_t, 3\u0026gt;\u0026amp; dim() const { return dim_; } size_t size() { return std::vector\u0026lt;System\u0026gt;::size(); }; const System\u0026amp; at(size_t n) { return std::vector\u0026lt;System\u0026gt;::at(n); }; void Swap(System\u0026amp; that, size_t n); UniformSystemList\u0026amp; operator=(const UniformSystemList\u0026amp; that); UniformSystemList\u0026amp; operator=(UniformSystemList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim); std::array\u0026lt;size_t, 3\u0026gt; dim_{}; }; template \u0026lt;typename System\u0026gt; inline void UniformSystemList\u0026lt;System\u0026gt;::Swap(System\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformSystemList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = (dim_[0] == that.n_u()) \u0026amp;\u0026amp; (dim_[1] == that.n_x()) \u0026amp;\u0026amp; (dim_[2] == that.n_y()); if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformSystemList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap System tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( const UniformSystemList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_[0] + dim_[1] + dim_[2]) { std::array\u0026lt;size_t, 3\u0026gt; other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign systems of size \u0026#34; \u0026lt;\u0026lt; dim_[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2] \u0026lt;\u0026lt; \u0026#34; with systems of size \u0026#34; \u0026lt;\u0026lt; other_dim[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other_dim[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2]; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( UniformSystemList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. if (dim_[0] + dim_[1] + dim_[2]) { // bool does_match = (dim_[0] == that.at(0).n_u()) \u0026amp;\u0026amp; // (dim_[1] == that.at(0).n_x()) \u0026amp;\u0026amp; // (dim_[2] == that.at(0).n_y()); // if (!does_match) { // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformSystemList element for an element of \u0026#34; // \u0026#34;different size. Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;System\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(std::move(systems)) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList( std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const UniformSystemList\u0026amp; that) : std::vector\u0026lt;System\u0026gt;(that) { (*this) = that; } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept : std::vector\u0026lt;System\u0026gt;(std::move(that)) { this-\u0026gt;dim_[0] = this-\u0026gt;at(0).n_u(); this-\u0026gt;dim_[1] = this-\u0026gt;at(0).n_x(); this-\u0026gt;dim_[2] = this-\u0026gt;at(0).n_y(); } template \u0026lt;typename System\u0026gt; void UniformSystemList\u0026lt;System\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim) { if (dim[0] + dim[1] + dim[2]) { dim_ = dim; } else { dim_[0] = this-\u0026gt;at(0).n_u(); dim_[1] = this-\u0026gt;at(0).n_x(); dim_[2] = this-\u0026gt;at(0).n_y(); } // make sure dimensiolaties are all uniform bool does_match(true); for (const System\u0026amp; sys : *this) { does_match = does_match \u0026amp;\u0026amp; (sys.n_u() == dim_[0]); does_match = does_match \u0026amp;\u0026amp; (sys.n_x() == dim_[1]); does_match = does_match \u0026amp;\u0026amp; (sys.n_y() == dim_[2]); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input systems are not uniform.\u0026#34;); } } } } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":74,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/","title":"ldsCtrlEst_h/lds_uniform_vecs.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_vecs.h # List of uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformVectorList Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H #define LDSCTRLEST_LDS_UNIFORM_VECS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { class UniformVectorList : public std::vector\u0026lt;Vector\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Vector\u0026gt;::vector; using std::vector\u0026lt;Vector\u0026gt;::operator=; using std::vector\u0026lt;Vector\u0026gt;::operator[]; using std::vector\u0026lt;Vector\u0026gt;::at; using std::vector\u0026lt;Vector\u0026gt;::begin; using std::vector\u0026lt;Vector\u0026gt;::end; using std::vector\u0026lt;Vector\u0026gt;::size; public: UniformVectorList() = default; explicit UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim = 0); explicit UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim = 0); UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim = 0); UniformVectorList(const UniformVectorList\u0026amp; that); UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept; ~UniformVectorList() = default; size_t dim() const { return dim_; } size_t size() { return std::vector\u0026lt;Vector\u0026gt;::size(); }; const Vector\u0026amp; at(size_t n) { return std::vector\u0026lt;Vector\u0026gt;::at(n); }; void Swap(Vector\u0026amp; that, size_t n); UniformVectorList\u0026amp; operator=(const UniformVectorList\u0026amp; that); UniformVectorList\u0026amp; operator=(UniformVectorList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(size_t dim); size_t dim_{}; }; inline void UniformVectorList::Swap(Vector\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = dim_ == that.n_elem; if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap Vector tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( const UniformVectorList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_) { size_t other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ \u0026lt;\u0026lt; \u0026#34; with vectors of size \u0026#34; \u0026lt;\u0026lt; other_dim; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( UniformVectorList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors. Skipping.\\n\u0026#34;; // return (*this); // } // // if (dim_) { // size_t other_dim(that.dim()); // if (dim_ != other_dim) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ // \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; other_dim \u0026lt;\u0026lt; \u0026#34;. // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Vector\u0026gt;::operator=(std::move(that)); return (*this); } } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":75,"href":"/lds-ctrl-est/docs/api/files/lds_8h/","title":"ldsCtrlEst_h/lds.h","section":"Files","content":" ldsCtrlEst_h/lds.h # lds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)) observations.\nSource code # //===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_H #define LDSCTRLEST_LDS_H // #ifndef LDSCTRLEST // #include \u0026lt;ldsCtrlEst\u0026gt; // #endif #include \u0026lt;armadillo\u0026gt; namespace lds { using data_t = double; // may change to float (but breaks mex functions) using Vector = arma::Col\u0026lt;data_t\u0026gt;; using Matrix = arma::Mat\u0026lt;data_t\u0026gt;; using Cube = arma::Cube\u0026lt;data_t\u0026gt;; using View = arma::subview\u0026lt;data_t\u0026gt;; namespace fill = arma::fill; static const std::size_t kControlTypeDeltaU = 0x1; static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); static const data_t kPi = arma::datum::pi; static const data_t kDefaultP0 = 1e-6; static const data_t kDefaultQ0 = 1e-6; static const data_t kDefaultR0 = 1e-2; enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA }; enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2 }; // TODO(mfbolus): for SwitchedController, may want systems to have differing // numbers of states. Use this enum as template parameter? // enum SystemListFreeDim { // kSysFreeDimNone, // kSysFreeDimX ///\u0026lt; allow state dim (x) of systems in list to be hetero // }; // place hard limits on contents of vecors/mats void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub); void Limit(Vector\u0026amp; x, data_t lb, data_t ub); void Limit(Matrix\u0026amp; x, data_t lb, data_t ub); // in-place assign that errs if there are dimension mismatches: void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); // TODO(mfbolus): this is a fudge, but for some reason, cov mats often going // numerically asymm. void ForceSymPD(Matrix\u0026amp; X); void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min = 0); void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X); Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B); inline void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Vector\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Matrix\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if (other.n_elem != some.n_elem) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vector of size \u0026#34; \u0026lt;\u0026lt; some.n_elem \u0026lt;\u0026lt; \u0026#34; with vector of size \u0026#34; \u0026lt;\u0026lt; other.n_elem \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } inline void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrix of size \u0026#34; \u0026lt;\u0026lt; some.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; some.n_cols \u0026lt;\u0026lt; \u0026#34; with matrix of size \u0026#34; \u0026lt;\u0026lt; other.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other.n_cols \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } } // namespace lds #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":76,"href":"/lds-ctrl-est/docs/api/files/mex__c__util_8h/","title":"ldsCtrlEst_h/mex_c_util.h","section":"Files","content":" ldsCtrlEst_h/mex_c_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) More\u0026hellip;\nNamespaces # Name armamexc arma/mex interface using Matlab C API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab/Octave\u0026rsquo;s C mex API.\nSource code # //===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXC_UTIL_H #define LDSCTRLEST_MEXC_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.h\u0026#34; // // If Matlab_FOUND, include matrix.h. // // (Octave does not need/have it.) // #ifdef Matlab_FOUND // #include \u0026#34;matrix.h\u0026#34; // #endif namespace armamexc { template \u0026lt;class T\u0026gt; inline auto m2T_scalar(const mxArray *matlab_scalar) -\u0026gt; T { if (mxGetData(matlab_scalar)) { return static_cast\u0026lt;T\u0026gt;(mxGetScalar(matlab_scalar)); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return 0; } template \u0026lt;class T\u0026gt; inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false, bool strict = true) -\u0026gt; arma::Mat\u0026lt;T\u0026gt; { if (mxGetData(matlab_mat)) { const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat); if (n_dim == 2) { return arma::Mat\u0026lt;T\u0026gt;(static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)), mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem, strict); } mexErrMsgTxt(\u0026#34;Number of dimensions must be 2.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } // TODO(mfbolus): make these templated. template \u0026lt;typename T\u0026gt; inline auto a2m_mat(arma::Mat\u0026lt;T\u0026gt; const \u0026amp;arma_mat) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_mat.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Mat.\u0026#34;); return nullptr; } template \u0026lt;typename T\u0026gt; inline auto a2m_vec(arma::Col\u0026lt;T\u0026gt; const \u0026amp;arma_vec) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_vec.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Col.\u0026#34;); return nullptr; } } // namespace armamexc #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":77,"href":"/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/","title":"ldsCtrlEst_h/mex_cpp_util.h","section":"Files","content":" ldsCtrlEst_h/mex_cpp_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) More\u0026hellip;\nNamespaces # Name armamexcpp arma/mex interface using Matlab C++ API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab\u0026rsquo;s C++ mex API.\nSource code # //===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXCPP_UTIL_H #define LDSCTRLEST_MEXCPP_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.hpp\u0026#34; #include \u0026#34;mexAdapter.hpp\u0026#34; namespace armamexcpp { template \u0026lt;class T\u0026gt; std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; m2a_cellmat(matlab::data::CellArray\u0026amp; matlab_cell) { size_t n_cells = matlab_cell.getNumberOfElements(); std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; arma_mat(n_cells, arma::Mat\u0026lt;T\u0026gt;(1, 1, arma::fill::zeros)); for (size_t k = 0; k \u0026lt; n_cells; k++) { matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = matlab_cell[k]; auto dims = matlab_mat.getDimensions(); arma_mat[k] = arma::Mat\u0026lt;T\u0026gt;(matlab_mat.release().get(), dims[0], dims[1]); } return arma_mat; }; template \u0026lt;class T\u0026gt; std::vector\u0026lt;T\u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt;T\u0026gt;\u0026amp; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); T* ptr = matlab_array.release().get(); std::vector\u0026lt;T\u0026gt; vec(ptr, ptr + n_elem); return vec; }; template \u0026lt;class T\u0026gt; arma::Col\u0026lt;T\u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); // T* ptr = matlab_array.release().get(); // arma::Col\u0026lt;T\u0026gt; vec(ptr, n_elem); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? arma::Col\u0026lt;T\u0026gt; vec(n_elem, arma::fill::zeros); for (size_t k = 0; k \u0026lt; n_elem; k++) { vec[k] = matlab_array[k]; } return vec; }; template \u0026lt;class T\u0026gt; arma::Mat\u0026lt;T\u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { // ArrayDimensions == std::vector\u0026lt;size_t\u0026gt; auto dims = matlab_array.getDimensions(); // T* ptr = matlab_array.release().get(); // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false) // arma::Mat\u0026lt;T\u0026gt; mat(ptr, dims[0], dims[1]); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? // // armadillo and matlab both use column-major ordering, so this should work: size_t n_elem = dims[0] * dims[1]; arma::Mat\u0026lt;T\u0026gt; mat(dims[0], dims[1], arma::fill::zeros); size_t k(0); for (auto m: matlab_array) { mat[k] = m; k++; } return mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_mat(const arma::Mat\u0026lt;T\u0026gt;\u0026amp; arma_mat, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(), arma_mat.memptr() + arma_mat.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_vec(const arma::Col\u0026lt;T\u0026gt;\u0026amp; arma_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;({arma_vec.n_elem, 1}, arma_vec.memptr(), arma_vec.memptr() + arma_vec.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; s2m_vec(const std::vector\u0026lt;T\u0026gt;\u0026amp; std_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size()); return matlab_mat; }; } // namespace armamexcpp #endif Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":78,"href":"/lds-ctrl-est/docs/terminology/model/","title":"Models","section":"LDS C+E Documentation","content":" Model Definitions # This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form: \\[\\mathbf{x}_{t\u0026#43;1} = f\\left( \\mathbf{x}_{t}, \\mathbf{v}_{t} \\right) = \\mathbf{A} \\mathbf{x}_{t} \u0026#43; \\mathbf{B} \\mathbf{v}_{t} \u0026#43; \\mathbf{m}_{t} \u0026#43; \\mathbf{w}_{t}\\] \\[\\mathbf{y}_{t} = h\\left( \\mathbf{x}_{t} \\right)\\] t : time index x : system state v = g%u : input (e.g., in physical units used for model fit) u : control signal sent to actuator (e.g., in Volts) y : system output m : process disturbance w ~ N(0, Q) : process noise/disturbance A : state matrix B : input coupling matrix g : input gain (e.g., for converting to control signal actuator voltage) n.b., assumes this conversion is linear Q : process noise covariance % : element-wise multiplication LDS with Gaussian Observations # For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.\n\\[\\mathbf{y}_{t} = \\mathbf{C} \\mathbf{x}_{t} \u0026#43; \\mathbf{d}\\] \\[\\mathbf{z}_{t} \\sim \\mathcal{N}\\left(\\mathbf{y}_{t} , \\mathbf{R} \\right)\\] z : measurement C : output matrix d : output bias R : measurement noise covariance LDS with Poisson Observations # For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.\n\\[y_{t}^{i} = \\exp \\left(\\mathbf{c}^i \\mathbf{x}_{t} \u0026#43; d^i\\right)\\] \\[z_{t}^i \\sim \\rm{Poisson} \\left(y_{t}^i \\right)\\] i : output index z : measurement (count data) c : i^th row of output matrix (C) d : output bias Model Predictive Control (MPC) # Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.\nIn the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.\n"},{"id":79,"href":"/lds-ctrl-est/docs/api/modules/","title":"Modules","section":"LDS C+E Documentation","content":" Modules # Control Mode Bit Masks provides fill types for constructing new armadillo vectors, matrices\nDefaults\nUpdated on 5 March 2025 at 16:42:06 EST\n"},{"id":80,"href":"/lds-ctrl-est/docs/api/namespaces/","title":"Namespaces","section":"LDS C+E Documentation","content":" Namespaces # armamexc arma/mex interface using Matlab C API\narmamexcpp arma/mex interface using Matlab C++ API\nlds::gaussian Linear Dynamical Systems with Gaussian observations.\nlds::poisson Linear Dynamical Systems with Poisson observations.\nstd\nUpdated on 5 March 2025 at 16:42:06 EST\n"},{"id":81,"href":"/lds-ctrl-est/docs/api/pages/","title":"Pages","section":"LDS C+E Documentation","content":" Pages # Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":82,"href":"/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/","title":"PLDS State Estimation","section":"LDS C+E Examples","content":" PLDS State Estimation Tutorial # This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[x_{t\u0026#43;1} = x_t \u0026#43; w_t\\] \\[y_{t} = \\exp\\left(x_t\\right)\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state Finally, assign the parameters using corresponding set-methods.\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); Creating the estimator # Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. To ensure robust estimates, adaptively re-estimate the process disturbance.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); Simulating estimation # In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.\n// Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.\nWith this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.\n"},{"id":83,"href":"/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/","title":"PLDS Switched Control","section":"LDS C+E Examples","content":" PLDS Switched Control Tutorial # This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.\nIn the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating the simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); The system\u0026rsquo;s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 Initially, the system will be in \u0026ldquo;mode\u0026rdquo; 1, where B = b1.\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.\nCreating the controller # Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.\nSimilar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.\nMoreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.\nPutting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying systems: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nNow that the SwitchedController is instantiated, assign its parameters.\n// Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).\nThe control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.\n// Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system\u0026rsquo;s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.\nExample simulation result # Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller\u0026rsquo;s online estimates of the output and state are shown in purple.\nNote that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.\n"},{"id":84,"href":"/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/","title":"src","section":"Files","content":" src # Files # Name src/lds.cpp misc lds namespace functions src/lds_gaussian_sys.cpp GLDS base type. src/lds_poisson_sys.cpp PLDS base type. src/lds_sys.cpp LDS base type. src/lds_uniform_vecs.cpp Uniformly sized vectors. Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":85,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/","title":"src/lds_gaussian_sys.cpp","section":"Files","content":" src/lds_gaussian_sys.cpp # GLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_gaussian_sys.h\u0026gt; lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0, data_t r0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { R_.zeros(n_y, n_y); R_.diag().fill(r0); do_recurse_Ke_=true; }; // recursively estimate Ke void lds::gaussian::System::RecurseKe() { if (!do_recurse_Ke_) { return; } // predict covariance P_ = A_ * P_ * A_.t() + Q_; // calc Kalman gain Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_); // update covariance // Reference: Ghahramani et Hinton (1996) P_ = P_ - Ke_ * C_ * P_; if (do_adapt_m) { P_m_ += Q_m_; // A_m = I (i.e., random walk) Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_); P_m_ = P_m_ - Ke_m_ * C_ * P_m_; } } // Simulate const lds::Vector\u0026amp; lds::gaussian::System::Simulate(const Vector\u0026amp; u_tm1){ f(u_tm1, true);//simulate dynamics with noise added h();//output z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure return z_; } void lds::gaussian::System::Print() { lds::System::Print(); std::cout \u0026lt;\u0026lt; \u0026#34;R: \\n\u0026#34; \u0026lt;\u0026lt; R_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":86,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/","title":"src/lds_poisson_sys.cpp","section":"Files","content":" src/lds_poisson_sys.cpp # PLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_poisson_sys.h\u0026gt; lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { diag_y_ = diagmat(y_); pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(0); }; // Correct: Given measurement (z) and current input (u), update estimate of the // state, covar, output. // // see Eden et al. 2004 void lds::poisson::System::RecurseKe() { // predict covariance P_ = A_ * P_ * A_.t() + Q_; // update cov P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_); Ke_ = P_ * C_.t(); if (do_adapt_m) { P_m_ += Q_m_; // predict (A_m = I) P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_); // update Ke_m_ = P_m_ * C_.t(); } } // Simulate Measurement: z ~ Poisson(y) const lds::Vector\u0026amp; lds::poisson::System::Simulate(const Vector\u0026amp; u_tm1) { f(u_tm1, true); // simulate dynamics with noise added h(); // output z_.zeros(); for (std::size_t k = 0; k \u0026lt; n_y_; k++) { // construct a Poisson distribution object with mean y[k] pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(y_[k]); // pull random sample from this distribution z_[k] = pd_(rng); } return z_; } // ******************* SYS_T ******************* Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":87,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8cpp/","title":"src/lds_sys.cpp","section":"Files","content":" src/lds_sys.cpp # LDS base type. More\u0026hellip;\nDetailed Description # This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- lds_sys.cpp - LDS -------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_sys.h\u0026gt; #include \u0026lt;vector\u0026gt; lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) { InitVars(p0, q0); } void lds::System::InitVars(data_t p0, data_t q0) { // initial conditions. x0_ = Vector(n_x_, fill::zeros); // includes bias (nY) and g (nU) P0_ = p0 * Matrix(n_x_, n_x_, fill::eye); m0_ = x0_; P0_m_ = P0_; // signals x_ = x0_; P_ = P0_; m_ = m0_; P_m_ = P0_m_; y_ = Vector(n_y_, fill::zeros); cx_ = Vector(n_y_, fill::zeros); z_ = Vector(n_y_, fill::zeros); // By default, random walk where each state is independent // In this way, provides independent estimates of rate per channel of output. A_ = Matrix(n_x_, n_x_, fill::eye); B_ = Matrix(n_x_, n_u_, fill::zeros); g_ = Vector(n_u_, fill::ones); Q_ = q0 * Matrix(n_x_, n_x_, fill::eye); Q_m_ = Q_; C_ = Matrix(n_y_, n_x_, fill::eye); // each state will map to an output by d_ = Vector(n_y_, fill::zeros); Ke_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain. Ke_m_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain for m adaptation. do_adapt_m = false; } // Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state // and update estimate of the state, covar, output using Kalman filter void lds::System::Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z_t) { // predict mean f(u_tm1); // dynamics h(); // output // recursively calculate esimator gains (or just keep existing values) // (also predicts+updates estimate covariance) RecurseKe(); // update x_ += Ke_ * (z_t - y_); if (do_adapt_m) { m_ += Ke_m_ * (z_t - y_); // adaptively estimating disturbance } // With new state, estimate output. h(); // --\u0026gt; posterior } void lds::System::Reset() { // reset to initial conditions x_ = x0_; // mean P_ = P0_; // cov of state estimate m_ = m0_; // process disturbance P_m_ = P0_m_; // cov of disturbance estimate h(); } std::vector\u0026lt;lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt;\u0026gt; lds::System::nstep_pred_block(lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; u, lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; z, size_t n_pred) { lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_filt; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_pred; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; y_pred; for (size_t k = 0; k \u0026lt; u.size(); k++) { Reset(); size_t n_t = arma::size(u[k])[1]; Matrix x_filt_k(n_x_, n_t, fill::zeros); Matrix x_pred_k(n_x_, n_t - n_pred, fill::zeros); Matrix y_pred_k(n_y_, n_t - n_pred, fill::zeros); for (size_t t = 0; t \u0026lt; n_t - n_pred; t++) { Vector x_pred_ahead = x_; for (size_t t_u = t; t_u \u0026lt; t + n_pred; t_u++) { x_pred_ahead = A_ * x_pred_ahead + B_ * u[k].col(t_u); } x_pred_k.col(t) = x_pred_ahead; y_pred_k.col(t) = h_(x_pred_ahead); if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; // given previous measurment } for (size_t t = n_t - n_pred; t \u0026lt; n_t; t++) { if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; } x_filt.append(x_filt_k); x_pred.append(x_pred_k); y_pred.append(y_pred_k); } return {x_filt, x_pred, y_pred}; } void lds::System::Print() { std::cout \u0026lt;\u0026lt; \u0026#34;\\n ********** SYSTEM ********** \\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;x: \\n\u0026#34; \u0026lt;\u0026lt; x_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;P: \\n\u0026#34; \u0026lt;\u0026lt; P_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;A: \\n\u0026#34; \u0026lt;\u0026lt; A_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B: \\n\u0026#34; \u0026lt;\u0026lt; B_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;g: \\n\u0026#34; \u0026lt;\u0026lt; g_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;m: \\n\u0026#34; \u0026lt;\u0026lt; m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q: \\n\u0026#34; \u0026lt;\u0026lt; Q_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q_m: \\n\u0026#34; \u0026lt;\u0026lt; Q_m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d: \\n\u0026#34; \u0026lt;\u0026lt; d_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;C: \\n\u0026#34; \u0026lt;\u0026lt; C_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;y: \\n\u0026#34; \u0026lt;\u0026lt; y_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } //******************* SYS_T ******************* Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":88,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/","title":"src/lds_uniform_vecs.cpp","section":"Files","content":" src/lds_uniform_vecs.cpp # Uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_uniform_vecs.h\u0026gt; namespace lds { UniformVectorList::UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); } UniformVectorList::UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim) : vector(std::move(vecs)) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(const UniformVectorList\u0026amp; that) : vector(that) { (*this) = that; } UniformVectorList::UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { this-\u0026gt;dim_ = this-\u0026gt;at(0).n_elem; } void UniformVectorList::CheckDimensions(size_t dim) { if (dim) { dim_ = dim; } else { dim_ = this-\u0026gt;at(0).n_elem; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Vector\u0026amp; vec : *this) { does_match = does_match \u0026amp;\u0026amp; (vec.n_elem == dim_); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } } } // namespace lds Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":89,"href":"/lds-ctrl-est/docs/api/files/lds_8cpp/","title":"src/lds.cpp","section":"Files","content":" src/lds.cpp # misc lds namespace functions More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file implements miscellaneous lds namespace functions not bound to a class.\nSource code # //===-- lds.cpp - LDS -----------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds.h\u0026gt; // insert any necessary function definitions here. namespace lds { void ForceSymPD(Matrix\u0026amp; X) { if (X.is_sympd() || !X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; // for eigenval decomp bool did_succeed(true); Vector d; Matrix u; // see first method (which may not be ideal): // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/ size_t k(1); bool is_sympd = X.is_sympd(); Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye); while (!is_sympd) { if (k \u0026gt; 100) { did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); data_t min_eig = arma::min(d); std::cerr \u0026lt;\u0026lt; \u0026#34;After multiple iterations, min eigen val = \u0026#34; \u0026lt;\u0026lt; min_eig \u0026lt;\u0026lt; \u0026#34;.\\n\u0026#34;; throw std::runtime_error( \u0026#34;Failed to make matrix symmetric positive definite.\u0026#34;); return; } // Limit(d, arma::eps(0), kInf); // force to be positive... // Matrix d_diag = arma::diagmat(d); // X = u * d_diag * u.t(); did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymPD failed.\u0026#34;); } data_t min_eig = arma::min(d); X += id * abs(min_eig) + arma::datum::eps; // make sure symm: X = (X + X.t()) / 2; // double check eigenvals positive after symmetrizing: arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); min_eig = arma::min(d); is_sympd = min_eig \u0026gt; 0; k++; } } void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min) { if (!X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; bool did_succeed(true); Vector d; Matrix u; did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymMinEig failed.\u0026#34;); } Limit(d, eig_min + arma::eps(eig_min), kInf); // enforce lower bound Matrix d_diag = arma::diagmat(d); X = u * d_diag * u.t(); // double check symmetric X = (X + X.t()) / 2; } void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X) { bool did_succeed(true); did_succeed = arma::qr_econ(Qt, L, X.t()); if (!did_succeed) { throw std::runtime_error(\u0026#34;LQ decomposition failed.\u0026#34;); } arma::inplace_trans(L); arma::inplace_trans(Qt); } Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B) { // subtract out mean auto m_a = arma::mean(A, 1); Matrix a0 = A; a0.each_col() -= m_a; auto m_b = arma::mean(B, 1); Matrix b0 = B; b0.each_col() -= m_b; Matrix cov = a0 * b0.t() / a0.n_cols; return cov; } } // namespace lds Updated on 5 March 2025 at 16:42:06 EST\n"},{"id":90,"href":"/lds-ctrl-est/docs/api/namespaces/namespacestd/","title":"std","section":"Namespaces","content":" std # Updated on 5 March 2025 at 16:42:06 EST\n"}]
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+[{"id":0,"href":"/lds-ctrl-est/docs/","title":"LDS C+E Documentation","section":"LDS Control \u0026 Estimation","content":" LDS Control \u0026amp; Estimation Documentation # "},{"id":1,"href":"/lds-ctrl-est/docs/tutorials/","title":"LDS C+E Examples","section":"LDS C+E Documentation","content":" Examples # "},{"id":2,"href":"/lds-ctrl-est/acknowledgements/","title":"Acknowledgements","section":"LDS Control \u0026 Estimation","content":" Acknowledgements # Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.\n"},{"id":3,"href":"/lds-ctrl-est/docs/getting-started/getting-started/","title":"Getting Started","section":"LDS C+E Documentation","content":" Getting Started # This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.\nldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.\nTested Configurations # Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the RelWithDebInfo build type in the CMake configure command (-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo):\nUbuntu 18.04 with GCC 7.5 compiler macOS 11 (Big Sur) with Apple Clang 12 compiler Windows 10 with Visual Studio 16.11 (2019 release) and Clang 12 compiler That being said, if you want to debug a build for a single platform, here are some things you can try:\nUse different compilers (or even different versions of a single compiler) Use different versions of vcpkg (which you can control by checking out a different commit in the vcpkg submodule) Mac Pre-requisities # Xcode Command Line Tools will get you clang, gcc, make, and git:\nxcode-select --install Homebrew is \u0026ldquo;The Missing Package Manager for macOS\u0026rdquo; which will make installing lots of things easy. Install like this:\n/bin/bash -c \u0026#34;$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\u0026#34; You can then use it to install CMake, gfortran, and pkg-config:\nbrew install cmake gfortran pkg-config Linux Pre-requisites # You\u0026rsquo;ll need Git, CMake, GCC, gfortran, etc.\nsudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential ninja-build Windows Installation # Look here for Windows-specific instructions.\nDownloading the Library # First, clone the repository along with submodules:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est\rgit submodule update --init Compilation + Installation # Now generate the cache and build using your IDE or from the command line as follows.\nmkdir build \u0026amp;\u0026amp; cd build\rcmake ..\rcmake --build . The first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.\nIf you want to use vcpkg set up somewhere besides this repo\u0026rsquo;s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE\u0026rsquo;s settings.\nYou can verify the build is working by running ctest from the build folder, which runs all the example scripts.\nOptions # This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\nLDSCTRLEST_BUILD_EXAMPLES : [default=ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake to configure/build the library.\nFor basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build cmake --build #build the project sudo make install #[optional] installs to default location (OS-specific) To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location cmake --build #build the project make install #install to /your/install/prefix To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\nLD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.\nPython bindings package ldsctrlest # With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython3_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn\u0026rsquo;t use an undesired version. That environment needs to have NumPy installed.\ncmake --build . --target python_modules The bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory (pip install pytest matplotlib first if you need to).\nSee python/ldsctrlest/README.md for usage details.\nAlso, beware that a single build will probably not work for both the standalone library and the Python package, since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.\nCommon issues # \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.\nexport CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix vcpkg fails on configuration Try running ./bootstrap-vcpkg from the vcpkg folder and try again. If that doesn\u0026rsquo;t work, try updating vcpkg to a newer version (in the source control tab, click on the commit hash by the vcpkg repo then select from the dropdown) and running boostsrap-vcpkg again. You can also try upgrading your system (e.g., apt update, apt upgrade).\nCould not find Python3 (missing: Python3_NumPy_INCLUDE_DIRS NumPy)\nMake sure NumPy is installed in the Python environment you specified. If CMake still can\u0026rsquo;t find it, you may need to tell CMake exactly where to find it by adding an argument to the configure command: -DPython3_NumPy_INCLUDE_DIR=.... You can find that location like this: python -c 'import numpy; print(numpy.get_include())'\n"},{"id":4,"href":"/lds-ctrl-est/docs/getting-started/windows/","title":"Windows","section":"LDS C+E Documentation","content":" Windows Installation # Windows Pre-requisites # Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:\nSet-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time iwr get.scoop.sh | Invoke-Expression Install Git and CMake if you don\u0026rsquo;t already have them:\nscoop install git cmake If that didn\u0026rsquo;t work, follow more detailed instructions here.\nThe easiest way to compile C++ project on Windows is with Visual Studio\u0026rsquo;s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on \u0026ldquo;Desktop development with C++.\u0026rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the \u0026ldquo;Installation details\u0026rdquo; sidebar under optional features.\nAnd the easiest way to use Visual Studio\u0026rsquo;s build tools is with VS Code, along with the CMake Tools extension. Install them and you should be ready to go.\nDownloading the Library # First, clone the repository, either from VS Code or the command line:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est You\u0026rsquo;ll need to initialize the submodules from the command line after the repo is cloned:\ngit submodule update --init Installation # When you open the folder in VS Code, you will like be prompted by the CMake Tools extension to configure the project. Make sure you select the kit (you\u0026rsquo;ll be prompted when you configure\u0026ndash;else there\u0026rsquo;s an icon in the bar on the bottom of the window or type Ctrl+Shift+P, then \u0026ldquo;cmake select kit\u0026rdquo;). Choose Clang [latest version] with GNU CLI ... amd64 assuming you are running a 64-bit OS. (MSVC may work okay too if you don\u0026rsquo;t need to build Python bindings.)\nFollow along with the \u0026ldquo;Getting Started\u0026rdquo; instructions, but where you see config options specified as -DLDSCTREST_BUILD_STATIC=OFF or -DPython3_ROOT_DIR=..., you will enter those in settings: open with Ctrl+,, click \u0026ldquo;workspace\u0026rdquo;, then search for \u0026ldquo;CMake: Configure Args\u0026rdquo; and enter each of your desired arguments as a separate item.\nTo configure, use Ctrl+Shift+P and search for the \u0026ldquo;CMake: Configure\u0026rdquo; command. To build, click the \u0026ldquo;Build\u0026rdquo; button on the bottom bar. Then click the \u0026ldquo;CTest\u0026rdquo; button to run the example scripts.\nConsiderations # Development on Windows has been more prone to bugs than on Unix systems, so if you encounter many problems, consider switching—WSL (Windows Subsystem for Linux) is a good option for Windows users who don\u0026rsquo;t want to work on a different machine.\nCompilation has been successfully tested in VS Code using the following kit, using the \u0026ldquo;RelWithDebInfo\u0026rdquo; config:\nClang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64) Troubleshooting # The build appears to work, but tests fail with code 0xc0000135 OR \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; Have you installed the library? In VS Code, use Shift+F7 to build a specific target, in this case INSTALL. If that doesn\u0026rsquo;t solve your problem, you will likely need to add the build or install folder to your PATH environment variable, which you can do using the settings GUI (search for \u0026ldquo;Edit the system environment variables\u0026rdquo;).\nOn Windows, \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted. Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":5,"href":"/lds-ctrl-est/issues-contributing/","title":"Issues Contributing","section":"LDS Control \u0026 Estimation","content":" Reporting Issues # If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.\nContributing # We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.\nWhen editing any documentation/guides, please use the markdown docs in misc/docs-hugo instead of directly editing the HTML docs. This may require having hugo, graphviz and doxygen installed through brew, as well as doxybook2 installed through a git clone.\nClone the doxybook2 repository online and place the executable in your usr/local/bin folder or another bin folder. Run sudo chmod +x /usr/local/bin/doxybook2 to give doxybook2 permissions. Run doxybook2 --help to ensure that this works properly. If permission is still denied, navigate to System Preferences \u0026gt; Security \u0026amp; Privacy \u0026gt; General. You should see a message at the bottom that says: \u0026ldquo;doxybook2 was blocked from use because it is not from an identified developer.\u0026rdquo; Click Allow Anyway.\nUpdated docs can be built by running cd scripts and ./update-docs.sh. After a successful build, navigate to /misc/docs-hugo/ and run hugo server to create a local host of the website. Access the website using local host to ensure the correct results.\n"},{"id":6,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/","title":"armamexc","section":"Namespaces","content":" armamexc # arma/mex interface using Matlab C API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; T m2T_scalar(const mxArray * matlab_scalar)\nConvert Matlab mxArray to scalar of type T. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)\nConvert matlab matrix to armadillo. template \u0026lt;typename T \u0026gt; mxArray * a2m_mat(arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat)\nConvert armadillo to matlab matrix. template \u0026lt;typename T \u0026gt; mxArray * a2m_vec(arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec)\nConvert armadillo to matlab vector. Detailed Description # Utilities for arma/mex interface using Matlab C API\nFunction Details # m2T_scalar # template \u0026lt;class T \u0026gt; inline T m2T_scalar( const mxArray * matlab_scalar ) Parameters:\nmatlab_scalar matlab scalar Template Parameters:\nT type Return: scalar of type T\nm2a_mat # template \u0026lt;class T \u0026gt; inline arma::Mat\u0026lt; T \u0026gt; m2a_mat( const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true ) Parameters:\nmatlab_mat matlab matrix copy_aux_mem [optional] whether to copy auxiliary memory strict [optional] strictly enforce the above Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_mat( arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat ) Parameters:\narma_mat armadillo matrix Return: matlab matrix\na2m_vec # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_vec( arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec ) Parameters:\narma_vec armadillo vector Return: matlab vector\nUpdated on 5 March 2025 at 21:41:26 EST\n"},{"id":7,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/","title":"armamexcpp","section":"Namespaces","content":" armamexcpp # arma/mex interface using Matlab C++ API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat(matlab::data::CellArray \u0026amp; matlab_cell)\nConvert matlab cell array to vector of armadillo matrices. template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array)\nConvert matlab matrix to a vector of scalars. template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo vector. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat(const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec(const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab vector. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec(const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert vector of scalar T to matlab matrix. Detailed Description # utilities for arma/mex interface using Matlab C++ API\nFunction Details # m2a_cellmat # template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat( matlab::data::CellArray \u0026amp; matlab_cell ) Parameters:\nmatlab_cell matlab cell Template Parameters:\nT type Return: vector of armadillo matrices of type T\nm2s_vec # template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: vector of type T\nm2a_vec # template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: armadillo vector of type T\nm2a_mat # template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab matrix Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat( const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_mat arma matrix factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\na2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec( const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_vec armadillo vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\ns2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec( const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\nstd_vec standard vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\nUpdated on 5 March 2025 at 21:41:26 EST\n"},{"id":8,"href":"/lds-ctrl-est/docs/terminology/control-estimation/","title":"C\u0026E","section":"LDS C+E Documentation","content":" Control \u0026amp; Estimation # The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.\nState estimation # In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by \\[\\widehat{\\mathbf{x}}_{t|t-1} = \\mathbf{A}\\widehat{\\mathbf{x}}_{t-1|t-1} \u0026#43; \\mathbf{B} u_{t-1} \u0026#43; \\mathbf{m}_{t-1} \\;,\\] \\[\\widehat{\\mathbf{x}}_{t|t} = \\widehat{\\mathbf{x}}_{t|t-1} \u0026#43; \\mathbf{K}^{\\rm e}_t \\left(\\mathbf{z}_t - \\widehat{\\mathbf{y}}_{t|t-1}\\right)\\;,\\] where \\( \\hat{\\left(\\cdot\\right)}_{t|j} \\) indicates an estimate at time \\( t \\) given data up to time \\( j \\) inclusive, \\( \\mathbf{K}^{\\rm e} \\) is the estimator gain, and\n\\[ \\widehat{\\mathbf{y}}_{t|t-1} = h\\left( \\widehat{\\mathbf{x}}_{t|t-1} \\right) \\; .\\] In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.\nIn the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.\nAdaptive estimation of process disturbance # Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.\nWhen parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk \\[\\mathbf{m}_{t} = \\mathbf{m}_{t-1} \u0026#43; \\mathbf{w}^m_{t-1} \\;,\\] where \\( \\mathbf{w}^m \\sim \\mathcal{N}\\left(0, \\mathbf{Q}_m\\right)\\) . Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.\nControl # Given the estimated state, the controller updates the inputs to the system according to the following law: \\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] where \\( \\left( \\cdot \\right)^{\\rm ref} \\) correspond to reference/target signals and \\( \\mathbf{K}^c_x \\) is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.\nIf users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as\n\\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right) - \\mathbf{K}^c_{\\rm inty} \\sum_{j=1}^{t}\\left( \\widehat{\\mathbf{y}}_j - \\mathbf{y}^{\\rm ref}_j \\right) \\;.\\] An additional option available to users is a control law that updates the change in u,\n\\[\\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\left(\\mathbf{u}_{t-1} - \\mathbf{u}^{\\rm ref}_{t-1} \\right) - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] \\[\\mathbf{u}_{t} = \\mathbf{u}_{t-1} \u0026#43; \\Delta\\mathbf{u}_{t} \\; .\\] Notice that this takes the form of a first-order difference equation for updating control (i.e., \\( \\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\mathbf{u}_{t-1} \u0026#43; \\epsilon_{t-1} \\) ), effectively low-pass filtering the input depending on the characteristics of \\( \\mathbf{K}^c_u \\) . This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.\nIntegral action and the \\( \\Delta \\mathbf{u} \\) control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR\u0026rsquo;d to use in combination.\nCalculating reference state-control from output # In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce \\( \\mathbf{x}^{\\rm ref} \\) and \\( \\mathbf{u}^{\\rm ref} \\) . Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.\nModel Predictive Control # Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.\nIn the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.\n"},{"id":9,"href":"/lds-ctrl-est/docs/api/classes/","title":"Classes","section":"LDS C+E Documentation","content":" Classes # lds::Controller\nlds::EM\nlds::Fit LDS Fit Type.\nlds::SSID\nlds::SwitchedController SwitchedController Type.\nlds::System Linear Dynamical System Type.\nlds::UniformMatrixList\nlds::UniformSystemList\nlds::UniformVectorList\nlds::gaussian::Controller Gaussian-observation Controller Type.\nlds::gaussian::Fit GLDS Fit Type.\nlds::gaussian::FitEM GLDS E-M Fit Type.\nlds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.\nlds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.\nlds::gaussian::System Gaussian LDS Type.\nlds::poisson::Controller PLDS Controller Type.\nlds::poisson::Fit PLDS Fit Type.\nlds::poisson::FitEM PLDS E-M Fit Type.\nlds::poisson::FitSSID Subspace Identification (SSID) for PLDS.\nlds::poisson::SwitchedController Poisson-observation SwitchedController Type.\nlds::poisson::System Poisson System type.\nUpdated on 5 March 2025 at 21:41:27 EST\n"},{"id":10,"href":"/lds-ctrl-est/docs/api/modules/group__control__masks/","title":"Control Mode Bit Masks","section":"Modules","content":" Control Mode Bit Masks # provides fill types for constructing new armadillo vectors, matrices More\u0026hellip; Attributes # Name const std::size_t kControlTypeDeltaU control designed to penalize change in input const std::size_t kControlTypeIntY control using integral action const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m Detailed Description # Control mode bit masks. These can be bit-wise OR\u0026rsquo;d to use in combination.\nAttribute Details # kControlTypeDeltaU # static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY # static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM # static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\nUpdated on 5 March 2025 at 21:41:27 EST\n"},{"id":11,"href":"/lds-ctrl-est/docs/api/modules/group__defaults/","title":"Defaults","section":"Modules","content":" Defaults # More\u0026hellip; Attributes # Name const data_t kDefaultP0 default state estimate covar const data_t kDefaultQ0 default process noise covar const data_t kDefaultR0 default output noise covar Detailed Description # Default values for common variables (e.g., default diagonal elements of covariances)\nAttribute Details # kDefaultP0 # static const data_t kDefaultP0 = 1e-6; kDefaultQ0 # static const data_t kDefaultQ0 = 1e-6; kDefaultR0 # static const data_t kDefaultR0 = 1e-2; Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":12,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/","title":"eg_glds_ctrl.cpp","section":"Examples","content":" eg_glds_ctrl.cpp # Example GLDS Control ```cpp\n//===\u0026ndash; eg_glds_ctrl.cpp - Example GLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS Control ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\n// output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false;\n// Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err\n// setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]);\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; }\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 21:41:27 EST "},{"id":13,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/","title":"eg_glds_du_plds_ctrl.cpp","section":"Examples","content":" eg_glds_du_plds_ctrl.cpp # Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u). ```cpp\n//===\u0026ndash; eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt);\n// to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU;\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_du_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 21:41:27 EST "},{"id":14,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/","title":"eg_plds_ctrl.cpp","section":"Examples","content":" eg_plds_ctrl.cpp # Example PLDS Control ```cpp\n//===\u0026ndash; eg_plds_ctrl.cpp - Example PLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Control ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt);\n// Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err\n// Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\nsize_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\nVector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled\u0026hellip; lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system);\n// for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type);\n// set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty);\n// to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0;\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros);\n// set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y();\nx_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x();\nm_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 21:41:27 EST "},{"id":15,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/","title":"eg_plds_est.cpp","section":"Examples","content":" eg_plds_est.cpp # Example PLDS Estimation ```cpp\n//===\u0026ndash; eg_plds_est.cpp - Example PLDS Estimation \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0);\nint main() { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.\n// construct ground truth system\u0026hellip; lds::poisson::System system_true(n_u, n_x, n_y, dt);\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset();\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt);\n// Can copy parameters from another system object system_estimator = system_true;\n// wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est);\n// set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true;\n// set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;estimator:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; system_estimator.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));\n// create matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros);\n// states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\nMatrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simlation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1));\n// Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simlation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows;\nif ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026ldquo;Q must be n x n.\u0026rdquo;); }\nMatrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); }\nreturn x; }\n_Filename: eg_plds_est.cpp_ ------------------------------- Updated on 5 March 2025 at 21:41:27 EST "},{"id":16,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/","title":"eg_plds_switched_ctrl.cpp","section":"Examples","content":" eg_plds_switched_ctrl.cpp # Example Switched PLDS Control ```cpp\n//===\u0026ndash; eg_plds_switched_ctrl.cpp - Example Switched PLDS Control \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026quot;;\n// whether to do switched control bool do_switch_ctrl = true;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt);\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices. data_t scale_sys_b = 2;\nMatrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));\ncontrolled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions\n// reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);\n// Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system);\n// set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys1:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys2:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x));\nswitched_controller.set_y_ref(y_ref0);\nstd::vectorlds::poisson::System systems_vec(3, lds::poisson::System()); lds::UniformSystemListlds::poisson::System systems(std::move(systems_vec));\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;switched_controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; switched_controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Fake measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros);\n// create Matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);\n// modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones);\n// set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } }\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace)); mode.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;mode\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_switched_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 21:41:27 EST "},{"id":17,"href":"/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/","title":"examples","section":"Files","content":" examples # Files # Name examples/eg_glds_ctrl.cpp examples/eg_glds_du_plds_ctrl.cpp examples/eg_plds_ctrl.cpp examples/eg_plds_est.cpp examples/eg_plds_switched_ctrl.cpp Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":18,"href":"/lds-ctrl-est/docs/api/examples/","title":"Examples","section":"LDS C+E Documentation","content":" Examples # eg_glds_ctrl.cpp Example GLDS Control.\neg_glds_du_plds_ctrl.cpp Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).\neg_plds_ctrl.cpp Example PLDS Control.\neg_plds_est.cpp Example PLDS Estimation.\neg_plds_switched_ctrl.cpp Example Switched PLDS Control.\nUpdated on 5 March 2025 at 21:41:27 EST\n"},{"id":19,"href":"/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/","title":"examples/eg_glds_ctrl.cpp","section":"Files","content":" examples/eg_glds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":20,"href":"/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/","title":"examples/eg_glds_du_plds_ctrl.cpp","section":"Files","content":" examples/eg_glds_du_plds_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU; // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":21,"href":"/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/","title":"examples/eg_plds_ctrl.cpp","section":"Files","content":" examples/eg_plds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err // Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled... lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type); // set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":22,"href":"/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/","title":"examples/eg_plds_est.cpp","section":"Files","content":" examples/eg_plds_est.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name Matrix random_walk(size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0) int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # random_walk # Matrix random_walk( size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0 ) main # int main() Source code # //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. // construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state // Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. // turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; } Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":23,"href":"/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/","title":"examples/eg_plds_switched_ctrl.cpp","section":"Files","content":" examples/eg_plds_switched_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Source code # //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 // simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions // reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":24,"href":"/lds-ctrl-est/docs/api/files/","title":"Files","section":"LDS C+E Documentation","content":" Files # examples/eg_glds_ctrl.cpp\nexamples/eg_glds_du_plds_ctrl.cpp\nexamples/eg_plds_ctrl.cpp\nexamples/eg_plds_est.cpp\nexamples/eg_plds_switched_ctrl.cpp\nldsCtrlEst_h/lds.h lds namespace\nldsCtrlEst_h/lds_ctrl.h Controller.\nldsCtrlEst_h/lds_fit.h LDS base fit type.\nldsCtrlEst_h/lds_fit_em.h subspace identification\nldsCtrlEst_h/lds_fit_ssid.h subspace identification\nldsCtrlEst_h/lds_gaussian.h glds namespace\nldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.\nldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.\nldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.\nldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.\nldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.\nldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.\nldsCtrlEst_h/lds_poisson.h plds namespace\nldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.\nldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.\nldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.\nldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.\nldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.\nldsCtrlEst_h/lds_poisson_sys.h PLDS base type.\nldsCtrlEst_h/lds_sctrl.h SwitchedController type.\nldsCtrlEst_h/lds_sys.h LDS base type.\nldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.\nldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.\nldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.\nldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)\nldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)\nsrc/lds.cpp misc lds namespace functions\nsrc/lds_gaussian_sys.cpp GLDS base type.\nsrc/lds_poisson_sys.cpp PLDS base type.\nsrc/lds_sys.cpp LDS base type.\nsrc/lds_uniform_vecs.cpp Uniformly sized vectors.\nUpdated on 5 March 2025 at 21:41:27 EST\n"},{"id":25,"href":"/lds-ctrl-est/docs/tutorials/eg_glds_control/","title":"GLDS Control","section":"LDS C+E Examples","content":" GLDS Control Tutorial # This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\] \\[\ry_{t} = x_t\r\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.\n/// Going to simulate a switching disturbance (m) acting on system size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_y).fill(m_low); Finally, assign the parameters using corresponding set-methods.\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); Creating the controller # Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.\nFor the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.\n// make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nWith the controller constructed, control variables may be set.\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.\nThe control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller\u0026rsquo;s online estimates of the output, state, and disturbance are given in purple.\n"},{"id":26,"href":"/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/","title":"include","section":"Files","content":" include # Directories # Name ldsCtrlEst_h Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":27,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds/","title":"lds","section":"Namespaces","content":" lds # Linear Dynamical Systems (LDS) namespace. Namespaces # Name lds::gaussian Linear Dynamical Systems with Gaussian observations. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::Controller class lds::EM class lds::Fit LDS Fit Type. class lds::SSID class lds::SwitchedController SwitchedController Type. class lds::System Linear Dynamical System Type. class lds::UniformMatrixList class lds::UniformSystemList class lds::UniformVectorList Types # Name enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}\nweighting options for SSID enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2} using double data_t using arma::Col\u0026lt; data_t \u0026gt; Vector using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Cube\u0026lt; data_t \u0026gt; Cube using arma::subview\u0026lt; data_t \u0026gt; View Functions # Name void Limit(std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub) void Limit(Vector \u0026amp; x, data_t lb, data_t ub) void Limit(Matrix \u0026amp; x, data_t lb, data_t ub) void Reassign(Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Vector in place void Reassign(Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Matrix in place void ForceSymPD(Matrix \u0026amp; X)\nforces matrix to be symmetric positive-definite void ForceSymMinEig(Matrix \u0026amp; X, data_t eig_min =0)\nforces matrix to be symmetric and have a minimum eigenvalue void lq(Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X)\nLQ decomposition. Matrix calcCov(const Matrix \u0026amp; A, const Matrix \u0026amp; B)\nCalculate covariance matrix. Attributes # Name const data_t kInf Some useful numbers. const data_t kPi Type Details # SSIDWt # Enumerator Value Description kSSIDNone None. kSSIDMOESP MOESP (AKA \u0026ldquo;robust method\u0026rdquo; in van Overschee 1996) kSSIDCVA CVA \u0026ldquo;Canonical Variate Analysis\u0026rdquo;. Weighting options for singular value decomposition performed during subspace identification (SSID)\nReference:\nvan Overschee, de Moor. 1996. Subspace Identification for Linear Systems.\nMatrixListFreeDim # Enumerator Value Description kMatFreeDimNone neither dim free to be hetero in mat list kMatFreeDim1 allow 1st dim of mats in list to be hetero kMatFreeDim2 allow 2nd dim of mats in list to be hetero data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nVector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Cube # using lds::Cube = arma::Cube\u0026lt;data_t\u0026gt;; View # using lds::View = arma::subview\u0026lt;data_t\u0026gt;; Function Details # Limit # inline void Limit( std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Vector \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Matrix \u0026amp; x, data_t lb, data_t ub ) Reassign # inline void Reassign( Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Vector other other Vector parenthetical optional description provided by caller to ease debugging Reassign # inline void Reassign( Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Matrix other other Matrix parenthetical optional description provided by caller to ease debugging ForceSymPD # void ForceSymPD( Matrix \u0026amp; X ) Parameters:\nX mutated matrix ForceSymMinEig # void ForceSymMinEig( Matrix \u0026amp; X, data_t eig_min =0 ) Parameters:\nX mutated matrix eig_min [optional] minimum eigen value lq # void lq( Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X ) Parameters:\nL lower triangle matrix Qt orthonormal matrix (transposed cf QR decomp) X matrix being decomposed calcCov # Matrix calcCov( const Matrix \u0026amp; A, const Matrix \u0026amp; B ) Parameters:\nA some matrix B some other matrix Return: covariance\nAttribute Details # kInf # static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); kPi # static const data_t kPi = arma::datum::pi; Updated on 5 March 2025 at 21:41:26 EST\n"},{"id":28,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/","title":"lds::Controller","section":"Classes","content":" lds::Controller # More\u0026hellip;\nInherited by lds::SwitchedController\u0026lt; System \u0026gt;, lds::gaussian::Controller, lds::poisson::Controller\nPublic Functions # Name Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes # Name System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::Controller; Public Function Details # Controller # Controller() =default Controller # inline Controller( const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Controller # inline Controller( System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Control # inline const Vector \u0026amp; Control( const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step). This is the most flexible option, but requires user to have set the controller\u0026rsquo;s y_ref, x_ref, and u_ref variables.\nControlOutputReference # inline const Vector \u0026amp; ControlOutputReference( const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_estimation [optional] whether to update state estimate (if false, effectively open-loop control) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.\nsys # inline const System \u0026amp; sys() const Kc # inline const Matrix \u0026amp; Kc() const Kc_inty # inline const Matrix \u0026amp; Kc_inty() const Kc_u # inline const Matrix \u0026amp; Kc_u() const g_design # inline const Vector \u0026amp; g_design() const u_ref # inline const Vector \u0026amp; u_ref() const x_ref # inline const Vector \u0026amp; x_ref() const y_ref # inline const Vector \u0026amp; y_ref() const control_type # inline size_t control_type() const tau_awu # inline data_t tau_awu() const u_lb # inline data_t u_lb() const u_ub # inline data_t u_ub() const set_sys # inline void set_sys( const System \u0026amp; sys ) set_g_design # inline void set_g_design( const Vector \u0026amp; g_design ) set_u_ref # inline void set_u_ref( const Vector \u0026amp; u_ref ) set_x_ref # inline void set_x_ref( const Vector \u0026amp; x_ref ) set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) Reimplemented by: lds::gaussian::Controller::set_y_ref, lds::gaussian::SwitchedController::set_y_ref, lds::poisson::Controller::set_y_ref, lds::poisson::SwitchedController::set_y_ref\nset_Kc # inline void set_Kc( const Matrix \u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const Matrix \u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const Matrix \u0026amp; Kc_u ) set_tau_awu # inline void set_tau_awu( data_t tau ) set_control_type # inline void set_control_type( size_t control_type ) Parameters:\ncontrol_type control type bit mask Template Parameters:\nSystem type derived from lds::System set_u_lb # inline void set_u_lb( data_t u_lb ) Parameters:\nu_lb control lower bound set_u_ub # inline void set_u_ub( data_t u_ub ) Parameters:\nu_ub control upper bound Reset # inline void Reset() Print # inline void Print() Protected Attribute Details # sys_ # System sys_; u_ # Vector u_; u_return_ # Vector u_return_; g_design_ # Vector g_design_; u_ref_ # Vector u_ref_; u_ref_prev_ # Vector u_ref_prev_; x_ref_ # Vector x_ref_; y_ref_ # Vector y_ref_; cx_ref_ # Vector cx_ref_; Kc_ # Matrix Kc_; Kc_u_ # Matrix Kc_u_; Kc_inty_ # Matrix Kc_inty_; du_ref_ # Vector du_ref_; dv_ref_ # Vector dv_ref_; v_ref_ # Vector v_ref_; dv_ # Vector dv_; v_ # Vector v_; int_e_ # Vector int_e_; int_e_awu_adjust_ # Vector int_e_awu_adjust_; u_sat_ # Vector u_sat_; do_control_prev_ # bool do_control_prev_ = false; do_lock_control_prev_ # bool do_lock_control_prev_ = false; u_saturated_ # bool u_saturated_ = false; u_lb_ # data_t u_lb_ {}; u_ub_ # data_t u_ub_ {}; tau_awu_ # data_t tau_awu_ {}; k_awu_ # data_t k_awu_ = 0; t_since_control_onset_ # data_t t_since_control_onset_ = 0; control_type_ # size_t control_type_ {}; Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":29,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/","title":"lds::EM","section":"Classes","content":" lds::EM # More\u0026hellip;\nInherited by lds::gaussian::FitEM, lds::poisson::FitEM\nPublic Functions # Name EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions # Name void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() virtual void MaximizeOutput() =0 virtual void MaximizeMeasurement() =0 void Smooth(bool force_common_initial)\nget smoothed estimates virtual void RecurseKe(Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t) =0\nrecursively update estimator gain Ke void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::EM; Public Function Details # EM # EM() =default EM # EM( size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nn_x number of states dt sample period u_train input training data z_train measurement training data EM # EM( const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nfit0 initial fit u_train input training data z_train measurement training data ~EM # virtual ~EM() =default Run # const Fit \u0026amp; Run( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2 ) Parameters:\ncalc_dynamics [optional] whether to calculate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law max_iter max number of iterations tol convergence tolerance (max fractional abs change) Return: Fit\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nx # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const y # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const sum_E_x_t_x_t # inline const Matrix \u0026amp; sum_E_x_t_x_t() const sum_E_xu_tm1_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const sum_E_xu_t_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const n_t_tot # inline size_t n_t_tot() theta # inline const Vector \u0026amp; theta() const Protected Function Details # Expectation # void Expectation( bool force_common_initial =false ) Parameters:\nforce_common_initial whether to force common initial condition for all trials Maximization # void Maximization( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false ) Parameters:\ncalc_dynamics [optional] whether to caclulate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law MaximizeDynamics # void MaximizeDynamics() MaximizeQ # void MaximizeQ() MaximizeInitial # void MaximizeInitial() MaximizeOutput # virtual void MaximizeOutput() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput\nMaximizeMeasurement # virtual void MaximizeMeasurement() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement\nSmooth # void Smooth( bool force_common_initial ) Parameters:\nforce_common_initial whether to force common initial conditions RecurseKe # virtual void RecurseKe( Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t ) =0 Parameters:\nKe estimator gain P_pre cov of predicted state est. P_post cov of postior sate est. t time Reimplemented by: lds::gaussian::FitEM::RecurseKe, lds::poisson::FitEM::RecurseKe\nReset # void Reset() InitVars # void InitVars() UpdateTheta # Vector UpdateTheta() Return: parameter list\nProtected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; x_ # std::vector\u0026lt; Matrix \u0026gt; x_; P_ # std::vector\u0026lt; Cube \u0026gt; P_; P_t_tm1_ # std::vector\u0026lt; Cube \u0026gt; P_t_tm1_; y_ # std::vector\u0026lt; Matrix \u0026gt; y_; diag_y_ # Matrix diag_y_; sum_E_x_t_x_t_ # Matrix sum_E_x_t_x_t_; sum_E_xu_tm1_xu_tm1_ # Matrix sum_E_xu_tm1_xu_tm1_; sum_E_xu_t_xu_tm1_ # Matrix sum_E_xu_t_xu_tm1_; fit_ # Fit fit_; theta_ # Vector theta_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":30,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/","title":"lds::Fit","section":"Classes","content":" lds::Fit # LDS Fit Type. #include \u0026lt;lds_fit.h\u0026gt;\nInherited by lds::gaussian::Fit, lds::poisson::Fit\nPublic Functions # Name Fit() =default\nConstructs a new Fit. Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias virtual const Matrix \u0026amp; R() const =0 void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance virtual void set_R(const Matrix \u0026amp; R) =0\nsets output noise covariance (if any) void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) =0\noutput function Protected Attributes # Name data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period ~Fit # virtual ~Fit() =default n_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const m # inline const Vector \u0026amp; m() const Q # inline const Matrix \u0026amp; Q() const x0 # inline const Vector \u0026amp; x0() const P0 # inline const Matrix \u0026amp; P0() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const R # virtual const Matrix \u0026amp; R() const =0 Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R\nset_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_g # inline void set_g( const Vector \u0026amp; g ) set_m # inline void set_m( const Vector \u0026amp; m ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # virtual void set_R( const Matrix \u0026amp; R ) =0 Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R\nset_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) f # inline View f( Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t ) Parameters:\nx state estimate (over time) u input (over time) t time index Return: view of updated state\nf # inline View f( Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t ) Parameters:\nx_pre predicted state est. x_post posterior state est. u input (over time) t time index Return: view of predicted state\nh # virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) =0 Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplemented by: lds::gaussian::Fit::h, lds::poisson::Fit::h\nProtected Attribute Details # dt_ # data_t dt_ {}; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; m_ # Vector m_; Q_ # Matrix Q_; C_ # Matrix C_; d_ # Vector d_; R_ # Matrix R_; x0_ # Vector x0_; P0_ # Matrix P0_; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":31,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/","title":"lds::gaussian","section":"Namespaces","content":" lds::gaussian # Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. class lds::gaussian::Fit GLDS Fit Type. class lds::gaussian::FitEM GLDS E-M Fit Type. class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. class lds::gaussian::System Gaussian LDS Type. Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":32,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/","title":"lds::gaussian::Controller","section":"Classes","content":" lds::gaussian::Controller # Gaussian-observation Controller Type. #include \u0026lt;lds_gaussian_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 21:41:27 EST\n"},{"id":33,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/","title":"lds::gaussian::Fit","section":"Classes","content":" lds::gaussian::Fit # GLDS Fit Type. #include \u0026lt;lds_gaussian_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual const Matrix \u0026amp; R() const override\ngets measurement noise covariance virtual void set_R(const Matrix \u0026amp; R) override\nsets measurement noise covariance virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period R # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nh # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nUpdated on 5 March 2025 at 21:41:27 EST\n"},{"id":34,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/","title":"lds::gaussian::FitEM","section":"Classes","content":" lds::gaussian::FitEM # GLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_gaussian_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::gaussian::FitEM; This type is used in the process of fitting GLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":35,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/","title":"lds::gaussian::FitSSID","section":"Classes","content":" lds::gaussian::FitSSID # Subspace Identification (SSID) for GLDS. #include \u0026lt;lds_gaussian_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":36,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/","title":"lds::gaussian::SwitchedController","section":"Classes","content":" lds::gaussian::SwitchedController # Gaussian-observation SwitchedController Type. #include \u0026lt;lds_gaussian_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 21:41:27 EST\n"},{"id":37,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/","title":"lds::gaussian::System","section":"Classes","content":" lds::gaussian::System # Gaussian LDS Type. #include \u0026lt;lds_gaussian_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)\nConstructs a new Gaussian System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. const Matrix \u0026amp; R() const\nGet output noise covariance. void set_Q(const Matrix \u0026amp; Q) void set_R(const Matrix \u0026amp; R)\nSet output noise covariance. void set_Ke(const Matrix \u0026amp; Ke)\nSet estimator gain. void set_Ke_m(const Matrix \u0026amp; Ke_m)\nSet disturbance estimator gain. void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively update estimator gain. Protected Attributes # Name Matrix R_ covariance of output noise bool do_recurse_Ke_ whether to recursively calculate estimator gain Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0 ) Parameters:\nn_u number of inputs (u) n_x number of states (x) n_y number of outputs (y) dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) r0 [optional] initial diagonal elements of output noise covariance (R) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nR # inline const Matrix \u0026amp; R() const set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # inline void set_R( const Matrix \u0026amp; R ) set_Ke # inline void set_Ke( const Matrix \u0026amp; Ke ) set_Ke_m # inline void set_Ke_m( const Matrix \u0026amp; Ke_m ) Print # void Print() Protected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nProtected Attribute Details # R_ # Matrix R_; do_recurse_Ke_ # bool do_recurse_Ke_ {}; Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":38,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/","title":"lds::poisson","section":"Namespaces","content":" lds::poisson # Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. class lds::poisson::Fit PLDS Fit Type. class lds::poisson::FitEM PLDS E-M Fit Type. class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. class lds::poisson::System Poisson System type. Attributes # Name std::random_device rd random device for simulating poisson data std::mt19937 rng random number generator for simulating poisson data Attribute Details # rd # static std::random_device rd; rng # static std::mt19937 rng = std::mt19937( rd()); Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":39,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/","title":"lds::poisson::Controller","section":"Classes","content":" lds::poisson::Controller # PLDS Controller Type. #include \u0026lt;lds_poisson_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 21:41:27 EST\n"},{"id":40,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/","title":"lds::poisson::Fit","section":"Classes","content":" lds::poisson::Fit # PLDS Fit Type. #include \u0026lt;lds_poisson_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function virtual void set_R(const Matrix \u0026amp; R) override\nsets output noise covariance (if any) virtual const Matrix \u0026amp; R() const override Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # inline Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period h # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nR # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nUpdated on 5 March 2025 at 21:41:27 EST\n"},{"id":41,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/","title":"lds::poisson::FitEM","section":"Classes","content":" lds::poisson::FitEM # PLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_poisson_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::poisson::FitEM; This type is used in the process of fitting PLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":42,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/","title":"lds::poisson::FitSSID","section":"Classes","content":" lds::poisson::FitSSID # Subspace Identification (SSID) for PLDS. #include \u0026lt;lds_poisson_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":43,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/","title":"lds::poisson::SwitchedController","section":"Classes","content":" lds::poisson::SwitchedController # Poisson-observation SwitchedController Type. #include \u0026lt;lds_poisson_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 21:41:27 EST\n"},{"id":44,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/","title":"lds::poisson::System","section":"Classes","content":" lds::poisson::System # Poisson System type. #include \u0026lt;lds_poisson_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nConstructs a new Poisson System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively recalculate estimator gain (Ke) Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nProtected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nRecursively recalculate estimator gain (Ke).\nReferences:\nSmith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.\nEden UT, \u0026hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.\nUpdated on 5 March 2025 at 21:41:27 EST\n"},{"id":45,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/","title":"lds::SSID","section":"Classes","content":" lds::SSID # More\u0026hellip;\nInherited by lds::gaussian::FitSSID, lds::poisson::FitSSID\nPublic Functions # Name SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions # Name void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. virtual void DecomposeData() =0\nDecompose data to lower-triangular matrix (used in Solve) void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::SSID; Public Function Details # SSID # SSID() =default SSID # SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf) ) Parameters:\nn_x number of states n_h size of block-hankel data matrix dt sample period u_train input training data z_train measurement training data d output bias Run # std::tuple\u0026lt; Fit, Vector \u0026gt; Run( SSIDWt ssid_wt ) Parameters:\nssid_wt weight for singular value decomp Return: tuple (Fit, singular values)\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nProtected Function Details # CalcD # void CalcD( data_t t_silence =0.1, data_t thresh_silence =0.001 ) Parameters:\nt_silence threshold on period of time that qualifies as \u0026ldquo;silence\u0026rdquo; thresh_silence threshold on input amplitude u that qualifies as \u0026ldquo;silence\u0026rdquo; CreateHankelDataMat # void CreateHankelDataMat() Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.\nDecomposeData # virtual void DecomposeData() =0 Reimplemented by: lds::gaussian::FitSSID::DecomposeData, lds::poisson::FitSSID::DecomposeData\nCalcSVD # void CalcSVD( SSIDWt wt ) Parameters:\nssid_wt weight for SVD Solve # void Solve( data_t wt_dc ) Parameters:\nwt_dc weight placed on getting correct DC I/O gain RecomputeExtObs # void RecomputeExtObs() Protected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; D_ # Matrix D_; fit_ # Fit fit_; g_dc_ # Matrix g_dc_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_h_ # size_t n_h_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; L_ # Matrix L_; s_ # Vector s_; ext_obs_t_ # Matrix ext_obs_t_; Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":46,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/","title":"lds::SwitchedController","section":"Classes","content":" lds::SwitchedController # SwitchedController Type. More\u0026hellip;\n#include \u0026lt;lds_sctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nInherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController\nPublic Functions # Name SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes # Name std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::SwitchedController; Public Function Details # SwitchedController # SwitchedController() =default SwitchedController # inline SwitchedController( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask SwitchedController # inline SwitchedController( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Switch # inline void Switch( size_t idx, bool do_force_switch =false ) Parameters:\nidx index do_force_switch whether to force a system switch even if already there. set_Kc # inline void set_Kc( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc ) set_Kc # inline void set_Kc( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty ) set_Kc_inty # inline void set_Kc_inty( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u ) set_Kc_u # inline void set_Kc_u( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u ) set_g_design # inline void set_g_design( const UniformVectorList \u0026amp; g ) set_g_design # inline void set_g_design( UniformVectorList \u0026amp;\u0026amp; g ) Protected Attribute Details # systems_ # std::vector\u0026lt; System \u0026gt; systems_; n_sys_ # size_t n_sys_ {}; idx_ # size_t idx_ {}; Kc_list_ # UniformMatrixList Kc_list_; Kc_inty_list_ # UniformMatrixList Kc_inty_list_; Kc_u_list_ # UniformMatrixList Kc_u_list_; g_design_list_ # UniformVectorList g_design_list_; Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":47,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_system/","title":"lds::System","section":"Classes","content":" lds::System # Linear Dynamical System Type. #include \u0026lt;lds_sys.h\u0026gt;\nInherited by lds::gaussian::System, lds::poisson::System\nPublic Functions # Name System() =default\nConstructs a new System. System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nconstructs a new System virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) =0\nsimulates system (single time step) void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function virtual void h() =0\nsystem output function virtual Vector h_(Vector x) =0\nsystem output function (stateless) size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void RecurseKe() =0\nRecursively recalculate estimator gain (Ke) void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes # Name bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes # Name std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 diagonal elements for state estimate covariance q0 diagonal elements for process noise covariance ~System # inline virtual ~System() Filter # void Filter( const Vector \u0026amp; u_tm1, const Vector \u0026amp; z ) Parameters:\nu_tm1 input at t-minus-1 z_t current measurement Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.\nSimulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) =0 Parameters:\nu_tm1 input at time t-1 Return: simulated measurement at time t\nReimplemented by: lds::gaussian::System::Simulate, lds::poisson::System::Simulate\nf # inline void f( const Vector \u0026amp; u, bool do_add_noise =false ) Parameters:\nu input do_add_noise whether to add simulated process noise h # virtual void h() =0 Reimplemented by: lds::gaussian::System::h, lds::poisson::System::h\nh_ # virtual Vector h_( Vector x ) =0 Parameters:\nx_t state at time t Return: predicted state at time t + 1\nReimplemented by: lds::gaussian::System::h_, lds::poisson::System::h_\nn_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const x # inline const Vector \u0026amp; x() const P # inline const Matrix \u0026amp; P() const m # inline const Vector \u0026amp; m() const P_m # inline const Matrix \u0026amp; P_m() const cx # inline const Vector \u0026amp; cx() const y # inline const Vector \u0026amp; y() const x0 # inline const Vector \u0026amp; x0() const m0 # inline const Vector \u0026amp; m0() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const Ke # inline const Matrix \u0026amp; Ke() const Ke_m # inline const Matrix \u0026amp; Ke_m() const Q # inline const Matrix \u0026amp; Q() Q_m # inline const Matrix \u0026amp; Q_m() P0 # inline const Matrix \u0026amp; P0() P0_m # inline const Matrix \u0026amp; P0_m() set_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_m # inline void set_m( const Vector \u0026amp; m, bool do_force_assign =false ) set_g # inline void set_g( const Vector \u0026amp; g ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_Q_m # inline void set_Q_m( const Matrix \u0026amp; Q_m ) set_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_P0_m # inline void set_P0_m( const Matrix \u0026amp; P0_m ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) set_x # inline void set_x( const Vector \u0026amp; x ) Reset # void Reset() nstep_pred_block # std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block( UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1 ) Print # void Print() Protected Function Details # RecurseKe # virtual void RecurseKe() =0 Reimplemented by: lds::gaussian::System::RecurseKe, lds::poisson::System::RecurseKe\nInitVars # void InitVars( data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Public Attribute Details # do_adapt_m # bool do_adapt_m {}; Protected Attribute Details # n_x_ # std::size_t n_x_ {}; n_u_ # std::size_t n_u_ {}; n_y_ # std::size_t n_y_ {}; dt_ # data_t dt_ {}; x_ # Vector x_; P_ # Matrix P_; m_ # Vector m_; P_m_ # Matrix P_m_; cx_ # Vector cx_; y_ # Vector y_; z_ # Vector z_; x0_ # Vector x0_; P0_ # Matrix P0_; m0_ # Vector m0_; P0_m_ # Matrix P0_m_; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; Q_ # Matrix Q_; Q_m_ # Matrix Q_m_; C_ # Matrix C_; d_ # Vector d_; Ke_ # Matrix Ke_; Ke_m_ # Matrix Ke_m_; Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":48,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/","title":"lds::UniformMatrixList","section":"Classes","content":" lds::UniformMatrixList # More\u0026hellip;\nInherits from std::vector\u0026lt; Matrix \u0026gt;\nPublic Functions # Name UniformMatrixList() =default\nConstructs a new UniformMatrixList. UniformMatrixList(const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent. UniformMatrixList(std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent. UniformMatrixList(std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent. UniformMatrixList(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nConstructs a new UniformMatrixList (copy). UniformMatrixList(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nConstructs a new UniformMatrixList (move). ~UniformMatrixList() =default\nDestroys the object. const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim(size_t n =0) const\ngets dimensions of uniformly sized matrices size_t size()\nsize of container const Matrix \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Matrix \u0026amp; that, size_t n)\nswaps input matrix with n^th matrix of list UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nassigns the contents (copy) UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nassigns the contents (move) void append(const Matrix \u0026amp; mat)\nappends a matrix to the list Detailed Description # template \u0026lt;MatrixListFreeDim D =kMatFreeDimNone\u0026gt; class lds::UniformMatrixList; Public Function Details # UniformMatrixList # UniformMatrixList() =default UniformMatrixList # explicit UniformMatrixList( const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # explicit UniformMatrixList( std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList UniformMatrixList # UniformMatrixList( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList ~UniformMatrixList # ~UniformMatrixList() =default dim # inline const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim( size_t n =0 ) const Parameters:\nn [optional] index in list of matrices Return: dimensions\nsize # inline size_t size() at # inline const Matrix \u0026amp; at( size_t n ) Swap # inline void Swap( Matrix \u0026amp; that, size_t n ) Parameters:\nthat input matrix n index where the matrix is moved operator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\noperator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\nappend # void append( const Matrix \u0026amp; mat ) Parameters:\nmat input matrix Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":49,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/","title":"lds::UniformSystemList","section":"Classes","content":" lds::UniformSystemList # More\u0026hellip;\nInherits from std::vector\u0026lt; System \u0026gt;\nPublic Functions # Name UniformSystemList() =default\nConstructs a new UniformSystemList. UniformSystemList(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by copying existing vector of System if dimensions consistent. UniformSystemList(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by moving existing vector of System if dimensions consistent. UniformSystemList(std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList from initializer_list of System if dimensions consistent. UniformSystemList(const UniformSystemList \u0026amp; that)\nConstructs a new UniformSystemList (copy). UniformSystemList(UniformSystemList \u0026amp;\u0026amp; that)\nConstructs a new UniformSystemList (move). ~UniformSystemList() =default\nDestroys the object. const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const\ngets dimensions of the uniformly sized systems size_t size()\nsize of container const System \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(System \u0026amp; that, size_t n)\nswaps input system with n^th system of list UniformSystemList \u0026amp; operator=(const UniformSystemList \u0026amp; that)\nassigns the contents (copy) UniformSystemList \u0026amp; operator=(UniformSystemList \u0026amp;\u0026amp; that)\nassigns the contents (move) Detailed Description # template \u0026lt;typename System \u0026gt; class lds::UniformSystemList; Public Function Details # UniformSystemList # UniformSystemList() =default UniformSystemList # explicit UniformSystemList( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # explicit UniformSystemList( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList UniformSystemList # UniformSystemList( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList ~UniformSystemList # ~UniformSystemList() =default dim # inline const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const size # inline size_t size() at # inline const System \u0026amp; at( size_t n ) Swap # inline void Swap( System \u0026amp; that, size_t n ) Parameters:\nthat input system n index where the system is moved operator= # inline UniformSystemList \u0026amp; operator=( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\noperator= # inline UniformSystemList \u0026amp; operator=( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\nUpdated on 5 March 2025 at 21:41:27 EST\n"},{"id":50,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/","title":"lds::UniformVectorList","section":"Classes","content":" lds::UniformVectorList # Inherits from std::vector\u0026lt; Vector \u0026gt;\nPublic Functions # Name UniformVectorList() =default\nConstructs a new UniformVectorList. UniformVectorList(const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent. UniformVectorList(std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent. UniformVectorList(std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0)\nConstructs a new UniformVectorList from initializer_list of Vector if dimensions consistent. UniformVectorList(const UniformVectorList \u0026amp; that)\nConstructs a new UniformVectorList (copy) UniformVectorList(UniformVectorList \u0026amp;\u0026amp; that)\nConstructs a new UniformVectorList (move) ~UniformVectorList() =default\nDestroys the object. size_t dim() const\ngets dimensions of the uniformly sized matrices size_t size()\nsize of container const Vector \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Vector \u0026amp; that, size_t n)\nswaps input matrix with n^th vector of list UniformVectorList \u0026amp; operator=(const UniformVectorList \u0026amp; that)\nassigns the contents (copy) UniformVectorList \u0026amp; operator=(UniformVectorList \u0026amp;\u0026amp; that)\nassigns the contents (move) Public Function Details # UniformVectorList # UniformVectorList() =default UniformVectorList # explicit UniformVectorList( const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dims dimension UniformVectorList # explicit UniformVectorList( std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList UniformVectorList # UniformVectorList( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList ~UniformVectorList # ~UniformVectorList() =default dim # inline size_t dim() const size # inline size_t size() at # inline const Vector \u0026amp; at( size_t n ) Swap # inline void Swap( Vector \u0026amp; that, size_t n ) Parameters:\nthat input vector n index where the vector is moved operator= # inline UniformVectorList \u0026amp; operator=( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\noperator= # inline UniformVectorList \u0026amp; operator=( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\nUpdated on 5 March 2025 at 21:41:27 EST\n"},{"id":51,"href":"/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/","title":"ldsCtrlEst_h","section":"Files","content":" ldsCtrlEst_h # Files # Name ldsCtrlEst_h/lds.h lds namespace ldsCtrlEst_h/lds_ctrl.h Controller. ldsCtrlEst_h/lds_fit.h LDS base fit type. ldsCtrlEst_h/lds_fit_em.h subspace identification ldsCtrlEst_h/lds_fit_ssid.h subspace identification ldsCtrlEst_h/lds_gaussian.h glds namespace ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller. ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type. ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type. ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type. ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type. ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type. ldsCtrlEst_h/lds_poisson.h plds namespace ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type. ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type. ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type. ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type. ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type. ldsCtrlEst_h/lds_poisson_sys.h PLDS base type. ldsCtrlEst_h/lds_sctrl.h SwitchedController type. ldsCtrlEst_h/lds_sys.h LDS base type. ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices. ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems. ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors. ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":52,"href":"/lds-ctrl-est/docs/api/files/lds__ctrl_8h/","title":"ldsCtrlEst_h/lds_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_ctrl.h # Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Controller Detailed Description # This file declares the type for control of a linear dynamical system (lds::Controller).\nSource code # //===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_CTRL_H #define LDSCTRLEST_LDS_CTRL_H // namespace #include \u0026#34;lds.h\u0026#34; // system type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class Controller { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); public: Controller() = default; Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); const Vector\u0026amp; Control(const Vector\u0026amp; z, bool do_control = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); const Vector\u0026amp; ControlOutputReference(const Vector\u0026amp; z, bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); // get methods: const System\u0026amp; sys() const { return sys_; }; const Matrix\u0026amp; Kc() const { return Kc_; }; const Matrix\u0026amp; Kc_inty() const { return Kc_inty_; }; const Matrix\u0026amp; Kc_u() const { return Kc_u_; }; const Vector\u0026amp; g_design() const { return g_design_; }; const Vector\u0026amp; u_ref() const { return u_ref_; }; const Vector\u0026amp; x_ref() const { return x_ref_; }; const Vector\u0026amp; y_ref() const { return y_ref_; }; size_t control_type() const { return control_type_; }; data_t tau_awu() const { return tau_awu_; }; data_t u_lb() const { return u_lb_; }; data_t u_ub() const { return u_ub_; }; // set methods void set_sys(const System\u0026amp; sys) { bool does_match = sys_.n_u() == sys.n_u(); does_match = does_match \u0026amp;\u0026amp; (sys_.n_x() == sys.n_x()); does_match = does_match \u0026amp;\u0026amp; (sys_.n_y() == sys.n_y()); if (does_match) { sys_ = sys; } else { throw std::runtime_error( \u0026#34;new system argument to `set_sys` does not match dimensionality of \u0026#34; \u0026#34;existing system\u0026#34;); } }; void set_g_design(const Vector\u0026amp; g_design) { Reassign(g_design_, g_design); }; void set_u_ref(const Vector\u0026amp; u_ref) { Reassign(u_ref_, u_ref); }; void set_x_ref(const Vector\u0026amp; x_ref) { Reassign(x_ref_, x_ref); cx_ref_ = sys_.C() * x_ref_; }; // y_ref needs to be handled differently depending on output fn. // (need to populate cx_ref_ too, which depends on output fn) virtual void set_y_ref(const Vector\u0026amp; y_ref) { Reassign(y_ref_, y_ref); }; void set_Kc(const Matrix\u0026amp; Kc) { Reassign(Kc_, Kc); }; void set_Kc_inty(const Matrix\u0026amp; Kc_inty) { Reassign(Kc_inty_, Kc_inty); }; void set_Kc_u(const Matrix\u0026amp; Kc_u) { Reassign(Kc_u_, Kc_u); }; void set_tau_awu(data_t tau) { tau_awu_ = tau; k_awu_ = sys_.dt() / tau_awu_; }; void set_control_type(size_t control_type); // There is no reason u_lb/ub should not be public, but making set methods // anyway. void set_u_lb(data_t u_lb) { u_lb_ = u_lb; }; void set_u_ub(data_t u_ub) { u_ub_ = u_ub; }; void Reset() { sys_.Reset(); u_ref_.zeros(); u_ref_prev_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); u_saturated_ = false; t_since_control_onset_ = 0.0; }; void Print() { sys_.Print(); std::cout \u0026lt;\u0026lt; \u0026#34;g_design : \u0026#34; \u0026lt;\u0026lt; g_design_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_lb : \u0026#34; \u0026lt;\u0026lt; u_lb_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_ub : \u0026#34; \u0026lt;\u0026lt; u_ub_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }; protected: System sys_; Vector u_; Vector u_return_; Vector g_design_; // reference signals Vector u_ref_; // create no set method for this: Vector u_ref_prev_; Vector x_ref_; Vector y_ref_; Vector cx_ref_; // Controller gains Matrix Kc_; Matrix Kc_u_; Matrix Kc_inty_; // control after g inversion // do not need set methods for these. Vector du_ref_; Vector dv_ref_; Vector v_ref_; Vector dv_; Vector v_; // integral error // do not need set method for this Vector int_e_; Vector int_e_awu_adjust_; Vector u_sat_; bool do_control_prev_ = false; bool do_lock_control_prev_ = false; // whether the g of system has become inverted from what you think it is // (gain_ref) bool u_saturated_ = false; // should be safe to have references here bc nothing needs to be done // (like reset vars) when it changes... data_t u_lb_{}; data_t u_ub_{}; data_t tau_awu_{}; data_t k_awu_ = 0; data_t t_since_control_onset_ = 0; size_t control_type_{}; private: void CalcControl(bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); void CalcSteadyStateSetPoint(); void AntiWindup(); void InitVars(size_t control_type); }; // Implement the above: template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(sys), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(std::move(sys)), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::set_control_type(size_t control_type) { if (control_type_ == control_type) { return; } // creating a blank slate... control_type_ = 0; Kc_inty_.zeros(0, 0); Kc_u_.zeros(0, 0); int_e_.zeros(0, 0); int_e_awu_adjust_.zeros(0, 0); // controller was designed to minimize integral error if (control_type \u0026amp; kControlTypeIntY) { Kc_inty_.zeros(sys_.n_u(), sys_.n_y()); int_e_.zeros(sys_.n_y()); int_e_awu_adjust_.zeros(sys_.n_u()); control_type_ = control_type_ | kControlTypeIntY; } // controller was designed to minimize deltaU // (i.e. state augmented with u) if (control_type \u0026amp; kControlTypeDeltaU) { Kc_u_.zeros(sys_.n_u(), sys_.n_u()); control_type_ = control_type_ | kControlTypeDeltaU; } // whether to adapt set point calculate with (re-estimated) process // disturbance (m) if (control_type \u0026amp; kControlTypeAdaptM) { if (sys_.do_adapt_m) // only if adapting m... { control_type_ = control_type_ | kControlTypeAdaptM; } } } // set_control_type template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::Control( const Vector\u0026amp; z, bool do_control, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement sys_.Filter(u_, z); bool do_estimation = true; // always have estimator on in this case // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::ControlOutputReference( const Vector\u0026amp; z, bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement if (do_estimation) { sys_.Filter(u_, z); } else { sys_.f(u_); } // calculate the set point // solves for u_ref and x_ref when output is at y_ref at steady state. if (do_control) { CalcSteadyStateSetPoint(); } // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcControl(bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { if (do_control \u0026amp;\u0026amp; do_estimation) { if (!do_control_prev_) { if (do_reset_at_control_onset) { Reset(); } t_since_control_onset_ = 0.0; } else { t_since_control_onset_ += sys_.dt(); } // enforce softstart on control vars. if (sigma_soft_start \u0026gt; 0) { // half-Gaussian soft-start scaling factor data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) / (2 * pow(sigma_soft_start, 2))); u_ref_ *= soft_start_sf; // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too // x_ref_ *= soft_start_sf; // cx_ref_ *= soft_start_sf; // y_ref_ *= soft_start_sf; } if (!do_lock_control) { // first do u -\u0026gt; v change of vars. (v = g.*u) // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver // control voltage u[=]V) v_ref_ = g_design_ % u_ref_; // Given FB, calc. the change in control if (control_type_ \u0026amp; kControlTypeDeltaU) { // if control designed to minimize not u but deltaU (i.e. state aug with // u): // TODO(mfbolus): Commented out for now. See note below. // du_ref_ = u_ref_ - u_ref_prev_; // dv_ref_ = g_design_ % du_ref_; // TODO(mfbolus): Assuming users want *smooth* control signals if using // kControlTypeDeltaU, it should be the case that dv_ref_ is --\u0026gt; 0. May // want to revisit, but I am going to force it to be zero unless a // situation arises that argues for keeping the above. dv_ref_.zeros(); dv_ = dv_ref_; // nominally-optimal. dv_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error dv_ -= Kc_u_ * (v_ - v_ref_); // penalty on amp u (rel to ref) if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if(!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error dv_ -= Kc_inty_ * int_e_; // control for integrated error } // update the control v_ += dv_; } else { v_ = v_ref_; // nominally-optimal. v_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if (!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error v_ -= Kc_inty_ * int_e_; // control for integrated error } } // convert back to control voltage u[=]V u_ = v_ / sys_.g(); } // else do nothing until lock is low } else { // if not control // feed through u_ref in open loop u_ = u_ref_ % g_design_ / sys_.g(); v_ = sys_.g() % u_; u_ref_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); } // ends do_control // enforce box constraints (and antiwindup) AntiWindup(); // add noise to input? // The value for u that is *returned* to user after addition of any noise, // while keeping controller/estimator blind to this addition. u_return_ = u_; if ((sigma_u_noise \u0026gt; 0.0) \u0026amp;\u0026amp; (do_control \u0026amp;\u0026amp; !do_lock_control)) { u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn); Limit(u_return_, u_lb_, u_ub_); }; // For next time step: u_ref_prev_ = u_ref_; do_control_prev_ = do_control; do_lock_control_prev_ = do_lock_control; } // CalcControl template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcSteadyStateSetPoint() { // Linearly-constrained least squares (ls). // // _reference: // Boyd \u0026amp; Vandenberghe (2018) Introduction to Applied Linear Algebra // Matrix a_ls = join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros)); Vector b_ls = cx_ref_; Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye), sys_.B() * arma::diagmat(sys_.g())); Vector d_ls = -sys_.m0(); if (control_type_ \u0026amp; kControlTypeAdaptM) { d_ls = -sys_.m(); // adapt setpoint calc with disturbance? } Matrix a_ls_t = a_ls.t(); // TODO(mfbolus): not sure why but causes seg // fault if I do not do this. Matrix phi_ls = join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()), join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros))); // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse: Matrix inv_phi = pinv(phi_ls); Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls); x_ref_ = xulam.subvec(0, sys_.n_x() - 1); u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1); cx_ref_ = sys_.C() * x_ref_; } // CalcSteadyStateSetPoint template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::AntiWindup() { u_saturated_ = false; u_sat_ = u_; // limit u and flag whether saturated for (size_t k = 0; k \u0026lt; u_.n_elem; k++) { if (u_[k] \u0026lt; u_lb_) { u_sat_[k] = u_lb_; u_saturated_ = true; } if (u_[k] \u0026gt; u_ub_) { u_sat_[k] = u_ub_; u_saturated_ = true; } } if ((control_type_ \u0026amp; kControlTypeIntY) \u0026amp;\u0026amp; (tau_awu_ \u0026lt; lds::kInf)) { // one-step back-calculation (calculate intE for u=u_sat) // (Astroem, Rundqwist 1989 warn against using this...) // int_e_awu_adjust_ = // solve(Kc_inty_, (u_ - u_sat_)); // pinv(Kc_inty) * (u-uSat); // gradual: see Astroem, Rundqwist 1989 // this is a fudge for doing MIMO gradual // n.b., went ahead and multiplied 1/T by dt so don\u0026#39;t have to do that here. int_e_awu_adjust_ = k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_); // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_); int_e_ += int_e_awu_adjust_; } // set u to saturated version u_ = u_sat_; } template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::InitVars(size_t control_type) { // initialize to default values u_ref_ = Vector(sys_.n_u(), fill::zeros); u_ref_prev_ = Vector(sys_.n_u(), fill::zeros); x_ref_ = Vector(sys_.n_x(), fill::zeros); y_ref_ = Vector(sys_.n_y(), fill::zeros); cx_ref_ = Vector(sys_.n_y(), fill::zeros); u_ = Vector(sys_.n_u(), fill::zeros); u_return_ = Vector(sys_.n_u(), fill::zeros); u_sat_ = Vector(sys_.n_u(), fill::zeros); // Might not need all these, so zero elements until later. Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros); Kc_u_ = Matrix(0, 0, fill::zeros); Kc_inty_ = Matrix(0, 0, fill::zeros); g_design_ = sys_.g(); // by default, same as model dv_ = Vector(sys_.n_u(), fill::zeros); v_ = Vector(sys_.n_u(), fill::zeros); du_ref_ = Vector(sys_.n_u(), fill::zeros); dv_ref_ = Vector(sys_.n_u(), fill::zeros); v_ref_ = Vector(sys_.n_u(), fill::zeros); int_e_ = Vector(0, fill::zeros); int_e_awu_adjust_ = Vector(0, fill::zeros); set_control_type(control_type); } } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":53,"href":"/lds-ctrl-est/docs/api/files/lds__fit__em_8h/","title":"ldsCtrlEst_h/lds_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_em.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::EM Detailed Description # This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).\nSource code # //===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_EMAX_H #define LDSCTRLEST_LDS_EMAX_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class EM { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: EM() = default; EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); virtual ~EM() = default; const Fit\u0026amp; Run(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = true, bool calc_output = true, bool calc_measurement = true, size_t max_iter = 100, data_t tol = 1e-2); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); // auto tuple = std::make_tuple(u_, z_); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; x() const { return x_; }; const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; y() const { return y_; }; const Matrix\u0026amp; sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; }; const Matrix\u0026amp; sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; }; const Matrix\u0026amp; sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; }; size_t n_t_tot() { return n_t_tot_; } const Vector\u0026amp; theta() const { return theta_; }; protected: void Expectation(bool force_common_initial = false); void Maximization(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = false, bool calc_output = false, bool calc_measurement = false); void MaximizeDynamics(); void MaximizeQ(); void MaximizeInitial(); virtual void MaximizeOutput() = 0; virtual void MaximizeMeasurement() = 0; void Smooth(bool force_common_initial); virtual void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) = 0; void Reset(); void InitVars(); Vector UpdateTheta(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; std::vector\u0026lt;Matrix\u0026gt; x_; std::vector\u0026lt;Cube\u0026gt; P_; std::vector\u0026lt;Cube\u0026gt; P_t_tm1_; std::vector\u0026lt;Matrix\u0026gt; y_; Matrix diag_y_; // expectations calculated in E-step Matrix sum_E_x_t_x_t_; Matrix sum_E_xu_tm1_xu_tm1_; Matrix sum_E_xu_t_xu_tm1_; Fit fit_; Vector theta_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; }; template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; fit_ = Fit(n_u_, n_x, n_y_, dt); u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { // make sure fit dims match I/O data if (fit0.n_u() != u_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and input training data have inconsistent dimensions\u0026#34;); } if (fit0.n_y() != z_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and output training data have inconsistent dimensions\u0026#34;); } fit_ = fit0; u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::InitVars() { // check input/output data dimensions are consistent if (z_.size() != u_.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_.at(trial).n_cols != u_.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } n_u_ = fit_.n_u(); n_x_ = fit_.n_x(); n_y_ = fit_.n_y(); dt_ = fit_.dt(); x_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); P_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); P_t_tm1_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); y_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros); P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros); } diag_y_ = Matrix(n_y_, n_y_, fill::zeros); // covariances in expectation step sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); } template \u0026lt;typename Fit\u0026gt; const Fit\u0026amp; EM\u0026lt;Fit\u0026gt;::Run(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement, size_t max_iter, data_t tol) { Reset(); // to initial conditions size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_; Vector theta(n_params); Vector theta_new(n_params); data_t max_dtheta = 1; // if solving for initial conditions, allow them be varied. // otherwise, freeze at provided values. bool force_common_initial = !calc_init; // go until parameter convergence for (size_t l = 0; l \u0026lt; max_iter; l++) { theta_ = UpdateTheta(); std::cout \u0026lt;\u0026lt; \u0026#34;Iteration \u0026#34; \u0026lt;\u0026lt; l + 1 \u0026lt;\u0026lt; \u0026#34;/\u0026#34; \u0026lt;\u0026lt; max_iter \u0026lt;\u0026lt; \u0026#34; ...\\n\u0026#34;; Expectation(force_common_initial); Maximization(calc_dynamics, calc_Q, calc_init, calc_output, calc_measurement); // check convergence theta_new = UpdateTheta(); Vector dtheta = abs(theta_new - theta_) / abs(theta_); // some parameters could be zero... arma::uvec ubi_finite = find_finite(dtheta); max_dtheta = max(dtheta.elem(ubi_finite)); std::cout \u0026lt;\u0026lt; \u0026#34;max dtheta: \u0026#34; \u0026lt;\u0026lt; max_dtheta \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; if (max_dtheta \u0026lt; tol) { std::cout \u0026lt;\u0026lt; \u0026#34;Converged.\\n\u0026#34;; break; } std::cout \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } return fit_; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Smooth(bool force_common_initial) { Matrix k_e(n_x_, n_y_); // estimator gain Cube k_backfilt; // back-filtering gains // TODO(mfbolus): this loop could be made parallel for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { Matrix x_pre(n_x_, n_t_[trial], fill::zeros); Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros); Matrix x_post(n_x_, n_t_[trial], fill::zeros); Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros); if (force_common_initial) // forces all trials to have same initial // conditions. { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); } y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); // This *should not* be necessary but make sure P is symmetric. ForceSymPD(P_[trial].slice(0)); x_pre.col(0) = x_[trial].col(0); p_pre.slice(0) = P_[trial].slice(0); x_post.col(0) = x_[trial].col(0); p_post.slice(0) = P_[trial].slice(0); // filter for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // predict fit_.f(x_pre, x_post, u_.at(trial), t); fit_.h(y_[trial], x_pre, t); diag_y_.diag() = y_[trial].col(t); // TODO(mfbolus): change if parallel // update --\u0026gt; posterior estimation RecurseKe(k_e, p_pre, p_post, t); x_post.col(t) = x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t)); y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d(); } // backfilter -\u0026gt; Smoothed estimate // Reference: // Shumway et Stoffer (1982) ForceSymPD(p_post.slice(n_t_[trial] - 1)); k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1); P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 0; t--) { // TODO(mfmbolus): should not be necessary to force symm positive def ForceSymPD(p_pre.slice(t)); ForceSymPD(p_post.slice(t - 1)); ForceSymPD(P_[trial].slice(t)); k_backfilt.slice(t - 1) = p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t)); x_[trial].col(t - 1) = x_post.col(t - 1) + k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t)); P_[trial].slice(t - 1) = p_post.slice(t - 1) + k_backfilt.slice(t - 1) * (P_[trial].slice(t) - p_pre.slice(t)) * k_backfilt.slice(t - 1).t(); } // do the same for P_t_tm1 Matrix id(n_x_, n_x_, fill::eye); P_t_tm1_[trial].slice(n_t_[trial] - 1) = (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 1; t--) { P_t_tm1_[trial].slice(t - 1) = p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() + k_backfilt.slice(t - 1) * (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) * k_backfilt.slice(t - 2).t(); } // finally, get smoothed estimate of output for (size_t t = 0; t \u0026lt; n_t_[trial]; t++) { fit_.h(y_[trial], x_[trial], t); } // samps loop } // trial loop } // Smooth // template \u0026lt;typename Fit\u0026gt; // void EM\u0026lt;Fit\u0026gt;::RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) { // // predict covar // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q(); // // update Ke // Ke = P_pre.slice(t) * fit_.C().t() * // inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R()); // // update cov // // Reference: Ghahramani et Hinton (1996) // P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t); // // // n.b. for poisson : // // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + // fit_.Q(); // // // update cov // // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ * // // fit_.C()); // // // update Ke // // Ke = P_post.slice(t) * fit_.C(); // } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Expectation(bool force_common_initial) { // calculate the mean/cov of state needed for maximizing E[pr(z|theta)] Smooth(force_common_initial); // now get the various forms of sum(E[xx\u0026#39;]) needed // n.b. Going to start at t=1 rather than 0 bc most max terms need that. // so really \u0026#34;n_t_tot_\u0026#34; is (n_t_tot_-1) n_t_tot_ = 0; sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); Vector xu_tm1(n_x_ + n_u_, fill::zeros); Vector xu_t(n_x_ + n_u_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // ------------ sum_E_x_t_x_t ------------ sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t(); sum_E_x_t_x_t_ += P_[trial].slice(t); // ------------ sum_E_xu_tm1_xu_tm1 ------------ xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1)); sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t(); sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t - 1); // ------------ sum_E_xu_t_xu_tm1 ------------ xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t)); sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t(); sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_t_tm1_[trial].slice(t); n_t_tot_ += 1; } // time } // trial } // Expectation template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement) { if (calc_output) { MaximizeOutput(); } if (calc_measurement) { MaximizeMeasurement(); } if (calc_dynamics) { MaximizeDynamics(); } if (calc_Q) { MaximizeQ(); } if (calc_init) { MaximizeInitial(); } } // Maximization template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeDynamics() { // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) * inv_sympd(sum_E_xu_tm1_xu_tm1_); fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1)); std::cout \u0026lt;\u0026lt; \u0026#34;A_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.A()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.B()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeQ() { // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 * // inv_sympd(sum_E_xu_tm1_xu_tm1_) * // sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // this way is same as above iff dynamics were just updated: // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix ab = arma::join_horiz(fit_.A(), fit_.B()); // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // From scratch method: // Q is covariance of the error between state and dynamics-predicted state // (aka process noise) // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})\u0026#39;] // t-1 terms: View sum_e_x_tm1_x_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_u_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1); View sum_e_x_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); // t, t-1 terms: View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_x_t_u_tm1 = sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); Matrix q = sum_E_x_t_x_t_; q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t(); q -= sum_e_x_t_x_tm1 * fit_.A().t(); q -= fit_.A() * sum_e_x_t_x_tm1.t(); // input-related terms: q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t(); q -= sum_e_x_t_u_tm1 * fit_.B().t(); q -= fit_.B() * sum_e_x_t_u_tm1.t(); q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t(); q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t(); q /= n_t_tot_; fit_.set_Q(q); std::cout \u0026lt;\u0026lt; \u0026#34;Q_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.Q()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // std::cout \u0026lt;\u0026lt; \u0026#34;Q_new: \\n\u0026#34; \u0026lt;\u0026lt; fit_.Q() \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeInitial() { Vector x0 = fit_.x0(); x0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { x0 += x_[trial].col(0); } x0 /= z_.size(); std::cout \u0026lt;\u0026lt; \u0026#34;x0_new[0]: \u0026#34; \u0026lt;\u0026lt; x0[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // always recalc P0 even if the initial state is fixed (at zero, for // example) Matrix e_var(n_x_, n_x_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t(); } e_var /= z_.size(); // go ahead and subtract x0*x0\u0026#39; so don\u0026#39;t have to below. e_var -= x0 * x0.t(); // To get P0, going to get initial P_ per trial and average. // (which might be wrong, but need a single number) Matrix p0 = fit_.P0(); p0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { p0 += (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var; } p0 /= z_.size(); fit_.set_P0(p0); std::cout \u0026lt;\u0026lt; \u0026#34;P0_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.P0()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeOutput() { // solve for C+d: Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros); Vector x1(n_x_ + 1, fill::zeros); x1[n_x_] = 1.0; // augment with one to solve for bias Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { x1.subvec(0, n_x_ - 1) = x_[trial].col(t); sum_zx += z_.at(trial).col(t) * x1.t(); sum_e_x1_x1 += x1 * x1.t(); sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t); } } Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1); fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1)); fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_))); std::cout \u0026lt;\u0026lt; \u0026#34;C_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.C()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.d()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeMeasurement() { // Solve for measurement noise covar size_t n_t_tot = 0; // Ghahgramani, Hinton 1996: Matrix sum_zz(n_y_, n_y_, fill::zeros); Matrix sum_yz(n_y_, n_y_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t(); // Use Cnew: sum_yz += (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t(); n_t_tot += 1; } } fit_.set_R((sum_zz - sum_yz) / n_t_tot); std::cout \u0026lt;\u0026lt; \u0026#34;R_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.R()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Reset() { // reset to initial conditions for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); } } template \u0026lt;typename Fit\u0026gt; Vector EM\u0026lt;Fit\u0026gt;::UpdateTheta() { // TODO(mfbolus): This should include n_y_ more params for d. size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_; if (fit_.R().n_elem \u0026gt; 0) { n_params += n_y_ * n_y_; } Vector theta(n_params); size_t idx_start = 0; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B()); idx_start += n_x_ * n_u_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0()); idx_start += n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C()); idx_start += n_y_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d()); idx_start += n_y_; if (fit_.R().n_elem \u0026gt; 0) { theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R()); } return theta; } } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":54,"href":"/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_ssid.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SSID Detailed Description # This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)\u0026lt;Fit\u0026gt;).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_FIT_SSID_H #define LDSCTRLEST_LDS_FIT_SSID_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class SSID { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: SSID() = default; SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d = Vector(1).fill(-kInf)); std::tuple\u0026lt;Fit, Vector\u0026gt; Run(SSIDWt ssid_wt); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } protected: void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001); void CreateHankelDataMat(); virtual void DecomposeData() = 0; void CalcSVD(SSIDWt wt); void Solve(data_t wt_dc); void RecomputeExtObs(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; Matrix D_; Fit fit_; Matrix g_dc_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_h_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; Matrix L_; Vector s_; Matrix ext_obs_t_; }; template \u0026lt;typename Fit\u0026gt; SSID\u0026lt;Fit\u0026gt;::SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d) { // check input/output data dimensions are consistent if (z_train.size() != u_train.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_train.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_train.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } dt_ = dt; n_x_ = n_x; n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; n_h_ = n_h; // dimensionality check for eventual block-hankel data matrix size_t len = n_t_tot_ - 2 * n_h_ + 1; if (len \u0026lt; (2 * n_h_ * (n_u_ + n_y_))) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;Dataset problem! More rows than columns in block-hankel data \u0026#34; \u0026#34;matrix: 2*(n_u+n_y)*n_h \u0026gt; data-length! Need higher data-length or \u0026#34; \u0026#34;lower n_h.\u0026#34;; throw std::runtime_error(ss.str()); } fit_ = Fit(n_u_, n_x_, n_y_, dt_); u_ = std::move(u_train); z_ = std::move(z_train); if (!d.is_finite() || (d.n_rows != n_y_)) { // TODO(mfbolus): implement least-square solution for impulse response with // a second input of ones. Data-driven way of accounting for offset *not* // driven by an input. // // For now, calculate output bias (d) as the // output wherever the stimulus has not been on for some amount of time. // convolve u with rectangle and take all samples. This is a reasonable // approach, since often when autonomous systems are fit (i.e., systems with // no input), they will subtract off the mean of the output. This // essentially amounts to setting output bias to the mean of the output when // there is no stimulation. data_t t_silence = 0.1; data_t thresh_silence = 0.001; CalcD(t_silence, thresh_silence); } else { fit_.set_d(d); } } template \u0026lt;typename Fit\u0026gt; std::tuple\u0026lt;Fit, Vector\u0026gt; SSID\u0026lt;Fit\u0026gt;::Run(SSIDWt ssid_wt) { // the weight on minimizing dc I/O gain only works for gaussian, // and hopefully not necessary with appropriate dataset. data_t wt_dc = 0; // std::cout \u0026lt;\u0026lt; \u0026#34;creating hankel mat\\n\u0026#34;; CreateHankelDataMat(); // std::cout \u0026lt;\u0026lt; \u0026#34;decomposing data\\n\u0026#34;; DecomposeData(); // std::cout \u0026lt;\u0026lt; \u0026#34;calculating svd\\n\u0026#34;; CalcSVD(ssid_wt); // std::cout \u0026lt;\u0026lt; \u0026#34;solving for params\\n\u0026#34;; Solve(wt_dc); // std::cout \u0026lt;\u0026lt; \u0026#34;fin\\n\u0026#34;; return std::make_tuple(fit_, s_); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcD(data_t t_silence, data_t thresh_silence) { Vector d(z_.at(0).n_rows, fill::zeros); Vector win(static_cast\u0026lt;size_t\u0026gt;(t_silence / dt_), fill::ones); Vector sum_z_silence(n_y_, fill::zeros); size_t n_silence(0); for (size_t trial = 0; trial \u0026lt; u_.size(); trial++) { // find silent samples // start by convolving with Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0)); Vector u_conv = conv(sum_u, win, \u0026#34;same\u0026#34;); // get only the samples that are silent... arma::uvec ubi_silence = find(u_conv \u0026lt;= thresh_silence); if (ubi_silence.n_elem \u0026gt; 0) { sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1); n_silence += ubi_silence.n_elem; } } if (n_silence \u0026gt; 0) { d = sum_z_silence / n_silence; } fit_.set_d(d); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat() { // temporary copy of data Matrix z(n_y_, n_t_tot_, fill::zeros); Matrix u(n_u_, n_t_tot_, fill::zeros); size_t so_far(0); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial); u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial); so_far += n_t_.at(trial); } // remove output bias z.each_col() -= fit_.d(); // calculate I/O gain @ DC while data in convenient form g_dc_ = z * pinv(u); // std::cout \u0026lt;\u0026lt; \u0026#34;G0_data = \u0026#34; \u0026lt;\u0026lt; g_dc_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // create hankel data matrix size_t len = z.n_cols - 2 * n_h_ + 1; // data length in hankel mat // block-hankel data matrix D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros); // past input auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1); // future input auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1); // past output auto y_p = D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1); // future output auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, len - 1); size_t idx = 0; for (size_t k = 0; k \u0026lt; len; k++) { idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } } D_ /= sqrt(static_cast\u0026lt;data_t\u0026gt;(len)); } // template \u0026lt;typename Fit\u0026gt; // void SSID\u0026lt;Fit\u0026gt;::DecomposeData() { // // do LQ decomp instead of calculating covariance expensive way // // Note that \u0026#34;R\u0026#34; in van Overschee is lower-triangular (L), not \u0026#34;R\u0026#34; in QR // // decomp. Very confusing. // Matrix q_t; // lq(L_, q_t, D_); // // van Overschee zeros out the other elements. // L_ = trimatl(L_); // } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcSVD(SSIDWt wt) { // submats that will be needed: auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1); auto R_23_13 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1); auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_) - 1); auto R_44 = L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14); auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1); auto Luf = Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1); auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); // aka: R_f Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1); // from van Overschee subid.m: // Rf = R((2*m+l)*i+1:2*(m+l)*i,:); % Future outputs Matrix U; Matrix V; switch (wt) { case kSSIDNone: { // No weighting. (what van Overschee calls \u0026#34;N4SID\u0026#34;) Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14; arma::svd(U, s_, V, O_k_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDMOESP: { // MOESP weighting // This is what they use in the \u0026#34;robust\u0026#34; algorithm van Overschee, de Moor // 1996 Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); svd(U, s_, V, O_k_ortho_Uf_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDCVA: { // CVA weighting // See van Overschee\u0026#39;s matlab code (subid.m): // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); Matrix inv_w1; Matrix qt1; lq(inv_w1, qt1, R_56_16); // lq decomp of R_f (future output data) inv_w1 = trimatl(inv_w1); inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1); Matrix w_o_w = arma::solve( inv_w1, O_k_ortho_Uf_sans_Qt); // alternatively // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt svd(U, s_, V, w_o_w, \u0026#34;std\u0026#34;); U = inv_w1 * U; break; } } // Truncate to model order (heart of ssid method) auto s_hat = s_.subvec(0, n_x_ - 1); Matrix diag_sqrt_s = diagmat(sqrt(s_hat)); auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1); // get extended observability and controllability mats ext_obs_t_ = u_hat * diag_sqrt_s; // extended observability matrix } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::Solve(data_t wt_dc) { // required submats auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_56_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); // Solve for params using appropriate algorithm: // robust deterministic/stochastic algorithm in van Overschee 1996 // algorithm that the authors say \u0026#34;works\u0026#34; in practice. auto ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix // This is what textbook (1996) says: // // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15); // // HOWEVER, do not know why but have to fill the last place with zeros like // authors\u0026#39; matlab implementation (see `subid.m`) // Otherwise, get ridiculous covariances (although A,C estimates are close to // same...) Matrix Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Matrix S = Tl * pinv(Tr); // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure // stability. fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1)); Matrix ext_obs_t_p1 = join_vert( ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1), Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros)); fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1); // At this point, van Overschee \u0026amp; de Moor suggest re-calculating ext_obs_t_, // ext_obs_tm1 from (A, C) because it was just an approximation. This is RecomputeExtObs(); ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); S = Tl * pinv(Tr); Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_); Matrix Mcurly = pinv(ext_obs_tm1); Matrix Pcurly = Tl - Lcurly * R_56_15; Vector Pvec = vectorise(Pcurly); Matrix Qcurly = R_23_15; // Identify [D; B], assuming D=0 and ensuring DC gain is correct Matrix sum_QcurlyT_kron_Ncurly( (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_), fill::zeros); Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols, fill::eye); eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1, eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1; // van Overschee (1996) p. 126 Matrix N1_Tl = -Lcurly; N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) += join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly); N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) += Matrix(n_y_, n_y_, fill::eye); Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros); Matrix N_k; for (size_t k = 0; k \u0026lt; n_h_; k++) { auto Qcurly_k = Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1); Nk_Tl.zeros(); Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) = N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1); N_k = Nk_Tl * eye_ext_obs_tm1; sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k); } Matrix err_vec; if (wt_dc \u0026gt; 0) { // Constraints enforced by weighted least squares // // Reference: // // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly // Excited Industrial Systems. Conference in Decision and Control. // constraint 1: assume D=0 --\u0026gt; remove the components for Dvec (this is // actually a hard constraint in that it ignores D) Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly; sum_QcurlyT_kron_Ncurly = Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_); size_t kkk = 0; for (size_t k = 1; k \u0026lt; (n_u_ + 1); k++) { size_t start_idx = k * (n_y_ + n_x_) - n_x_; for (size_t kk = 0; kk \u0026lt; n_x_; kk++) { sum_QcurlyT_kron_Ncurly.col(kkk) = sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk); kkk++; } } // constraint 2: Make sure DC I/O gain is correct Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A()); Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_)); Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0); Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 = join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0); // WEIGHTED LS // Important in practice because I care a lot about at least getting the DC // gain correct. Put x weight on minimizing error at DC, relative to others Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye); // Make weight on minimizing DC error immense so at least that // should be nailed. size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows; size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows; size_t stop_row = w.n_rows - 1; size_t stop_col = w.n_cols - 1; // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale // weight with data length? w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc; Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * sum_QcurlyT_kron_Ncurly_b_to_g0) * sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec; fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_)); // Calculate residuals and their cov. // Because I\u0026#39;ve added constraints, I need to re-calculate the right term // with b_vec instead of how van Overschee do in final algorithm. err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec; } else { // default way: *no* constraint on G0 or D=0 Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec; // TODO(mfbolus) n.b., this gets thrown away... // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_); fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_)); err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec; } // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols); // TODO(mfbolus): Something is wrong with the error calculation above. // Use the way van overschee does it in `subid.m` // WARNING: this ignores any above constraints, so Q, R will be approximate... Matrix err = Tl - S * Tr; Matrix cov_err = err * err.t(); fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1)); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::RecomputeExtObs() { ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C(); for (size_t k = 2; k \u0026lt; (n_h_ + 1); k++) { ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) = ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1, ext_obs_t_.n_cols - 1) * fit_.A(); } } } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":55,"href":"/lds-ctrl-est/docs/api/files/lds__fit_8h/","title":"ldsCtrlEst_h/lds_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_fit.h # LDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Fit LDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.\nSource code # //===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDS_FIT_HPP #define LDS_FIT_HPP // namespace #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); virtual ~Fit() = default; // get methods size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; Q() const { return Q_; }; const Vector\u0026amp; x0() const { return x0_; }; const Matrix\u0026amp; P0() const { return P0_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; // gets measurement noise virtual const Matrix\u0026amp; R() const = 0; // set methods (e.g., seeding initial fit values) void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_m(const Vector\u0026amp; m) { Reassign(m_, m); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); ForceSymPD(Q_); }; virtual void set_R(const Matrix\u0026amp; R) = 0; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); ForceSymPD(P0_); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; View f(Matrix\u0026amp; x, const Matrix\u0026amp; u, size_t t) { x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x.col(t); }; View f(Matrix\u0026amp; x_pre, const Matrix\u0026amp; x_post, const Matrix\u0026amp; u, size_t t) { x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x_pre.col(t); }; virtual View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) = 0; protected: data_t dt_{}; // Dynamics Matrix A_; Matrix B_; Vector g_; Vector m_; Matrix Q_; // Output Matrix C_; Vector d_; Matrix R_; // initial conditions Vector x0_; Matrix P0_; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; }; } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":56,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_ctrl.h # GLDS Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. Detailed Description # This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_gaussian_sys.h\u0026#34; // controller #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace gaussian { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); cx_ref_ = y_ref - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":57,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_em.h # GLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitEM GLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":58,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_ssid.h # GLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; using SSID\u0026lt;Fit\u0026gt;::Run; private: using SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat; using SSID\u0026lt;Fit\u0026gt;::CalcSVD; using SSID\u0026lt;Fit\u0026gt;::Solve; void DecomposeData() override; void SolveVanOverschee(); }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":59,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit.h # GLDS fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Fit GLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // fit type #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace gaussian { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); const Matrix\u0026amp; R() const override { return R_; }; void set_R(const Matrix\u0026amp; R) override { Reassign(R_, R); ForceSymPD(R_); }; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = C_ * x.col(t) + d_; return y.col(t); }; }; }; // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":60,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sctrl.h # GLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H // controller type #include \u0026#34;lds_gaussian_ctrl.h\u0026#34; // switched controller #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace gaussian { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); cx_ref_ = y_ref - sys_.d(); } // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; }; // SwitchedController } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":61,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/","title":"ldsCtrlEst_h/lds_gaussian_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sys.h # GLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::System Gaussian LDS Type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H #define LDSCTRLEST_LDS_GAUSSIAN_SYS_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; namespace lds { namespace gaussian { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0, data_t r0 = kDefaultR0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; // get methods const Matrix\u0026amp; R() const { return R_; }; // set methods void set_Q(const Matrix\u0026amp; Q) { lds::System::set_Q(Q); do_recurse_Ke_ = true; } void set_R(const Matrix\u0026amp; R) { Reassign(R_, R); do_recurse_Ke_ = true; }; void set_Ke(const Matrix\u0026amp; Ke) { Reassign(Ke_, Ke); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void set_Ke_m(const Matrix\u0026amp; Ke_m) { Reassign(Ke_m_, Ke_m); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void Print(); protected: void h() override { cx_ = C_ * x_; y_ = cx_ + d_; }; Vector h_(Vector x) override { return C_ * x + d_; }; void RecurseKe() override; // Gaussian-output-specific Matrix R_; bool do_recurse_Ke_{}; }; // System } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":62,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian_8h/","title":"ldsCtrlEst_h/lds_gaussian.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian.h # glds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_H #define LDSCTRLEST_LDS_GAUSSIAN_H // namespace #include \u0026#34;lds.h\u0026#34; namespace lds { namespace gaussian { // insert any Gaussian-specific things here... } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":63,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_ctrl.h # PLDS controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. Detailed Description # This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_CTRL_H #define LDSCTRLEST_LDS_POISSON_CTRL_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system type #include \u0026#34;lds_poisson_sys.h\u0026#34; // control type #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace poisson { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); lds::Limit(y_ref_, kYRefLb, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; private: constexpr static const data_t kYRefLb = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":64,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_em.h # PLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitEM PLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\n[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H #define LDSCTRLEST_LDS_POISSON_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override{}; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; data_t NewtonSolveC(); void AnalyticalSolveD(); }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":65,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_ssid.h # PLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. Detailed Description # This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/)).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H #define LDSCTRLEST_LDS_POISSON_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; private: void DecomposeData() override; void CalcCov(); void PoissonToGaussianMoments(); Matrix cov_; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":66,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/","title":"ldsCtrlEst_h/lds_poisson_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit.h # PLDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Fit PLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_H #define LDSCTRLEST_LDS_POISSON_FIT_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // fit #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace poisson { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) : lds::Fit(n_u, n_x, n_y, dt){}; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = exp(C_ * x.col(t) + d_); return y.col(t); }; void set_R(const Matrix\u0026amp; R) override { std::cerr \u0026lt;\u0026lt; \u0026#34;WARNING: Cannot set R (R[0] = \u0026#34; \u0026lt;\u0026lt; R.at(0) \u0026lt;\u0026lt; \u0026#34;). No Gaussian measurement noise in Poisson observation model.\\n\u0026#34;; }; const Matrix\u0026amp; R() const override { return R_; }; }; }; // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":67,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sctrl.h # PLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #include \u0026#34;lds_poisson_ctrl.h\u0026#34; #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace poisson { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); lds::Limit(y_ref_, kYRefLB, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; private: constexpr static data_t kYRefLB = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":68,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/","title":"ldsCtrlEst_h/lds_poisson_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sys.h # PLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::System Poisson System type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SYS_H #define LDSCTRLEST_LDS_POISSON_SYS_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; // needed for Poisson random number generation #include \u0026lt;random\u0026gt; namespace lds { namespace poisson { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; protected: void h() override { cx_ = C_ * x_; y_ = exp(cx_ + d_); diag_y_.diag() = y_; }; Vector h_(Vector x) override { return exp(C_ * x + d_); }; void RecurseKe() override; private: // Poisson-output-specific Matrix diag_y_; std::poisson_distribution\u0026lt;size_t\u0026gt; pd_; }; // System } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":69,"href":"/lds-ctrl-est/docs/api/files/lds__poisson_8h/","title":"ldsCtrlEst_h/lds_poisson.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson.h # plds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_H #define LDSCTRLEST_LDS_POISSON_H #include \u0026#34;lds.h\u0026#34; namespace lds { namespace poisson { // TODO(mfbolus): Not sure if defining these as static here makes the most // sense. Is there a downside to letting multiple poisson System objects share a // common random number generator? static std::random_device rd; static std::mt19937 rng = std::mt19937( rd()); } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":70,"href":"/lds-ctrl-est/docs/api/files/lds__sctrl_8h/","title":"ldsCtrlEst_h/lds_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_sctrl.h # SwitchedController type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SwitchedController SwitchedController Type. Detailed Description # This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SCTRL_H #define LDSCTRLEST_LDS_SCTRL_H #include \u0026#34;lds_ctrl.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; #include \u0026#34;lds_uniform_vecs.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class SwitchedController : public Controller\u0026lt;System\u0026gt; { public: SwitchedController() = default; SwitchedController(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); SwitchedController(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); void Switch(size_t idx, bool do_force_switch = false); void set_Kc(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc) { Kc_list_ = Kc; Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc) { Kc_list_ = std::move(Kc); Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_inty) { Kc_inty_list_ = Kc_inty; Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_inty) { Kc_inty_list_ = std::move(Kc_inty); Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_u) { Kc_u_list_ = Kc_u; Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_u) { Kc_u_list_ = std::move(Kc_u); Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(const UniformVectorList\u0026amp; g) { g_design_list_ = g; g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(UniformVectorList\u0026amp;\u0026amp; g) { g_design_list_ = std::move(g); g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; protected: std::vector\u0026lt;System\u0026gt; systems_; size_t n_sys_{}; size_t idx_{}; // controller gains could be different for each UniformMatrixList\u0026lt;\u0026gt; Kc_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_inty_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_u_list_; // design-phase input gain could also be different UniformVectorList g_design_list_; // TODO(mfbolus): not sure why I need to do this. using Controller\u0026lt;System\u0026gt;::Kc_; using Controller\u0026lt;System\u0026gt;::Kc_inty_; using Controller\u0026lt;System\u0026gt;::Kc_u_; using Controller\u0026lt;System\u0026gt;::g_design_; using Controller\u0026lt;System\u0026gt;::sys_; // using Controller\u0026lt;System\u0026gt;::u_ref_; // using Controller\u0026lt;System\u0026gt;::x_ref_; // using Controller\u0026lt;System\u0026gt;::y_ref_; // using Controller\u0026lt;System\u0026gt;::control_type_; private: void InitVars(); using lds::Controller\u0026lt;System\u0026gt;::set_sys; // using Controller\u0026lt;System\u0026gt;::set_Kc; // using Controller\u0026lt;System\u0026gt;::set_Kc_inty; // using Controller\u0026lt;System\u0026gt;::set_Kc_u; // using Controller\u0026lt;System\u0026gt;::set_g_design; }; template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(systems.at(0), u_lb, u_ub, control_type), systems_(systems) { InitVars(); } template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(System(systems.at(0).n_u(), systems.at(0).n_x(), systems.at(0).n_y(), systems.at(0).dt()), u_lb, u_ub, control_type), systems_(std::move(systems)) { InitVars(); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::InitVars() { n_sys_ = systems_.size(); sys_ = systems_.at(0); Kc_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_)); Kc_inty_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); Kc_u_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); g_design_list_ = UniformVectorList(std::vector\u0026lt;Vector\u0026gt;(n_sys_, g_design_)); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::Switch(size_t idx, bool do_force_switch) { if ((idx == idx_) \u0026amp;\u0026amp; !do_force_switch) { return; // already there. } // put old up and get new one out systems_.at(idx_) = std::move(sys_); sys_ = std::move(systems_.at(idx)); // set the state of this system to that of the previous system // TODO(mfbolus): This will only work as intended if state matrix is the same. // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp. sys_.set_m(systems_.at(idx_).m(), true); sys_.set_x(systems_.at(idx_).x()); // swap controller gains Kc_list_.Swap(Kc_, idx_); Kc_list_.Swap(Kc_, idx); if (control_type_ \u0026amp; kControlTypeIntY) { Kc_inty_list_.Swap(Kc_inty_, idx_); Kc_inty_list_.Swap(Kc_inty_, idx); } if (control_type_ \u0026amp; kControlTypeDeltaU) { Kc_u_list_.Swap(Kc_u_, idx_); Kc_u_list_.Swap(Kc_u_, idx); } g_design_list_.Swap(g_design_, idx_); g_design_list_.Swap(g_design_, idx); idx_ = idx; } // Switch } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":71,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8h/","title":"ldsCtrlEst_h/lds_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_sys.h # LDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::System Linear Dynamical System Type. Detailed Description # This file declares and partially defines the base type for linear dynamical systems ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SYS_H #define LDSCTRLEST_LDS_SYS_H #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class System { public: System() = default; System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); virtual ~System() {} void Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z); virtual const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) = 0; void f(const Vector\u0026amp; u, bool do_add_noise = false) { x_ = A_ * x_ + B_ * (g_ % u) + m_; if (do_add_noise) { x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_); } }; virtual void h() = 0; virtual Vector h_(Vector x) = 0; size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Vector\u0026amp; x() const { return x_; }; const Matrix\u0026amp; P() const { return P_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; P_m() const { return P_m_; }; const Vector\u0026amp; cx() const { return cx_; }; const Vector\u0026amp; y() const { return y_; }; const Vector\u0026amp; x0() const { return x0_; }; const Vector\u0026amp; m0() const { return m0_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; const Matrix\u0026amp; Ke() const { return Ke_; }; const Matrix\u0026amp; Ke_m() const { return Ke_m_; }; const Matrix\u0026amp; Q() { return Q_; }; const Matrix\u0026amp; Q_m() { return Q_m_; }; const Matrix\u0026amp; P0() { return P0_; }; const Matrix\u0026amp; P0_m() { return P0_m_; }; void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_m(const Vector\u0026amp; m, bool do_force_assign = false) { Reassign(m0_, m); if ((!do_adapt_m) || do_force_assign) { Reassign(m_, m); } }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); }; void set_Q_m(const Matrix\u0026amp; Q_m) { Reassign(Q_m_, Q_m); }; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); }; void set_P0_m(const Matrix\u0026amp; P0_m) { Reassign(P0_m_, P0_m); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; void set_x(const Vector\u0026amp; x) { Reassign(x_, x); h(); }; void Reset(); std::vector\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; nstep_pred_block( UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z, size_t n_pred = 1); void Print(); // safe to leave this public and non-const bool do_adapt_m{}; protected: virtual void RecurseKe() = 0; void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); std::size_t n_x_{}; std::size_t n_u_{}; std::size_t n_y_{}; data_t dt_{}; // Signals: Vector x_; Matrix P_; Vector m_; Matrix P_m_; Vector cx_; Vector y_; Vector z_; // Parameters: Vector x0_; Matrix P0_; Vector m0_; Matrix P0_m_; Matrix A_; Matrix B_; Vector g_; Matrix Q_; Matrix Q_m_; Matrix C_; Vector d_; Matrix Ke_; Matrix Ke_m_; }; // System } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":72,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/","title":"ldsCtrlEst_h/lds_uniform_mats.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_mats.h # List of uniformly sized matrices. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformMatrixList Detailed Description # This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H #define LDSCTRLEST_LDS_UNIFORM_MATS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { template \u0026lt;MatrixListFreeDim D = kMatFreeDimNone\u0026gt; class UniformMatrixList : public std::vector\u0026lt;Matrix\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Matrix\u0026gt;::vector; // don\u0026#39;t allow push_back to be used since it doesn\u0026#39;t check dims using std::vector\u0026lt;Matrix\u0026gt;::push_back; public: using std::vector\u0026lt;Matrix\u0026gt;::operator=; using std::vector\u0026lt;Matrix\u0026gt;::operator[]; using std::vector\u0026lt;Matrix\u0026gt;::begin; using std::vector\u0026lt;Matrix\u0026gt;::end; using std::vector\u0026lt;Matrix\u0026gt;::size; using std::vector\u0026lt;Matrix\u0026gt;::at; UniformMatrixList() = default; explicit UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); explicit UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; ~UniformMatrixList() = default; const std::array\u0026lt;size_t, 2\u0026gt;\u0026amp; dim(size_t n = 0) const { return dim_.at(n); } size_t size() { return std::vector\u0026lt;Matrix\u0026gt;::size(); }; const Matrix\u0026amp; at(size_t n) { return std::vector\u0026lt;Matrix\u0026gt;::at(n); }; void Swap(Matrix\u0026amp; that, size_t n); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; void append(const Matrix\u0026amp; mat); private: void CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim); std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt; dim_; }; template \u0026lt;MatrixListFreeDim D\u0026gt; inline void UniformMatrixList\u0026lt;D\u0026gt;::Swap(Matrix\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.n_cols); } if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap // not moving, since it causes memory issues. // so this method isn\u0026#39;t a memory-saver as designed for now Matrix tmp = (*this)[n]; (*this)[n] = that; that = tmp; if (D == kMatFreeDim1) { this-\u0026gt;dim_[n][0] = (*this)[n].n_rows; } if (D == kMatFreeDim2) { this-\u0026gt;dim_[n][1] = (*this)[n].n_cols; } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::append(const Matrix\u0026amp; mat) { std::array\u0026lt;size_t, 2\u0026gt; dim({mat.n_rows, mat.n_cols}); CheckDimensions(dim); std::vector\u0026lt;Matrix\u0026gt;::push_back(mat); dim_.push_back(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) { // make sure dim_ vector is initialized if (dim_.empty()) { dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(that.size(), {0, 0}); } // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices\u0026#34;; throw std::runtime_error(ss.str()); } // if dimensions a not zero and do not match, skip move with error message. bool dims_nonzero = true; for (auto d : dim_) { if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; d[0] \u0026lt; 1) { dims_nonzero = false; break; } if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; d[1] \u0026lt; 1) { dims_nonzero = false; break; } } if (dims_nonzero) { bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); } if (!does_match) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrices of size \u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; dim_[k] = that.dim(k); } return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. bool dims_nonzero = true; for (auto d : dim_) { // if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; (d[0] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; (d[1] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // } // // if (dims_nonzero) { // bool does_match = true; // if (!(D == kMatFreeDim1)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); // } // // if (!(D == kMatFreeDim2)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); // } // // if (!does_match) { // this-\u0026gt;at(0).print(\u0026#34;this[0] = \u0026#34;); // that.at(0).print(\u0026#34;that[0] = \u0026#34;); // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformMatrixList element of size (\u0026#34; \u0026lt;\u0026lt; // that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols \u0026lt;\u0026lt; \u0026#34;) for an // element of size (\u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34;). // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Matrix\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(std::move(mats)) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) : vector(that) { (*this) = that; } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { std::array\u0026lt;size_t, 2\u0026gt; dim_k({this-\u0026gt;at(k).n_rows, this-\u0026gt;at(k).n_cols}); dim_.push_back(dim_k); } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim) { // change behavior based on free dim D if ((dim[0] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim1)) { dim[0] = this-\u0026gt;at(0).n_rows; } if ((dim[1] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim2)) { dim[1] = this-\u0026gt;at(0).n_cols; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Matrix\u0026amp; mat : *this) { if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_rows == dim[0]); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_cols == dim[1]); } if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(this-\u0026gt;size(), dim); for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { dim_[k][0] = (*this)[k].n_rows; dim_[k][1] = (*this)[k].n_cols; } } } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":73,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/","title":"ldsCtrlEst_h/lds_uniform_systems.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_systems.h # List of uniformly sized Systems. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformSystemList Detailed Description # This file provides a container for uniformly sized Systems.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector // namespace #include \u0026#34;lds.h\u0026#34; // System type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class UniformSystemList : public std::vector\u0026lt;System\u0026gt; { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;System\u0026gt;::vector; using std::vector\u0026lt;System\u0026gt;::operator=; using std::vector\u0026lt;System\u0026gt;::operator[]; using std::vector\u0026lt;System\u0026gt;::at; using std::vector\u0026lt;System\u0026gt;::begin; using std::vector\u0026lt;System\u0026gt;::end; using std::vector\u0026lt;System\u0026gt;::size; public: UniformSystemList() = default; explicit UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); explicit UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(const UniformSystemList\u0026amp; that); UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept; ~UniformSystemList() = default; const std::array\u0026lt;size_t, 3\u0026gt;\u0026amp; dim() const { return dim_; } size_t size() { return std::vector\u0026lt;System\u0026gt;::size(); }; const System\u0026amp; at(size_t n) { return std::vector\u0026lt;System\u0026gt;::at(n); }; void Swap(System\u0026amp; that, size_t n); UniformSystemList\u0026amp; operator=(const UniformSystemList\u0026amp; that); UniformSystemList\u0026amp; operator=(UniformSystemList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim); std::array\u0026lt;size_t, 3\u0026gt; dim_{}; }; template \u0026lt;typename System\u0026gt; inline void UniformSystemList\u0026lt;System\u0026gt;::Swap(System\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformSystemList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = (dim_[0] == that.n_u()) \u0026amp;\u0026amp; (dim_[1] == that.n_x()) \u0026amp;\u0026amp; (dim_[2] == that.n_y()); if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformSystemList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap System tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( const UniformSystemList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_[0] + dim_[1] + dim_[2]) { std::array\u0026lt;size_t, 3\u0026gt; other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign systems of size \u0026#34; \u0026lt;\u0026lt; dim_[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2] \u0026lt;\u0026lt; \u0026#34; with systems of size \u0026#34; \u0026lt;\u0026lt; other_dim[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other_dim[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2]; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( UniformSystemList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. if (dim_[0] + dim_[1] + dim_[2]) { // bool does_match = (dim_[0] == that.at(0).n_u()) \u0026amp;\u0026amp; // (dim_[1] == that.at(0).n_x()) \u0026amp;\u0026amp; // (dim_[2] == that.at(0).n_y()); // if (!does_match) { // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformSystemList element for an element of \u0026#34; // \u0026#34;different size. Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;System\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(std::move(systems)) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList( std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const UniformSystemList\u0026amp; that) : std::vector\u0026lt;System\u0026gt;(that) { (*this) = that; } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept : std::vector\u0026lt;System\u0026gt;(std::move(that)) { this-\u0026gt;dim_[0] = this-\u0026gt;at(0).n_u(); this-\u0026gt;dim_[1] = this-\u0026gt;at(0).n_x(); this-\u0026gt;dim_[2] = this-\u0026gt;at(0).n_y(); } template \u0026lt;typename System\u0026gt; void UniformSystemList\u0026lt;System\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim) { if (dim[0] + dim[1] + dim[2]) { dim_ = dim; } else { dim_[0] = this-\u0026gt;at(0).n_u(); dim_[1] = this-\u0026gt;at(0).n_x(); dim_[2] = this-\u0026gt;at(0).n_y(); } // make sure dimensiolaties are all uniform bool does_match(true); for (const System\u0026amp; sys : *this) { does_match = does_match \u0026amp;\u0026amp; (sys.n_u() == dim_[0]); does_match = does_match \u0026amp;\u0026amp; (sys.n_x() == dim_[1]); does_match = does_match \u0026amp;\u0026amp; (sys.n_y() == dim_[2]); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input systems are not uniform.\u0026#34;); } } } } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":74,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/","title":"ldsCtrlEst_h/lds_uniform_vecs.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_vecs.h # List of uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformVectorList Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H #define LDSCTRLEST_LDS_UNIFORM_VECS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { class UniformVectorList : public std::vector\u0026lt;Vector\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Vector\u0026gt;::vector; using std::vector\u0026lt;Vector\u0026gt;::operator=; using std::vector\u0026lt;Vector\u0026gt;::operator[]; using std::vector\u0026lt;Vector\u0026gt;::at; using std::vector\u0026lt;Vector\u0026gt;::begin; using std::vector\u0026lt;Vector\u0026gt;::end; using std::vector\u0026lt;Vector\u0026gt;::size; public: UniformVectorList() = default; explicit UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim = 0); explicit UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim = 0); UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim = 0); UniformVectorList(const UniformVectorList\u0026amp; that); UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept; ~UniformVectorList() = default; size_t dim() const { return dim_; } size_t size() { return std::vector\u0026lt;Vector\u0026gt;::size(); }; const Vector\u0026amp; at(size_t n) { return std::vector\u0026lt;Vector\u0026gt;::at(n); }; void Swap(Vector\u0026amp; that, size_t n); UniformVectorList\u0026amp; operator=(const UniformVectorList\u0026amp; that); UniformVectorList\u0026amp; operator=(UniformVectorList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(size_t dim); size_t dim_{}; }; inline void UniformVectorList::Swap(Vector\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = dim_ == that.n_elem; if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap Vector tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( const UniformVectorList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_) { size_t other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ \u0026lt;\u0026lt; \u0026#34; with vectors of size \u0026#34; \u0026lt;\u0026lt; other_dim; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( UniformVectorList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors. Skipping.\\n\u0026#34;; // return (*this); // } // // if (dim_) { // size_t other_dim(that.dim()); // if (dim_ != other_dim) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ // \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; other_dim \u0026lt;\u0026lt; \u0026#34;. // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Vector\u0026gt;::operator=(std::move(that)); return (*this); } } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":75,"href":"/lds-ctrl-est/docs/api/files/lds_8h/","title":"ldsCtrlEst_h/lds.h","section":"Files","content":" ldsCtrlEst_h/lds.h # lds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)) observations.\nSource code # //===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_H #define LDSCTRLEST_LDS_H // #ifndef LDSCTRLEST // #include \u0026lt;ldsCtrlEst\u0026gt; // #endif #include \u0026lt;armadillo\u0026gt; namespace lds { using data_t = double; // may change to float (but breaks mex functions) using Vector = arma::Col\u0026lt;data_t\u0026gt;; using Matrix = arma::Mat\u0026lt;data_t\u0026gt;; using Cube = arma::Cube\u0026lt;data_t\u0026gt;; using View = arma::subview\u0026lt;data_t\u0026gt;; namespace fill = arma::fill; static const std::size_t kControlTypeDeltaU = 0x1; static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); static const data_t kPi = arma::datum::pi; static const data_t kDefaultP0 = 1e-6; static const data_t kDefaultQ0 = 1e-6; static const data_t kDefaultR0 = 1e-2; enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA }; enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2 }; // TODO(mfbolus): for SwitchedController, may want systems to have differing // numbers of states. Use this enum as template parameter? // enum SystemListFreeDim { // kSysFreeDimNone, // kSysFreeDimX ///\u0026lt; allow state dim (x) of systems in list to be hetero // }; // place hard limits on contents of vecors/mats void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub); void Limit(Vector\u0026amp; x, data_t lb, data_t ub); void Limit(Matrix\u0026amp; x, data_t lb, data_t ub); // in-place assign that errs if there are dimension mismatches: void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); // TODO(mfbolus): this is a fudge, but for some reason, cov mats often going // numerically asymm. void ForceSymPD(Matrix\u0026amp; X); void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min = 0); void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X); Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B); inline void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Vector\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Matrix\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if (other.n_elem != some.n_elem) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vector of size \u0026#34; \u0026lt;\u0026lt; some.n_elem \u0026lt;\u0026lt; \u0026#34; with vector of size \u0026#34; \u0026lt;\u0026lt; other.n_elem \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } inline void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrix of size \u0026#34; \u0026lt;\u0026lt; some.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; some.n_cols \u0026lt;\u0026lt; \u0026#34; with matrix of size \u0026#34; \u0026lt;\u0026lt; other.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other.n_cols \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } } // namespace lds #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":76,"href":"/lds-ctrl-est/docs/api/files/mex__c__util_8h/","title":"ldsCtrlEst_h/mex_c_util.h","section":"Files","content":" ldsCtrlEst_h/mex_c_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) More\u0026hellip;\nNamespaces # Name armamexc arma/mex interface using Matlab C API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab/Octave\u0026rsquo;s C mex API.\nSource code # //===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXC_UTIL_H #define LDSCTRLEST_MEXC_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.h\u0026#34; // // If Matlab_FOUND, include matrix.h. // // (Octave does not need/have it.) // #ifdef Matlab_FOUND // #include \u0026#34;matrix.h\u0026#34; // #endif namespace armamexc { template \u0026lt;class T\u0026gt; inline auto m2T_scalar(const mxArray *matlab_scalar) -\u0026gt; T { if (mxGetData(matlab_scalar)) { return static_cast\u0026lt;T\u0026gt;(mxGetScalar(matlab_scalar)); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return 0; } template \u0026lt;class T\u0026gt; inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false, bool strict = true) -\u0026gt; arma::Mat\u0026lt;T\u0026gt; { if (mxGetData(matlab_mat)) { const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat); if (n_dim == 2) { return arma::Mat\u0026lt;T\u0026gt;(static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)), mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem, strict); } mexErrMsgTxt(\u0026#34;Number of dimensions must be 2.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } // TODO(mfbolus): make these templated. template \u0026lt;typename T\u0026gt; inline auto a2m_mat(arma::Mat\u0026lt;T\u0026gt; const \u0026amp;arma_mat) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_mat.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Mat.\u0026#34;); return nullptr; } template \u0026lt;typename T\u0026gt; inline auto a2m_vec(arma::Col\u0026lt;T\u0026gt; const \u0026amp;arma_vec) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_vec.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Col.\u0026#34;); return nullptr; } } // namespace armamexc #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":77,"href":"/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/","title":"ldsCtrlEst_h/mex_cpp_util.h","section":"Files","content":" ldsCtrlEst_h/mex_cpp_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) More\u0026hellip;\nNamespaces # Name armamexcpp arma/mex interface using Matlab C++ API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab\u0026rsquo;s C++ mex API.\nSource code # //===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXCPP_UTIL_H #define LDSCTRLEST_MEXCPP_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.hpp\u0026#34; #include \u0026#34;mexAdapter.hpp\u0026#34; namespace armamexcpp { template \u0026lt;class T\u0026gt; std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; m2a_cellmat(matlab::data::CellArray\u0026amp; matlab_cell) { size_t n_cells = matlab_cell.getNumberOfElements(); std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; arma_mat(n_cells, arma::Mat\u0026lt;T\u0026gt;(1, 1, arma::fill::zeros)); for (size_t k = 0; k \u0026lt; n_cells; k++) { matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = matlab_cell[k]; auto dims = matlab_mat.getDimensions(); arma_mat[k] = arma::Mat\u0026lt;T\u0026gt;(matlab_mat.release().get(), dims[0], dims[1]); } return arma_mat; }; template \u0026lt;class T\u0026gt; std::vector\u0026lt;T\u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt;T\u0026gt;\u0026amp; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); T* ptr = matlab_array.release().get(); std::vector\u0026lt;T\u0026gt; vec(ptr, ptr + n_elem); return vec; }; template \u0026lt;class T\u0026gt; arma::Col\u0026lt;T\u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); // T* ptr = matlab_array.release().get(); // arma::Col\u0026lt;T\u0026gt; vec(ptr, n_elem); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? arma::Col\u0026lt;T\u0026gt; vec(n_elem, arma::fill::zeros); for (size_t k = 0; k \u0026lt; n_elem; k++) { vec[k] = matlab_array[k]; } return vec; }; template \u0026lt;class T\u0026gt; arma::Mat\u0026lt;T\u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { // ArrayDimensions == std::vector\u0026lt;size_t\u0026gt; auto dims = matlab_array.getDimensions(); // T* ptr = matlab_array.release().get(); // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false) // arma::Mat\u0026lt;T\u0026gt; mat(ptr, dims[0], dims[1]); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? // // armadillo and matlab both use column-major ordering, so this should work: size_t n_elem = dims[0] * dims[1]; arma::Mat\u0026lt;T\u0026gt; mat(dims[0], dims[1], arma::fill::zeros); size_t k(0); for (auto m: matlab_array) { mat[k] = m; k++; } return mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_mat(const arma::Mat\u0026lt;T\u0026gt;\u0026amp; arma_mat, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(), arma_mat.memptr() + arma_mat.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_vec(const arma::Col\u0026lt;T\u0026gt;\u0026amp; arma_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;({arma_vec.n_elem, 1}, arma_vec.memptr(), arma_vec.memptr() + arma_vec.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; s2m_vec(const std::vector\u0026lt;T\u0026gt;\u0026amp; std_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size()); return matlab_mat; }; } // namespace armamexcpp #endif Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":78,"href":"/lds-ctrl-est/docs/terminology/model/","title":"Models","section":"LDS C+E Documentation","content":" Model Definitions # This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form: \\[\\mathbf{x}_{t\u0026#43;1} = f\\left( \\mathbf{x}_{t}, \\mathbf{v}_{t} \\right) = \\mathbf{A} \\mathbf{x}_{t} \u0026#43; \\mathbf{B} \\mathbf{v}_{t} \u0026#43; \\mathbf{m}_{t} \u0026#43; \\mathbf{w}_{t}\\] \\[\\mathbf{y}_{t} = h\\left( \\mathbf{x}_{t} \\right)\\] t : time index x : system state v = g%u : input (e.g., in physical units used for model fit) u : control signal sent to actuator (e.g., in Volts) y : system output m : process disturbance w ~ N(0, Q) : process noise/disturbance A : state matrix B : input coupling matrix g : input gain (e.g., for converting to control signal actuator voltage) n.b., assumes this conversion is linear Q : process noise covariance % : element-wise multiplication LDS with Gaussian Observations # For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.\n\\[\\mathbf{y}_{t} = \\mathbf{C} \\mathbf{x}_{t} \u0026#43; \\mathbf{d}\\] \\[\\mathbf{z}_{t} \\sim \\mathcal{N}\\left(\\mathbf{y}_{t} , \\mathbf{R} \\right)\\] z : measurement C : output matrix d : output bias R : measurement noise covariance LDS with Poisson Observations # For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.\n\\[y_{t}^{i} = \\exp \\left(\\mathbf{c}^i \\mathbf{x}_{t} \u0026#43; d^i\\right)\\] \\[z_{t}^i \\sim \\rm{Poisson} \\left(y_{t}^i \\right)\\] i : output index z : measurement (count data) c : i^th row of output matrix (C) d : output bias Model Predictive Control (MPC) # Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.\nIn the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.\n"},{"id":79,"href":"/lds-ctrl-est/docs/api/modules/","title":"Modules","section":"LDS C+E Documentation","content":" Modules # Control Mode Bit Masks provides fill types for constructing new armadillo vectors, matrices\nDefaults\nUpdated on 5 March 2025 at 21:41:27 EST\n"},{"id":80,"href":"/lds-ctrl-est/docs/api/namespaces/","title":"Namespaces","section":"LDS C+E Documentation","content":" Namespaces # armamexc arma/mex interface using Matlab C API\narmamexcpp arma/mex interface using Matlab C++ API\nlds::gaussian Linear Dynamical Systems with Gaussian observations.\nlds::poisson Linear Dynamical Systems with Poisson observations.\nstd\nUpdated on 5 March 2025 at 21:41:27 EST\n"},{"id":81,"href":"/lds-ctrl-est/docs/api/pages/","title":"Pages","section":"LDS C+E Documentation","content":" Pages # Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":82,"href":"/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/","title":"PLDS State Estimation","section":"LDS C+E Examples","content":" PLDS State Estimation Tutorial # This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[x_{t\u0026#43;1} = x_t \u0026#43; w_t\\] \\[y_{t} = \\exp\\left(x_t\\right)\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state Finally, assign the parameters using corresponding set-methods.\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); Creating the estimator # Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. To ensure robust estimates, adaptively re-estimate the process disturbance.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); Simulating estimation # In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.\n// Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.\nWith this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.\n"},{"id":83,"href":"/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/","title":"PLDS Switched Control","section":"LDS C+E Examples","content":" PLDS Switched Control Tutorial # This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.\nIn the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating the simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); The system\u0026rsquo;s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 Initially, the system will be in \u0026ldquo;mode\u0026rdquo; 1, where B = b1.\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.\nCreating the controller # Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.\nSimilar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.\nMoreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.\nPutting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying systems: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nNow that the SwitchedController is instantiated, assign its parameters.\n// Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).\nThe control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.\n// Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system\u0026rsquo;s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.\nExample simulation result # Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller\u0026rsquo;s online estimates of the output and state are shown in purple.\nNote that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.\n"},{"id":84,"href":"/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/","title":"src","section":"Files","content":" src # Files # Name src/lds.cpp misc lds namespace functions src/lds_gaussian_sys.cpp GLDS base type. src/lds_poisson_sys.cpp PLDS base type. src/lds_sys.cpp LDS base type. src/lds_uniform_vecs.cpp Uniformly sized vectors. Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":85,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/","title":"src/lds_gaussian_sys.cpp","section":"Files","content":" src/lds_gaussian_sys.cpp # GLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_gaussian_sys.h\u0026gt; lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0, data_t r0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { R_.zeros(n_y, n_y); R_.diag().fill(r0); do_recurse_Ke_=true; }; // recursively estimate Ke void lds::gaussian::System::RecurseKe() { if (!do_recurse_Ke_) { return; } // predict covariance P_ = A_ * P_ * A_.t() + Q_; // calc Kalman gain Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_); // update covariance // Reference: Ghahramani et Hinton (1996) P_ = P_ - Ke_ * C_ * P_; if (do_adapt_m) { P_m_ += Q_m_; // A_m = I (i.e., random walk) Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_); P_m_ = P_m_ - Ke_m_ * C_ * P_m_; } } // Simulate const lds::Vector\u0026amp; lds::gaussian::System::Simulate(const Vector\u0026amp; u_tm1){ f(u_tm1, true);//simulate dynamics with noise added h();//output z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure return z_; } void lds::gaussian::System::Print() { lds::System::Print(); std::cout \u0026lt;\u0026lt; \u0026#34;R: \\n\u0026#34; \u0026lt;\u0026lt; R_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":86,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/","title":"src/lds_poisson_sys.cpp","section":"Files","content":" src/lds_poisson_sys.cpp # PLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_poisson_sys.h\u0026gt; lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { diag_y_ = diagmat(y_); pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(0); }; // Correct: Given measurement (z) and current input (u), update estimate of the // state, covar, output. // // see Eden et al. 2004 void lds::poisson::System::RecurseKe() { // predict covariance P_ = A_ * P_ * A_.t() + Q_; // update cov P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_); Ke_ = P_ * C_.t(); if (do_adapt_m) { P_m_ += Q_m_; // predict (A_m = I) P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_); // update Ke_m_ = P_m_ * C_.t(); } } // Simulate Measurement: z ~ Poisson(y) const lds::Vector\u0026amp; lds::poisson::System::Simulate(const Vector\u0026amp; u_tm1) { f(u_tm1, true); // simulate dynamics with noise added h(); // output z_.zeros(); for (std::size_t k = 0; k \u0026lt; n_y_; k++) { // construct a Poisson distribution object with mean y[k] pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(y_[k]); // pull random sample from this distribution z_[k] = pd_(rng); } return z_; } // ******************* SYS_T ******************* Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":87,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8cpp/","title":"src/lds_sys.cpp","section":"Files","content":" src/lds_sys.cpp # LDS base type. More\u0026hellip;\nDetailed Description # This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- lds_sys.cpp - LDS -------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_sys.h\u0026gt; #include \u0026lt;vector\u0026gt; lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) { InitVars(p0, q0); } void lds::System::InitVars(data_t p0, data_t q0) { // initial conditions. x0_ = Vector(n_x_, fill::zeros); // includes bias (nY) and g (nU) P0_ = p0 * Matrix(n_x_, n_x_, fill::eye); m0_ = x0_; P0_m_ = P0_; // signals x_ = x0_; P_ = P0_; m_ = m0_; P_m_ = P0_m_; y_ = Vector(n_y_, fill::zeros); cx_ = Vector(n_y_, fill::zeros); z_ = Vector(n_y_, fill::zeros); // By default, random walk where each state is independent // In this way, provides independent estimates of rate per channel of output. A_ = Matrix(n_x_, n_x_, fill::eye); B_ = Matrix(n_x_, n_u_, fill::zeros); g_ = Vector(n_u_, fill::ones); Q_ = q0 * Matrix(n_x_, n_x_, fill::eye); Q_m_ = Q_; C_ = Matrix(n_y_, n_x_, fill::eye); // each state will map to an output by d_ = Vector(n_y_, fill::zeros); Ke_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain. Ke_m_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain for m adaptation. do_adapt_m = false; } // Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state // and update estimate of the state, covar, output using Kalman filter void lds::System::Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z_t) { // predict mean f(u_tm1); // dynamics h(); // output // recursively calculate esimator gains (or just keep existing values) // (also predicts+updates estimate covariance) RecurseKe(); // update x_ += Ke_ * (z_t - y_); if (do_adapt_m) { m_ += Ke_m_ * (z_t - y_); // adaptively estimating disturbance } // With new state, estimate output. h(); // --\u0026gt; posterior } void lds::System::Reset() { // reset to initial conditions x_ = x0_; // mean P_ = P0_; // cov of state estimate m_ = m0_; // process disturbance P_m_ = P0_m_; // cov of disturbance estimate h(); } std::vector\u0026lt;lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt;\u0026gt; lds::System::nstep_pred_block(lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; u, lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; z, size_t n_pred) { lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_filt; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_pred; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; y_pred; for (size_t k = 0; k \u0026lt; u.size(); k++) { Reset(); size_t n_t = arma::size(u[k])[1]; Matrix x_filt_k(n_x_, n_t, fill::zeros); Matrix x_pred_k(n_x_, n_t - n_pred, fill::zeros); Matrix y_pred_k(n_y_, n_t - n_pred, fill::zeros); for (size_t t = 0; t \u0026lt; n_t - n_pred; t++) { Vector x_pred_ahead = x_; for (size_t t_u = t; t_u \u0026lt; t + n_pred; t_u++) { x_pred_ahead = A_ * x_pred_ahead + B_ * u[k].col(t_u); } x_pred_k.col(t) = x_pred_ahead; y_pred_k.col(t) = h_(x_pred_ahead); if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; // given previous measurment } for (size_t t = n_t - n_pred; t \u0026lt; n_t; t++) { if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; } x_filt.append(x_filt_k); x_pred.append(x_pred_k); y_pred.append(y_pred_k); } return {x_filt, x_pred, y_pred}; } void lds::System::Print() { std::cout \u0026lt;\u0026lt; \u0026#34;\\n ********** SYSTEM ********** \\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;x: \\n\u0026#34; \u0026lt;\u0026lt; x_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;P: \\n\u0026#34; \u0026lt;\u0026lt; P_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;A: \\n\u0026#34; \u0026lt;\u0026lt; A_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B: \\n\u0026#34; \u0026lt;\u0026lt; B_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;g: \\n\u0026#34; \u0026lt;\u0026lt; g_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;m: \\n\u0026#34; \u0026lt;\u0026lt; m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q: \\n\u0026#34; \u0026lt;\u0026lt; Q_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q_m: \\n\u0026#34; \u0026lt;\u0026lt; Q_m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d: \\n\u0026#34; \u0026lt;\u0026lt; d_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;C: \\n\u0026#34; \u0026lt;\u0026lt; C_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;y: \\n\u0026#34; \u0026lt;\u0026lt; y_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } //******************* SYS_T ******************* Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":88,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/","title":"src/lds_uniform_vecs.cpp","section":"Files","content":" src/lds_uniform_vecs.cpp # Uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_uniform_vecs.h\u0026gt; namespace lds { UniformVectorList::UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); } UniformVectorList::UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim) : vector(std::move(vecs)) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(const UniformVectorList\u0026amp; that) : vector(that) { (*this) = that; } UniformVectorList::UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { this-\u0026gt;dim_ = this-\u0026gt;at(0).n_elem; } void UniformVectorList::CheckDimensions(size_t dim) { if (dim) { dim_ = dim; } else { dim_ = this-\u0026gt;at(0).n_elem; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Vector\u0026amp; vec : *this) { does_match = does_match \u0026amp;\u0026amp; (vec.n_elem == dim_); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } } } // namespace lds Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":89,"href":"/lds-ctrl-est/docs/api/files/lds_8cpp/","title":"src/lds.cpp","section":"Files","content":" src/lds.cpp # misc lds namespace functions More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file implements miscellaneous lds namespace functions not bound to a class.\nSource code # //===-- lds.cpp - LDS -----------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds.h\u0026gt; // insert any necessary function definitions here. namespace lds { void ForceSymPD(Matrix\u0026amp; X) { if (X.is_sympd() || !X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; // for eigenval decomp bool did_succeed(true); Vector d; Matrix u; // see first method (which may not be ideal): // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/ size_t k(1); bool is_sympd = X.is_sympd(); Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye); while (!is_sympd) { if (k \u0026gt; 100) { did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); data_t min_eig = arma::min(d); std::cerr \u0026lt;\u0026lt; \u0026#34;After multiple iterations, min eigen val = \u0026#34; \u0026lt;\u0026lt; min_eig \u0026lt;\u0026lt; \u0026#34;.\\n\u0026#34;; throw std::runtime_error( \u0026#34;Failed to make matrix symmetric positive definite.\u0026#34;); return; } // Limit(d, arma::eps(0), kInf); // force to be positive... // Matrix d_diag = arma::diagmat(d); // X = u * d_diag * u.t(); did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymPD failed.\u0026#34;); } data_t min_eig = arma::min(d); X += id * abs(min_eig) + arma::datum::eps; // make sure symm: X = (X + X.t()) / 2; // double check eigenvals positive after symmetrizing: arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); min_eig = arma::min(d); is_sympd = min_eig \u0026gt; 0; k++; } } void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min) { if (!X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; bool did_succeed(true); Vector d; Matrix u; did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymMinEig failed.\u0026#34;); } Limit(d, eig_min + arma::eps(eig_min), kInf); // enforce lower bound Matrix d_diag = arma::diagmat(d); X = u * d_diag * u.t(); // double check symmetric X = (X + X.t()) / 2; } void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X) { bool did_succeed(true); did_succeed = arma::qr_econ(Qt, L, X.t()); if (!did_succeed) { throw std::runtime_error(\u0026#34;LQ decomposition failed.\u0026#34;); } arma::inplace_trans(L); arma::inplace_trans(Qt); } Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B) { // subtract out mean auto m_a = arma::mean(A, 1); Matrix a0 = A; a0.each_col() -= m_a; auto m_b = arma::mean(B, 1); Matrix b0 = B; b0.each_col() -= m_b; Matrix cov = a0 * b0.t() / a0.n_cols; return cov; } } // namespace lds Updated on 5 March 2025 at 21:41:27 EST\n"},{"id":90,"href":"/lds-ctrl-est/docs/api/namespaces/namespacestd/","title":"std","section":"Namespaces","content":" std # Updated on 5 March 2025 at 21:41:27 EST\n"}]
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+[{"id":0,"href":"/lds-ctrl-est/docs/","title":"LDS C+E Documentation","section":"LDS Control \u0026 Estimation","content":" LDS Control \u0026amp; Estimation Documentation # "},{"id":1,"href":"/lds-ctrl-est/docs/tutorials/","title":"LDS C+E Examples","section":"LDS C+E Documentation","content":" Examples # "},{"id":2,"href":"/lds-ctrl-est/acknowledgements/","title":"Acknowledgements","section":"LDS Control \u0026 Estimation","content":" Acknowledgements # Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.\n"},{"id":3,"href":"/lds-ctrl-est/docs/getting-started/getting-started/","title":"Getting Started","section":"LDS C+E Documentation","content":" Getting Started # This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.\nldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.\nTested Configurations # Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the RelWithDebInfo build type in the CMake configure command (-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo):\nUbuntu 18.04 with GCC 7.5 compiler macOS 11 (Big Sur) with Apple Clang 12 compiler Windows 10 with Visual Studio 16.11 (2019 release) and Clang 12 compiler That being said, if you want to debug a build for a single platform, here are some things you can try:\nUse different compilers (or even different versions of a single compiler) Use different versions of vcpkg (which you can control by checking out a different commit in the vcpkg submodule) Mac Pre-requisities # Xcode Command Line Tools will get you clang, gcc, make, and git:\nxcode-select --install Homebrew is \u0026ldquo;The Missing Package Manager for macOS\u0026rdquo; which will make installing lots of things easy. Install like this:\n/bin/bash -c \u0026#34;$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\u0026#34; You can then use it to install CMake, gfortran, and pkg-config:\nbrew install cmake gfortran pkg-config Linux Pre-requisites # You\u0026rsquo;ll need Git, CMake, GCC, gfortran, etc.\nsudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential ninja-build Windows Installation # Look here for Windows-specific instructions.\nDownloading the Library # First, clone the repository along with submodules:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est\rgit submodule update --init Compilation + Installation # Now generate the cache and build using your IDE or from the command line as follows.\nmkdir build \u0026amp;\u0026amp; cd build\rcmake ..\rcmake --build . The first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.\nIf you want to use vcpkg set up somewhere besides this repo\u0026rsquo;s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE\u0026rsquo;s settings.\nYou can verify the build is working by running ctest from the build folder, which runs all the example scripts.\nOptions # This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\nLDSCTRLEST_BUILD_EXAMPLES : [default=ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake to configure/build the library.\nFor basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build cmake --build #build the project sudo make install #[optional] installs to default location (OS-specific) To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location cmake --build #build the project make install #install to /your/install/prefix To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\nLD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.\nPython bindings package ldsctrlest # With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython3_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn\u0026rsquo;t use an undesired version. That environment needs to have NumPy installed.\ncmake --build . --target python_modules The bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory (pip install pytest matplotlib first if you need to).\nSee python/ldsctrlest/README.md for usage details.\nAlso, beware that a single build will probably not work for both the standalone library and the Python package, since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.\nCommon issues # \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.\nexport CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix vcpkg fails on configuration Try running ./bootstrap-vcpkg from the vcpkg folder and try again. If that doesn\u0026rsquo;t work, try updating vcpkg to a newer version (in the source control tab, click on the commit hash by the vcpkg repo then select from the dropdown) and running boostsrap-vcpkg again. You can also try upgrading your system (e.g., apt update, apt upgrade).\nCould not find Python3 (missing: Python3_NumPy_INCLUDE_DIRS NumPy)\nMake sure NumPy is installed in the Python environment you specified. If CMake still can\u0026rsquo;t find it, you may need to tell CMake exactly where to find it by adding an argument to the configure command: -DPython3_NumPy_INCLUDE_DIR=.... You can find that location like this: python -c 'import numpy; print(numpy.get_include())'\n"},{"id":4,"href":"/lds-ctrl-est/docs/getting-started/windows/","title":"Windows","section":"LDS C+E Documentation","content":" Windows Installation # Windows Pre-requisites # Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:\nSet-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time iwr get.scoop.sh | Invoke-Expression Install Git and CMake if you don\u0026rsquo;t already have them:\nscoop install git cmake If that didn\u0026rsquo;t work, follow more detailed instructions here.\nThe easiest way to compile C++ project on Windows is with Visual Studio\u0026rsquo;s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on \u0026ldquo;Desktop development with C++.\u0026rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the \u0026ldquo;Installation details\u0026rdquo; sidebar under optional features.\nAnd the easiest way to use Visual Studio\u0026rsquo;s build tools is with VS Code, along with the CMake Tools extension. Install them and you should be ready to go.\nDownloading the Library # First, clone the repository, either from VS Code or the command line:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est You\u0026rsquo;ll need to initialize the submodules from the command line after the repo is cloned:\ngit submodule update --init Installation # When you open the folder in VS Code, you will like be prompted by the CMake Tools extension to configure the project. Make sure you select the kit (you\u0026rsquo;ll be prompted when you configure\u0026ndash;else there\u0026rsquo;s an icon in the bar on the bottom of the window or type Ctrl+Shift+P, then \u0026ldquo;cmake select kit\u0026rdquo;). Choose Clang [latest version] with GNU CLI ... amd64 assuming you are running a 64-bit OS. (MSVC may work okay too if you don\u0026rsquo;t need to build Python bindings.)\nFollow along with the \u0026ldquo;Getting Started\u0026rdquo; instructions, but where you see config options specified as -DLDSCTREST_BUILD_STATIC=OFF or -DPython3_ROOT_DIR=..., you will enter those in settings: open with Ctrl+,, click \u0026ldquo;workspace\u0026rdquo;, then search for \u0026ldquo;CMake: Configure Args\u0026rdquo; and enter each of your desired arguments as a separate item.\nTo configure, use Ctrl+Shift+P and search for the \u0026ldquo;CMake: Configure\u0026rdquo; command. To build, click the \u0026ldquo;Build\u0026rdquo; button on the bottom bar. Then click the \u0026ldquo;CTest\u0026rdquo; button to run the example scripts.\nConsiderations # Development on Windows has been more prone to bugs than on Unix systems, so if you encounter many problems, consider switching—WSL (Windows Subsystem for Linux) is a good option for Windows users who don\u0026rsquo;t want to work on a different machine.\nCompilation has been successfully tested in VS Code using the following kit, using the \u0026ldquo;RelWithDebInfo\u0026rdquo; config:\nClang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64) Troubleshooting # The build appears to work, but tests fail with code 0xc0000135 OR \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; Have you installed the library? In VS Code, use Shift+F7 to build a specific target, in this case INSTALL. If that doesn\u0026rsquo;t solve your problem, you will likely need to add the build or install folder to your PATH environment variable, which you can do using the settings GUI (search for \u0026ldquo;Edit the system environment variables\u0026rdquo;).\nOn Windows, \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted. Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":5,"href":"/lds-ctrl-est/issues-contributing/","title":"Issues Contributing","section":"LDS Control \u0026 Estimation","content":" Reporting Issues # If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.\nContributing # We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.\nWhen editing any documentation/guides, please use the markdown docs in misc/docs-hugo instead of directly editing the HTML docs. This may require having hugo, graphviz and doxygen installed through brew, as well as doxybook2 installed through a git clone.\nClone the doxybook2 repository online and place the executable in your usr/local/bin folder or another bin folder. Run sudo chmod +x /usr/local/bin/doxybook2 to give doxybook2 permissions. Run doxybook2 --help to ensure that this works properly. If permission is still denied, navigate to System Preferences \u0026gt; Security \u0026amp; Privacy \u0026gt; General. You should see a message at the bottom that says: \u0026ldquo;doxybook2 was blocked from use because it is not from an identified developer.\u0026rdquo; Click Allow Anyway.\nUpdated docs can be built by running cd scripts and ./update-docs.sh. After a successful build, navigate to /misc/docs-hugo/ and run hugo server to create a local host of the website. Access the website using local host to ensure the correct results.\n"},{"id":6,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/","title":"armamexc","section":"Namespaces","content":" armamexc # arma/mex interface using Matlab C API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; T m2T_scalar(const mxArray * matlab_scalar)\nConvert Matlab mxArray to scalar of type T. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)\nConvert matlab matrix to armadillo. template \u0026lt;typename T \u0026gt; mxArray * a2m_mat(arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat)\nConvert armadillo to matlab matrix. template \u0026lt;typename T \u0026gt; mxArray * a2m_vec(arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec)\nConvert armadillo to matlab vector. Detailed Description # Utilities for arma/mex interface using Matlab C API\nFunction Details # m2T_scalar # template \u0026lt;class T \u0026gt; inline T m2T_scalar( const mxArray * matlab_scalar ) Parameters:\nmatlab_scalar matlab scalar Template Parameters:\nT type Return: scalar of type T\nm2a_mat # template \u0026lt;class T \u0026gt; inline arma::Mat\u0026lt; T \u0026gt; m2a_mat( const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true ) Parameters:\nmatlab_mat matlab matrix copy_aux_mem [optional] whether to copy auxiliary memory strict [optional] strictly enforce the above Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_mat( arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat ) Parameters:\narma_mat armadillo matrix Return: matlab matrix\na2m_vec # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_vec( arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec ) Parameters:\narma_vec armadillo vector Return: matlab vector\nUpdated on 31 March 2025 at 16:04:30 EDT\n"},{"id":7,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/","title":"armamexcpp","section":"Namespaces","content":" armamexcpp # arma/mex interface using Matlab C++ API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat(matlab::data::CellArray \u0026amp; matlab_cell)\nConvert matlab cell array to vector of armadillo matrices. template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array)\nConvert matlab matrix to a vector of scalars. template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo vector. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat(const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec(const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab vector. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec(const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert vector of scalar T to matlab matrix. Detailed Description # utilities for arma/mex interface using Matlab C++ API\nFunction Details # m2a_cellmat # template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat( matlab::data::CellArray \u0026amp; matlab_cell ) Parameters:\nmatlab_cell matlab cell Template Parameters:\nT type Return: vector of armadillo matrices of type T\nm2s_vec # template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: vector of type T\nm2a_vec # template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: armadillo vector of type T\nm2a_mat # template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab matrix Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat( const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_mat arma matrix factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\na2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec( const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_vec armadillo vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\ns2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec( const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\nstd_vec standard vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\nUpdated on 31 March 2025 at 16:04:30 EDT\n"},{"id":8,"href":"/lds-ctrl-est/docs/terminology/control-estimation/","title":"C\u0026E","section":"LDS C+E Documentation","content":" Control \u0026amp; Estimation # The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.\nState estimation # In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by \\[\\widehat{\\mathbf{x}}_{t|t-1} = \\mathbf{A}\\widehat{\\mathbf{x}}_{t-1|t-1} \u0026#43; \\mathbf{B} u_{t-1} \u0026#43; \\mathbf{m}_{t-1} \\;,\\] \\[\\widehat{\\mathbf{x}}_{t|t} = \\widehat{\\mathbf{x}}_{t|t-1} \u0026#43; \\mathbf{K}^{\\rm e}_t \\left(\\mathbf{z}_t - \\widehat{\\mathbf{y}}_{t|t-1}\\right)\\;,\\] where \\( \\hat{\\left(\\cdot\\right)}_{t|j} \\) indicates an estimate at time \\( t \\) given data up to time \\( j \\) inclusive, \\( \\mathbf{K}^{\\rm e} \\) is the estimator gain, and\n\\[ \\widehat{\\mathbf{y}}_{t|t-1} = h\\left( \\widehat{\\mathbf{x}}_{t|t-1} \\right) \\; .\\] In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.\nIn the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.\nAdaptive estimation of process disturbance # Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.\nWhen parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk \\[\\mathbf{m}_{t} = \\mathbf{m}_{t-1} \u0026#43; \\mathbf{w}^m_{t-1} \\;,\\] where \\( \\mathbf{w}^m \\sim \\mathcal{N}\\left(0, \\mathbf{Q}_m\\right)\\) . Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.\nControl # Given the estimated state, the controller updates the inputs to the system according to the following law: \\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] where \\( \\left( \\cdot \\right)^{\\rm ref} \\) correspond to reference/target signals and \\( \\mathbf{K}^c_x \\) is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.\nIf users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as\n\\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right) - \\mathbf{K}^c_{\\rm inty} \\sum_{j=1}^{t}\\left( \\widehat{\\mathbf{y}}_j - \\mathbf{y}^{\\rm ref}_j \\right) \\;.\\] An additional option available to users is a control law that updates the change in u,\n\\[\\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\left(\\mathbf{u}_{t-1} - \\mathbf{u}^{\\rm ref}_{t-1} \\right) - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] \\[\\mathbf{u}_{t} = \\mathbf{u}_{t-1} \u0026#43; \\Delta\\mathbf{u}_{t} \\; .\\] Notice that this takes the form of a first-order difference equation for updating control (i.e., \\( \\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\mathbf{u}_{t-1} \u0026#43; \\epsilon_{t-1} \\) ), effectively low-pass filtering the input depending on the characteristics of \\( \\mathbf{K}^c_u \\) . This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.\nIntegral action and the \\( \\Delta \\mathbf{u} \\) control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR\u0026rsquo;d to use in combination.\nCalculating reference state-control from output # In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce \\( \\mathbf{x}^{\\rm ref} \\) and \\( \\mathbf{u}^{\\rm ref} \\) . Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.\nModel Predictive Control # Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.\nIn the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.\n"},{"id":9,"href":"/lds-ctrl-est/docs/api/classes/","title":"Classes","section":"LDS C+E Documentation","content":" Classes # lds::Controller\nlds::EM\nlds::Fit LDS Fit Type.\nlds::SSID\nlds::SwitchedController SwitchedController Type.\nlds::System Linear Dynamical System Type.\nlds::UniformMatrixList\nlds::UniformSystemList\nlds::UniformVectorList\nlds::gaussian::Controller Gaussian-observation Controller Type.\nlds::gaussian::Fit GLDS Fit Type.\nlds::gaussian::FitEM GLDS E-M Fit Type.\nlds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.\nlds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.\nlds::gaussian::System Gaussian LDS Type.\nlds::poisson::Controller PLDS Controller Type.\nlds::poisson::Fit PLDS Fit Type.\nlds::poisson::FitEM PLDS E-M Fit Type.\nlds::poisson::FitSSID Subspace Identification (SSID) for PLDS.\nlds::poisson::SwitchedController Poisson-observation SwitchedController Type.\nlds::poisson::System Poisson System type.\nUpdated on 31 March 2025 at 16:04:30 EDT\n"},{"id":10,"href":"/lds-ctrl-est/docs/api/modules/group__control__masks/","title":"Control Mode Bit Masks","section":"Modules","content":" Control Mode Bit Masks # provides fill types for constructing new armadillo vectors, matrices More\u0026hellip; Attributes # Name const std::size_t kControlTypeDeltaU control designed to penalize change in input const std::size_t kControlTypeIntY control using integral action const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m Detailed Description # Control mode bit masks. These can be bit-wise OR\u0026rsquo;d to use in combination.\nAttribute Details # kControlTypeDeltaU # static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY # static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM # static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\nUpdated on 31 March 2025 at 16:04:30 EDT\n"},{"id":11,"href":"/lds-ctrl-est/docs/api/modules/group__defaults/","title":"Defaults","section":"Modules","content":" Defaults # More\u0026hellip; Attributes # Name const data_t kDefaultP0 default state estimate covar const data_t kDefaultQ0 default process noise covar const data_t kDefaultR0 default output noise covar Detailed Description # Default values for common variables (e.g., default diagonal elements of covariances)\nAttribute Details # kDefaultP0 # static const data_t kDefaultP0 = 1e-6; kDefaultQ0 # static const data_t kDefaultQ0 = 1e-6; kDefaultR0 # static const data_t kDefaultR0 = 1e-2; Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":12,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/","title":"eg_glds_ctrl.cpp","section":"Examples","content":" eg_glds_ctrl.cpp # Example GLDS Control ```cpp\n//===\u0026ndash; eg_glds_ctrl.cpp - Example GLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS Control ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\n// output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false;\n// Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err\n// setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]);\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; }\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_ctrl.cpp_ ------------------------------- Updated on 31 March 2025 at 16:04:30 EDT "},{"id":13,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/","title":"eg_glds_du_plds_ctrl.cpp","section":"Examples","content":" eg_glds_du_plds_ctrl.cpp # Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u). ```cpp\n//===\u0026ndash; eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt);\n// to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU;\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_du_plds_ctrl.cpp_ ------------------------------- Updated on 31 March 2025 at 16:04:30 EDT "},{"id":14,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/","title":"eg_plds_ctrl.cpp","section":"Examples","content":" eg_plds_ctrl.cpp # Example PLDS Control ```cpp\n//===\u0026ndash; eg_plds_ctrl.cpp - Example PLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Control ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt);\n// Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err\n// Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\nsize_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\nVector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled\u0026hellip; lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system);\n// for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type);\n// set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty);\n// to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0;\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros);\n// set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y();\nx_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x();\nm_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_ctrl.cpp_ ------------------------------- Updated on 31 March 2025 at 16:04:30 EDT "},{"id":15,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/","title":"eg_plds_est.cpp","section":"Examples","content":" eg_plds_est.cpp # Example PLDS Estimation ```cpp\n//===\u0026ndash; eg_plds_est.cpp - Example PLDS Estimation \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0);\nint main() { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.\n// construct ground truth system\u0026hellip; lds::poisson::System system_true(n_u, n_x, n_y, dt);\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset();\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt);\n// Can copy parameters from another system object system_estimator = system_true;\n// wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est);\n// set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true;\n// set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;estimator:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; system_estimator.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));\n// create matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros);\n// states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\nMatrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simlation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1));\n// Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simlation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows;\nif ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026ldquo;Q must be n x n.\u0026rdquo;); }\nMatrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); }\nreturn x; }\n_Filename: eg_plds_est.cpp_ ------------------------------- Updated on 31 March 2025 at 16:04:30 EDT "},{"id":16,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/","title":"eg_plds_switched_ctrl.cpp","section":"Examples","content":" eg_plds_switched_ctrl.cpp # Example Switched PLDS Control ```cpp\n//===\u0026ndash; eg_plds_switched_ctrl.cpp - Example Switched PLDS Control \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026quot;;\n// whether to do switched control bool do_switch_ctrl = true;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt);\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices. data_t scale_sys_b = 2;\nMatrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));\ncontrolled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions\n// reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);\n// Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system);\n// set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys1:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys2:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x));\nswitched_controller.set_y_ref(y_ref0);\nstd::vectorlds::poisson::System systems_vec(3, lds::poisson::System()); lds::UniformSystemListlds::poisson::System systems(std::move(systems_vec));\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;switched_controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; switched_controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Fake measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros);\n// create Matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);\n// modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones);\n// set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } }\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace)); mode.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;mode\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_switched_ctrl.cpp_ ------------------------------- Updated on 31 March 2025 at 16:04:30 EDT "},{"id":17,"href":"/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/","title":"examples","section":"Files","content":" examples # Files # Name examples/eg_glds_ctrl.cpp examples/eg_glds_du_plds_ctrl.cpp examples/eg_plds_ctrl.cpp examples/eg_plds_est.cpp examples/eg_plds_switched_ctrl.cpp Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":18,"href":"/lds-ctrl-est/docs/api/examples/","title":"Examples","section":"LDS C+E Documentation","content":" Examples # eg_glds_ctrl.cpp Example GLDS Control.\neg_glds_du_plds_ctrl.cpp Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).\neg_plds_ctrl.cpp Example PLDS Control.\neg_plds_est.cpp Example PLDS Estimation.\neg_plds_switched_ctrl.cpp Example Switched PLDS Control.\nUpdated on 31 March 2025 at 16:04:30 EDT\n"},{"id":19,"href":"/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/","title":"examples/eg_glds_ctrl.cpp","section":"Files","content":" examples/eg_glds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":20,"href":"/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/","title":"examples/eg_glds_du_plds_ctrl.cpp","section":"Files","content":" examples/eg_glds_du_plds_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU; // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":21,"href":"/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/","title":"examples/eg_plds_ctrl.cpp","section":"Files","content":" examples/eg_plds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err // Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled... lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type); // set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":22,"href":"/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/","title":"examples/eg_plds_est.cpp","section":"Files","content":" examples/eg_plds_est.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name Matrix random_walk(size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0) int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # random_walk # Matrix random_walk( size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0 ) main # int main() Source code # //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. // construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state // Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. // turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; } Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":23,"href":"/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/","title":"examples/eg_plds_switched_ctrl.cpp","section":"Files","content":" examples/eg_plds_switched_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Source code # //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 // simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions // reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; } Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":24,"href":"/lds-ctrl-est/docs/api/files/","title":"Files","section":"LDS C+E Documentation","content":" Files # examples/eg_glds_ctrl.cpp\nexamples/eg_glds_du_plds_ctrl.cpp\nexamples/eg_plds_ctrl.cpp\nexamples/eg_plds_est.cpp\nexamples/eg_plds_switched_ctrl.cpp\nldsCtrlEst_h/lds.h lds namespace\nldsCtrlEst_h/lds_ctrl.h Controller.\nldsCtrlEst_h/lds_fit.h LDS base fit type.\nldsCtrlEst_h/lds_fit_em.h subspace identification\nldsCtrlEst_h/lds_fit_ssid.h subspace identification\nldsCtrlEst_h/lds_gaussian.h glds namespace\nldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.\nldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.\nldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.\nldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.\nldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.\nldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.\nldsCtrlEst_h/lds_poisson.h plds namespace\nldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.\nldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.\nldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.\nldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.\nldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.\nldsCtrlEst_h/lds_poisson_sys.h PLDS base type.\nldsCtrlEst_h/lds_sctrl.h SwitchedController type.\nldsCtrlEst_h/lds_sys.h LDS base type.\nldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.\nldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.\nldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.\nldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)\nldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)\nsrc/lds.cpp misc lds namespace functions\nsrc/lds_gaussian_sys.cpp GLDS base type.\nsrc/lds_poisson_sys.cpp PLDS base type.\nsrc/lds_sys.cpp LDS base type.\nsrc/lds_uniform_vecs.cpp Uniformly sized vectors.\nUpdated on 31 March 2025 at 16:04:30 EDT\n"},{"id":25,"href":"/lds-ctrl-est/docs/tutorials/eg_glds_control/","title":"GLDS Control","section":"LDS C+E Examples","content":" GLDS Control Tutorial # This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\] \\[\ry_{t} = x_t\r\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.\n/// Going to simulate a switching disturbance (m) acting on system size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_y).fill(m_low); Finally, assign the parameters using corresponding set-methods.\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); Creating the controller # Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.\nFor the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.\n// make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nWith the controller constructed, control variables may be set.\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.\nThe control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller\u0026rsquo;s online estimates of the output, state, and disturbance are given in purple.\n"},{"id":26,"href":"/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/","title":"include","section":"Files","content":" include # Directories # Name ldsCtrlEst_h Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":27,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds/","title":"lds","section":"Namespaces","content":" lds # Linear Dynamical Systems (LDS) namespace. Namespaces # Name lds::gaussian Linear Dynamical Systems with Gaussian observations. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::Controller class lds::EM class lds::Fit LDS Fit Type. class lds::SSID class lds::SwitchedController SwitchedController Type. class lds::System Linear Dynamical System Type. class lds::UniformMatrixList class lds::UniformSystemList class lds::UniformVectorList Types # Name enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}\nweighting options for SSID enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2} using double data_t using arma::Col\u0026lt; data_t \u0026gt; Vector using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Cube\u0026lt; data_t \u0026gt; Cube using arma::subview\u0026lt; data_t \u0026gt; View Functions # Name void Limit(std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub) void Limit(Vector \u0026amp; x, data_t lb, data_t ub) void Limit(Matrix \u0026amp; x, data_t lb, data_t ub) void Reassign(Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Vector in place void Reassign(Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Matrix in place void ForceSymPD(Matrix \u0026amp; X)\nforces matrix to be symmetric positive-definite void ForceSymMinEig(Matrix \u0026amp; X, data_t eig_min =0)\nforces matrix to be symmetric and have a minimum eigenvalue void lq(Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X)\nLQ decomposition. Matrix calcCov(const Matrix \u0026amp; A, const Matrix \u0026amp; B)\nCalculate covariance matrix. Attributes # Name const data_t kInf Some useful numbers. const data_t kPi Type Details # SSIDWt # Enumerator Value Description kSSIDNone None. kSSIDMOESP MOESP (AKA \u0026ldquo;robust method\u0026rdquo; in van Overschee 1996) kSSIDCVA CVA \u0026ldquo;Canonical Variate Analysis\u0026rdquo;. Weighting options for singular value decomposition performed during subspace identification (SSID)\nReference:\nvan Overschee, de Moor. 1996. Subspace Identification for Linear Systems.\nMatrixListFreeDim # Enumerator Value Description kMatFreeDimNone neither dim free to be hetero in mat list kMatFreeDim1 allow 1st dim of mats in list to be hetero kMatFreeDim2 allow 2nd dim of mats in list to be hetero data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nVector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Cube # using lds::Cube = arma::Cube\u0026lt;data_t\u0026gt;; View # using lds::View = arma::subview\u0026lt;data_t\u0026gt;; Function Details # Limit # inline void Limit( std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Vector \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Matrix \u0026amp; x, data_t lb, data_t ub ) Reassign # inline void Reassign( Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Vector other other Vector parenthetical optional description provided by caller to ease debugging Reassign # inline void Reassign( Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Matrix other other Matrix parenthetical optional description provided by caller to ease debugging ForceSymPD # void ForceSymPD( Matrix \u0026amp; X ) Parameters:\nX mutated matrix ForceSymMinEig # void ForceSymMinEig( Matrix \u0026amp; X, data_t eig_min =0 ) Parameters:\nX mutated matrix eig_min [optional] minimum eigen value lq # void lq( Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X ) Parameters:\nL lower triangle matrix Qt orthonormal matrix (transposed cf QR decomp) X matrix being decomposed calcCov # Matrix calcCov( const Matrix \u0026amp; A, const Matrix \u0026amp; B ) Parameters:\nA some matrix B some other matrix Return: covariance\nAttribute Details # kInf # static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); kPi # static const data_t kPi = arma::datum::pi; Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":28,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/","title":"lds::Controller","section":"Classes","content":" lds::Controller # More\u0026hellip;\nInherited by lds::SwitchedController\u0026lt; System \u0026gt;, lds::gaussian::Controller, lds::poisson::Controller\nPublic Functions # Name Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes # Name System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::Controller; Public Function Details # Controller # Controller() =default Controller # inline Controller( const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Controller # inline Controller( System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Control # inline const Vector \u0026amp; Control( const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step). This is the most flexible option, but requires user to have set the controller\u0026rsquo;s y_ref, x_ref, and u_ref variables.\nControlOutputReference # inline const Vector \u0026amp; ControlOutputReference( const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_estimation [optional] whether to update state estimate (if false, effectively open-loop control) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.\nsys # inline const System \u0026amp; sys() const Kc # inline const Matrix \u0026amp; Kc() const Kc_inty # inline const Matrix \u0026amp; Kc_inty() const Kc_u # inline const Matrix \u0026amp; Kc_u() const g_design # inline const Vector \u0026amp; g_design() const u_ref # inline const Vector \u0026amp; u_ref() const x_ref # inline const Vector \u0026amp; x_ref() const y_ref # inline const Vector \u0026amp; y_ref() const control_type # inline size_t control_type() const tau_awu # inline data_t tau_awu() const u_lb # inline data_t u_lb() const u_ub # inline data_t u_ub() const set_sys # inline void set_sys( const System \u0026amp; sys ) set_g_design # inline void set_g_design( const Vector \u0026amp; g_design ) set_u_ref # inline void set_u_ref( const Vector \u0026amp; u_ref ) set_x_ref # inline void set_x_ref( const Vector \u0026amp; x_ref ) set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) Reimplemented by: lds::gaussian::Controller::set_y_ref, lds::gaussian::SwitchedController::set_y_ref, lds::poisson::Controller::set_y_ref, lds::poisson::SwitchedController::set_y_ref\nset_Kc # inline void set_Kc( const Matrix \u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const Matrix \u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const Matrix \u0026amp; Kc_u ) set_tau_awu # inline void set_tau_awu( data_t tau ) set_control_type # inline void set_control_type( size_t control_type ) Parameters:\ncontrol_type control type bit mask Template Parameters:\nSystem type derived from lds::System set_u_lb # inline void set_u_lb( data_t u_lb ) Parameters:\nu_lb control lower bound set_u_ub # inline void set_u_ub( data_t u_ub ) Parameters:\nu_ub control upper bound Reset # inline void Reset() Print # inline void Print() Protected Attribute Details # sys_ # System sys_; u_ # Vector u_; u_return_ # Vector u_return_; g_design_ # Vector g_design_; u_ref_ # Vector u_ref_; u_ref_prev_ # Vector u_ref_prev_; x_ref_ # Vector x_ref_; y_ref_ # Vector y_ref_; cx_ref_ # Vector cx_ref_; Kc_ # Matrix Kc_; Kc_u_ # Matrix Kc_u_; Kc_inty_ # Matrix Kc_inty_; du_ref_ # Vector du_ref_; dv_ref_ # Vector dv_ref_; v_ref_ # Vector v_ref_; dv_ # Vector dv_; v_ # Vector v_; int_e_ # Vector int_e_; int_e_awu_adjust_ # Vector int_e_awu_adjust_; u_sat_ # Vector u_sat_; do_control_prev_ # bool do_control_prev_ = false; do_lock_control_prev_ # bool do_lock_control_prev_ = false; u_saturated_ # bool u_saturated_ = false; u_lb_ # data_t u_lb_ {}; u_ub_ # data_t u_ub_ {}; tau_awu_ # data_t tau_awu_ {}; k_awu_ # data_t k_awu_ = 0; t_since_control_onset_ # data_t t_since_control_onset_ = 0; control_type_ # size_t control_type_ {}; Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":29,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/","title":"lds::EM","section":"Classes","content":" lds::EM # More\u0026hellip;\nInherited by lds::gaussian::FitEM, lds::poisson::FitEM\nPublic Functions # Name EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions # Name void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() virtual void MaximizeOutput() =0 virtual void MaximizeMeasurement() =0 void Smooth(bool force_common_initial)\nget smoothed estimates virtual void RecurseKe(Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t) =0\nrecursively update estimator gain Ke void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::EM; Public Function Details # EM # EM() =default EM # EM( size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nn_x number of states dt sample period u_train input training data z_train measurement training data EM # EM( const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nfit0 initial fit u_train input training data z_train measurement training data ~EM # virtual ~EM() =default Run # const Fit \u0026amp; Run( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2 ) Parameters:\ncalc_dynamics [optional] whether to calculate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law max_iter max number of iterations tol convergence tolerance (max fractional abs change) Return: Fit\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nx # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const y # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const sum_E_x_t_x_t # inline const Matrix \u0026amp; sum_E_x_t_x_t() const sum_E_xu_tm1_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const sum_E_xu_t_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const n_t_tot # inline size_t n_t_tot() theta # inline const Vector \u0026amp; theta() const Protected Function Details # Expectation # void Expectation( bool force_common_initial =false ) Parameters:\nforce_common_initial whether to force common initial condition for all trials Maximization # void Maximization( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false ) Parameters:\ncalc_dynamics [optional] whether to caclulate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law MaximizeDynamics # void MaximizeDynamics() MaximizeQ # void MaximizeQ() MaximizeInitial # void MaximizeInitial() MaximizeOutput # virtual void MaximizeOutput() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput\nMaximizeMeasurement # virtual void MaximizeMeasurement() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement\nSmooth # void Smooth( bool force_common_initial ) Parameters:\nforce_common_initial whether to force common initial conditions RecurseKe # virtual void RecurseKe( Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t ) =0 Parameters:\nKe estimator gain P_pre cov of predicted state est. P_post cov of postior sate est. t time Reimplemented by: lds::gaussian::FitEM::RecurseKe, lds::poisson::FitEM::RecurseKe\nReset # void Reset() InitVars # void InitVars() UpdateTheta # Vector UpdateTheta() Return: parameter list\nProtected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; x_ # std::vector\u0026lt; Matrix \u0026gt; x_; P_ # std::vector\u0026lt; Cube \u0026gt; P_; P_t_tm1_ # std::vector\u0026lt; Cube \u0026gt; P_t_tm1_; y_ # std::vector\u0026lt; Matrix \u0026gt; y_; diag_y_ # Matrix diag_y_; sum_E_x_t_x_t_ # Matrix sum_E_x_t_x_t_; sum_E_xu_tm1_xu_tm1_ # Matrix sum_E_xu_tm1_xu_tm1_; sum_E_xu_t_xu_tm1_ # Matrix sum_E_xu_t_xu_tm1_; fit_ # Fit fit_; theta_ # Vector theta_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":30,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/","title":"lds::Fit","section":"Classes","content":" lds::Fit # LDS Fit Type. #include \u0026lt;lds_fit.h\u0026gt;\nInherited by lds::gaussian::Fit, lds::poisson::Fit\nPublic Functions # Name Fit() =default\nConstructs a new Fit. Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias virtual const Matrix \u0026amp; R() const =0 void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance virtual void set_R(const Matrix \u0026amp; R) =0\nsets output noise covariance (if any) void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) =0\noutput function Protected Attributes # Name data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period ~Fit # virtual ~Fit() =default n_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const m # inline const Vector \u0026amp; m() const Q # inline const Matrix \u0026amp; Q() const x0 # inline const Vector \u0026amp; x0() const P0 # inline const Matrix \u0026amp; P0() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const R # virtual const Matrix \u0026amp; R() const =0 Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R\nset_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_g # inline void set_g( const Vector \u0026amp; g ) set_m # inline void set_m( const Vector \u0026amp; m ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # virtual void set_R( const Matrix \u0026amp; R ) =0 Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R\nset_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) f # inline View f( Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t ) Parameters:\nx state estimate (over time) u input (over time) t time index Return: view of updated state\nf # inline View f( Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t ) Parameters:\nx_pre predicted state est. x_post posterior state est. u input (over time) t time index Return: view of predicted state\nh # virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) =0 Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplemented by: lds::gaussian::Fit::h, lds::poisson::Fit::h\nProtected Attribute Details # dt_ # data_t dt_ {}; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; m_ # Vector m_; Q_ # Matrix Q_; C_ # Matrix C_; d_ # Vector d_; R_ # Matrix R_; x0_ # Vector x0_; P0_ # Matrix P0_; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":31,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/","title":"lds::gaussian","section":"Namespaces","content":" lds::gaussian # Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. class lds::gaussian::Fit GLDS Fit Type. class lds::gaussian::FitEM GLDS E-M Fit Type. class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. class lds::gaussian::System Gaussian LDS Type. Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":32,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/","title":"lds::gaussian::Controller","section":"Classes","content":" lds::gaussian::Controller # Gaussian-observation Controller Type. #include \u0026lt;lds_gaussian_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 31 March 2025 at 16:04:30 EDT\n"},{"id":33,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/","title":"lds::gaussian::Fit","section":"Classes","content":" lds::gaussian::Fit # GLDS Fit Type. #include \u0026lt;lds_gaussian_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual const Matrix \u0026amp; R() const override\ngets measurement noise covariance virtual void set_R(const Matrix \u0026amp; R) override\nsets measurement noise covariance virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period R # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nh # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nUpdated on 31 March 2025 at 16:04:30 EDT\n"},{"id":34,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/","title":"lds::gaussian::FitEM","section":"Classes","content":" lds::gaussian::FitEM # GLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_gaussian_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::gaussian::FitEM; This type is used in the process of fitting GLDS models by expectation-maximization (EM). Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":35,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/","title":"lds::gaussian::FitSSID","section":"Classes","content":" lds::gaussian::FitSSID # Subspace Identification (SSID) for GLDS. #include \u0026lt;lds_gaussian_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":36,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/","title":"lds::gaussian::SwitchedController","section":"Classes","content":" lds::gaussian::SwitchedController # Gaussian-observation SwitchedController Type. #include \u0026lt;lds_gaussian_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 31 March 2025 at 16:04:30 EDT\n"},{"id":37,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/","title":"lds::gaussian::System","section":"Classes","content":" lds::gaussian::System # Gaussian LDS Type. #include \u0026lt;lds_gaussian_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)\nConstructs a new Gaussian System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. const Matrix \u0026amp; R() const\nGet output noise covariance. void set_Q(const Matrix \u0026amp; Q) void set_R(const Matrix \u0026amp; R)\nSet output noise covariance. void set_Ke(const Matrix \u0026amp; Ke)\nSet estimator gain. void set_Ke_m(const Matrix \u0026amp; Ke_m)\nSet disturbance estimator gain. void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively update estimator gain. Protected Attributes # Name Matrix R_ covariance of output noise bool do_recurse_Ke_ whether to recursively calculate estimator gain Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0 ) Parameters:\nn_u number of inputs (u) n_x number of states (x) n_y number of outputs (y) dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) r0 [optional] initial diagonal elements of output noise covariance (R) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nR # inline const Matrix \u0026amp; R() const set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # inline void set_R( const Matrix \u0026amp; R ) set_Ke # inline void set_Ke( const Matrix \u0026amp; Ke ) set_Ke_m # inline void set_Ke_m( const Matrix \u0026amp; Ke_m ) Print # void Print() Protected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nProtected Attribute Details # R_ # Matrix R_; do_recurse_Ke_ # bool do_recurse_Ke_ {}; Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":38,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/","title":"lds::poisson","section":"Namespaces","content":" lds::poisson # Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. class lds::poisson::Fit PLDS Fit Type. class lds::poisson::FitEM PLDS E-M Fit Type. class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. class lds::poisson::System Poisson System type. Attributes # Name std::random_device rd random device for simulating poisson data std::mt19937 rng random number generator for simulating poisson data Attribute Details # rd # static std::random_device rd; rng # static std::mt19937 rng = std::mt19937( rd()); Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":39,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/","title":"lds::poisson::Controller","section":"Classes","content":" lds::poisson::Controller # PLDS Controller Type. #include \u0026lt;lds_poisson_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 31 March 2025 at 16:04:30 EDT\n"},{"id":40,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/","title":"lds::poisson::Fit","section":"Classes","content":" lds::poisson::Fit # PLDS Fit Type. #include \u0026lt;lds_poisson_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function virtual void set_R(const Matrix \u0026amp; R) override\nsets output noise covariance (if any) virtual const Matrix \u0026amp; R() const override Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # inline Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period h # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nR # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nUpdated on 31 March 2025 at 16:04:30 EDT\n"},{"id":41,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/","title":"lds::poisson::FitEM","section":"Classes","content":" lds::poisson::FitEM # PLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_poisson_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::poisson::FitEM; This type is used in the process of fitting PLDS models by expectation-maximization (EM). Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":42,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/","title":"lds::poisson::FitSSID","section":"Classes","content":" lds::poisson::FitSSID # Subspace Identification (SSID) for PLDS. #include \u0026lt;lds_poisson_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":43,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/","title":"lds::poisson::SwitchedController","section":"Classes","content":" lds::poisson::SwitchedController # Poisson-observation SwitchedController Type. #include \u0026lt;lds_poisson_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 31 March 2025 at 16:04:30 EDT\n"},{"id":44,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/","title":"lds::poisson::System","section":"Classes","content":" lds::poisson::System # Poisson System type. #include \u0026lt;lds_poisson_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nConstructs a new Poisson System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively recalculate estimator gain (Ke) Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nProtected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nRecursively recalculate estimator gain (Ke).\nReferences:\nSmith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.\nEden UT, \u0026hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.\nUpdated on 31 March 2025 at 16:04:30 EDT\n"},{"id":45,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/","title":"lds::SSID","section":"Classes","content":" lds::SSID # More\u0026hellip;\nInherited by lds::gaussian::FitSSID, lds::poisson::FitSSID\nPublic Functions # Name SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions # Name void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. virtual void DecomposeData() =0\nDecompose data to lower-triangular matrix (used in Solve) void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::SSID; Public Function Details # SSID # SSID() =default SSID # SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf) ) Parameters:\nn_x number of states n_h size of block-hankel data matrix dt sample period u_train input training data z_train measurement training data d output bias Run # std::tuple\u0026lt; Fit, Vector \u0026gt; Run( SSIDWt ssid_wt ) Parameters:\nssid_wt weight for singular value decomp Return: tuple (Fit, singular values)\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nProtected Function Details # CalcD # void CalcD( data_t t_silence =0.1, data_t thresh_silence =0.001 ) Parameters:\nt_silence threshold on period of time that qualifies as \u0026ldquo;silence\u0026rdquo; thresh_silence threshold on input amplitude u that qualifies as \u0026ldquo;silence\u0026rdquo; CreateHankelDataMat # void CreateHankelDataMat() Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.\nDecomposeData # virtual void DecomposeData() =0 Reimplemented by: lds::gaussian::FitSSID::DecomposeData, lds::poisson::FitSSID::DecomposeData\nCalcSVD # void CalcSVD( SSIDWt wt ) Parameters:\nssid_wt weight for SVD Solve # void Solve( data_t wt_dc ) Parameters:\nwt_dc weight placed on getting correct DC I/O gain RecomputeExtObs # void RecomputeExtObs() Protected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; D_ # Matrix D_; fit_ # Fit fit_; g_dc_ # Matrix g_dc_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_h_ # size_t n_h_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; L_ # Matrix L_; s_ # Vector s_; ext_obs_t_ # Matrix ext_obs_t_; Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":46,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/","title":"lds::SwitchedController","section":"Classes","content":" lds::SwitchedController # SwitchedController Type. More\u0026hellip;\n#include \u0026lt;lds_sctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nInherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController\nPublic Functions # Name SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes # Name std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::SwitchedController; Public Function Details # SwitchedController # SwitchedController() =default SwitchedController # inline SwitchedController( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask SwitchedController # inline SwitchedController( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Switch # inline void Switch( size_t idx, bool do_force_switch =false ) Parameters:\nidx index do_force_switch whether to force a system switch even if already there. set_Kc # inline void set_Kc( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc ) set_Kc # inline void set_Kc( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty ) set_Kc_inty # inline void set_Kc_inty( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u ) set_Kc_u # inline void set_Kc_u( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u ) set_g_design # inline void set_g_design( const UniformVectorList \u0026amp; g ) set_g_design # inline void set_g_design( UniformVectorList \u0026amp;\u0026amp; g ) Protected Attribute Details # systems_ # std::vector\u0026lt; System \u0026gt; systems_; n_sys_ # size_t n_sys_ {}; idx_ # size_t idx_ {}; Kc_list_ # UniformMatrixList Kc_list_; Kc_inty_list_ # UniformMatrixList Kc_inty_list_; Kc_u_list_ # UniformMatrixList Kc_u_list_; g_design_list_ # UniformVectorList g_design_list_; Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":47,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_system/","title":"lds::System","section":"Classes","content":" lds::System # Linear Dynamical System Type. #include \u0026lt;lds_sys.h\u0026gt;\nInherited by lds::gaussian::System, lds::poisson::System\nPublic Functions # Name System() =default\nConstructs a new System. System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nconstructs a new System virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) =0\nsimulates system (single time step) void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function virtual void h() =0\nsystem output function virtual Vector h_(Vector x) =0\nsystem output function (stateless) size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void RecurseKe() =0\nRecursively recalculate estimator gain (Ke) void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes # Name bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes # Name std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 diagonal elements for state estimate covariance q0 diagonal elements for process noise covariance ~System # inline virtual ~System() Filter # void Filter( const Vector \u0026amp; u_tm1, const Vector \u0026amp; z ) Parameters:\nu_tm1 input at t-minus-1 z_t current measurement Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.\nSimulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) =0 Parameters:\nu_tm1 input at time t-1 Return: simulated measurement at time t\nReimplemented by: lds::gaussian::System::Simulate, lds::poisson::System::Simulate\nf # inline void f( const Vector \u0026amp; u, bool do_add_noise =false ) Parameters:\nu input do_add_noise whether to add simulated process noise h # virtual void h() =0 Reimplemented by: lds::gaussian::System::h, lds::poisson::System::h\nh_ # virtual Vector h_( Vector x ) =0 Parameters:\nx_t state at time t Return: predicted state at time t + 1\nReimplemented by: lds::gaussian::System::h_, lds::poisson::System::h_\nn_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const x # inline const Vector \u0026amp; x() const P # inline const Matrix \u0026amp; P() const m # inline const Vector \u0026amp; m() const P_m # inline const Matrix \u0026amp; P_m() const cx # inline const Vector \u0026amp; cx() const y # inline const Vector \u0026amp; y() const x0 # inline const Vector \u0026amp; x0() const m0 # inline const Vector \u0026amp; m0() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const Ke # inline const Matrix \u0026amp; Ke() const Ke_m # inline const Matrix \u0026amp; Ke_m() const Q # inline const Matrix \u0026amp; Q() Q_m # inline const Matrix \u0026amp; Q_m() P0 # inline const Matrix \u0026amp; P0() P0_m # inline const Matrix \u0026amp; P0_m() set_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_m # inline void set_m( const Vector \u0026amp; m, bool do_force_assign =false ) set_g # inline void set_g( const Vector \u0026amp; g ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_Q_m # inline void set_Q_m( const Matrix \u0026amp; Q_m ) set_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_P0_m # inline void set_P0_m( const Matrix \u0026amp; P0_m ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) set_x # inline void set_x( const Vector \u0026amp; x ) Reset # void Reset() nstep_pred_block # std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block( UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1 ) Print # void Print() Protected Function Details # RecurseKe # virtual void RecurseKe() =0 Reimplemented by: lds::gaussian::System::RecurseKe, lds::poisson::System::RecurseKe\nInitVars # void InitVars( data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Public Attribute Details # do_adapt_m # bool do_adapt_m {}; Protected Attribute Details # n_x_ # std::size_t n_x_ {}; n_u_ # std::size_t n_u_ {}; n_y_ # std::size_t n_y_ {}; dt_ # data_t dt_ {}; x_ # Vector x_; P_ # Matrix P_; m_ # Vector m_; P_m_ # Matrix P_m_; cx_ # Vector cx_; y_ # Vector y_; z_ # Vector z_; x0_ # Vector x0_; P0_ # Matrix P0_; m0_ # Vector m0_; P0_m_ # Matrix P0_m_; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; Q_ # Matrix Q_; Q_m_ # Matrix Q_m_; C_ # Matrix C_; d_ # Vector d_; Ke_ # Matrix Ke_; Ke_m_ # Matrix Ke_m_; Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":48,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/","title":"lds::UniformMatrixList","section":"Classes","content":" lds::UniformMatrixList # More\u0026hellip;\nInherits from std::vector\u0026lt; Matrix \u0026gt;\nPublic Functions # Name UniformMatrixList() =default\nConstructs a new UniformMatrixList. UniformMatrixList(const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent. UniformMatrixList(std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent. UniformMatrixList(std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent. UniformMatrixList(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nConstructs a new UniformMatrixList (copy). UniformMatrixList(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nConstructs a new UniformMatrixList (move). ~UniformMatrixList() =default\nDestroys the object. const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim(size_t n =0) const\ngets dimensions of uniformly sized matrices size_t size()\nsize of container const Matrix \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Matrix \u0026amp; that, size_t n)\nswaps input matrix with n^th matrix of list UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nassigns the contents (copy) UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nassigns the contents (move) void append(const Matrix \u0026amp; mat)\nappends a matrix to the list Detailed Description # template \u0026lt;MatrixListFreeDim D =kMatFreeDimNone\u0026gt; class lds::UniformMatrixList; Public Function Details # UniformMatrixList # UniformMatrixList() =default UniformMatrixList # explicit UniformMatrixList( const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # explicit UniformMatrixList( std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList UniformMatrixList # UniformMatrixList( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList ~UniformMatrixList # ~UniformMatrixList() =default dim # inline const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim( size_t n =0 ) const Parameters:\nn [optional] index in list of matrices Return: dimensions\nsize # inline size_t size() at # inline const Matrix \u0026amp; at( size_t n ) Swap # inline void Swap( Matrix \u0026amp; that, size_t n ) Parameters:\nthat input matrix n index where the matrix is moved operator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\noperator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\nappend # void append( const Matrix \u0026amp; mat ) Parameters:\nmat input matrix Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":49,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/","title":"lds::UniformSystemList","section":"Classes","content":" lds::UniformSystemList # More\u0026hellip;\nInherits from std::vector\u0026lt; System \u0026gt;\nPublic Functions # Name UniformSystemList() =default\nConstructs a new UniformSystemList. UniformSystemList(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by copying existing vector of System if dimensions consistent. UniformSystemList(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by moving existing vector of System if dimensions consistent. UniformSystemList(std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList from initializer_list of System if dimensions consistent. UniformSystemList(const UniformSystemList \u0026amp; that)\nConstructs a new UniformSystemList (copy). UniformSystemList(UniformSystemList \u0026amp;\u0026amp; that)\nConstructs a new UniformSystemList (move). ~UniformSystemList() =default\nDestroys the object. const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const\ngets dimensions of the uniformly sized systems size_t size()\nsize of container const System \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(System \u0026amp; that, size_t n)\nswaps input system with n^th system of list UniformSystemList \u0026amp; operator=(const UniformSystemList \u0026amp; that)\nassigns the contents (copy) UniformSystemList \u0026amp; operator=(UniformSystemList \u0026amp;\u0026amp; that)\nassigns the contents (move) Detailed Description # template \u0026lt;typename System \u0026gt; class lds::UniformSystemList; Public Function Details # UniformSystemList # UniformSystemList() =default UniformSystemList # explicit UniformSystemList( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # explicit UniformSystemList( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList UniformSystemList # UniformSystemList( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList ~UniformSystemList # ~UniformSystemList() =default dim # inline const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const size # inline size_t size() at # inline const System \u0026amp; at( size_t n ) Swap # inline void Swap( System \u0026amp; that, size_t n ) Parameters:\nthat input system n index where the system is moved operator= # inline UniformSystemList \u0026amp; operator=( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\noperator= # inline UniformSystemList \u0026amp; operator=( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\nUpdated on 31 March 2025 at 16:04:30 EDT\n"},{"id":50,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/","title":"lds::UniformVectorList","section":"Classes","content":" lds::UniformVectorList # Inherits from std::vector\u0026lt; Vector \u0026gt;\nPublic Functions # Name UniformVectorList() =default\nConstructs a new UniformVectorList. UniformVectorList(const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent. UniformVectorList(std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent. UniformVectorList(std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0)\nConstructs a new UniformVectorList from initializer_list of Vector if dimensions consistent. UniformVectorList(const UniformVectorList \u0026amp; that)\nConstructs a new UniformVectorList (copy) UniformVectorList(UniformVectorList \u0026amp;\u0026amp; that)\nConstructs a new UniformVectorList (move) ~UniformVectorList() =default\nDestroys the object. size_t dim() const\ngets dimensions of the uniformly sized matrices size_t size()\nsize of container const Vector \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Vector \u0026amp; that, size_t n)\nswaps input matrix with n^th vector of list UniformVectorList \u0026amp; operator=(const UniformVectorList \u0026amp; that)\nassigns the contents (copy) UniformVectorList \u0026amp; operator=(UniformVectorList \u0026amp;\u0026amp; that)\nassigns the contents (move) Public Function Details # UniformVectorList # UniformVectorList() =default UniformVectorList # explicit UniformVectorList( const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dims dimension UniformVectorList # explicit UniformVectorList( std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList UniformVectorList # UniformVectorList( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList ~UniformVectorList # ~UniformVectorList() =default dim # inline size_t dim() const size # inline size_t size() at # inline const Vector \u0026amp; at( size_t n ) Swap # inline void Swap( Vector \u0026amp; that, size_t n ) Parameters:\nthat input vector n index where the vector is moved operator= # inline UniformVectorList \u0026amp; operator=( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\noperator= # inline UniformVectorList \u0026amp; operator=( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\nUpdated on 31 March 2025 at 16:04:30 EDT\n"},{"id":51,"href":"/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/","title":"ldsCtrlEst_h","section":"Files","content":" ldsCtrlEst_h # Files # Name ldsCtrlEst_h/lds.h lds namespace ldsCtrlEst_h/lds_ctrl.h Controller. ldsCtrlEst_h/lds_fit.h LDS base fit type. ldsCtrlEst_h/lds_fit_em.h subspace identification ldsCtrlEst_h/lds_fit_ssid.h subspace identification ldsCtrlEst_h/lds_gaussian.h glds namespace ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller. ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type. ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type. ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type. ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type. ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type. ldsCtrlEst_h/lds_poisson.h plds namespace ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type. ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type. ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type. ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type. ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type. ldsCtrlEst_h/lds_poisson_sys.h PLDS base type. ldsCtrlEst_h/lds_sctrl.h SwitchedController type. ldsCtrlEst_h/lds_sys.h LDS base type. ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices. ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems. ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors. ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":52,"href":"/lds-ctrl-est/docs/api/files/lds__ctrl_8h/","title":"ldsCtrlEst_h/lds_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_ctrl.h # Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Controller Detailed Description # This file declares the type for control of a linear dynamical system (lds::Controller).\nSource code # //===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_CTRL_H #define LDSCTRLEST_LDS_CTRL_H // namespace #include \u0026#34;lds.h\u0026#34; // system type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class Controller { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); public: Controller() = default; Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); const Vector\u0026amp; Control(const Vector\u0026amp; z, bool do_control = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); const Vector\u0026amp; ControlOutputReference(const Vector\u0026amp; z, bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); // get methods: const System\u0026amp; sys() const { return sys_; }; const Matrix\u0026amp; Kc() const { return Kc_; }; const Matrix\u0026amp; Kc_inty() const { return Kc_inty_; }; const Matrix\u0026amp; Kc_u() const { return Kc_u_; }; const Vector\u0026amp; g_design() const { return g_design_; }; const Vector\u0026amp; u_ref() const { return u_ref_; }; const Vector\u0026amp; x_ref() const { return x_ref_; }; const Vector\u0026amp; y_ref() const { return y_ref_; }; size_t control_type() const { return control_type_; }; data_t tau_awu() const { return tau_awu_; }; data_t u_lb() const { return u_lb_; }; data_t u_ub() const { return u_ub_; }; // set methods void set_sys(const System\u0026amp; sys) { bool does_match = sys_.n_u() == sys.n_u(); does_match = does_match \u0026amp;\u0026amp; (sys_.n_x() == sys.n_x()); does_match = does_match \u0026amp;\u0026amp; (sys_.n_y() == sys.n_y()); if (does_match) { sys_ = sys; } else { throw std::runtime_error( \u0026#34;new system argument to `set_sys` does not match dimensionality of \u0026#34; \u0026#34;existing system\u0026#34;); } }; void set_g_design(const Vector\u0026amp; g_design) { Reassign(g_design_, g_design); }; void set_u_ref(const Vector\u0026amp; u_ref) { Reassign(u_ref_, u_ref); }; void set_x_ref(const Vector\u0026amp; x_ref) { Reassign(x_ref_, x_ref); cx_ref_ = sys_.C() * x_ref_; }; // y_ref needs to be handled differently depending on output fn. // (need to populate cx_ref_ too, which depends on output fn) virtual void set_y_ref(const Vector\u0026amp; y_ref) { Reassign(y_ref_, y_ref); }; void set_Kc(const Matrix\u0026amp; Kc) { Reassign(Kc_, Kc); }; void set_Kc_inty(const Matrix\u0026amp; Kc_inty) { Reassign(Kc_inty_, Kc_inty); }; void set_Kc_u(const Matrix\u0026amp; Kc_u) { Reassign(Kc_u_, Kc_u); }; void set_tau_awu(data_t tau) { tau_awu_ = tau; k_awu_ = sys_.dt() / tau_awu_; }; void set_control_type(size_t control_type); // There is no reason u_lb/ub should not be public, but making set methods // anyway. void set_u_lb(data_t u_lb) { u_lb_ = u_lb; }; void set_u_ub(data_t u_ub) { u_ub_ = u_ub; }; void Reset() { sys_.Reset(); u_ref_.zeros(); u_ref_prev_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); u_saturated_ = false; t_since_control_onset_ = 0.0; }; void Print() { sys_.Print(); std::cout \u0026lt;\u0026lt; \u0026#34;g_design : \u0026#34; \u0026lt;\u0026lt; g_design_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_lb : \u0026#34; \u0026lt;\u0026lt; u_lb_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_ub : \u0026#34; \u0026lt;\u0026lt; u_ub_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }; protected: System sys_; Vector u_; Vector u_return_; Vector g_design_; // reference signals Vector u_ref_; // create no set method for this: Vector u_ref_prev_; Vector x_ref_; Vector y_ref_; Vector cx_ref_; // Controller gains Matrix Kc_; Matrix Kc_u_; Matrix Kc_inty_; // control after g inversion // do not need set methods for these. Vector du_ref_; Vector dv_ref_; Vector v_ref_; Vector dv_; Vector v_; // integral error // do not need set method for this Vector int_e_; Vector int_e_awu_adjust_; Vector u_sat_; bool do_control_prev_ = false; bool do_lock_control_prev_ = false; // whether the g of system has become inverted from what you think it is // (gain_ref) bool u_saturated_ = false; // should be safe to have references here bc nothing needs to be done // (like reset vars) when it changes... data_t u_lb_{}; data_t u_ub_{}; data_t tau_awu_{}; data_t k_awu_ = 0; data_t t_since_control_onset_ = 0; size_t control_type_{}; private: void CalcControl(bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); void CalcSteadyStateSetPoint(); void AntiWindup(); void InitVars(size_t control_type); }; // Implement the above: template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(sys), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(std::move(sys)), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::set_control_type(size_t control_type) { if (control_type_ == control_type) { return; } // creating a blank slate... control_type_ = 0; Kc_inty_.zeros(0, 0); Kc_u_.zeros(0, 0); int_e_.zeros(0, 0); int_e_awu_adjust_.zeros(0, 0); // controller was designed to minimize integral error if (control_type \u0026amp; kControlTypeIntY) { Kc_inty_.zeros(sys_.n_u(), sys_.n_y()); int_e_.zeros(sys_.n_y()); int_e_awu_adjust_.zeros(sys_.n_u()); control_type_ = control_type_ | kControlTypeIntY; } // controller was designed to minimize deltaU // (i.e. state augmented with u) if (control_type \u0026amp; kControlTypeDeltaU) { Kc_u_.zeros(sys_.n_u(), sys_.n_u()); control_type_ = control_type_ | kControlTypeDeltaU; } // whether to adapt set point calculate with (re-estimated) process // disturbance (m) if (control_type \u0026amp; kControlTypeAdaptM) { if (sys_.do_adapt_m) // only if adapting m... { control_type_ = control_type_ | kControlTypeAdaptM; } } } // set_control_type template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::Control( const Vector\u0026amp; z, bool do_control, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement sys_.Filter(u_, z); bool do_estimation = true; // always have estimator on in this case // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::ControlOutputReference( const Vector\u0026amp; z, bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement if (do_estimation) { sys_.Filter(u_, z); } else { sys_.f(u_); } // calculate the set point // solves for u_ref and x_ref when output is at y_ref at steady state. if (do_control) { CalcSteadyStateSetPoint(); } // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcControl(bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { if (do_control \u0026amp;\u0026amp; do_estimation) { if (!do_control_prev_) { if (do_reset_at_control_onset) { Reset(); } t_since_control_onset_ = 0.0; } else { t_since_control_onset_ += sys_.dt(); } // enforce softstart on control vars. if (sigma_soft_start \u0026gt; 0) { // half-Gaussian soft-start scaling factor data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) / (2 * pow(sigma_soft_start, 2))); u_ref_ *= soft_start_sf; // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too // x_ref_ *= soft_start_sf; // cx_ref_ *= soft_start_sf; // y_ref_ *= soft_start_sf; } if (!do_lock_control) { // first do u -\u0026gt; v change of vars. (v = g.*u) // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver // control voltage u[=]V) v_ref_ = g_design_ % u_ref_; // Given FB, calc. the change in control if (control_type_ \u0026amp; kControlTypeDeltaU) { // if control designed to minimize not u but deltaU (i.e. state aug with // u): // TODO(mfbolus): Commented out for now. See note below. // du_ref_ = u_ref_ - u_ref_prev_; // dv_ref_ = g_design_ % du_ref_; // TODO(mfbolus): Assuming users want *smooth* control signals if using // kControlTypeDeltaU, it should be the case that dv_ref_ is --\u0026gt; 0. May // want to revisit, but I am going to force it to be zero unless a // situation arises that argues for keeping the above. dv_ref_.zeros(); dv_ = dv_ref_; // nominally-optimal. dv_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error dv_ -= Kc_u_ * (v_ - v_ref_); // penalty on amp u (rel to ref) if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if(!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error dv_ -= Kc_inty_ * int_e_; // control for integrated error } // update the control v_ += dv_; } else { v_ = v_ref_; // nominally-optimal. v_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if (!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error v_ -= Kc_inty_ * int_e_; // control for integrated error } } // convert back to control voltage u[=]V u_ = v_ / sys_.g(); } // else do nothing until lock is low } else { // if not control // feed through u_ref in open loop u_ = u_ref_ % g_design_ / sys_.g(); v_ = sys_.g() % u_; u_ref_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); } // ends do_control // enforce box constraints (and antiwindup) AntiWindup(); // add noise to input? // The value for u that is *returned* to user after addition of any noise, // while keeping controller/estimator blind to this addition. u_return_ = u_; if ((sigma_u_noise \u0026gt; 0.0) \u0026amp;\u0026amp; (do_control \u0026amp;\u0026amp; !do_lock_control)) { u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn); Limit(u_return_, u_lb_, u_ub_); }; // For next time step: u_ref_prev_ = u_ref_; do_control_prev_ = do_control; do_lock_control_prev_ = do_lock_control; } // CalcControl template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcSteadyStateSetPoint() { // Linearly-constrained least squares (ls). // // _reference: // Boyd \u0026amp; Vandenberghe (2018) Introduction to Applied Linear Algebra // Matrix a_ls = join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros)); Vector b_ls = cx_ref_; Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye), sys_.B() * arma::diagmat(sys_.g())); Vector d_ls = -sys_.m0(); if (control_type_ \u0026amp; kControlTypeAdaptM) { d_ls = -sys_.m(); // adapt setpoint calc with disturbance? } Matrix a_ls_t = a_ls.t(); // TODO(mfbolus): not sure why but causes seg // fault if I do not do this. Matrix phi_ls = join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()), join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros))); // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse: Matrix inv_phi = pinv(phi_ls); Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls); x_ref_ = xulam.subvec(0, sys_.n_x() - 1); u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1); cx_ref_ = sys_.C() * x_ref_; } // CalcSteadyStateSetPoint template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::AntiWindup() { u_saturated_ = false; u_sat_ = u_; // limit u and flag whether saturated for (size_t k = 0; k \u0026lt; u_.n_elem; k++) { if (u_[k] \u0026lt; u_lb_) { u_sat_[k] = u_lb_; u_saturated_ = true; } if (u_[k] \u0026gt; u_ub_) { u_sat_[k] = u_ub_; u_saturated_ = true; } } if ((control_type_ \u0026amp; kControlTypeIntY) \u0026amp;\u0026amp; (tau_awu_ \u0026lt; lds::kInf)) { // one-step back-calculation (calculate intE for u=u_sat) // (Astroem, Rundqwist 1989 warn against using this...) // int_e_awu_adjust_ = // solve(Kc_inty_, (u_ - u_sat_)); // pinv(Kc_inty) * (u-uSat); // gradual: see Astroem, Rundqwist 1989 // this is a fudge for doing MIMO gradual // n.b., went ahead and multiplied 1/T by dt so don\u0026#39;t have to do that here. int_e_awu_adjust_ = k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_); // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_); int_e_ += int_e_awu_adjust_; } // set u to saturated version u_ = u_sat_; } template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::InitVars(size_t control_type) { // initialize to default values u_ref_ = Vector(sys_.n_u(), fill::zeros); u_ref_prev_ = Vector(sys_.n_u(), fill::zeros); x_ref_ = Vector(sys_.n_x(), fill::zeros); y_ref_ = Vector(sys_.n_y(), fill::zeros); cx_ref_ = Vector(sys_.n_y(), fill::zeros); u_ = Vector(sys_.n_u(), fill::zeros); u_return_ = Vector(sys_.n_u(), fill::zeros); u_sat_ = Vector(sys_.n_u(), fill::zeros); // Might not need all these, so zero elements until later. Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros); Kc_u_ = Matrix(0, 0, fill::zeros); Kc_inty_ = Matrix(0, 0, fill::zeros); g_design_ = sys_.g(); // by default, same as model dv_ = Vector(sys_.n_u(), fill::zeros); v_ = Vector(sys_.n_u(), fill::zeros); du_ref_ = Vector(sys_.n_u(), fill::zeros); dv_ref_ = Vector(sys_.n_u(), fill::zeros); v_ref_ = Vector(sys_.n_u(), fill::zeros); int_e_ = Vector(0, fill::zeros); int_e_awu_adjust_ = Vector(0, fill::zeros); set_control_type(control_type); } } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":53,"href":"/lds-ctrl-est/docs/api/files/lds__fit__em_8h/","title":"ldsCtrlEst_h/lds_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_em.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::EM Detailed Description # This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).\nSource code # //===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_EMAX_H #define LDSCTRLEST_LDS_EMAX_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class EM { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: EM() = default; EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); virtual ~EM() = default; const Fit\u0026amp; Run(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = true, bool calc_output = true, bool calc_measurement = true, size_t max_iter = 100, data_t tol = 1e-2); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); // auto tuple = std::make_tuple(u_, z_); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; x() const { return x_; }; const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; y() const { return y_; }; const Matrix\u0026amp; sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; }; const Matrix\u0026amp; sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; }; const Matrix\u0026amp; sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; }; size_t n_t_tot() { return n_t_tot_; } const Vector\u0026amp; theta() const { return theta_; }; protected: void Expectation(bool force_common_initial = false); void Maximization(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = false, bool calc_output = false, bool calc_measurement = false); void MaximizeDynamics(); void MaximizeQ(); void MaximizeInitial(); virtual void MaximizeOutput() = 0; virtual void MaximizeMeasurement() = 0; void Smooth(bool force_common_initial); virtual void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) = 0; void Reset(); void InitVars(); Vector UpdateTheta(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; std::vector\u0026lt;Matrix\u0026gt; x_; std::vector\u0026lt;Cube\u0026gt; P_; std::vector\u0026lt;Cube\u0026gt; P_t_tm1_; std::vector\u0026lt;Matrix\u0026gt; y_; Matrix diag_y_; // expectations calculated in E-step Matrix sum_E_x_t_x_t_; Matrix sum_E_xu_tm1_xu_tm1_; Matrix sum_E_xu_t_xu_tm1_; Fit fit_; Vector theta_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; }; template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; fit_ = Fit(n_u_, n_x, n_y_, dt); u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { // make sure fit dims match I/O data if (fit0.n_u() != u_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and input training data have inconsistent dimensions\u0026#34;); } if (fit0.n_y() != z_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and output training data have inconsistent dimensions\u0026#34;); } fit_ = fit0; u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::InitVars() { // check input/output data dimensions are consistent if (z_.size() != u_.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_.at(trial).n_cols != u_.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } n_u_ = fit_.n_u(); n_x_ = fit_.n_x(); n_y_ = fit_.n_y(); dt_ = fit_.dt(); x_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); P_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); P_t_tm1_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); y_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros); P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros); } diag_y_ = Matrix(n_y_, n_y_, fill::zeros); // covariances in expectation step sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); } template \u0026lt;typename Fit\u0026gt; const Fit\u0026amp; EM\u0026lt;Fit\u0026gt;::Run(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement, size_t max_iter, data_t tol) { Reset(); // to initial conditions size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_; Vector theta(n_params); Vector theta_new(n_params); data_t max_dtheta = 1; // if solving for initial conditions, allow them be varied. // otherwise, freeze at provided values. bool force_common_initial = !calc_init; // go until parameter convergence for (size_t l = 0; l \u0026lt; max_iter; l++) { theta_ = UpdateTheta(); std::cout \u0026lt;\u0026lt; \u0026#34;Iteration \u0026#34; \u0026lt;\u0026lt; l + 1 \u0026lt;\u0026lt; \u0026#34;/\u0026#34; \u0026lt;\u0026lt; max_iter \u0026lt;\u0026lt; \u0026#34; ...\\n\u0026#34;; Expectation(force_common_initial); Maximization(calc_dynamics, calc_Q, calc_init, calc_output, calc_measurement); // check convergence theta_new = UpdateTheta(); Vector dtheta = abs(theta_new - theta_) / abs(theta_); // some parameters could be zero... arma::uvec ubi_finite = find_finite(dtheta); max_dtheta = max(dtheta.elem(ubi_finite)); std::cout \u0026lt;\u0026lt; \u0026#34;max dtheta: \u0026#34; \u0026lt;\u0026lt; max_dtheta \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; if (max_dtheta \u0026lt; tol) { std::cout \u0026lt;\u0026lt; \u0026#34;Converged.\\n\u0026#34;; break; } std::cout \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } return fit_; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Smooth(bool force_common_initial) { Matrix k_e(n_x_, n_y_); // estimator gain Cube k_backfilt; // back-filtering gains // TODO(mfbolus): this loop could be made parallel for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { Matrix x_pre(n_x_, n_t_[trial], fill::zeros); Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros); Matrix x_post(n_x_, n_t_[trial], fill::zeros); Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros); if (force_common_initial) // forces all trials to have same initial // conditions. { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); } y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); // This *should not* be necessary but make sure P is symmetric. ForceSymPD(P_[trial].slice(0)); x_pre.col(0) = x_[trial].col(0); p_pre.slice(0) = P_[trial].slice(0); x_post.col(0) = x_[trial].col(0); p_post.slice(0) = P_[trial].slice(0); // filter for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // predict fit_.f(x_pre, x_post, u_.at(trial), t); fit_.h(y_[trial], x_pre, t); diag_y_.diag() = y_[trial].col(t); // TODO(mfbolus): change if parallel // update --\u0026gt; posterior estimation RecurseKe(k_e, p_pre, p_post, t); x_post.col(t) = x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t)); y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d(); } // backfilter -\u0026gt; Smoothed estimate // Reference: // Shumway et Stoffer (1982) ForceSymPD(p_post.slice(n_t_[trial] - 1)); k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1); P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 0; t--) { // TODO(mfmbolus): should not be necessary to force symm positive def ForceSymPD(p_pre.slice(t)); ForceSymPD(p_post.slice(t - 1)); ForceSymPD(P_[trial].slice(t)); k_backfilt.slice(t - 1) = p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t)); x_[trial].col(t - 1) = x_post.col(t - 1) + k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t)); P_[trial].slice(t - 1) = p_post.slice(t - 1) + k_backfilt.slice(t - 1) * (P_[trial].slice(t) - p_pre.slice(t)) * k_backfilt.slice(t - 1).t(); } // do the same for P_t_tm1 Matrix id(n_x_, n_x_, fill::eye); P_t_tm1_[trial].slice(n_t_[trial] - 1) = (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 1; t--) { P_t_tm1_[trial].slice(t - 1) = p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() + k_backfilt.slice(t - 1) * (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) * k_backfilt.slice(t - 2).t(); } // finally, get smoothed estimate of output for (size_t t = 0; t \u0026lt; n_t_[trial]; t++) { fit_.h(y_[trial], x_[trial], t); } // samps loop } // trial loop } // Smooth // template \u0026lt;typename Fit\u0026gt; // void EM\u0026lt;Fit\u0026gt;::RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) { // // predict covar // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q(); // // update Ke // Ke = P_pre.slice(t) * fit_.C().t() * // inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R()); // // update cov // // Reference: Ghahramani et Hinton (1996) // P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t); // // // n.b. for poisson : // // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + // fit_.Q(); // // // update cov // // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ * // // fit_.C()); // // // update Ke // // Ke = P_post.slice(t) * fit_.C(); // } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Expectation(bool force_common_initial) { // calculate the mean/cov of state needed for maximizing E[pr(z|theta)] Smooth(force_common_initial); // now get the various forms of sum(E[xx\u0026#39;]) needed // n.b. Going to start at t=1 rather than 0 bc most max terms need that. // so really \u0026#34;n_t_tot_\u0026#34; is (n_t_tot_-1) n_t_tot_ = 0; sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); Vector xu_tm1(n_x_ + n_u_, fill::zeros); Vector xu_t(n_x_ + n_u_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // ------------ sum_E_x_t_x_t ------------ sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t(); sum_E_x_t_x_t_ += P_[trial].slice(t); // ------------ sum_E_xu_tm1_xu_tm1 ------------ xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1)); sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t(); sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t - 1); // ------------ sum_E_xu_t_xu_tm1 ------------ xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t)); sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t(); sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_t_tm1_[trial].slice(t); n_t_tot_ += 1; } // time } // trial } // Expectation template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement) { if (calc_output) { MaximizeOutput(); } if (calc_measurement) { MaximizeMeasurement(); } if (calc_dynamics) { MaximizeDynamics(); } if (calc_Q) { MaximizeQ(); } if (calc_init) { MaximizeInitial(); } } // Maximization template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeDynamics() { // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) * inv_sympd(sum_E_xu_tm1_xu_tm1_); fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1)); std::cout \u0026lt;\u0026lt; \u0026#34;A_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.A()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.B()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeQ() { // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 * // inv_sympd(sum_E_xu_tm1_xu_tm1_) * // sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // this way is same as above iff dynamics were just updated: // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix ab = arma::join_horiz(fit_.A(), fit_.B()); // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // From scratch method: // Q is covariance of the error between state and dynamics-predicted state // (aka process noise) // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})\u0026#39;] // t-1 terms: View sum_e_x_tm1_x_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_u_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1); View sum_e_x_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); // t, t-1 terms: View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_x_t_u_tm1 = sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); Matrix q = sum_E_x_t_x_t_; q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t(); q -= sum_e_x_t_x_tm1 * fit_.A().t(); q -= fit_.A() * sum_e_x_t_x_tm1.t(); // input-related terms: q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t(); q -= sum_e_x_t_u_tm1 * fit_.B().t(); q -= fit_.B() * sum_e_x_t_u_tm1.t(); q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t(); q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t(); q /= n_t_tot_; fit_.set_Q(q); std::cout \u0026lt;\u0026lt; \u0026#34;Q_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.Q()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // std::cout \u0026lt;\u0026lt; \u0026#34;Q_new: \\n\u0026#34; \u0026lt;\u0026lt; fit_.Q() \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeInitial() { Vector x0 = fit_.x0(); x0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { x0 += x_[trial].col(0); } x0 /= z_.size(); std::cout \u0026lt;\u0026lt; \u0026#34;x0_new[0]: \u0026#34; \u0026lt;\u0026lt; x0[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // always recalc P0 even if the initial state is fixed (at zero, for // example) Matrix e_var(n_x_, n_x_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t(); } e_var /= z_.size(); // go ahead and subtract x0*x0\u0026#39; so don\u0026#39;t have to below. e_var -= x0 * x0.t(); // To get P0, going to get initial P_ per trial and average. // (which might be wrong, but need a single number) Matrix p0 = fit_.P0(); p0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { p0 += (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var; } p0 /= z_.size(); fit_.set_P0(p0); std::cout \u0026lt;\u0026lt; \u0026#34;P0_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.P0()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeOutput() { // solve for C+d: Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros); Vector x1(n_x_ + 1, fill::zeros); x1[n_x_] = 1.0; // augment with one to solve for bias Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { x1.subvec(0, n_x_ - 1) = x_[trial].col(t); sum_zx += z_.at(trial).col(t) * x1.t(); sum_e_x1_x1 += x1 * x1.t(); sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t); } } Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1); fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1)); fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_))); std::cout \u0026lt;\u0026lt; \u0026#34;C_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.C()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.d()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeMeasurement() { // Solve for measurement noise covar size_t n_t_tot = 0; // Ghahgramani, Hinton 1996: Matrix sum_zz(n_y_, n_y_, fill::zeros); Matrix sum_yz(n_y_, n_y_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t(); // Use Cnew: sum_yz += (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t(); n_t_tot += 1; } } fit_.set_R((sum_zz - sum_yz) / n_t_tot); std::cout \u0026lt;\u0026lt; \u0026#34;R_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.R()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Reset() { // reset to initial conditions for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); } } template \u0026lt;typename Fit\u0026gt; Vector EM\u0026lt;Fit\u0026gt;::UpdateTheta() { // TODO(mfbolus): This should include n_y_ more params for d. size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_; if (fit_.R().n_elem \u0026gt; 0) { n_params += n_y_ * n_y_; } Vector theta(n_params); size_t idx_start = 0; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B()); idx_start += n_x_ * n_u_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0()); idx_start += n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C()); idx_start += n_y_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d()); idx_start += n_y_; if (fit_.R().n_elem \u0026gt; 0) { theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R()); } return theta; } } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":54,"href":"/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_ssid.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SSID Detailed Description # This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)\u0026lt;Fit\u0026gt;).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_FIT_SSID_H #define LDSCTRLEST_LDS_FIT_SSID_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class SSID { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: SSID() = default; SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d = Vector(1).fill(-kInf)); std::tuple\u0026lt;Fit, Vector\u0026gt; Run(SSIDWt ssid_wt); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } protected: void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001); void CreateHankelDataMat(); virtual void DecomposeData() = 0; void CalcSVD(SSIDWt wt); void Solve(data_t wt_dc); void RecomputeExtObs(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; Matrix D_; Fit fit_; Matrix g_dc_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_h_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; Matrix L_; Vector s_; Matrix ext_obs_t_; }; template \u0026lt;typename Fit\u0026gt; SSID\u0026lt;Fit\u0026gt;::SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d) { // check input/output data dimensions are consistent if (z_train.size() != u_train.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_train.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_train.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } dt_ = dt; n_x_ = n_x; n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; n_h_ = n_h; // dimensionality check for eventual block-hankel data matrix size_t len = n_t_tot_ - 2 * n_h_ + 1; if (len \u0026lt; (2 * n_h_ * (n_u_ + n_y_))) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;Dataset problem! More rows than columns in block-hankel data \u0026#34; \u0026#34;matrix: 2*(n_u+n_y)*n_h \u0026gt; data-length! Need higher data-length or \u0026#34; \u0026#34;lower n_h.\u0026#34;; throw std::runtime_error(ss.str()); } fit_ = Fit(n_u_, n_x_, n_y_, dt_); u_ = std::move(u_train); z_ = std::move(z_train); if (!d.is_finite() || (d.n_rows != n_y_)) { // TODO(mfbolus): implement least-square solution for impulse response with // a second input of ones. Data-driven way of accounting for offset *not* // driven by an input. // // For now, calculate output bias (d) as the // output wherever the stimulus has not been on for some amount of time. // convolve u with rectangle and take all samples. This is a reasonable // approach, since often when autonomous systems are fit (i.e., systems with // no input), they will subtract off the mean of the output. This // essentially amounts to setting output bias to the mean of the output when // there is no stimulation. data_t t_silence = 0.1; data_t thresh_silence = 0.001; CalcD(t_silence, thresh_silence); } else { fit_.set_d(d); } } template \u0026lt;typename Fit\u0026gt; std::tuple\u0026lt;Fit, Vector\u0026gt; SSID\u0026lt;Fit\u0026gt;::Run(SSIDWt ssid_wt) { // the weight on minimizing dc I/O gain only works for gaussian, // and hopefully not necessary with appropriate dataset. data_t wt_dc = 0; // std::cout \u0026lt;\u0026lt; \u0026#34;creating hankel mat\\n\u0026#34;; CreateHankelDataMat(); // std::cout \u0026lt;\u0026lt; \u0026#34;decomposing data\\n\u0026#34;; DecomposeData(); // std::cout \u0026lt;\u0026lt; \u0026#34;calculating svd\\n\u0026#34;; CalcSVD(ssid_wt); // std::cout \u0026lt;\u0026lt; \u0026#34;solving for params\\n\u0026#34;; Solve(wt_dc); // std::cout \u0026lt;\u0026lt; \u0026#34;fin\\n\u0026#34;; return std::make_tuple(fit_, s_); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcD(data_t t_silence, data_t thresh_silence) { Vector d(z_.at(0).n_rows, fill::zeros); Vector win(static_cast\u0026lt;size_t\u0026gt;(t_silence / dt_), fill::ones); Vector sum_z_silence(n_y_, fill::zeros); size_t n_silence(0); for (size_t trial = 0; trial \u0026lt; u_.size(); trial++) { // find silent samples // start by convolving with Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0)); Vector u_conv = conv(sum_u, win, \u0026#34;same\u0026#34;); // get only the samples that are silent... arma::uvec ubi_silence = find(u_conv \u0026lt;= thresh_silence); if (ubi_silence.n_elem \u0026gt; 0) { sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1); n_silence += ubi_silence.n_elem; } } if (n_silence \u0026gt; 0) { d = sum_z_silence / n_silence; } fit_.set_d(d); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat() { // temporary copy of data Matrix z(n_y_, n_t_tot_, fill::zeros); Matrix u(n_u_, n_t_tot_, fill::zeros); size_t so_far(0); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial); u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial); so_far += n_t_.at(trial); } // remove output bias z.each_col() -= fit_.d(); // calculate I/O gain @ DC while data in convenient form g_dc_ = z * pinv(u); // std::cout \u0026lt;\u0026lt; \u0026#34;G0_data = \u0026#34; \u0026lt;\u0026lt; g_dc_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // create hankel data matrix size_t len = z.n_cols - 2 * n_h_ + 1; // data length in hankel mat // block-hankel data matrix D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros); // past input auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1); // future input auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1); // past output auto y_p = D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1); // future output auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, len - 1); size_t idx = 0; for (size_t k = 0; k \u0026lt; len; k++) { idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } } D_ /= sqrt(static_cast\u0026lt;data_t\u0026gt;(len)); } // template \u0026lt;typename Fit\u0026gt; // void SSID\u0026lt;Fit\u0026gt;::DecomposeData() { // // do LQ decomp instead of calculating covariance expensive way // // Note that \u0026#34;R\u0026#34; in van Overschee is lower-triangular (L), not \u0026#34;R\u0026#34; in QR // // decomp. Very confusing. // Matrix q_t; // lq(L_, q_t, D_); // // van Overschee zeros out the other elements. // L_ = trimatl(L_); // } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcSVD(SSIDWt wt) { // submats that will be needed: auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1); auto R_23_13 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1); auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_) - 1); auto R_44 = L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14); auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1); auto Luf = Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1); auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); // aka: R_f Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1); // from van Overschee subid.m: // Rf = R((2*m+l)*i+1:2*(m+l)*i,:); % Future outputs Matrix U; Matrix V; switch (wt) { case kSSIDNone: { // No weighting. (what van Overschee calls \u0026#34;N4SID\u0026#34;) Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14; arma::svd(U, s_, V, O_k_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDMOESP: { // MOESP weighting // This is what they use in the \u0026#34;robust\u0026#34; algorithm van Overschee, de Moor // 1996 Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); svd(U, s_, V, O_k_ortho_Uf_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDCVA: { // CVA weighting // See van Overschee\u0026#39;s matlab code (subid.m): // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); Matrix inv_w1; Matrix qt1; lq(inv_w1, qt1, R_56_16); // lq decomp of R_f (future output data) inv_w1 = trimatl(inv_w1); inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1); Matrix w_o_w = arma::solve( inv_w1, O_k_ortho_Uf_sans_Qt); // alternatively // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt svd(U, s_, V, w_o_w, \u0026#34;std\u0026#34;); U = inv_w1 * U; break; } } // Truncate to model order (heart of ssid method) auto s_hat = s_.subvec(0, n_x_ - 1); Matrix diag_sqrt_s = diagmat(sqrt(s_hat)); auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1); // get extended observability and controllability mats ext_obs_t_ = u_hat * diag_sqrt_s; // extended observability matrix } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::Solve(data_t wt_dc) { // required submats auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_56_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); // Solve for params using appropriate algorithm: // robust deterministic/stochastic algorithm in van Overschee 1996 // algorithm that the authors say \u0026#34;works\u0026#34; in practice. auto ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix // This is what textbook (1996) says: // // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15); // // HOWEVER, do not know why but have to fill the last place with zeros like // authors\u0026#39; matlab implementation (see `subid.m`) // Otherwise, get ridiculous covariances (although A,C estimates are close to // same...) Matrix Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Matrix S = Tl * pinv(Tr); // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure // stability. fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1)); Matrix ext_obs_t_p1 = join_vert( ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1), Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros)); fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1); // At this point, van Overschee \u0026amp; de Moor suggest re-calculating ext_obs_t_, // ext_obs_tm1 from (A, C) because it was just an approximation. This is RecomputeExtObs(); ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); S = Tl * pinv(Tr); Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_); Matrix Mcurly = pinv(ext_obs_tm1); Matrix Pcurly = Tl - Lcurly * R_56_15; Vector Pvec = vectorise(Pcurly); Matrix Qcurly = R_23_15; // Identify [D; B], assuming D=0 and ensuring DC gain is correct Matrix sum_QcurlyT_kron_Ncurly( (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_), fill::zeros); Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols, fill::eye); eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1, eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1; // van Overschee (1996) p. 126 Matrix N1_Tl = -Lcurly; N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) += join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly); N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) += Matrix(n_y_, n_y_, fill::eye); Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros); Matrix N_k; for (size_t k = 0; k \u0026lt; n_h_; k++) { auto Qcurly_k = Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1); Nk_Tl.zeros(); Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) = N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1); N_k = Nk_Tl * eye_ext_obs_tm1; sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k); } Matrix err_vec; if (wt_dc \u0026gt; 0) { // Constraints enforced by weighted least squares // // Reference: // // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly // Excited Industrial Systems. Conference in Decision and Control. // constraint 1: assume D=0 --\u0026gt; remove the components for Dvec (this is // actually a hard constraint in that it ignores D) Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly; sum_QcurlyT_kron_Ncurly = Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_); size_t kkk = 0; for (size_t k = 1; k \u0026lt; (n_u_ + 1); k++) { size_t start_idx = k * (n_y_ + n_x_) - n_x_; for (size_t kk = 0; kk \u0026lt; n_x_; kk++) { sum_QcurlyT_kron_Ncurly.col(kkk) = sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk); kkk++; } } // constraint 2: Make sure DC I/O gain is correct Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A()); Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_)); Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0); Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 = join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0); // WEIGHTED LS // Important in practice because I care a lot about at least getting the DC // gain correct. Put x weight on minimizing error at DC, relative to others Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye); // Make weight on minimizing DC error immense so at least that // should be nailed. size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows; size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows; size_t stop_row = w.n_rows - 1; size_t stop_col = w.n_cols - 1; // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale // weight with data length? w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc; Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * sum_QcurlyT_kron_Ncurly_b_to_g0) * sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec; fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_)); // Calculate residuals and their cov. // Because I\u0026#39;ve added constraints, I need to re-calculate the right term // with b_vec instead of how van Overschee do in final algorithm. err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec; } else { // default way: *no* constraint on G0 or D=0 Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec; // TODO(mfbolus) n.b., this gets thrown away... // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_); fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_)); err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec; } // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols); // TODO(mfbolus): Something is wrong with the error calculation above. // Use the way van overschee does it in `subid.m` // WARNING: this ignores any above constraints, so Q, R will be approximate... Matrix err = Tl - S * Tr; Matrix cov_err = err * err.t(); fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1)); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::RecomputeExtObs() { ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C(); for (size_t k = 2; k \u0026lt; (n_h_ + 1); k++) { ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) = ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1, ext_obs_t_.n_cols - 1) * fit_.A(); } } } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":55,"href":"/lds-ctrl-est/docs/api/files/lds__fit_8h/","title":"ldsCtrlEst_h/lds_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_fit.h # LDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Fit LDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.\nSource code # //===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDS_FIT_HPP #define LDS_FIT_HPP // namespace #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); virtual ~Fit() = default; // get methods size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; Q() const { return Q_; }; const Vector\u0026amp; x0() const { return x0_; }; const Matrix\u0026amp; P0() const { return P0_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; // gets measurement noise virtual const Matrix\u0026amp; R() const = 0; // set methods (e.g., seeding initial fit values) void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_m(const Vector\u0026amp; m) { Reassign(m_, m); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); ForceSymPD(Q_); }; virtual void set_R(const Matrix\u0026amp; R) = 0; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); ForceSymPD(P0_); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; View f(Matrix\u0026amp; x, const Matrix\u0026amp; u, size_t t) { x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x.col(t); }; View f(Matrix\u0026amp; x_pre, const Matrix\u0026amp; x_post, const Matrix\u0026amp; u, size_t t) { x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x_pre.col(t); }; virtual View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) = 0; protected: data_t dt_{}; // Dynamics Matrix A_; Matrix B_; Vector g_; Vector m_; Matrix Q_; // Output Matrix C_; Vector d_; Matrix R_; // initial conditions Vector x0_; Matrix P0_; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; }; } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":56,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_ctrl.h # GLDS Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. Detailed Description # This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_gaussian_sys.h\u0026#34; // controller #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace gaussian { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); cx_ref_ = y_ref - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; }; } // namespace gaussian } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":57,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_em.h # GLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitEM GLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; }; } // namespace gaussian } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":58,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_ssid.h # GLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; using SSID\u0026lt;Fit\u0026gt;::Run; private: using SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat; using SSID\u0026lt;Fit\u0026gt;::CalcSVD; using SSID\u0026lt;Fit\u0026gt;::Solve; void DecomposeData() override; void SolveVanOverschee(); }; } // namespace gaussian } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":59,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit.h # GLDS fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Fit GLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // fit type #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace gaussian { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); const Matrix\u0026amp; R() const override { return R_; }; void set_R(const Matrix\u0026amp; R) override { Reassign(R_, R); ForceSymPD(R_); }; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = C_ * x.col(t) + d_; return y.col(t); }; }; }; // namespace gaussian } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":60,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sctrl.h # GLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H // controller type #include \u0026#34;lds_gaussian_ctrl.h\u0026#34; // switched controller #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace gaussian { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); cx_ref_ = y_ref - sys_.d(); } // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; }; // SwitchedController } // namespace gaussian } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":61,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/","title":"ldsCtrlEst_h/lds_gaussian_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sys.h # GLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::System Gaussian LDS Type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H #define LDSCTRLEST_LDS_GAUSSIAN_SYS_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; namespace lds { namespace gaussian { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0, data_t r0 = kDefaultR0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; // get methods const Matrix\u0026amp; R() const { return R_; }; // set methods void set_Q(const Matrix\u0026amp; Q) { lds::System::set_Q(Q); do_recurse_Ke_ = true; } void set_R(const Matrix\u0026amp; R) { Reassign(R_, R); do_recurse_Ke_ = true; }; void set_Ke(const Matrix\u0026amp; Ke) { Reassign(Ke_, Ke); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void set_Ke_m(const Matrix\u0026amp; Ke_m) { Reassign(Ke_m_, Ke_m); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void Print(); protected: void h() override { cx_ = C_ * x_; y_ = cx_ + d_; }; Vector h_(Vector x) override { return C_ * x + d_; }; void RecurseKe() override; // Gaussian-output-specific Matrix R_; bool do_recurse_Ke_{}; }; // System } // namespace gaussian } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":62,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian_8h/","title":"ldsCtrlEst_h/lds_gaussian.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian.h # glds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_H #define LDSCTRLEST_LDS_GAUSSIAN_H // namespace #include \u0026#34;lds.h\u0026#34; namespace lds { namespace gaussian { // insert any Gaussian-specific things here... } // namespace gaussian } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":63,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_ctrl.h # PLDS controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. Detailed Description # This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_CTRL_H #define LDSCTRLEST_LDS_POISSON_CTRL_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system type #include \u0026#34;lds_poisson_sys.h\u0026#34; // control type #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace poisson { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); lds::Limit(y_ref_, kYRefLb, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; private: constexpr static const data_t kYRefLb = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":64,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_em.h # PLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitEM PLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\n[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H #define LDSCTRLEST_LDS_POISSON_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override{}; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; data_t NewtonSolveC(); void AnalyticalSolveD(); }; } // namespace poisson } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":65,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_ssid.h # PLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. Detailed Description # This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/)).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H #define LDSCTRLEST_LDS_POISSON_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; private: void DecomposeData() override; void CalcCov(); void PoissonToGaussianMoments(); Matrix cov_; }; } // namespace poisson } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":66,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/","title":"ldsCtrlEst_h/lds_poisson_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit.h # PLDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Fit PLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_H #define LDSCTRLEST_LDS_POISSON_FIT_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // fit #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace poisson { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) : lds::Fit(n_u, n_x, n_y, dt){}; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = exp(C_ * x.col(t) + d_); return y.col(t); }; void set_R(const Matrix\u0026amp; R) override { std::cerr \u0026lt;\u0026lt; \u0026#34;WARNING: Cannot set R (R[0] = \u0026#34; \u0026lt;\u0026lt; R.at(0) \u0026lt;\u0026lt; \u0026#34;). No Gaussian measurement noise in Poisson observation model.\\n\u0026#34;; }; const Matrix\u0026amp; R() const override { return R_; }; }; }; // namespace poisson } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":67,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sctrl.h # PLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #include \u0026#34;lds_poisson_ctrl.h\u0026#34; #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace poisson { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); lds::Limit(y_ref_, kYRefLB, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; private: constexpr static data_t kYRefLB = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":68,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/","title":"ldsCtrlEst_h/lds_poisson_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sys.h # PLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::System Poisson System type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SYS_H #define LDSCTRLEST_LDS_POISSON_SYS_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; // needed for Poisson random number generation #include \u0026lt;random\u0026gt; namespace lds { namespace poisson { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; protected: void h() override { cx_ = C_ * x_; y_ = exp(cx_ + d_); diag_y_.diag() = y_; }; Vector h_(Vector x) override { return exp(C_ * x + d_); }; void RecurseKe() override; private: // Poisson-output-specific Matrix diag_y_; std::poisson_distribution\u0026lt;size_t\u0026gt; pd_; }; // System } // namespace poisson } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":69,"href":"/lds-ctrl-est/docs/api/files/lds__poisson_8h/","title":"ldsCtrlEst_h/lds_poisson.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson.h # plds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_H #define LDSCTRLEST_LDS_POISSON_H #include \u0026#34;lds.h\u0026#34; namespace lds { namespace poisson { // TODO(mfbolus): Not sure if defining these as static here makes the most // sense. Is there a downside to letting multiple poisson System objects share a // common random number generator? static std::random_device rd; static std::mt19937 rng = std::mt19937( rd()); } // namespace poisson } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":70,"href":"/lds-ctrl-est/docs/api/files/lds__sctrl_8h/","title":"ldsCtrlEst_h/lds_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_sctrl.h # SwitchedController type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SwitchedController SwitchedController Type. Detailed Description # This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SCTRL_H #define LDSCTRLEST_LDS_SCTRL_H #include \u0026#34;lds_ctrl.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; #include \u0026#34;lds_uniform_vecs.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class SwitchedController : public Controller\u0026lt;System\u0026gt; { public: SwitchedController() = default; SwitchedController(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); SwitchedController(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); void Switch(size_t idx, bool do_force_switch = false); void set_Kc(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc) { Kc_list_ = Kc; Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc) { Kc_list_ = std::move(Kc); Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_inty) { Kc_inty_list_ = Kc_inty; Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_inty) { Kc_inty_list_ = std::move(Kc_inty); Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_u) { Kc_u_list_ = Kc_u; Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_u) { Kc_u_list_ = std::move(Kc_u); Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(const UniformVectorList\u0026amp; g) { g_design_list_ = g; g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(UniformVectorList\u0026amp;\u0026amp; g) { g_design_list_ = std::move(g); g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; protected: std::vector\u0026lt;System\u0026gt; systems_; size_t n_sys_{}; size_t idx_{}; // controller gains could be different for each UniformMatrixList\u0026lt;\u0026gt; Kc_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_inty_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_u_list_; // design-phase input gain could also be different UniformVectorList g_design_list_; // TODO(mfbolus): not sure why I need to do this. using Controller\u0026lt;System\u0026gt;::Kc_; using Controller\u0026lt;System\u0026gt;::Kc_inty_; using Controller\u0026lt;System\u0026gt;::Kc_u_; using Controller\u0026lt;System\u0026gt;::g_design_; using Controller\u0026lt;System\u0026gt;::sys_; // using Controller\u0026lt;System\u0026gt;::u_ref_; // using Controller\u0026lt;System\u0026gt;::x_ref_; // using Controller\u0026lt;System\u0026gt;::y_ref_; // using Controller\u0026lt;System\u0026gt;::control_type_; private: void InitVars(); using lds::Controller\u0026lt;System\u0026gt;::set_sys; // using Controller\u0026lt;System\u0026gt;::set_Kc; // using Controller\u0026lt;System\u0026gt;::set_Kc_inty; // using Controller\u0026lt;System\u0026gt;::set_Kc_u; // using Controller\u0026lt;System\u0026gt;::set_g_design; }; template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(systems.at(0), u_lb, u_ub, control_type), systems_(systems) { InitVars(); } template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(System(systems.at(0).n_u(), systems.at(0).n_x(), systems.at(0).n_y(), systems.at(0).dt()), u_lb, u_ub, control_type), systems_(std::move(systems)) { InitVars(); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::InitVars() { n_sys_ = systems_.size(); sys_ = systems_.at(0); Kc_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_)); Kc_inty_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); Kc_u_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); g_design_list_ = UniformVectorList(std::vector\u0026lt;Vector\u0026gt;(n_sys_, g_design_)); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::Switch(size_t idx, bool do_force_switch) { if ((idx == idx_) \u0026amp;\u0026amp; !do_force_switch) { return; // already there. } // put old up and get new one out systems_.at(idx_) = std::move(sys_); sys_ = std::move(systems_.at(idx)); // set the state of this system to that of the previous system // TODO(mfbolus): This will only work as intended if state matrix is the same. // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp. sys_.set_m(systems_.at(idx_).m(), true); sys_.set_x(systems_.at(idx_).x()); // swap controller gains Kc_list_.Swap(Kc_, idx_); Kc_list_.Swap(Kc_, idx); if (control_type_ \u0026amp; kControlTypeIntY) { Kc_inty_list_.Swap(Kc_inty_, idx_); Kc_inty_list_.Swap(Kc_inty_, idx); } if (control_type_ \u0026amp; kControlTypeDeltaU) { Kc_u_list_.Swap(Kc_u_, idx_); Kc_u_list_.Swap(Kc_u_, idx); } g_design_list_.Swap(g_design_, idx_); g_design_list_.Swap(g_design_, idx); idx_ = idx; } // Switch } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":71,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8h/","title":"ldsCtrlEst_h/lds_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_sys.h # LDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::System Linear Dynamical System Type. Detailed Description # This file declares and partially defines the base type for linear dynamical systems ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SYS_H #define LDSCTRLEST_LDS_SYS_H #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class System { public: System() = default; System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); virtual ~System() {} void Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z); virtual const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) = 0; void f(const Vector\u0026amp; u, bool do_add_noise = false) { x_ = A_ * x_ + B_ * (g_ % u) + m_; if (do_add_noise) { x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_); } }; virtual void h() = 0; virtual Vector h_(Vector x) = 0; size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Vector\u0026amp; x() const { return x_; }; const Matrix\u0026amp; P() const { return P_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; P_m() const { return P_m_; }; const Vector\u0026amp; cx() const { return cx_; }; const Vector\u0026amp; y() const { return y_; }; const Vector\u0026amp; x0() const { return x0_; }; const Vector\u0026amp; m0() const { return m0_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; const Matrix\u0026amp; Ke() const { return Ke_; }; const Matrix\u0026amp; Ke_m() const { return Ke_m_; }; const Matrix\u0026amp; Q() { return Q_; }; const Matrix\u0026amp; Q_m() { return Q_m_; }; const Matrix\u0026amp; P0() { return P0_; }; const Matrix\u0026amp; P0_m() { return P0_m_; }; void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_m(const Vector\u0026amp; m, bool do_force_assign = false) { Reassign(m0_, m); if ((!do_adapt_m) || do_force_assign) { Reassign(m_, m); } }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); }; void set_Q_m(const Matrix\u0026amp; Q_m) { Reassign(Q_m_, Q_m); }; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); }; void set_P0_m(const Matrix\u0026amp; P0_m) { Reassign(P0_m_, P0_m); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; void set_x(const Vector\u0026amp; x) { Reassign(x_, x); h(); }; void Reset(); std::vector\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; nstep_pred_block( UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z, size_t n_pred = 1); void Print(); // safe to leave this public and non-const bool do_adapt_m{}; protected: virtual void RecurseKe() = 0; void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); std::size_t n_x_{}; std::size_t n_u_{}; std::size_t n_y_{}; data_t dt_{}; // Signals: Vector x_; Matrix P_; Vector m_; Matrix P_m_; Vector cx_; Vector y_; Vector z_; // Parameters: Vector x0_; Matrix P0_; Vector m0_; Matrix P0_m_; Matrix A_; Matrix B_; Vector g_; Matrix Q_; Matrix Q_m_; Matrix C_; Vector d_; Matrix Ke_; Matrix Ke_m_; }; // System } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":72,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/","title":"ldsCtrlEst_h/lds_uniform_mats.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_mats.h # List of uniformly sized matrices. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformMatrixList Detailed Description # This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H #define LDSCTRLEST_LDS_UNIFORM_MATS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { template \u0026lt;MatrixListFreeDim D = kMatFreeDimNone\u0026gt; class UniformMatrixList : public std::vector\u0026lt;Matrix\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Matrix\u0026gt;::vector; // don\u0026#39;t allow push_back to be used since it doesn\u0026#39;t check dims using std::vector\u0026lt;Matrix\u0026gt;::push_back; public: using std::vector\u0026lt;Matrix\u0026gt;::operator=; using std::vector\u0026lt;Matrix\u0026gt;::operator[]; using std::vector\u0026lt;Matrix\u0026gt;::begin; using std::vector\u0026lt;Matrix\u0026gt;::end; using std::vector\u0026lt;Matrix\u0026gt;::size; using std::vector\u0026lt;Matrix\u0026gt;::at; UniformMatrixList() = default; explicit UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); explicit UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; ~UniformMatrixList() = default; const std::array\u0026lt;size_t, 2\u0026gt;\u0026amp; dim(size_t n = 0) const { return dim_.at(n); } size_t size() { return std::vector\u0026lt;Matrix\u0026gt;::size(); }; const Matrix\u0026amp; at(size_t n) { return std::vector\u0026lt;Matrix\u0026gt;::at(n); }; void Swap(Matrix\u0026amp; that, size_t n); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; void append(const Matrix\u0026amp; mat); private: void CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim); std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt; dim_; }; template \u0026lt;MatrixListFreeDim D\u0026gt; inline void UniformMatrixList\u0026lt;D\u0026gt;::Swap(Matrix\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.n_cols); } if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap // not moving, since it causes memory issues. // so this method isn\u0026#39;t a memory-saver as designed for now Matrix tmp = (*this)[n]; (*this)[n] = that; that = tmp; if (D == kMatFreeDim1) { this-\u0026gt;dim_[n][0] = (*this)[n].n_rows; } if (D == kMatFreeDim2) { this-\u0026gt;dim_[n][1] = (*this)[n].n_cols; } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::append(const Matrix\u0026amp; mat) { std::array\u0026lt;size_t, 2\u0026gt; dim({mat.n_rows, mat.n_cols}); CheckDimensions(dim); std::vector\u0026lt;Matrix\u0026gt;::push_back(mat); dim_.push_back(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) { // make sure dim_ vector is initialized if (dim_.empty()) { dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(that.size(), {0, 0}); } // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices\u0026#34;; throw std::runtime_error(ss.str()); } // if dimensions a not zero and do not match, skip move with error message. bool dims_nonzero = true; for (auto d : dim_) { if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; d[0] \u0026lt; 1) { dims_nonzero = false; break; } if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; d[1] \u0026lt; 1) { dims_nonzero = false; break; } } if (dims_nonzero) { bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); } if (!does_match) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrices of size \u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; dim_[k] = that.dim(k); } return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. bool dims_nonzero = true; for (auto d : dim_) { // if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; (d[0] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; (d[1] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // } // // if (dims_nonzero) { // bool does_match = true; // if (!(D == kMatFreeDim1)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); // } // // if (!(D == kMatFreeDim2)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); // } // // if (!does_match) { // this-\u0026gt;at(0).print(\u0026#34;this[0] = \u0026#34;); // that.at(0).print(\u0026#34;that[0] = \u0026#34;); // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformMatrixList element of size (\u0026#34; \u0026lt;\u0026lt; // that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols \u0026lt;\u0026lt; \u0026#34;) for an // element of size (\u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34;). // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Matrix\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(std::move(mats)) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) : vector(that) { (*this) = that; } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { std::array\u0026lt;size_t, 2\u0026gt; dim_k({this-\u0026gt;at(k).n_rows, this-\u0026gt;at(k).n_cols}); dim_.push_back(dim_k); } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim) { // change behavior based on free dim D if ((dim[0] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim1)) { dim[0] = this-\u0026gt;at(0).n_rows; } if ((dim[1] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim2)) { dim[1] = this-\u0026gt;at(0).n_cols; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Matrix\u0026amp; mat : *this) { if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_rows == dim[0]); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_cols == dim[1]); } if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(this-\u0026gt;size(), dim); for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { dim_[k][0] = (*this)[k].n_rows; dim_[k][1] = (*this)[k].n_cols; } } } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":73,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/","title":"ldsCtrlEst_h/lds_uniform_systems.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_systems.h # List of uniformly sized Systems. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformSystemList Detailed Description # This file provides a container for uniformly sized Systems.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector // namespace #include \u0026#34;lds.h\u0026#34; // System type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class UniformSystemList : public std::vector\u0026lt;System\u0026gt; { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;System\u0026gt;::vector; using std::vector\u0026lt;System\u0026gt;::operator=; using std::vector\u0026lt;System\u0026gt;::operator[]; using std::vector\u0026lt;System\u0026gt;::at; using std::vector\u0026lt;System\u0026gt;::begin; using std::vector\u0026lt;System\u0026gt;::end; using std::vector\u0026lt;System\u0026gt;::size; public: UniformSystemList() = default; explicit UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); explicit UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(const UniformSystemList\u0026amp; that); UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept; ~UniformSystemList() = default; const std::array\u0026lt;size_t, 3\u0026gt;\u0026amp; dim() const { return dim_; } size_t size() { return std::vector\u0026lt;System\u0026gt;::size(); }; const System\u0026amp; at(size_t n) { return std::vector\u0026lt;System\u0026gt;::at(n); }; void Swap(System\u0026amp; that, size_t n); UniformSystemList\u0026amp; operator=(const UniformSystemList\u0026amp; that); UniformSystemList\u0026amp; operator=(UniformSystemList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim); std::array\u0026lt;size_t, 3\u0026gt; dim_{}; }; template \u0026lt;typename System\u0026gt; inline void UniformSystemList\u0026lt;System\u0026gt;::Swap(System\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformSystemList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = (dim_[0] == that.n_u()) \u0026amp;\u0026amp; (dim_[1] == that.n_x()) \u0026amp;\u0026amp; (dim_[2] == that.n_y()); if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformSystemList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap System tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( const UniformSystemList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_[0] + dim_[1] + dim_[2]) { std::array\u0026lt;size_t, 3\u0026gt; other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign systems of size \u0026#34; \u0026lt;\u0026lt; dim_[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2] \u0026lt;\u0026lt; \u0026#34; with systems of size \u0026#34; \u0026lt;\u0026lt; other_dim[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other_dim[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2]; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( UniformSystemList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. if (dim_[0] + dim_[1] + dim_[2]) { // bool does_match = (dim_[0] == that.at(0).n_u()) \u0026amp;\u0026amp; // (dim_[1] == that.at(0).n_x()) \u0026amp;\u0026amp; // (dim_[2] == that.at(0).n_y()); // if (!does_match) { // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformSystemList element for an element of \u0026#34; // \u0026#34;different size. Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;System\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(std::move(systems)) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList( std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const UniformSystemList\u0026amp; that) : std::vector\u0026lt;System\u0026gt;(that) { (*this) = that; } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept : std::vector\u0026lt;System\u0026gt;(std::move(that)) { this-\u0026gt;dim_[0] = this-\u0026gt;at(0).n_u(); this-\u0026gt;dim_[1] = this-\u0026gt;at(0).n_x(); this-\u0026gt;dim_[2] = this-\u0026gt;at(0).n_y(); } template \u0026lt;typename System\u0026gt; void UniformSystemList\u0026lt;System\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim) { if (dim[0] + dim[1] + dim[2]) { dim_ = dim; } else { dim_[0] = this-\u0026gt;at(0).n_u(); dim_[1] = this-\u0026gt;at(0).n_x(); dim_[2] = this-\u0026gt;at(0).n_y(); } // make sure dimensiolaties are all uniform bool does_match(true); for (const System\u0026amp; sys : *this) { does_match = does_match \u0026amp;\u0026amp; (sys.n_u() == dim_[0]); does_match = does_match \u0026amp;\u0026amp; (sys.n_x() == dim_[1]); does_match = does_match \u0026amp;\u0026amp; (sys.n_y() == dim_[2]); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input systems are not uniform.\u0026#34;); } } } } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":74,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/","title":"ldsCtrlEst_h/lds_uniform_vecs.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_vecs.h # List of uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformVectorList Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H #define LDSCTRLEST_LDS_UNIFORM_VECS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { class UniformVectorList : public std::vector\u0026lt;Vector\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Vector\u0026gt;::vector; using std::vector\u0026lt;Vector\u0026gt;::operator=; using std::vector\u0026lt;Vector\u0026gt;::operator[]; using std::vector\u0026lt;Vector\u0026gt;::at; using std::vector\u0026lt;Vector\u0026gt;::begin; using std::vector\u0026lt;Vector\u0026gt;::end; using std::vector\u0026lt;Vector\u0026gt;::size; public: UniformVectorList() = default; explicit UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim = 0); explicit UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim = 0); UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim = 0); UniformVectorList(const UniformVectorList\u0026amp; that); UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept; ~UniformVectorList() = default; size_t dim() const { return dim_; } size_t size() { return std::vector\u0026lt;Vector\u0026gt;::size(); }; const Vector\u0026amp; at(size_t n) { return std::vector\u0026lt;Vector\u0026gt;::at(n); }; void Swap(Vector\u0026amp; that, size_t n); UniformVectorList\u0026amp; operator=(const UniformVectorList\u0026amp; that); UniformVectorList\u0026amp; operator=(UniformVectorList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(size_t dim); size_t dim_{}; }; inline void UniformVectorList::Swap(Vector\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = dim_ == that.n_elem; if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap Vector tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( const UniformVectorList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_) { size_t other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ \u0026lt;\u0026lt; \u0026#34; with vectors of size \u0026#34; \u0026lt;\u0026lt; other_dim; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( UniformVectorList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors. Skipping.\\n\u0026#34;; // return (*this); // } // // if (dim_) { // size_t other_dim(that.dim()); // if (dim_ != other_dim) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ // \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; other_dim \u0026lt;\u0026lt; \u0026#34;. // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Vector\u0026gt;::operator=(std::move(that)); return (*this); } } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":75,"href":"/lds-ctrl-est/docs/api/files/lds_8h/","title":"ldsCtrlEst_h/lds.h","section":"Files","content":" ldsCtrlEst_h/lds.h # lds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)) observations.\nSource code # //===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_H #define LDSCTRLEST_LDS_H // #ifndef LDSCTRLEST // #include \u0026lt;ldsCtrlEst\u0026gt; // #endif #include \u0026lt;armadillo\u0026gt; namespace lds { using data_t = double; // may change to float (but breaks mex functions) using Vector = arma::Col\u0026lt;data_t\u0026gt;; using Matrix = arma::Mat\u0026lt;data_t\u0026gt;; using Cube = arma::Cube\u0026lt;data_t\u0026gt;; using View = arma::subview\u0026lt;data_t\u0026gt;; namespace fill = arma::fill; static const std::size_t kControlTypeDeltaU = 0x1; static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); static const data_t kPi = arma::datum::pi; static const data_t kDefaultP0 = 1e-6; static const data_t kDefaultQ0 = 1e-6; static const data_t kDefaultR0 = 1e-2; enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA }; enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2 }; // TODO(mfbolus): for SwitchedController, may want systems to have differing // numbers of states. Use this enum as template parameter? // enum SystemListFreeDim { // kSysFreeDimNone, // kSysFreeDimX ///\u0026lt; allow state dim (x) of systems in list to be hetero // }; // place hard limits on contents of vecors/mats void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub); void Limit(Vector\u0026amp; x, data_t lb, data_t ub); void Limit(Matrix\u0026amp; x, data_t lb, data_t ub); // in-place assign that errs if there are dimension mismatches: void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); // TODO(mfbolus): this is a fudge, but for some reason, cov mats often going // numerically asymm. void ForceSymPD(Matrix\u0026amp; X); void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min = 0); void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X); Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B); inline void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Vector\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Matrix\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if (other.n_elem != some.n_elem) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vector of size \u0026#34; \u0026lt;\u0026lt; some.n_elem \u0026lt;\u0026lt; \u0026#34; with vector of size \u0026#34; \u0026lt;\u0026lt; other.n_elem \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } inline void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrix of size \u0026#34; \u0026lt;\u0026lt; some.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; some.n_cols \u0026lt;\u0026lt; \u0026#34; with matrix of size \u0026#34; \u0026lt;\u0026lt; other.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other.n_cols \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } } // namespace lds #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":76,"href":"/lds-ctrl-est/docs/api/files/mex__c__util_8h/","title":"ldsCtrlEst_h/mex_c_util.h","section":"Files","content":" ldsCtrlEst_h/mex_c_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) More\u0026hellip;\nNamespaces # Name armamexc arma/mex interface using Matlab C API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab/Octave\u0026rsquo;s C mex API.\nSource code # //===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXC_UTIL_H #define LDSCTRLEST_MEXC_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.h\u0026#34; // // If Matlab_FOUND, include matrix.h. // // (Octave does not need/have it.) // #ifdef Matlab_FOUND // #include \u0026#34;matrix.h\u0026#34; // #endif namespace armamexc { template \u0026lt;class T\u0026gt; inline auto m2T_scalar(const mxArray *matlab_scalar) -\u0026gt; T { if (mxGetData(matlab_scalar)) { return static_cast\u0026lt;T\u0026gt;(mxGetScalar(matlab_scalar)); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return 0; } template \u0026lt;class T\u0026gt; inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false, bool strict = true) -\u0026gt; arma::Mat\u0026lt;T\u0026gt; { if (mxGetData(matlab_mat)) { const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat); if (n_dim == 2) { return arma::Mat\u0026lt;T\u0026gt;(static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)), mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem, strict); } mexErrMsgTxt(\u0026#34;Number of dimensions must be 2.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } // TODO(mfbolus): make these templated. template \u0026lt;typename T\u0026gt; inline auto a2m_mat(arma::Mat\u0026lt;T\u0026gt; const \u0026amp;arma_mat) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_mat.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Mat.\u0026#34;); return nullptr; } template \u0026lt;typename T\u0026gt; inline auto a2m_vec(arma::Col\u0026lt;T\u0026gt; const \u0026amp;arma_vec) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_vec.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Col.\u0026#34;); return nullptr; } } // namespace armamexc #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":77,"href":"/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/","title":"ldsCtrlEst_h/mex_cpp_util.h","section":"Files","content":" ldsCtrlEst_h/mex_cpp_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) More\u0026hellip;\nNamespaces # Name armamexcpp arma/mex interface using Matlab C++ API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab\u0026rsquo;s C++ mex API.\nSource code # //===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXCPP_UTIL_H #define LDSCTRLEST_MEXCPP_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.hpp\u0026#34; #include \u0026#34;mexAdapter.hpp\u0026#34; namespace armamexcpp { template \u0026lt;class T\u0026gt; std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; m2a_cellmat(matlab::data::CellArray\u0026amp; matlab_cell) { size_t n_cells = matlab_cell.getNumberOfElements(); std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; arma_mat(n_cells, arma::Mat\u0026lt;T\u0026gt;(1, 1, arma::fill::zeros)); for (size_t k = 0; k \u0026lt; n_cells; k++) { matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = matlab_cell[k]; auto dims = matlab_mat.getDimensions(); arma_mat[k] = arma::Mat\u0026lt;T\u0026gt;(matlab_mat.release().get(), dims[0], dims[1]); } return arma_mat; }; template \u0026lt;class T\u0026gt; std::vector\u0026lt;T\u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt;T\u0026gt;\u0026amp; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); T* ptr = matlab_array.release().get(); std::vector\u0026lt;T\u0026gt; vec(ptr, ptr + n_elem); return vec; }; template \u0026lt;class T\u0026gt; arma::Col\u0026lt;T\u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); // T* ptr = matlab_array.release().get(); // arma::Col\u0026lt;T\u0026gt; vec(ptr, n_elem); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? arma::Col\u0026lt;T\u0026gt; vec(n_elem, arma::fill::zeros); for (size_t k = 0; k \u0026lt; n_elem; k++) { vec[k] = matlab_array[k]; } return vec; }; template \u0026lt;class T\u0026gt; arma::Mat\u0026lt;T\u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { // ArrayDimensions == std::vector\u0026lt;size_t\u0026gt; auto dims = matlab_array.getDimensions(); // T* ptr = matlab_array.release().get(); // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false) // arma::Mat\u0026lt;T\u0026gt; mat(ptr, dims[0], dims[1]); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? // // armadillo and matlab both use column-major ordering, so this should work: size_t n_elem = dims[0] * dims[1]; arma::Mat\u0026lt;T\u0026gt; mat(dims[0], dims[1], arma::fill::zeros); size_t k(0); for (auto m: matlab_array) { mat[k] = m; k++; } return mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_mat(const arma::Mat\u0026lt;T\u0026gt;\u0026amp; arma_mat, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(), arma_mat.memptr() + arma_mat.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_vec(const arma::Col\u0026lt;T\u0026gt;\u0026amp; arma_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;({arma_vec.n_elem, 1}, arma_vec.memptr(), arma_vec.memptr() + arma_vec.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; s2m_vec(const std::vector\u0026lt;T\u0026gt;\u0026amp; std_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size()); return matlab_mat; }; } // namespace armamexcpp #endif Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":78,"href":"/lds-ctrl-est/docs/terminology/model/","title":"Models","section":"LDS C+E Documentation","content":" Model Definitions # This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form: \\[\\mathbf{x}_{t\u0026#43;1} = f\\left( \\mathbf{x}_{t}, \\mathbf{v}_{t} \\right) = \\mathbf{A} \\mathbf{x}_{t} \u0026#43; \\mathbf{B} \\mathbf{v}_{t} \u0026#43; \\mathbf{m}_{t} \u0026#43; \\mathbf{w}_{t}\\] \\[\\mathbf{y}_{t} = h\\left( \\mathbf{x}_{t} \\right)\\] t : time index x : system state v = g%u : input (e.g., in physical units used for model fit) u : control signal sent to actuator (e.g., in Volts) y : system output m : process disturbance w ~ N(0, Q) : process noise/disturbance A : state matrix B : input coupling matrix g : input gain (e.g., for converting to control signal actuator voltage) n.b., assumes this conversion is linear Q : process noise covariance % : element-wise multiplication LDS with Gaussian Observations # For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.\n\\[\\mathbf{y}_{t} = \\mathbf{C} \\mathbf{x}_{t} \u0026#43; \\mathbf{d}\\] \\[\\mathbf{z}_{t} \\sim \\mathcal{N}\\left(\\mathbf{y}_{t} , \\mathbf{R} \\right)\\] z : measurement C : output matrix d : output bias R : measurement noise covariance LDS with Poisson Observations # For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.\n\\[y_{t}^{i} = \\exp \\left(\\mathbf{c}^i \\mathbf{x}_{t} \u0026#43; d^i\\right)\\] \\[z_{t}^i \\sim \\rm{Poisson} \\left(y_{t}^i \\right)\\] i : output index z : measurement (count data) c : i^th row of output matrix (C) d : output bias Model Predictive Control (MPC) # Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.\nIn the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.\n"},{"id":79,"href":"/lds-ctrl-est/docs/api/modules/","title":"Modules","section":"LDS C+E Documentation","content":" Modules # Control Mode Bit Masks provides fill types for constructing new armadillo vectors, matrices\nDefaults\nUpdated on 31 March 2025 at 16:04:30 EDT\n"},{"id":80,"href":"/lds-ctrl-est/docs/api/namespaces/","title":"Namespaces","section":"LDS C+E Documentation","content":" Namespaces # armamexc arma/mex interface using Matlab C API\narmamexcpp arma/mex interface using Matlab C++ API\nlds::gaussian Linear Dynamical Systems with Gaussian observations.\nlds::poisson Linear Dynamical Systems with Poisson observations.\nstd\nUpdated on 31 March 2025 at 16:04:30 EDT\n"},{"id":81,"href":"/lds-ctrl-est/docs/api/pages/","title":"Pages","section":"LDS C+E Documentation","content":" Pages # Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":82,"href":"/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/","title":"PLDS State Estimation","section":"LDS C+E Examples","content":" PLDS State Estimation Tutorial # This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[x_{t\u0026#43;1} = x_t \u0026#43; w_t\\] \\[y_{t} = \\exp\\left(x_t\\right)\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state Finally, assign the parameters using corresponding set-methods.\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); Creating the estimator # Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. To ensure robust estimates, adaptively re-estimate the process disturbance.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); Simulating estimation # In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.\n// Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.\nWith this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.\n"},{"id":83,"href":"/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/","title":"PLDS Switched Control","section":"LDS C+E Examples","content":" PLDS Switched Control Tutorial # This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.\nIn the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating the simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); The system\u0026rsquo;s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 Initially, the system will be in \u0026ldquo;mode\u0026rdquo; 1, where B = b1.\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.\nCreating the controller # Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.\nSimilar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.\nMoreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.\nPutting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying systems: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nNow that the SwitchedController is instantiated, assign its parameters.\n// Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).\nThe control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.\n// Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system\u0026rsquo;s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.\nExample simulation result # Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller\u0026rsquo;s online estimates of the output and state are shown in purple.\nNote that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.\n"},{"id":84,"href":"/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/","title":"src","section":"Files","content":" src # Files # Name src/lds.cpp misc lds namespace functions src/lds_gaussian_sys.cpp GLDS base type. src/lds_poisson_sys.cpp PLDS base type. src/lds_sys.cpp LDS base type. src/lds_uniform_vecs.cpp Uniformly sized vectors. Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":85,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/","title":"src/lds_gaussian_sys.cpp","section":"Files","content":" src/lds_gaussian_sys.cpp # GLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_gaussian_sys.h\u0026gt; lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0, data_t r0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { R_.zeros(n_y, n_y); R_.diag().fill(r0); do_recurse_Ke_=true; }; // recursively estimate Ke void lds::gaussian::System::RecurseKe() { if (!do_recurse_Ke_) { return; } // predict covariance P_ = A_ * P_ * A_.t() + Q_; // calc Kalman gain Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_); // update covariance // Reference: Ghahramani et Hinton (1996) P_ = P_ - Ke_ * C_ * P_; if (do_adapt_m) { P_m_ += Q_m_; // A_m = I (i.e., random walk) Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_); P_m_ = P_m_ - Ke_m_ * C_ * P_m_; } } // Simulate const lds::Vector\u0026amp; lds::gaussian::System::Simulate(const Vector\u0026amp; u_tm1){ f(u_tm1, true);//simulate dynamics with noise added h();//output z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure return z_; } void lds::gaussian::System::Print() { lds::System::Print(); std::cout \u0026lt;\u0026lt; \u0026#34;R: \\n\u0026#34; \u0026lt;\u0026lt; R_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":86,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/","title":"src/lds_poisson_sys.cpp","section":"Files","content":" src/lds_poisson_sys.cpp # PLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_poisson_sys.h\u0026gt; lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { diag_y_ = diagmat(y_); pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(0); }; // Correct: Given measurement (z) and current input (u), update estimate of the // state, covar, output. // // see Eden et al. 2004 void lds::poisson::System::RecurseKe() { // predict covariance P_ = A_ * P_ * A_.t() + Q_; // update cov P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_); Ke_ = P_ * C_.t(); if (do_adapt_m) { P_m_ += Q_m_; // predict (A_m = I) P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_); // update Ke_m_ = P_m_ * C_.t(); } } // Simulate Measurement: z ~ Poisson(y) const lds::Vector\u0026amp; lds::poisson::System::Simulate(const Vector\u0026amp; u_tm1) { f(u_tm1, true); // simulate dynamics with noise added h(); // output z_.zeros(); for (std::size_t k = 0; k \u0026lt; n_y_; k++) { // construct a Poisson distribution object with mean y[k] pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(y_[k]); // pull random sample from this distribution z_[k] = pd_(rng); } return z_; } // ******************* SYS_T ******************* Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":87,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8cpp/","title":"src/lds_sys.cpp","section":"Files","content":" src/lds_sys.cpp # LDS base type. More\u0026hellip;\nDetailed Description # This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- lds_sys.cpp - LDS -------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_sys.h\u0026gt; #include \u0026lt;vector\u0026gt; lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) { InitVars(p0, q0); } void lds::System::InitVars(data_t p0, data_t q0) { // initial conditions. x0_ = Vector(n_x_, fill::zeros); // includes bias (nY) and g (nU) P0_ = p0 * Matrix(n_x_, n_x_, fill::eye); m0_ = x0_; P0_m_ = P0_; // signals x_ = x0_; P_ = P0_; m_ = m0_; P_m_ = P0_m_; y_ = Vector(n_y_, fill::zeros); cx_ = Vector(n_y_, fill::zeros); z_ = Vector(n_y_, fill::zeros); // By default, random walk where each state is independent // In this way, provides independent estimates of rate per channel of output. A_ = Matrix(n_x_, n_x_, fill::eye); B_ = Matrix(n_x_, n_u_, fill::zeros); g_ = Vector(n_u_, fill::ones); Q_ = q0 * Matrix(n_x_, n_x_, fill::eye); Q_m_ = Q_; C_ = Matrix(n_y_, n_x_, fill::eye); // each state will map to an output by d_ = Vector(n_y_, fill::zeros); Ke_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain. Ke_m_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain for m adaptation. do_adapt_m = false; } // Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state // and update estimate of the state, covar, output using Kalman filter void lds::System::Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z_t) { // predict mean f(u_tm1); // dynamics h(); // output // recursively calculate esimator gains (or just keep existing values) // (also predicts+updates estimate covariance) RecurseKe(); // update x_ += Ke_ * (z_t - y_); if (do_adapt_m) { m_ += Ke_m_ * (z_t - y_); // adaptively estimating disturbance } // With new state, estimate output. h(); // --\u0026gt; posterior } void lds::System::Reset() { // reset to initial conditions x_ = x0_; // mean P_ = P0_; // cov of state estimate m_ = m0_; // process disturbance P_m_ = P0_m_; // cov of disturbance estimate h(); } std::vector\u0026lt;lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt;\u0026gt; lds::System::nstep_pred_block(lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; u, lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; z, size_t n_pred) { lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_filt; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_pred; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; y_pred; for (size_t k = 0; k \u0026lt; u.size(); k++) { Reset(); size_t n_t = arma::size(u[k])[1]; Matrix x_filt_k(n_x_, n_t, fill::zeros); Matrix x_pred_k(n_x_, n_t - n_pred, fill::zeros); Matrix y_pred_k(n_y_, n_t - n_pred, fill::zeros); for (size_t t = 0; t \u0026lt; n_t - n_pred; t++) { Vector x_pred_ahead = x_; for (size_t t_u = t; t_u \u0026lt; t + n_pred; t_u++) { x_pred_ahead = A_ * x_pred_ahead + B_ * u[k].col(t_u); } x_pred_k.col(t) = x_pred_ahead; y_pred_k.col(t) = h_(x_pred_ahead); if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; // given previous measurment } for (size_t t = n_t - n_pred; t \u0026lt; n_t; t++) { if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; } x_filt.append(x_filt_k); x_pred.append(x_pred_k); y_pred.append(y_pred_k); } return {x_filt, x_pred, y_pred}; } void lds::System::Print() { std::cout \u0026lt;\u0026lt; \u0026#34;\\n ********** SYSTEM ********** \\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;x: \\n\u0026#34; \u0026lt;\u0026lt; x_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;P: \\n\u0026#34; \u0026lt;\u0026lt; P_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;A: \\n\u0026#34; \u0026lt;\u0026lt; A_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B: \\n\u0026#34; \u0026lt;\u0026lt; B_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;g: \\n\u0026#34; \u0026lt;\u0026lt; g_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;m: \\n\u0026#34; \u0026lt;\u0026lt; m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q: \\n\u0026#34; \u0026lt;\u0026lt; Q_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q_m: \\n\u0026#34; \u0026lt;\u0026lt; Q_m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d: \\n\u0026#34; \u0026lt;\u0026lt; d_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;C: \\n\u0026#34; \u0026lt;\u0026lt; C_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;y: \\n\u0026#34; \u0026lt;\u0026lt; y_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } //******************* SYS_T ******************* Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":88,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/","title":"src/lds_uniform_vecs.cpp","section":"Files","content":" src/lds_uniform_vecs.cpp # Uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_uniform_vecs.h\u0026gt; namespace lds { UniformVectorList::UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); } UniformVectorList::UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim) : vector(std::move(vecs)) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(const UniformVectorList\u0026amp; that) : vector(that) { (*this) = that; } UniformVectorList::UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { this-\u0026gt;dim_ = this-\u0026gt;at(0).n_elem; } void UniformVectorList::CheckDimensions(size_t dim) { if (dim) { dim_ = dim; } else { dim_ = this-\u0026gt;at(0).n_elem; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Vector\u0026amp; vec : *this) { does_match = does_match \u0026amp;\u0026amp; (vec.n_elem == dim_); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } } } // namespace lds Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":89,"href":"/lds-ctrl-est/docs/api/files/lds_8cpp/","title":"src/lds.cpp","section":"Files","content":" src/lds.cpp # misc lds namespace functions More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file implements miscellaneous lds namespace functions not bound to a class.\nSource code # //===-- lds.cpp - LDS -----------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds.h\u0026gt; // insert any necessary function definitions here. namespace lds { void ForceSymPD(Matrix\u0026amp; X) { if (X.is_sympd() || !X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; // for eigenval decomp bool did_succeed(true); Vector d; Matrix u; // see first method (which may not be ideal): // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/ size_t k(1); bool is_sympd = X.is_sympd(); Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye); while (!is_sympd) { if (k \u0026gt; 100) { did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); data_t min_eig = arma::min(d); std::cerr \u0026lt;\u0026lt; \u0026#34;After multiple iterations, min eigen val = \u0026#34; \u0026lt;\u0026lt; min_eig \u0026lt;\u0026lt; \u0026#34;.\\n\u0026#34;; throw std::runtime_error( \u0026#34;Failed to make matrix symmetric positive definite.\u0026#34;); return; } // Limit(d, arma::eps(0), kInf); // force to be positive... // Matrix d_diag = arma::diagmat(d); // X = u * d_diag * u.t(); did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymPD failed.\u0026#34;); } data_t min_eig = arma::min(d); X += id * abs(min_eig) + arma::datum::eps; // make sure symm: X = (X + X.t()) / 2; // double check eigenvals positive after symmetrizing: arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); min_eig = arma::min(d); is_sympd = min_eig \u0026gt; 0; k++; } } void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min) { if (!X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; bool did_succeed(true); Vector d; Matrix u; did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymMinEig failed.\u0026#34;); } Limit(d, eig_min + arma::eps(eig_min), kInf); // enforce lower bound Matrix d_diag = arma::diagmat(d); X = u * d_diag * u.t(); // double check symmetric X = (X + X.t()) / 2; } void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X) { bool did_succeed(true); did_succeed = arma::qr_econ(Qt, L, X.t()); if (!did_succeed) { throw std::runtime_error(\u0026#34;LQ decomposition failed.\u0026#34;); } arma::inplace_trans(L); arma::inplace_trans(Qt); } Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B) { // subtract out mean auto m_a = arma::mean(A, 1); Matrix a0 = A; a0.each_col() -= m_a; auto m_b = arma::mean(B, 1); Matrix b0 = B; b0.each_col() -= m_b; Matrix cov = a0 * b0.t() / a0.n_cols; return cov; } } // namespace lds Updated on 31 March 2025 at 16:04:30 EDT\n"},{"id":90,"href":"/lds-ctrl-est/docs/api/namespaces/namespacestd/","title":"std","section":"Namespaces","content":" std # Updated on 31 March 2025 at 16:04:30 EDT\n"}]
\ No newline at end of file
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+[{"id":0,"href":"/docs/","title":"LDS C+E Documentation","section":"LDS Control \u0026 Estimation","content":" LDS Control \u0026amp; Estimation Documentation # "},{"id":1,"href":"/docs/tutorials/","title":"LDS C+E Examples","section":"LDS C+E Documentation","content":" Examples # "},{"id":2,"href":"/docs/api/","title":"API Reference","section":"LDS C+E Documentation","content":" API Reference # The API documentation is organized into the following sections:\nClasses - Documentation for all classes Namespaces - Documentation for all namespaces Files - Documentation for all files Modules - Documentation for all modules Examples - Documentation for all examples "},{"id":3,"href":"/acknowledgements/","title":"Acknowledgements","section":"LDS Control \u0026 Estimation","content":" Acknowledgements # Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.\n"},{"id":4,"href":"/docs/getting-started/getting-started/","title":"Getting Started","section":"LDS C+E Documentation","content":" Getting Started # This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.\nldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.\nTested Configurations # Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the RelWithDebInfo build type in the CMake configure command (-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo):\nUbuntu 18.04 with GCC 7.5 compiler macOS 11 (Big Sur) with Apple Clang 12 compiler Windows 10 with Visual Studio 16.11 (2019 release) and Clang 12 compiler That being said, if you want to debug a build for a single platform, here are some things you can try:\nUse different compilers (or even different versions of a single compiler) Use different versions of vcpkg (which you can control by checking out a different commit in the vcpkg submodule) Mac Pre-requisities # Xcode Command Line Tools will get you clang, gcc, make, and git:\nxcode-select --install Homebrew is \u0026ldquo;The Missing Package Manager for macOS\u0026rdquo; which will make installing lots of things easy. Install like this:\n/bin/bash -c \u0026#34;$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\u0026#34; You can then use it to install CMake, gfortran, and pkg-config:\nbrew install cmake gfortran pkg-config Linux Pre-requisites # You\u0026rsquo;ll need Git, CMake, GCC, gfortran, etc.\nsudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential ninja-build Windows Installation # Look here for Windows-specific instructions.\nDownloading the Library # First, clone the repository along with submodules:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est\rgit submodule update --init Compilation + Installation # Now generate the cache and build using your IDE or from the command line as follows.\nmkdir build \u0026amp;\u0026amp; cd build\rcmake ..\rcmake --build . The first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.\nIf you want to use vcpkg set up somewhere besides this repo\u0026rsquo;s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE\u0026rsquo;s settings.\nYou can verify the build is working by running ctest from the build folder, which runs all the example scripts.\nOptions # This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\nLDSCTRLEST_BUILD_EXAMPLES : [default=ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake to configure/build the library.\nFor basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build cmake --build #build the project sudo make install #[optional] installs to default location (OS-specific) To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location cmake --build #build the project make install #install to /your/install/prefix To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\nLD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.\nPython bindings package ldsctrlest # With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython3_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn\u0026rsquo;t use an undesired version. That environment needs to have NumPy installed.\ncmake --build . --target python_modules The bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory (pip install pytest matplotlib first if you need to).\nSee python/ldsctrlest/README.md for usage details.\nAlso, beware that a single build will probably not work for both the standalone library and the Python package, since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.\nCommon issues # \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.\nexport CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix vcpkg fails on configuration Try running ./bootstrap-vcpkg from the vcpkg folder and try again. If that doesn\u0026rsquo;t work, try updating vcpkg to a newer version (in the source control tab, click on the commit hash by the vcpkg repo then select from the dropdown) and running boostsrap-vcpkg again. You can also try upgrading your system (e.g., apt update, apt upgrade).\nCould not find Python3 (missing: Python3_NumPy_INCLUDE_DIRS NumPy)\nMake sure NumPy is installed in the Python environment you specified. If CMake still can\u0026rsquo;t find it, you may need to tell CMake exactly where to find it by adding an argument to the configure command: -DPython3_NumPy_INCLUDE_DIR=.... You can find that location like this: python -c 'import numpy; print(numpy.get_include())'\n"},{"id":5,"href":"/docs/getting-started/windows/","title":"Windows","section":"LDS C+E Documentation","content":" Windows Installation # Windows Pre-requisites # Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:\nSet-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time iwr get.scoop.sh | Invoke-Expression Install Git and CMake if you don\u0026rsquo;t already have them:\nscoop install git cmake If that didn\u0026rsquo;t work, follow more detailed instructions here.\nThe easiest way to compile C++ project on Windows is with Visual Studio\u0026rsquo;s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on \u0026ldquo;Desktop development with C++.\u0026rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the \u0026ldquo;Installation details\u0026rdquo; sidebar under optional features.\nAnd the easiest way to use Visual Studio\u0026rsquo;s build tools is with VS Code, along with the CMake Tools extension. Install them and you should be ready to go.\nDownloading the Library # First, clone the repository, either from VS Code or the command line:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est You\u0026rsquo;ll need to initialize the submodules from the command line after the repo is cloned:\ngit submodule update --init Installation # When you open the folder in VS Code, you will like be prompted by the CMake Tools extension to configure the project. Make sure you select the kit (you\u0026rsquo;ll be prompted when you configure\u0026ndash;else there\u0026rsquo;s an icon in the bar on the bottom of the window or type Ctrl+Shift+P, then \u0026ldquo;cmake select kit\u0026rdquo;). Choose Clang [latest version] with GNU CLI ... amd64 assuming you are running a 64-bit OS. (MSVC may work okay too if you don\u0026rsquo;t need to build Python bindings.)\nFollow along with the \u0026ldquo;Getting Started\u0026rdquo; instructions, but where you see config options specified as -DLDSCTREST_BUILD_STATIC=OFF or -DPython3_ROOT_DIR=..., you will enter those in settings: open with Ctrl+,, click \u0026ldquo;workspace\u0026rdquo;, then search for \u0026ldquo;CMake: Configure Args\u0026rdquo; and enter each of your desired arguments as a separate item.\nTo configure, use Ctrl+Shift+P and search for the \u0026ldquo;CMake: Configure\u0026rdquo; command. To build, click the \u0026ldquo;Build\u0026rdquo; button on the bottom bar. Then click the \u0026ldquo;CTest\u0026rdquo; button to run the example scripts.\nConsiderations # Development on Windows has been more prone to bugs than on Unix systems, so if you encounter many problems, consider switching—WSL (Windows Subsystem for Linux) is a good option for Windows users who don\u0026rsquo;t want to work on a different machine.\nCompilation has been successfully tested in VS Code using the following kit, using the \u0026ldquo;RelWithDebInfo\u0026rdquo; config:\nClang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64) Troubleshooting # The build appears to work, but tests fail with code 0xc0000135 OR \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; Have you installed the library? In VS Code, use Shift+F7 to build a specific target, in this case INSTALL. If that doesn\u0026rsquo;t solve your problem, you will likely need to add the build or install folder to your PATH environment variable, which you can do using the settings GUI (search for \u0026ldquo;Edit the system environment variables\u0026rdquo;).\nOn Windows, \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted. Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":6,"href":"/issues-contributing/","title":"Issues Contributing","section":"LDS Control \u0026 Estimation","content":" Reporting Issues # If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.\nContributing # We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.\n"},{"id":7,"href":"/docs/api/namespaces/namespacearmamexc/","title":"armamexc","section":"Namespaces","content":" armamexc # arma/mex interface using Matlab C API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; T m2T_scalar(const mxArray * matlab_scalar)\nConvert Matlab mxArray to scalar of type T. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)\nConvert matlab matrix to armadillo. template \u0026lt;typename T \u0026gt; mxArray * a2m_mat(arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat)\nConvert armadillo to matlab matrix. template \u0026lt;typename T \u0026gt; mxArray * a2m_vec(arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec)\nConvert armadillo to matlab vector. Detailed Description # Utilities for arma/mex interface using Matlab C API\nFunction Details # m2T_scalar # template \u0026lt;class T \u0026gt; inline T m2T_scalar( const mxArray * matlab_scalar ) Parameters:\nmatlab_scalar matlab scalar Template Parameters:\nT type Return: scalar of type T\nm2a_mat # template \u0026lt;class T \u0026gt; inline arma::Mat\u0026lt; T \u0026gt; m2a_mat( const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true ) Parameters:\nmatlab_mat matlab matrix copy_aux_mem [optional] whether to copy auxiliary memory strict [optional] strictly enforce the above Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_mat( arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat ) Parameters:\narma_mat armadillo matrix Return: matlab matrix\na2m_vec # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_vec( arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec ) Parameters:\narma_vec armadillo vector Return: matlab vector\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":8,"href":"/docs/api/namespaces/namespacearmamexcpp/","title":"armamexcpp","section":"Namespaces","content":" armamexcpp # arma/mex interface using Matlab C++ API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat(matlab::data::CellArray \u0026amp; matlab_cell)\nConvert matlab cell array to vector of armadillo matrices. template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array)\nConvert matlab matrix to a vector of scalars. template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo vector. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat(const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec(const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab vector. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec(const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert vector of scalar T to matlab matrix. Detailed Description # utilities for arma/mex interface using Matlab C++ API\nFunction Details # m2a_cellmat # template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat( matlab::data::CellArray \u0026amp; matlab_cell ) Parameters:\nmatlab_cell matlab cell Template Parameters:\nT type Return: vector of armadillo matrices of type T\nm2s_vec # template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: vector of type T\nm2a_vec # template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: armadillo vector of type T\nm2a_mat # template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab matrix Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat( const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_mat arma matrix factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\na2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec( const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_vec armadillo vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\ns2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec( const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\nstd_vec standard vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":9,"href":"/docs/terminology/control-estimation/","title":"C\u0026E","section":"LDS C+E Documentation","content":" Control \u0026amp; Estimation # The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.\nState estimation # In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by \\[\\widehat{\\mathbf{x}}_{t|t-1} = \\mathbf{A}\\widehat{\\mathbf{x}}_{t-1|t-1} \u0026#43; \\mathbf{B} u_{t-1} \u0026#43; \\mathbf{m}_{t-1} \\;,\\] \\[\\widehat{\\mathbf{x}}_{t|t} = \\widehat{\\mathbf{x}}_{t|t-1} \u0026#43; \\mathbf{K}^{\\rm e}_t \\left(\\mathbf{z}_t - \\widehat{\\mathbf{y}}_{t|t-1}\\right)\\;,\\] where \\( \\hat{\\left(\\cdot\\right)}_{t|j} \\) indicates an estimate at time \\( t \\) given data up to time \\( j \\) inclusive, \\( \\mathbf{K}^{\\rm e} \\) is the estimator gain, and\n\\[ \\widehat{\\mathbf{y}}_{t|t-1} = h\\left( \\widehat{\\mathbf{x}}_{t|t-1} \\right) \\; .\\] In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.\nIn the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.\nAdaptive estimation of process disturbance # Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.\nWhen parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk \\[\\mathbf{m}_{t} = \\mathbf{m}_{t-1} \u0026#43; \\mathbf{w}^m_{t-1} \\;,\\] where \\( \\mathbf{w}^m \\sim \\mathcal{N}\\left(0, \\mathbf{Q}_m\\right)\\) . Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.\nControl # Given the estimated state, the controller updates the inputs to the system according to the following law: \\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] where \\( \\left( \\cdot \\right)^{\\rm ref} \\) correspond to reference/target signals and \\( \\mathbf{K}^c_x \\) is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.\nIf users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as\n\\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right) - \\mathbf{K}^c_{\\rm inty} \\sum_{j=1}^{t}\\left( \\widehat{\\mathbf{y}}_j - \\mathbf{y}^{\\rm ref}_j \\right) \\;.\\] An additional option available to users is a control law that updates the change in u,\n\\[\\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\left(\\mathbf{u}_{t-1} - \\mathbf{u}^{\\rm ref}_{t-1} \\right) - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] \\[\\mathbf{u}_{t} = \\mathbf{u}_{t-1} \u0026#43; \\Delta\\mathbf{u}_{t} \\; .\\] Notice that this takes the form of a first-order difference equation for updating control (i.e., \\( \\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\mathbf{u}_{t-1} \u0026#43; \\epsilon_{t-1} \\) ), effectively low-pass filtering the input depending on the characteristics of \\( \\mathbf{K}^c_u \\) . This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.\nIntegral action and the \\( \\Delta \\mathbf{u} \\) control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR\u0026rsquo;d to use in combination.\nCalculating reference state-control from output # In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce \\( \\mathbf{x}^{\\rm ref} \\) and \\( \\mathbf{u}^{\\rm ref} \\) . Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.\n"},{"id":10,"href":"/docs/api/classes/","title":"Classes","section":"API Reference","content":" Classes # "},{"id":11,"href":"/docs/api/modules/group__control__masks/","title":"Control Mode Bit Masks","section":"Modules","content":" Control Mode Bit Masks # provides fill types for constructing new armadillo vectors, matrices More\u0026hellip; Attributes # Name const std::size_t kControlTypeDeltaU control designed to penalize change in input const std::size_t kControlTypeIntY control using integral action const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m Detailed Description # Control mode bit masks. These can be bit-wise OR\u0026rsquo;d to use in combination.\nAttribute Details # kControlTypeDeltaU # static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY # static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM # static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":12,"href":"/docs/api/modules/group__defaults/","title":"Defaults","section":"Modules","content":" Defaults # More\u0026hellip; Attributes # Name const data_t kDefaultP0 default state estimate covar const data_t kDefaultQ0 default process noise covar const data_t kDefaultR0 default output noise covar Detailed Description # Default values for common variables (e.g., default diagonal elements of covariances)\nAttribute Details # kDefaultP0 # static const data_t kDefaultP0 = 1e-6; kDefaultQ0 # static const data_t kDefaultQ0 = 1e-6; kDefaultR0 # static const data_t kDefaultR0 = 1e-2; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":13,"href":"/docs/api/examples/eg_glds_ctrl_8cpp-example/","title":"eg_glds_ctrl.cpp","section":"Examples","content":" eg_glds_ctrl.cpp # Example GLDS Control ```cpp\n//===\u0026ndash; eg_glds_ctrl.cpp - Example GLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS Control ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\n// output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false;\n// Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err\n// setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]);\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; }\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:35:01 EST "},{"id":14,"href":"/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/","title":"eg_glds_du_plds_ctrl.cpp","section":"Examples","content":" eg_glds_du_plds_ctrl.cpp # Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u). ```cpp\n//===\u0026ndash; eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt);\n// to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU;\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_du_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:35:01 EST "},{"id":15,"href":"/docs/api/examples/eg_plds_ctrl_8cpp-example/","title":"eg_plds_ctrl.cpp","section":"Examples","content":" eg_plds_ctrl.cpp # Example PLDS Control ```cpp\n//===\u0026ndash; eg_plds_ctrl.cpp - Example PLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Control ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt);\n// Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err\n// Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\nsize_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\nVector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled\u0026hellip; lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system);\n// for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type);\n// set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty);\n// to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0;\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros);\n// set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y();\nx_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x();\nm_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:35:01 EST "},{"id":16,"href":"/docs/api/examples/eg_plds_est_8cpp-example/","title":"eg_plds_est.cpp","section":"Examples","content":" eg_plds_est.cpp # Example PLDS Estimation ```cpp\n//===\u0026ndash; eg_plds_est.cpp - Example PLDS Estimation \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0);\nint main() { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.\n// construct ground truth system\u0026hellip; lds::poisson::System system_true(n_u, n_x, n_y, dt);\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset();\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt);\n// Can copy parameters from another system object system_estimator = system_true;\n// wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est);\n// set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true;\n// set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;estimator:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; system_estimator.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));\n// create matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros);\n// states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\nMatrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simlation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1));\n// Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simlation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows;\nif ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026ldquo;Q must be n x n.\u0026rdquo;); }\nMatrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); }\nreturn x; }\n_Filename: eg_plds_est.cpp_ ------------------------------- Updated on 5 March 2025 at 16:35:01 EST "},{"id":17,"href":"/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/","title":"eg_plds_switched_ctrl.cpp","section":"Examples","content":" eg_plds_switched_ctrl.cpp # Example Switched PLDS Control ```cpp\n//===\u0026ndash; eg_plds_switched_ctrl.cpp - Example Switched PLDS Control \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026quot;;\n// whether to do switched control bool do_switch_ctrl = true;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt);\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices. data_t scale_sys_b = 2;\nMatrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));\ncontrolled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions\n// reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);\n// Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system);\n// set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys1:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys2:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x));\nswitched_controller.set_y_ref(y_ref0);\nstd::vectorlds::poisson::System systems_vec(3, lds::poisson::System()); lds::UniformSystemListlds::poisson::System systems(std::move(systems_vec));\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;switched_controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; switched_controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Fake measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros);\n// create Matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);\n// modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones);\n// set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } }\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace)); mode.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;mode\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_switched_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:35:01 EST "},{"id":18,"href":"/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/","title":"examples","section":"Files","content":" examples # Files # Name examples/eg_glds_ctrl.cpp examples/eg_glds_du_plds_ctrl.cpp examples/eg_plds_ctrl.cpp examples/eg_plds_est.cpp examples/eg_plds_switched_ctrl.cpp Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":19,"href":"/docs/api/examples/","title":"Examples","section":"API Reference","content":" Examples # "},{"id":20,"href":"/docs/api/files/eg__glds__ctrl_8cpp/","title":"examples/eg_glds_ctrl.cpp","section":"Files","content":" examples/eg_glds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":21,"href":"/docs/api/files/eg__glds__du__plds__ctrl_8cpp/","title":"examples/eg_glds_du_plds_ctrl.cpp","section":"Files","content":" examples/eg_glds_du_plds_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU; // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":22,"href":"/docs/api/files/eg__plds__ctrl_8cpp/","title":"examples/eg_plds_ctrl.cpp","section":"Files","content":" examples/eg_plds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err // Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled... lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type); // set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":23,"href":"/docs/api/files/eg__plds__est_8cpp/","title":"examples/eg_plds_est.cpp","section":"Files","content":" examples/eg_plds_est.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name Matrix random_walk(size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0) int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # random_walk # Matrix random_walk( size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0 ) main # int main() Source code # //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. // construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state // Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. // turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":24,"href":"/docs/api/files/eg__plds__switched__ctrl_8cpp/","title":"examples/eg_plds_switched_ctrl.cpp","section":"Files","content":" examples/eg_plds_switched_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Source code # //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 // simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions // reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":25,"href":"/docs/api/files/","title":"Files","section":"API Reference","content":" Files # "},{"id":26,"href":"/docs/tutorials/eg_glds_control/","title":"GLDS Control","section":"LDS C+E Examples","content":" GLDS Control Tutorial # This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\] \\[\ry_{t} = x_t\r\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.\n/// Going to simulate a switching disturbance (m) acting on system size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_y).fill(m_low); Finally, assign the parameters using corresponding set-methods.\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); Creating the controller # Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.\nFor the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.\n// make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nWith the controller constructed, control variables may be set.\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.\nThe control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller\u0026rsquo;s online estimates of the output, state, and disturbance are given in purple.\n"},{"id":27,"href":"/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/","title":"include","section":"Files","content":" include # Directories # Name ldsCtrlEst_h Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":28,"href":"/docs/api/namespaces/namespacelds/","title":"lds","section":"Namespaces","content":" lds # Linear Dynamical Systems (LDS) namespace. Namespaces # Name lds::gaussian Linear Dynamical Systems with Gaussian observations. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::Controller class lds::EM class lds::Fit LDS Fit Type. class lds::SSID class lds::SwitchedController SwitchedController Type. class lds::System Linear Dynamical System Type. class lds::UniformMatrixList class lds::UniformSystemList class lds::UniformVectorList Types # Name enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}\nweighting options for SSID enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2} using double data_t using arma::Col\u0026lt; data_t \u0026gt; Vector using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Cube\u0026lt; data_t \u0026gt; Cube using arma::subview\u0026lt; data_t \u0026gt; View Functions # Name void Limit(std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub) void Limit(Vector \u0026amp; x, data_t lb, data_t ub) void Limit(Matrix \u0026amp; x, data_t lb, data_t ub) void Reassign(Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Vector in place void Reassign(Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Matrix in place void ForceSymPD(Matrix \u0026amp; X)\nforces matrix to be symmetric positive-definite void ForceSymMinEig(Matrix \u0026amp; X, data_t eig_min =0)\nforces matrix to be symmetric and have a minimum eigenvalue void lq(Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X)\nLQ decomposition. Matrix calcCov(const Matrix \u0026amp; A, const Matrix \u0026amp; B)\nCalculate covariance matrix. Attributes # Name const data_t kInf Some useful numbers. const data_t kPi Type Details # SSIDWt # Enumerator Value Description kSSIDNone None. kSSIDMOESP MOESP (AKA \u0026ldquo;robust method\u0026rdquo; in van Overschee 1996) kSSIDCVA CVA \u0026ldquo;Canonical Variate Analysis\u0026rdquo;. Weighting options for singular value decomposition performed during subspace identification (SSID)\nReference:\nvan Overschee, de Moor. 1996. Subspace Identification for Linear Systems.\nMatrixListFreeDim # Enumerator Value Description kMatFreeDimNone neither dim free to be hetero in mat list kMatFreeDim1 allow 1st dim of mats in list to be hetero kMatFreeDim2 allow 2nd dim of mats in list to be hetero data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nVector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Cube # using lds::Cube = arma::Cube\u0026lt;data_t\u0026gt;; View # using lds::View = arma::subview\u0026lt;data_t\u0026gt;; Function Details # Limit # inline void Limit( std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Vector \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Matrix \u0026amp; x, data_t lb, data_t ub ) Reassign # inline void Reassign( Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Vector other other Vector parenthetical optional description provided by caller to ease debugging Reassign # inline void Reassign( Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Matrix other other Matrix parenthetical optional description provided by caller to ease debugging ForceSymPD # void ForceSymPD( Matrix \u0026amp; X ) Parameters:\nX mutated matrix ForceSymMinEig # void ForceSymMinEig( Matrix \u0026amp; X, data_t eig_min =0 ) Parameters:\nX mutated matrix eig_min [optional] minimum eigen value lq # void lq( Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X ) Parameters:\nL lower triangle matrix Qt orthonormal matrix (transposed cf QR decomp) X matrix being decomposed calcCov # Matrix calcCov( const Matrix \u0026amp; A, const Matrix \u0026amp; B ) Parameters:\nA some matrix B some other matrix Return: covariance\nAttribute Details # kInf # static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); kPi # static const data_t kPi = arma::datum::pi; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":29,"href":"/docs/api/classes/classlds_1_1_controller/","title":"lds::Controller","section":"Classes","content":" lds::Controller # More\u0026hellip;\nInherited by lds::SwitchedController\u0026lt; System \u0026gt;, lds::gaussian::Controller, lds::poisson::Controller\nPublic Functions # Name Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes # Name System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::Controller; Public Function Details # Controller # Controller() =default Controller # inline Controller( const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Controller # inline Controller( System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Control # inline const Vector \u0026amp; Control( const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step). This is the most flexible option, but requires user to have set the controller\u0026rsquo;s y_ref, x_ref, and u_ref variables.\nControlOutputReference # inline const Vector \u0026amp; ControlOutputReference( const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_estimation [optional] whether to update state estimate (if false, effectively open-loop control) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.\nsys # inline const System \u0026amp; sys() const Kc # inline const Matrix \u0026amp; Kc() const Kc_inty # inline const Matrix \u0026amp; Kc_inty() const Kc_u # inline const Matrix \u0026amp; Kc_u() const g_design # inline const Vector \u0026amp; g_design() const u_ref # inline const Vector \u0026amp; u_ref() const x_ref # inline const Vector \u0026amp; x_ref() const y_ref # inline const Vector \u0026amp; y_ref() const control_type # inline size_t control_type() const tau_awu # inline data_t tau_awu() const u_lb # inline data_t u_lb() const u_ub # inline data_t u_ub() const set_sys # inline void set_sys( const System \u0026amp; sys ) set_g_design # inline void set_g_design( const Vector \u0026amp; g_design ) set_u_ref # inline void set_u_ref( const Vector \u0026amp; u_ref ) set_x_ref # inline void set_x_ref( const Vector \u0026amp; x_ref ) set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) Reimplemented by: lds::gaussian::Controller::set_y_ref, lds::gaussian::SwitchedController::set_y_ref, lds::poisson::Controller::set_y_ref, lds::poisson::SwitchedController::set_y_ref\nset_Kc # inline void set_Kc( const Matrix \u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const Matrix \u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const Matrix \u0026amp; Kc_u ) set_tau_awu # inline void set_tau_awu( data_t tau ) set_control_type # inline void set_control_type( size_t control_type ) Parameters:\ncontrol_type control type bit mask Template Parameters:\nSystem type derived from lds::System set_u_lb # inline void set_u_lb( data_t u_lb ) Parameters:\nu_lb control lower bound set_u_ub # inline void set_u_ub( data_t u_ub ) Parameters:\nu_ub control upper bound Reset # inline void Reset() Print # inline void Print() Protected Attribute Details # sys_ # System sys_; u_ # Vector u_; u_return_ # Vector u_return_; g_design_ # Vector g_design_; u_ref_ # Vector u_ref_; u_ref_prev_ # Vector u_ref_prev_; x_ref_ # Vector x_ref_; y_ref_ # Vector y_ref_; cx_ref_ # Vector cx_ref_; Kc_ # Matrix Kc_; Kc_u_ # Matrix Kc_u_; Kc_inty_ # Matrix Kc_inty_; du_ref_ # Vector du_ref_; dv_ref_ # Vector dv_ref_; v_ref_ # Vector v_ref_; dv_ # Vector dv_; v_ # Vector v_; int_e_ # Vector int_e_; int_e_awu_adjust_ # Vector int_e_awu_adjust_; u_sat_ # Vector u_sat_; do_control_prev_ # bool do_control_prev_ = false; do_lock_control_prev_ # bool do_lock_control_prev_ = false; u_saturated_ # bool u_saturated_ = false; u_lb_ # data_t u_lb_ {}; u_ub_ # data_t u_ub_ {}; tau_awu_ # data_t tau_awu_ {}; k_awu_ # data_t k_awu_ = 0; t_since_control_onset_ # data_t t_since_control_onset_ = 0; control_type_ # size_t control_type_ {}; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":30,"href":"/docs/api/classes/classlds_1_1_e_m/","title":"lds::EM","section":"Classes","content":" lds::EM # More\u0026hellip;\nInherited by lds::gaussian::FitEM, lds::poisson::FitEM\nPublic Functions # Name EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions # Name void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() virtual void MaximizeOutput() =0 virtual void MaximizeMeasurement() =0 void Smooth(bool force_common_initial)\nget smoothed estimates virtual void RecurseKe(Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t) =0\nrecursively update estimator gain Ke void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::EM; Public Function Details # EM # EM() =default EM # EM( size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nn_x number of states dt sample period u_train input training data z_train measurement training data EM # EM( const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nfit0 initial fit u_train input training data z_train measurement training data ~EM # virtual ~EM() =default Run # const Fit \u0026amp; Run( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2 ) Parameters:\ncalc_dynamics [optional] whether to calculate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law max_iter max number of iterations tol convergence tolerance (max fractional abs change) Return: Fit\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nx # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const y # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const sum_E_x_t_x_t # inline const Matrix \u0026amp; sum_E_x_t_x_t() const sum_E_xu_tm1_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const sum_E_xu_t_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const n_t_tot # inline size_t n_t_tot() theta # inline const Vector \u0026amp; theta() const Protected Function Details # Expectation # void Expectation( bool force_common_initial =false ) Parameters:\nforce_common_initial whether to force common initial condition for all trials Maximization # void Maximization( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false ) Parameters:\ncalc_dynamics [optional] whether to caclulate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law MaximizeDynamics # void MaximizeDynamics() MaximizeQ # void MaximizeQ() MaximizeInitial # void MaximizeInitial() MaximizeOutput # virtual void MaximizeOutput() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput\nMaximizeMeasurement # virtual void MaximizeMeasurement() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement\nSmooth # void Smooth( bool force_common_initial ) Parameters:\nforce_common_initial whether to force common initial conditions RecurseKe # virtual void RecurseKe( Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t ) =0 Parameters:\nKe estimator gain P_pre cov of predicted state est. P_post cov of postior sate est. t time Reimplemented by: lds::gaussian::FitEM::RecurseKe, lds::poisson::FitEM::RecurseKe\nReset # void Reset() InitVars # void InitVars() UpdateTheta # Vector UpdateTheta() Return: parameter list\nProtected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; x_ # std::vector\u0026lt; Matrix \u0026gt; x_; P_ # std::vector\u0026lt; Cube \u0026gt; P_; P_t_tm1_ # std::vector\u0026lt; Cube \u0026gt; P_t_tm1_; y_ # std::vector\u0026lt; Matrix \u0026gt; y_; diag_y_ # Matrix diag_y_; sum_E_x_t_x_t_ # Matrix sum_E_x_t_x_t_; sum_E_xu_tm1_xu_tm1_ # Matrix sum_E_xu_tm1_xu_tm1_; sum_E_xu_t_xu_tm1_ # Matrix sum_E_xu_t_xu_tm1_; fit_ # Fit fit_; theta_ # Vector theta_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":31,"href":"/docs/api/classes/classlds_1_1_fit/","title":"lds::Fit","section":"Classes","content":" lds::Fit # LDS Fit Type. #include \u0026lt;lds_fit.h\u0026gt;\nInherited by lds::gaussian::Fit, lds::poisson::Fit\nPublic Functions # Name Fit() =default\nConstructs a new Fit. Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias virtual const Matrix \u0026amp; R() const =0 void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance virtual void set_R(const Matrix \u0026amp; R) =0\nsets output noise covariance (if any) void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) =0\noutput function Protected Attributes # Name data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period ~Fit # virtual ~Fit() =default n_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const m # inline const Vector \u0026amp; m() const Q # inline const Matrix \u0026amp; Q() const x0 # inline const Vector \u0026amp; x0() const P0 # inline const Matrix \u0026amp; P0() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const R # virtual const Matrix \u0026amp; R() const =0 Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R\nset_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_g # inline void set_g( const Vector \u0026amp; g ) set_m # inline void set_m( const Vector \u0026amp; m ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # virtual void set_R( const Matrix \u0026amp; R ) =0 Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R\nset_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) f # inline View f( Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t ) Parameters:\nx state estimate (over time) u input (over time) t time index Return: view of updated state\nf # inline View f( Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t ) Parameters:\nx_pre predicted state est. x_post posterior state est. u input (over time) t time index Return: view of predicted state\nh # virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) =0 Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplemented by: lds::gaussian::Fit::h, lds::poisson::Fit::h\nProtected Attribute Details # dt_ # data_t dt_ {}; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; m_ # Vector m_; Q_ # Matrix Q_; C_ # Matrix C_; d_ # Vector d_; R_ # Matrix R_; x0_ # Vector x0_; P0_ # Matrix P0_; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":32,"href":"/docs/api/namespaces/namespacelds_1_1gaussian/","title":"lds::gaussian","section":"Namespaces","content":" lds::gaussian # Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. class lds::gaussian::Fit GLDS Fit Type. class lds::gaussian::FitEM GLDS E-M Fit Type. class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. class lds::gaussian::System Gaussian LDS Type. Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":33,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_controller/","title":"lds::gaussian::Controller","section":"Classes","content":" lds::gaussian::Controller # Gaussian-observation Controller Type. #include \u0026lt;lds_gaussian_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":34,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_fit/","title":"lds::gaussian::Fit","section":"Classes","content":" lds::gaussian::Fit # GLDS Fit Type. #include \u0026lt;lds_gaussian_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual const Matrix \u0026amp; R() const override\ngets measurement noise covariance virtual void set_R(const Matrix \u0026amp; R) override\nsets measurement noise covariance virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period R # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nh # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":35,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/","title":"lds::gaussian::FitEM","section":"Classes","content":" lds::gaussian::FitEM # GLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_gaussian_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::gaussian::FitEM; This type is used in the process of fitting GLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":36,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/","title":"lds::gaussian::FitSSID","section":"Classes","content":" lds::gaussian::FitSSID # Subspace Identification (SSID) for GLDS. #include \u0026lt;lds_gaussian_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":37,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/","title":"lds::gaussian::SwitchedController","section":"Classes","content":" lds::gaussian::SwitchedController # Gaussian-observation SwitchedController Type. #include \u0026lt;lds_gaussian_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":38,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_system/","title":"lds::gaussian::System","section":"Classes","content":" lds::gaussian::System # Gaussian LDS Type. #include \u0026lt;lds_gaussian_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)\nConstructs a new Gaussian System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. const Matrix \u0026amp; R() const\nGet output noise covariance. void set_Q(const Matrix \u0026amp; Q) void set_R(const Matrix \u0026amp; R)\nSet output noise covariance. void set_Ke(const Matrix \u0026amp; Ke)\nSet estimator gain. void set_Ke_m(const Matrix \u0026amp; Ke_m)\nSet disturbance estimator gain. void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively update estimator gain. Protected Attributes # Name Matrix R_ covariance of output noise bool do_recurse_Ke_ whether to recursively calculate estimator gain Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0 ) Parameters:\nn_u number of inputs (u) n_x number of states (x) n_y number of outputs (y) dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) r0 [optional] initial diagonal elements of output noise covariance (R) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nR # inline const Matrix \u0026amp; R() const set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # inline void set_R( const Matrix \u0026amp; R ) set_Ke # inline void set_Ke( const Matrix \u0026amp; Ke ) set_Ke_m # inline void set_Ke_m( const Matrix \u0026amp; Ke_m ) Print # void Print() Protected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nProtected Attribute Details # R_ # Matrix R_; do_recurse_Ke_ # bool do_recurse_Ke_ {}; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":39,"href":"/docs/api/namespaces/namespacelds_1_1poisson/","title":"lds::poisson","section":"Namespaces","content":" lds::poisson # Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. class lds::poisson::Fit PLDS Fit Type. class lds::poisson::FitEM PLDS E-M Fit Type. class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. class lds::poisson::System Poisson System type. Attributes # Name std::random_device rd random device for simulating poisson data std::mt19937 rng random number generator for simulating poisson data Attribute Details # rd # static std::random_device rd; rng # static std::mt19937 rng = std::mt19937( rd()); Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":40,"href":"/docs/api/classes/classlds_1_1poisson_1_1_controller/","title":"lds::poisson::Controller","section":"Classes","content":" lds::poisson::Controller # PLDS Controller Type. #include \u0026lt;lds_poisson_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":41,"href":"/docs/api/classes/classlds_1_1poisson_1_1_fit/","title":"lds::poisson::Fit","section":"Classes","content":" lds::poisson::Fit # PLDS Fit Type. #include \u0026lt;lds_poisson_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function virtual void set_R(const Matrix \u0026amp; R) override\nsets output noise covariance (if any) virtual const Matrix \u0026amp; R() const override Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # inline Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period h # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nR # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":42,"href":"/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/","title":"lds::poisson::FitEM","section":"Classes","content":" lds::poisson::FitEM # PLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_poisson_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::poisson::FitEM; This type is used in the process of fitting PLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":43,"href":"/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/","title":"lds::poisson::FitSSID","section":"Classes","content":" lds::poisson::FitSSID # Subspace Identification (SSID) for PLDS. #include \u0026lt;lds_poisson_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":44,"href":"/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/","title":"lds::poisson::SwitchedController","section":"Classes","content":" lds::poisson::SwitchedController # Poisson-observation SwitchedController Type. #include \u0026lt;lds_poisson_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":45,"href":"/docs/api/classes/classlds_1_1poisson_1_1_system/","title":"lds::poisson::System","section":"Classes","content":" lds::poisson::System # Poisson System type. #include \u0026lt;lds_poisson_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nConstructs a new Poisson System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively recalculate estimator gain (Ke) Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nProtected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nRecursively recalculate estimator gain (Ke).\nReferences:\nSmith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.\nEden UT, \u0026hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":46,"href":"/docs/api/classes/classlds_1_1_s_s_i_d/","title":"lds::SSID","section":"Classes","content":" lds::SSID # More\u0026hellip;\nInherited by lds::gaussian::FitSSID, lds::poisson::FitSSID\nPublic Functions # Name SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions # Name void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. virtual void DecomposeData() =0\nDecompose data to lower-triangular matrix (used in Solve) void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::SSID; Public Function Details # SSID # SSID() =default SSID # SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf) ) Parameters:\nn_x number of states n_h size of block-hankel data matrix dt sample period u_train input training data z_train measurement training data d output bias Run # std::tuple\u0026lt; Fit, Vector \u0026gt; Run( SSIDWt ssid_wt ) Parameters:\nssid_wt weight for singular value decomp Return: tuple (Fit, singular values)\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nProtected Function Details # CalcD # void CalcD( data_t t_silence =0.1, data_t thresh_silence =0.001 ) Parameters:\nt_silence threshold on period of time that qualifies as \u0026ldquo;silence\u0026rdquo; thresh_silence threshold on input amplitude u that qualifies as \u0026ldquo;silence\u0026rdquo; CreateHankelDataMat # void CreateHankelDataMat() Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.\nDecomposeData # virtual void DecomposeData() =0 Reimplemented by: lds::gaussian::FitSSID::DecomposeData, lds::poisson::FitSSID::DecomposeData\nCalcSVD # void CalcSVD( SSIDWt wt ) Parameters:\nssid_wt weight for SVD Solve # void Solve( data_t wt_dc ) Parameters:\nwt_dc weight placed on getting correct DC I/O gain RecomputeExtObs # void RecomputeExtObs() Protected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; D_ # Matrix D_; fit_ # Fit fit_; g_dc_ # Matrix g_dc_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_h_ # size_t n_h_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; L_ # Matrix L_; s_ # Vector s_; ext_obs_t_ # Matrix ext_obs_t_; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":47,"href":"/docs/api/classes/classlds_1_1_switched_controller/","title":"lds::SwitchedController","section":"Classes","content":" lds::SwitchedController # SwitchedController Type. More\u0026hellip;\n#include \u0026lt;lds_sctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nInherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController\nPublic Functions # Name SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes # Name std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::SwitchedController; Public Function Details # SwitchedController # SwitchedController() =default SwitchedController # inline SwitchedController( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask SwitchedController # inline SwitchedController( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Switch # inline void Switch( size_t idx, bool do_force_switch =false ) Parameters:\nidx index do_force_switch whether to force a system switch even if already there. set_Kc # inline void set_Kc( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc ) set_Kc # inline void set_Kc( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty ) set_Kc_inty # inline void set_Kc_inty( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u ) set_Kc_u # inline void set_Kc_u( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u ) set_g_design # inline void set_g_design( const UniformVectorList \u0026amp; g ) set_g_design # inline void set_g_design( UniformVectorList \u0026amp;\u0026amp; g ) Protected Attribute Details # systems_ # std::vector\u0026lt; System \u0026gt; systems_; n_sys_ # size_t n_sys_ {}; idx_ # size_t idx_ {}; Kc_list_ # UniformMatrixList Kc_list_; Kc_inty_list_ # UniformMatrixList Kc_inty_list_; Kc_u_list_ # UniformMatrixList Kc_u_list_; g_design_list_ # UniformVectorList g_design_list_; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":48,"href":"/docs/api/classes/classlds_1_1_system/","title":"lds::System","section":"Classes","content":" lds::System # Linear Dynamical System Type. #include \u0026lt;lds_sys.h\u0026gt;\nInherited by lds::gaussian::System, lds::poisson::System\nPublic Functions # Name System() =default\nConstructs a new System. System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nconstructs a new System virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) =0\nsimulates system (single time step) void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function virtual void h() =0\nsystem output function virtual Vector h_(Vector x) =0\nsystem output function (stateless) size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void RecurseKe() =0\nRecursively recalculate estimator gain (Ke) void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes # Name bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes # Name std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 diagonal elements for state estimate covariance q0 diagonal elements for process noise covariance ~System # inline virtual ~System() Filter # void Filter( const Vector \u0026amp; u_tm1, const Vector \u0026amp; z ) Parameters:\nu_tm1 input at t-minus-1 z_t current measurement Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.\nSimulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) =0 Parameters:\nu_tm1 input at time t-1 Return: simulated measurement at time t\nReimplemented by: lds::gaussian::System::Simulate, lds::poisson::System::Simulate\nf # inline void f( const Vector \u0026amp; u, bool do_add_noise =false ) Parameters:\nu input do_add_noise whether to add simulated process noise h # virtual void h() =0 Reimplemented by: lds::gaussian::System::h, lds::poisson::System::h\nh_ # virtual Vector h_( Vector x ) =0 Parameters:\nx_t state at time t Return: predicted state at time t + 1\nReimplemented by: lds::gaussian::System::h_, lds::poisson::System::h_\nn_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const x # inline const Vector \u0026amp; x() const P # inline const Matrix \u0026amp; P() const m # inline const Vector \u0026amp; m() const P_m # inline const Matrix \u0026amp; P_m() const cx # inline const Vector \u0026amp; cx() const y # inline const Vector \u0026amp; y() const x0 # inline const Vector \u0026amp; x0() const m0 # inline const Vector \u0026amp; m0() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const Ke # inline const Matrix \u0026amp; Ke() const Ke_m # inline const Matrix \u0026amp; Ke_m() const Q # inline const Matrix \u0026amp; Q() Q_m # inline const Matrix \u0026amp; Q_m() P0 # inline const Matrix \u0026amp; P0() P0_m # inline const Matrix \u0026amp; P0_m() set_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_m # inline void set_m( const Vector \u0026amp; m, bool do_force_assign =false ) set_g # inline void set_g( const Vector \u0026amp; g ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_Q_m # inline void set_Q_m( const Matrix \u0026amp; Q_m ) set_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_P0_m # inline void set_P0_m( const Matrix \u0026amp; P0_m ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) set_x # inline void set_x( const Vector \u0026amp; x ) Reset # void Reset() nstep_pred_block # std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block( UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1 ) Print # void Print() Protected Function Details # RecurseKe # virtual void RecurseKe() =0 Reimplemented by: lds::gaussian::System::RecurseKe, lds::poisson::System::RecurseKe\nInitVars # void InitVars( data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Public Attribute Details # do_adapt_m # bool do_adapt_m {}; Protected Attribute Details # n_x_ # std::size_t n_x_ {}; n_u_ # std::size_t n_u_ {}; n_y_ # std::size_t n_y_ {}; dt_ # data_t dt_ {}; x_ # Vector x_; P_ # Matrix P_; m_ # Vector m_; P_m_ # Matrix P_m_; cx_ # Vector cx_; y_ # Vector y_; z_ # Vector z_; x0_ # Vector x0_; P0_ # Matrix P0_; m0_ # Vector m0_; P0_m_ # Matrix P0_m_; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; Q_ # Matrix Q_; Q_m_ # Matrix Q_m_; C_ # Matrix C_; d_ # Vector d_; Ke_ # Matrix Ke_; Ke_m_ # Matrix Ke_m_; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":49,"href":"/docs/api/classes/classlds_1_1_uniform_matrix_list/","title":"lds::UniformMatrixList","section":"Classes","content":" lds::UniformMatrixList # More\u0026hellip;\nInherits from std::vector\u0026lt; Matrix \u0026gt;\nPublic Functions # Name UniformMatrixList() =default\nConstructs a new UniformMatrixList. UniformMatrixList(const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent. UniformMatrixList(std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent. UniformMatrixList(std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent. UniformMatrixList(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nConstructs a new UniformMatrixList (copy). UniformMatrixList(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nConstructs a new UniformMatrixList (move). ~UniformMatrixList() =default\nDestroys the object. const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim(size_t n =0) const\ngets dimensions of uniformly sized matrices size_t size()\nsize of container const Matrix \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Matrix \u0026amp; that, size_t n)\nswaps input matrix with n^th matrix of list UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nassigns the contents (copy) UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nassigns the contents (move) void append(const Matrix \u0026amp; mat)\nappends a matrix to the list Detailed Description # template \u0026lt;MatrixListFreeDim D =kMatFreeDimNone\u0026gt; class lds::UniformMatrixList; Public Function Details # UniformMatrixList # UniformMatrixList() =default UniformMatrixList # explicit UniformMatrixList( const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # explicit UniformMatrixList( std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList UniformMatrixList # UniformMatrixList( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList ~UniformMatrixList # ~UniformMatrixList() =default dim # inline const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim( size_t n =0 ) const Parameters:\nn [optional] index in list of matrices Return: dimensions\nsize # inline size_t size() at # inline const Matrix \u0026amp; at( size_t n ) Swap # inline void Swap( Matrix \u0026amp; that, size_t n ) Parameters:\nthat input matrix n index where the matrix is moved operator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\noperator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\nappend # void append( const Matrix \u0026amp; mat ) Parameters:\nmat input matrix Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":50,"href":"/docs/api/classes/classlds_1_1_uniform_system_list/","title":"lds::UniformSystemList","section":"Classes","content":" lds::UniformSystemList # More\u0026hellip;\nInherits from std::vector\u0026lt; System \u0026gt;\nPublic Functions # Name UniformSystemList() =default\nConstructs a new UniformSystemList. UniformSystemList(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by copying existing vector of System if dimensions consistent. UniformSystemList(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by moving existing vector of System if dimensions consistent. UniformSystemList(std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList from initializer_list of System if dimensions consistent. UniformSystemList(const UniformSystemList \u0026amp; that)\nConstructs a new UniformSystemList (copy). UniformSystemList(UniformSystemList \u0026amp;\u0026amp; that)\nConstructs a new UniformSystemList (move). ~UniformSystemList() =default\nDestroys the object. const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const\ngets dimensions of the uniformly sized systems size_t size()\nsize of container const System \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(System \u0026amp; that, size_t n)\nswaps input system with n^th system of list UniformSystemList \u0026amp; operator=(const UniformSystemList \u0026amp; that)\nassigns the contents (copy) UniformSystemList \u0026amp; operator=(UniformSystemList \u0026amp;\u0026amp; that)\nassigns the contents (move) Detailed Description # template \u0026lt;typename System \u0026gt; class lds::UniformSystemList; Public Function Details # UniformSystemList # UniformSystemList() =default UniformSystemList # explicit UniformSystemList( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # explicit UniformSystemList( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList UniformSystemList # UniformSystemList( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList ~UniformSystemList # ~UniformSystemList() =default dim # inline const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const size # inline size_t size() at # inline const System \u0026amp; at( size_t n ) Swap # inline void Swap( System \u0026amp; that, size_t n ) Parameters:\nthat input system n index where the system is moved operator= # inline UniformSystemList \u0026amp; operator=( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\noperator= # inline UniformSystemList \u0026amp; operator=( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":51,"href":"/docs/api/classes/classlds_1_1_uniform_vector_list/","title":"lds::UniformVectorList","section":"Classes","content":" lds::UniformVectorList # Inherits from std::vector\u0026lt; Vector \u0026gt;\nPublic Functions # Name UniformVectorList() =default\nConstructs a new UniformVectorList. UniformVectorList(const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent. UniformVectorList(std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent. UniformVectorList(std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0)\nConstructs a new UniformVectorList from initializer_list of Vector if dimensions consistent. UniformVectorList(const UniformVectorList \u0026amp; that)\nConstructs a new UniformVectorList (copy) UniformVectorList(UniformVectorList \u0026amp;\u0026amp; that)\nConstructs a new UniformVectorList (move) ~UniformVectorList() =default\nDestroys the object. size_t dim() const\ngets dimensions of the uniformly sized matrices size_t size()\nsize of container const Vector \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Vector \u0026amp; that, size_t n)\nswaps input matrix with n^th vector of list UniformVectorList \u0026amp; operator=(const UniformVectorList \u0026amp; that)\nassigns the contents (copy) UniformVectorList \u0026amp; operator=(UniformVectorList \u0026amp;\u0026amp; that)\nassigns the contents (move) Public Function Details # UniformVectorList # UniformVectorList() =default UniformVectorList # explicit UniformVectorList( const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dims dimension UniformVectorList # explicit UniformVectorList( std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList UniformVectorList # UniformVectorList( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList ~UniformVectorList # ~UniformVectorList() =default dim # inline size_t dim() const size # inline size_t size() at # inline const Vector \u0026amp; at( size_t n ) Swap # inline void Swap( Vector \u0026amp; that, size_t n ) Parameters:\nthat input vector n index where the vector is moved operator= # inline UniformVectorList \u0026amp; operator=( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\noperator= # inline UniformVectorList \u0026amp; operator=( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":52,"href":"/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/","title":"ldsCtrlEst_h","section":"Files","content":" ldsCtrlEst_h # Files # Name ldsCtrlEst_h/lds.h lds namespace ldsCtrlEst_h/lds_ctrl.h Controller. ldsCtrlEst_h/lds_fit.h LDS base fit type. ldsCtrlEst_h/lds_fit_em.h subspace identification ldsCtrlEst_h/lds_fit_ssid.h subspace identification ldsCtrlEst_h/lds_gaussian.h glds namespace ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller. ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type. ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type. ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type. ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type. ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type. ldsCtrlEst_h/lds_poisson.h plds namespace ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type. ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type. ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type. ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type. ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type. ldsCtrlEst_h/lds_poisson_sys.h PLDS base type. ldsCtrlEst_h/lds_sctrl.h SwitchedController type. ldsCtrlEst_h/lds_sys.h LDS base type. ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices. ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems. ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors. ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":53,"href":"/docs/api/files/lds__ctrl_8h/","title":"ldsCtrlEst_h/lds_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_ctrl.h # Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Controller Detailed Description # This file declares the type for control of a linear dynamical system (lds::Controller).\nSource code # //===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_CTRL_H #define LDSCTRLEST_LDS_CTRL_H // namespace #include \u0026#34;lds.h\u0026#34; // system type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class Controller { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); public: Controller() = default; Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); const Vector\u0026amp; Control(const Vector\u0026amp; z, bool do_control = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); const Vector\u0026amp; ControlOutputReference(const Vector\u0026amp; z, bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); // get methods: const System\u0026amp; sys() const { return sys_; }; const Matrix\u0026amp; Kc() const { return Kc_; }; const Matrix\u0026amp; Kc_inty() const { return Kc_inty_; }; const Matrix\u0026amp; Kc_u() const { return Kc_u_; }; const Vector\u0026amp; g_design() const { return g_design_; }; const Vector\u0026amp; u_ref() const { return u_ref_; }; const Vector\u0026amp; x_ref() const { return x_ref_; }; const Vector\u0026amp; y_ref() const { return y_ref_; }; size_t control_type() const { return control_type_; }; data_t tau_awu() const { return tau_awu_; }; data_t u_lb() const { return u_lb_; }; data_t u_ub() const { return u_ub_; }; // set methods void set_sys(const System\u0026amp; sys) { bool does_match = sys_.n_u() == sys.n_u(); does_match = does_match \u0026amp;\u0026amp; (sys_.n_x() == sys.n_x()); does_match = does_match \u0026amp;\u0026amp; (sys_.n_y() == sys.n_y()); if (does_match) { sys_ = sys; } else { throw std::runtime_error( \u0026#34;new system argument to `set_sys` does not match dimensionality of \u0026#34; \u0026#34;existing system\u0026#34;); } }; void set_g_design(const Vector\u0026amp; g_design) { Reassign(g_design_, g_design); }; void set_u_ref(const Vector\u0026amp; u_ref) { Reassign(u_ref_, u_ref); }; void set_x_ref(const Vector\u0026amp; x_ref) { Reassign(x_ref_, x_ref); cx_ref_ = sys_.C() * x_ref_; }; // y_ref needs to be handled differently depending on output fn. // (need to populate cx_ref_ too, which depends on output fn) virtual void set_y_ref(const Vector\u0026amp; y_ref) { Reassign(y_ref_, y_ref); }; void set_Kc(const Matrix\u0026amp; Kc) { Reassign(Kc_, Kc); }; void set_Kc_inty(const Matrix\u0026amp; Kc_inty) { Reassign(Kc_inty_, Kc_inty); }; void set_Kc_u(const Matrix\u0026amp; Kc_u) { Reassign(Kc_u_, Kc_u); }; void set_tau_awu(data_t tau) { tau_awu_ = tau; k_awu_ = sys_.dt() / tau_awu_; }; void set_control_type(size_t control_type); // There is no reason u_lb/ub should not be public, but making set methods // anyway. void set_u_lb(data_t u_lb) { u_lb_ = u_lb; }; void set_u_ub(data_t u_ub) { u_ub_ = u_ub; }; void Reset() { sys_.Reset(); u_ref_.zeros(); u_ref_prev_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); u_saturated_ = false; t_since_control_onset_ = 0.0; }; void Print() { sys_.Print(); std::cout \u0026lt;\u0026lt; \u0026#34;g_design : \u0026#34; \u0026lt;\u0026lt; g_design_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_lb : \u0026#34; \u0026lt;\u0026lt; u_lb_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_ub : \u0026#34; \u0026lt;\u0026lt; u_ub_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }; protected: System sys_; Vector u_; Vector u_return_; Vector g_design_; // reference signals Vector u_ref_; // create no set method for this: Vector u_ref_prev_; Vector x_ref_; Vector y_ref_; Vector cx_ref_; // Controller gains Matrix Kc_; Matrix Kc_u_; Matrix Kc_inty_; // control after g inversion // do not need set methods for these. Vector du_ref_; Vector dv_ref_; Vector v_ref_; Vector dv_; Vector v_; // integral error // do not need set method for this Vector int_e_; Vector int_e_awu_adjust_; Vector u_sat_; bool do_control_prev_ = false; bool do_lock_control_prev_ = false; // whether the g of system has become inverted from what you think it is // (gain_ref) bool u_saturated_ = false; // should be safe to have references here bc nothing needs to be done // (like reset vars) when it changes... data_t u_lb_{}; data_t u_ub_{}; data_t tau_awu_{}; data_t k_awu_ = 0; data_t t_since_control_onset_ = 0; size_t control_type_{}; private: void CalcControl(bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); void CalcSteadyStateSetPoint(); void AntiWindup(); void InitVars(size_t control_type); }; // Implement the above: template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(sys), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(std::move(sys)), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::set_control_type(size_t control_type) { if (control_type_ == control_type) { return; } // creating a blank slate... control_type_ = 0; Kc_inty_.zeros(0, 0); Kc_u_.zeros(0, 0); int_e_.zeros(0, 0); int_e_awu_adjust_.zeros(0, 0); // controller was designed to minimize integral error if (control_type \u0026amp; kControlTypeIntY) { Kc_inty_.zeros(sys_.n_u(), sys_.n_y()); int_e_.zeros(sys_.n_y()); int_e_awu_adjust_.zeros(sys_.n_u()); control_type_ = control_type_ | kControlTypeIntY; } // controller was designed to minimize deltaU // (i.e. state augmented with u) if (control_type \u0026amp; kControlTypeDeltaU) { Kc_u_.zeros(sys_.n_u(), sys_.n_u()); control_type_ = control_type_ | kControlTypeDeltaU; } // whether to adapt set point calculate with (re-estimated) process // disturbance (m) if (control_type \u0026amp; kControlTypeAdaptM) { if (sys_.do_adapt_m) // only if adapting m... { control_type_ = control_type_ | kControlTypeAdaptM; } } } // set_control_type template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::Control( const Vector\u0026amp; z, bool do_control, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement sys_.Filter(u_, z); bool do_estimation = true; // always have estimator on in this case // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::ControlOutputReference( const Vector\u0026amp; z, bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement if (do_estimation) { sys_.Filter(u_, z); } else { sys_.f(u_); } // calculate the set point // solves for u_ref and x_ref when output is at y_ref at steady state. if (do_control) { CalcSteadyStateSetPoint(); } // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcControl(bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { if (do_control \u0026amp;\u0026amp; do_estimation) { if (!do_control_prev_) { if (do_reset_at_control_onset) { Reset(); } t_since_control_onset_ = 0.0; } else { t_since_control_onset_ += sys_.dt(); } // enforce softstart on control vars. if (sigma_soft_start \u0026gt; 0) { // half-Gaussian soft-start scaling factor data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) / (2 * pow(sigma_soft_start, 2))); u_ref_ *= soft_start_sf; // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too // x_ref_ *= soft_start_sf; // cx_ref_ *= soft_start_sf; // y_ref_ *= soft_start_sf; } if (!do_lock_control) { // first do u -\u0026gt; v change of vars. (v = g.*u) // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver // control voltage u[=]V) v_ref_ = g_design_ % u_ref_; // Given FB, calc. the change in control if (control_type_ \u0026amp; kControlTypeDeltaU) { // if control designed to minimize not u but deltaU (i.e. state aug with // u): // TODO(mfbolus): Commented out for now. See note below. // du_ref_ = u_ref_ - u_ref_prev_; // dv_ref_ = g_design_ % du_ref_; // TODO(mfbolus): Assuming users want *smooth* control signals if using // kControlTypeDeltaU, it should be the case that dv_ref_ is --\u0026gt; 0. May // want to revisit, but I am going to force it to be zero unless a // situation arises that argues for keeping the above. dv_ref_.zeros(); dv_ = dv_ref_; // nominally-optimal. dv_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error dv_ -= Kc_u_ * (v_ - v_ref_); // penalty on amp u (rel to ref) if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if(!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error dv_ -= Kc_inty_ * int_e_; // control for integrated error } // update the control v_ += dv_; } else { v_ = v_ref_; // nominally-optimal. v_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if (!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error v_ -= Kc_inty_ * int_e_; // control for integrated error } } // convert back to control voltage u[=]V u_ = v_ / sys_.g(); } // else do nothing until lock is low } else { // if not control // feed through u_ref in open loop u_ = u_ref_ % g_design_ / sys_.g(); v_ = sys_.g() % u_; u_ref_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); } // ends do_control // enforce box constraints (and antiwindup) AntiWindup(); // add noise to input? // The value for u that is *returned* to user after addition of any noise, // while keeping controller/estimator blind to this addition. u_return_ = u_; if ((sigma_u_noise \u0026gt; 0.0) \u0026amp;\u0026amp; (do_control \u0026amp;\u0026amp; !do_lock_control)) { u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn); Limit(u_return_, u_lb_, u_ub_); }; // For next time step: u_ref_prev_ = u_ref_; do_control_prev_ = do_control; do_lock_control_prev_ = do_lock_control; } // CalcControl template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcSteadyStateSetPoint() { // Linearly-constrained least squares (ls). // // _reference: // Boyd \u0026amp; Vandenberghe (2018) Introduction to Applied Linear Algebra // Matrix a_ls = join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros)); Vector b_ls = cx_ref_; Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye), sys_.B() * arma::diagmat(sys_.g())); Vector d_ls = -sys_.m0(); if (control_type_ \u0026amp; kControlTypeAdaptM) { d_ls = -sys_.m(); // adapt setpoint calc with disturbance? } Matrix a_ls_t = a_ls.t(); // TODO(mfbolus): not sure why but causes seg // fault if I do not do this. Matrix phi_ls = join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()), join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros))); // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse: Matrix inv_phi = pinv(phi_ls); Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls); x_ref_ = xulam.subvec(0, sys_.n_x() - 1); u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1); cx_ref_ = sys_.C() * x_ref_; } // CalcSteadyStateSetPoint template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::AntiWindup() { u_saturated_ = false; u_sat_ = u_; // limit u and flag whether saturated for (size_t k = 0; k \u0026lt; u_.n_elem; k++) { if (u_[k] \u0026lt; u_lb_) { u_sat_[k] = u_lb_; u_saturated_ = true; } if (u_[k] \u0026gt; u_ub_) { u_sat_[k] = u_ub_; u_saturated_ = true; } } if ((control_type_ \u0026amp; kControlTypeIntY) \u0026amp;\u0026amp; (tau_awu_ \u0026lt; lds::kInf)) { // one-step back-calculation (calculate intE for u=u_sat) // (Astroem, Rundqwist 1989 warn against using this...) // int_e_awu_adjust_ = // solve(Kc_inty_, (u_ - u_sat_)); // pinv(Kc_inty) * (u-uSat); // gradual: see Astroem, Rundqwist 1989 // this is a fudge for doing MIMO gradual // n.b., went ahead and multiplied 1/T by dt so don\u0026#39;t have to do that here. int_e_awu_adjust_ = k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_); // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_); int_e_ += int_e_awu_adjust_; } // set u to saturated version u_ = u_sat_; } template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::InitVars(size_t control_type) { // initialize to default values u_ref_ = Vector(sys_.n_u(), fill::zeros); u_ref_prev_ = Vector(sys_.n_u(), fill::zeros); x_ref_ = Vector(sys_.n_x(), fill::zeros); y_ref_ = Vector(sys_.n_y(), fill::zeros); cx_ref_ = Vector(sys_.n_y(), fill::zeros); u_ = Vector(sys_.n_u(), fill::zeros); u_return_ = Vector(sys_.n_u(), fill::zeros); u_sat_ = Vector(sys_.n_u(), fill::zeros); // Might not need all these, so zero elements until later. Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros); Kc_u_ = Matrix(0, 0, fill::zeros); Kc_inty_ = Matrix(0, 0, fill::zeros); g_design_ = sys_.g(); // by default, same as model dv_ = Vector(sys_.n_u(), fill::zeros); v_ = Vector(sys_.n_u(), fill::zeros); du_ref_ = Vector(sys_.n_u(), fill::zeros); dv_ref_ = Vector(sys_.n_u(), fill::zeros); v_ref_ = Vector(sys_.n_u(), fill::zeros); int_e_ = Vector(0, fill::zeros); int_e_awu_adjust_ = Vector(0, fill::zeros); set_control_type(control_type); } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":54,"href":"/docs/api/files/lds__fit__em_8h/","title":"ldsCtrlEst_h/lds_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_em.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::EM Detailed Description # This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).\nSource code # //===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_EMAX_H #define LDSCTRLEST_LDS_EMAX_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class EM { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: EM() = default; EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); virtual ~EM() = default; const Fit\u0026amp; Run(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = true, bool calc_output = true, bool calc_measurement = true, size_t max_iter = 100, data_t tol = 1e-2); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); // auto tuple = std::make_tuple(u_, z_); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; x() const { return x_; }; const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; y() const { return y_; }; const Matrix\u0026amp; sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; }; const Matrix\u0026amp; sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; }; const Matrix\u0026amp; sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; }; size_t n_t_tot() { return n_t_tot_; } const Vector\u0026amp; theta() const { return theta_; }; protected: void Expectation(bool force_common_initial = false); void Maximization(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = false, bool calc_output = false, bool calc_measurement = false); void MaximizeDynamics(); void MaximizeQ(); void MaximizeInitial(); virtual void MaximizeOutput() = 0; virtual void MaximizeMeasurement() = 0; void Smooth(bool force_common_initial); virtual void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) = 0; void Reset(); void InitVars(); Vector UpdateTheta(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; std::vector\u0026lt;Matrix\u0026gt; x_; std::vector\u0026lt;Cube\u0026gt; P_; std::vector\u0026lt;Cube\u0026gt; P_t_tm1_; std::vector\u0026lt;Matrix\u0026gt; y_; Matrix diag_y_; // expectations calculated in E-step Matrix sum_E_x_t_x_t_; Matrix sum_E_xu_tm1_xu_tm1_; Matrix sum_E_xu_t_xu_tm1_; Fit fit_; Vector theta_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; }; template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; fit_ = Fit(n_u_, n_x, n_y_, dt); u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { // make sure fit dims match I/O data if (fit0.n_u() != u_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and input training data have inconsistent dimensions\u0026#34;); } if (fit0.n_y() != z_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and output training data have inconsistent dimensions\u0026#34;); } fit_ = fit0; u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::InitVars() { // check input/output data dimensions are consistent if (z_.size() != u_.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_.at(trial).n_cols != u_.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } n_u_ = fit_.n_u(); n_x_ = fit_.n_x(); n_y_ = fit_.n_y(); dt_ = fit_.dt(); x_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); P_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); P_t_tm1_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); y_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros); P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros); } diag_y_ = Matrix(n_y_, n_y_, fill::zeros); // covariances in expectation step sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); } template \u0026lt;typename Fit\u0026gt; const Fit\u0026amp; EM\u0026lt;Fit\u0026gt;::Run(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement, size_t max_iter, data_t tol) { Reset(); // to initial conditions size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_; Vector theta(n_params); Vector theta_new(n_params); data_t max_dtheta = 1; // if solving for initial conditions, allow them be varied. // otherwise, freeze at provided values. bool force_common_initial = !calc_init; // go until parameter convergence for (size_t l = 0; l \u0026lt; max_iter; l++) { theta_ = UpdateTheta(); std::cout \u0026lt;\u0026lt; \u0026#34;Iteration \u0026#34; \u0026lt;\u0026lt; l + 1 \u0026lt;\u0026lt; \u0026#34;/\u0026#34; \u0026lt;\u0026lt; max_iter \u0026lt;\u0026lt; \u0026#34; ...\\n\u0026#34;; Expectation(force_common_initial); Maximization(calc_dynamics, calc_Q, calc_init, calc_output, calc_measurement); // check convergence theta_new = UpdateTheta(); Vector dtheta = abs(theta_new - theta_) / abs(theta_); // some parameters could be zero... arma::uvec ubi_finite = find_finite(dtheta); max_dtheta = max(dtheta.elem(ubi_finite)); std::cout \u0026lt;\u0026lt; \u0026#34;max dtheta: \u0026#34; \u0026lt;\u0026lt; max_dtheta \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; if (max_dtheta \u0026lt; tol) { std::cout \u0026lt;\u0026lt; \u0026#34;Converged.\\n\u0026#34;; break; } std::cout \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } return fit_; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Smooth(bool force_common_initial) { Matrix k_e(n_x_, n_y_); // estimator gain Cube k_backfilt; // back-filtering gains // TODO(mfbolus): this loop could be made parallel for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { Matrix x_pre(n_x_, n_t_[trial], fill::zeros); Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros); Matrix x_post(n_x_, n_t_[trial], fill::zeros); Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros); if (force_common_initial) // forces all trials to have same initial // conditions. { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); } y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); // This *should not* be necessary but make sure P is symmetric. ForceSymPD(P_[trial].slice(0)); x_pre.col(0) = x_[trial].col(0); p_pre.slice(0) = P_[trial].slice(0); x_post.col(0) = x_[trial].col(0); p_post.slice(0) = P_[trial].slice(0); // filter for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // predict fit_.f(x_pre, x_post, u_.at(trial), t); fit_.h(y_[trial], x_pre, t); diag_y_.diag() = y_[trial].col(t); // TODO(mfbolus): change if parallel // update --\u0026gt; posterior estimation RecurseKe(k_e, p_pre, p_post, t); x_post.col(t) = x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t)); y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d(); } // backfilter -\u0026gt; Smoothed estimate // Reference: // Shumway et Stoffer (1982) ForceSymPD(p_post.slice(n_t_[trial] - 1)); k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1); P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 0; t--) { // TODO(mfmbolus): should not be necessary to force symm positive def ForceSymPD(p_pre.slice(t)); ForceSymPD(p_post.slice(t - 1)); ForceSymPD(P_[trial].slice(t)); k_backfilt.slice(t - 1) = p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t)); x_[trial].col(t - 1) = x_post.col(t - 1) + k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t)); P_[trial].slice(t - 1) = p_post.slice(t - 1) + k_backfilt.slice(t - 1) * (P_[trial].slice(t) - p_pre.slice(t)) * k_backfilt.slice(t - 1).t(); } // do the same for P_t_tm1 Matrix id(n_x_, n_x_, fill::eye); P_t_tm1_[trial].slice(n_t_[trial] - 1) = (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 1; t--) { P_t_tm1_[trial].slice(t - 1) = p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() + k_backfilt.slice(t - 1) * (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) * k_backfilt.slice(t - 2).t(); } // finally, get smoothed estimate of output for (size_t t = 0; t \u0026lt; n_t_[trial]; t++) { fit_.h(y_[trial], x_[trial], t); } // samps loop } // trial loop } // Smooth // template \u0026lt;typename Fit\u0026gt; // void EM\u0026lt;Fit\u0026gt;::RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) { // // predict covar // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q(); // // update Ke // Ke = P_pre.slice(t) * fit_.C().t() * // inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R()); // // update cov // // Reference: Ghahramani et Hinton (1996) // P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t); // // // n.b. for poisson : // // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + // fit_.Q(); // // // update cov // // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ * // // fit_.C()); // // // update Ke // // Ke = P_post.slice(t) * fit_.C(); // } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Expectation(bool force_common_initial) { // calculate the mean/cov of state needed for maximizing E[pr(z|theta)] Smooth(force_common_initial); // now get the various forms of sum(E[xx\u0026#39;]) needed // n.b. Going to start at t=1 rather than 0 bc most max terms need that. // so really \u0026#34;n_t_tot_\u0026#34; is (n_t_tot_-1) n_t_tot_ = 0; sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); Vector xu_tm1(n_x_ + n_u_, fill::zeros); Vector xu_t(n_x_ + n_u_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // ------------ sum_E_x_t_x_t ------------ sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t(); sum_E_x_t_x_t_ += P_[trial].slice(t); // ------------ sum_E_xu_tm1_xu_tm1 ------------ xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1)); sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t(); sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t - 1); // ------------ sum_E_xu_t_xu_tm1 ------------ xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t)); sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t(); sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_t_tm1_[trial].slice(t); n_t_tot_ += 1; } // time } // trial } // Expectation template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement) { if (calc_output) { MaximizeOutput(); } if (calc_measurement) { MaximizeMeasurement(); } if (calc_dynamics) { MaximizeDynamics(); } if (calc_Q) { MaximizeQ(); } if (calc_init) { MaximizeInitial(); } } // Maximization template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeDynamics() { // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) * inv_sympd(sum_E_xu_tm1_xu_tm1_); fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1)); std::cout \u0026lt;\u0026lt; \u0026#34;A_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.A()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.B()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeQ() { // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 * // inv_sympd(sum_E_xu_tm1_xu_tm1_) * // sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // this way is same as above iff dynamics were just updated: // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix ab = arma::join_horiz(fit_.A(), fit_.B()); // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // From scratch method: // Q is covariance of the error between state and dynamics-predicted state // (aka process noise) // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})\u0026#39;] // t-1 terms: View sum_e_x_tm1_x_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_u_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1); View sum_e_x_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); // t, t-1 terms: View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_x_t_u_tm1 = sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); Matrix q = sum_E_x_t_x_t_; q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t(); q -= sum_e_x_t_x_tm1 * fit_.A().t(); q -= fit_.A() * sum_e_x_t_x_tm1.t(); // input-related terms: q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t(); q -= sum_e_x_t_u_tm1 * fit_.B().t(); q -= fit_.B() * sum_e_x_t_u_tm1.t(); q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t(); q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t(); q /= n_t_tot_; fit_.set_Q(q); std::cout \u0026lt;\u0026lt; \u0026#34;Q_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.Q()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // std::cout \u0026lt;\u0026lt; \u0026#34;Q_new: \\n\u0026#34; \u0026lt;\u0026lt; fit_.Q() \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeInitial() { Vector x0 = fit_.x0(); x0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { x0 += x_[trial].col(0); } x0 /= z_.size(); std::cout \u0026lt;\u0026lt; \u0026#34;x0_new[0]: \u0026#34; \u0026lt;\u0026lt; x0[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // always recalc P0 even if the initial state is fixed (at zero, for // example) Matrix e_var(n_x_, n_x_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t(); } e_var /= z_.size(); // go ahead and subtract x0*x0\u0026#39; so don\u0026#39;t have to below. e_var -= x0 * x0.t(); // To get P0, going to get initial P_ per trial and average. // (which might be wrong, but need a single number) Matrix p0 = fit_.P0(); p0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { p0 += (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var; } p0 /= z_.size(); fit_.set_P0(p0); std::cout \u0026lt;\u0026lt; \u0026#34;P0_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.P0()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeOutput() { // solve for C+d: Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros); Vector x1(n_x_ + 1, fill::zeros); x1[n_x_] = 1.0; // augment with one to solve for bias Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { x1.subvec(0, n_x_ - 1) = x_[trial].col(t); sum_zx += z_.at(trial).col(t) * x1.t(); sum_e_x1_x1 += x1 * x1.t(); sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t); } } Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1); fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1)); fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_))); std::cout \u0026lt;\u0026lt; \u0026#34;C_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.C()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.d()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeMeasurement() { // Solve for measurement noise covar size_t n_t_tot = 0; // Ghahgramani, Hinton 1996: Matrix sum_zz(n_y_, n_y_, fill::zeros); Matrix sum_yz(n_y_, n_y_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t(); // Use Cnew: sum_yz += (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t(); n_t_tot += 1; } } fit_.set_R((sum_zz - sum_yz) / n_t_tot); std::cout \u0026lt;\u0026lt; \u0026#34;R_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.R()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Reset() { // reset to initial conditions for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); } } template \u0026lt;typename Fit\u0026gt; Vector EM\u0026lt;Fit\u0026gt;::UpdateTheta() { // TODO(mfbolus): This should include n_y_ more params for d. size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_; if (fit_.R().n_elem \u0026gt; 0) { n_params += n_y_ * n_y_; } Vector theta(n_params); size_t idx_start = 0; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B()); idx_start += n_x_ * n_u_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0()); idx_start += n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C()); idx_start += n_y_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d()); idx_start += n_y_; if (fit_.R().n_elem \u0026gt; 0) { theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R()); } return theta; } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":55,"href":"/docs/api/files/lds__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_ssid.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SSID Detailed Description # This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/docs/api/classes/classlds_1_1_s_s_i_d/)\u0026lt;Fit\u0026gt;).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_FIT_SSID_H #define LDSCTRLEST_LDS_FIT_SSID_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class SSID { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: SSID() = default; SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d = Vector(1).fill(-kInf)); std::tuple\u0026lt;Fit, Vector\u0026gt; Run(SSIDWt ssid_wt); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } protected: void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001); void CreateHankelDataMat(); virtual void DecomposeData() = 0; void CalcSVD(SSIDWt wt); void Solve(data_t wt_dc); void RecomputeExtObs(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; Matrix D_; Fit fit_; Matrix g_dc_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_h_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; Matrix L_; Vector s_; Matrix ext_obs_t_; }; template \u0026lt;typename Fit\u0026gt; SSID\u0026lt;Fit\u0026gt;::SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d) { // check input/output data dimensions are consistent if (z_train.size() != u_train.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_train.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_train.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } dt_ = dt; n_x_ = n_x; n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; n_h_ = n_h; // dimensionality check for eventual block-hankel data matrix size_t len = n_t_tot_ - 2 * n_h_ + 1; if (len \u0026lt; (2 * n_h_ * (n_u_ + n_y_))) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;Dataset problem! More rows than columns in block-hankel data \u0026#34; \u0026#34;matrix: 2*(n_u+n_y)*n_h \u0026gt; data-length! Need higher data-length or \u0026#34; \u0026#34;lower n_h.\u0026#34;; throw std::runtime_error(ss.str()); } fit_ = Fit(n_u_, n_x_, n_y_, dt_); u_ = std::move(u_train); z_ = std::move(z_train); if (!d.is_finite() || (d.n_rows != n_y_)) { // TODO(mfbolus): implement least-square solution for impulse response with // a second input of ones. Data-driven way of accounting for offset *not* // driven by an input. // // For now, calculate output bias (d) as the // output wherever the stimulus has not been on for some amount of time. // convolve u with rectangle and take all samples. This is a reasonable // approach, since often when autonomous systems are fit (i.e., systems with // no input), they will subtract off the mean of the output. This // essentially amounts to setting output bias to the mean of the output when // there is no stimulation. data_t t_silence = 0.1; data_t thresh_silence = 0.001; CalcD(t_silence, thresh_silence); } else { fit_.set_d(d); } } template \u0026lt;typename Fit\u0026gt; std::tuple\u0026lt;Fit, Vector\u0026gt; SSID\u0026lt;Fit\u0026gt;::Run(SSIDWt ssid_wt) { // the weight on minimizing dc I/O gain only works for gaussian, // and hopefully not necessary with appropriate dataset. data_t wt_dc = 0; // std::cout \u0026lt;\u0026lt; \u0026#34;creating hankel mat\\n\u0026#34;; CreateHankelDataMat(); // std::cout \u0026lt;\u0026lt; \u0026#34;decomposing data\\n\u0026#34;; DecomposeData(); // std::cout \u0026lt;\u0026lt; \u0026#34;calculating svd\\n\u0026#34;; CalcSVD(ssid_wt); // std::cout \u0026lt;\u0026lt; \u0026#34;solving for params\\n\u0026#34;; Solve(wt_dc); // std::cout \u0026lt;\u0026lt; \u0026#34;fin\\n\u0026#34;; return std::make_tuple(fit_, s_); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcD(data_t t_silence, data_t thresh_silence) { Vector d(z_.at(0).n_rows, fill::zeros); Vector win(static_cast\u0026lt;size_t\u0026gt;(t_silence / dt_), fill::ones); Vector sum_z_silence(n_y_, fill::zeros); size_t n_silence(0); for (size_t trial = 0; trial \u0026lt; u_.size(); trial++) { // find silent samples // start by convolving with Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0)); Vector u_conv = conv(sum_u, win, \u0026#34;same\u0026#34;); // get only the samples that are silent... arma::uvec ubi_silence = find(u_conv \u0026lt;= thresh_silence); if (ubi_silence.n_elem \u0026gt; 0) { sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1); n_silence += ubi_silence.n_elem; } } if (n_silence \u0026gt; 0) { d = sum_z_silence / n_silence; } fit_.set_d(d); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat() { // temporary copy of data Matrix z(n_y_, n_t_tot_, fill::zeros); Matrix u(n_u_, n_t_tot_, fill::zeros); size_t so_far(0); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial); u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial); so_far += n_t_.at(trial); } // remove output bias z.each_col() -= fit_.d(); // calculate I/O gain @ DC while data in convenient form g_dc_ = z * pinv(u); // std::cout \u0026lt;\u0026lt; \u0026#34;G0_data = \u0026#34; \u0026lt;\u0026lt; g_dc_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // create hankel data matrix size_t len = z.n_cols - 2 * n_h_ + 1; // data length in hankel mat // block-hankel data matrix D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros); // past input auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1); // future input auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1); // past output auto y_p = D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1); // future output auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, len - 1); size_t idx = 0; for (size_t k = 0; k \u0026lt; len; k++) { idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } } D_ /= sqrt(static_cast\u0026lt;data_t\u0026gt;(len)); } // template \u0026lt;typename Fit\u0026gt; // void SSID\u0026lt;Fit\u0026gt;::DecomposeData() { // // do LQ decomp instead of calculating covariance expensive way // // Note that \u0026#34;R\u0026#34; in van Overschee is lower-triangular (L), not \u0026#34;R\u0026#34; in QR // // decomp. Very confusing. // Matrix q_t; // lq(L_, q_t, D_); // // van Overschee zeros out the other elements. // L_ = trimatl(L_); // } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcSVD(SSIDWt wt) { // submats that will be needed: auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1); auto R_23_13 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1); auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_) - 1); auto R_44 = L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14); auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1); auto Luf = Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1); auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); // aka: R_f Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1); // from van Overschee subid.m: // Rf = R((2*m+l)*i+1:2*(m+l)*i,:); % Future outputs Matrix U; Matrix V; switch (wt) { case kSSIDNone: { // No weighting. (what van Overschee calls \u0026#34;N4SID\u0026#34;) Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14; arma::svd(U, s_, V, O_k_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDMOESP: { // MOESP weighting // This is what they use in the \u0026#34;robust\u0026#34; algorithm van Overschee, de Moor // 1996 Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); svd(U, s_, V, O_k_ortho_Uf_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDCVA: { // CVA weighting // See van Overschee\u0026#39;s matlab code (subid.m): // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); Matrix inv_w1; Matrix qt1; lq(inv_w1, qt1, R_56_16); // lq decomp of R_f (future output data) inv_w1 = trimatl(inv_w1); inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1); Matrix w_o_w = arma::solve( inv_w1, O_k_ortho_Uf_sans_Qt); // alternatively // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt svd(U, s_, V, w_o_w, \u0026#34;std\u0026#34;); U = inv_w1 * U; break; } } // Truncate to model order (heart of ssid method) auto s_hat = s_.subvec(0, n_x_ - 1); Matrix diag_sqrt_s = diagmat(sqrt(s_hat)); auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1); // get extended observability and controllability mats ext_obs_t_ = u_hat * diag_sqrt_s; // extended observability matrix } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::Solve(data_t wt_dc) { // required submats auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_56_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); // Solve for params using appropriate algorithm: // robust deterministic/stochastic algorithm in van Overschee 1996 // algorithm that the authors say \u0026#34;works\u0026#34; in practice. auto ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix // This is what textbook (1996) says: // // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15); // // HOWEVER, do not know why but have to fill the last place with zeros like // authors\u0026#39; matlab implementation (see `subid.m`) // Otherwise, get ridiculous covariances (although A,C estimates are close to // same...) Matrix Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Matrix S = Tl * pinv(Tr); // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure // stability. fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1)); Matrix ext_obs_t_p1 = join_vert( ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1), Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros)); fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1); // At this point, van Overschee \u0026amp; de Moor suggest re-calculating ext_obs_t_, // ext_obs_tm1 from (A, C) because it was just an approximation. This is RecomputeExtObs(); ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); S = Tl * pinv(Tr); Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_); Matrix Mcurly = pinv(ext_obs_tm1); Matrix Pcurly = Tl - Lcurly * R_56_15; Vector Pvec = vectorise(Pcurly); Matrix Qcurly = R_23_15; // Identify [D; B], assuming D=0 and ensuring DC gain is correct Matrix sum_QcurlyT_kron_Ncurly( (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_), fill::zeros); Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols, fill::eye); eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1, eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1; // van Overschee (1996) p. 126 Matrix N1_Tl = -Lcurly; N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) += join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly); N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) += Matrix(n_y_, n_y_, fill::eye); Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros); Matrix N_k; for (size_t k = 0; k \u0026lt; n_h_; k++) { auto Qcurly_k = Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1); Nk_Tl.zeros(); Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) = N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1); N_k = Nk_Tl * eye_ext_obs_tm1; sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k); } Matrix err_vec; if (wt_dc \u0026gt; 0) { // Constraints enforced by weighted least squares // // Reference: // // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly // Excited Industrial Systems. Conference in Decision and Control. // constraint 1: assume D=0 --\u0026gt; remove the components for Dvec (this is // actually a hard constraint in that it ignores D) Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly; sum_QcurlyT_kron_Ncurly = Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_); size_t kkk = 0; for (size_t k = 1; k \u0026lt; (n_u_ + 1); k++) { size_t start_idx = k * (n_y_ + n_x_) - n_x_; for (size_t kk = 0; kk \u0026lt; n_x_; kk++) { sum_QcurlyT_kron_Ncurly.col(kkk) = sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk); kkk++; } } // constraint 2: Make sure DC I/O gain is correct Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A()); Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_)); Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0); Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 = join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0); // WEIGHTED LS // Important in practice because I care a lot about at least getting the DC // gain correct. Put x weight on minimizing error at DC, relative to others Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye); // Make weight on minimizing DC error immense so at least that // should be nailed. size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows; size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows; size_t stop_row = w.n_rows - 1; size_t stop_col = w.n_cols - 1; // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale // weight with data length? w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc; Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * sum_QcurlyT_kron_Ncurly_b_to_g0) * sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec; fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_)); // Calculate residuals and their cov. // Because I\u0026#39;ve added constraints, I need to re-calculate the right term // with b_vec instead of how van Overschee do in final algorithm. err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec; } else { // default way: *no* constraint on G0 or D=0 Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec; // TODO(mfbolus) n.b., this gets thrown away... // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_); fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_)); err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec; } // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols); // TODO(mfbolus): Something is wrong with the error calculation above. // Use the way van overschee does it in `subid.m` // WARNING: this ignores any above constraints, so Q, R will be approximate... Matrix err = Tl - S * Tr; Matrix cov_err = err * err.t(); fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1)); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::RecomputeExtObs() { ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C(); for (size_t k = 2; k \u0026lt; (n_h_ + 1); k++) { ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) = ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1, ext_obs_t_.n_cols - 1) * fit_.A(); } } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":56,"href":"/docs/api/files/lds__fit_8h/","title":"ldsCtrlEst_h/lds_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_fit.h # LDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Fit LDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.\nSource code # //===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDS_FIT_HPP #define LDS_FIT_HPP // namespace #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); virtual ~Fit() = default; // get methods size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; Q() const { return Q_; }; const Vector\u0026amp; x0() const { return x0_; }; const Matrix\u0026amp; P0() const { return P0_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; // gets measurement noise virtual const Matrix\u0026amp; R() const = 0; // set methods (e.g., seeding initial fit values) void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_m(const Vector\u0026amp; m) { Reassign(m_, m); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); ForceSymPD(Q_); }; virtual void set_R(const Matrix\u0026amp; R) = 0; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); ForceSymPD(P0_); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; View f(Matrix\u0026amp; x, const Matrix\u0026amp; u, size_t t) { x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x.col(t); }; View f(Matrix\u0026amp; x_pre, const Matrix\u0026amp; x_post, const Matrix\u0026amp; u, size_t t) { x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x_pre.col(t); }; virtual View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) = 0; protected: data_t dt_{}; // Dynamics Matrix A_; Matrix B_; Vector g_; Vector m_; Matrix Q_; // Output Matrix C_; Vector d_; Matrix R_; // initial conditions Vector x0_; Matrix P0_; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; }; } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":57,"href":"/docs/api/files/lds__gaussian__ctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_ctrl.h # GLDS Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. Detailed Description # This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_gaussian_sys.h\u0026#34; // controller #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace gaussian { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); cx_ref_ = y_ref - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":58,"href":"/docs/api/files/lds__gaussian__fit__em_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_em.h # GLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitEM GLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":59,"href":"/docs/api/files/lds__gaussian__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_ssid.h # GLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; using SSID\u0026lt;Fit\u0026gt;::Run; private: using SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat; using SSID\u0026lt;Fit\u0026gt;::CalcSVD; using SSID\u0026lt;Fit\u0026gt;::Solve; void DecomposeData() override; void SolveVanOverschee(); }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":60,"href":"/docs/api/files/lds__gaussian__fit_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit.h # GLDS fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Fit GLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // fit type #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace gaussian { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); const Matrix\u0026amp; R() const override { return R_; }; void set_R(const Matrix\u0026amp; R) override { Reassign(R_, R); ForceSymPD(R_); }; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = C_ * x.col(t) + d_; return y.col(t); }; }; }; // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":61,"href":"/docs/api/files/lds__gaussian__sctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sctrl.h # GLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H // controller type #include \u0026#34;lds_gaussian_ctrl.h\u0026#34; // switched controller #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace gaussian { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); cx_ref_ = y_ref - sys_.d(); } // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; }; // SwitchedController } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":62,"href":"/docs/api/files/lds__gaussian__sys_8h/","title":"ldsCtrlEst_h/lds_gaussian_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sys.h # GLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::System Gaussian LDS Type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/docs/api/classes/classlds_1_1gaussian_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H #define LDSCTRLEST_LDS_GAUSSIAN_SYS_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; namespace lds { namespace gaussian { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0, data_t r0 = kDefaultR0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; // get methods const Matrix\u0026amp; R() const { return R_; }; // set methods void set_Q(const Matrix\u0026amp; Q) { lds::System::set_Q(Q); do_recurse_Ke_ = true; } void set_R(const Matrix\u0026amp; R) { Reassign(R_, R); do_recurse_Ke_ = true; }; void set_Ke(const Matrix\u0026amp; Ke) { Reassign(Ke_, Ke); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void set_Ke_m(const Matrix\u0026amp; Ke_m) { Reassign(Ke_m_, Ke_m); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void Print(); protected: void h() override { cx_ = C_ * x_; y_ = cx_ + d_; }; Vector h_(Vector x) override { return C_ * x + d_; }; void RecurseKe() override; // Gaussian-output-specific Matrix R_; bool do_recurse_Ke_{}; }; // System } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":63,"href":"/docs/api/files/lds__gaussian_8h/","title":"ldsCtrlEst_h/lds_gaussian.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian.h # glds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/docs/api/namespaces/namespacelds_1_1gaussian/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_H #define LDSCTRLEST_LDS_GAUSSIAN_H // namespace #include \u0026#34;lds.h\u0026#34; namespace lds { namespace gaussian { // insert any Gaussian-specific things here... } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":64,"href":"/docs/api/files/lds__poisson__ctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_ctrl.h # PLDS controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. Detailed Description # This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/docs/api/classes/classlds_1_1poisson_1_1_controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/docs/api/classes/classlds_1_1poisson_1_1_system/)), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_CTRL_H #define LDSCTRLEST_LDS_POISSON_CTRL_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system type #include \u0026#34;lds_poisson_sys.h\u0026#34; // control type #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace poisson { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); lds::Limit(y_ref_, kYRefLb, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; private: constexpr static const data_t kYRefLb = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":65,"href":"/docs/api/files/lds__poisson__fit__em_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_em.h # PLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitEM PLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\n[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H #define LDSCTRLEST_LDS_POISSON_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override{}; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; data_t NewtonSolveC(); void AnalyticalSolveD(); }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":66,"href":"/docs/api/files/lds__poisson__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_ssid.h # PLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. Detailed Description # This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/)).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H #define LDSCTRLEST_LDS_POISSON_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; private: void DecomposeData() override; void CalcCov(); void PoissonToGaussianMoments(); Matrix cov_; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":67,"href":"/docs/api/files/lds__poisson__fit_8h/","title":"ldsCtrlEst_h/lds_poisson_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit.h # PLDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Fit PLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_H #define LDSCTRLEST_LDS_POISSON_FIT_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // fit #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace poisson { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) : lds::Fit(n_u, n_x, n_y, dt){}; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = exp(C_ * x.col(t) + d_); return y.col(t); }; void set_R(const Matrix\u0026amp; R) override { std::cerr \u0026lt;\u0026lt; \u0026#34;WARNING: Cannot set R (R[0] = \u0026#34; \u0026lt;\u0026lt; R.at(0) \u0026lt;\u0026lt; \u0026#34;). No Gaussian measurement noise in Poisson observation model.\\n\u0026#34;; }; const Matrix\u0026amp; R() const override { return R_; }; }; }; // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":68,"href":"/docs/api/files/lds__poisson__sctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sctrl.h # PLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #include \u0026#34;lds_poisson_ctrl.h\u0026#34; #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace poisson { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); lds::Limit(y_ref_, kYRefLB, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; private: constexpr static data_t kYRefLB = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":69,"href":"/docs/api/files/lds__poisson__sys_8h/","title":"ldsCtrlEst_h/lds_poisson_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sys.h # PLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::System Poisson System type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/docs/api/classes/classlds_1_1poisson_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SYS_H #define LDSCTRLEST_LDS_POISSON_SYS_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; // needed for Poisson random number generation #include \u0026lt;random\u0026gt; namespace lds { namespace poisson { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; protected: void h() override { cx_ = C_ * x_; y_ = exp(cx_ + d_); diag_y_.diag() = y_; }; Vector h_(Vector x) override { return exp(C_ * x + d_); }; void RecurseKe() override; private: // Poisson-output-specific Matrix diag_y_; std::poisson_distribution\u0026lt;size_t\u0026gt; pd_; }; // System } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":70,"href":"/docs/api/files/lds__poisson_8h/","title":"ldsCtrlEst_h/lds_poisson.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson.h # plds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/docs/api/namespaces/namespacelds_1_1poisson/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_H #define LDSCTRLEST_LDS_POISSON_H #include \u0026#34;lds.h\u0026#34; namespace lds { namespace poisson { // TODO(mfbolus): Not sure if defining these as static here makes the most // sense. Is there a downside to letting multiple poisson System objects share a // common random number generator? static std::random_device rd; static std::mt19937 rng = std::mt19937( rd()); } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":71,"href":"/docs/api/files/lds__sctrl_8h/","title":"ldsCtrlEst_h/lds_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_sctrl.h # SwitchedController type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SwitchedController SwitchedController Type. Detailed Description # This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SCTRL_H #define LDSCTRLEST_LDS_SCTRL_H #include \u0026#34;lds_ctrl.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; #include \u0026#34;lds_uniform_vecs.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class SwitchedController : public Controller\u0026lt;System\u0026gt; { public: SwitchedController() = default; SwitchedController(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); SwitchedController(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); void Switch(size_t idx, bool do_force_switch = false); void set_Kc(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc) { Kc_list_ = Kc; Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc) { Kc_list_ = std::move(Kc); Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_inty) { Kc_inty_list_ = Kc_inty; Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_inty) { Kc_inty_list_ = std::move(Kc_inty); Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_u) { Kc_u_list_ = Kc_u; Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_u) { Kc_u_list_ = std::move(Kc_u); Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(const UniformVectorList\u0026amp; g) { g_design_list_ = g; g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(UniformVectorList\u0026amp;\u0026amp; g) { g_design_list_ = std::move(g); g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; protected: std::vector\u0026lt;System\u0026gt; systems_; size_t n_sys_{}; size_t idx_{}; // controller gains could be different for each UniformMatrixList\u0026lt;\u0026gt; Kc_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_inty_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_u_list_; // design-phase input gain could also be different UniformVectorList g_design_list_; // TODO(mfbolus): not sure why I need to do this. using Controller\u0026lt;System\u0026gt;::Kc_; using Controller\u0026lt;System\u0026gt;::Kc_inty_; using Controller\u0026lt;System\u0026gt;::Kc_u_; using Controller\u0026lt;System\u0026gt;::g_design_; using Controller\u0026lt;System\u0026gt;::sys_; // using Controller\u0026lt;System\u0026gt;::u_ref_; // using Controller\u0026lt;System\u0026gt;::x_ref_; // using Controller\u0026lt;System\u0026gt;::y_ref_; // using Controller\u0026lt;System\u0026gt;::control_type_; private: void InitVars(); using lds::Controller\u0026lt;System\u0026gt;::set_sys; // using Controller\u0026lt;System\u0026gt;::set_Kc; // using Controller\u0026lt;System\u0026gt;::set_Kc_inty; // using Controller\u0026lt;System\u0026gt;::set_Kc_u; // using Controller\u0026lt;System\u0026gt;::set_g_design; }; template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(systems.at(0), u_lb, u_ub, control_type), systems_(systems) { InitVars(); } template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(System(systems.at(0).n_u(), systems.at(0).n_x(), systems.at(0).n_y(), systems.at(0).dt()), u_lb, u_ub, control_type), systems_(std::move(systems)) { InitVars(); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::InitVars() { n_sys_ = systems_.size(); sys_ = systems_.at(0); Kc_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_)); Kc_inty_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); Kc_u_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); g_design_list_ = UniformVectorList(std::vector\u0026lt;Vector\u0026gt;(n_sys_, g_design_)); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::Switch(size_t idx, bool do_force_switch) { if ((idx == idx_) \u0026amp;\u0026amp; !do_force_switch) { return; // already there. } // put old up and get new one out systems_.at(idx_) = std::move(sys_); sys_ = std::move(systems_.at(idx)); // set the state of this system to that of the previous system // TODO(mfbolus): This will only work as intended if state matrix is the same. // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp. sys_.set_m(systems_.at(idx_).m(), true); sys_.set_x(systems_.at(idx_).x()); // swap controller gains Kc_list_.Swap(Kc_, idx_); Kc_list_.Swap(Kc_, idx); if (control_type_ \u0026amp; kControlTypeIntY) { Kc_inty_list_.Swap(Kc_inty_, idx_); Kc_inty_list_.Swap(Kc_inty_, idx); } if (control_type_ \u0026amp; kControlTypeDeltaU) { Kc_u_list_.Swap(Kc_u_, idx_); Kc_u_list_.Swap(Kc_u_, idx); } g_design_list_.Swap(g_design_, idx_); g_design_list_.Swap(g_design_, idx); idx_ = idx; } // Switch } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":72,"href":"/docs/api/files/lds__sys_8h/","title":"ldsCtrlEst_h/lds_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_sys.h # LDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::System Linear Dynamical System Type. Detailed Description # This file declares and partially defines the base type for linear dynamical systems ([lds::System](/docs/api/classes/classlds_1_1_system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SYS_H #define LDSCTRLEST_LDS_SYS_H #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class System { public: System() = default; System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); virtual ~System() {} void Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z); virtual const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) = 0; void f(const Vector\u0026amp; u, bool do_add_noise = false) { x_ = A_ * x_ + B_ * (g_ % u) + m_; if (do_add_noise) { x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_); } }; virtual void h() = 0; virtual Vector h_(Vector x) = 0; size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Vector\u0026amp; x() const { return x_; }; const Matrix\u0026amp; P() const { return P_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; P_m() const { return P_m_; }; const Vector\u0026amp; cx() const { return cx_; }; const Vector\u0026amp; y() const { return y_; }; const Vector\u0026amp; x0() const { return x0_; }; const Vector\u0026amp; m0() const { return m0_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; const Matrix\u0026amp; Ke() const { return Ke_; }; const Matrix\u0026amp; Ke_m() const { return Ke_m_; }; const Matrix\u0026amp; Q() { return Q_; }; const Matrix\u0026amp; Q_m() { return Q_m_; }; const Matrix\u0026amp; P0() { return P0_; }; const Matrix\u0026amp; P0_m() { return P0_m_; }; void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_m(const Vector\u0026amp; m, bool do_force_assign = false) { Reassign(m0_, m); if ((!do_adapt_m) || do_force_assign) { Reassign(m_, m); } }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); }; void set_Q_m(const Matrix\u0026amp; Q_m) { Reassign(Q_m_, Q_m); }; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); }; void set_P0_m(const Matrix\u0026amp; P0_m) { Reassign(P0_m_, P0_m); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; void set_x(const Vector\u0026amp; x) { Reassign(x_, x); h(); }; void Reset(); std::vector\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; nstep_pred_block( UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z, size_t n_pred = 1); void Print(); // safe to leave this public and non-const bool do_adapt_m{}; protected: virtual void RecurseKe() = 0; void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); std::size_t n_x_{}; std::size_t n_u_{}; std::size_t n_y_{}; data_t dt_{}; // Signals: Vector x_; Matrix P_; Vector m_; Matrix P_m_; Vector cx_; Vector y_; Vector z_; // Parameters: Vector x0_; Matrix P0_; Vector m0_; Matrix P0_m_; Matrix A_; Matrix B_; Vector g_; Matrix Q_; Matrix Q_m_; Matrix C_; Vector d_; Matrix Ke_; Matrix Ke_m_; }; // System } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":73,"href":"/docs/api/files/lds__uniform__mats_8h/","title":"ldsCtrlEst_h/lds_uniform_mats.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_mats.h # List of uniformly sized matrices. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformMatrixList Detailed Description # This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H #define LDSCTRLEST_LDS_UNIFORM_MATS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { template \u0026lt;MatrixListFreeDim D = kMatFreeDimNone\u0026gt; class UniformMatrixList : public std::vector\u0026lt;Matrix\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Matrix\u0026gt;::vector; // don\u0026#39;t allow push_back to be used since it doesn\u0026#39;t check dims using std::vector\u0026lt;Matrix\u0026gt;::push_back; public: using std::vector\u0026lt;Matrix\u0026gt;::operator=; using std::vector\u0026lt;Matrix\u0026gt;::operator[]; using std::vector\u0026lt;Matrix\u0026gt;::begin; using std::vector\u0026lt;Matrix\u0026gt;::end; using std::vector\u0026lt;Matrix\u0026gt;::size; using std::vector\u0026lt;Matrix\u0026gt;::at; UniformMatrixList() = default; explicit UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); explicit UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; ~UniformMatrixList() = default; const std::array\u0026lt;size_t, 2\u0026gt;\u0026amp; dim(size_t n = 0) const { return dim_.at(n); } size_t size() { return std::vector\u0026lt;Matrix\u0026gt;::size(); }; const Matrix\u0026amp; at(size_t n) { return std::vector\u0026lt;Matrix\u0026gt;::at(n); }; void Swap(Matrix\u0026amp; that, size_t n); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; void append(const Matrix\u0026amp; mat); private: void CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim); std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt; dim_; }; template \u0026lt;MatrixListFreeDim D\u0026gt; inline void UniformMatrixList\u0026lt;D\u0026gt;::Swap(Matrix\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.n_cols); } if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap // not moving, since it causes memory issues. // so this method isn\u0026#39;t a memory-saver as designed for now Matrix tmp = (*this)[n]; (*this)[n] = that; that = tmp; if (D == kMatFreeDim1) { this-\u0026gt;dim_[n][0] = (*this)[n].n_rows; } if (D == kMatFreeDim2) { this-\u0026gt;dim_[n][1] = (*this)[n].n_cols; } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::append(const Matrix\u0026amp; mat) { std::array\u0026lt;size_t, 2\u0026gt; dim({mat.n_rows, mat.n_cols}); CheckDimensions(dim); std::vector\u0026lt;Matrix\u0026gt;::push_back(mat); dim_.push_back(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) { // make sure dim_ vector is initialized if (dim_.empty()) { dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(that.size(), {0, 0}); } // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices\u0026#34;; throw std::runtime_error(ss.str()); } // if dimensions a not zero and do not match, skip move with error message. bool dims_nonzero = true; for (auto d : dim_) { if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; d[0] \u0026lt; 1) { dims_nonzero = false; break; } if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; d[1] \u0026lt; 1) { dims_nonzero = false; break; } } if (dims_nonzero) { bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); } if (!does_match) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrices of size \u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; dim_[k] = that.dim(k); } return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. bool dims_nonzero = true; for (auto d : dim_) { // if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; (d[0] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; (d[1] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // } // // if (dims_nonzero) { // bool does_match = true; // if (!(D == kMatFreeDim1)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); // } // // if (!(D == kMatFreeDim2)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); // } // // if (!does_match) { // this-\u0026gt;at(0).print(\u0026#34;this[0] = \u0026#34;); // that.at(0).print(\u0026#34;that[0] = \u0026#34;); // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformMatrixList element of size (\u0026#34; \u0026lt;\u0026lt; // that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols \u0026lt;\u0026lt; \u0026#34;) for an // element of size (\u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34;). // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Matrix\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(std::move(mats)) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) : vector(that) { (*this) = that; } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { std::array\u0026lt;size_t, 2\u0026gt; dim_k({this-\u0026gt;at(k).n_rows, this-\u0026gt;at(k).n_cols}); dim_.push_back(dim_k); } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim) { // change behavior based on free dim D if ((dim[0] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim1)) { dim[0] = this-\u0026gt;at(0).n_rows; } if ((dim[1] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim2)) { dim[1] = this-\u0026gt;at(0).n_cols; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Matrix\u0026amp; mat : *this) { if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_rows == dim[0]); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_cols == dim[1]); } if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(this-\u0026gt;size(), dim); for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { dim_[k][0] = (*this)[k].n_rows; dim_[k][1] = (*this)[k].n_cols; } } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":74,"href":"/docs/api/files/lds__uniform__systems_8h/","title":"ldsCtrlEst_h/lds_uniform_systems.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_systems.h # List of uniformly sized Systems. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformSystemList Detailed Description # This file provides a container for uniformly sized Systems.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector // namespace #include \u0026#34;lds.h\u0026#34; // System type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class UniformSystemList : public std::vector\u0026lt;System\u0026gt; { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;System\u0026gt;::vector; using std::vector\u0026lt;System\u0026gt;::operator=; using std::vector\u0026lt;System\u0026gt;::operator[]; using std::vector\u0026lt;System\u0026gt;::at; using std::vector\u0026lt;System\u0026gt;::begin; using std::vector\u0026lt;System\u0026gt;::end; using std::vector\u0026lt;System\u0026gt;::size; public: UniformSystemList() = default; explicit UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); explicit UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(const UniformSystemList\u0026amp; that); UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept; ~UniformSystemList() = default; const std::array\u0026lt;size_t, 3\u0026gt;\u0026amp; dim() const { return dim_; } size_t size() { return std::vector\u0026lt;System\u0026gt;::size(); }; const System\u0026amp; at(size_t n) { return std::vector\u0026lt;System\u0026gt;::at(n); }; void Swap(System\u0026amp; that, size_t n); UniformSystemList\u0026amp; operator=(const UniformSystemList\u0026amp; that); UniformSystemList\u0026amp; operator=(UniformSystemList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim); std::array\u0026lt;size_t, 3\u0026gt; dim_{}; }; template \u0026lt;typename System\u0026gt; inline void UniformSystemList\u0026lt;System\u0026gt;::Swap(System\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformSystemList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = (dim_[0] == that.n_u()) \u0026amp;\u0026amp; (dim_[1] == that.n_x()) \u0026amp;\u0026amp; (dim_[2] == that.n_y()); if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformSystemList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap System tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( const UniformSystemList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_[0] + dim_[1] + dim_[2]) { std::array\u0026lt;size_t, 3\u0026gt; other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign systems of size \u0026#34; \u0026lt;\u0026lt; dim_[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2] \u0026lt;\u0026lt; \u0026#34; with systems of size \u0026#34; \u0026lt;\u0026lt; other_dim[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other_dim[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2]; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( UniformSystemList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. if (dim_[0] + dim_[1] + dim_[2]) { // bool does_match = (dim_[0] == that.at(0).n_u()) \u0026amp;\u0026amp; // (dim_[1] == that.at(0).n_x()) \u0026amp;\u0026amp; // (dim_[2] == that.at(0).n_y()); // if (!does_match) { // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformSystemList element for an element of \u0026#34; // \u0026#34;different size. Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;System\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(std::move(systems)) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList( std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const UniformSystemList\u0026amp; that) : std::vector\u0026lt;System\u0026gt;(that) { (*this) = that; } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept : std::vector\u0026lt;System\u0026gt;(std::move(that)) { this-\u0026gt;dim_[0] = this-\u0026gt;at(0).n_u(); this-\u0026gt;dim_[1] = this-\u0026gt;at(0).n_x(); this-\u0026gt;dim_[2] = this-\u0026gt;at(0).n_y(); } template \u0026lt;typename System\u0026gt; void UniformSystemList\u0026lt;System\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim) { if (dim[0] + dim[1] + dim[2]) { dim_ = dim; } else { dim_[0] = this-\u0026gt;at(0).n_u(); dim_[1] = this-\u0026gt;at(0).n_x(); dim_[2] = this-\u0026gt;at(0).n_y(); } // make sure dimensiolaties are all uniform bool does_match(true); for (const System\u0026amp; sys : *this) { does_match = does_match \u0026amp;\u0026amp; (sys.n_u() == dim_[0]); does_match = does_match \u0026amp;\u0026amp; (sys.n_x() == dim_[1]); does_match = does_match \u0026amp;\u0026amp; (sys.n_y() == dim_[2]); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input systems are not uniform.\u0026#34;); } } } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":75,"href":"/docs/api/files/lds__uniform__vecs_8h/","title":"ldsCtrlEst_h/lds_uniform_vecs.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_vecs.h # List of uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformVectorList Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H #define LDSCTRLEST_LDS_UNIFORM_VECS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { class UniformVectorList : public std::vector\u0026lt;Vector\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Vector\u0026gt;::vector; using std::vector\u0026lt;Vector\u0026gt;::operator=; using std::vector\u0026lt;Vector\u0026gt;::operator[]; using std::vector\u0026lt;Vector\u0026gt;::at; using std::vector\u0026lt;Vector\u0026gt;::begin; using std::vector\u0026lt;Vector\u0026gt;::end; using std::vector\u0026lt;Vector\u0026gt;::size; public: UniformVectorList() = default; explicit UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim = 0); explicit UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim = 0); UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim = 0); UniformVectorList(const UniformVectorList\u0026amp; that); UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept; ~UniformVectorList() = default; size_t dim() const { return dim_; } size_t size() { return std::vector\u0026lt;Vector\u0026gt;::size(); }; const Vector\u0026amp; at(size_t n) { return std::vector\u0026lt;Vector\u0026gt;::at(n); }; void Swap(Vector\u0026amp; that, size_t n); UniformVectorList\u0026amp; operator=(const UniformVectorList\u0026amp; that); UniformVectorList\u0026amp; operator=(UniformVectorList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(size_t dim); size_t dim_{}; }; inline void UniformVectorList::Swap(Vector\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = dim_ == that.n_elem; if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap Vector tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( const UniformVectorList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_) { size_t other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ \u0026lt;\u0026lt; \u0026#34; with vectors of size \u0026#34; \u0026lt;\u0026lt; other_dim; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( UniformVectorList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors. Skipping.\\n\u0026#34;; // return (*this); // } // // if (dim_) { // size_t other_dim(that.dim()); // if (dim_ != other_dim) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ // \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; other_dim \u0026lt;\u0026lt; \u0026#34;. // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Vector\u0026gt;::operator=(std::move(that)); return (*this); } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":76,"href":"/docs/api/files/lds_8h/","title":"ldsCtrlEst_h/lds.h","section":"Files","content":" ldsCtrlEst_h/lds.h # lds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/docs/api/namespaces/namespacelds_1_1poisson/)) observations.\nSource code # //===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_H #define LDSCTRLEST_LDS_H // #ifndef LDSCTRLEST // #include \u0026lt;ldsCtrlEst\u0026gt; // #endif #include \u0026lt;armadillo\u0026gt; namespace lds { using data_t = double; // may change to float (but breaks mex functions) using Vector = arma::Col\u0026lt;data_t\u0026gt;; using Matrix = arma::Mat\u0026lt;data_t\u0026gt;; using Cube = arma::Cube\u0026lt;data_t\u0026gt;; using View = arma::subview\u0026lt;data_t\u0026gt;; namespace fill = arma::fill; static const std::size_t kControlTypeDeltaU = 0x1; static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); static const data_t kPi = arma::datum::pi; static const data_t kDefaultP0 = 1e-6; static const data_t kDefaultQ0 = 1e-6; static const data_t kDefaultR0 = 1e-2; enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA }; enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2 }; // TODO(mfbolus): for SwitchedController, may want systems to have differing // numbers of states. Use this enum as template parameter? // enum SystemListFreeDim { // kSysFreeDimNone, // kSysFreeDimX ///\u0026lt; allow state dim (x) of systems in list to be hetero // }; // place hard limits on contents of vecors/mats void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub); void Limit(Vector\u0026amp; x, data_t lb, data_t ub); void Limit(Matrix\u0026amp; x, data_t lb, data_t ub); // in-place assign that errs if there are dimension mismatches: void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); // TODO(mfbolus): this is a fudge, but for some reason, cov mats often going // numerically asymm. void ForceSymPD(Matrix\u0026amp; X); void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min = 0); void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X); Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B); inline void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Vector\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Matrix\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if (other.n_elem != some.n_elem) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vector of size \u0026#34; \u0026lt;\u0026lt; some.n_elem \u0026lt;\u0026lt; \u0026#34; with vector of size \u0026#34; \u0026lt;\u0026lt; other.n_elem \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } inline void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrix of size \u0026#34; \u0026lt;\u0026lt; some.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; some.n_cols \u0026lt;\u0026lt; \u0026#34; with matrix of size \u0026#34; \u0026lt;\u0026lt; other.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other.n_cols \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":77,"href":"/docs/api/files/mex__c__util_8h/","title":"ldsCtrlEst_h/mex_c_util.h","section":"Files","content":" ldsCtrlEst_h/mex_c_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) More\u0026hellip;\nNamespaces # Name armamexc arma/mex interface using Matlab C API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab/Octave\u0026rsquo;s C mex API.\nSource code # //===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXC_UTIL_H #define LDSCTRLEST_MEXC_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.h\u0026#34; // // If Matlab_FOUND, include matrix.h. // // (Octave does not need/have it.) // #ifdef Matlab_FOUND // #include \u0026#34;matrix.h\u0026#34; // #endif namespace armamexc { template \u0026lt;class T\u0026gt; inline auto m2T_scalar(const mxArray *matlab_scalar) -\u0026gt; T { if (mxGetData(matlab_scalar)) { return static_cast\u0026lt;T\u0026gt;(mxGetScalar(matlab_scalar)); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return 0; } template \u0026lt;class T\u0026gt; inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false, bool strict = true) -\u0026gt; arma::Mat\u0026lt;T\u0026gt; { if (mxGetData(matlab_mat)) { const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat); if (n_dim == 2) { return arma::Mat\u0026lt;T\u0026gt;(static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)), mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem, strict); } mexErrMsgTxt(\u0026#34;Number of dimensions must be 2.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } // TODO(mfbolus): make these templated. template \u0026lt;typename T\u0026gt; inline auto a2m_mat(arma::Mat\u0026lt;T\u0026gt; const \u0026amp;arma_mat) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_mat.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Mat.\u0026#34;); return nullptr; } template \u0026lt;typename T\u0026gt; inline auto a2m_vec(arma::Col\u0026lt;T\u0026gt; const \u0026amp;arma_vec) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_vec.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Col.\u0026#34;); return nullptr; } } // namespace armamexc #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":78,"href":"/docs/api/files/mex__cpp__util_8h/","title":"ldsCtrlEst_h/mex_cpp_util.h","section":"Files","content":" ldsCtrlEst_h/mex_cpp_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) More\u0026hellip;\nNamespaces # Name armamexcpp arma/mex interface using Matlab C++ API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab\u0026rsquo;s C++ mex API.\nSource code # //===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXCPP_UTIL_H #define LDSCTRLEST_MEXCPP_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.hpp\u0026#34; #include \u0026#34;mexAdapter.hpp\u0026#34; namespace armamexcpp { template \u0026lt;class T\u0026gt; std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; m2a_cellmat(matlab::data::CellArray\u0026amp; matlab_cell) { size_t n_cells = matlab_cell.getNumberOfElements(); std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; arma_mat(n_cells, arma::Mat\u0026lt;T\u0026gt;(1, 1, arma::fill::zeros)); for (size_t k = 0; k \u0026lt; n_cells; k++) { matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = matlab_cell[k]; auto dims = matlab_mat.getDimensions(); arma_mat[k] = arma::Mat\u0026lt;T\u0026gt;(matlab_mat.release().get(), dims[0], dims[1]); } return arma_mat; }; template \u0026lt;class T\u0026gt; std::vector\u0026lt;T\u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt;T\u0026gt;\u0026amp; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); T* ptr = matlab_array.release().get(); std::vector\u0026lt;T\u0026gt; vec(ptr, ptr + n_elem); return vec; }; template \u0026lt;class T\u0026gt; arma::Col\u0026lt;T\u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); // T* ptr = matlab_array.release().get(); // arma::Col\u0026lt;T\u0026gt; vec(ptr, n_elem); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? arma::Col\u0026lt;T\u0026gt; vec(n_elem, arma::fill::zeros); for (size_t k = 0; k \u0026lt; n_elem; k++) { vec[k] = matlab_array[k]; } return vec; }; template \u0026lt;class T\u0026gt; arma::Mat\u0026lt;T\u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { // ArrayDimensions == std::vector\u0026lt;size_t\u0026gt; auto dims = matlab_array.getDimensions(); // T* ptr = matlab_array.release().get(); // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false) // arma::Mat\u0026lt;T\u0026gt; mat(ptr, dims[0], dims[1]); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? // // armadillo and matlab both use column-major ordering, so this should work: size_t n_elem = dims[0] * dims[1]; arma::Mat\u0026lt;T\u0026gt; mat(dims[0], dims[1], arma::fill::zeros); size_t k(0); for (auto m: matlab_array) { mat[k] = m; k++; } return mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_mat(const arma::Mat\u0026lt;T\u0026gt;\u0026amp; arma_mat, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(), arma_mat.memptr() + arma_mat.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_vec(const arma::Col\u0026lt;T\u0026gt;\u0026amp; arma_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;({arma_vec.n_elem, 1}, arma_vec.memptr(), arma_vec.memptr() + arma_vec.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; s2m_vec(const std::vector\u0026lt;T\u0026gt;\u0026amp; std_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size()); return matlab_mat; }; } // namespace armamexcpp #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":79,"href":"/docs/terminology/model/","title":"Models","section":"LDS C+E Documentation","content":" Model Definitions # This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form: \\[\\mathbf{x}_{t\u0026#43;1} = f\\left( \\mathbf{x}_{t}, \\mathbf{v}_{t} \\right) = \\mathbf{A} \\mathbf{x}_{t} \u0026#43; \\mathbf{B} \\mathbf{v}_{t} \u0026#43; \\mathbf{m}_{t} \u0026#43; \\mathbf{w}_{t}\\] \\[\\mathbf{y}_{t} = h\\left( \\mathbf{x}_{t} \\right)\\] t : time index x : system state v = g%u : input (e.g., in physical units used for model fit) u : control signal sent to actuator (e.g., in Volts) y : system output m : process disturbance w ~ N(0, Q) : process noise/disturbance A : state matrix B : input coupling matrix g : input gain (e.g., for converting to control signal actuator voltage) n.b., assumes this conversion is linear Q : process noise covariance % : element-wise multiplication LDS with Gaussian Observations # For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.\n\\[\\mathbf{y}_{t} = \\mathbf{C} \\mathbf{x}_{t} \u0026#43; \\mathbf{d}\\] \\[\\mathbf{z}_{t} \\sim \\mathcal{N}\\left(\\mathbf{y}_{t} , \\mathbf{R} \\right)\\] z : measurement C : output matrix d : output bias R : measurement noise covariance LDS with Poisson Observations # For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.\n\\[y_{t}^{i} = \\exp \\left(\\mathbf{c}^i \\mathbf{x}_{t} \u0026#43; d^i\\right)\\] \\[z_{t}^i \\sim \\rm{Poisson} \\left(y_{t}^i \\right)\\] i : output index z : measurement (count data) c : i^th row of output matrix (C) d : output bias Model Predictive Control (MPC) # Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.\nIn the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.\n"},{"id":80,"href":"/docs/api/modules/","title":"Modules","section":"API Reference","content":" Modules # "},{"id":81,"href":"/docs/api/namespaces/","title":"Namespaces","section":"API Reference","content":" Namespaces # "},{"id":82,"href":"/docs/api/pages/","title":"Pages","section":"API Reference","content":" Pages # Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":83,"href":"/docs/tutorials/eg_plds_state_estimation/","title":"PLDS State Estimation","section":"LDS C+E Examples","content":" PLDS State Estimation Tutorial # This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[x_{t\u0026#43;1} = x_t \u0026#43; w_t\\] \\[y_{t} = \\exp\\left(x_t\\right)\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state Finally, assign the parameters using corresponding set-methods.\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); Creating the estimator # Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. To ensure robust estimates, adaptively re-estimate the process disturbance.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); Simulating estimation # In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.\n// Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.\nWith this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.\n"},{"id":84,"href":"/docs/tutorials/eg_switched_plds_control/","title":"PLDS Switched Control","section":"LDS C+E Examples","content":" PLDS Switched Control Tutorial # This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.\nIn the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating the simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); The system\u0026rsquo;s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 Initially, the system will be in \u0026ldquo;mode\u0026rdquo; 1, where B = b1.\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.\nCreating the controller # Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.\nSimilar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.\nMoreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.\nPutting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying systems: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nNow that the SwitchedController is instantiated, assign its parameters.\n// Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).\nThe control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.\n// Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system\u0026rsquo;s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.\nExample simulation result # Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller\u0026rsquo;s online estimates of the output and state are shown in purple.\nNote that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.\n"},{"id":85,"href":"/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/","title":"src","section":"Files","content":" src # Files # Name src/lds.cpp misc lds namespace functions src/lds_gaussian_sys.cpp GLDS base type. src/lds_poisson_sys.cpp PLDS base type. src/lds_sys.cpp LDS base type. src/lds_uniform_vecs.cpp Uniformly sized vectors. Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":86,"href":"/docs/api/files/lds__gaussian__sys_8cpp/","title":"src/lds_gaussian_sys.cpp","section":"Files","content":" src/lds_gaussian_sys.cpp # GLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_gaussian_sys.h\u0026gt; lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0, data_t r0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { R_.zeros(n_y, n_y); R_.diag().fill(r0); do_recurse_Ke_=true; }; // recursively estimate Ke void lds::gaussian::System::RecurseKe() { if (!do_recurse_Ke_) { return; } // predict covariance P_ = A_ * P_ * A_.t() + Q_; // calc Kalman gain Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_); // update covariance // Reference: Ghahramani et Hinton (1996) P_ = P_ - Ke_ * C_ * P_; if (do_adapt_m) { P_m_ += Q_m_; // A_m = I (i.e., random walk) Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_); P_m_ = P_m_ - Ke_m_ * C_ * P_m_; } } // Simulate const lds::Vector\u0026amp; lds::gaussian::System::Simulate(const Vector\u0026amp; u_tm1){ f(u_tm1, true);//simulate dynamics with noise added h();//output z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure return z_; } void lds::gaussian::System::Print() { lds::System::Print(); std::cout \u0026lt;\u0026lt; \u0026#34;R: \\n\u0026#34; \u0026lt;\u0026lt; R_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":87,"href":"/docs/api/files/lds__poisson__sys_8cpp/","title":"src/lds_poisson_sys.cpp","section":"Files","content":" src/lds_poisson_sys.cpp # PLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_poisson_sys.h\u0026gt; lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { diag_y_ = diagmat(y_); pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(0); }; // Correct: Given measurement (z) and current input (u), update estimate of the // state, covar, output. // // see Eden et al. 2004 void lds::poisson::System::RecurseKe() { // predict covariance P_ = A_ * P_ * A_.t() + Q_; // update cov P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_); Ke_ = P_ * C_.t(); if (do_adapt_m) { P_m_ += Q_m_; // predict (A_m = I) P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_); // update Ke_m_ = P_m_ * C_.t(); } } // Simulate Measurement: z ~ Poisson(y) const lds::Vector\u0026amp; lds::poisson::System::Simulate(const Vector\u0026amp; u_tm1) { f(u_tm1, true); // simulate dynamics with noise added h(); // output z_.zeros(); for (std::size_t k = 0; k \u0026lt; n_y_; k++) { // construct a Poisson distribution object with mean y[k] pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(y_[k]); // pull random sample from this distribution z_[k] = pd_(rng); } return z_; } // ******************* SYS_T ******************* Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":88,"href":"/docs/api/files/lds__sys_8cpp/","title":"src/lds_sys.cpp","section":"Files","content":" src/lds_sys.cpp # LDS base type. More\u0026hellip;\nDetailed Description # This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- lds_sys.cpp - LDS -------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_sys.h\u0026gt; #include \u0026lt;vector\u0026gt; lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) { InitVars(p0, q0); } void lds::System::InitVars(data_t p0, data_t q0) { // initial conditions. x0_ = Vector(n_x_, fill::zeros); // includes bias (nY) and g (nU) P0_ = p0 * Matrix(n_x_, n_x_, fill::eye); m0_ = x0_; P0_m_ = P0_; // signals x_ = x0_; P_ = P0_; m_ = m0_; P_m_ = P0_m_; y_ = Vector(n_y_, fill::zeros); cx_ = Vector(n_y_, fill::zeros); z_ = Vector(n_y_, fill::zeros); // By default, random walk where each state is independent // In this way, provides independent estimates of rate per channel of output. A_ = Matrix(n_x_, n_x_, fill::eye); B_ = Matrix(n_x_, n_u_, fill::zeros); g_ = Vector(n_u_, fill::ones); Q_ = q0 * Matrix(n_x_, n_x_, fill::eye); Q_m_ = Q_; C_ = Matrix(n_y_, n_x_, fill::eye); // each state will map to an output by d_ = Vector(n_y_, fill::zeros); Ke_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain. Ke_m_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain for m adaptation. do_adapt_m = false; } // Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state // and update estimate of the state, covar, output using Kalman filter void lds::System::Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z_t) { // predict mean f(u_tm1); // dynamics h(); // output // recursively calculate esimator gains (or just keep existing values) // (also predicts+updates estimate covariance) RecurseKe(); // update x_ += Ke_ * (z_t - y_); if (do_adapt_m) { m_ += Ke_m_ * (z_t - y_); // adaptively estimating disturbance } // With new state, estimate output. h(); // --\u0026gt; posterior } void lds::System::Reset() { // reset to initial conditions x_ = x0_; // mean P_ = P0_; // cov of state estimate m_ = m0_; // process disturbance P_m_ = P0_m_; // cov of disturbance estimate h(); } std::vector\u0026lt;lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt;\u0026gt; lds::System::nstep_pred_block(lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; u, lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; z, size_t n_pred) { lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_filt; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_pred; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; y_pred; for (size_t k = 0; k \u0026lt; u.size(); k++) { Reset(); size_t n_t = arma::size(u[k])[1]; Matrix x_filt_k(n_x_, n_t, fill::zeros); Matrix x_pred_k(n_x_, n_t - n_pred, fill::zeros); Matrix y_pred_k(n_y_, n_t - n_pred, fill::zeros); for (size_t t = 0; t \u0026lt; n_t - n_pred; t++) { Vector x_pred_ahead = x_; for (size_t t_u = t; t_u \u0026lt; t + n_pred; t_u++) { x_pred_ahead = A_ * x_pred_ahead + B_ * u[k].col(t_u); } x_pred_k.col(t) = x_pred_ahead; y_pred_k.col(t) = h_(x_pred_ahead); if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; // given previous measurment } for (size_t t = n_t - n_pred; t \u0026lt; n_t; t++) { if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; } x_filt.append(x_filt_k); x_pred.append(x_pred_k); y_pred.append(y_pred_k); } return {x_filt, x_pred, y_pred}; } void lds::System::Print() { std::cout \u0026lt;\u0026lt; \u0026#34;\\n ********** SYSTEM ********** \\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;x: \\n\u0026#34; \u0026lt;\u0026lt; x_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;P: \\n\u0026#34; \u0026lt;\u0026lt; P_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;A: \\n\u0026#34; \u0026lt;\u0026lt; A_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B: \\n\u0026#34; \u0026lt;\u0026lt; B_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;g: \\n\u0026#34; \u0026lt;\u0026lt; g_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;m: \\n\u0026#34; \u0026lt;\u0026lt; m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q: \\n\u0026#34; \u0026lt;\u0026lt; Q_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q_m: \\n\u0026#34; \u0026lt;\u0026lt; Q_m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d: \\n\u0026#34; \u0026lt;\u0026lt; d_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;C: \\n\u0026#34; \u0026lt;\u0026lt; C_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;y: \\n\u0026#34; \u0026lt;\u0026lt; y_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } //******************* SYS_T ******************* Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":89,"href":"/docs/api/files/lds__uniform__vecs_8cpp/","title":"src/lds_uniform_vecs.cpp","section":"Files","content":" src/lds_uniform_vecs.cpp # Uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_uniform_vecs.h\u0026gt; namespace lds { UniformVectorList::UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); } UniformVectorList::UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim) : vector(std::move(vecs)) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(const UniformVectorList\u0026amp; that) : vector(that) { (*this) = that; } UniformVectorList::UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { this-\u0026gt;dim_ = this-\u0026gt;at(0).n_elem; } void UniformVectorList::CheckDimensions(size_t dim) { if (dim) { dim_ = dim; } else { dim_ = this-\u0026gt;at(0).n_elem; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Vector\u0026amp; vec : *this) { does_match = does_match \u0026amp;\u0026amp; (vec.n_elem == dim_); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } } } // namespace lds Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":90,"href":"/docs/api/files/lds_8cpp/","title":"src/lds.cpp","section":"Files","content":" src/lds.cpp # misc lds namespace functions More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file implements miscellaneous lds namespace functions not bound to a class.\nSource code # //===-- lds.cpp - LDS -----------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds.h\u0026gt; // insert any necessary function definitions here. namespace lds { void ForceSymPD(Matrix\u0026amp; X) { if (X.is_sympd() || !X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; // for eigenval decomp bool did_succeed(true); Vector d; Matrix u; // see first method (which may not be ideal): // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/ size_t k(1); bool is_sympd = X.is_sympd(); Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye); while (!is_sympd) { if (k \u0026gt; 100) { did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); data_t min_eig = arma::min(d); std::cerr \u0026lt;\u0026lt; \u0026#34;After multiple iterations, min eigen val = \u0026#34; \u0026lt;\u0026lt; min_eig \u0026lt;\u0026lt; \u0026#34;.\\n\u0026#34;; throw std::runtime_error( \u0026#34;Failed to make matrix symmetric positive definite.\u0026#34;); return; } // Limit(d, arma::eps(0), kInf); // force to be positive... // Matrix d_diag = arma::diagmat(d); // X = u * d_diag * u.t(); did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymPD failed.\u0026#34;); } data_t min_eig = arma::min(d); X += id * abs(min_eig) + arma::datum::eps; // make sure symm: X = (X + X.t()) / 2; // double check eigenvals positive after symmetrizing: arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); min_eig = arma::min(d); is_sympd = min_eig \u0026gt; 0; k++; } } void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min) { if (!X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; bool did_succeed(true); Vector d; Matrix u; did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymMinEig failed.\u0026#34;); } Limit(d, eig_min + arma::eps(eig_min), kInf); // enforce lower bound Matrix d_diag = arma::diagmat(d); X = u * d_diag * u.t(); // double check symmetric X = (X + X.t()) / 2; } void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X) { bool did_succeed(true); did_succeed = arma::qr_econ(Qt, L, X.t()); if (!did_succeed) { throw std::runtime_error(\u0026#34;LQ decomposition failed.\u0026#34;); } arma::inplace_trans(L); arma::inplace_trans(Qt); } Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B) { // subtract out mean auto m_a = arma::mean(A, 1); Matrix a0 = A; a0.each_col() -= m_a; auto m_b = arma::mean(B, 1); Matrix b0 = B; b0.each_col() -= m_b; Matrix cov = a0 * b0.t() / a0.n_cols; return cov; } } // namespace lds Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":91,"href":"/docs/api/namespaces/namespacestd/","title":"std","section":"Namespaces","content":" std # Updated on 5 March 2025 at 16:35:01 EST\n"}]
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+[{"id":0,"href":"/lds-ctrl-est/docs/","title":"LDS C+E Documentation","section":"LDS Control \u0026 Estimation","content":" LDS Control \u0026amp; Estimation Documentation # "},{"id":1,"href":"/lds-ctrl-est/docs/tutorials/","title":"LDS C+E Examples","section":"LDS C+E Documentation","content":" Examples # "},{"id":2,"href":"/lds-ctrl-est/acknowledgements/","title":"Acknowledgements","section":"LDS Control \u0026 Estimation","content":" Acknowledgements # Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.\n"},{"id":3,"href":"/lds-ctrl-est/docs/getting-started/getting-started/","title":"Getting Started","section":"LDS C+E Documentation","content":" Getting Started # This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.\nldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.\nTested Configurations # Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the RelWithDebInfo build type in the CMake configure command (-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo):\nUbuntu 18.04 with GCC 7.5 compiler macOS 11 (Big Sur) with Apple Clang 12 compiler Windows 10 with Visual Studio 16.11 (2019 release) and Clang 12 compiler That being said, if you want to debug a build for a single platform, here are some things you can try:\nUse different compilers (or even different versions of a single compiler) Use different versions of vcpkg (which you can control by checking out a different commit in the vcpkg submodule) Mac Pre-requisities # Xcode Command Line Tools will get you clang, gcc, make, and git:\nxcode-select --install Homebrew is \u0026ldquo;The Missing Package Manager for macOS\u0026rdquo; which will make installing lots of things easy. Install like this:\n/bin/bash -c \u0026#34;$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\u0026#34; You can then use it to install CMake, gfortran, and pkg-config:\nbrew install cmake gfortran pkg-config Linux Pre-requisites # You\u0026rsquo;ll need Git, CMake, GCC, gfortran, etc.\nsudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential ninja-build Windows Installation # Look here for Windows-specific instructions.\nDownloading the Library # First, clone the repository along with submodules:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est\rgit submodule update --init Compilation + Installation # Now generate the cache and build using your IDE or from the command line as follows.\nmkdir build \u0026amp;\u0026amp; cd build\rcmake ..\rcmake --build . The first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.\nIf you want to use vcpkg set up somewhere besides this repo\u0026rsquo;s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE\u0026rsquo;s settings.\nYou can verify the build is working by running ctest from the build folder, which runs all the example scripts.\nOptions # This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\nLDSCTRLEST_BUILD_EXAMPLES : [default=ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake to configure/build the library.\nFor basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build cmake --build #build the project sudo make install #[optional] installs to default location (OS-specific) To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location cmake --build #build the project make install #install to /your/install/prefix To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\nLD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.\nPython bindings package ldsctrlest # With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython3_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn\u0026rsquo;t use an undesired version. That environment needs to have NumPy installed.\ncmake --build . --target python_modules The bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory (pip install pytest matplotlib first if you need to).\nSee python/ldsctrlest/README.md for usage details.\nAlso, beware that a single build will probably not work for both the standalone library and the Python package, since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.\nCommon issues # \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.\nexport CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix vcpkg fails on configuration Try running ./bootstrap-vcpkg from the vcpkg folder and try again. If that doesn\u0026rsquo;t work, try updating vcpkg to a newer version (in the source control tab, click on the commit hash by the vcpkg repo then select from the dropdown) and running boostsrap-vcpkg again. You can also try upgrading your system (e.g., apt update, apt upgrade).\nCould not find Python3 (missing: Python3_NumPy_INCLUDE_DIRS NumPy)\nMake sure NumPy is installed in the Python environment you specified. If CMake still can\u0026rsquo;t find it, you may need to tell CMake exactly where to find it by adding an argument to the configure command: -DPython3_NumPy_INCLUDE_DIR=.... You can find that location like this: python -c 'import numpy; print(numpy.get_include())'\n"},{"id":4,"href":"/lds-ctrl-est/docs/getting-started/windows/","title":"Windows","section":"LDS C+E Documentation","content":" Windows Installation # Windows Pre-requisites # Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:\nSet-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time iwr get.scoop.sh | Invoke-Expression Install Git and CMake if you don\u0026rsquo;t already have them:\nscoop install git cmake If that didn\u0026rsquo;t work, follow more detailed instructions here.\nThe easiest way to compile C++ project on Windows is with Visual Studio\u0026rsquo;s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on \u0026ldquo;Desktop development with C++.\u0026rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the \u0026ldquo;Installation details\u0026rdquo; sidebar under optional features.\nAnd the easiest way to use Visual Studio\u0026rsquo;s build tools is with VS Code, along with the CMake Tools extension. Install them and you should be ready to go.\nDownloading the Library # First, clone the repository, either from VS Code or the command line:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est You\u0026rsquo;ll need to initialize the submodules from the command line after the repo is cloned:\ngit submodule update --init Installation # When you open the folder in VS Code, you will like be prompted by the CMake Tools extension to configure the project. Make sure you select the kit (you\u0026rsquo;ll be prompted when you configure\u0026ndash;else there\u0026rsquo;s an icon in the bar on the bottom of the window or type Ctrl+Shift+P, then \u0026ldquo;cmake select kit\u0026rdquo;). Choose Clang [latest version] with GNU CLI ... amd64 assuming you are running a 64-bit OS. (MSVC may work okay too if you don\u0026rsquo;t need to build Python bindings.)\nFollow along with the \u0026ldquo;Getting Started\u0026rdquo; instructions, but where you see config options specified as -DLDSCTREST_BUILD_STATIC=OFF or -DPython3_ROOT_DIR=..., you will enter those in settings: open with Ctrl+,, click \u0026ldquo;workspace\u0026rdquo;, then search for \u0026ldquo;CMake: Configure Args\u0026rdquo; and enter each of your desired arguments as a separate item.\nTo configure, use Ctrl+Shift+P and search for the \u0026ldquo;CMake: Configure\u0026rdquo; command. To build, click the \u0026ldquo;Build\u0026rdquo; button on the bottom bar. Then click the \u0026ldquo;CTest\u0026rdquo; button to run the example scripts.\nConsiderations # Development on Windows has been more prone to bugs than on Unix systems, so if you encounter many problems, consider switching—WSL (Windows Subsystem for Linux) is a good option for Windows users who don\u0026rsquo;t want to work on a different machine.\nCompilation has been successfully tested in VS Code using the following kit, using the \u0026ldquo;RelWithDebInfo\u0026rdquo; config:\nClang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64) Troubleshooting # The build appears to work, but tests fail with code 0xc0000135 OR \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; Have you installed the library? In VS Code, use Shift+F7 to build a specific target, in this case INSTALL. If that doesn\u0026rsquo;t solve your problem, you will likely need to add the build or install folder to your PATH environment variable, which you can do using the settings GUI (search for \u0026ldquo;Edit the system environment variables\u0026rdquo;).\nOn Windows, \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted. Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":5,"href":"/lds-ctrl-est/issues-contributing/","title":"Issues Contributing","section":"LDS Control \u0026 Estimation","content":" Reporting Issues # If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.\nContributing # We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.\n"},{"id":6,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/","title":"armamexc","section":"Namespaces","content":" armamexc # arma/mex interface using Matlab C API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; T m2T_scalar(const mxArray * matlab_scalar)\nConvert Matlab mxArray to scalar of type T. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)\nConvert matlab matrix to armadillo. template \u0026lt;typename T \u0026gt; mxArray * a2m_mat(arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat)\nConvert armadillo to matlab matrix. template \u0026lt;typename T \u0026gt; mxArray * a2m_vec(arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec)\nConvert armadillo to matlab vector. Detailed Description # Utilities for arma/mex interface using Matlab C API\nFunction Details # m2T_scalar # template \u0026lt;class T \u0026gt; inline T m2T_scalar( const mxArray * matlab_scalar ) Parameters:\nmatlab_scalar matlab scalar Template Parameters:\nT type Return: scalar of type T\nm2a_mat # template \u0026lt;class T \u0026gt; inline arma::Mat\u0026lt; T \u0026gt; m2a_mat( const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true ) Parameters:\nmatlab_mat matlab matrix copy_aux_mem [optional] whether to copy auxiliary memory strict [optional] strictly enforce the above Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_mat( arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat ) Parameters:\narma_mat armadillo matrix Return: matlab matrix\na2m_vec # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_vec( arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec ) Parameters:\narma_vec armadillo vector Return: matlab vector\nUpdated on 5 March 2025 at 21:00:41 EST\n"},{"id":7,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/","title":"armamexcpp","section":"Namespaces","content":" armamexcpp # arma/mex interface using Matlab C++ API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat(matlab::data::CellArray \u0026amp; matlab_cell)\nConvert matlab cell array to vector of armadillo matrices. template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array)\nConvert matlab matrix to a vector of scalars. template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo vector. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat(const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec(const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab vector. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec(const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert vector of scalar T to matlab matrix. Detailed Description # utilities for arma/mex interface using Matlab C++ API\nFunction Details # m2a_cellmat # template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat( matlab::data::CellArray \u0026amp; matlab_cell ) Parameters:\nmatlab_cell matlab cell Template Parameters:\nT type Return: vector of armadillo matrices of type T\nm2s_vec # template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: vector of type T\nm2a_vec # template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: armadillo vector of type T\nm2a_mat # template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab matrix Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat( const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_mat arma matrix factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\na2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec( const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_vec armadillo vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\ns2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec( const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\nstd_vec standard vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\nUpdated on 5 March 2025 at 21:00:41 EST\n"},{"id":8,"href":"/lds-ctrl-est/docs/terminology/control-estimation/","title":"C\u0026E","section":"LDS C+E Documentation","content":" Control \u0026amp; Estimation # The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.\nState estimation # In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by \\[\\widehat{\\mathbf{x}}_{t|t-1} = \\mathbf{A}\\widehat{\\mathbf{x}}_{t-1|t-1} \u0026#43; \\mathbf{B} u_{t-1} \u0026#43; \\mathbf{m}_{t-1} \\;,\\] \\[\\widehat{\\mathbf{x}}_{t|t} = \\widehat{\\mathbf{x}}_{t|t-1} \u0026#43; \\mathbf{K}^{\\rm e}_t \\left(\\mathbf{z}_t - \\widehat{\\mathbf{y}}_{t|t-1}\\right)\\;,\\] where \\( \\hat{\\left(\\cdot\\right)}_{t|j} \\) indicates an estimate at time \\( t \\) given data up to time \\( j \\) inclusive, \\( \\mathbf{K}^{\\rm e} \\) is the estimator gain, and\n\\[ \\widehat{\\mathbf{y}}_{t|t-1} = h\\left( \\widehat{\\mathbf{x}}_{t|t-1} \\right) \\; .\\] In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.\nIn the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.\nAdaptive estimation of process disturbance # Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.\nWhen parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk \\[\\mathbf{m}_{t} = \\mathbf{m}_{t-1} \u0026#43; \\mathbf{w}^m_{t-1} \\;,\\] where \\( \\mathbf{w}^m \\sim \\mathcal{N}\\left(0, \\mathbf{Q}_m\\right)\\) . Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.\nControl # Given the estimated state, the controller updates the inputs to the system according to the following law: \\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] where \\( \\left( \\cdot \\right)^{\\rm ref} \\) correspond to reference/target signals and \\( \\mathbf{K}^c_x \\) is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.\nIf users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as\n\\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right) - \\mathbf{K}^c_{\\rm inty} \\sum_{j=1}^{t}\\left( \\widehat{\\mathbf{y}}_j - \\mathbf{y}^{\\rm ref}_j \\right) \\;.\\] An additional option available to users is a control law that updates the change in u,\n\\[\\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\left(\\mathbf{u}_{t-1} - \\mathbf{u}^{\\rm ref}_{t-1} \\right) - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] \\[\\mathbf{u}_{t} = \\mathbf{u}_{t-1} \u0026#43; \\Delta\\mathbf{u}_{t} \\; .\\] Notice that this takes the form of a first-order difference equation for updating control (i.e., \\( \\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\mathbf{u}_{t-1} \u0026#43; \\epsilon_{t-1} \\) ), effectively low-pass filtering the input depending on the characteristics of \\( \\mathbf{K}^c_u \\) . This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.\nIntegral action and the \\( \\Delta \\mathbf{u} \\) control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR\u0026rsquo;d to use in combination.\nCalculating reference state-control from output # In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce \\( \\mathbf{x}^{\\rm ref} \\) and \\( \\mathbf{u}^{\\rm ref} \\) . Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.\n"},{"id":9,"href":"/lds-ctrl-est/docs/api/classes/","title":"Classes","section":"LDS C+E Documentation","content":" Classes # lds::Controller\nlds::EM\nlds::Fit LDS Fit Type.\nlds::SSID\nlds::SwitchedController SwitchedController Type.\nlds::System Linear Dynamical System Type.\nlds::UniformMatrixList\nlds::UniformSystemList\nlds::UniformVectorList\nlds::gaussian::Controller Gaussian-observation Controller Type.\nlds::gaussian::Fit GLDS Fit Type.\nlds::gaussian::FitEM GLDS E-M Fit Type.\nlds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.\nlds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.\nlds::gaussian::System Gaussian LDS Type.\nlds::poisson::Controller PLDS Controller Type.\nlds::poisson::Fit PLDS Fit Type.\nlds::poisson::FitEM PLDS E-M Fit Type.\nlds::poisson::FitSSID Subspace Identification (SSID) for PLDS.\nlds::poisson::SwitchedController Poisson-observation SwitchedController Type.\nlds::poisson::System Poisson System type.\nUpdated on 5 March 2025 at 21:00:41 EST\n"},{"id":10,"href":"/lds-ctrl-est/docs/api/modules/group__control__masks/","title":"Control Mode Bit Masks","section":"Modules","content":" Control Mode Bit Masks # provides fill types for constructing new armadillo vectors, matrices More\u0026hellip; Attributes # Name const std::size_t kControlTypeDeltaU control designed to penalize change in input const std::size_t kControlTypeIntY control using integral action const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m Detailed Description # Control mode bit masks. These can be bit-wise OR\u0026rsquo;d to use in combination.\nAttribute Details # kControlTypeDeltaU # static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY # static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM # static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\nUpdated on 5 March 2025 at 21:00:41 EST\n"},{"id":11,"href":"/lds-ctrl-est/docs/api/modules/group__defaults/","title":"Defaults","section":"Modules","content":" Defaults # More\u0026hellip; Attributes # Name const data_t kDefaultP0 default state estimate covar const data_t kDefaultQ0 default process noise covar const data_t kDefaultR0 default output noise covar Detailed Description # Default values for common variables (e.g., default diagonal elements of covariances)\nAttribute Details # kDefaultP0 # static const data_t kDefaultP0 = 1e-6; kDefaultQ0 # static const data_t kDefaultQ0 = 1e-6; kDefaultR0 # static const data_t kDefaultR0 = 1e-2; Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":12,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/","title":"eg_glds_ctrl.cpp","section":"Examples","content":" eg_glds_ctrl.cpp # Example GLDS Control ```cpp\n//===\u0026ndash; eg_glds_ctrl.cpp - Example GLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS Control ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\n// output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false;\n// Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err\n// setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]);\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; }\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 21:00:41 EST "},{"id":13,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/","title":"eg_glds_du_plds_ctrl.cpp","section":"Examples","content":" eg_glds_du_plds_ctrl.cpp # Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u). ```cpp\n//===\u0026ndash; eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt);\n// to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU;\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_du_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 21:00:41 EST "},{"id":14,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/","title":"eg_plds_ctrl.cpp","section":"Examples","content":" eg_plds_ctrl.cpp # Example PLDS Control ```cpp\n//===\u0026ndash; eg_plds_ctrl.cpp - Example PLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Control ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt);\n// Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err\n// Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\nsize_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\nVector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled\u0026hellip; lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system);\n// for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type);\n// set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty);\n// to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0;\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros);\n// set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y();\nx_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x();\nm_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 21:00:41 EST "},{"id":15,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/","title":"eg_plds_est.cpp","section":"Examples","content":" eg_plds_est.cpp # Example PLDS Estimation ```cpp\n//===\u0026ndash; eg_plds_est.cpp - Example PLDS Estimation \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0);\nint main() { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.\n// construct ground truth system\u0026hellip; lds::poisson::System system_true(n_u, n_x, n_y, dt);\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset();\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt);\n// Can copy parameters from another system object system_estimator = system_true;\n// wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est);\n// set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true;\n// set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;estimator:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; system_estimator.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));\n// create matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros);\n// states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\nMatrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simlation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1));\n// Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simlation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows;\nif ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026ldquo;Q must be n x n.\u0026rdquo;); }\nMatrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); }\nreturn x; }\n_Filename: eg_plds_est.cpp_ ------------------------------- Updated on 5 March 2025 at 21:00:41 EST "},{"id":16,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/","title":"eg_plds_switched_ctrl.cpp","section":"Examples","content":" eg_plds_switched_ctrl.cpp # Example Switched PLDS Control ```cpp\n//===\u0026ndash; eg_plds_switched_ctrl.cpp - Example Switched PLDS Control \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026quot;;\n// whether to do switched control bool do_switch_ctrl = true;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt);\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices. data_t scale_sys_b = 2;\nMatrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));\ncontrolled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions\n// reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);\n// Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system);\n// set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys1:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys2:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x));\nswitched_controller.set_y_ref(y_ref0);\nstd::vectorlds::poisson::System systems_vec(3, lds::poisson::System()); lds::UniformSystemListlds::poisson::System systems(std::move(systems_vec));\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;switched_controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; switched_controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Fake measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros);\n// create Matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);\n// modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones);\n// set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } }\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace)); mode.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;mode\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_switched_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 21:00:41 EST "},{"id":17,"href":"/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/","title":"examples","section":"Files","content":" examples # Files # Name examples/eg_glds_ctrl.cpp examples/eg_glds_du_plds_ctrl.cpp examples/eg_plds_ctrl.cpp examples/eg_plds_est.cpp examples/eg_plds_switched_ctrl.cpp Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":18,"href":"/lds-ctrl-est/docs/api/examples/","title":"Examples","section":"LDS C+E Documentation","content":" Examples # eg_glds_ctrl.cpp Example GLDS Control.\neg_glds_du_plds_ctrl.cpp Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).\neg_plds_ctrl.cpp Example PLDS Control.\neg_plds_est.cpp Example PLDS Estimation.\neg_plds_switched_ctrl.cpp Example Switched PLDS Control.\nUpdated on 5 March 2025 at 21:00:41 EST\n"},{"id":19,"href":"/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/","title":"examples/eg_glds_ctrl.cpp","section":"Files","content":" examples/eg_glds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":20,"href":"/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/","title":"examples/eg_glds_du_plds_ctrl.cpp","section":"Files","content":" examples/eg_glds_du_plds_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU; // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":21,"href":"/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/","title":"examples/eg_plds_ctrl.cpp","section":"Files","content":" examples/eg_plds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err // Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled... lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type); // set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":22,"href":"/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/","title":"examples/eg_plds_est.cpp","section":"Files","content":" examples/eg_plds_est.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name Matrix random_walk(size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0) int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # random_walk # Matrix random_walk( size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0 ) main # int main() Source code # //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. // construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state // Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. // turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; } Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":23,"href":"/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/","title":"examples/eg_plds_switched_ctrl.cpp","section":"Files","content":" examples/eg_plds_switched_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Source code # //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 // simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions // reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":24,"href":"/lds-ctrl-est/docs/api/files/","title":"Files","section":"LDS C+E Documentation","content":" Files # examples/eg_glds_ctrl.cpp\nexamples/eg_glds_du_plds_ctrl.cpp\nexamples/eg_plds_ctrl.cpp\nexamples/eg_plds_est.cpp\nexamples/eg_plds_switched_ctrl.cpp\nldsCtrlEst_h/lds.h lds namespace\nldsCtrlEst_h/lds_ctrl.h Controller.\nldsCtrlEst_h/lds_fit.h LDS base fit type.\nldsCtrlEst_h/lds_fit_em.h subspace identification\nldsCtrlEst_h/lds_fit_ssid.h subspace identification\nldsCtrlEst_h/lds_gaussian.h glds namespace\nldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.\nldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.\nldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.\nldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.\nldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.\nldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.\nldsCtrlEst_h/lds_poisson.h plds namespace\nldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.\nldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.\nldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.\nldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.\nldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.\nldsCtrlEst_h/lds_poisson_sys.h PLDS base type.\nldsCtrlEst_h/lds_sctrl.h SwitchedController type.\nldsCtrlEst_h/lds_sys.h LDS base type.\nldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.\nldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.\nldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.\nldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)\nldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)\nsrc/lds.cpp misc lds namespace functions\nsrc/lds_gaussian_sys.cpp GLDS base type.\nsrc/lds_poisson_sys.cpp PLDS base type.\nsrc/lds_sys.cpp LDS base type.\nsrc/lds_uniform_vecs.cpp Uniformly sized vectors.\nUpdated on 5 March 2025 at 21:00:41 EST\n"},{"id":25,"href":"/lds-ctrl-est/docs/tutorials/eg_glds_control/","title":"GLDS Control","section":"LDS C+E Examples","content":" GLDS Control Tutorial # This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\] \\[\ry_{t} = x_t\r\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.\n/// Going to simulate a switching disturbance (m) acting on system size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_y).fill(m_low); Finally, assign the parameters using corresponding set-methods.\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); Creating the controller # Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.\nFor the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.\n// make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nWith the controller constructed, control variables may be set.\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.\nThe control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller\u0026rsquo;s online estimates of the output, state, and disturbance are given in purple.\n"},{"id":26,"href":"/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/","title":"include","section":"Files","content":" include # Directories # Name ldsCtrlEst_h Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":27,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds/","title":"lds","section":"Namespaces","content":" lds # Linear Dynamical Systems (LDS) namespace. Namespaces # Name lds::gaussian Linear Dynamical Systems with Gaussian observations. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::Controller class lds::EM class lds::Fit LDS Fit Type. class lds::SSID class lds::SwitchedController SwitchedController Type. class lds::System Linear Dynamical System Type. class lds::UniformMatrixList class lds::UniformSystemList class lds::UniformVectorList Types # Name enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}\nweighting options for SSID enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2} using double data_t using arma::Col\u0026lt; data_t \u0026gt; Vector using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Cube\u0026lt; data_t \u0026gt; Cube using arma::subview\u0026lt; data_t \u0026gt; View Functions # Name void Limit(std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub) void Limit(Vector \u0026amp; x, data_t lb, data_t ub) void Limit(Matrix \u0026amp; x, data_t lb, data_t ub) void Reassign(Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Vector in place void Reassign(Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Matrix in place void ForceSymPD(Matrix \u0026amp; X)\nforces matrix to be symmetric positive-definite void ForceSymMinEig(Matrix \u0026amp; X, data_t eig_min =0)\nforces matrix to be symmetric and have a minimum eigenvalue void lq(Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X)\nLQ decomposition. Matrix calcCov(const Matrix \u0026amp; A, const Matrix \u0026amp; B)\nCalculate covariance matrix. Attributes # Name const data_t kInf Some useful numbers. const data_t kPi Type Details # SSIDWt # Enumerator Value Description kSSIDNone None. kSSIDMOESP MOESP (AKA \u0026ldquo;robust method\u0026rdquo; in van Overschee 1996) kSSIDCVA CVA \u0026ldquo;Canonical Variate Analysis\u0026rdquo;. Weighting options for singular value decomposition performed during subspace identification (SSID)\nReference:\nvan Overschee, de Moor. 1996. Subspace Identification for Linear Systems.\nMatrixListFreeDim # Enumerator Value Description kMatFreeDimNone neither dim free to be hetero in mat list kMatFreeDim1 allow 1st dim of mats in list to be hetero kMatFreeDim2 allow 2nd dim of mats in list to be hetero data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nVector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Cube # using lds::Cube = arma::Cube\u0026lt;data_t\u0026gt;; View # using lds::View = arma::subview\u0026lt;data_t\u0026gt;; Function Details # Limit # inline void Limit( std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Vector \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Matrix \u0026amp; x, data_t lb, data_t ub ) Reassign # inline void Reassign( Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Vector other other Vector parenthetical optional description provided by caller to ease debugging Reassign # inline void Reassign( Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Matrix other other Matrix parenthetical optional description provided by caller to ease debugging ForceSymPD # void ForceSymPD( Matrix \u0026amp; X ) Parameters:\nX mutated matrix ForceSymMinEig # void ForceSymMinEig( Matrix \u0026amp; X, data_t eig_min =0 ) Parameters:\nX mutated matrix eig_min [optional] minimum eigen value lq # void lq( Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X ) Parameters:\nL lower triangle matrix Qt orthonormal matrix (transposed cf QR decomp) X matrix being decomposed calcCov # Matrix calcCov( const Matrix \u0026amp; A, const Matrix \u0026amp; B ) Parameters:\nA some matrix B some other matrix Return: covariance\nAttribute Details # kInf # static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); kPi # static const data_t kPi = arma::datum::pi; Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":28,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/","title":"lds::Controller","section":"Classes","content":" lds::Controller # More\u0026hellip;\nInherited by lds::SwitchedController\u0026lt; System \u0026gt;, lds::gaussian::Controller, lds::poisson::Controller\nPublic Functions # Name Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes # Name System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::Controller; Public Function Details # Controller # Controller() =default Controller # inline Controller( const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Controller # inline Controller( System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Control # inline const Vector \u0026amp; Control( const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step). This is the most flexible option, but requires user to have set the controller\u0026rsquo;s y_ref, x_ref, and u_ref variables.\nControlOutputReference # inline const Vector \u0026amp; ControlOutputReference( const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_estimation [optional] whether to update state estimate (if false, effectively open-loop control) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.\nsys # inline const System \u0026amp; sys() const Kc # inline const Matrix \u0026amp; Kc() const Kc_inty # inline const Matrix \u0026amp; Kc_inty() const Kc_u # inline const Matrix \u0026amp; Kc_u() const g_design # inline const Vector \u0026amp; g_design() const u_ref # inline const Vector \u0026amp; u_ref() const x_ref # inline const Vector \u0026amp; x_ref() const y_ref # inline const Vector \u0026amp; y_ref() const control_type # inline size_t control_type() const tau_awu # inline data_t tau_awu() const u_lb # inline data_t u_lb() const u_ub # inline data_t u_ub() const set_sys # inline void set_sys( const System \u0026amp; sys ) set_g_design # inline void set_g_design( const Vector \u0026amp; g_design ) set_u_ref # inline void set_u_ref( const Vector \u0026amp; u_ref ) set_x_ref # inline void set_x_ref( const Vector \u0026amp; x_ref ) set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) Reimplemented by: lds::gaussian::Controller::set_y_ref, lds::gaussian::SwitchedController::set_y_ref, lds::poisson::Controller::set_y_ref, lds::poisson::SwitchedController::set_y_ref\nset_Kc # inline void set_Kc( const Matrix \u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const Matrix \u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const Matrix \u0026amp; Kc_u ) set_tau_awu # inline void set_tau_awu( data_t tau ) set_control_type # inline void set_control_type( size_t control_type ) Parameters:\ncontrol_type control type bit mask Template Parameters:\nSystem type derived from lds::System set_u_lb # inline void set_u_lb( data_t u_lb ) Parameters:\nu_lb control lower bound set_u_ub # inline void set_u_ub( data_t u_ub ) Parameters:\nu_ub control upper bound Reset # inline void Reset() Print # inline void Print() Protected Attribute Details # sys_ # System sys_; u_ # Vector u_; u_return_ # Vector u_return_; g_design_ # Vector g_design_; u_ref_ # Vector u_ref_; u_ref_prev_ # Vector u_ref_prev_; x_ref_ # Vector x_ref_; y_ref_ # Vector y_ref_; cx_ref_ # Vector cx_ref_; Kc_ # Matrix Kc_; Kc_u_ # Matrix Kc_u_; Kc_inty_ # Matrix Kc_inty_; du_ref_ # Vector du_ref_; dv_ref_ # Vector dv_ref_; v_ref_ # Vector v_ref_; dv_ # Vector dv_; v_ # Vector v_; int_e_ # Vector int_e_; int_e_awu_adjust_ # Vector int_e_awu_adjust_; u_sat_ # Vector u_sat_; do_control_prev_ # bool do_control_prev_ = false; do_lock_control_prev_ # bool do_lock_control_prev_ = false; u_saturated_ # bool u_saturated_ = false; u_lb_ # data_t u_lb_ {}; u_ub_ # data_t u_ub_ {}; tau_awu_ # data_t tau_awu_ {}; k_awu_ # data_t k_awu_ = 0; t_since_control_onset_ # data_t t_since_control_onset_ = 0; control_type_ # size_t control_type_ {}; Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":29,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/","title":"lds::EM","section":"Classes","content":" lds::EM # More\u0026hellip;\nInherited by lds::gaussian::FitEM, lds::poisson::FitEM\nPublic Functions # Name EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions # Name void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() virtual void MaximizeOutput() =0 virtual void MaximizeMeasurement() =0 void Smooth(bool force_common_initial)\nget smoothed estimates virtual void RecurseKe(Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t) =0\nrecursively update estimator gain Ke void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::EM; Public Function Details # EM # EM() =default EM # EM( size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nn_x number of states dt sample period u_train input training data z_train measurement training data EM # EM( const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nfit0 initial fit u_train input training data z_train measurement training data ~EM # virtual ~EM() =default Run # const Fit \u0026amp; Run( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2 ) Parameters:\ncalc_dynamics [optional] whether to calculate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law max_iter max number of iterations tol convergence tolerance (max fractional abs change) Return: Fit\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nx # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const y # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const sum_E_x_t_x_t # inline const Matrix \u0026amp; sum_E_x_t_x_t() const sum_E_xu_tm1_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const sum_E_xu_t_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const n_t_tot # inline size_t n_t_tot() theta # inline const Vector \u0026amp; theta() const Protected Function Details # Expectation # void Expectation( bool force_common_initial =false ) Parameters:\nforce_common_initial whether to force common initial condition for all trials Maximization # void Maximization( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false ) Parameters:\ncalc_dynamics [optional] whether to caclulate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law MaximizeDynamics # void MaximizeDynamics() MaximizeQ # void MaximizeQ() MaximizeInitial # void MaximizeInitial() MaximizeOutput # virtual void MaximizeOutput() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput\nMaximizeMeasurement # virtual void MaximizeMeasurement() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement\nSmooth # void Smooth( bool force_common_initial ) Parameters:\nforce_common_initial whether to force common initial conditions RecurseKe # virtual void RecurseKe( Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t ) =0 Parameters:\nKe estimator gain P_pre cov of predicted state est. P_post cov of postior sate est. t time Reimplemented by: lds::gaussian::FitEM::RecurseKe, lds::poisson::FitEM::RecurseKe\nReset # void Reset() InitVars # void InitVars() UpdateTheta # Vector UpdateTheta() Return: parameter list\nProtected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; x_ # std::vector\u0026lt; Matrix \u0026gt; x_; P_ # std::vector\u0026lt; Cube \u0026gt; P_; P_t_tm1_ # std::vector\u0026lt; Cube \u0026gt; P_t_tm1_; y_ # std::vector\u0026lt; Matrix \u0026gt; y_; diag_y_ # Matrix diag_y_; sum_E_x_t_x_t_ # Matrix sum_E_x_t_x_t_; sum_E_xu_tm1_xu_tm1_ # Matrix sum_E_xu_tm1_xu_tm1_; sum_E_xu_t_xu_tm1_ # Matrix sum_E_xu_t_xu_tm1_; fit_ # Fit fit_; theta_ # Vector theta_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":30,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/","title":"lds::Fit","section":"Classes","content":" lds::Fit # LDS Fit Type. #include \u0026lt;lds_fit.h\u0026gt;\nInherited by lds::gaussian::Fit, lds::poisson::Fit\nPublic Functions # Name Fit() =default\nConstructs a new Fit. Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias virtual const Matrix \u0026amp; R() const =0 void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance virtual void set_R(const Matrix \u0026amp; R) =0\nsets output noise covariance (if any) void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) =0\noutput function Protected Attributes # Name data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period ~Fit # virtual ~Fit() =default n_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const m # inline const Vector \u0026amp; m() const Q # inline const Matrix \u0026amp; Q() const x0 # inline const Vector \u0026amp; x0() const P0 # inline const Matrix \u0026amp; P0() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const R # virtual const Matrix \u0026amp; R() const =0 Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R\nset_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_g # inline void set_g( const Vector \u0026amp; g ) set_m # inline void set_m( const Vector \u0026amp; m ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # virtual void set_R( const Matrix \u0026amp; R ) =0 Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R\nset_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) f # inline View f( Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t ) Parameters:\nx state estimate (over time) u input (over time) t time index Return: view of updated state\nf # inline View f( Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t ) Parameters:\nx_pre predicted state est. x_post posterior state est. u input (over time) t time index Return: view of predicted state\nh # virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) =0 Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplemented by: lds::gaussian::Fit::h, lds::poisson::Fit::h\nProtected Attribute Details # dt_ # data_t dt_ {}; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; m_ # Vector m_; Q_ # Matrix Q_; C_ # Matrix C_; d_ # Vector d_; R_ # Matrix R_; x0_ # Vector x0_; P0_ # Matrix P0_; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":31,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/","title":"lds::gaussian","section":"Namespaces","content":" lds::gaussian # Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. class lds::gaussian::Fit GLDS Fit Type. class lds::gaussian::FitEM GLDS E-M Fit Type. class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. class lds::gaussian::System Gaussian LDS Type. Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":32,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/","title":"lds::gaussian::Controller","section":"Classes","content":" lds::gaussian::Controller # Gaussian-observation Controller Type. #include \u0026lt;lds_gaussian_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 21:00:41 EST\n"},{"id":33,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/","title":"lds::gaussian::Fit","section":"Classes","content":" lds::gaussian::Fit # GLDS Fit Type. #include \u0026lt;lds_gaussian_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual const Matrix \u0026amp; R() const override\ngets measurement noise covariance virtual void set_R(const Matrix \u0026amp; R) override\nsets measurement noise covariance virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period R # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nh # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nUpdated on 5 March 2025 at 21:00:41 EST\n"},{"id":34,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/","title":"lds::gaussian::FitEM","section":"Classes","content":" lds::gaussian::FitEM # GLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_gaussian_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::gaussian::FitEM; This type is used in the process of fitting GLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":35,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/","title":"lds::gaussian::FitSSID","section":"Classes","content":" lds::gaussian::FitSSID # Subspace Identification (SSID) for GLDS. #include \u0026lt;lds_gaussian_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":36,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/","title":"lds::gaussian::SwitchedController","section":"Classes","content":" lds::gaussian::SwitchedController # Gaussian-observation SwitchedController Type. #include \u0026lt;lds_gaussian_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 21:00:41 EST\n"},{"id":37,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/","title":"lds::gaussian::System","section":"Classes","content":" lds::gaussian::System # Gaussian LDS Type. #include \u0026lt;lds_gaussian_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)\nConstructs a new Gaussian System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. const Matrix \u0026amp; R() const\nGet output noise covariance. void set_Q(const Matrix \u0026amp; Q) void set_R(const Matrix \u0026amp; R)\nSet output noise covariance. void set_Ke(const Matrix \u0026amp; Ke)\nSet estimator gain. void set_Ke_m(const Matrix \u0026amp; Ke_m)\nSet disturbance estimator gain. void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively update estimator gain. Protected Attributes # Name Matrix R_ covariance of output noise bool do_recurse_Ke_ whether to recursively calculate estimator gain Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0 ) Parameters:\nn_u number of inputs (u) n_x number of states (x) n_y number of outputs (y) dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) r0 [optional] initial diagonal elements of output noise covariance (R) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nR # inline const Matrix \u0026amp; R() const set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # inline void set_R( const Matrix \u0026amp; R ) set_Ke # inline void set_Ke( const Matrix \u0026amp; Ke ) set_Ke_m # inline void set_Ke_m( const Matrix \u0026amp; Ke_m ) Print # void Print() Protected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nProtected Attribute Details # R_ # Matrix R_; do_recurse_Ke_ # bool do_recurse_Ke_ {}; Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":38,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/","title":"lds::poisson","section":"Namespaces","content":" lds::poisson # Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. class lds::poisson::Fit PLDS Fit Type. class lds::poisson::FitEM PLDS E-M Fit Type. class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. class lds::poisson::System Poisson System type. Attributes # Name std::random_device rd random device for simulating poisson data std::mt19937 rng random number generator for simulating poisson data Attribute Details # rd # static std::random_device rd; rng # static std::mt19937 rng = std::mt19937( rd()); Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":39,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/","title":"lds::poisson::Controller","section":"Classes","content":" lds::poisson::Controller # PLDS Controller Type. #include \u0026lt;lds_poisson_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 21:00:41 EST\n"},{"id":40,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/","title":"lds::poisson::Fit","section":"Classes","content":" lds::poisson::Fit # PLDS Fit Type. #include \u0026lt;lds_poisson_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function virtual void set_R(const Matrix \u0026amp; R) override\nsets output noise covariance (if any) virtual const Matrix \u0026amp; R() const override Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # inline Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period h # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nR # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nUpdated on 5 March 2025 at 21:00:41 EST\n"},{"id":41,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/","title":"lds::poisson::FitEM","section":"Classes","content":" lds::poisson::FitEM # PLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_poisson_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::poisson::FitEM; This type is used in the process of fitting PLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":42,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/","title":"lds::poisson::FitSSID","section":"Classes","content":" lds::poisson::FitSSID # Subspace Identification (SSID) for PLDS. #include \u0026lt;lds_poisson_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":43,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/","title":"lds::poisson::SwitchedController","section":"Classes","content":" lds::poisson::SwitchedController # Poisson-observation SwitchedController Type. #include \u0026lt;lds_poisson_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 21:00:41 EST\n"},{"id":44,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/","title":"lds::poisson::System","section":"Classes","content":" lds::poisson::System # Poisson System type. #include \u0026lt;lds_poisson_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nConstructs a new Poisson System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively recalculate estimator gain (Ke) Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nProtected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nRecursively recalculate estimator gain (Ke).\nReferences:\nSmith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.\nEden UT, \u0026hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.\nUpdated on 5 March 2025 at 21:00:41 EST\n"},{"id":45,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/","title":"lds::SSID","section":"Classes","content":" lds::SSID # More\u0026hellip;\nInherited by lds::gaussian::FitSSID, lds::poisson::FitSSID\nPublic Functions # Name SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions # Name void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. virtual void DecomposeData() =0\nDecompose data to lower-triangular matrix (used in Solve) void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::SSID; Public Function Details # SSID # SSID() =default SSID # SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf) ) Parameters:\nn_x number of states n_h size of block-hankel data matrix dt sample period u_train input training data z_train measurement training data d output bias Run # std::tuple\u0026lt; Fit, Vector \u0026gt; Run( SSIDWt ssid_wt ) Parameters:\nssid_wt weight for singular value decomp Return: tuple (Fit, singular values)\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nProtected Function Details # CalcD # void CalcD( data_t t_silence =0.1, data_t thresh_silence =0.001 ) Parameters:\nt_silence threshold on period of time that qualifies as \u0026ldquo;silence\u0026rdquo; thresh_silence threshold on input amplitude u that qualifies as \u0026ldquo;silence\u0026rdquo; CreateHankelDataMat # void CreateHankelDataMat() Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.\nDecomposeData # virtual void DecomposeData() =0 Reimplemented by: lds::gaussian::FitSSID::DecomposeData, lds::poisson::FitSSID::DecomposeData\nCalcSVD # void CalcSVD( SSIDWt wt ) Parameters:\nssid_wt weight for SVD Solve # void Solve( data_t wt_dc ) Parameters:\nwt_dc weight placed on getting correct DC I/O gain RecomputeExtObs # void RecomputeExtObs() Protected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; D_ # Matrix D_; fit_ # Fit fit_; g_dc_ # Matrix g_dc_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_h_ # size_t n_h_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; L_ # Matrix L_; s_ # Vector s_; ext_obs_t_ # Matrix ext_obs_t_; Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":46,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/","title":"lds::SwitchedController","section":"Classes","content":" lds::SwitchedController # SwitchedController Type. More\u0026hellip;\n#include \u0026lt;lds_sctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nInherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController\nPublic Functions # Name SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes # Name std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::SwitchedController; Public Function Details # SwitchedController # SwitchedController() =default SwitchedController # inline SwitchedController( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask SwitchedController # inline SwitchedController( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Switch # inline void Switch( size_t idx, bool do_force_switch =false ) Parameters:\nidx index do_force_switch whether to force a system switch even if already there. set_Kc # inline void set_Kc( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc ) set_Kc # inline void set_Kc( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty ) set_Kc_inty # inline void set_Kc_inty( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u ) set_Kc_u # inline void set_Kc_u( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u ) set_g_design # inline void set_g_design( const UniformVectorList \u0026amp; g ) set_g_design # inline void set_g_design( UniformVectorList \u0026amp;\u0026amp; g ) Protected Attribute Details # systems_ # std::vector\u0026lt; System \u0026gt; systems_; n_sys_ # size_t n_sys_ {}; idx_ # size_t idx_ {}; Kc_list_ # UniformMatrixList Kc_list_; Kc_inty_list_ # UniformMatrixList Kc_inty_list_; Kc_u_list_ # UniformMatrixList Kc_u_list_; g_design_list_ # UniformVectorList g_design_list_; Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":47,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_system/","title":"lds::System","section":"Classes","content":" lds::System # Linear Dynamical System Type. #include \u0026lt;lds_sys.h\u0026gt;\nInherited by lds::gaussian::System, lds::poisson::System\nPublic Functions # Name System() =default\nConstructs a new System. System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nconstructs a new System virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) =0\nsimulates system (single time step) void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function virtual void h() =0\nsystem output function virtual Vector h_(Vector x) =0\nsystem output function (stateless) size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void RecurseKe() =0\nRecursively recalculate estimator gain (Ke) void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes # Name bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes # Name std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 diagonal elements for state estimate covariance q0 diagonal elements for process noise covariance ~System # inline virtual ~System() Filter # void Filter( const Vector \u0026amp; u_tm1, const Vector \u0026amp; z ) Parameters:\nu_tm1 input at t-minus-1 z_t current measurement Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.\nSimulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) =0 Parameters:\nu_tm1 input at time t-1 Return: simulated measurement at time t\nReimplemented by: lds::gaussian::System::Simulate, lds::poisson::System::Simulate\nf # inline void f( const Vector \u0026amp; u, bool do_add_noise =false ) Parameters:\nu input do_add_noise whether to add simulated process noise h # virtual void h() =0 Reimplemented by: lds::gaussian::System::h, lds::poisson::System::h\nh_ # virtual Vector h_( Vector x ) =0 Parameters:\nx_t state at time t Return: predicted state at time t + 1\nReimplemented by: lds::gaussian::System::h_, lds::poisson::System::h_\nn_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const x # inline const Vector \u0026amp; x() const P # inline const Matrix \u0026amp; P() const m # inline const Vector \u0026amp; m() const P_m # inline const Matrix \u0026amp; P_m() const cx # inline const Vector \u0026amp; cx() const y # inline const Vector \u0026amp; y() const x0 # inline const Vector \u0026amp; x0() const m0 # inline const Vector \u0026amp; m0() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const Ke # inline const Matrix \u0026amp; Ke() const Ke_m # inline const Matrix \u0026amp; Ke_m() const Q # inline const Matrix \u0026amp; Q() Q_m # inline const Matrix \u0026amp; Q_m() P0 # inline const Matrix \u0026amp; P0() P0_m # inline const Matrix \u0026amp; P0_m() set_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_m # inline void set_m( const Vector \u0026amp; m, bool do_force_assign =false ) set_g # inline void set_g( const Vector \u0026amp; g ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_Q_m # inline void set_Q_m( const Matrix \u0026amp; Q_m ) set_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_P0_m # inline void set_P0_m( const Matrix \u0026amp; P0_m ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) set_x # inline void set_x( const Vector \u0026amp; x ) Reset # void Reset() nstep_pred_block # std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block( UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1 ) Print # void Print() Protected Function Details # RecurseKe # virtual void RecurseKe() =0 Reimplemented by: lds::gaussian::System::RecurseKe, lds::poisson::System::RecurseKe\nInitVars # void InitVars( data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Public Attribute Details # do_adapt_m # bool do_adapt_m {}; Protected Attribute Details # n_x_ # std::size_t n_x_ {}; n_u_ # std::size_t n_u_ {}; n_y_ # std::size_t n_y_ {}; dt_ # data_t dt_ {}; x_ # Vector x_; P_ # Matrix P_; m_ # Vector m_; P_m_ # Matrix P_m_; cx_ # Vector cx_; y_ # Vector y_; z_ # Vector z_; x0_ # Vector x0_; P0_ # Matrix P0_; m0_ # Vector m0_; P0_m_ # Matrix P0_m_; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; Q_ # Matrix Q_; Q_m_ # Matrix Q_m_; C_ # Matrix C_; d_ # Vector d_; Ke_ # Matrix Ke_; Ke_m_ # Matrix Ke_m_; Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":48,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/","title":"lds::UniformMatrixList","section":"Classes","content":" lds::UniformMatrixList # More\u0026hellip;\nInherits from std::vector\u0026lt; Matrix \u0026gt;\nPublic Functions # Name UniformMatrixList() =default\nConstructs a new UniformMatrixList. UniformMatrixList(const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent. UniformMatrixList(std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent. UniformMatrixList(std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent. UniformMatrixList(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nConstructs a new UniformMatrixList (copy). UniformMatrixList(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nConstructs a new UniformMatrixList (move). ~UniformMatrixList() =default\nDestroys the object. const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim(size_t n =0) const\ngets dimensions of uniformly sized matrices size_t size()\nsize of container const Matrix \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Matrix \u0026amp; that, size_t n)\nswaps input matrix with n^th matrix of list UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nassigns the contents (copy) UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nassigns the contents (move) void append(const Matrix \u0026amp; mat)\nappends a matrix to the list Detailed Description # template \u0026lt;MatrixListFreeDim D =kMatFreeDimNone\u0026gt; class lds::UniformMatrixList; Public Function Details # UniformMatrixList # UniformMatrixList() =default UniformMatrixList # explicit UniformMatrixList( const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # explicit UniformMatrixList( std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList UniformMatrixList # UniformMatrixList( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList ~UniformMatrixList # ~UniformMatrixList() =default dim # inline const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim( size_t n =0 ) const Parameters:\nn [optional] index in list of matrices Return: dimensions\nsize # inline size_t size() at # inline const Matrix \u0026amp; at( size_t n ) Swap # inline void Swap( Matrix \u0026amp; that, size_t n ) Parameters:\nthat input matrix n index where the matrix is moved operator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\noperator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\nappend # void append( const Matrix \u0026amp; mat ) Parameters:\nmat input matrix Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":49,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/","title":"lds::UniformSystemList","section":"Classes","content":" lds::UniformSystemList # More\u0026hellip;\nInherits from std::vector\u0026lt; System \u0026gt;\nPublic Functions # Name UniformSystemList() =default\nConstructs a new UniformSystemList. UniformSystemList(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by copying existing vector of System if dimensions consistent. UniformSystemList(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by moving existing vector of System if dimensions consistent. UniformSystemList(std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList from initializer_list of System if dimensions consistent. UniformSystemList(const UniformSystemList \u0026amp; that)\nConstructs a new UniformSystemList (copy). UniformSystemList(UniformSystemList \u0026amp;\u0026amp; that)\nConstructs a new UniformSystemList (move). ~UniformSystemList() =default\nDestroys the object. const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const\ngets dimensions of the uniformly sized systems size_t size()\nsize of container const System \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(System \u0026amp; that, size_t n)\nswaps input system with n^th system of list UniformSystemList \u0026amp; operator=(const UniformSystemList \u0026amp; that)\nassigns the contents (copy) UniformSystemList \u0026amp; operator=(UniformSystemList \u0026amp;\u0026amp; that)\nassigns the contents (move) Detailed Description # template \u0026lt;typename System \u0026gt; class lds::UniformSystemList; Public Function Details # UniformSystemList # UniformSystemList() =default UniformSystemList # explicit UniformSystemList( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # explicit UniformSystemList( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList UniformSystemList # UniformSystemList( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList ~UniformSystemList # ~UniformSystemList() =default dim # inline const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const size # inline size_t size() at # inline const System \u0026amp; at( size_t n ) Swap # inline void Swap( System \u0026amp; that, size_t n ) Parameters:\nthat input system n index where the system is moved operator= # inline UniformSystemList \u0026amp; operator=( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\noperator= # inline UniformSystemList \u0026amp; operator=( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\nUpdated on 5 March 2025 at 21:00:41 EST\n"},{"id":50,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/","title":"lds::UniformVectorList","section":"Classes","content":" lds::UniformVectorList # Inherits from std::vector\u0026lt; Vector \u0026gt;\nPublic Functions # Name UniformVectorList() =default\nConstructs a new UniformVectorList. UniformVectorList(const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent. UniformVectorList(std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent. UniformVectorList(std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0)\nConstructs a new UniformVectorList from initializer_list of Vector if dimensions consistent. UniformVectorList(const UniformVectorList \u0026amp; that)\nConstructs a new UniformVectorList (copy) UniformVectorList(UniformVectorList \u0026amp;\u0026amp; that)\nConstructs a new UniformVectorList (move) ~UniformVectorList() =default\nDestroys the object. size_t dim() const\ngets dimensions of the uniformly sized matrices size_t size()\nsize of container const Vector \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Vector \u0026amp; that, size_t n)\nswaps input matrix with n^th vector of list UniformVectorList \u0026amp; operator=(const UniformVectorList \u0026amp; that)\nassigns the contents (copy) UniformVectorList \u0026amp; operator=(UniformVectorList \u0026amp;\u0026amp; that)\nassigns the contents (move) Public Function Details # UniformVectorList # UniformVectorList() =default UniformVectorList # explicit UniformVectorList( const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dims dimension UniformVectorList # explicit UniformVectorList( std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList UniformVectorList # UniformVectorList( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList ~UniformVectorList # ~UniformVectorList() =default dim # inline size_t dim() const size # inline size_t size() at # inline const Vector \u0026amp; at( size_t n ) Swap # inline void Swap( Vector \u0026amp; that, size_t n ) Parameters:\nthat input vector n index where the vector is moved operator= # inline UniformVectorList \u0026amp; operator=( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\noperator= # inline UniformVectorList \u0026amp; operator=( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\nUpdated on 5 March 2025 at 21:00:41 EST\n"},{"id":51,"href":"/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/","title":"ldsCtrlEst_h","section":"Files","content":" ldsCtrlEst_h # Files # Name ldsCtrlEst_h/lds.h lds namespace ldsCtrlEst_h/lds_ctrl.h Controller. ldsCtrlEst_h/lds_fit.h LDS base fit type. ldsCtrlEst_h/lds_fit_em.h subspace identification ldsCtrlEst_h/lds_fit_ssid.h subspace identification ldsCtrlEst_h/lds_gaussian.h glds namespace ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller. ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type. ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type. ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type. ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type. ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type. ldsCtrlEst_h/lds_poisson.h plds namespace ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type. ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type. ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type. ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type. ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type. ldsCtrlEst_h/lds_poisson_sys.h PLDS base type. ldsCtrlEst_h/lds_sctrl.h SwitchedController type. ldsCtrlEst_h/lds_sys.h LDS base type. ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices. ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems. ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors. ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":52,"href":"/lds-ctrl-est/docs/api/files/lds__ctrl_8h/","title":"ldsCtrlEst_h/lds_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_ctrl.h # Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Controller Detailed Description # This file declares the type for control of a linear dynamical system (lds::Controller).\nSource code # //===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_CTRL_H #define LDSCTRLEST_LDS_CTRL_H // namespace #include \u0026#34;lds.h\u0026#34; // system type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class Controller { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); public: Controller() = default; Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); const Vector\u0026amp; Control(const Vector\u0026amp; z, bool do_control = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); const Vector\u0026amp; ControlOutputReference(const Vector\u0026amp; z, bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); // get methods: const System\u0026amp; sys() const { return sys_; }; const Matrix\u0026amp; Kc() const { return Kc_; }; const Matrix\u0026amp; Kc_inty() const { return Kc_inty_; }; const Matrix\u0026amp; Kc_u() const { return Kc_u_; }; const Vector\u0026amp; g_design() const { return g_design_; }; const Vector\u0026amp; u_ref() const { return u_ref_; }; const Vector\u0026amp; x_ref() const { return x_ref_; }; const Vector\u0026amp; y_ref() const { return y_ref_; }; size_t control_type() const { return control_type_; }; data_t tau_awu() const { return tau_awu_; }; data_t u_lb() const { return u_lb_; }; data_t u_ub() const { return u_ub_; }; // set methods void set_sys(const System\u0026amp; sys) { bool does_match = sys_.n_u() == sys.n_u(); does_match = does_match \u0026amp;\u0026amp; (sys_.n_x() == sys.n_x()); does_match = does_match \u0026amp;\u0026amp; (sys_.n_y() == sys.n_y()); if (does_match) { sys_ = sys; } else { throw std::runtime_error( \u0026#34;new system argument to `set_sys` does not match dimensionality of \u0026#34; \u0026#34;existing system\u0026#34;); } }; void set_g_design(const Vector\u0026amp; g_design) { Reassign(g_design_, g_design); }; void set_u_ref(const Vector\u0026amp; u_ref) { Reassign(u_ref_, u_ref); }; void set_x_ref(const Vector\u0026amp; x_ref) { Reassign(x_ref_, x_ref); cx_ref_ = sys_.C() * x_ref_; }; // y_ref needs to be handled differently depending on output fn. // (need to populate cx_ref_ too, which depends on output fn) virtual void set_y_ref(const Vector\u0026amp; y_ref) { Reassign(y_ref_, y_ref); }; void set_Kc(const Matrix\u0026amp; Kc) { Reassign(Kc_, Kc); }; void set_Kc_inty(const Matrix\u0026amp; Kc_inty) { Reassign(Kc_inty_, Kc_inty); }; void set_Kc_u(const Matrix\u0026amp; Kc_u) { Reassign(Kc_u_, Kc_u); }; void set_tau_awu(data_t tau) { tau_awu_ = tau; k_awu_ = sys_.dt() / tau_awu_; }; void set_control_type(size_t control_type); // There is no reason u_lb/ub should not be public, but making set methods // anyway. void set_u_lb(data_t u_lb) { u_lb_ = u_lb; }; void set_u_ub(data_t u_ub) { u_ub_ = u_ub; }; void Reset() { sys_.Reset(); u_ref_.zeros(); u_ref_prev_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); u_saturated_ = false; t_since_control_onset_ = 0.0; }; void Print() { sys_.Print(); std::cout \u0026lt;\u0026lt; \u0026#34;g_design : \u0026#34; \u0026lt;\u0026lt; g_design_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_lb : \u0026#34; \u0026lt;\u0026lt; u_lb_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_ub : \u0026#34; \u0026lt;\u0026lt; u_ub_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }; protected: System sys_; Vector u_; Vector u_return_; Vector g_design_; // reference signals Vector u_ref_; // create no set method for this: Vector u_ref_prev_; Vector x_ref_; Vector y_ref_; Vector cx_ref_; // Controller gains Matrix Kc_; Matrix Kc_u_; Matrix Kc_inty_; // control after g inversion // do not need set methods for these. Vector du_ref_; Vector dv_ref_; Vector v_ref_; Vector dv_; Vector v_; // integral error // do not need set method for this Vector int_e_; Vector int_e_awu_adjust_; Vector u_sat_; bool do_control_prev_ = false; bool do_lock_control_prev_ = false; // whether the g of system has become inverted from what you think it is // (gain_ref) bool u_saturated_ = false; // should be safe to have references here bc nothing needs to be done // (like reset vars) when it changes... data_t u_lb_{}; data_t u_ub_{}; data_t tau_awu_{}; data_t k_awu_ = 0; data_t t_since_control_onset_ = 0; size_t control_type_{}; private: void CalcControl(bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); void CalcSteadyStateSetPoint(); void AntiWindup(); void InitVars(size_t control_type); }; // Implement the above: template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(sys), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(std::move(sys)), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::set_control_type(size_t control_type) { if (control_type_ == control_type) { return; } // creating a blank slate... control_type_ = 0; Kc_inty_.zeros(0, 0); Kc_u_.zeros(0, 0); int_e_.zeros(0, 0); int_e_awu_adjust_.zeros(0, 0); // controller was designed to minimize integral error if (control_type \u0026amp; kControlTypeIntY) { Kc_inty_.zeros(sys_.n_u(), sys_.n_y()); int_e_.zeros(sys_.n_y()); int_e_awu_adjust_.zeros(sys_.n_u()); control_type_ = control_type_ | kControlTypeIntY; } // controller was designed to minimize deltaU // (i.e. state augmented with u) if (control_type \u0026amp; kControlTypeDeltaU) { Kc_u_.zeros(sys_.n_u(), sys_.n_u()); control_type_ = control_type_ | kControlTypeDeltaU; } // whether to adapt set point calculate with (re-estimated) process // disturbance (m) if (control_type \u0026amp; kControlTypeAdaptM) { if (sys_.do_adapt_m) // only if adapting m... { control_type_ = control_type_ | kControlTypeAdaptM; } } } // set_control_type template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::Control( const Vector\u0026amp; z, bool do_control, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement sys_.Filter(u_, z); bool do_estimation = true; // always have estimator on in this case // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::ControlOutputReference( const Vector\u0026amp; z, bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement if (do_estimation) { sys_.Filter(u_, z); } else { sys_.f(u_); } // calculate the set point // solves for u_ref and x_ref when output is at y_ref at steady state. if (do_control) { CalcSteadyStateSetPoint(); } // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcControl(bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { if (do_control \u0026amp;\u0026amp; do_estimation) { if (!do_control_prev_) { if (do_reset_at_control_onset) { Reset(); } t_since_control_onset_ = 0.0; } else { t_since_control_onset_ += sys_.dt(); } // enforce softstart on control vars. if (sigma_soft_start \u0026gt; 0) { // half-Gaussian soft-start scaling factor data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) / (2 * pow(sigma_soft_start, 2))); u_ref_ *= soft_start_sf; // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too // x_ref_ *= soft_start_sf; // cx_ref_ *= soft_start_sf; // y_ref_ *= soft_start_sf; } if (!do_lock_control) { // first do u -\u0026gt; v change of vars. (v = g.*u) // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver // control voltage u[=]V) v_ref_ = g_design_ % u_ref_; // Given FB, calc. the change in control if (control_type_ \u0026amp; kControlTypeDeltaU) { // if control designed to minimize not u but deltaU (i.e. state aug with // u): // TODO(mfbolus): Commented out for now. See note below. // du_ref_ = u_ref_ - u_ref_prev_; // dv_ref_ = g_design_ % du_ref_; // TODO(mfbolus): Assuming users want *smooth* control signals if using // kControlTypeDeltaU, it should be the case that dv_ref_ is --\u0026gt; 0. May // want to revisit, but I am going to force it to be zero unless a // situation arises that argues for keeping the above. dv_ref_.zeros(); dv_ = dv_ref_; // nominally-optimal. dv_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error dv_ -= Kc_u_ * (v_ - v_ref_); // penalty on amp u (rel to ref) if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if(!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error dv_ -= Kc_inty_ * int_e_; // control for integrated error } // update the control v_ += dv_; } else { v_ = v_ref_; // nominally-optimal. v_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if (!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error v_ -= Kc_inty_ * int_e_; // control for integrated error } } // convert back to control voltage u[=]V u_ = v_ / sys_.g(); } // else do nothing until lock is low } else { // if not control // feed through u_ref in open loop u_ = u_ref_ % g_design_ / sys_.g(); v_ = sys_.g() % u_; u_ref_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); } // ends do_control // enforce box constraints (and antiwindup) AntiWindup(); // add noise to input? // The value for u that is *returned* to user after addition of any noise, // while keeping controller/estimator blind to this addition. u_return_ = u_; if ((sigma_u_noise \u0026gt; 0.0) \u0026amp;\u0026amp; (do_control \u0026amp;\u0026amp; !do_lock_control)) { u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn); Limit(u_return_, u_lb_, u_ub_); }; // For next time step: u_ref_prev_ = u_ref_; do_control_prev_ = do_control; do_lock_control_prev_ = do_lock_control; } // CalcControl template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcSteadyStateSetPoint() { // Linearly-constrained least squares (ls). // // _reference: // Boyd \u0026amp; Vandenberghe (2018) Introduction to Applied Linear Algebra // Matrix a_ls = join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros)); Vector b_ls = cx_ref_; Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye), sys_.B() * arma::diagmat(sys_.g())); Vector d_ls = -sys_.m0(); if (control_type_ \u0026amp; kControlTypeAdaptM) { d_ls = -sys_.m(); // adapt setpoint calc with disturbance? } Matrix a_ls_t = a_ls.t(); // TODO(mfbolus): not sure why but causes seg // fault if I do not do this. Matrix phi_ls = join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()), join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros))); // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse: Matrix inv_phi = pinv(phi_ls); Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls); x_ref_ = xulam.subvec(0, sys_.n_x() - 1); u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1); cx_ref_ = sys_.C() * x_ref_; } // CalcSteadyStateSetPoint template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::AntiWindup() { u_saturated_ = false; u_sat_ = u_; // limit u and flag whether saturated for (size_t k = 0; k \u0026lt; u_.n_elem; k++) { if (u_[k] \u0026lt; u_lb_) { u_sat_[k] = u_lb_; u_saturated_ = true; } if (u_[k] \u0026gt; u_ub_) { u_sat_[k] = u_ub_; u_saturated_ = true; } } if ((control_type_ \u0026amp; kControlTypeIntY) \u0026amp;\u0026amp; (tau_awu_ \u0026lt; lds::kInf)) { // one-step back-calculation (calculate intE for u=u_sat) // (Astroem, Rundqwist 1989 warn against using this...) // int_e_awu_adjust_ = // solve(Kc_inty_, (u_ - u_sat_)); // pinv(Kc_inty) * (u-uSat); // gradual: see Astroem, Rundqwist 1989 // this is a fudge for doing MIMO gradual // n.b., went ahead and multiplied 1/T by dt so don\u0026#39;t have to do that here. int_e_awu_adjust_ = k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_); // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_); int_e_ += int_e_awu_adjust_; } // set u to saturated version u_ = u_sat_; } template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::InitVars(size_t control_type) { // initialize to default values u_ref_ = Vector(sys_.n_u(), fill::zeros); u_ref_prev_ = Vector(sys_.n_u(), fill::zeros); x_ref_ = Vector(sys_.n_x(), fill::zeros); y_ref_ = Vector(sys_.n_y(), fill::zeros); cx_ref_ = Vector(sys_.n_y(), fill::zeros); u_ = Vector(sys_.n_u(), fill::zeros); u_return_ = Vector(sys_.n_u(), fill::zeros); u_sat_ = Vector(sys_.n_u(), fill::zeros); // Might not need all these, so zero elements until later. Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros); Kc_u_ = Matrix(0, 0, fill::zeros); Kc_inty_ = Matrix(0, 0, fill::zeros); g_design_ = sys_.g(); // by default, same as model dv_ = Vector(sys_.n_u(), fill::zeros); v_ = Vector(sys_.n_u(), fill::zeros); du_ref_ = Vector(sys_.n_u(), fill::zeros); dv_ref_ = Vector(sys_.n_u(), fill::zeros); v_ref_ = Vector(sys_.n_u(), fill::zeros); int_e_ = Vector(0, fill::zeros); int_e_awu_adjust_ = Vector(0, fill::zeros); set_control_type(control_type); } } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":53,"href":"/lds-ctrl-est/docs/api/files/lds__fit__em_8h/","title":"ldsCtrlEst_h/lds_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_em.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::EM Detailed Description # This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).\nSource code # //===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_EMAX_H #define LDSCTRLEST_LDS_EMAX_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class EM { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: EM() = default; EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); virtual ~EM() = default; const Fit\u0026amp; Run(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = true, bool calc_output = true, bool calc_measurement = true, size_t max_iter = 100, data_t tol = 1e-2); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); // auto tuple = std::make_tuple(u_, z_); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; x() const { return x_; }; const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; y() const { return y_; }; const Matrix\u0026amp; sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; }; const Matrix\u0026amp; sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; }; const Matrix\u0026amp; sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; }; size_t n_t_tot() { return n_t_tot_; } const Vector\u0026amp; theta() const { return theta_; }; protected: void Expectation(bool force_common_initial = false); void Maximization(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = false, bool calc_output = false, bool calc_measurement = false); void MaximizeDynamics(); void MaximizeQ(); void MaximizeInitial(); virtual void MaximizeOutput() = 0; virtual void MaximizeMeasurement() = 0; void Smooth(bool force_common_initial); virtual void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) = 0; void Reset(); void InitVars(); Vector UpdateTheta(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; std::vector\u0026lt;Matrix\u0026gt; x_; std::vector\u0026lt;Cube\u0026gt; P_; std::vector\u0026lt;Cube\u0026gt; P_t_tm1_; std::vector\u0026lt;Matrix\u0026gt; y_; Matrix diag_y_; // expectations calculated in E-step Matrix sum_E_x_t_x_t_; Matrix sum_E_xu_tm1_xu_tm1_; Matrix sum_E_xu_t_xu_tm1_; Fit fit_; Vector theta_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; }; template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; fit_ = Fit(n_u_, n_x, n_y_, dt); u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { // make sure fit dims match I/O data if (fit0.n_u() != u_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and input training data have inconsistent dimensions\u0026#34;); } if (fit0.n_y() != z_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and output training data have inconsistent dimensions\u0026#34;); } fit_ = fit0; u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::InitVars() { // check input/output data dimensions are consistent if (z_.size() != u_.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_.at(trial).n_cols != u_.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } n_u_ = fit_.n_u(); n_x_ = fit_.n_x(); n_y_ = fit_.n_y(); dt_ = fit_.dt(); x_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); P_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); P_t_tm1_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); y_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros); P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros); } diag_y_ = Matrix(n_y_, n_y_, fill::zeros); // covariances in expectation step sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); } template \u0026lt;typename Fit\u0026gt; const Fit\u0026amp; EM\u0026lt;Fit\u0026gt;::Run(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement, size_t max_iter, data_t tol) { Reset(); // to initial conditions size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_; Vector theta(n_params); Vector theta_new(n_params); data_t max_dtheta = 1; // if solving for initial conditions, allow them be varied. // otherwise, freeze at provided values. bool force_common_initial = !calc_init; // go until parameter convergence for (size_t l = 0; l \u0026lt; max_iter; l++) { theta_ = UpdateTheta(); std::cout \u0026lt;\u0026lt; \u0026#34;Iteration \u0026#34; \u0026lt;\u0026lt; l + 1 \u0026lt;\u0026lt; \u0026#34;/\u0026#34; \u0026lt;\u0026lt; max_iter \u0026lt;\u0026lt; \u0026#34; ...\\n\u0026#34;; Expectation(force_common_initial); Maximization(calc_dynamics, calc_Q, calc_init, calc_output, calc_measurement); // check convergence theta_new = UpdateTheta(); Vector dtheta = abs(theta_new - theta_) / abs(theta_); // some parameters could be zero... arma::uvec ubi_finite = find_finite(dtheta); max_dtheta = max(dtheta.elem(ubi_finite)); std::cout \u0026lt;\u0026lt; \u0026#34;max dtheta: \u0026#34; \u0026lt;\u0026lt; max_dtheta \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; if (max_dtheta \u0026lt; tol) { std::cout \u0026lt;\u0026lt; \u0026#34;Converged.\\n\u0026#34;; break; } std::cout \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } return fit_; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Smooth(bool force_common_initial) { Matrix k_e(n_x_, n_y_); // estimator gain Cube k_backfilt; // back-filtering gains // TODO(mfbolus): this loop could be made parallel for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { Matrix x_pre(n_x_, n_t_[trial], fill::zeros); Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros); Matrix x_post(n_x_, n_t_[trial], fill::zeros); Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros); if (force_common_initial) // forces all trials to have same initial // conditions. { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); } y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); // This *should not* be necessary but make sure P is symmetric. ForceSymPD(P_[trial].slice(0)); x_pre.col(0) = x_[trial].col(0); p_pre.slice(0) = P_[trial].slice(0); x_post.col(0) = x_[trial].col(0); p_post.slice(0) = P_[trial].slice(0); // filter for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // predict fit_.f(x_pre, x_post, u_.at(trial), t); fit_.h(y_[trial], x_pre, t); diag_y_.diag() = y_[trial].col(t); // TODO(mfbolus): change if parallel // update --\u0026gt; posterior estimation RecurseKe(k_e, p_pre, p_post, t); x_post.col(t) = x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t)); y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d(); } // backfilter -\u0026gt; Smoothed estimate // Reference: // Shumway et Stoffer (1982) ForceSymPD(p_post.slice(n_t_[trial] - 1)); k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1); P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 0; t--) { // TODO(mfmbolus): should not be necessary to force symm positive def ForceSymPD(p_pre.slice(t)); ForceSymPD(p_post.slice(t - 1)); ForceSymPD(P_[trial].slice(t)); k_backfilt.slice(t - 1) = p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t)); x_[trial].col(t - 1) = x_post.col(t - 1) + k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t)); P_[trial].slice(t - 1) = p_post.slice(t - 1) + k_backfilt.slice(t - 1) * (P_[trial].slice(t) - p_pre.slice(t)) * k_backfilt.slice(t - 1).t(); } // do the same for P_t_tm1 Matrix id(n_x_, n_x_, fill::eye); P_t_tm1_[trial].slice(n_t_[trial] - 1) = (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 1; t--) { P_t_tm1_[trial].slice(t - 1) = p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() + k_backfilt.slice(t - 1) * (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) * k_backfilt.slice(t - 2).t(); } // finally, get smoothed estimate of output for (size_t t = 0; t \u0026lt; n_t_[trial]; t++) { fit_.h(y_[trial], x_[trial], t); } // samps loop } // trial loop } // Smooth // template \u0026lt;typename Fit\u0026gt; // void EM\u0026lt;Fit\u0026gt;::RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) { // // predict covar // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q(); // // update Ke // Ke = P_pre.slice(t) * fit_.C().t() * // inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R()); // // update cov // // Reference: Ghahramani et Hinton (1996) // P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t); // // // n.b. for poisson : // // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + // fit_.Q(); // // // update cov // // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ * // // fit_.C()); // // // update Ke // // Ke = P_post.slice(t) * fit_.C(); // } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Expectation(bool force_common_initial) { // calculate the mean/cov of state needed for maximizing E[pr(z|theta)] Smooth(force_common_initial); // now get the various forms of sum(E[xx\u0026#39;]) needed // n.b. Going to start at t=1 rather than 0 bc most max terms need that. // so really \u0026#34;n_t_tot_\u0026#34; is (n_t_tot_-1) n_t_tot_ = 0; sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); Vector xu_tm1(n_x_ + n_u_, fill::zeros); Vector xu_t(n_x_ + n_u_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // ------------ sum_E_x_t_x_t ------------ sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t(); sum_E_x_t_x_t_ += P_[trial].slice(t); // ------------ sum_E_xu_tm1_xu_tm1 ------------ xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1)); sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t(); sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t - 1); // ------------ sum_E_xu_t_xu_tm1 ------------ xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t)); sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t(); sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_t_tm1_[trial].slice(t); n_t_tot_ += 1; } // time } // trial } // Expectation template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement) { if (calc_output) { MaximizeOutput(); } if (calc_measurement) { MaximizeMeasurement(); } if (calc_dynamics) { MaximizeDynamics(); } if (calc_Q) { MaximizeQ(); } if (calc_init) { MaximizeInitial(); } } // Maximization template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeDynamics() { // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) * inv_sympd(sum_E_xu_tm1_xu_tm1_); fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1)); std::cout \u0026lt;\u0026lt; \u0026#34;A_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.A()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.B()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeQ() { // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 * // inv_sympd(sum_E_xu_tm1_xu_tm1_) * // sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // this way is same as above iff dynamics were just updated: // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix ab = arma::join_horiz(fit_.A(), fit_.B()); // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // From scratch method: // Q is covariance of the error between state and dynamics-predicted state // (aka process noise) // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})\u0026#39;] // t-1 terms: View sum_e_x_tm1_x_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_u_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1); View sum_e_x_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); // t, t-1 terms: View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_x_t_u_tm1 = sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); Matrix q = sum_E_x_t_x_t_; q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t(); q -= sum_e_x_t_x_tm1 * fit_.A().t(); q -= fit_.A() * sum_e_x_t_x_tm1.t(); // input-related terms: q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t(); q -= sum_e_x_t_u_tm1 * fit_.B().t(); q -= fit_.B() * sum_e_x_t_u_tm1.t(); q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t(); q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t(); q /= n_t_tot_; fit_.set_Q(q); std::cout \u0026lt;\u0026lt; \u0026#34;Q_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.Q()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // std::cout \u0026lt;\u0026lt; \u0026#34;Q_new: \\n\u0026#34; \u0026lt;\u0026lt; fit_.Q() \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeInitial() { Vector x0 = fit_.x0(); x0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { x0 += x_[trial].col(0); } x0 /= z_.size(); std::cout \u0026lt;\u0026lt; \u0026#34;x0_new[0]: \u0026#34; \u0026lt;\u0026lt; x0[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // always recalc P0 even if the initial state is fixed (at zero, for // example) Matrix e_var(n_x_, n_x_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t(); } e_var /= z_.size(); // go ahead and subtract x0*x0\u0026#39; so don\u0026#39;t have to below. e_var -= x0 * x0.t(); // To get P0, going to get initial P_ per trial and average. // (which might be wrong, but need a single number) Matrix p0 = fit_.P0(); p0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { p0 += (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var; } p0 /= z_.size(); fit_.set_P0(p0); std::cout \u0026lt;\u0026lt; \u0026#34;P0_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.P0()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeOutput() { // solve for C+d: Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros); Vector x1(n_x_ + 1, fill::zeros); x1[n_x_] = 1.0; // augment with one to solve for bias Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { x1.subvec(0, n_x_ - 1) = x_[trial].col(t); sum_zx += z_.at(trial).col(t) * x1.t(); sum_e_x1_x1 += x1 * x1.t(); sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t); } } Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1); fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1)); fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_))); std::cout \u0026lt;\u0026lt; \u0026#34;C_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.C()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.d()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeMeasurement() { // Solve for measurement noise covar size_t n_t_tot = 0; // Ghahgramani, Hinton 1996: Matrix sum_zz(n_y_, n_y_, fill::zeros); Matrix sum_yz(n_y_, n_y_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t(); // Use Cnew: sum_yz += (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t(); n_t_tot += 1; } } fit_.set_R((sum_zz - sum_yz) / n_t_tot); std::cout \u0026lt;\u0026lt; \u0026#34;R_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.R()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Reset() { // reset to initial conditions for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); } } template \u0026lt;typename Fit\u0026gt; Vector EM\u0026lt;Fit\u0026gt;::UpdateTheta() { // TODO(mfbolus): This should include n_y_ more params for d. size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_; if (fit_.R().n_elem \u0026gt; 0) { n_params += n_y_ * n_y_; } Vector theta(n_params); size_t idx_start = 0; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B()); idx_start += n_x_ * n_u_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0()); idx_start += n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C()); idx_start += n_y_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d()); idx_start += n_y_; if (fit_.R().n_elem \u0026gt; 0) { theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R()); } return theta; } } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":54,"href":"/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_ssid.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SSID Detailed Description # This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)\u0026lt;Fit\u0026gt;).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_FIT_SSID_H #define LDSCTRLEST_LDS_FIT_SSID_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class SSID { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: SSID() = default; SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d = Vector(1).fill(-kInf)); std::tuple\u0026lt;Fit, Vector\u0026gt; Run(SSIDWt ssid_wt); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } protected: void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001); void CreateHankelDataMat(); virtual void DecomposeData() = 0; void CalcSVD(SSIDWt wt); void Solve(data_t wt_dc); void RecomputeExtObs(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; Matrix D_; Fit fit_; Matrix g_dc_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_h_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; Matrix L_; Vector s_; Matrix ext_obs_t_; }; template \u0026lt;typename Fit\u0026gt; SSID\u0026lt;Fit\u0026gt;::SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d) { // check input/output data dimensions are consistent if (z_train.size() != u_train.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_train.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_train.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } dt_ = dt; n_x_ = n_x; n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; n_h_ = n_h; // dimensionality check for eventual block-hankel data matrix size_t len = n_t_tot_ - 2 * n_h_ + 1; if (len \u0026lt; (2 * n_h_ * (n_u_ + n_y_))) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;Dataset problem! More rows than columns in block-hankel data \u0026#34; \u0026#34;matrix: 2*(n_u+n_y)*n_h \u0026gt; data-length! Need higher data-length or \u0026#34; \u0026#34;lower n_h.\u0026#34;; throw std::runtime_error(ss.str()); } fit_ = Fit(n_u_, n_x_, n_y_, dt_); u_ = std::move(u_train); z_ = std::move(z_train); if (!d.is_finite() || (d.n_rows != n_y_)) { // TODO(mfbolus): implement least-square solution for impulse response with // a second input of ones. Data-driven way of accounting for offset *not* // driven by an input. // // For now, calculate output bias (d) as the // output wherever the stimulus has not been on for some amount of time. // convolve u with rectangle and take all samples. This is a reasonable // approach, since often when autonomous systems are fit (i.e., systems with // no input), they will subtract off the mean of the output. This // essentially amounts to setting output bias to the mean of the output when // there is no stimulation. data_t t_silence = 0.1; data_t thresh_silence = 0.001; CalcD(t_silence, thresh_silence); } else { fit_.set_d(d); } } template \u0026lt;typename Fit\u0026gt; std::tuple\u0026lt;Fit, Vector\u0026gt; SSID\u0026lt;Fit\u0026gt;::Run(SSIDWt ssid_wt) { // the weight on minimizing dc I/O gain only works for gaussian, // and hopefully not necessary with appropriate dataset. data_t wt_dc = 0; // std::cout \u0026lt;\u0026lt; \u0026#34;creating hankel mat\\n\u0026#34;; CreateHankelDataMat(); // std::cout \u0026lt;\u0026lt; \u0026#34;decomposing data\\n\u0026#34;; DecomposeData(); // std::cout \u0026lt;\u0026lt; \u0026#34;calculating svd\\n\u0026#34;; CalcSVD(ssid_wt); // std::cout \u0026lt;\u0026lt; \u0026#34;solving for params\\n\u0026#34;; Solve(wt_dc); // std::cout \u0026lt;\u0026lt; \u0026#34;fin\\n\u0026#34;; return std::make_tuple(fit_, s_); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcD(data_t t_silence, data_t thresh_silence) { Vector d(z_.at(0).n_rows, fill::zeros); Vector win(static_cast\u0026lt;size_t\u0026gt;(t_silence / dt_), fill::ones); Vector sum_z_silence(n_y_, fill::zeros); size_t n_silence(0); for (size_t trial = 0; trial \u0026lt; u_.size(); trial++) { // find silent samples // start by convolving with Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0)); Vector u_conv = conv(sum_u, win, \u0026#34;same\u0026#34;); // get only the samples that are silent... arma::uvec ubi_silence = find(u_conv \u0026lt;= thresh_silence); if (ubi_silence.n_elem \u0026gt; 0) { sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1); n_silence += ubi_silence.n_elem; } } if (n_silence \u0026gt; 0) { d = sum_z_silence / n_silence; } fit_.set_d(d); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat() { // temporary copy of data Matrix z(n_y_, n_t_tot_, fill::zeros); Matrix u(n_u_, n_t_tot_, fill::zeros); size_t so_far(0); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial); u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial); so_far += n_t_.at(trial); } // remove output bias z.each_col() -= fit_.d(); // calculate I/O gain @ DC while data in convenient form g_dc_ = z * pinv(u); // std::cout \u0026lt;\u0026lt; \u0026#34;G0_data = \u0026#34; \u0026lt;\u0026lt; g_dc_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // create hankel data matrix size_t len = z.n_cols - 2 * n_h_ + 1; // data length in hankel mat // block-hankel data matrix D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros); // past input auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1); // future input auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1); // past output auto y_p = D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1); // future output auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, len - 1); size_t idx = 0; for (size_t k = 0; k \u0026lt; len; k++) { idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } } D_ /= sqrt(static_cast\u0026lt;data_t\u0026gt;(len)); } // template \u0026lt;typename Fit\u0026gt; // void SSID\u0026lt;Fit\u0026gt;::DecomposeData() { // // do LQ decomp instead of calculating covariance expensive way // // Note that \u0026#34;R\u0026#34; in van Overschee is lower-triangular (L), not \u0026#34;R\u0026#34; in QR // // decomp. Very confusing. // Matrix q_t; // lq(L_, q_t, D_); // // van Overschee zeros out the other elements. // L_ = trimatl(L_); // } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcSVD(SSIDWt wt) { // submats that will be needed: auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1); auto R_23_13 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1); auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_) - 1); auto R_44 = L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14); auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1); auto Luf = Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1); auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); // aka: R_f Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1); // from van Overschee subid.m: // Rf = R((2*m+l)*i+1:2*(m+l)*i,:); % Future outputs Matrix U; Matrix V; switch (wt) { case kSSIDNone: { // No weighting. (what van Overschee calls \u0026#34;N4SID\u0026#34;) Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14; arma::svd(U, s_, V, O_k_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDMOESP: { // MOESP weighting // This is what they use in the \u0026#34;robust\u0026#34; algorithm van Overschee, de Moor // 1996 Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); svd(U, s_, V, O_k_ortho_Uf_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDCVA: { // CVA weighting // See van Overschee\u0026#39;s matlab code (subid.m): // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); Matrix inv_w1; Matrix qt1; lq(inv_w1, qt1, R_56_16); // lq decomp of R_f (future output data) inv_w1 = trimatl(inv_w1); inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1); Matrix w_o_w = arma::solve( inv_w1, O_k_ortho_Uf_sans_Qt); // alternatively // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt svd(U, s_, V, w_o_w, \u0026#34;std\u0026#34;); U = inv_w1 * U; break; } } // Truncate to model order (heart of ssid method) auto s_hat = s_.subvec(0, n_x_ - 1); Matrix diag_sqrt_s = diagmat(sqrt(s_hat)); auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1); // get extended observability and controllability mats ext_obs_t_ = u_hat * diag_sqrt_s; // extended observability matrix } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::Solve(data_t wt_dc) { // required submats auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_56_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); // Solve for params using appropriate algorithm: // robust deterministic/stochastic algorithm in van Overschee 1996 // algorithm that the authors say \u0026#34;works\u0026#34; in practice. auto ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix // This is what textbook (1996) says: // // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15); // // HOWEVER, do not know why but have to fill the last place with zeros like // authors\u0026#39; matlab implementation (see `subid.m`) // Otherwise, get ridiculous covariances (although A,C estimates are close to // same...) Matrix Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Matrix S = Tl * pinv(Tr); // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure // stability. fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1)); Matrix ext_obs_t_p1 = join_vert( ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1), Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros)); fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1); // At this point, van Overschee \u0026amp; de Moor suggest re-calculating ext_obs_t_, // ext_obs_tm1 from (A, C) because it was just an approximation. This is RecomputeExtObs(); ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); S = Tl * pinv(Tr); Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_); Matrix Mcurly = pinv(ext_obs_tm1); Matrix Pcurly = Tl - Lcurly * R_56_15; Vector Pvec = vectorise(Pcurly); Matrix Qcurly = R_23_15; // Identify [D; B], assuming D=0 and ensuring DC gain is correct Matrix sum_QcurlyT_kron_Ncurly( (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_), fill::zeros); Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols, fill::eye); eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1, eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1; // van Overschee (1996) p. 126 Matrix N1_Tl = -Lcurly; N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) += join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly); N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) += Matrix(n_y_, n_y_, fill::eye); Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros); Matrix N_k; for (size_t k = 0; k \u0026lt; n_h_; k++) { auto Qcurly_k = Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1); Nk_Tl.zeros(); Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) = N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1); N_k = Nk_Tl * eye_ext_obs_tm1; sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k); } Matrix err_vec; if (wt_dc \u0026gt; 0) { // Constraints enforced by weighted least squares // // Reference: // // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly // Excited Industrial Systems. Conference in Decision and Control. // constraint 1: assume D=0 --\u0026gt; remove the components for Dvec (this is // actually a hard constraint in that it ignores D) Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly; sum_QcurlyT_kron_Ncurly = Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_); size_t kkk = 0; for (size_t k = 1; k \u0026lt; (n_u_ + 1); k++) { size_t start_idx = k * (n_y_ + n_x_) - n_x_; for (size_t kk = 0; kk \u0026lt; n_x_; kk++) { sum_QcurlyT_kron_Ncurly.col(kkk) = sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk); kkk++; } } // constraint 2: Make sure DC I/O gain is correct Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A()); Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_)); Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0); Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 = join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0); // WEIGHTED LS // Important in practice because I care a lot about at least getting the DC // gain correct. Put x weight on minimizing error at DC, relative to others Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye); // Make weight on minimizing DC error immense so at least that // should be nailed. size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows; size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows; size_t stop_row = w.n_rows - 1; size_t stop_col = w.n_cols - 1; // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale // weight with data length? w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc; Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * sum_QcurlyT_kron_Ncurly_b_to_g0) * sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec; fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_)); // Calculate residuals and their cov. // Because I\u0026#39;ve added constraints, I need to re-calculate the right term // with b_vec instead of how van Overschee do in final algorithm. err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec; } else { // default way: *no* constraint on G0 or D=0 Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec; // TODO(mfbolus) n.b., this gets thrown away... // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_); fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_)); err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec; } // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols); // TODO(mfbolus): Something is wrong with the error calculation above. // Use the way van overschee does it in `subid.m` // WARNING: this ignores any above constraints, so Q, R will be approximate... Matrix err = Tl - S * Tr; Matrix cov_err = err * err.t(); fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1)); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::RecomputeExtObs() { ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C(); for (size_t k = 2; k \u0026lt; (n_h_ + 1); k++) { ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) = ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1, ext_obs_t_.n_cols - 1) * fit_.A(); } } } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":55,"href":"/lds-ctrl-est/docs/api/files/lds__fit_8h/","title":"ldsCtrlEst_h/lds_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_fit.h # LDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Fit LDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.\nSource code # //===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDS_FIT_HPP #define LDS_FIT_HPP // namespace #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); virtual ~Fit() = default; // get methods size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; Q() const { return Q_; }; const Vector\u0026amp; x0() const { return x0_; }; const Matrix\u0026amp; P0() const { return P0_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; // gets measurement noise virtual const Matrix\u0026amp; R() const = 0; // set methods (e.g., seeding initial fit values) void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_m(const Vector\u0026amp; m) { Reassign(m_, m); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); ForceSymPD(Q_); }; virtual void set_R(const Matrix\u0026amp; R) = 0; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); ForceSymPD(P0_); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; View f(Matrix\u0026amp; x, const Matrix\u0026amp; u, size_t t) { x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x.col(t); }; View f(Matrix\u0026amp; x_pre, const Matrix\u0026amp; x_post, const Matrix\u0026amp; u, size_t t) { x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x_pre.col(t); }; virtual View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) = 0; protected: data_t dt_{}; // Dynamics Matrix A_; Matrix B_; Vector g_; Vector m_; Matrix Q_; // Output Matrix C_; Vector d_; Matrix R_; // initial conditions Vector x0_; Matrix P0_; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; }; } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":56,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_ctrl.h # GLDS Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. Detailed Description # This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_gaussian_sys.h\u0026#34; // controller #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace gaussian { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); cx_ref_ = y_ref - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":57,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_em.h # GLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitEM GLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":58,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_ssid.h # GLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; using SSID\u0026lt;Fit\u0026gt;::Run; private: using SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat; using SSID\u0026lt;Fit\u0026gt;::CalcSVD; using SSID\u0026lt;Fit\u0026gt;::Solve; void DecomposeData() override; void SolveVanOverschee(); }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":59,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit.h # GLDS fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Fit GLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // fit type #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace gaussian { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); const Matrix\u0026amp; R() const override { return R_; }; void set_R(const Matrix\u0026amp; R) override { Reassign(R_, R); ForceSymPD(R_); }; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = C_ * x.col(t) + d_; return y.col(t); }; }; }; // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":60,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sctrl.h # GLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H // controller type #include \u0026#34;lds_gaussian_ctrl.h\u0026#34; // switched controller #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace gaussian { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); cx_ref_ = y_ref - sys_.d(); } // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; }; // SwitchedController } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":61,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/","title":"ldsCtrlEst_h/lds_gaussian_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sys.h # GLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::System Gaussian LDS Type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H #define LDSCTRLEST_LDS_GAUSSIAN_SYS_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; namespace lds { namespace gaussian { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0, data_t r0 = kDefaultR0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; // get methods const Matrix\u0026amp; R() const { return R_; }; // set methods void set_Q(const Matrix\u0026amp; Q) { lds::System::set_Q(Q); do_recurse_Ke_ = true; } void set_R(const Matrix\u0026amp; R) { Reassign(R_, R); do_recurse_Ke_ = true; }; void set_Ke(const Matrix\u0026amp; Ke) { Reassign(Ke_, Ke); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void set_Ke_m(const Matrix\u0026amp; Ke_m) { Reassign(Ke_m_, Ke_m); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void Print(); protected: void h() override { cx_ = C_ * x_; y_ = cx_ + d_; }; Vector h_(Vector x) override { return C_ * x + d_; }; void RecurseKe() override; // Gaussian-output-specific Matrix R_; bool do_recurse_Ke_{}; }; // System } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":62,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian_8h/","title":"ldsCtrlEst_h/lds_gaussian.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian.h # glds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_H #define LDSCTRLEST_LDS_GAUSSIAN_H // namespace #include \u0026#34;lds.h\u0026#34; namespace lds { namespace gaussian { // insert any Gaussian-specific things here... } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":63,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_ctrl.h # PLDS controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. Detailed Description # This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_CTRL_H #define LDSCTRLEST_LDS_POISSON_CTRL_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system type #include \u0026#34;lds_poisson_sys.h\u0026#34; // control type #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace poisson { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); lds::Limit(y_ref_, kYRefLb, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; private: constexpr static const data_t kYRefLb = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":64,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_em.h # PLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitEM PLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\n[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H #define LDSCTRLEST_LDS_POISSON_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override{}; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; data_t NewtonSolveC(); void AnalyticalSolveD(); }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":65,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_ssid.h # PLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. Detailed Description # This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/)).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H #define LDSCTRLEST_LDS_POISSON_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; private: void DecomposeData() override; void CalcCov(); void PoissonToGaussianMoments(); Matrix cov_; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":66,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/","title":"ldsCtrlEst_h/lds_poisson_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit.h # PLDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Fit PLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_H #define LDSCTRLEST_LDS_POISSON_FIT_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // fit #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace poisson { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) : lds::Fit(n_u, n_x, n_y, dt){}; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = exp(C_ * x.col(t) + d_); return y.col(t); }; void set_R(const Matrix\u0026amp; R) override { std::cerr \u0026lt;\u0026lt; \u0026#34;WARNING: Cannot set R (R[0] = \u0026#34; \u0026lt;\u0026lt; R.at(0) \u0026lt;\u0026lt; \u0026#34;). No Gaussian measurement noise in Poisson observation model.\\n\u0026#34;; }; const Matrix\u0026amp; R() const override { return R_; }; }; }; // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":67,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sctrl.h # PLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #include \u0026#34;lds_poisson_ctrl.h\u0026#34; #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace poisson { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); lds::Limit(y_ref_, kYRefLB, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; private: constexpr static data_t kYRefLB = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":68,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/","title":"ldsCtrlEst_h/lds_poisson_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sys.h # PLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::System Poisson System type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SYS_H #define LDSCTRLEST_LDS_POISSON_SYS_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; // needed for Poisson random number generation #include \u0026lt;random\u0026gt; namespace lds { namespace poisson { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; protected: void h() override { cx_ = C_ * x_; y_ = exp(cx_ + d_); diag_y_.diag() = y_; }; Vector h_(Vector x) override { return exp(C_ * x + d_); }; void RecurseKe() override; private: // Poisson-output-specific Matrix diag_y_; std::poisson_distribution\u0026lt;size_t\u0026gt; pd_; }; // System } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":69,"href":"/lds-ctrl-est/docs/api/files/lds__poisson_8h/","title":"ldsCtrlEst_h/lds_poisson.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson.h # plds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_H #define LDSCTRLEST_LDS_POISSON_H #include \u0026#34;lds.h\u0026#34; namespace lds { namespace poisson { // TODO(mfbolus): Not sure if defining these as static here makes the most // sense. Is there a downside to letting multiple poisson System objects share a // common random number generator? static std::random_device rd; static std::mt19937 rng = std::mt19937( rd()); } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":70,"href":"/lds-ctrl-est/docs/api/files/lds__sctrl_8h/","title":"ldsCtrlEst_h/lds_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_sctrl.h # SwitchedController type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SwitchedController SwitchedController Type. Detailed Description # This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SCTRL_H #define LDSCTRLEST_LDS_SCTRL_H #include \u0026#34;lds_ctrl.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; #include \u0026#34;lds_uniform_vecs.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class SwitchedController : public Controller\u0026lt;System\u0026gt; { public: SwitchedController() = default; SwitchedController(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); SwitchedController(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); void Switch(size_t idx, bool do_force_switch = false); void set_Kc(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc) { Kc_list_ = Kc; Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc) { Kc_list_ = std::move(Kc); Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_inty) { Kc_inty_list_ = Kc_inty; Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_inty) { Kc_inty_list_ = std::move(Kc_inty); Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_u) { Kc_u_list_ = Kc_u; Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_u) { Kc_u_list_ = std::move(Kc_u); Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(const UniformVectorList\u0026amp; g) { g_design_list_ = g; g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(UniformVectorList\u0026amp;\u0026amp; g) { g_design_list_ = std::move(g); g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; protected: std::vector\u0026lt;System\u0026gt; systems_; size_t n_sys_{}; size_t idx_{}; // controller gains could be different for each UniformMatrixList\u0026lt;\u0026gt; Kc_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_inty_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_u_list_; // design-phase input gain could also be different UniformVectorList g_design_list_; // TODO(mfbolus): not sure why I need to do this. using Controller\u0026lt;System\u0026gt;::Kc_; using Controller\u0026lt;System\u0026gt;::Kc_inty_; using Controller\u0026lt;System\u0026gt;::Kc_u_; using Controller\u0026lt;System\u0026gt;::g_design_; using Controller\u0026lt;System\u0026gt;::sys_; // using Controller\u0026lt;System\u0026gt;::u_ref_; // using Controller\u0026lt;System\u0026gt;::x_ref_; // using Controller\u0026lt;System\u0026gt;::y_ref_; // using Controller\u0026lt;System\u0026gt;::control_type_; private: void InitVars(); using lds::Controller\u0026lt;System\u0026gt;::set_sys; // using Controller\u0026lt;System\u0026gt;::set_Kc; // using Controller\u0026lt;System\u0026gt;::set_Kc_inty; // using Controller\u0026lt;System\u0026gt;::set_Kc_u; // using Controller\u0026lt;System\u0026gt;::set_g_design; }; template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(systems.at(0), u_lb, u_ub, control_type), systems_(systems) { InitVars(); } template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(System(systems.at(0).n_u(), systems.at(0).n_x(), systems.at(0).n_y(), systems.at(0).dt()), u_lb, u_ub, control_type), systems_(std::move(systems)) { InitVars(); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::InitVars() { n_sys_ = systems_.size(); sys_ = systems_.at(0); Kc_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_)); Kc_inty_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); Kc_u_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); g_design_list_ = UniformVectorList(std::vector\u0026lt;Vector\u0026gt;(n_sys_, g_design_)); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::Switch(size_t idx, bool do_force_switch) { if ((idx == idx_) \u0026amp;\u0026amp; !do_force_switch) { return; // already there. } // put old up and get new one out systems_.at(idx_) = std::move(sys_); sys_ = std::move(systems_.at(idx)); // set the state of this system to that of the previous system // TODO(mfbolus): This will only work as intended if state matrix is the same. // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp. sys_.set_m(systems_.at(idx_).m(), true); sys_.set_x(systems_.at(idx_).x()); // swap controller gains Kc_list_.Swap(Kc_, idx_); Kc_list_.Swap(Kc_, idx); if (control_type_ \u0026amp; kControlTypeIntY) { Kc_inty_list_.Swap(Kc_inty_, idx_); Kc_inty_list_.Swap(Kc_inty_, idx); } if (control_type_ \u0026amp; kControlTypeDeltaU) { Kc_u_list_.Swap(Kc_u_, idx_); Kc_u_list_.Swap(Kc_u_, idx); } g_design_list_.Swap(g_design_, idx_); g_design_list_.Swap(g_design_, idx); idx_ = idx; } // Switch } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":71,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8h/","title":"ldsCtrlEst_h/lds_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_sys.h # LDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::System Linear Dynamical System Type. Detailed Description # This file declares and partially defines the base type for linear dynamical systems ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SYS_H #define LDSCTRLEST_LDS_SYS_H #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class System { public: System() = default; System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); virtual ~System() {} void Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z); virtual const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) = 0; void f(const Vector\u0026amp; u, bool do_add_noise = false) { x_ = A_ * x_ + B_ * (g_ % u) + m_; if (do_add_noise) { x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_); } }; virtual void h() = 0; virtual Vector h_(Vector x) = 0; size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Vector\u0026amp; x() const { return x_; }; const Matrix\u0026amp; P() const { return P_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; P_m() const { return P_m_; }; const Vector\u0026amp; cx() const { return cx_; }; const Vector\u0026amp; y() const { return y_; }; const Vector\u0026amp; x0() const { return x0_; }; const Vector\u0026amp; m0() const { return m0_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; const Matrix\u0026amp; Ke() const { return Ke_; }; const Matrix\u0026amp; Ke_m() const { return Ke_m_; }; const Matrix\u0026amp; Q() { return Q_; }; const Matrix\u0026amp; Q_m() { return Q_m_; }; const Matrix\u0026amp; P0() { return P0_; }; const Matrix\u0026amp; P0_m() { return P0_m_; }; void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_m(const Vector\u0026amp; m, bool do_force_assign = false) { Reassign(m0_, m); if ((!do_adapt_m) || do_force_assign) { Reassign(m_, m); } }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); }; void set_Q_m(const Matrix\u0026amp; Q_m) { Reassign(Q_m_, Q_m); }; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); }; void set_P0_m(const Matrix\u0026amp; P0_m) { Reassign(P0_m_, P0_m); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; void set_x(const Vector\u0026amp; x) { Reassign(x_, x); h(); }; void Reset(); std::vector\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; nstep_pred_block( UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z, size_t n_pred = 1); void Print(); // safe to leave this public and non-const bool do_adapt_m{}; protected: virtual void RecurseKe() = 0; void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); std::size_t n_x_{}; std::size_t n_u_{}; std::size_t n_y_{}; data_t dt_{}; // Signals: Vector x_; Matrix P_; Vector m_; Matrix P_m_; Vector cx_; Vector y_; Vector z_; // Parameters: Vector x0_; Matrix P0_; Vector m0_; Matrix P0_m_; Matrix A_; Matrix B_; Vector g_; Matrix Q_; Matrix Q_m_; Matrix C_; Vector d_; Matrix Ke_; Matrix Ke_m_; }; // System } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":72,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/","title":"ldsCtrlEst_h/lds_uniform_mats.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_mats.h # List of uniformly sized matrices. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformMatrixList Detailed Description # This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H #define LDSCTRLEST_LDS_UNIFORM_MATS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { template \u0026lt;MatrixListFreeDim D = kMatFreeDimNone\u0026gt; class UniformMatrixList : public std::vector\u0026lt;Matrix\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Matrix\u0026gt;::vector; // don\u0026#39;t allow push_back to be used since it doesn\u0026#39;t check dims using std::vector\u0026lt;Matrix\u0026gt;::push_back; public: using std::vector\u0026lt;Matrix\u0026gt;::operator=; using std::vector\u0026lt;Matrix\u0026gt;::operator[]; using std::vector\u0026lt;Matrix\u0026gt;::begin; using std::vector\u0026lt;Matrix\u0026gt;::end; using std::vector\u0026lt;Matrix\u0026gt;::size; using std::vector\u0026lt;Matrix\u0026gt;::at; UniformMatrixList() = default; explicit UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); explicit UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; ~UniformMatrixList() = default; const std::array\u0026lt;size_t, 2\u0026gt;\u0026amp; dim(size_t n = 0) const { return dim_.at(n); } size_t size() { return std::vector\u0026lt;Matrix\u0026gt;::size(); }; const Matrix\u0026amp; at(size_t n) { return std::vector\u0026lt;Matrix\u0026gt;::at(n); }; void Swap(Matrix\u0026amp; that, size_t n); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; void append(const Matrix\u0026amp; mat); private: void CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim); std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt; dim_; }; template \u0026lt;MatrixListFreeDim D\u0026gt; inline void UniformMatrixList\u0026lt;D\u0026gt;::Swap(Matrix\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.n_cols); } if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap // not moving, since it causes memory issues. // so this method isn\u0026#39;t a memory-saver as designed for now Matrix tmp = (*this)[n]; (*this)[n] = that; that = tmp; if (D == kMatFreeDim1) { this-\u0026gt;dim_[n][0] = (*this)[n].n_rows; } if (D == kMatFreeDim2) { this-\u0026gt;dim_[n][1] = (*this)[n].n_cols; } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::append(const Matrix\u0026amp; mat) { std::array\u0026lt;size_t, 2\u0026gt; dim({mat.n_rows, mat.n_cols}); CheckDimensions(dim); std::vector\u0026lt;Matrix\u0026gt;::push_back(mat); dim_.push_back(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) { // make sure dim_ vector is initialized if (dim_.empty()) { dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(that.size(), {0, 0}); } // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices\u0026#34;; throw std::runtime_error(ss.str()); } // if dimensions a not zero and do not match, skip move with error message. bool dims_nonzero = true; for (auto d : dim_) { if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; d[0] \u0026lt; 1) { dims_nonzero = false; break; } if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; d[1] \u0026lt; 1) { dims_nonzero = false; break; } } if (dims_nonzero) { bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); } if (!does_match) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrices of size \u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; dim_[k] = that.dim(k); } return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. bool dims_nonzero = true; for (auto d : dim_) { // if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; (d[0] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; (d[1] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // } // // if (dims_nonzero) { // bool does_match = true; // if (!(D == kMatFreeDim1)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); // } // // if (!(D == kMatFreeDim2)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); // } // // if (!does_match) { // this-\u0026gt;at(0).print(\u0026#34;this[0] = \u0026#34;); // that.at(0).print(\u0026#34;that[0] = \u0026#34;); // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformMatrixList element of size (\u0026#34; \u0026lt;\u0026lt; // that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols \u0026lt;\u0026lt; \u0026#34;) for an // element of size (\u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34;). // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Matrix\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(std::move(mats)) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) : vector(that) { (*this) = that; } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { std::array\u0026lt;size_t, 2\u0026gt; dim_k({this-\u0026gt;at(k).n_rows, this-\u0026gt;at(k).n_cols}); dim_.push_back(dim_k); } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim) { // change behavior based on free dim D if ((dim[0] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim1)) { dim[0] = this-\u0026gt;at(0).n_rows; } if ((dim[1] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim2)) { dim[1] = this-\u0026gt;at(0).n_cols; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Matrix\u0026amp; mat : *this) { if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_rows == dim[0]); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_cols == dim[1]); } if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(this-\u0026gt;size(), dim); for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { dim_[k][0] = (*this)[k].n_rows; dim_[k][1] = (*this)[k].n_cols; } } } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":73,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/","title":"ldsCtrlEst_h/lds_uniform_systems.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_systems.h # List of uniformly sized Systems. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformSystemList Detailed Description # This file provides a container for uniformly sized Systems.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector // namespace #include \u0026#34;lds.h\u0026#34; // System type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class UniformSystemList : public std::vector\u0026lt;System\u0026gt; { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;System\u0026gt;::vector; using std::vector\u0026lt;System\u0026gt;::operator=; using std::vector\u0026lt;System\u0026gt;::operator[]; using std::vector\u0026lt;System\u0026gt;::at; using std::vector\u0026lt;System\u0026gt;::begin; using std::vector\u0026lt;System\u0026gt;::end; using std::vector\u0026lt;System\u0026gt;::size; public: UniformSystemList() = default; explicit UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); explicit UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(const UniformSystemList\u0026amp; that); UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept; ~UniformSystemList() = default; const std::array\u0026lt;size_t, 3\u0026gt;\u0026amp; dim() const { return dim_; } size_t size() { return std::vector\u0026lt;System\u0026gt;::size(); }; const System\u0026amp; at(size_t n) { return std::vector\u0026lt;System\u0026gt;::at(n); }; void Swap(System\u0026amp; that, size_t n); UniformSystemList\u0026amp; operator=(const UniformSystemList\u0026amp; that); UniformSystemList\u0026amp; operator=(UniformSystemList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim); std::array\u0026lt;size_t, 3\u0026gt; dim_{}; }; template \u0026lt;typename System\u0026gt; inline void UniformSystemList\u0026lt;System\u0026gt;::Swap(System\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformSystemList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = (dim_[0] == that.n_u()) \u0026amp;\u0026amp; (dim_[1] == that.n_x()) \u0026amp;\u0026amp; (dim_[2] == that.n_y()); if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformSystemList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap System tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( const UniformSystemList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_[0] + dim_[1] + dim_[2]) { std::array\u0026lt;size_t, 3\u0026gt; other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign systems of size \u0026#34; \u0026lt;\u0026lt; dim_[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2] \u0026lt;\u0026lt; \u0026#34; with systems of size \u0026#34; \u0026lt;\u0026lt; other_dim[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other_dim[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2]; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( UniformSystemList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. if (dim_[0] + dim_[1] + dim_[2]) { // bool does_match = (dim_[0] == that.at(0).n_u()) \u0026amp;\u0026amp; // (dim_[1] == that.at(0).n_x()) \u0026amp;\u0026amp; // (dim_[2] == that.at(0).n_y()); // if (!does_match) { // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformSystemList element for an element of \u0026#34; // \u0026#34;different size. Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;System\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(std::move(systems)) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList( std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const UniformSystemList\u0026amp; that) : std::vector\u0026lt;System\u0026gt;(that) { (*this) = that; } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept : std::vector\u0026lt;System\u0026gt;(std::move(that)) { this-\u0026gt;dim_[0] = this-\u0026gt;at(0).n_u(); this-\u0026gt;dim_[1] = this-\u0026gt;at(0).n_x(); this-\u0026gt;dim_[2] = this-\u0026gt;at(0).n_y(); } template \u0026lt;typename System\u0026gt; void UniformSystemList\u0026lt;System\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim) { if (dim[0] + dim[1] + dim[2]) { dim_ = dim; } else { dim_[0] = this-\u0026gt;at(0).n_u(); dim_[1] = this-\u0026gt;at(0).n_x(); dim_[2] = this-\u0026gt;at(0).n_y(); } // make sure dimensiolaties are all uniform bool does_match(true); for (const System\u0026amp; sys : *this) { does_match = does_match \u0026amp;\u0026amp; (sys.n_u() == dim_[0]); does_match = does_match \u0026amp;\u0026amp; (sys.n_x() == dim_[1]); does_match = does_match \u0026amp;\u0026amp; (sys.n_y() == dim_[2]); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input systems are not uniform.\u0026#34;); } } } } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":74,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/","title":"ldsCtrlEst_h/lds_uniform_vecs.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_vecs.h # List of uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformVectorList Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H #define LDSCTRLEST_LDS_UNIFORM_VECS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { class UniformVectorList : public std::vector\u0026lt;Vector\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Vector\u0026gt;::vector; using std::vector\u0026lt;Vector\u0026gt;::operator=; using std::vector\u0026lt;Vector\u0026gt;::operator[]; using std::vector\u0026lt;Vector\u0026gt;::at; using std::vector\u0026lt;Vector\u0026gt;::begin; using std::vector\u0026lt;Vector\u0026gt;::end; using std::vector\u0026lt;Vector\u0026gt;::size; public: UniformVectorList() = default; explicit UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim = 0); explicit UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim = 0); UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim = 0); UniformVectorList(const UniformVectorList\u0026amp; that); UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept; ~UniformVectorList() = default; size_t dim() const { return dim_; } size_t size() { return std::vector\u0026lt;Vector\u0026gt;::size(); }; const Vector\u0026amp; at(size_t n) { return std::vector\u0026lt;Vector\u0026gt;::at(n); }; void Swap(Vector\u0026amp; that, size_t n); UniformVectorList\u0026amp; operator=(const UniformVectorList\u0026amp; that); UniformVectorList\u0026amp; operator=(UniformVectorList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(size_t dim); size_t dim_{}; }; inline void UniformVectorList::Swap(Vector\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = dim_ == that.n_elem; if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap Vector tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( const UniformVectorList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_) { size_t other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ \u0026lt;\u0026lt; \u0026#34; with vectors of size \u0026#34; \u0026lt;\u0026lt; other_dim; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( UniformVectorList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors. Skipping.\\n\u0026#34;; // return (*this); // } // // if (dim_) { // size_t other_dim(that.dim()); // if (dim_ != other_dim) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ // \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; other_dim \u0026lt;\u0026lt; \u0026#34;. // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Vector\u0026gt;::operator=(std::move(that)); return (*this); } } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":75,"href":"/lds-ctrl-est/docs/api/files/lds_8h/","title":"ldsCtrlEst_h/lds.h","section":"Files","content":" ldsCtrlEst_h/lds.h # lds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)) observations.\nSource code # //===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_H #define LDSCTRLEST_LDS_H // #ifndef LDSCTRLEST // #include \u0026lt;ldsCtrlEst\u0026gt; // #endif #include \u0026lt;armadillo\u0026gt; namespace lds { using data_t = double; // may change to float (but breaks mex functions) using Vector = arma::Col\u0026lt;data_t\u0026gt;; using Matrix = arma::Mat\u0026lt;data_t\u0026gt;; using Cube = arma::Cube\u0026lt;data_t\u0026gt;; using View = arma::subview\u0026lt;data_t\u0026gt;; namespace fill = arma::fill; static const std::size_t kControlTypeDeltaU = 0x1; static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); static const data_t kPi = arma::datum::pi; static const data_t kDefaultP0 = 1e-6; static const data_t kDefaultQ0 = 1e-6; static const data_t kDefaultR0 = 1e-2; enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA }; enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2 }; // TODO(mfbolus): for SwitchedController, may want systems to have differing // numbers of states. Use this enum as template parameter? // enum SystemListFreeDim { // kSysFreeDimNone, // kSysFreeDimX ///\u0026lt; allow state dim (x) of systems in list to be hetero // }; // place hard limits on contents of vecors/mats void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub); void Limit(Vector\u0026amp; x, data_t lb, data_t ub); void Limit(Matrix\u0026amp; x, data_t lb, data_t ub); // in-place assign that errs if there are dimension mismatches: void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); // TODO(mfbolus): this is a fudge, but for some reason, cov mats often going // numerically asymm. void ForceSymPD(Matrix\u0026amp; X); void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min = 0); void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X); Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B); inline void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Vector\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Matrix\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if (other.n_elem != some.n_elem) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vector of size \u0026#34; \u0026lt;\u0026lt; some.n_elem \u0026lt;\u0026lt; \u0026#34; with vector of size \u0026#34; \u0026lt;\u0026lt; other.n_elem \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } inline void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrix of size \u0026#34; \u0026lt;\u0026lt; some.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; some.n_cols \u0026lt;\u0026lt; \u0026#34; with matrix of size \u0026#34; \u0026lt;\u0026lt; other.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other.n_cols \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } } // namespace lds #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":76,"href":"/lds-ctrl-est/docs/api/files/mex__c__util_8h/","title":"ldsCtrlEst_h/mex_c_util.h","section":"Files","content":" ldsCtrlEst_h/mex_c_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) More\u0026hellip;\nNamespaces # Name armamexc arma/mex interface using Matlab C API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab/Octave\u0026rsquo;s C mex API.\nSource code # //===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXC_UTIL_H #define LDSCTRLEST_MEXC_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.h\u0026#34; // // If Matlab_FOUND, include matrix.h. // // (Octave does not need/have it.) // #ifdef Matlab_FOUND // #include \u0026#34;matrix.h\u0026#34; // #endif namespace armamexc { template \u0026lt;class T\u0026gt; inline auto m2T_scalar(const mxArray *matlab_scalar) -\u0026gt; T { if (mxGetData(matlab_scalar)) { return static_cast\u0026lt;T\u0026gt;(mxGetScalar(matlab_scalar)); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return 0; } template \u0026lt;class T\u0026gt; inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false, bool strict = true) -\u0026gt; arma::Mat\u0026lt;T\u0026gt; { if (mxGetData(matlab_mat)) { const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat); if (n_dim == 2) { return arma::Mat\u0026lt;T\u0026gt;(static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)), mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem, strict); } mexErrMsgTxt(\u0026#34;Number of dimensions must be 2.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } // TODO(mfbolus): make these templated. template \u0026lt;typename T\u0026gt; inline auto a2m_mat(arma::Mat\u0026lt;T\u0026gt; const \u0026amp;arma_mat) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_mat.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Mat.\u0026#34;); return nullptr; } template \u0026lt;typename T\u0026gt; inline auto a2m_vec(arma::Col\u0026lt;T\u0026gt; const \u0026amp;arma_vec) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_vec.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Col.\u0026#34;); return nullptr; } } // namespace armamexc #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":77,"href":"/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/","title":"ldsCtrlEst_h/mex_cpp_util.h","section":"Files","content":" ldsCtrlEst_h/mex_cpp_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) More\u0026hellip;\nNamespaces # Name armamexcpp arma/mex interface using Matlab C++ API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab\u0026rsquo;s C++ mex API.\nSource code # //===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXCPP_UTIL_H #define LDSCTRLEST_MEXCPP_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.hpp\u0026#34; #include \u0026#34;mexAdapter.hpp\u0026#34; namespace armamexcpp { template \u0026lt;class T\u0026gt; std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; m2a_cellmat(matlab::data::CellArray\u0026amp; matlab_cell) { size_t n_cells = matlab_cell.getNumberOfElements(); std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; arma_mat(n_cells, arma::Mat\u0026lt;T\u0026gt;(1, 1, arma::fill::zeros)); for (size_t k = 0; k \u0026lt; n_cells; k++) { matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = matlab_cell[k]; auto dims = matlab_mat.getDimensions(); arma_mat[k] = arma::Mat\u0026lt;T\u0026gt;(matlab_mat.release().get(), dims[0], dims[1]); } return arma_mat; }; template \u0026lt;class T\u0026gt; std::vector\u0026lt;T\u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt;T\u0026gt;\u0026amp; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); T* ptr = matlab_array.release().get(); std::vector\u0026lt;T\u0026gt; vec(ptr, ptr + n_elem); return vec; }; template \u0026lt;class T\u0026gt; arma::Col\u0026lt;T\u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); // T* ptr = matlab_array.release().get(); // arma::Col\u0026lt;T\u0026gt; vec(ptr, n_elem); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? arma::Col\u0026lt;T\u0026gt; vec(n_elem, arma::fill::zeros); for (size_t k = 0; k \u0026lt; n_elem; k++) { vec[k] = matlab_array[k]; } return vec; }; template \u0026lt;class T\u0026gt; arma::Mat\u0026lt;T\u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { // ArrayDimensions == std::vector\u0026lt;size_t\u0026gt; auto dims = matlab_array.getDimensions(); // T* ptr = matlab_array.release().get(); // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false) // arma::Mat\u0026lt;T\u0026gt; mat(ptr, dims[0], dims[1]); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? // // armadillo and matlab both use column-major ordering, so this should work: size_t n_elem = dims[0] * dims[1]; arma::Mat\u0026lt;T\u0026gt; mat(dims[0], dims[1], arma::fill::zeros); size_t k(0); for (auto m: matlab_array) { mat[k] = m; k++; } return mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_mat(const arma::Mat\u0026lt;T\u0026gt;\u0026amp; arma_mat, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(), arma_mat.memptr() + arma_mat.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_vec(const arma::Col\u0026lt;T\u0026gt;\u0026amp; arma_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;({arma_vec.n_elem, 1}, arma_vec.memptr(), arma_vec.memptr() + arma_vec.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; s2m_vec(const std::vector\u0026lt;T\u0026gt;\u0026amp; std_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size()); return matlab_mat; }; } // namespace armamexcpp #endif Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":78,"href":"/lds-ctrl-est/docs/terminology/model/","title":"Models","section":"LDS C+E Documentation","content":" Model Definitions # This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form: \\[\\mathbf{x}_{t\u0026#43;1} = f\\left( \\mathbf{x}_{t}, \\mathbf{v}_{t} \\right) = \\mathbf{A} \\mathbf{x}_{t} \u0026#43; \\mathbf{B} \\mathbf{v}_{t} \u0026#43; \\mathbf{m}_{t} \u0026#43; \\mathbf{w}_{t}\\] \\[\\mathbf{y}_{t} = h\\left( \\mathbf{x}_{t} \\right)\\] t : time index x : system state v = g%u : input (e.g., in physical units used for model fit) u : control signal sent to actuator (e.g., in Volts) y : system output m : process disturbance w ~ N(0, Q) : process noise/disturbance A : state matrix B : input coupling matrix g : input gain (e.g., for converting to control signal actuator voltage) n.b., assumes this conversion is linear Q : process noise covariance % : element-wise multiplication LDS with Gaussian Observations # For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.\n\\[\\mathbf{y}_{t} = \\mathbf{C} \\mathbf{x}_{t} \u0026#43; \\mathbf{d}\\] \\[\\mathbf{z}_{t} \\sim \\mathcal{N}\\left(\\mathbf{y}_{t} , \\mathbf{R} \\right)\\] z : measurement C : output matrix d : output bias R : measurement noise covariance LDS with Poisson Observations # For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.\n\\[y_{t}^{i} = \\exp \\left(\\mathbf{c}^i \\mathbf{x}_{t} \u0026#43; d^i\\right)\\] \\[z_{t}^i \\sim \\rm{Poisson} \\left(y_{t}^i \\right)\\] i : output index z : measurement (count data) c : i^th row of output matrix (C) d : output bias Model Predictive Control (MPC) # Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.\nIn the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.\n"},{"id":79,"href":"/lds-ctrl-est/docs/api/modules/","title":"Modules","section":"LDS C+E Documentation","content":" Modules # Control Mode Bit Masks provides fill types for constructing new armadillo vectors, matrices\nDefaults\nUpdated on 5 March 2025 at 21:00:41 EST\n"},{"id":80,"href":"/lds-ctrl-est/docs/api/namespaces/","title":"Namespaces","section":"LDS C+E Documentation","content":" Namespaces # armamexc arma/mex interface using Matlab C API\narmamexcpp arma/mex interface using Matlab C++ API\nlds::gaussian Linear Dynamical Systems with Gaussian observations.\nlds::poisson Linear Dynamical Systems with Poisson observations.\nstd\nUpdated on 5 March 2025 at 21:00:41 EST\n"},{"id":81,"href":"/lds-ctrl-est/docs/api/pages/","title":"Pages","section":"LDS C+E Documentation","content":" Pages # Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":82,"href":"/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/","title":"PLDS State Estimation","section":"LDS C+E Examples","content":" PLDS State Estimation Tutorial # This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[x_{t\u0026#43;1} = x_t \u0026#43; w_t\\] \\[y_{t} = \\exp\\left(x_t\\right)\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state Finally, assign the parameters using corresponding set-methods.\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); Creating the estimator # Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. To ensure robust estimates, adaptively re-estimate the process disturbance.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); Simulating estimation # In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.\n// Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.\nWith this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.\n"},{"id":83,"href":"/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/","title":"PLDS Switched Control","section":"LDS C+E Examples","content":" PLDS Switched Control Tutorial # This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.\nIn the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating the simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); The system\u0026rsquo;s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 Initially, the system will be in \u0026ldquo;mode\u0026rdquo; 1, where B = b1.\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.\nCreating the controller # Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.\nSimilar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.\nMoreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.\nPutting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying systems: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nNow that the SwitchedController is instantiated, assign its parameters.\n// Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).\nThe control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.\n// Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system\u0026rsquo;s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.\nExample simulation result # Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller\u0026rsquo;s online estimates of the output and state are shown in purple.\nNote that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.\n"},{"id":84,"href":"/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/","title":"src","section":"Files","content":" src # Files # Name src/lds.cpp misc lds namespace functions src/lds_gaussian_sys.cpp GLDS base type. src/lds_poisson_sys.cpp PLDS base type. src/lds_sys.cpp LDS base type. src/lds_uniform_vecs.cpp Uniformly sized vectors. Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":85,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/","title":"src/lds_gaussian_sys.cpp","section":"Files","content":" src/lds_gaussian_sys.cpp # GLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_gaussian_sys.h\u0026gt; lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0, data_t r0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { R_.zeros(n_y, n_y); R_.diag().fill(r0); do_recurse_Ke_=true; }; // recursively estimate Ke void lds::gaussian::System::RecurseKe() { if (!do_recurse_Ke_) { return; } // predict covariance P_ = A_ * P_ * A_.t() + Q_; // calc Kalman gain Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_); // update covariance // Reference: Ghahramani et Hinton (1996) P_ = P_ - Ke_ * C_ * P_; if (do_adapt_m) { P_m_ += Q_m_; // A_m = I (i.e., random walk) Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_); P_m_ = P_m_ - Ke_m_ * C_ * P_m_; } } // Simulate const lds::Vector\u0026amp; lds::gaussian::System::Simulate(const Vector\u0026amp; u_tm1){ f(u_tm1, true);//simulate dynamics with noise added h();//output z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure return z_; } void lds::gaussian::System::Print() { lds::System::Print(); std::cout \u0026lt;\u0026lt; \u0026#34;R: \\n\u0026#34; \u0026lt;\u0026lt; R_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":86,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/","title":"src/lds_poisson_sys.cpp","section":"Files","content":" src/lds_poisson_sys.cpp # PLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_poisson_sys.h\u0026gt; lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { diag_y_ = diagmat(y_); pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(0); }; // Correct: Given measurement (z) and current input (u), update estimate of the // state, covar, output. // // see Eden et al. 2004 void lds::poisson::System::RecurseKe() { // predict covariance P_ = A_ * P_ * A_.t() + Q_; // update cov P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_); Ke_ = P_ * C_.t(); if (do_adapt_m) { P_m_ += Q_m_; // predict (A_m = I) P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_); // update Ke_m_ = P_m_ * C_.t(); } } // Simulate Measurement: z ~ Poisson(y) const lds::Vector\u0026amp; lds::poisson::System::Simulate(const Vector\u0026amp; u_tm1) { f(u_tm1, true); // simulate dynamics with noise added h(); // output z_.zeros(); for (std::size_t k = 0; k \u0026lt; n_y_; k++) { // construct a Poisson distribution object with mean y[k] pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(y_[k]); // pull random sample from this distribution z_[k] = pd_(rng); } return z_; } // ******************* SYS_T ******************* Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":87,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8cpp/","title":"src/lds_sys.cpp","section":"Files","content":" src/lds_sys.cpp # LDS base type. More\u0026hellip;\nDetailed Description # This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- lds_sys.cpp - LDS -------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_sys.h\u0026gt; #include \u0026lt;vector\u0026gt; lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) { InitVars(p0, q0); } void lds::System::InitVars(data_t p0, data_t q0) { // initial conditions. x0_ = Vector(n_x_, fill::zeros); // includes bias (nY) and g (nU) P0_ = p0 * Matrix(n_x_, n_x_, fill::eye); m0_ = x0_; P0_m_ = P0_; // signals x_ = x0_; P_ = P0_; m_ = m0_; P_m_ = P0_m_; y_ = Vector(n_y_, fill::zeros); cx_ = Vector(n_y_, fill::zeros); z_ = Vector(n_y_, fill::zeros); // By default, random walk where each state is independent // In this way, provides independent estimates of rate per channel of output. A_ = Matrix(n_x_, n_x_, fill::eye); B_ = Matrix(n_x_, n_u_, fill::zeros); g_ = Vector(n_u_, fill::ones); Q_ = q0 * Matrix(n_x_, n_x_, fill::eye); Q_m_ = Q_; C_ = Matrix(n_y_, n_x_, fill::eye); // each state will map to an output by d_ = Vector(n_y_, fill::zeros); Ke_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain. Ke_m_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain for m adaptation. do_adapt_m = false; } // Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state // and update estimate of the state, covar, output using Kalman filter void lds::System::Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z_t) { // predict mean f(u_tm1); // dynamics h(); // output // recursively calculate esimator gains (or just keep existing values) // (also predicts+updates estimate covariance) RecurseKe(); // update x_ += Ke_ * (z_t - y_); if (do_adapt_m) { m_ += Ke_m_ * (z_t - y_); // adaptively estimating disturbance } // With new state, estimate output. h(); // --\u0026gt; posterior } void lds::System::Reset() { // reset to initial conditions x_ = x0_; // mean P_ = P0_; // cov of state estimate m_ = m0_; // process disturbance P_m_ = P0_m_; // cov of disturbance estimate h(); } std::vector\u0026lt;lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt;\u0026gt; lds::System::nstep_pred_block(lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; u, lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; z, size_t n_pred) { lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_filt; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_pred; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; y_pred; for (size_t k = 0; k \u0026lt; u.size(); k++) { Reset(); size_t n_t = arma::size(u[k])[1]; Matrix x_filt_k(n_x_, n_t, fill::zeros); Matrix x_pred_k(n_x_, n_t - n_pred, fill::zeros); Matrix y_pred_k(n_y_, n_t - n_pred, fill::zeros); for (size_t t = 0; t \u0026lt; n_t - n_pred; t++) { Vector x_pred_ahead = x_; for (size_t t_u = t; t_u \u0026lt; t + n_pred; t_u++) { x_pred_ahead = A_ * x_pred_ahead + B_ * u[k].col(t_u); } x_pred_k.col(t) = x_pred_ahead; y_pred_k.col(t) = h_(x_pred_ahead); if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; // given previous measurment } for (size_t t = n_t - n_pred; t \u0026lt; n_t; t++) { if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; } x_filt.append(x_filt_k); x_pred.append(x_pred_k); y_pred.append(y_pred_k); } return {x_filt, x_pred, y_pred}; } void lds::System::Print() { std::cout \u0026lt;\u0026lt; \u0026#34;\\n ********** SYSTEM ********** \\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;x: \\n\u0026#34; \u0026lt;\u0026lt; x_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;P: \\n\u0026#34; \u0026lt;\u0026lt; P_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;A: \\n\u0026#34; \u0026lt;\u0026lt; A_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B: \\n\u0026#34; \u0026lt;\u0026lt; B_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;g: \\n\u0026#34; \u0026lt;\u0026lt; g_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;m: \\n\u0026#34; \u0026lt;\u0026lt; m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q: \\n\u0026#34; \u0026lt;\u0026lt; Q_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q_m: \\n\u0026#34; \u0026lt;\u0026lt; Q_m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d: \\n\u0026#34; \u0026lt;\u0026lt; d_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;C: \\n\u0026#34; \u0026lt;\u0026lt; C_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;y: \\n\u0026#34; \u0026lt;\u0026lt; y_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } //******************* SYS_T ******************* Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":88,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/","title":"src/lds_uniform_vecs.cpp","section":"Files","content":" src/lds_uniform_vecs.cpp # Uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_uniform_vecs.h\u0026gt; namespace lds { UniformVectorList::UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); } UniformVectorList::UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim) : vector(std::move(vecs)) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(const UniformVectorList\u0026amp; that) : vector(that) { (*this) = that; } UniformVectorList::UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { this-\u0026gt;dim_ = this-\u0026gt;at(0).n_elem; } void UniformVectorList::CheckDimensions(size_t dim) { if (dim) { dim_ = dim; } else { dim_ = this-\u0026gt;at(0).n_elem; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Vector\u0026amp; vec : *this) { does_match = does_match \u0026amp;\u0026amp; (vec.n_elem == dim_); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } } } // namespace lds Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":89,"href":"/lds-ctrl-est/docs/api/files/lds_8cpp/","title":"src/lds.cpp","section":"Files","content":" src/lds.cpp # misc lds namespace functions More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file implements miscellaneous lds namespace functions not bound to a class.\nSource code # //===-- lds.cpp - LDS -----------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds.h\u0026gt; // insert any necessary function definitions here. namespace lds { void ForceSymPD(Matrix\u0026amp; X) { if (X.is_sympd() || !X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; // for eigenval decomp bool did_succeed(true); Vector d; Matrix u; // see first method (which may not be ideal): // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/ size_t k(1); bool is_sympd = X.is_sympd(); Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye); while (!is_sympd) { if (k \u0026gt; 100) { did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); data_t min_eig = arma::min(d); std::cerr \u0026lt;\u0026lt; \u0026#34;After multiple iterations, min eigen val = \u0026#34; \u0026lt;\u0026lt; min_eig \u0026lt;\u0026lt; \u0026#34;.\\n\u0026#34;; throw std::runtime_error( \u0026#34;Failed to make matrix symmetric positive definite.\u0026#34;); return; } // Limit(d, arma::eps(0), kInf); // force to be positive... // Matrix d_diag = arma::diagmat(d); // X = u * d_diag * u.t(); did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymPD failed.\u0026#34;); } data_t min_eig = arma::min(d); X += id * abs(min_eig) + arma::datum::eps; // make sure symm: X = (X + X.t()) / 2; // double check eigenvals positive after symmetrizing: arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); min_eig = arma::min(d); is_sympd = min_eig \u0026gt; 0; k++; } } void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min) { if (!X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; bool did_succeed(true); Vector d; Matrix u; did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymMinEig failed.\u0026#34;); } Limit(d, eig_min + arma::eps(eig_min), kInf); // enforce lower bound Matrix d_diag = arma::diagmat(d); X = u * d_diag * u.t(); // double check symmetric X = (X + X.t()) / 2; } void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X) { bool did_succeed(true); did_succeed = arma::qr_econ(Qt, L, X.t()); if (!did_succeed) { throw std::runtime_error(\u0026#34;LQ decomposition failed.\u0026#34;); } arma::inplace_trans(L); arma::inplace_trans(Qt); } Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B) { // subtract out mean auto m_a = arma::mean(A, 1); Matrix a0 = A; a0.each_col() -= m_a; auto m_b = arma::mean(B, 1); Matrix b0 = B; b0.each_col() -= m_b; Matrix cov = a0 * b0.t() / a0.n_cols; return cov; } } // namespace lds Updated on 5 March 2025 at 21:00:41 EST\n"},{"id":90,"href":"/lds-ctrl-est/docs/api/namespaces/namespacestd/","title":"std","section":"Namespaces","content":" std # Updated on 5 March 2025 at 21:00:41 EST\n"}]
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+[{"id":0,"href":"/lds-ctrl-est/docs/","title":"LDS C+E Documentation","section":"LDS Control \u0026 Estimation","content":" LDS Control \u0026amp; Estimation Documentation # "},{"id":1,"href":"/lds-ctrl-est/docs/tutorials/","title":"LDS C+E Examples","section":"LDS C+E Documentation","content":" Examples # "},{"id":2,"href":"/lds-ctrl-est/acknowledgements/","title":"Acknowledgements","section":"LDS Control \u0026 Estimation","content":" Acknowledgements # Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.\n"},{"id":3,"href":"/lds-ctrl-est/docs/getting-started/getting-started/","title":"Getting Started","section":"LDS C+E Documentation","content":" Getting Started # This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.\nldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.\nTested Configurations # Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the RelWithDebInfo build type in the CMake configure command (-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo):\nUbuntu 18.04 with GCC 7.5 compiler macOS 11 (Big Sur) with Apple Clang 12 compiler Windows 10 with Visual Studio 16.11 (2019 release) and Clang 12 compiler That being said, if you want to debug a build for a single platform, here are some things you can try:\nUse different compilers (or even different versions of a single compiler) Use different versions of vcpkg (which you can control by checking out a different commit in the vcpkg submodule) Mac Pre-requisities # Xcode Command Line Tools will get you clang, gcc, make, and git:\nxcode-select --install Homebrew is \u0026ldquo;The Missing Package Manager for macOS\u0026rdquo; which will make installing lots of things easy. Install like this:\n/bin/bash -c \u0026#34;$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\u0026#34; You can then use it to install CMake, gfortran, and pkg-config:\nbrew install cmake gfortran pkg-config Linux Pre-requisites # You\u0026rsquo;ll need Git, CMake, GCC, gfortran, etc.\nsudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential ninja-build Windows Installation # Look here for Windows-specific instructions.\nDownloading the Library # First, clone the repository along with submodules:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est\rgit submodule update --init Compilation + Installation # Now generate the cache and build using your IDE or from the command line as follows.\nmkdir build \u0026amp;\u0026amp; cd build\rcmake ..\rcmake --build . The first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.\nIf you want to use vcpkg set up somewhere besides this repo\u0026rsquo;s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE\u0026rsquo;s settings.\nYou can verify the build is working by running ctest from the build folder, which runs all the example scripts.\nOptions # This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\nLDSCTRLEST_BUILD_EXAMPLES : [default=ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake to configure/build the library.\nFor basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build cmake --build #build the project sudo make install #[optional] installs to default location (OS-specific) To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location cmake --build #build the project make install #install to /your/install/prefix To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\nLD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.\nPython bindings package ldsctrlest # With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython3_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn\u0026rsquo;t use an undesired version. That environment needs to have NumPy installed.\ncmake --build . --target python_modules The bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory (pip install pytest matplotlib first if you need to).\nSee python/ldsctrlest/README.md for usage details.\nAlso, beware that a single build will probably not work for both the standalone library and the Python package, since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.\nCommon issues # \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.\nexport CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix vcpkg fails on configuration Try running ./bootstrap-vcpkg from the vcpkg folder and try again. If that doesn\u0026rsquo;t work, try updating vcpkg to a newer version (in the source control tab, click on the commit hash by the vcpkg repo then select from the dropdown) and running boostsrap-vcpkg again. You can also try upgrading your system (e.g., apt update, apt upgrade).\nCould not find Python3 (missing: Python3_NumPy_INCLUDE_DIRS NumPy)\nMake sure NumPy is installed in the Python environment you specified. If CMake still can\u0026rsquo;t find it, you may need to tell CMake exactly where to find it by adding an argument to the configure command: -DPython3_NumPy_INCLUDE_DIR=.... You can find that location like this: python -c 'import numpy; print(numpy.get_include())'\n"},{"id":4,"href":"/lds-ctrl-est/docs/getting-started/windows/","title":"Windows","section":"LDS C+E Documentation","content":" Windows Installation # Windows Pre-requisites # Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:\nSet-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time iwr get.scoop.sh | Invoke-Expression Install Git and CMake if you don\u0026rsquo;t already have them:\nscoop install git cmake If that didn\u0026rsquo;t work, follow more detailed instructions here.\nThe easiest way to compile C++ project on Windows is with Visual Studio\u0026rsquo;s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on \u0026ldquo;Desktop development with C++.\u0026rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the \u0026ldquo;Installation details\u0026rdquo; sidebar under optional features.\nAnd the easiest way to use Visual Studio\u0026rsquo;s build tools is with VS Code, along with the CMake Tools extension. Install them and you should be ready to go.\nDownloading the Library # First, clone the repository, either from VS Code or the command line:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est You\u0026rsquo;ll need to initialize the submodules from the command line after the repo is cloned:\ngit submodule update --init Installation # When you open the folder in VS Code, you will like be prompted by the CMake Tools extension to configure the project. Make sure you select the kit (you\u0026rsquo;ll be prompted when you configure\u0026ndash;else there\u0026rsquo;s an icon in the bar on the bottom of the window or type Ctrl+Shift+P, then \u0026ldquo;cmake select kit\u0026rdquo;). Choose Clang [latest version] with GNU CLI ... amd64 assuming you are running a 64-bit OS. (MSVC may work okay too if you don\u0026rsquo;t need to build Python bindings.)\nFollow along with the \u0026ldquo;Getting Started\u0026rdquo; instructions, but where you see config options specified as -DLDSCTREST_BUILD_STATIC=OFF or -DPython3_ROOT_DIR=..., you will enter those in settings: open with Ctrl+,, click \u0026ldquo;workspace\u0026rdquo;, then search for \u0026ldquo;CMake: Configure Args\u0026rdquo; and enter each of your desired arguments as a separate item.\nTo configure, use Ctrl+Shift+P and search for the \u0026ldquo;CMake: Configure\u0026rdquo; command. To build, click the \u0026ldquo;Build\u0026rdquo; button on the bottom bar. Then click the \u0026ldquo;CTest\u0026rdquo; button to run the example scripts.\nConsiderations # Development on Windows has been more prone to bugs than on Unix systems, so if you encounter many problems, consider switching—WSL (Windows Subsystem for Linux) is a good option for Windows users who don\u0026rsquo;t want to work on a different machine.\nCompilation has been successfully tested in VS Code using the following kit, using the \u0026ldquo;RelWithDebInfo\u0026rdquo; config:\nClang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64) Troubleshooting # The build appears to work, but tests fail with code 0xc0000135 OR \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; Have you installed the library? In VS Code, use Shift+F7 to build a specific target, in this case INSTALL. If that doesn\u0026rsquo;t solve your problem, you will likely need to add the build or install folder to your PATH environment variable, which you can do using the settings GUI (search for \u0026ldquo;Edit the system environment variables\u0026rdquo;).\nOn Windows, \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted. Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":5,"href":"/lds-ctrl-est/issues-contributing/","title":"Issues Contributing","section":"LDS Control \u0026 Estimation","content":" Reporting Issues # If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.\nContributing # We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.\n"},{"id":6,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/","title":"armamexc","section":"Namespaces","content":" armamexc # arma/mex interface using Matlab C API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; T m2T_scalar(const mxArray * matlab_scalar)\nConvert Matlab mxArray to scalar of type T. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)\nConvert matlab matrix to armadillo. template \u0026lt;typename T \u0026gt; mxArray * a2m_mat(arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat)\nConvert armadillo to matlab matrix. template \u0026lt;typename T \u0026gt; mxArray * a2m_vec(arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec)\nConvert armadillo to matlab vector. Detailed Description # Utilities for arma/mex interface using Matlab C API\nFunction Details # m2T_scalar # template \u0026lt;class T \u0026gt; inline T m2T_scalar( const mxArray * matlab_scalar ) Parameters:\nmatlab_scalar matlab scalar Template Parameters:\nT type Return: scalar of type T\nm2a_mat # template \u0026lt;class T \u0026gt; inline arma::Mat\u0026lt; T \u0026gt; m2a_mat( const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true ) Parameters:\nmatlab_mat matlab matrix copy_aux_mem [optional] whether to copy auxiliary memory strict [optional] strictly enforce the above Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_mat( arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat ) Parameters:\narma_mat armadillo matrix Return: matlab matrix\na2m_vec # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_vec( arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec ) Parameters:\narma_vec armadillo vector Return: matlab vector\nUpdated on 5 March 2025 at 16:18:09 EST\n"},{"id":7,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/","title":"armamexcpp","section":"Namespaces","content":" armamexcpp # arma/mex interface using Matlab C++ API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat(matlab::data::CellArray \u0026amp; matlab_cell)\nConvert matlab cell array to vector of armadillo matrices. template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array)\nConvert matlab matrix to a vector of scalars. template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo vector. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat(const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec(const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab vector. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec(const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert vector of scalar T to matlab matrix. Detailed Description # utilities for arma/mex interface using Matlab C++ API\nFunction Details # m2a_cellmat # template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat( matlab::data::CellArray \u0026amp; matlab_cell ) Parameters:\nmatlab_cell matlab cell Template Parameters:\nT type Return: vector of armadillo matrices of type T\nm2s_vec # template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: vector of type T\nm2a_vec # template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: armadillo vector of type T\nm2a_mat # template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab matrix Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat( const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_mat arma matrix factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\na2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec( const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_vec armadillo vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\ns2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec( const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\nstd_vec standard vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\nUpdated on 5 March 2025 at 16:18:09 EST\n"},{"id":8,"href":"/lds-ctrl-est/docs/terminology/control-estimation/","title":"C\u0026E","section":"LDS C+E Documentation","content":" Control \u0026amp; Estimation # The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.\nState estimation # In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by \\[\\widehat{\\mathbf{x}}_{t|t-1} = \\mathbf{A}\\widehat{\\mathbf{x}}_{t-1|t-1} \u0026#43; \\mathbf{B} u_{t-1} \u0026#43; \\mathbf{m}_{t-1} \\;,\\] \\[\\widehat{\\mathbf{x}}_{t|t} = \\widehat{\\mathbf{x}}_{t|t-1} \u0026#43; \\mathbf{K}^{\\rm e}_t \\left(\\mathbf{z}_t - \\widehat{\\mathbf{y}}_{t|t-1}\\right)\\;,\\] where \\( \\hat{\\left(\\cdot\\right)}_{t|j} \\) indicates an estimate at time \\( t \\) given data up to time \\( j \\) inclusive, \\( \\mathbf{K}^{\\rm e} \\) is the estimator gain, and\n\\[ \\widehat{\\mathbf{y}}_{t|t-1} = h\\left( \\widehat{\\mathbf{x}}_{t|t-1} \\right) \\; .\\] In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.\nIn the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.\nAdaptive estimation of process disturbance # Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.\nWhen parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk \\[\\mathbf{m}_{t} = \\mathbf{m}_{t-1} \u0026#43; \\mathbf{w}^m_{t-1} \\;,\\] where \\( \\mathbf{w}^m \\sim \\mathcal{N}\\left(0, \\mathbf{Q}_m\\right)\\) . Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.\nControl # Given the estimated state, the controller updates the inputs to the system according to the following law: \\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] where \\( \\left( \\cdot \\right)^{\\rm ref} \\) correspond to reference/target signals and \\( \\mathbf{K}^c_x \\) is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.\nIf users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as\n\\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right) - \\mathbf{K}^c_{\\rm inty} \\sum_{j=1}^{t}\\left( \\widehat{\\mathbf{y}}_j - \\mathbf{y}^{\\rm ref}_j \\right) \\;.\\] An additional option available to users is a control law that updates the change in u,\n\\[\\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\left(\\mathbf{u}_{t-1} - \\mathbf{u}^{\\rm ref}_{t-1} \\right) - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] \\[\\mathbf{u}_{t} = \\mathbf{u}_{t-1} \u0026#43; \\Delta\\mathbf{u}_{t} \\; .\\] Notice that this takes the form of a first-order difference equation for updating control (i.e., \\( \\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\mathbf{u}_{t-1} \u0026#43; \\epsilon_{t-1} \\) ), effectively low-pass filtering the input depending on the characteristics of \\( \\mathbf{K}^c_u \\) . This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.\nIntegral action and the \\( \\Delta \\mathbf{u} \\) control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR\u0026rsquo;d to use in combination.\nCalculating reference state-control from output # In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce \\( \\mathbf{x}^{\\rm ref} \\) and \\( \\mathbf{u}^{\\rm ref} \\) . Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.\n"},{"id":9,"href":"/lds-ctrl-est/docs/api/classes/","title":"Classes","section":"LDS C+E Documentation","content":" Classes # lds::Controller\nlds::EM\nlds::Fit LDS Fit Type.\nlds::SSID\nlds::SwitchedController SwitchedController Type.\nlds::System Linear Dynamical System Type.\nlds::UniformMatrixList\nlds::UniformSystemList\nlds::UniformVectorList\nlds::gaussian::Controller Gaussian-observation Controller Type.\nlds::gaussian::Fit GLDS Fit Type.\nlds::gaussian::FitEM GLDS E-M Fit Type.\nlds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.\nlds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.\nlds::gaussian::System Gaussian LDS Type.\nlds::poisson::Controller PLDS Controller Type.\nlds::poisson::Fit PLDS Fit Type.\nlds::poisson::FitEM PLDS E-M Fit Type.\nlds::poisson::FitSSID Subspace Identification (SSID) for PLDS.\nlds::poisson::SwitchedController Poisson-observation SwitchedController Type.\nlds::poisson::System Poisson System type.\nUpdated on 5 March 2025 at 16:18:10 EST\n"},{"id":10,"href":"/lds-ctrl-est/docs/api/modules/group__control__masks/","title":"Control Mode Bit Masks","section":"Modules","content":" Control Mode Bit Masks # provides fill types for constructing new armadillo vectors, matrices More\u0026hellip; Attributes # Name const std::size_t kControlTypeDeltaU control designed to penalize change in input const std::size_t kControlTypeIntY control using integral action const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m Detailed Description # Control mode bit masks. These can be bit-wise OR\u0026rsquo;d to use in combination.\nAttribute Details # kControlTypeDeltaU # static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY # static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM # static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\nUpdated on 5 March 2025 at 16:18:10 EST\n"},{"id":11,"href":"/lds-ctrl-est/docs/api/modules/group__defaults/","title":"Defaults","section":"Modules","content":" Defaults # More\u0026hellip; Attributes # Name const data_t kDefaultP0 default state estimate covar const data_t kDefaultQ0 default process noise covar const data_t kDefaultR0 default output noise covar Detailed Description # Default values for common variables (e.g., default diagonal elements of covariances)\nAttribute Details # kDefaultP0 # static const data_t kDefaultP0 = 1e-6; kDefaultQ0 # static const data_t kDefaultQ0 = 1e-6; kDefaultR0 # static const data_t kDefaultR0 = 1e-2; Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":12,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/","title":"eg_glds_ctrl.cpp","section":"Examples","content":" eg_glds_ctrl.cpp # Example GLDS Control ```cpp\n//===\u0026ndash; eg_glds_ctrl.cpp - Example GLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS Control ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\n// output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false;\n// Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err\n// setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]);\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; }\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:18:10 EST "},{"id":13,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/","title":"eg_glds_du_plds_ctrl.cpp","section":"Examples","content":" eg_glds_du_plds_ctrl.cpp # Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u). ```cpp\n//===\u0026ndash; eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt);\n// to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU;\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_du_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:18:10 EST "},{"id":14,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/","title":"eg_plds_ctrl.cpp","section":"Examples","content":" eg_plds_ctrl.cpp # Example PLDS Control ```cpp\n//===\u0026ndash; eg_plds_ctrl.cpp - Example PLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Control ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt);\n// Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err\n// Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\nsize_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\nVector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled\u0026hellip; lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system);\n// for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type);\n// set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty);\n// to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0;\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros);\n// set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y();\nx_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x();\nm_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:18:10 EST "},{"id":15,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/","title":"eg_plds_est.cpp","section":"Examples","content":" eg_plds_est.cpp # Example PLDS Estimation ```cpp\n//===\u0026ndash; eg_plds_est.cpp - Example PLDS Estimation \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0);\nint main() { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.\n// construct ground truth system\u0026hellip; lds::poisson::System system_true(n_u, n_x, n_y, dt);\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset();\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt);\n// Can copy parameters from another system object system_estimator = system_true;\n// wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est);\n// set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true;\n// set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;estimator:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; system_estimator.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));\n// create matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros);\n// states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\nMatrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simlation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1));\n// Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simlation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows;\nif ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026ldquo;Q must be n x n.\u0026rdquo;); }\nMatrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); }\nreturn x; }\n_Filename: eg_plds_est.cpp_ ------------------------------- Updated on 5 March 2025 at 16:18:10 EST "},{"id":16,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/","title":"eg_plds_switched_ctrl.cpp","section":"Examples","content":" eg_plds_switched_ctrl.cpp # Example Switched PLDS Control ```cpp\n//===\u0026ndash; eg_plds_switched_ctrl.cpp - Example Switched PLDS Control \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026quot;;\n// whether to do switched control bool do_switch_ctrl = true;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt);\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices. data_t scale_sys_b = 2;\nMatrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));\ncontrolled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions\n// reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);\n// Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system);\n// set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys1:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys2:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x));\nswitched_controller.set_y_ref(y_ref0);\nstd::vectorlds::poisson::System systems_vec(3, lds::poisson::System()); lds::UniformSystemListlds::poisson::System systems(std::move(systems_vec));\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;switched_controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; switched_controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Fake measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros);\n// create Matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);\n// modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones);\n// set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } }\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace)); mode.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;mode\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_switched_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:18:10 EST "},{"id":17,"href":"/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/","title":"examples","section":"Files","content":" examples # Files # Name examples/eg_glds_ctrl.cpp examples/eg_glds_du_plds_ctrl.cpp examples/eg_plds_ctrl.cpp examples/eg_plds_est.cpp examples/eg_plds_switched_ctrl.cpp Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":18,"href":"/lds-ctrl-est/docs/api/examples/","title":"Examples","section":"LDS C+E Documentation","content":" Examples # eg_glds_ctrl.cpp Example GLDS Control.\neg_glds_du_plds_ctrl.cpp Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).\neg_plds_ctrl.cpp Example PLDS Control.\neg_plds_est.cpp Example PLDS Estimation.\neg_plds_switched_ctrl.cpp Example Switched PLDS Control.\nUpdated on 5 March 2025 at 16:18:10 EST\n"},{"id":19,"href":"/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/","title":"examples/eg_glds_ctrl.cpp","section":"Files","content":" examples/eg_glds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":20,"href":"/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/","title":"examples/eg_glds_du_plds_ctrl.cpp","section":"Files","content":" examples/eg_glds_du_plds_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU; // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":21,"href":"/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/","title":"examples/eg_plds_ctrl.cpp","section":"Files","content":" examples/eg_plds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err // Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled... lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type); // set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":22,"href":"/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/","title":"examples/eg_plds_est.cpp","section":"Files","content":" examples/eg_plds_est.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name Matrix random_walk(size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0) int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # random_walk # Matrix random_walk( size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0 ) main # int main() Source code # //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. // construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state // Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. // turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; } Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":23,"href":"/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/","title":"examples/eg_plds_switched_ctrl.cpp","section":"Files","content":" examples/eg_plds_switched_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Source code # //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 // simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions // reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":24,"href":"/lds-ctrl-est/docs/api/files/","title":"Files","section":"LDS C+E Documentation","content":" Files # examples/eg_glds_ctrl.cpp\nexamples/eg_glds_du_plds_ctrl.cpp\nexamples/eg_plds_ctrl.cpp\nexamples/eg_plds_est.cpp\nexamples/eg_plds_switched_ctrl.cpp\nldsCtrlEst_h/lds.h lds namespace\nldsCtrlEst_h/lds_ctrl.h Controller.\nldsCtrlEst_h/lds_fit.h LDS base fit type.\nldsCtrlEst_h/lds_fit_em.h subspace identification\nldsCtrlEst_h/lds_fit_ssid.h subspace identification\nldsCtrlEst_h/lds_gaussian.h glds namespace\nldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.\nldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.\nldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.\nldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.\nldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.\nldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.\nldsCtrlEst_h/lds_poisson.h plds namespace\nldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.\nldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.\nldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.\nldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.\nldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.\nldsCtrlEst_h/lds_poisson_sys.h PLDS base type.\nldsCtrlEst_h/lds_sctrl.h SwitchedController type.\nldsCtrlEst_h/lds_sys.h LDS base type.\nldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.\nldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.\nldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.\nldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)\nldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)\nsrc/lds.cpp misc lds namespace functions\nsrc/lds_gaussian_sys.cpp GLDS base type.\nsrc/lds_poisson_sys.cpp PLDS base type.\nsrc/lds_sys.cpp LDS base type.\nsrc/lds_uniform_vecs.cpp Uniformly sized vectors.\nUpdated on 5 March 2025 at 16:18:10 EST\n"},{"id":25,"href":"/lds-ctrl-est/docs/tutorials/eg_glds_control/","title":"GLDS Control","section":"LDS C+E Examples","content":" GLDS Control Tutorial # This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\] \\[\ry_{t} = x_t\r\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.\n/// Going to simulate a switching disturbance (m) acting on system size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_y).fill(m_low); Finally, assign the parameters using corresponding set-methods.\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); Creating the controller # Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.\nFor the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.\n// make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nWith the controller constructed, control variables may be set.\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.\nThe control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller\u0026rsquo;s online estimates of the output, state, and disturbance are given in purple.\n"},{"id":26,"href":"/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/","title":"include","section":"Files","content":" include # Directories # Name ldsCtrlEst_h Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":27,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds/","title":"lds","section":"Namespaces","content":" lds # Linear Dynamical Systems (LDS) namespace. Namespaces # Name lds::gaussian Linear Dynamical Systems with Gaussian observations. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::Controller class lds::EM class lds::Fit LDS Fit Type. class lds::SSID class lds::SwitchedController SwitchedController Type. class lds::System Linear Dynamical System Type. class lds::UniformMatrixList class lds::UniformSystemList class lds::UniformVectorList Types # Name enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}\nweighting options for SSID enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2} using double data_t using arma::Col\u0026lt; data_t \u0026gt; Vector using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Cube\u0026lt; data_t \u0026gt; Cube using arma::subview\u0026lt; data_t \u0026gt; View Functions # Name void Limit(std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub) void Limit(Vector \u0026amp; x, data_t lb, data_t ub) void Limit(Matrix \u0026amp; x, data_t lb, data_t ub) void Reassign(Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Vector in place void Reassign(Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Matrix in place void ForceSymPD(Matrix \u0026amp; X)\nforces matrix to be symmetric positive-definite void ForceSymMinEig(Matrix \u0026amp; X, data_t eig_min =0)\nforces matrix to be symmetric and have a minimum eigenvalue void lq(Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X)\nLQ decomposition. Matrix calcCov(const Matrix \u0026amp; A, const Matrix \u0026amp; B)\nCalculate covariance matrix. Attributes # Name const data_t kInf Some useful numbers. const data_t kPi Type Details # SSIDWt # Enumerator Value Description kSSIDNone None. kSSIDMOESP MOESP (AKA \u0026ldquo;robust method\u0026rdquo; in van Overschee 1996) kSSIDCVA CVA \u0026ldquo;Canonical Variate Analysis\u0026rdquo;. Weighting options for singular value decomposition performed during subspace identification (SSID)\nReference:\nvan Overschee, de Moor. 1996. Subspace Identification for Linear Systems.\nMatrixListFreeDim # Enumerator Value Description kMatFreeDimNone neither dim free to be hetero in mat list kMatFreeDim1 allow 1st dim of mats in list to be hetero kMatFreeDim2 allow 2nd dim of mats in list to be hetero data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nVector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Cube # using lds::Cube = arma::Cube\u0026lt;data_t\u0026gt;; View # using lds::View = arma::subview\u0026lt;data_t\u0026gt;; Function Details # Limit # inline void Limit( std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Vector \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Matrix \u0026amp; x, data_t lb, data_t ub ) Reassign # inline void Reassign( Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Vector other other Vector parenthetical optional description provided by caller to ease debugging Reassign # inline void Reassign( Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Matrix other other Matrix parenthetical optional description provided by caller to ease debugging ForceSymPD # void ForceSymPD( Matrix \u0026amp; X ) Parameters:\nX mutated matrix ForceSymMinEig # void ForceSymMinEig( Matrix \u0026amp; X, data_t eig_min =0 ) Parameters:\nX mutated matrix eig_min [optional] minimum eigen value lq # void lq( Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X ) Parameters:\nL lower triangle matrix Qt orthonormal matrix (transposed cf QR decomp) X matrix being decomposed calcCov # Matrix calcCov( const Matrix \u0026amp; A, const Matrix \u0026amp; B ) Parameters:\nA some matrix B some other matrix Return: covariance\nAttribute Details # kInf # static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); kPi # static const data_t kPi = arma::datum::pi; Updated on 5 March 2025 at 16:18:09 EST\n"},{"id":28,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/","title":"lds::Controller","section":"Classes","content":" lds::Controller # More\u0026hellip;\nInherited by lds::SwitchedController\u0026lt; System \u0026gt;, lds::gaussian::Controller, lds::poisson::Controller\nPublic Functions # Name Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes # Name System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::Controller; Public Function Details # Controller # Controller() =default Controller # inline Controller( const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Controller # inline Controller( System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Control # inline const Vector \u0026amp; Control( const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step). This is the most flexible option, but requires user to have set the controller\u0026rsquo;s y_ref, x_ref, and u_ref variables.\nControlOutputReference # inline const Vector \u0026amp; ControlOutputReference( const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_estimation [optional] whether to update state estimate (if false, effectively open-loop control) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.\nsys # inline const System \u0026amp; sys() const Kc # inline const Matrix \u0026amp; Kc() const Kc_inty # inline const Matrix \u0026amp; Kc_inty() const Kc_u # inline const Matrix \u0026amp; Kc_u() const g_design # inline const Vector \u0026amp; g_design() const u_ref # inline const Vector \u0026amp; u_ref() const x_ref # inline const Vector \u0026amp; x_ref() const y_ref # inline const Vector \u0026amp; y_ref() const control_type # inline size_t control_type() const tau_awu # inline data_t tau_awu() const u_lb # inline data_t u_lb() const u_ub # inline data_t u_ub() const set_sys # inline void set_sys( const System \u0026amp; sys ) set_g_design # inline void set_g_design( const Vector \u0026amp; g_design ) set_u_ref # inline void set_u_ref( const Vector \u0026amp; u_ref ) set_x_ref # inline void set_x_ref( const Vector \u0026amp; x_ref ) set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) Reimplemented by: lds::gaussian::Controller::set_y_ref, lds::gaussian::SwitchedController::set_y_ref, lds::poisson::Controller::set_y_ref, lds::poisson::SwitchedController::set_y_ref\nset_Kc # inline void set_Kc( const Matrix \u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const Matrix \u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const Matrix \u0026amp; Kc_u ) set_tau_awu # inline void set_tau_awu( data_t tau ) set_control_type # inline void set_control_type( size_t control_type ) Parameters:\ncontrol_type control type bit mask Template Parameters:\nSystem type derived from lds::System set_u_lb # inline void set_u_lb( data_t u_lb ) Parameters:\nu_lb control lower bound set_u_ub # inline void set_u_ub( data_t u_ub ) Parameters:\nu_ub control upper bound Reset # inline void Reset() Print # inline void Print() Protected Attribute Details # sys_ # System sys_; u_ # Vector u_; u_return_ # Vector u_return_; g_design_ # Vector g_design_; u_ref_ # Vector u_ref_; u_ref_prev_ # Vector u_ref_prev_; x_ref_ # Vector x_ref_; y_ref_ # Vector y_ref_; cx_ref_ # Vector cx_ref_; Kc_ # Matrix Kc_; Kc_u_ # Matrix Kc_u_; Kc_inty_ # Matrix Kc_inty_; du_ref_ # Vector du_ref_; dv_ref_ # Vector dv_ref_; v_ref_ # Vector v_ref_; dv_ # Vector dv_; v_ # Vector v_; int_e_ # Vector int_e_; int_e_awu_adjust_ # Vector int_e_awu_adjust_; u_sat_ # Vector u_sat_; do_control_prev_ # bool do_control_prev_ = false; do_lock_control_prev_ # bool do_lock_control_prev_ = false; u_saturated_ # bool u_saturated_ = false; u_lb_ # data_t u_lb_ {}; u_ub_ # data_t u_ub_ {}; tau_awu_ # data_t tau_awu_ {}; k_awu_ # data_t k_awu_ = 0; t_since_control_onset_ # data_t t_since_control_onset_ = 0; control_type_ # size_t control_type_ {}; Updated on 5 March 2025 at 16:18:09 EST\n"},{"id":29,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/","title":"lds::EM","section":"Classes","content":" lds::EM # More\u0026hellip;\nInherited by lds::gaussian::FitEM, lds::poisson::FitEM\nPublic Functions # Name EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions # Name void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() virtual void MaximizeOutput() =0 virtual void MaximizeMeasurement() =0 void Smooth(bool force_common_initial)\nget smoothed estimates virtual void RecurseKe(Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t) =0\nrecursively update estimator gain Ke void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::EM; Public Function Details # EM # EM() =default EM # EM( size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nn_x number of states dt sample period u_train input training data z_train measurement training data EM # EM( const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nfit0 initial fit u_train input training data z_train measurement training data ~EM # virtual ~EM() =default Run # const Fit \u0026amp; Run( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2 ) Parameters:\ncalc_dynamics [optional] whether to calculate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law max_iter max number of iterations tol convergence tolerance (max fractional abs change) Return: Fit\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nx # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const y # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const sum_E_x_t_x_t # inline const Matrix \u0026amp; sum_E_x_t_x_t() const sum_E_xu_tm1_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const sum_E_xu_t_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const n_t_tot # inline size_t n_t_tot() theta # inline const Vector \u0026amp; theta() const Protected Function Details # Expectation # void Expectation( bool force_common_initial =false ) Parameters:\nforce_common_initial whether to force common initial condition for all trials Maximization # void Maximization( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false ) Parameters:\ncalc_dynamics [optional] whether to caclulate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law MaximizeDynamics # void MaximizeDynamics() MaximizeQ # void MaximizeQ() MaximizeInitial # void MaximizeInitial() MaximizeOutput # virtual void MaximizeOutput() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput\nMaximizeMeasurement # virtual void MaximizeMeasurement() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement\nSmooth # void Smooth( bool force_common_initial ) Parameters:\nforce_common_initial whether to force common initial conditions RecurseKe # virtual void RecurseKe( Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t ) =0 Parameters:\nKe estimator gain P_pre cov of predicted state est. P_post cov of postior sate est. t time Reimplemented by: lds::gaussian::FitEM::RecurseKe, lds::poisson::FitEM::RecurseKe\nReset # void Reset() InitVars # void InitVars() UpdateTheta # Vector UpdateTheta() Return: parameter list\nProtected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; x_ # std::vector\u0026lt; Matrix \u0026gt; x_; P_ # std::vector\u0026lt; Cube \u0026gt; P_; P_t_tm1_ # std::vector\u0026lt; Cube \u0026gt; P_t_tm1_; y_ # std::vector\u0026lt; Matrix \u0026gt; y_; diag_y_ # Matrix diag_y_; sum_E_x_t_x_t_ # Matrix sum_E_x_t_x_t_; sum_E_xu_tm1_xu_tm1_ # Matrix sum_E_xu_tm1_xu_tm1_; sum_E_xu_t_xu_tm1_ # Matrix sum_E_xu_t_xu_tm1_; fit_ # Fit fit_; theta_ # Vector theta_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; Updated on 5 March 2025 at 16:18:09 EST\n"},{"id":30,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/","title":"lds::Fit","section":"Classes","content":" lds::Fit # LDS Fit Type. #include \u0026lt;lds_fit.h\u0026gt;\nInherited by lds::gaussian::Fit, lds::poisson::Fit\nPublic Functions # Name Fit() =default\nConstructs a new Fit. Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias virtual const Matrix \u0026amp; R() const =0 void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance virtual void set_R(const Matrix \u0026amp; R) =0\nsets output noise covariance (if any) void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) =0\noutput function Protected Attributes # Name data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period ~Fit # virtual ~Fit() =default n_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const m # inline const Vector \u0026amp; m() const Q # inline const Matrix \u0026amp; Q() const x0 # inline const Vector \u0026amp; x0() const P0 # inline const Matrix \u0026amp; P0() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const R # virtual const Matrix \u0026amp; R() const =0 Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R\nset_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_g # inline void set_g( const Vector \u0026amp; g ) set_m # inline void set_m( const Vector \u0026amp; m ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # virtual void set_R( const Matrix \u0026amp; R ) =0 Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R\nset_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) f # inline View f( Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t ) Parameters:\nx state estimate (over time) u input (over time) t time index Return: view of updated state\nf # inline View f( Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t ) Parameters:\nx_pre predicted state est. x_post posterior state est. u input (over time) t time index Return: view of predicted state\nh # virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) =0 Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplemented by: lds::gaussian::Fit::h, lds::poisson::Fit::h\nProtected Attribute Details # dt_ # data_t dt_ {}; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; m_ # Vector m_; Q_ # Matrix Q_; C_ # Matrix C_; d_ # Vector d_; R_ # Matrix R_; x0_ # Vector x0_; P0_ # Matrix P0_; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; Updated on 5 March 2025 at 16:18:09 EST\n"},{"id":31,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/","title":"lds::gaussian","section":"Namespaces","content":" lds::gaussian # Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. class lds::gaussian::Fit GLDS Fit Type. class lds::gaussian::FitEM GLDS E-M Fit Type. class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. class lds::gaussian::System Gaussian LDS Type. Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":32,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/","title":"lds::gaussian::Controller","section":"Classes","content":" lds::gaussian::Controller # Gaussian-observation Controller Type. #include \u0026lt;lds_gaussian_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:18:10 EST\n"},{"id":33,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/","title":"lds::gaussian::Fit","section":"Classes","content":" lds::gaussian::Fit # GLDS Fit Type. #include \u0026lt;lds_gaussian_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual const Matrix \u0026amp; R() const override\ngets measurement noise covariance virtual void set_R(const Matrix \u0026amp; R) override\nsets measurement noise covariance virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period R # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nh # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nUpdated on 5 March 2025 at 16:18:10 EST\n"},{"id":34,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/","title":"lds::gaussian::FitEM","section":"Classes","content":" lds::gaussian::FitEM # GLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_gaussian_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::gaussian::FitEM; This type is used in the process of fitting GLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":35,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/","title":"lds::gaussian::FitSSID","section":"Classes","content":" lds::gaussian::FitSSID # Subspace Identification (SSID) for GLDS. #include \u0026lt;lds_gaussian_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":36,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/","title":"lds::gaussian::SwitchedController","section":"Classes","content":" lds::gaussian::SwitchedController # Gaussian-observation SwitchedController Type. #include \u0026lt;lds_gaussian_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:18:10 EST\n"},{"id":37,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/","title":"lds::gaussian::System","section":"Classes","content":" lds::gaussian::System # Gaussian LDS Type. #include \u0026lt;lds_gaussian_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)\nConstructs a new Gaussian System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. const Matrix \u0026amp; R() const\nGet output noise covariance. void set_Q(const Matrix \u0026amp; Q) void set_R(const Matrix \u0026amp; R)\nSet output noise covariance. void set_Ke(const Matrix \u0026amp; Ke)\nSet estimator gain. void set_Ke_m(const Matrix \u0026amp; Ke_m)\nSet disturbance estimator gain. void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively update estimator gain. Protected Attributes # Name Matrix R_ covariance of output noise bool do_recurse_Ke_ whether to recursively calculate estimator gain Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0 ) Parameters:\nn_u number of inputs (u) n_x number of states (x) n_y number of outputs (y) dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) r0 [optional] initial diagonal elements of output noise covariance (R) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nR # inline const Matrix \u0026amp; R() const set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # inline void set_R( const Matrix \u0026amp; R ) set_Ke # inline void set_Ke( const Matrix \u0026amp; Ke ) set_Ke_m # inline void set_Ke_m( const Matrix \u0026amp; Ke_m ) Print # void Print() Protected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nProtected Attribute Details # R_ # Matrix R_; do_recurse_Ke_ # bool do_recurse_Ke_ {}; Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":38,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/","title":"lds::poisson","section":"Namespaces","content":" lds::poisson # Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. class lds::poisson::Fit PLDS Fit Type. class lds::poisson::FitEM PLDS E-M Fit Type. class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. class lds::poisson::System Poisson System type. Attributes # Name std::random_device rd random device for simulating poisson data std::mt19937 rng random number generator for simulating poisson data Attribute Details # rd # static std::random_device rd; rng # static std::mt19937 rng = std::mt19937( rd()); Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":39,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/","title":"lds::poisson::Controller","section":"Classes","content":" lds::poisson::Controller # PLDS Controller Type. #include \u0026lt;lds_poisson_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:18:10 EST\n"},{"id":40,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/","title":"lds::poisson::Fit","section":"Classes","content":" lds::poisson::Fit # PLDS Fit Type. #include \u0026lt;lds_poisson_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function virtual void set_R(const Matrix \u0026amp; R) override\nsets output noise covariance (if any) virtual const Matrix \u0026amp; R() const override Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # inline Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period h # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nR # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nUpdated on 5 March 2025 at 16:18:10 EST\n"},{"id":41,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/","title":"lds::poisson::FitEM","section":"Classes","content":" lds::poisson::FitEM # PLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_poisson_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::poisson::FitEM; This type is used in the process of fitting PLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":42,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/","title":"lds::poisson::FitSSID","section":"Classes","content":" lds::poisson::FitSSID # Subspace Identification (SSID) for PLDS. #include \u0026lt;lds_poisson_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":43,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/","title":"lds::poisson::SwitchedController","section":"Classes","content":" lds::poisson::SwitchedController # Poisson-observation SwitchedController Type. #include \u0026lt;lds_poisson_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:18:10 EST\n"},{"id":44,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/","title":"lds::poisson::System","section":"Classes","content":" lds::poisson::System # Poisson System type. #include \u0026lt;lds_poisson_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nConstructs a new Poisson System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively recalculate estimator gain (Ke) Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nProtected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nRecursively recalculate estimator gain (Ke).\nReferences:\nSmith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.\nEden UT, \u0026hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.\nUpdated on 5 March 2025 at 16:18:10 EST\n"},{"id":45,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/","title":"lds::SSID","section":"Classes","content":" lds::SSID # More\u0026hellip;\nInherited by lds::gaussian::FitSSID, lds::poisson::FitSSID\nPublic Functions # Name SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions # Name void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. virtual void DecomposeData() =0\nDecompose data to lower-triangular matrix (used in Solve) void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::SSID; Public Function Details # SSID # SSID() =default SSID # SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf) ) Parameters:\nn_x number of states n_h size of block-hankel data matrix dt sample period u_train input training data z_train measurement training data d output bias Run # std::tuple\u0026lt; Fit, Vector \u0026gt; Run( SSIDWt ssid_wt ) Parameters:\nssid_wt weight for singular value decomp Return: tuple (Fit, singular values)\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nProtected Function Details # CalcD # void CalcD( data_t t_silence =0.1, data_t thresh_silence =0.001 ) Parameters:\nt_silence threshold on period of time that qualifies as \u0026ldquo;silence\u0026rdquo; thresh_silence threshold on input amplitude u that qualifies as \u0026ldquo;silence\u0026rdquo; CreateHankelDataMat # void CreateHankelDataMat() Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.\nDecomposeData # virtual void DecomposeData() =0 Reimplemented by: lds::gaussian::FitSSID::DecomposeData, lds::poisson::FitSSID::DecomposeData\nCalcSVD # void CalcSVD( SSIDWt wt ) Parameters:\nssid_wt weight for SVD Solve # void Solve( data_t wt_dc ) Parameters:\nwt_dc weight placed on getting correct DC I/O gain RecomputeExtObs # void RecomputeExtObs() Protected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; D_ # Matrix D_; fit_ # Fit fit_; g_dc_ # Matrix g_dc_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_h_ # size_t n_h_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; L_ # Matrix L_; s_ # Vector s_; ext_obs_t_ # Matrix ext_obs_t_; Updated on 5 March 2025 at 16:18:09 EST\n"},{"id":46,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/","title":"lds::SwitchedController","section":"Classes","content":" lds::SwitchedController # SwitchedController Type. More\u0026hellip;\n#include \u0026lt;lds_sctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nInherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController\nPublic Functions # Name SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes # Name std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::SwitchedController; Public Function Details # SwitchedController # SwitchedController() =default SwitchedController # inline SwitchedController( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask SwitchedController # inline SwitchedController( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Switch # inline void Switch( size_t idx, bool do_force_switch =false ) Parameters:\nidx index do_force_switch whether to force a system switch even if already there. set_Kc # inline void set_Kc( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc ) set_Kc # inline void set_Kc( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty ) set_Kc_inty # inline void set_Kc_inty( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u ) set_Kc_u # inline void set_Kc_u( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u ) set_g_design # inline void set_g_design( const UniformVectorList \u0026amp; g ) set_g_design # inline void set_g_design( UniformVectorList \u0026amp;\u0026amp; g ) Protected Attribute Details # systems_ # std::vector\u0026lt; System \u0026gt; systems_; n_sys_ # size_t n_sys_ {}; idx_ # size_t idx_ {}; Kc_list_ # UniformMatrixList Kc_list_; Kc_inty_list_ # UniformMatrixList Kc_inty_list_; Kc_u_list_ # UniformMatrixList Kc_u_list_; g_design_list_ # UniformVectorList g_design_list_; Updated on 5 March 2025 at 16:18:09 EST\n"},{"id":47,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_system/","title":"lds::System","section":"Classes","content":" lds::System # Linear Dynamical System Type. #include \u0026lt;lds_sys.h\u0026gt;\nInherited by lds::gaussian::System, lds::poisson::System\nPublic Functions # Name System() =default\nConstructs a new System. System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nconstructs a new System virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) =0\nsimulates system (single time step) void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function virtual void h() =0\nsystem output function virtual Vector h_(Vector x) =0\nsystem output function (stateless) size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void RecurseKe() =0\nRecursively recalculate estimator gain (Ke) void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes # Name bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes # Name std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 diagonal elements for state estimate covariance q0 diagonal elements for process noise covariance ~System # inline virtual ~System() Filter # void Filter( const Vector \u0026amp; u_tm1, const Vector \u0026amp; z ) Parameters:\nu_tm1 input at t-minus-1 z_t current measurement Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.\nSimulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) =0 Parameters:\nu_tm1 input at time t-1 Return: simulated measurement at time t\nReimplemented by: lds::gaussian::System::Simulate, lds::poisson::System::Simulate\nf # inline void f( const Vector \u0026amp; u, bool do_add_noise =false ) Parameters:\nu input do_add_noise whether to add simulated process noise h # virtual void h() =0 Reimplemented by: lds::gaussian::System::h, lds::poisson::System::h\nh_ # virtual Vector h_( Vector x ) =0 Parameters:\nx_t state at time t Return: predicted state at time t + 1\nReimplemented by: lds::gaussian::System::h_, lds::poisson::System::h_\nn_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const x # inline const Vector \u0026amp; x() const P # inline const Matrix \u0026amp; P() const m # inline const Vector \u0026amp; m() const P_m # inline const Matrix \u0026amp; P_m() const cx # inline const Vector \u0026amp; cx() const y # inline const Vector \u0026amp; y() const x0 # inline const Vector \u0026amp; x0() const m0 # inline const Vector \u0026amp; m0() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const Ke # inline const Matrix \u0026amp; Ke() const Ke_m # inline const Matrix \u0026amp; Ke_m() const Q # inline const Matrix \u0026amp; Q() Q_m # inline const Matrix \u0026amp; Q_m() P0 # inline const Matrix \u0026amp; P0() P0_m # inline const Matrix \u0026amp; P0_m() set_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_m # inline void set_m( const Vector \u0026amp; m, bool do_force_assign =false ) set_g # inline void set_g( const Vector \u0026amp; g ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_Q_m # inline void set_Q_m( const Matrix \u0026amp; Q_m ) set_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_P0_m # inline void set_P0_m( const Matrix \u0026amp; P0_m ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) set_x # inline void set_x( const Vector \u0026amp; x ) Reset # void Reset() nstep_pred_block # std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block( UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1 ) Print # void Print() Protected Function Details # RecurseKe # virtual void RecurseKe() =0 Reimplemented by: lds::gaussian::System::RecurseKe, lds::poisson::System::RecurseKe\nInitVars # void InitVars( data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Public Attribute Details # do_adapt_m # bool do_adapt_m {}; Protected Attribute Details # n_x_ # std::size_t n_x_ {}; n_u_ # std::size_t n_u_ {}; n_y_ # std::size_t n_y_ {}; dt_ # data_t dt_ {}; x_ # Vector x_; P_ # Matrix P_; m_ # Vector m_; P_m_ # Matrix P_m_; cx_ # Vector cx_; y_ # Vector y_; z_ # Vector z_; x0_ # Vector x0_; P0_ # Matrix P0_; m0_ # Vector m0_; P0_m_ # Matrix P0_m_; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; Q_ # Matrix Q_; Q_m_ # Matrix Q_m_; C_ # Matrix C_; d_ # Vector d_; Ke_ # Matrix Ke_; Ke_m_ # Matrix Ke_m_; Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":48,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/","title":"lds::UniformMatrixList","section":"Classes","content":" lds::UniformMatrixList # More\u0026hellip;\nInherits from std::vector\u0026lt; Matrix \u0026gt;\nPublic Functions # Name UniformMatrixList() =default\nConstructs a new UniformMatrixList. UniformMatrixList(const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent. UniformMatrixList(std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent. UniformMatrixList(std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent. UniformMatrixList(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nConstructs a new UniformMatrixList (copy). UniformMatrixList(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nConstructs a new UniformMatrixList (move). ~UniformMatrixList() =default\nDestroys the object. const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim(size_t n =0) const\ngets dimensions of uniformly sized matrices size_t size()\nsize of container const Matrix \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Matrix \u0026amp; that, size_t n)\nswaps input matrix with n^th matrix of list UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nassigns the contents (copy) UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nassigns the contents (move) void append(const Matrix \u0026amp; mat)\nappends a matrix to the list Detailed Description # template \u0026lt;MatrixListFreeDim D =kMatFreeDimNone\u0026gt; class lds::UniformMatrixList; Public Function Details # UniformMatrixList # UniformMatrixList() =default UniformMatrixList # explicit UniformMatrixList( const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # explicit UniformMatrixList( std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList UniformMatrixList # UniformMatrixList( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList ~UniformMatrixList # ~UniformMatrixList() =default dim # inline const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim( size_t n =0 ) const Parameters:\nn [optional] index in list of matrices Return: dimensions\nsize # inline size_t size() at # inline const Matrix \u0026amp; at( size_t n ) Swap # inline void Swap( Matrix \u0026amp; that, size_t n ) Parameters:\nthat input matrix n index where the matrix is moved operator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\noperator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\nappend # void append( const Matrix \u0026amp; mat ) Parameters:\nmat input matrix Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":49,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/","title":"lds::UniformSystemList","section":"Classes","content":" lds::UniformSystemList # More\u0026hellip;\nInherits from std::vector\u0026lt; System \u0026gt;\nPublic Functions # Name UniformSystemList() =default\nConstructs a new UniformSystemList. UniformSystemList(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by copying existing vector of System if dimensions consistent. UniformSystemList(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by moving existing vector of System if dimensions consistent. UniformSystemList(std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList from initializer_list of System if dimensions consistent. UniformSystemList(const UniformSystemList \u0026amp; that)\nConstructs a new UniformSystemList (copy). UniformSystemList(UniformSystemList \u0026amp;\u0026amp; that)\nConstructs a new UniformSystemList (move). ~UniformSystemList() =default\nDestroys the object. const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const\ngets dimensions of the uniformly sized systems size_t size()\nsize of container const System \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(System \u0026amp; that, size_t n)\nswaps input system with n^th system of list UniformSystemList \u0026amp; operator=(const UniformSystemList \u0026amp; that)\nassigns the contents (copy) UniformSystemList \u0026amp; operator=(UniformSystemList \u0026amp;\u0026amp; that)\nassigns the contents (move) Detailed Description # template \u0026lt;typename System \u0026gt; class lds::UniformSystemList; Public Function Details # UniformSystemList # UniformSystemList() =default UniformSystemList # explicit UniformSystemList( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # explicit UniformSystemList( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList UniformSystemList # UniformSystemList( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList ~UniformSystemList # ~UniformSystemList() =default dim # inline const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const size # inline size_t size() at # inline const System \u0026amp; at( size_t n ) Swap # inline void Swap( System \u0026amp; that, size_t n ) Parameters:\nthat input system n index where the system is moved operator= # inline UniformSystemList \u0026amp; operator=( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\noperator= # inline UniformSystemList \u0026amp; operator=( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\nUpdated on 5 March 2025 at 16:18:10 EST\n"},{"id":50,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/","title":"lds::UniformVectorList","section":"Classes","content":" lds::UniformVectorList # Inherits from std::vector\u0026lt; Vector \u0026gt;\nPublic Functions # Name UniformVectorList() =default\nConstructs a new UniformVectorList. UniformVectorList(const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent. UniformVectorList(std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent. UniformVectorList(std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0)\nConstructs a new UniformVectorList from initializer_list of Vector if dimensions consistent. UniformVectorList(const UniformVectorList \u0026amp; that)\nConstructs a new UniformVectorList (copy) UniformVectorList(UniformVectorList \u0026amp;\u0026amp; that)\nConstructs a new UniformVectorList (move) ~UniformVectorList() =default\nDestroys the object. size_t dim() const\ngets dimensions of the uniformly sized matrices size_t size()\nsize of container const Vector \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Vector \u0026amp; that, size_t n)\nswaps input matrix with n^th vector of list UniformVectorList \u0026amp; operator=(const UniformVectorList \u0026amp; that)\nassigns the contents (copy) UniformVectorList \u0026amp; operator=(UniformVectorList \u0026amp;\u0026amp; that)\nassigns the contents (move) Public Function Details # UniformVectorList # UniformVectorList() =default UniformVectorList # explicit UniformVectorList( const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dims dimension UniformVectorList # explicit UniformVectorList( std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList UniformVectorList # UniformVectorList( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList ~UniformVectorList # ~UniformVectorList() =default dim # inline size_t dim() const size # inline size_t size() at # inline const Vector \u0026amp; at( size_t n ) Swap # inline void Swap( Vector \u0026amp; that, size_t n ) Parameters:\nthat input vector n index where the vector is moved operator= # inline UniformVectorList \u0026amp; operator=( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\noperator= # inline UniformVectorList \u0026amp; operator=( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\nUpdated on 5 March 2025 at 16:18:10 EST\n"},{"id":51,"href":"/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/","title":"ldsCtrlEst_h","section":"Files","content":" ldsCtrlEst_h # Files # Name ldsCtrlEst_h/lds.h lds namespace ldsCtrlEst_h/lds_ctrl.h Controller. ldsCtrlEst_h/lds_fit.h LDS base fit type. ldsCtrlEst_h/lds_fit_em.h subspace identification ldsCtrlEst_h/lds_fit_ssid.h subspace identification ldsCtrlEst_h/lds_gaussian.h glds namespace ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller. ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type. ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type. ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type. ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type. ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type. ldsCtrlEst_h/lds_poisson.h plds namespace ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type. ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type. ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type. ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type. ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type. ldsCtrlEst_h/lds_poisson_sys.h PLDS base type. ldsCtrlEst_h/lds_sctrl.h SwitchedController type. ldsCtrlEst_h/lds_sys.h LDS base type. ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices. ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems. ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors. ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":52,"href":"/lds-ctrl-est/docs/api/files/lds__ctrl_8h/","title":"ldsCtrlEst_h/lds_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_ctrl.h # Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Controller Detailed Description # This file declares the type for control of a linear dynamical system (lds::Controller).\nSource code # //===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_CTRL_H #define LDSCTRLEST_LDS_CTRL_H // namespace #include \u0026#34;lds.h\u0026#34; // system type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class Controller { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); public: Controller() = default; Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); const Vector\u0026amp; Control(const Vector\u0026amp; z, bool do_control = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); const Vector\u0026amp; ControlOutputReference(const Vector\u0026amp; z, bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); // get methods: const System\u0026amp; sys() const { return sys_; }; const Matrix\u0026amp; Kc() const { return Kc_; }; const Matrix\u0026amp; Kc_inty() const { return Kc_inty_; }; const Matrix\u0026amp; Kc_u() const { return Kc_u_; }; const Vector\u0026amp; g_design() const { return g_design_; }; const Vector\u0026amp; u_ref() const { return u_ref_; }; const Vector\u0026amp; x_ref() const { return x_ref_; }; const Vector\u0026amp; y_ref() const { return y_ref_; }; size_t control_type() const { return control_type_; }; data_t tau_awu() const { return tau_awu_; }; data_t u_lb() const { return u_lb_; }; data_t u_ub() const { return u_ub_; }; // set methods void set_sys(const System\u0026amp; sys) { bool does_match = sys_.n_u() == sys.n_u(); does_match = does_match \u0026amp;\u0026amp; (sys_.n_x() == sys.n_x()); does_match = does_match \u0026amp;\u0026amp; (sys_.n_y() == sys.n_y()); if (does_match) { sys_ = sys; } else { throw std::runtime_error( \u0026#34;new system argument to `set_sys` does not match dimensionality of \u0026#34; \u0026#34;existing system\u0026#34;); } }; void set_g_design(const Vector\u0026amp; g_design) { Reassign(g_design_, g_design); }; void set_u_ref(const Vector\u0026amp; u_ref) { Reassign(u_ref_, u_ref); }; void set_x_ref(const Vector\u0026amp; x_ref) { Reassign(x_ref_, x_ref); cx_ref_ = sys_.C() * x_ref_; }; // y_ref needs to be handled differently depending on output fn. // (need to populate cx_ref_ too, which depends on output fn) virtual void set_y_ref(const Vector\u0026amp; y_ref) { Reassign(y_ref_, y_ref); }; void set_Kc(const Matrix\u0026amp; Kc) { Reassign(Kc_, Kc); }; void set_Kc_inty(const Matrix\u0026amp; Kc_inty) { Reassign(Kc_inty_, Kc_inty); }; void set_Kc_u(const Matrix\u0026amp; Kc_u) { Reassign(Kc_u_, Kc_u); }; void set_tau_awu(data_t tau) { tau_awu_ = tau; k_awu_ = sys_.dt() / tau_awu_; }; void set_control_type(size_t control_type); // There is no reason u_lb/ub should not be public, but making set methods // anyway. void set_u_lb(data_t u_lb) { u_lb_ = u_lb; }; void set_u_ub(data_t u_ub) { u_ub_ = u_ub; }; void Reset() { sys_.Reset(); u_ref_.zeros(); u_ref_prev_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); u_saturated_ = false; t_since_control_onset_ = 0.0; }; void Print() { sys_.Print(); std::cout \u0026lt;\u0026lt; \u0026#34;g_design : \u0026#34; \u0026lt;\u0026lt; g_design_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_lb : \u0026#34; \u0026lt;\u0026lt; u_lb_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_ub : \u0026#34; \u0026lt;\u0026lt; u_ub_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }; protected: System sys_; Vector u_; Vector u_return_; Vector g_design_; // reference signals Vector u_ref_; // create no set method for this: Vector u_ref_prev_; Vector x_ref_; Vector y_ref_; Vector cx_ref_; // Controller gains Matrix Kc_; Matrix Kc_u_; Matrix Kc_inty_; // control after g inversion // do not need set methods for these. Vector du_ref_; Vector dv_ref_; Vector v_ref_; Vector dv_; Vector v_; // integral error // do not need set method for this Vector int_e_; Vector int_e_awu_adjust_; Vector u_sat_; bool do_control_prev_ = false; bool do_lock_control_prev_ = false; // whether the g of system has become inverted from what you think it is // (gain_ref) bool u_saturated_ = false; // should be safe to have references here bc nothing needs to be done // (like reset vars) when it changes... data_t u_lb_{}; data_t u_ub_{}; data_t tau_awu_{}; data_t k_awu_ = 0; data_t t_since_control_onset_ = 0; size_t control_type_{}; private: void CalcControl(bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); void CalcSteadyStateSetPoint(); void AntiWindup(); void InitVars(size_t control_type); }; // Implement the above: template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(sys), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(std::move(sys)), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::set_control_type(size_t control_type) { if (control_type_ == control_type) { return; } // creating a blank slate... control_type_ = 0; Kc_inty_.zeros(0, 0); Kc_u_.zeros(0, 0); int_e_.zeros(0, 0); int_e_awu_adjust_.zeros(0, 0); // controller was designed to minimize integral error if (control_type \u0026amp; kControlTypeIntY) { Kc_inty_.zeros(sys_.n_u(), sys_.n_y()); int_e_.zeros(sys_.n_y()); int_e_awu_adjust_.zeros(sys_.n_u()); control_type_ = control_type_ | kControlTypeIntY; } // controller was designed to minimize deltaU // (i.e. state augmented with u) if (control_type \u0026amp; kControlTypeDeltaU) { Kc_u_.zeros(sys_.n_u(), sys_.n_u()); control_type_ = control_type_ | kControlTypeDeltaU; } // whether to adapt set point calculate with (re-estimated) process // disturbance (m) if (control_type \u0026amp; kControlTypeAdaptM) { if (sys_.do_adapt_m) // only if adapting m... { control_type_ = control_type_ | kControlTypeAdaptM; } } } // set_control_type template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::Control( const Vector\u0026amp; z, bool do_control, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement sys_.Filter(u_, z); bool do_estimation = true; // always have estimator on in this case // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::ControlOutputReference( const Vector\u0026amp; z, bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement if (do_estimation) { sys_.Filter(u_, z); } else { sys_.f(u_); } // calculate the set point // solves for u_ref and x_ref when output is at y_ref at steady state. if (do_control) { CalcSteadyStateSetPoint(); } // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcControl(bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { if (do_control \u0026amp;\u0026amp; do_estimation) { if (!do_control_prev_) { if (do_reset_at_control_onset) { Reset(); } t_since_control_onset_ = 0.0; } else { t_since_control_onset_ += sys_.dt(); } // enforce softstart on control vars. if (sigma_soft_start \u0026gt; 0) { // half-Gaussian soft-start scaling factor data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) / (2 * pow(sigma_soft_start, 2))); u_ref_ *= soft_start_sf; // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too // x_ref_ *= soft_start_sf; // cx_ref_ *= soft_start_sf; // y_ref_ *= soft_start_sf; } if (!do_lock_control) { // first do u -\u0026gt; v change of vars. (v = g.*u) // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver // control voltage u[=]V) v_ref_ = g_design_ % u_ref_; // Given FB, calc. the change in control if (control_type_ \u0026amp; kControlTypeDeltaU) { // if control designed to minimize not u but deltaU (i.e. state aug with // u): // TODO(mfbolus): Commented out for now. See note below. // du_ref_ = u_ref_ - u_ref_prev_; // dv_ref_ = g_design_ % du_ref_; // TODO(mfbolus): Assuming users want *smooth* control signals if using // kControlTypeDeltaU, it should be the case that dv_ref_ is --\u0026gt; 0. May // want to revisit, but I am going to force it to be zero unless a // situation arises that argues for keeping the above. dv_ref_.zeros(); dv_ = dv_ref_; // nominally-optimal. dv_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error dv_ -= Kc_u_ * (v_ - v_ref_); // penalty on amp u (rel to ref) if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if(!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error dv_ -= Kc_inty_ * int_e_; // control for integrated error } // update the control v_ += dv_; } else { v_ = v_ref_; // nominally-optimal. v_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if (!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error v_ -= Kc_inty_ * int_e_; // control for integrated error } } // convert back to control voltage u[=]V u_ = v_ / sys_.g(); } // else do nothing until lock is low } else { // if not control // feed through u_ref in open loop u_ = u_ref_ % g_design_ / sys_.g(); v_ = sys_.g() % u_; u_ref_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); } // ends do_control // enforce box constraints (and antiwindup) AntiWindup(); // add noise to input? // The value for u that is *returned* to user after addition of any noise, // while keeping controller/estimator blind to this addition. u_return_ = u_; if ((sigma_u_noise \u0026gt; 0.0) \u0026amp;\u0026amp; (do_control \u0026amp;\u0026amp; !do_lock_control)) { u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn); Limit(u_return_, u_lb_, u_ub_); }; // For next time step: u_ref_prev_ = u_ref_; do_control_prev_ = do_control; do_lock_control_prev_ = do_lock_control; } // CalcControl template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcSteadyStateSetPoint() { // Linearly-constrained least squares (ls). // // _reference: // Boyd \u0026amp; Vandenberghe (2018) Introduction to Applied Linear Algebra // Matrix a_ls = join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros)); Vector b_ls = cx_ref_; Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye), sys_.B() * arma::diagmat(sys_.g())); Vector d_ls = -sys_.m0(); if (control_type_ \u0026amp; kControlTypeAdaptM) { d_ls = -sys_.m(); // adapt setpoint calc with disturbance? } Matrix a_ls_t = a_ls.t(); // TODO(mfbolus): not sure why but causes seg // fault if I do not do this. Matrix phi_ls = join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()), join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros))); // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse: Matrix inv_phi = pinv(phi_ls); Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls); x_ref_ = xulam.subvec(0, sys_.n_x() - 1); u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1); cx_ref_ = sys_.C() * x_ref_; } // CalcSteadyStateSetPoint template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::AntiWindup() { u_saturated_ = false; u_sat_ = u_; // limit u and flag whether saturated for (size_t k = 0; k \u0026lt; u_.n_elem; k++) { if (u_[k] \u0026lt; u_lb_) { u_sat_[k] = u_lb_; u_saturated_ = true; } if (u_[k] \u0026gt; u_ub_) { u_sat_[k] = u_ub_; u_saturated_ = true; } } if ((control_type_ \u0026amp; kControlTypeIntY) \u0026amp;\u0026amp; (tau_awu_ \u0026lt; lds::kInf)) { // one-step back-calculation (calculate intE for u=u_sat) // (Astroem, Rundqwist 1989 warn against using this...) // int_e_awu_adjust_ = // solve(Kc_inty_, (u_ - u_sat_)); // pinv(Kc_inty) * (u-uSat); // gradual: see Astroem, Rundqwist 1989 // this is a fudge for doing MIMO gradual // n.b., went ahead and multiplied 1/T by dt so don\u0026#39;t have to do that here. int_e_awu_adjust_ = k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_); // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_); int_e_ += int_e_awu_adjust_; } // set u to saturated version u_ = u_sat_; } template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::InitVars(size_t control_type) { // initialize to default values u_ref_ = Vector(sys_.n_u(), fill::zeros); u_ref_prev_ = Vector(sys_.n_u(), fill::zeros); x_ref_ = Vector(sys_.n_x(), fill::zeros); y_ref_ = Vector(sys_.n_y(), fill::zeros); cx_ref_ = Vector(sys_.n_y(), fill::zeros); u_ = Vector(sys_.n_u(), fill::zeros); u_return_ = Vector(sys_.n_u(), fill::zeros); u_sat_ = Vector(sys_.n_u(), fill::zeros); // Might not need all these, so zero elements until later. Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros); Kc_u_ = Matrix(0, 0, fill::zeros); Kc_inty_ = Matrix(0, 0, fill::zeros); g_design_ = sys_.g(); // by default, same as model dv_ = Vector(sys_.n_u(), fill::zeros); v_ = Vector(sys_.n_u(), fill::zeros); du_ref_ = Vector(sys_.n_u(), fill::zeros); dv_ref_ = Vector(sys_.n_u(), fill::zeros); v_ref_ = Vector(sys_.n_u(), fill::zeros); int_e_ = Vector(0, fill::zeros); int_e_awu_adjust_ = Vector(0, fill::zeros); set_control_type(control_type); } } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":53,"href":"/lds-ctrl-est/docs/api/files/lds__fit__em_8h/","title":"ldsCtrlEst_h/lds_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_em.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::EM Detailed Description # This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).\nSource code # //===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_EMAX_H #define LDSCTRLEST_LDS_EMAX_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class EM { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: EM() = default; EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); virtual ~EM() = default; const Fit\u0026amp; Run(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = true, bool calc_output = true, bool calc_measurement = true, size_t max_iter = 100, data_t tol = 1e-2); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); // auto tuple = std::make_tuple(u_, z_); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; x() const { return x_; }; const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; y() const { return y_; }; const Matrix\u0026amp; sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; }; const Matrix\u0026amp; sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; }; const Matrix\u0026amp; sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; }; size_t n_t_tot() { return n_t_tot_; } const Vector\u0026amp; theta() const { return theta_; }; protected: void Expectation(bool force_common_initial = false); void Maximization(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = false, bool calc_output = false, bool calc_measurement = false); void MaximizeDynamics(); void MaximizeQ(); void MaximizeInitial(); virtual void MaximizeOutput() = 0; virtual void MaximizeMeasurement() = 0; void Smooth(bool force_common_initial); virtual void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) = 0; void Reset(); void InitVars(); Vector UpdateTheta(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; std::vector\u0026lt;Matrix\u0026gt; x_; std::vector\u0026lt;Cube\u0026gt; P_; std::vector\u0026lt;Cube\u0026gt; P_t_tm1_; std::vector\u0026lt;Matrix\u0026gt; y_; Matrix diag_y_; // expectations calculated in E-step Matrix sum_E_x_t_x_t_; Matrix sum_E_xu_tm1_xu_tm1_; Matrix sum_E_xu_t_xu_tm1_; Fit fit_; Vector theta_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; }; template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; fit_ = Fit(n_u_, n_x, n_y_, dt); u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { // make sure fit dims match I/O data if (fit0.n_u() != u_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and input training data have inconsistent dimensions\u0026#34;); } if (fit0.n_y() != z_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and output training data have inconsistent dimensions\u0026#34;); } fit_ = fit0; u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::InitVars() { // check input/output data dimensions are consistent if (z_.size() != u_.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_.at(trial).n_cols != u_.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } n_u_ = fit_.n_u(); n_x_ = fit_.n_x(); n_y_ = fit_.n_y(); dt_ = fit_.dt(); x_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); P_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); P_t_tm1_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); y_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros); P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros); } diag_y_ = Matrix(n_y_, n_y_, fill::zeros); // covariances in expectation step sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); } template \u0026lt;typename Fit\u0026gt; const Fit\u0026amp; EM\u0026lt;Fit\u0026gt;::Run(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement, size_t max_iter, data_t tol) { Reset(); // to initial conditions size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_; Vector theta(n_params); Vector theta_new(n_params); data_t max_dtheta = 1; // if solving for initial conditions, allow them be varied. // otherwise, freeze at provided values. bool force_common_initial = !calc_init; // go until parameter convergence for (size_t l = 0; l \u0026lt; max_iter; l++) { theta_ = UpdateTheta(); std::cout \u0026lt;\u0026lt; \u0026#34;Iteration \u0026#34; \u0026lt;\u0026lt; l + 1 \u0026lt;\u0026lt; \u0026#34;/\u0026#34; \u0026lt;\u0026lt; max_iter \u0026lt;\u0026lt; \u0026#34; ...\\n\u0026#34;; Expectation(force_common_initial); Maximization(calc_dynamics, calc_Q, calc_init, calc_output, calc_measurement); // check convergence theta_new = UpdateTheta(); Vector dtheta = abs(theta_new - theta_) / abs(theta_); // some parameters could be zero... arma::uvec ubi_finite = find_finite(dtheta); max_dtheta = max(dtheta.elem(ubi_finite)); std::cout \u0026lt;\u0026lt; \u0026#34;max dtheta: \u0026#34; \u0026lt;\u0026lt; max_dtheta \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; if (max_dtheta \u0026lt; tol) { std::cout \u0026lt;\u0026lt; \u0026#34;Converged.\\n\u0026#34;; break; } std::cout \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } return fit_; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Smooth(bool force_common_initial) { Matrix k_e(n_x_, n_y_); // estimator gain Cube k_backfilt; // back-filtering gains // TODO(mfbolus): this loop could be made parallel for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { Matrix x_pre(n_x_, n_t_[trial], fill::zeros); Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros); Matrix x_post(n_x_, n_t_[trial], fill::zeros); Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros); if (force_common_initial) // forces all trials to have same initial // conditions. { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); } y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); // This *should not* be necessary but make sure P is symmetric. ForceSymPD(P_[trial].slice(0)); x_pre.col(0) = x_[trial].col(0); p_pre.slice(0) = P_[trial].slice(0); x_post.col(0) = x_[trial].col(0); p_post.slice(0) = P_[trial].slice(0); // filter for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // predict fit_.f(x_pre, x_post, u_.at(trial), t); fit_.h(y_[trial], x_pre, t); diag_y_.diag() = y_[trial].col(t); // TODO(mfbolus): change if parallel // update --\u0026gt; posterior estimation RecurseKe(k_e, p_pre, p_post, t); x_post.col(t) = x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t)); y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d(); } // backfilter -\u0026gt; Smoothed estimate // Reference: // Shumway et Stoffer (1982) ForceSymPD(p_post.slice(n_t_[trial] - 1)); k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1); P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 0; t--) { // TODO(mfmbolus): should not be necessary to force symm positive def ForceSymPD(p_pre.slice(t)); ForceSymPD(p_post.slice(t - 1)); ForceSymPD(P_[trial].slice(t)); k_backfilt.slice(t - 1) = p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t)); x_[trial].col(t - 1) = x_post.col(t - 1) + k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t)); P_[trial].slice(t - 1) = p_post.slice(t - 1) + k_backfilt.slice(t - 1) * (P_[trial].slice(t) - p_pre.slice(t)) * k_backfilt.slice(t - 1).t(); } // do the same for P_t_tm1 Matrix id(n_x_, n_x_, fill::eye); P_t_tm1_[trial].slice(n_t_[trial] - 1) = (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 1; t--) { P_t_tm1_[trial].slice(t - 1) = p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() + k_backfilt.slice(t - 1) * (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) * k_backfilt.slice(t - 2).t(); } // finally, get smoothed estimate of output for (size_t t = 0; t \u0026lt; n_t_[trial]; t++) { fit_.h(y_[trial], x_[trial], t); } // samps loop } // trial loop } // Smooth // template \u0026lt;typename Fit\u0026gt; // void EM\u0026lt;Fit\u0026gt;::RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) { // // predict covar // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q(); // // update Ke // Ke = P_pre.slice(t) * fit_.C().t() * // inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R()); // // update cov // // Reference: Ghahramani et Hinton (1996) // P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t); // // // n.b. for poisson : // // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + // fit_.Q(); // // // update cov // // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ * // // fit_.C()); // // // update Ke // // Ke = P_post.slice(t) * fit_.C(); // } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Expectation(bool force_common_initial) { // calculate the mean/cov of state needed for maximizing E[pr(z|theta)] Smooth(force_common_initial); // now get the various forms of sum(E[xx\u0026#39;]) needed // n.b. Going to start at t=1 rather than 0 bc most max terms need that. // so really \u0026#34;n_t_tot_\u0026#34; is (n_t_tot_-1) n_t_tot_ = 0; sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); Vector xu_tm1(n_x_ + n_u_, fill::zeros); Vector xu_t(n_x_ + n_u_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // ------------ sum_E_x_t_x_t ------------ sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t(); sum_E_x_t_x_t_ += P_[trial].slice(t); // ------------ sum_E_xu_tm1_xu_tm1 ------------ xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1)); sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t(); sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t - 1); // ------------ sum_E_xu_t_xu_tm1 ------------ xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t)); sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t(); sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_t_tm1_[trial].slice(t); n_t_tot_ += 1; } // time } // trial } // Expectation template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement) { if (calc_output) { MaximizeOutput(); } if (calc_measurement) { MaximizeMeasurement(); } if (calc_dynamics) { MaximizeDynamics(); } if (calc_Q) { MaximizeQ(); } if (calc_init) { MaximizeInitial(); } } // Maximization template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeDynamics() { // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) * inv_sympd(sum_E_xu_tm1_xu_tm1_); fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1)); std::cout \u0026lt;\u0026lt; \u0026#34;A_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.A()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.B()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeQ() { // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 * // inv_sympd(sum_E_xu_tm1_xu_tm1_) * // sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // this way is same as above iff dynamics were just updated: // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix ab = arma::join_horiz(fit_.A(), fit_.B()); // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // From scratch method: // Q is covariance of the error between state and dynamics-predicted state // (aka process noise) // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})\u0026#39;] // t-1 terms: View sum_e_x_tm1_x_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_u_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1); View sum_e_x_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); // t, t-1 terms: View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_x_t_u_tm1 = sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); Matrix q = sum_E_x_t_x_t_; q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t(); q -= sum_e_x_t_x_tm1 * fit_.A().t(); q -= fit_.A() * sum_e_x_t_x_tm1.t(); // input-related terms: q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t(); q -= sum_e_x_t_u_tm1 * fit_.B().t(); q -= fit_.B() * sum_e_x_t_u_tm1.t(); q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t(); q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t(); q /= n_t_tot_; fit_.set_Q(q); std::cout \u0026lt;\u0026lt; \u0026#34;Q_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.Q()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // std::cout \u0026lt;\u0026lt; \u0026#34;Q_new: \\n\u0026#34; \u0026lt;\u0026lt; fit_.Q() \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeInitial() { Vector x0 = fit_.x0(); x0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { x0 += x_[trial].col(0); } x0 /= z_.size(); std::cout \u0026lt;\u0026lt; \u0026#34;x0_new[0]: \u0026#34; \u0026lt;\u0026lt; x0[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // always recalc P0 even if the initial state is fixed (at zero, for // example) Matrix e_var(n_x_, n_x_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t(); } e_var /= z_.size(); // go ahead and subtract x0*x0\u0026#39; so don\u0026#39;t have to below. e_var -= x0 * x0.t(); // To get P0, going to get initial P_ per trial and average. // (which might be wrong, but need a single number) Matrix p0 = fit_.P0(); p0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { p0 += (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var; } p0 /= z_.size(); fit_.set_P0(p0); std::cout \u0026lt;\u0026lt; \u0026#34;P0_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.P0()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeOutput() { // solve for C+d: Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros); Vector x1(n_x_ + 1, fill::zeros); x1[n_x_] = 1.0; // augment with one to solve for bias Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { x1.subvec(0, n_x_ - 1) = x_[trial].col(t); sum_zx += z_.at(trial).col(t) * x1.t(); sum_e_x1_x1 += x1 * x1.t(); sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t); } } Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1); fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1)); fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_))); std::cout \u0026lt;\u0026lt; \u0026#34;C_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.C()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.d()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeMeasurement() { // Solve for measurement noise covar size_t n_t_tot = 0; // Ghahgramani, Hinton 1996: Matrix sum_zz(n_y_, n_y_, fill::zeros); Matrix sum_yz(n_y_, n_y_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t(); // Use Cnew: sum_yz += (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t(); n_t_tot += 1; } } fit_.set_R((sum_zz - sum_yz) / n_t_tot); std::cout \u0026lt;\u0026lt; \u0026#34;R_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.R()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Reset() { // reset to initial conditions for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); } } template \u0026lt;typename Fit\u0026gt; Vector EM\u0026lt;Fit\u0026gt;::UpdateTheta() { // TODO(mfbolus): This should include n_y_ more params for d. size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_; if (fit_.R().n_elem \u0026gt; 0) { n_params += n_y_ * n_y_; } Vector theta(n_params); size_t idx_start = 0; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B()); idx_start += n_x_ * n_u_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0()); idx_start += n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C()); idx_start += n_y_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d()); idx_start += n_y_; if (fit_.R().n_elem \u0026gt; 0) { theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R()); } return theta; } } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":54,"href":"/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_ssid.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SSID Detailed Description # This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)\u0026lt;Fit\u0026gt;).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_FIT_SSID_H #define LDSCTRLEST_LDS_FIT_SSID_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class SSID { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: SSID() = default; SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d = Vector(1).fill(-kInf)); std::tuple\u0026lt;Fit, Vector\u0026gt; Run(SSIDWt ssid_wt); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } protected: void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001); void CreateHankelDataMat(); virtual void DecomposeData() = 0; void CalcSVD(SSIDWt wt); void Solve(data_t wt_dc); void RecomputeExtObs(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; Matrix D_; Fit fit_; Matrix g_dc_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_h_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; Matrix L_; Vector s_; Matrix ext_obs_t_; }; template \u0026lt;typename Fit\u0026gt; SSID\u0026lt;Fit\u0026gt;::SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d) { // check input/output data dimensions are consistent if (z_train.size() != u_train.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_train.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_train.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } dt_ = dt; n_x_ = n_x; n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; n_h_ = n_h; // dimensionality check for eventual block-hankel data matrix size_t len = n_t_tot_ - 2 * n_h_ + 1; if (len \u0026lt; (2 * n_h_ * (n_u_ + n_y_))) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;Dataset problem! More rows than columns in block-hankel data \u0026#34; \u0026#34;matrix: 2*(n_u+n_y)*n_h \u0026gt; data-length! Need higher data-length or \u0026#34; \u0026#34;lower n_h.\u0026#34;; throw std::runtime_error(ss.str()); } fit_ = Fit(n_u_, n_x_, n_y_, dt_); u_ = std::move(u_train); z_ = std::move(z_train); if (!d.is_finite() || (d.n_rows != n_y_)) { // TODO(mfbolus): implement least-square solution for impulse response with // a second input of ones. Data-driven way of accounting for offset *not* // driven by an input. // // For now, calculate output bias (d) as the // output wherever the stimulus has not been on for some amount of time. // convolve u with rectangle and take all samples. This is a reasonable // approach, since often when autonomous systems are fit (i.e., systems with // no input), they will subtract off the mean of the output. This // essentially amounts to setting output bias to the mean of the output when // there is no stimulation. data_t t_silence = 0.1; data_t thresh_silence = 0.001; CalcD(t_silence, thresh_silence); } else { fit_.set_d(d); } } template \u0026lt;typename Fit\u0026gt; std::tuple\u0026lt;Fit, Vector\u0026gt; SSID\u0026lt;Fit\u0026gt;::Run(SSIDWt ssid_wt) { // the weight on minimizing dc I/O gain only works for gaussian, // and hopefully not necessary with appropriate dataset. data_t wt_dc = 0; // std::cout \u0026lt;\u0026lt; \u0026#34;creating hankel mat\\n\u0026#34;; CreateHankelDataMat(); // std::cout \u0026lt;\u0026lt; \u0026#34;decomposing data\\n\u0026#34;; DecomposeData(); // std::cout \u0026lt;\u0026lt; \u0026#34;calculating svd\\n\u0026#34;; CalcSVD(ssid_wt); // std::cout \u0026lt;\u0026lt; \u0026#34;solving for params\\n\u0026#34;; Solve(wt_dc); // std::cout \u0026lt;\u0026lt; \u0026#34;fin\\n\u0026#34;; return std::make_tuple(fit_, s_); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcD(data_t t_silence, data_t thresh_silence) { Vector d(z_.at(0).n_rows, fill::zeros); Vector win(static_cast\u0026lt;size_t\u0026gt;(t_silence / dt_), fill::ones); Vector sum_z_silence(n_y_, fill::zeros); size_t n_silence(0); for (size_t trial = 0; trial \u0026lt; u_.size(); trial++) { // find silent samples // start by convolving with Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0)); Vector u_conv = conv(sum_u, win, \u0026#34;same\u0026#34;); // get only the samples that are silent... arma::uvec ubi_silence = find(u_conv \u0026lt;= thresh_silence); if (ubi_silence.n_elem \u0026gt; 0) { sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1); n_silence += ubi_silence.n_elem; } } if (n_silence \u0026gt; 0) { d = sum_z_silence / n_silence; } fit_.set_d(d); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat() { // temporary copy of data Matrix z(n_y_, n_t_tot_, fill::zeros); Matrix u(n_u_, n_t_tot_, fill::zeros); size_t so_far(0); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial); u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial); so_far += n_t_.at(trial); } // remove output bias z.each_col() -= fit_.d(); // calculate I/O gain @ DC while data in convenient form g_dc_ = z * pinv(u); // std::cout \u0026lt;\u0026lt; \u0026#34;G0_data = \u0026#34; \u0026lt;\u0026lt; g_dc_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // create hankel data matrix size_t len = z.n_cols - 2 * n_h_ + 1; // data length in hankel mat // block-hankel data matrix D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros); // past input auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1); // future input auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1); // past output auto y_p = D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1); // future output auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, len - 1); size_t idx = 0; for (size_t k = 0; k \u0026lt; len; k++) { idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } } D_ /= sqrt(static_cast\u0026lt;data_t\u0026gt;(len)); } // template \u0026lt;typename Fit\u0026gt; // void SSID\u0026lt;Fit\u0026gt;::DecomposeData() { // // do LQ decomp instead of calculating covariance expensive way // // Note that \u0026#34;R\u0026#34; in van Overschee is lower-triangular (L), not \u0026#34;R\u0026#34; in QR // // decomp. Very confusing. // Matrix q_t; // lq(L_, q_t, D_); // // van Overschee zeros out the other elements. // L_ = trimatl(L_); // } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcSVD(SSIDWt wt) { // submats that will be needed: auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1); auto R_23_13 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1); auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_) - 1); auto R_44 = L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14); auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1); auto Luf = Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1); auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); // aka: R_f Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1); // from van Overschee subid.m: // Rf = R((2*m+l)*i+1:2*(m+l)*i,:); % Future outputs Matrix U; Matrix V; switch (wt) { case kSSIDNone: { // No weighting. (what van Overschee calls \u0026#34;N4SID\u0026#34;) Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14; arma::svd(U, s_, V, O_k_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDMOESP: { // MOESP weighting // This is what they use in the \u0026#34;robust\u0026#34; algorithm van Overschee, de Moor // 1996 Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); svd(U, s_, V, O_k_ortho_Uf_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDCVA: { // CVA weighting // See van Overschee\u0026#39;s matlab code (subid.m): // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); Matrix inv_w1; Matrix qt1; lq(inv_w1, qt1, R_56_16); // lq decomp of R_f (future output data) inv_w1 = trimatl(inv_w1); inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1); Matrix w_o_w = arma::solve( inv_w1, O_k_ortho_Uf_sans_Qt); // alternatively // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt svd(U, s_, V, w_o_w, \u0026#34;std\u0026#34;); U = inv_w1 * U; break; } } // Truncate to model order (heart of ssid method) auto s_hat = s_.subvec(0, n_x_ - 1); Matrix diag_sqrt_s = diagmat(sqrt(s_hat)); auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1); // get extended observability and controllability mats ext_obs_t_ = u_hat * diag_sqrt_s; // extended observability matrix } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::Solve(data_t wt_dc) { // required submats auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_56_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); // Solve for params using appropriate algorithm: // robust deterministic/stochastic algorithm in van Overschee 1996 // algorithm that the authors say \u0026#34;works\u0026#34; in practice. auto ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix // This is what textbook (1996) says: // // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15); // // HOWEVER, do not know why but have to fill the last place with zeros like // authors\u0026#39; matlab implementation (see `subid.m`) // Otherwise, get ridiculous covariances (although A,C estimates are close to // same...) Matrix Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Matrix S = Tl * pinv(Tr); // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure // stability. fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1)); Matrix ext_obs_t_p1 = join_vert( ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1), Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros)); fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1); // At this point, van Overschee \u0026amp; de Moor suggest re-calculating ext_obs_t_, // ext_obs_tm1 from (A, C) because it was just an approximation. This is RecomputeExtObs(); ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); S = Tl * pinv(Tr); Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_); Matrix Mcurly = pinv(ext_obs_tm1); Matrix Pcurly = Tl - Lcurly * R_56_15; Vector Pvec = vectorise(Pcurly); Matrix Qcurly = R_23_15; // Identify [D; B], assuming D=0 and ensuring DC gain is correct Matrix sum_QcurlyT_kron_Ncurly( (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_), fill::zeros); Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols, fill::eye); eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1, eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1; // van Overschee (1996) p. 126 Matrix N1_Tl = -Lcurly; N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) += join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly); N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) += Matrix(n_y_, n_y_, fill::eye); Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros); Matrix N_k; for (size_t k = 0; k \u0026lt; n_h_; k++) { auto Qcurly_k = Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1); Nk_Tl.zeros(); Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) = N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1); N_k = Nk_Tl * eye_ext_obs_tm1; sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k); } Matrix err_vec; if (wt_dc \u0026gt; 0) { // Constraints enforced by weighted least squares // // Reference: // // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly // Excited Industrial Systems. Conference in Decision and Control. // constraint 1: assume D=0 --\u0026gt; remove the components for Dvec (this is // actually a hard constraint in that it ignores D) Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly; sum_QcurlyT_kron_Ncurly = Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_); size_t kkk = 0; for (size_t k = 1; k \u0026lt; (n_u_ + 1); k++) { size_t start_idx = k * (n_y_ + n_x_) - n_x_; for (size_t kk = 0; kk \u0026lt; n_x_; kk++) { sum_QcurlyT_kron_Ncurly.col(kkk) = sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk); kkk++; } } // constraint 2: Make sure DC I/O gain is correct Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A()); Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_)); Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0); Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 = join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0); // WEIGHTED LS // Important in practice because I care a lot about at least getting the DC // gain correct. Put x weight on minimizing error at DC, relative to others Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye); // Make weight on minimizing DC error immense so at least that // should be nailed. size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows; size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows; size_t stop_row = w.n_rows - 1; size_t stop_col = w.n_cols - 1; // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale // weight with data length? w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc; Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * sum_QcurlyT_kron_Ncurly_b_to_g0) * sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec; fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_)); // Calculate residuals and their cov. // Because I\u0026#39;ve added constraints, I need to re-calculate the right term // with b_vec instead of how van Overschee do in final algorithm. err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec; } else { // default way: *no* constraint on G0 or D=0 Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec; // TODO(mfbolus) n.b., this gets thrown away... // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_); fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_)); err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec; } // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols); // TODO(mfbolus): Something is wrong with the error calculation above. // Use the way van overschee does it in `subid.m` // WARNING: this ignores any above constraints, so Q, R will be approximate... Matrix err = Tl - S * Tr; Matrix cov_err = err * err.t(); fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1)); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::RecomputeExtObs() { ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C(); for (size_t k = 2; k \u0026lt; (n_h_ + 1); k++) { ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) = ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1, ext_obs_t_.n_cols - 1) * fit_.A(); } } } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":55,"href":"/lds-ctrl-est/docs/api/files/lds__fit_8h/","title":"ldsCtrlEst_h/lds_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_fit.h # LDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Fit LDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.\nSource code # //===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDS_FIT_HPP #define LDS_FIT_HPP // namespace #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); virtual ~Fit() = default; // get methods size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; Q() const { return Q_; }; const Vector\u0026amp; x0() const { return x0_; }; const Matrix\u0026amp; P0() const { return P0_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; // gets measurement noise virtual const Matrix\u0026amp; R() const = 0; // set methods (e.g., seeding initial fit values) void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_m(const Vector\u0026amp; m) { Reassign(m_, m); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); ForceSymPD(Q_); }; virtual void set_R(const Matrix\u0026amp; R) = 0; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); ForceSymPD(P0_); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; View f(Matrix\u0026amp; x, const Matrix\u0026amp; u, size_t t) { x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x.col(t); }; View f(Matrix\u0026amp; x_pre, const Matrix\u0026amp; x_post, const Matrix\u0026amp; u, size_t t) { x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x_pre.col(t); }; virtual View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) = 0; protected: data_t dt_{}; // Dynamics Matrix A_; Matrix B_; Vector g_; Vector m_; Matrix Q_; // Output Matrix C_; Vector d_; Matrix R_; // initial conditions Vector x0_; Matrix P0_; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; }; } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":56,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_ctrl.h # GLDS Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. Detailed Description # This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_gaussian_sys.h\u0026#34; // controller #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace gaussian { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); cx_ref_ = y_ref - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":57,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_em.h # GLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitEM GLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":58,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_ssid.h # GLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; using SSID\u0026lt;Fit\u0026gt;::Run; private: using SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat; using SSID\u0026lt;Fit\u0026gt;::CalcSVD; using SSID\u0026lt;Fit\u0026gt;::Solve; void DecomposeData() override; void SolveVanOverschee(); }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":59,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit.h # GLDS fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Fit GLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // fit type #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace gaussian { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); const Matrix\u0026amp; R() const override { return R_; }; void set_R(const Matrix\u0026amp; R) override { Reassign(R_, R); ForceSymPD(R_); }; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = C_ * x.col(t) + d_; return y.col(t); }; }; }; // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":60,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sctrl.h # GLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H // controller type #include \u0026#34;lds_gaussian_ctrl.h\u0026#34; // switched controller #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace gaussian { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); cx_ref_ = y_ref - sys_.d(); } // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; }; // SwitchedController } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":61,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/","title":"ldsCtrlEst_h/lds_gaussian_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sys.h # GLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::System Gaussian LDS Type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H #define LDSCTRLEST_LDS_GAUSSIAN_SYS_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; namespace lds { namespace gaussian { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0, data_t r0 = kDefaultR0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; // get methods const Matrix\u0026amp; R() const { return R_; }; // set methods void set_Q(const Matrix\u0026amp; Q) { lds::System::set_Q(Q); do_recurse_Ke_ = true; } void set_R(const Matrix\u0026amp; R) { Reassign(R_, R); do_recurse_Ke_ = true; }; void set_Ke(const Matrix\u0026amp; Ke) { Reassign(Ke_, Ke); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void set_Ke_m(const Matrix\u0026amp; Ke_m) { Reassign(Ke_m_, Ke_m); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void Print(); protected: void h() override { cx_ = C_ * x_; y_ = cx_ + d_; }; Vector h_(Vector x) override { return C_ * x + d_; }; void RecurseKe() override; // Gaussian-output-specific Matrix R_; bool do_recurse_Ke_{}; }; // System } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":62,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian_8h/","title":"ldsCtrlEst_h/lds_gaussian.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian.h # glds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_H #define LDSCTRLEST_LDS_GAUSSIAN_H // namespace #include \u0026#34;lds.h\u0026#34; namespace lds { namespace gaussian { // insert any Gaussian-specific things here... } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":63,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_ctrl.h # PLDS controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. Detailed Description # This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_CTRL_H #define LDSCTRLEST_LDS_POISSON_CTRL_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system type #include \u0026#34;lds_poisson_sys.h\u0026#34; // control type #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace poisson { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); lds::Limit(y_ref_, kYRefLb, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; private: constexpr static const data_t kYRefLb = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":64,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_em.h # PLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitEM PLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\n[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H #define LDSCTRLEST_LDS_POISSON_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override{}; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; data_t NewtonSolveC(); void AnalyticalSolveD(); }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":65,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_ssid.h # PLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. Detailed Description # This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/)).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H #define LDSCTRLEST_LDS_POISSON_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; private: void DecomposeData() override; void CalcCov(); void PoissonToGaussianMoments(); Matrix cov_; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":66,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/","title":"ldsCtrlEst_h/lds_poisson_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit.h # PLDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Fit PLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_H #define LDSCTRLEST_LDS_POISSON_FIT_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // fit #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace poisson { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) : lds::Fit(n_u, n_x, n_y, dt){}; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = exp(C_ * x.col(t) + d_); return y.col(t); }; void set_R(const Matrix\u0026amp; R) override { std::cerr \u0026lt;\u0026lt; \u0026#34;WARNING: Cannot set R (R[0] = \u0026#34; \u0026lt;\u0026lt; R.at(0) \u0026lt;\u0026lt; \u0026#34;). No Gaussian measurement noise in Poisson observation model.\\n\u0026#34;; }; const Matrix\u0026amp; R() const override { return R_; }; }; }; // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":67,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sctrl.h # PLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #include \u0026#34;lds_poisson_ctrl.h\u0026#34; #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace poisson { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); lds::Limit(y_ref_, kYRefLB, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; private: constexpr static data_t kYRefLB = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":68,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/","title":"ldsCtrlEst_h/lds_poisson_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sys.h # PLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::System Poisson System type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SYS_H #define LDSCTRLEST_LDS_POISSON_SYS_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; // needed for Poisson random number generation #include \u0026lt;random\u0026gt; namespace lds { namespace poisson { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; protected: void h() override { cx_ = C_ * x_; y_ = exp(cx_ + d_); diag_y_.diag() = y_; }; Vector h_(Vector x) override { return exp(C_ * x + d_); }; void RecurseKe() override; private: // Poisson-output-specific Matrix diag_y_; std::poisson_distribution\u0026lt;size_t\u0026gt; pd_; }; // System } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":69,"href":"/lds-ctrl-est/docs/api/files/lds__poisson_8h/","title":"ldsCtrlEst_h/lds_poisson.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson.h # plds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_H #define LDSCTRLEST_LDS_POISSON_H #include \u0026#34;lds.h\u0026#34; namespace lds { namespace poisson { // TODO(mfbolus): Not sure if defining these as static here makes the most // sense. Is there a downside to letting multiple poisson System objects share a // common random number generator? static std::random_device rd; static std::mt19937 rng = std::mt19937( rd()); } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":70,"href":"/lds-ctrl-est/docs/api/files/lds__sctrl_8h/","title":"ldsCtrlEst_h/lds_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_sctrl.h # SwitchedController type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SwitchedController SwitchedController Type. Detailed Description # This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SCTRL_H #define LDSCTRLEST_LDS_SCTRL_H #include \u0026#34;lds_ctrl.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; #include \u0026#34;lds_uniform_vecs.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class SwitchedController : public Controller\u0026lt;System\u0026gt; { public: SwitchedController() = default; SwitchedController(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); SwitchedController(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); void Switch(size_t idx, bool do_force_switch = false); void set_Kc(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc) { Kc_list_ = Kc; Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc) { Kc_list_ = std::move(Kc); Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_inty) { Kc_inty_list_ = Kc_inty; Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_inty) { Kc_inty_list_ = std::move(Kc_inty); Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_u) { Kc_u_list_ = Kc_u; Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_u) { Kc_u_list_ = std::move(Kc_u); Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(const UniformVectorList\u0026amp; g) { g_design_list_ = g; g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(UniformVectorList\u0026amp;\u0026amp; g) { g_design_list_ = std::move(g); g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; protected: std::vector\u0026lt;System\u0026gt; systems_; size_t n_sys_{}; size_t idx_{}; // controller gains could be different for each UniformMatrixList\u0026lt;\u0026gt; Kc_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_inty_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_u_list_; // design-phase input gain could also be different UniformVectorList g_design_list_; // TODO(mfbolus): not sure why I need to do this. using Controller\u0026lt;System\u0026gt;::Kc_; using Controller\u0026lt;System\u0026gt;::Kc_inty_; using Controller\u0026lt;System\u0026gt;::Kc_u_; using Controller\u0026lt;System\u0026gt;::g_design_; using Controller\u0026lt;System\u0026gt;::sys_; // using Controller\u0026lt;System\u0026gt;::u_ref_; // using Controller\u0026lt;System\u0026gt;::x_ref_; // using Controller\u0026lt;System\u0026gt;::y_ref_; // using Controller\u0026lt;System\u0026gt;::control_type_; private: void InitVars(); using lds::Controller\u0026lt;System\u0026gt;::set_sys; // using Controller\u0026lt;System\u0026gt;::set_Kc; // using Controller\u0026lt;System\u0026gt;::set_Kc_inty; // using Controller\u0026lt;System\u0026gt;::set_Kc_u; // using Controller\u0026lt;System\u0026gt;::set_g_design; }; template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(systems.at(0), u_lb, u_ub, control_type), systems_(systems) { InitVars(); } template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(System(systems.at(0).n_u(), systems.at(0).n_x(), systems.at(0).n_y(), systems.at(0).dt()), u_lb, u_ub, control_type), systems_(std::move(systems)) { InitVars(); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::InitVars() { n_sys_ = systems_.size(); sys_ = systems_.at(0); Kc_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_)); Kc_inty_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); Kc_u_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); g_design_list_ = UniformVectorList(std::vector\u0026lt;Vector\u0026gt;(n_sys_, g_design_)); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::Switch(size_t idx, bool do_force_switch) { if ((idx == idx_) \u0026amp;\u0026amp; !do_force_switch) { return; // already there. } // put old up and get new one out systems_.at(idx_) = std::move(sys_); sys_ = std::move(systems_.at(idx)); // set the state of this system to that of the previous system // TODO(mfbolus): This will only work as intended if state matrix is the same. // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp. sys_.set_m(systems_.at(idx_).m(), true); sys_.set_x(systems_.at(idx_).x()); // swap controller gains Kc_list_.Swap(Kc_, idx_); Kc_list_.Swap(Kc_, idx); if (control_type_ \u0026amp; kControlTypeIntY) { Kc_inty_list_.Swap(Kc_inty_, idx_); Kc_inty_list_.Swap(Kc_inty_, idx); } if (control_type_ \u0026amp; kControlTypeDeltaU) { Kc_u_list_.Swap(Kc_u_, idx_); Kc_u_list_.Swap(Kc_u_, idx); } g_design_list_.Swap(g_design_, idx_); g_design_list_.Swap(g_design_, idx); idx_ = idx; } // Switch } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":71,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8h/","title":"ldsCtrlEst_h/lds_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_sys.h # LDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::System Linear Dynamical System Type. Detailed Description # This file declares and partially defines the base type for linear dynamical systems ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SYS_H #define LDSCTRLEST_LDS_SYS_H #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class System { public: System() = default; System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); virtual ~System() {} void Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z); virtual const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) = 0; void f(const Vector\u0026amp; u, bool do_add_noise = false) { x_ = A_ * x_ + B_ * (g_ % u) + m_; if (do_add_noise) { x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_); } }; virtual void h() = 0; virtual Vector h_(Vector x) = 0; size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Vector\u0026amp; x() const { return x_; }; const Matrix\u0026amp; P() const { return P_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; P_m() const { return P_m_; }; const Vector\u0026amp; cx() const { return cx_; }; const Vector\u0026amp; y() const { return y_; }; const Vector\u0026amp; x0() const { return x0_; }; const Vector\u0026amp; m0() const { return m0_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; const Matrix\u0026amp; Ke() const { return Ke_; }; const Matrix\u0026amp; Ke_m() const { return Ke_m_; }; const Matrix\u0026amp; Q() { return Q_; }; const Matrix\u0026amp; Q_m() { return Q_m_; }; const Matrix\u0026amp; P0() { return P0_; }; const Matrix\u0026amp; P0_m() { return P0_m_; }; void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_m(const Vector\u0026amp; m, bool do_force_assign = false) { Reassign(m0_, m); if ((!do_adapt_m) || do_force_assign) { Reassign(m_, m); } }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); }; void set_Q_m(const Matrix\u0026amp; Q_m) { Reassign(Q_m_, Q_m); }; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); }; void set_P0_m(const Matrix\u0026amp; P0_m) { Reassign(P0_m_, P0_m); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; void set_x(const Vector\u0026amp; x) { Reassign(x_, x); h(); }; void Reset(); std::vector\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; nstep_pred_block( UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z, size_t n_pred = 1); void Print(); // safe to leave this public and non-const bool do_adapt_m{}; protected: virtual void RecurseKe() = 0; void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); std::size_t n_x_{}; std::size_t n_u_{}; std::size_t n_y_{}; data_t dt_{}; // Signals: Vector x_; Matrix P_; Vector m_; Matrix P_m_; Vector cx_; Vector y_; Vector z_; // Parameters: Vector x0_; Matrix P0_; Vector m0_; Matrix P0_m_; Matrix A_; Matrix B_; Vector g_; Matrix Q_; Matrix Q_m_; Matrix C_; Vector d_; Matrix Ke_; Matrix Ke_m_; }; // System } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":72,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/","title":"ldsCtrlEst_h/lds_uniform_mats.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_mats.h # List of uniformly sized matrices. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformMatrixList Detailed Description # This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H #define LDSCTRLEST_LDS_UNIFORM_MATS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { template \u0026lt;MatrixListFreeDim D = kMatFreeDimNone\u0026gt; class UniformMatrixList : public std::vector\u0026lt;Matrix\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Matrix\u0026gt;::vector; // don\u0026#39;t allow push_back to be used since it doesn\u0026#39;t check dims using std::vector\u0026lt;Matrix\u0026gt;::push_back; public: using std::vector\u0026lt;Matrix\u0026gt;::operator=; using std::vector\u0026lt;Matrix\u0026gt;::operator[]; using std::vector\u0026lt;Matrix\u0026gt;::begin; using std::vector\u0026lt;Matrix\u0026gt;::end; using std::vector\u0026lt;Matrix\u0026gt;::size; using std::vector\u0026lt;Matrix\u0026gt;::at; UniformMatrixList() = default; explicit UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); explicit UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; ~UniformMatrixList() = default; const std::array\u0026lt;size_t, 2\u0026gt;\u0026amp; dim(size_t n = 0) const { return dim_.at(n); } size_t size() { return std::vector\u0026lt;Matrix\u0026gt;::size(); }; const Matrix\u0026amp; at(size_t n) { return std::vector\u0026lt;Matrix\u0026gt;::at(n); }; void Swap(Matrix\u0026amp; that, size_t n); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; void append(const Matrix\u0026amp; mat); private: void CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim); std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt; dim_; }; template \u0026lt;MatrixListFreeDim D\u0026gt; inline void UniformMatrixList\u0026lt;D\u0026gt;::Swap(Matrix\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.n_cols); } if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap // not moving, since it causes memory issues. // so this method isn\u0026#39;t a memory-saver as designed for now Matrix tmp = (*this)[n]; (*this)[n] = that; that = tmp; if (D == kMatFreeDim1) { this-\u0026gt;dim_[n][0] = (*this)[n].n_rows; } if (D == kMatFreeDim2) { this-\u0026gt;dim_[n][1] = (*this)[n].n_cols; } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::append(const Matrix\u0026amp; mat) { std::array\u0026lt;size_t, 2\u0026gt; dim({mat.n_rows, mat.n_cols}); CheckDimensions(dim); std::vector\u0026lt;Matrix\u0026gt;::push_back(mat); dim_.push_back(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) { // make sure dim_ vector is initialized if (dim_.empty()) { dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(that.size(), {0, 0}); } // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices\u0026#34;; throw std::runtime_error(ss.str()); } // if dimensions a not zero and do not match, skip move with error message. bool dims_nonzero = true; for (auto d : dim_) { if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; d[0] \u0026lt; 1) { dims_nonzero = false; break; } if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; d[1] \u0026lt; 1) { dims_nonzero = false; break; } } if (dims_nonzero) { bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); } if (!does_match) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrices of size \u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; dim_[k] = that.dim(k); } return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. bool dims_nonzero = true; for (auto d : dim_) { // if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; (d[0] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; (d[1] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // } // // if (dims_nonzero) { // bool does_match = true; // if (!(D == kMatFreeDim1)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); // } // // if (!(D == kMatFreeDim2)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); // } // // if (!does_match) { // this-\u0026gt;at(0).print(\u0026#34;this[0] = \u0026#34;); // that.at(0).print(\u0026#34;that[0] = \u0026#34;); // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformMatrixList element of size (\u0026#34; \u0026lt;\u0026lt; // that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols \u0026lt;\u0026lt; \u0026#34;) for an // element of size (\u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34;). // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Matrix\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(std::move(mats)) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) : vector(that) { (*this) = that; } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { std::array\u0026lt;size_t, 2\u0026gt; dim_k({this-\u0026gt;at(k).n_rows, this-\u0026gt;at(k).n_cols}); dim_.push_back(dim_k); } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim) { // change behavior based on free dim D if ((dim[0] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim1)) { dim[0] = this-\u0026gt;at(0).n_rows; } if ((dim[1] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim2)) { dim[1] = this-\u0026gt;at(0).n_cols; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Matrix\u0026amp; mat : *this) { if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_rows == dim[0]); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_cols == dim[1]); } if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(this-\u0026gt;size(), dim); for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { dim_[k][0] = (*this)[k].n_rows; dim_[k][1] = (*this)[k].n_cols; } } } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":73,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/","title":"ldsCtrlEst_h/lds_uniform_systems.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_systems.h # List of uniformly sized Systems. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformSystemList Detailed Description # This file provides a container for uniformly sized Systems.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector // namespace #include \u0026#34;lds.h\u0026#34; // System type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class UniformSystemList : public std::vector\u0026lt;System\u0026gt; { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;System\u0026gt;::vector; using std::vector\u0026lt;System\u0026gt;::operator=; using std::vector\u0026lt;System\u0026gt;::operator[]; using std::vector\u0026lt;System\u0026gt;::at; using std::vector\u0026lt;System\u0026gt;::begin; using std::vector\u0026lt;System\u0026gt;::end; using std::vector\u0026lt;System\u0026gt;::size; public: UniformSystemList() = default; explicit UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); explicit UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(const UniformSystemList\u0026amp; that); UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept; ~UniformSystemList() = default; const std::array\u0026lt;size_t, 3\u0026gt;\u0026amp; dim() const { return dim_; } size_t size() { return std::vector\u0026lt;System\u0026gt;::size(); }; const System\u0026amp; at(size_t n) { return std::vector\u0026lt;System\u0026gt;::at(n); }; void Swap(System\u0026amp; that, size_t n); UniformSystemList\u0026amp; operator=(const UniformSystemList\u0026amp; that); UniformSystemList\u0026amp; operator=(UniformSystemList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim); std::array\u0026lt;size_t, 3\u0026gt; dim_{}; }; template \u0026lt;typename System\u0026gt; inline void UniformSystemList\u0026lt;System\u0026gt;::Swap(System\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformSystemList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = (dim_[0] == that.n_u()) \u0026amp;\u0026amp; (dim_[1] == that.n_x()) \u0026amp;\u0026amp; (dim_[2] == that.n_y()); if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformSystemList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap System tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( const UniformSystemList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_[0] + dim_[1] + dim_[2]) { std::array\u0026lt;size_t, 3\u0026gt; other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign systems of size \u0026#34; \u0026lt;\u0026lt; dim_[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2] \u0026lt;\u0026lt; \u0026#34; with systems of size \u0026#34; \u0026lt;\u0026lt; other_dim[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other_dim[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2]; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( UniformSystemList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. if (dim_[0] + dim_[1] + dim_[2]) { // bool does_match = (dim_[0] == that.at(0).n_u()) \u0026amp;\u0026amp; // (dim_[1] == that.at(0).n_x()) \u0026amp;\u0026amp; // (dim_[2] == that.at(0).n_y()); // if (!does_match) { // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformSystemList element for an element of \u0026#34; // \u0026#34;different size. Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;System\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(std::move(systems)) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList( std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const UniformSystemList\u0026amp; that) : std::vector\u0026lt;System\u0026gt;(that) { (*this) = that; } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept : std::vector\u0026lt;System\u0026gt;(std::move(that)) { this-\u0026gt;dim_[0] = this-\u0026gt;at(0).n_u(); this-\u0026gt;dim_[1] = this-\u0026gt;at(0).n_x(); this-\u0026gt;dim_[2] = this-\u0026gt;at(0).n_y(); } template \u0026lt;typename System\u0026gt; void UniformSystemList\u0026lt;System\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim) { if (dim[0] + dim[1] + dim[2]) { dim_ = dim; } else { dim_[0] = this-\u0026gt;at(0).n_u(); dim_[1] = this-\u0026gt;at(0).n_x(); dim_[2] = this-\u0026gt;at(0).n_y(); } // make sure dimensiolaties are all uniform bool does_match(true); for (const System\u0026amp; sys : *this) { does_match = does_match \u0026amp;\u0026amp; (sys.n_u() == dim_[0]); does_match = does_match \u0026amp;\u0026amp; (sys.n_x() == dim_[1]); does_match = does_match \u0026amp;\u0026amp; (sys.n_y() == dim_[2]); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input systems are not uniform.\u0026#34;); } } } } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":74,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/","title":"ldsCtrlEst_h/lds_uniform_vecs.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_vecs.h # List of uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformVectorList Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H #define LDSCTRLEST_LDS_UNIFORM_VECS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { class UniformVectorList : public std::vector\u0026lt;Vector\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Vector\u0026gt;::vector; using std::vector\u0026lt;Vector\u0026gt;::operator=; using std::vector\u0026lt;Vector\u0026gt;::operator[]; using std::vector\u0026lt;Vector\u0026gt;::at; using std::vector\u0026lt;Vector\u0026gt;::begin; using std::vector\u0026lt;Vector\u0026gt;::end; using std::vector\u0026lt;Vector\u0026gt;::size; public: UniformVectorList() = default; explicit UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim = 0); explicit UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim = 0); UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim = 0); UniformVectorList(const UniformVectorList\u0026amp; that); UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept; ~UniformVectorList() = default; size_t dim() const { return dim_; } size_t size() { return std::vector\u0026lt;Vector\u0026gt;::size(); }; const Vector\u0026amp; at(size_t n) { return std::vector\u0026lt;Vector\u0026gt;::at(n); }; void Swap(Vector\u0026amp; that, size_t n); UniformVectorList\u0026amp; operator=(const UniformVectorList\u0026amp; that); UniformVectorList\u0026amp; operator=(UniformVectorList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(size_t dim); size_t dim_{}; }; inline void UniformVectorList::Swap(Vector\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = dim_ == that.n_elem; if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap Vector tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( const UniformVectorList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_) { size_t other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ \u0026lt;\u0026lt; \u0026#34; with vectors of size \u0026#34; \u0026lt;\u0026lt; other_dim; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( UniformVectorList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors. Skipping.\\n\u0026#34;; // return (*this); // } // // if (dim_) { // size_t other_dim(that.dim()); // if (dim_ != other_dim) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ // \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; other_dim \u0026lt;\u0026lt; \u0026#34;. // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Vector\u0026gt;::operator=(std::move(that)); return (*this); } } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":75,"href":"/lds-ctrl-est/docs/api/files/lds_8h/","title":"ldsCtrlEst_h/lds.h","section":"Files","content":" ldsCtrlEst_h/lds.h # lds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)) observations.\nSource code # //===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_H #define LDSCTRLEST_LDS_H // #ifndef LDSCTRLEST // #include \u0026lt;ldsCtrlEst\u0026gt; // #endif #include \u0026lt;armadillo\u0026gt; namespace lds { using data_t = double; // may change to float (but breaks mex functions) using Vector = arma::Col\u0026lt;data_t\u0026gt;; using Matrix = arma::Mat\u0026lt;data_t\u0026gt;; using Cube = arma::Cube\u0026lt;data_t\u0026gt;; using View = arma::subview\u0026lt;data_t\u0026gt;; namespace fill = arma::fill; static const std::size_t kControlTypeDeltaU = 0x1; static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); static const data_t kPi = arma::datum::pi; static const data_t kDefaultP0 = 1e-6; static const data_t kDefaultQ0 = 1e-6; static const data_t kDefaultR0 = 1e-2; enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA }; enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2 }; // TODO(mfbolus): for SwitchedController, may want systems to have differing // numbers of states. Use this enum as template parameter? // enum SystemListFreeDim { // kSysFreeDimNone, // kSysFreeDimX ///\u0026lt; allow state dim (x) of systems in list to be hetero // }; // place hard limits on contents of vecors/mats void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub); void Limit(Vector\u0026amp; x, data_t lb, data_t ub); void Limit(Matrix\u0026amp; x, data_t lb, data_t ub); // in-place assign that errs if there are dimension mismatches: void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); // TODO(mfbolus): this is a fudge, but for some reason, cov mats often going // numerically asymm. void ForceSymPD(Matrix\u0026amp; X); void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min = 0); void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X); Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B); inline void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Vector\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Matrix\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if (other.n_elem != some.n_elem) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vector of size \u0026#34; \u0026lt;\u0026lt; some.n_elem \u0026lt;\u0026lt; \u0026#34; with vector of size \u0026#34; \u0026lt;\u0026lt; other.n_elem \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } inline void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrix of size \u0026#34; \u0026lt;\u0026lt; some.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; some.n_cols \u0026lt;\u0026lt; \u0026#34; with matrix of size \u0026#34; \u0026lt;\u0026lt; other.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other.n_cols \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } } // namespace lds #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":76,"href":"/lds-ctrl-est/docs/api/files/mex__c__util_8h/","title":"ldsCtrlEst_h/mex_c_util.h","section":"Files","content":" ldsCtrlEst_h/mex_c_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) More\u0026hellip;\nNamespaces # Name armamexc arma/mex interface using Matlab C API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab/Octave\u0026rsquo;s C mex API.\nSource code # //===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXC_UTIL_H #define LDSCTRLEST_MEXC_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.h\u0026#34; // // If Matlab_FOUND, include matrix.h. // // (Octave does not need/have it.) // #ifdef Matlab_FOUND // #include \u0026#34;matrix.h\u0026#34; // #endif namespace armamexc { template \u0026lt;class T\u0026gt; inline auto m2T_scalar(const mxArray *matlab_scalar) -\u0026gt; T { if (mxGetData(matlab_scalar)) { return static_cast\u0026lt;T\u0026gt;(mxGetScalar(matlab_scalar)); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return 0; } template \u0026lt;class T\u0026gt; inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false, bool strict = true) -\u0026gt; arma::Mat\u0026lt;T\u0026gt; { if (mxGetData(matlab_mat)) { const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat); if (n_dim == 2) { return arma::Mat\u0026lt;T\u0026gt;(static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)), mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem, strict); } mexErrMsgTxt(\u0026#34;Number of dimensions must be 2.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } // TODO(mfbolus): make these templated. template \u0026lt;typename T\u0026gt; inline auto a2m_mat(arma::Mat\u0026lt;T\u0026gt; const \u0026amp;arma_mat) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_mat.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Mat.\u0026#34;); return nullptr; } template \u0026lt;typename T\u0026gt; inline auto a2m_vec(arma::Col\u0026lt;T\u0026gt; const \u0026amp;arma_vec) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_vec.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Col.\u0026#34;); return nullptr; } } // namespace armamexc #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":77,"href":"/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/","title":"ldsCtrlEst_h/mex_cpp_util.h","section":"Files","content":" ldsCtrlEst_h/mex_cpp_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) More\u0026hellip;\nNamespaces # Name armamexcpp arma/mex interface using Matlab C++ API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab\u0026rsquo;s C++ mex API.\nSource code # //===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXCPP_UTIL_H #define LDSCTRLEST_MEXCPP_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.hpp\u0026#34; #include \u0026#34;mexAdapter.hpp\u0026#34; namespace armamexcpp { template \u0026lt;class T\u0026gt; std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; m2a_cellmat(matlab::data::CellArray\u0026amp; matlab_cell) { size_t n_cells = matlab_cell.getNumberOfElements(); std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; arma_mat(n_cells, arma::Mat\u0026lt;T\u0026gt;(1, 1, arma::fill::zeros)); for (size_t k = 0; k \u0026lt; n_cells; k++) { matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = matlab_cell[k]; auto dims = matlab_mat.getDimensions(); arma_mat[k] = arma::Mat\u0026lt;T\u0026gt;(matlab_mat.release().get(), dims[0], dims[1]); } return arma_mat; }; template \u0026lt;class T\u0026gt; std::vector\u0026lt;T\u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt;T\u0026gt;\u0026amp; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); T* ptr = matlab_array.release().get(); std::vector\u0026lt;T\u0026gt; vec(ptr, ptr + n_elem); return vec; }; template \u0026lt;class T\u0026gt; arma::Col\u0026lt;T\u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); // T* ptr = matlab_array.release().get(); // arma::Col\u0026lt;T\u0026gt; vec(ptr, n_elem); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? arma::Col\u0026lt;T\u0026gt; vec(n_elem, arma::fill::zeros); for (size_t k = 0; k \u0026lt; n_elem; k++) { vec[k] = matlab_array[k]; } return vec; }; template \u0026lt;class T\u0026gt; arma::Mat\u0026lt;T\u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { // ArrayDimensions == std::vector\u0026lt;size_t\u0026gt; auto dims = matlab_array.getDimensions(); // T* ptr = matlab_array.release().get(); // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false) // arma::Mat\u0026lt;T\u0026gt; mat(ptr, dims[0], dims[1]); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? // // armadillo and matlab both use column-major ordering, so this should work: size_t n_elem = dims[0] * dims[1]; arma::Mat\u0026lt;T\u0026gt; mat(dims[0], dims[1], arma::fill::zeros); size_t k(0); for (auto m: matlab_array) { mat[k] = m; k++; } return mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_mat(const arma::Mat\u0026lt;T\u0026gt;\u0026amp; arma_mat, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(), arma_mat.memptr() + arma_mat.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_vec(const arma::Col\u0026lt;T\u0026gt;\u0026amp; arma_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;({arma_vec.n_elem, 1}, arma_vec.memptr(), arma_vec.memptr() + arma_vec.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; s2m_vec(const std::vector\u0026lt;T\u0026gt;\u0026amp; std_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size()); return matlab_mat; }; } // namespace armamexcpp #endif Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":78,"href":"/lds-ctrl-est/docs/terminology/model/","title":"Models","section":"LDS C+E Documentation","content":" Model Definitions # This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form: \\[\\mathbf{x}_{t\u0026#43;1} = f\\left( \\mathbf{x}_{t}, \\mathbf{v}_{t} \\right) = \\mathbf{A} \\mathbf{x}_{t} \u0026#43; \\mathbf{B} \\mathbf{v}_{t} \u0026#43; \\mathbf{m}_{t} \u0026#43; \\mathbf{w}_{t}\\] \\[\\mathbf{y}_{t} = h\\left( \\mathbf{x}_{t} \\right)\\] t : time index x : system state v = g%u : input (e.g., in physical units used for model fit) u : control signal sent to actuator (e.g., in Volts) y : system output m : process disturbance w ~ N(0, Q) : process noise/disturbance A : state matrix B : input coupling matrix g : input gain (e.g., for converting to control signal actuator voltage) n.b., assumes this conversion is linear Q : process noise covariance % : element-wise multiplication LDS with Gaussian Observations # For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.\n\\[\\mathbf{y}_{t} = \\mathbf{C} \\mathbf{x}_{t} \u0026#43; \\mathbf{d}\\] \\[\\mathbf{z}_{t} \\sim \\mathcal{N}\\left(\\mathbf{y}_{t} , \\mathbf{R} \\right)\\] z : measurement C : output matrix d : output bias R : measurement noise covariance LDS with Poisson Observations # For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.\n\\[y_{t}^{i} = \\exp \\left(\\mathbf{c}^i \\mathbf{x}_{t} \u0026#43; d^i\\right)\\] \\[z_{t}^i \\sim \\rm{Poisson} \\left(y_{t}^i \\right)\\] i : output index z : measurement (count data) c : i^th row of output matrix (C) d : output bias Model Predictive Control (MPC) # Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.\nIn the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.\n"},{"id":79,"href":"/lds-ctrl-est/docs/api/modules/","title":"Modules","section":"LDS C+E Documentation","content":" Modules # Control Mode Bit Masks provides fill types for constructing new armadillo vectors, matrices\nDefaults\nUpdated on 5 March 2025 at 16:18:10 EST\n"},{"id":80,"href":"/lds-ctrl-est/docs/api/namespaces/","title":"Namespaces","section":"LDS C+E Documentation","content":" Namespaces # armamexc arma/mex interface using Matlab C API\narmamexcpp arma/mex interface using Matlab C++ API\nlds::gaussian Linear Dynamical Systems with Gaussian observations.\nlds::poisson Linear Dynamical Systems with Poisson observations.\nstd\nUpdated on 5 March 2025 at 16:18:10 EST\n"},{"id":81,"href":"/lds-ctrl-est/docs/api/pages/","title":"Pages","section":"LDS C+E Documentation","content":" Pages # Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":82,"href":"/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/","title":"PLDS State Estimation","section":"LDS C+E Examples","content":" PLDS State Estimation Tutorial # This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[x_{t\u0026#43;1} = x_t \u0026#43; w_t\\] \\[y_{t} = \\exp\\left(x_t\\right)\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state Finally, assign the parameters using corresponding set-methods.\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); Creating the estimator # Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. To ensure robust estimates, adaptively re-estimate the process disturbance.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); Simulating estimation # In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.\n// Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.\nWith this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.\n"},{"id":83,"href":"/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/","title":"PLDS Switched Control","section":"LDS C+E Examples","content":" PLDS Switched Control Tutorial # This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.\nIn the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating the simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); The system\u0026rsquo;s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 Initially, the system will be in \u0026ldquo;mode\u0026rdquo; 1, where B = b1.\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.\nCreating the controller # Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.\nSimilar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.\nMoreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.\nPutting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying systems: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nNow that the SwitchedController is instantiated, assign its parameters.\n// Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).\nThe control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.\n// Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system\u0026rsquo;s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.\nExample simulation result # Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller\u0026rsquo;s online estimates of the output and state are shown in purple.\nNote that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.\n"},{"id":84,"href":"/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/","title":"src","section":"Files","content":" src # Files # Name src/lds.cpp misc lds namespace functions src/lds_gaussian_sys.cpp GLDS base type. src/lds_poisson_sys.cpp PLDS base type. src/lds_sys.cpp LDS base type. src/lds_uniform_vecs.cpp Uniformly sized vectors. Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":85,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/","title":"src/lds_gaussian_sys.cpp","section":"Files","content":" src/lds_gaussian_sys.cpp # GLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_gaussian_sys.h\u0026gt; lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0, data_t r0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { R_.zeros(n_y, n_y); R_.diag().fill(r0); do_recurse_Ke_=true; }; // recursively estimate Ke void lds::gaussian::System::RecurseKe() { if (!do_recurse_Ke_) { return; } // predict covariance P_ = A_ * P_ * A_.t() + Q_; // calc Kalman gain Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_); // update covariance // Reference: Ghahramani et Hinton (1996) P_ = P_ - Ke_ * C_ * P_; if (do_adapt_m) { P_m_ += Q_m_; // A_m = I (i.e., random walk) Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_); P_m_ = P_m_ - Ke_m_ * C_ * P_m_; } } // Simulate const lds::Vector\u0026amp; lds::gaussian::System::Simulate(const Vector\u0026amp; u_tm1){ f(u_tm1, true);//simulate dynamics with noise added h();//output z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure return z_; } void lds::gaussian::System::Print() { lds::System::Print(); std::cout \u0026lt;\u0026lt; \u0026#34;R: \\n\u0026#34; \u0026lt;\u0026lt; R_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":86,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/","title":"src/lds_poisson_sys.cpp","section":"Files","content":" src/lds_poisson_sys.cpp # PLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_poisson_sys.h\u0026gt; lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { diag_y_ = diagmat(y_); pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(0); }; // Correct: Given measurement (z) and current input (u), update estimate of the // state, covar, output. // // see Eden et al. 2004 void lds::poisson::System::RecurseKe() { // predict covariance P_ = A_ * P_ * A_.t() + Q_; // update cov P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_); Ke_ = P_ * C_.t(); if (do_adapt_m) { P_m_ += Q_m_; // predict (A_m = I) P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_); // update Ke_m_ = P_m_ * C_.t(); } } // Simulate Measurement: z ~ Poisson(y) const lds::Vector\u0026amp; lds::poisson::System::Simulate(const Vector\u0026amp; u_tm1) { f(u_tm1, true); // simulate dynamics with noise added h(); // output z_.zeros(); for (std::size_t k = 0; k \u0026lt; n_y_; k++) { // construct a Poisson distribution object with mean y[k] pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(y_[k]); // pull random sample from this distribution z_[k] = pd_(rng); } return z_; } // ******************* SYS_T ******************* Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":87,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8cpp/","title":"src/lds_sys.cpp","section":"Files","content":" src/lds_sys.cpp # LDS base type. More\u0026hellip;\nDetailed Description # This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- lds_sys.cpp - LDS -------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_sys.h\u0026gt; #include \u0026lt;vector\u0026gt; lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) { InitVars(p0, q0); } void lds::System::InitVars(data_t p0, data_t q0) { // initial conditions. x0_ = Vector(n_x_, fill::zeros); // includes bias (nY) and g (nU) P0_ = p0 * Matrix(n_x_, n_x_, fill::eye); m0_ = x0_; P0_m_ = P0_; // signals x_ = x0_; P_ = P0_; m_ = m0_; P_m_ = P0_m_; y_ = Vector(n_y_, fill::zeros); cx_ = Vector(n_y_, fill::zeros); z_ = Vector(n_y_, fill::zeros); // By default, random walk where each state is independent // In this way, provides independent estimates of rate per channel of output. A_ = Matrix(n_x_, n_x_, fill::eye); B_ = Matrix(n_x_, n_u_, fill::zeros); g_ = Vector(n_u_, fill::ones); Q_ = q0 * Matrix(n_x_, n_x_, fill::eye); Q_m_ = Q_; C_ = Matrix(n_y_, n_x_, fill::eye); // each state will map to an output by d_ = Vector(n_y_, fill::zeros); Ke_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain. Ke_m_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain for m adaptation. do_adapt_m = false; } // Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state // and update estimate of the state, covar, output using Kalman filter void lds::System::Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z_t) { // predict mean f(u_tm1); // dynamics h(); // output // recursively calculate esimator gains (or just keep existing values) // (also predicts+updates estimate covariance) RecurseKe(); // update x_ += Ke_ * (z_t - y_); if (do_adapt_m) { m_ += Ke_m_ * (z_t - y_); // adaptively estimating disturbance } // With new state, estimate output. h(); // --\u0026gt; posterior } void lds::System::Reset() { // reset to initial conditions x_ = x0_; // mean P_ = P0_; // cov of state estimate m_ = m0_; // process disturbance P_m_ = P0_m_; // cov of disturbance estimate h(); } std::vector\u0026lt;lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt;\u0026gt; lds::System::nstep_pred_block(lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; u, lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; z, size_t n_pred) { lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_filt; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_pred; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; y_pred; for (size_t k = 0; k \u0026lt; u.size(); k++) { Reset(); size_t n_t = arma::size(u[k])[1]; Matrix x_filt_k(n_x_, n_t, fill::zeros); Matrix x_pred_k(n_x_, n_t - n_pred, fill::zeros); Matrix y_pred_k(n_y_, n_t - n_pred, fill::zeros); for (size_t t = 0; t \u0026lt; n_t - n_pred; t++) { Vector x_pred_ahead = x_; for (size_t t_u = t; t_u \u0026lt; t + n_pred; t_u++) { x_pred_ahead = A_ * x_pred_ahead + B_ * u[k].col(t_u); } x_pred_k.col(t) = x_pred_ahead; y_pred_k.col(t) = h_(x_pred_ahead); if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; // given previous measurment } for (size_t t = n_t - n_pred; t \u0026lt; n_t; t++) { if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; } x_filt.append(x_filt_k); x_pred.append(x_pred_k); y_pred.append(y_pred_k); } return {x_filt, x_pred, y_pred}; } void lds::System::Print() { std::cout \u0026lt;\u0026lt; \u0026#34;\\n ********** SYSTEM ********** \\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;x: \\n\u0026#34; \u0026lt;\u0026lt; x_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;P: \\n\u0026#34; \u0026lt;\u0026lt; P_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;A: \\n\u0026#34; \u0026lt;\u0026lt; A_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B: \\n\u0026#34; \u0026lt;\u0026lt; B_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;g: \\n\u0026#34; \u0026lt;\u0026lt; g_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;m: \\n\u0026#34; \u0026lt;\u0026lt; m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q: \\n\u0026#34; \u0026lt;\u0026lt; Q_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q_m: \\n\u0026#34; \u0026lt;\u0026lt; Q_m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d: \\n\u0026#34; \u0026lt;\u0026lt; d_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;C: \\n\u0026#34; \u0026lt;\u0026lt; C_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;y: \\n\u0026#34; \u0026lt;\u0026lt; y_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } //******************* SYS_T ******************* Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":88,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/","title":"src/lds_uniform_vecs.cpp","section":"Files","content":" src/lds_uniform_vecs.cpp # Uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_uniform_vecs.h\u0026gt; namespace lds { UniformVectorList::UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); } UniformVectorList::UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim) : vector(std::move(vecs)) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(const UniformVectorList\u0026amp; that) : vector(that) { (*this) = that; } UniformVectorList::UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { this-\u0026gt;dim_ = this-\u0026gt;at(0).n_elem; } void UniformVectorList::CheckDimensions(size_t dim) { if (dim) { dim_ = dim; } else { dim_ = this-\u0026gt;at(0).n_elem; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Vector\u0026amp; vec : *this) { does_match = does_match \u0026amp;\u0026amp; (vec.n_elem == dim_); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } } } // namespace lds Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":89,"href":"/lds-ctrl-est/docs/api/files/lds_8cpp/","title":"src/lds.cpp","section":"Files","content":" src/lds.cpp # misc lds namespace functions More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file implements miscellaneous lds namespace functions not bound to a class.\nSource code # //===-- lds.cpp - LDS -----------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds.h\u0026gt; // insert any necessary function definitions here. namespace lds { void ForceSymPD(Matrix\u0026amp; X) { if (X.is_sympd() || !X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; // for eigenval decomp bool did_succeed(true); Vector d; Matrix u; // see first method (which may not be ideal): // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/ size_t k(1); bool is_sympd = X.is_sympd(); Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye); while (!is_sympd) { if (k \u0026gt; 100) { did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); data_t min_eig = arma::min(d); std::cerr \u0026lt;\u0026lt; \u0026#34;After multiple iterations, min eigen val = \u0026#34; \u0026lt;\u0026lt; min_eig \u0026lt;\u0026lt; \u0026#34;.\\n\u0026#34;; throw std::runtime_error( \u0026#34;Failed to make matrix symmetric positive definite.\u0026#34;); return; } // Limit(d, arma::eps(0), kInf); // force to be positive... // Matrix d_diag = arma::diagmat(d); // X = u * d_diag * u.t(); did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymPD failed.\u0026#34;); } data_t min_eig = arma::min(d); X += id * abs(min_eig) + arma::datum::eps; // make sure symm: X = (X + X.t()) / 2; // double check eigenvals positive after symmetrizing: arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); min_eig = arma::min(d); is_sympd = min_eig \u0026gt; 0; k++; } } void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min) { if (!X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; bool did_succeed(true); Vector d; Matrix u; did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymMinEig failed.\u0026#34;); } Limit(d, eig_min + arma::eps(eig_min), kInf); // enforce lower bound Matrix d_diag = arma::diagmat(d); X = u * d_diag * u.t(); // double check symmetric X = (X + X.t()) / 2; } void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X) { bool did_succeed(true); did_succeed = arma::qr_econ(Qt, L, X.t()); if (!did_succeed) { throw std::runtime_error(\u0026#34;LQ decomposition failed.\u0026#34;); } arma::inplace_trans(L); arma::inplace_trans(Qt); } Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B) { // subtract out mean auto m_a = arma::mean(A, 1); Matrix a0 = A; a0.each_col() -= m_a; auto m_b = arma::mean(B, 1); Matrix b0 = B; b0.each_col() -= m_b; Matrix cov = a0 * b0.t() / a0.n_cols; return cov; } } // namespace lds Updated on 5 March 2025 at 16:18:10 EST\n"},{"id":90,"href":"/lds-ctrl-est/docs/api/namespaces/namespacestd/","title":"std","section":"Namespaces","content":" std # Updated on 5 March 2025 at 16:18:10 EST\n"}]
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+[{"id":0,"href":"/lds-ctrl-est/docs/","title":"LDS C+E Documentation","section":"LDS Control \u0026 Estimation","content":" LDS Control \u0026amp; Estimation Documentation # "},{"id":1,"href":"/lds-ctrl-est/docs/tutorials/","title":"LDS C+E Examples","section":"LDS C+E Documentation","content":" Examples # "},{"id":2,"href":"/lds-ctrl-est/acknowledgements/","title":"Acknowledgements","section":"LDS Control \u0026 Estimation","content":" Acknowledgements # Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.\n"},{"id":3,"href":"/lds-ctrl-est/docs/getting-started/getting-started/","title":"Getting Started","section":"LDS C+E Documentation","content":" Getting Started # This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.\nldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.\nTested Configurations # Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the RelWithDebInfo build type in the CMake configure command (-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo):\nUbuntu 18.04 with GCC 7.5 compiler macOS 11 (Big Sur) with Apple Clang 12 compiler Windows 10 with Visual Studio 16.11 (2019 release) and Clang 12 compiler That being said, if you want to debug a build for a single platform, here are some things you can try:\nUse different compilers (or even different versions of a single compiler) Use different versions of vcpkg (which you can control by checking out a different commit in the vcpkg submodule) Mac Pre-requisities # Xcode Command Line Tools will get you clang, gcc, make, and git:\nxcode-select --install Homebrew is \u0026ldquo;The Missing Package Manager for macOS\u0026rdquo; which will make installing lots of things easy. Install like this:\n/bin/bash -c \u0026#34;$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\u0026#34; You can then use it to install CMake, gfortran, and pkg-config:\nbrew install cmake gfortran pkg-config Linux Pre-requisites # You\u0026rsquo;ll need Git, CMake, GCC, gfortran, etc.\nsudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential ninja-build Windows Installation # Look here for Windows-specific instructions.\nDownloading the Library # First, clone the repository along with submodules:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est\rgit submodule update --init Compilation + Installation # Now generate the cache and build using your IDE or from the command line as follows.\nmkdir build \u0026amp;\u0026amp; cd build\rcmake ..\rcmake --build . The first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.\nIf you want to use vcpkg set up somewhere besides this repo\u0026rsquo;s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE\u0026rsquo;s settings.\nYou can verify the build is working by running ctest from the build folder, which runs all the example scripts.\nOptions # This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\nLDSCTRLEST_BUILD_EXAMPLES : [default=ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake to configure/build the library.\nFor basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build cmake --build #build the project sudo make install #[optional] installs to default location (OS-specific) To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location cmake --build #build the project make install #install to /your/install/prefix To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\nLD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.\nPython bindings package ldsctrlest # With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython3_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn\u0026rsquo;t use an undesired version. That environment needs to have NumPy installed.\ncmake --build . --target python_modules The bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory (pip install pytest matplotlib first if you need to).\nSee python/ldsctrlest/README.md for usage details.\nAlso, beware that a single build will probably not work for both the standalone library and the Python package, since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.\nCommon issues # \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.\nexport CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix vcpkg fails on configuration Try running ./bootstrap-vcpkg from the vcpkg folder and try again. If that doesn\u0026rsquo;t work, try updating vcpkg to a newer version (in the source control tab, click on the commit hash by the vcpkg repo then select from the dropdown) and running boostsrap-vcpkg again. You can also try upgrading your system (e.g., apt update, apt upgrade).\nCould not find Python3 (missing: Python3_NumPy_INCLUDE_DIRS NumPy)\nMake sure NumPy is installed in the Python environment you specified. If CMake still can\u0026rsquo;t find it, you may need to tell CMake exactly where to find it by adding an argument to the configure command: -DPython3_NumPy_INCLUDE_DIR=.... You can find that location like this: python -c 'import numpy; print(numpy.get_include())'\n"},{"id":4,"href":"/lds-ctrl-est/docs/getting-started/windows/","title":"Windows","section":"LDS C+E Documentation","content":" Windows Installation # Windows Pre-requisites # Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:\nSet-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time iwr get.scoop.sh | Invoke-Expression Install Git and CMake if you don\u0026rsquo;t already have them:\nscoop install git cmake If that didn\u0026rsquo;t work, follow more detailed instructions here.\nThe easiest way to compile C++ project on Windows is with Visual Studio\u0026rsquo;s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on \u0026ldquo;Desktop development with C++.\u0026rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the \u0026ldquo;Installation details\u0026rdquo; sidebar under optional features.\nAnd the easiest way to use Visual Studio\u0026rsquo;s build tools is with VS Code, along with the CMake Tools extension. Install them and you should be ready to go.\nDownloading the Library # First, clone the repository, either from VS Code or the command line:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est You\u0026rsquo;ll need to initialize the submodules from the command line after the repo is cloned:\ngit submodule update --init Installation # When you open the folder in VS Code, you will like be prompted by the CMake Tools extension to configure the project. Make sure you select the kit (you\u0026rsquo;ll be prompted when you configure\u0026ndash;else there\u0026rsquo;s an icon in the bar on the bottom of the window or type Ctrl+Shift+P, then \u0026ldquo;cmake select kit\u0026rdquo;). Choose Clang [latest version] with GNU CLI ... amd64 assuming you are running a 64-bit OS. (MSVC may work okay too if you don\u0026rsquo;t need to build Python bindings.)\nFollow along with the \u0026ldquo;Getting Started\u0026rdquo; instructions, but where you see config options specified as -DLDSCTREST_BUILD_STATIC=OFF or -DPython3_ROOT_DIR=..., you will enter those in settings: open with Ctrl+,, click \u0026ldquo;workspace\u0026rdquo;, then search for \u0026ldquo;CMake: Configure Args\u0026rdquo; and enter each of your desired arguments as a separate item.\nTo configure, use Ctrl+Shift+P and search for the \u0026ldquo;CMake: Configure\u0026rdquo; command. To build, click the \u0026ldquo;Build\u0026rdquo; button on the bottom bar. Then click the \u0026ldquo;CTest\u0026rdquo; button to run the example scripts.\nConsiderations # Development on Windows has been more prone to bugs than on Unix systems, so if you encounter many problems, consider switching—WSL (Windows Subsystem for Linux) is a good option for Windows users who don\u0026rsquo;t want to work on a different machine.\nCompilation has been successfully tested in VS Code using the following kit, using the \u0026ldquo;RelWithDebInfo\u0026rdquo; config:\nClang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64) Troubleshooting # The build appears to work, but tests fail with code 0xc0000135 OR \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; Have you installed the library? In VS Code, use Shift+F7 to build a specific target, in this case INSTALL. If that doesn\u0026rsquo;t solve your problem, you will likely need to add the build or install folder to your PATH environment variable, which you can do using the settings GUI (search for \u0026ldquo;Edit the system environment variables\u0026rdquo;).\nOn Windows, \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted. Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":5,"href":"/lds-ctrl-est/issues-contributing/","title":"Issues Contributing","section":"LDS Control \u0026 Estimation","content":" Reporting Issues # If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.\nContributing # We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.\nWhen editing any documentation/guides, please use the markdown docs in misc/docs-hugo instead of directly editing the HTML docs. This may require having hugo, graphviz and doxygen installed through brew, as well as doxybook2 installed through a git clone.\nClone the doxybook2 repository online and place the executable in your usr/local/bin folder or another bin folder. Run sudo chmod +x /usr/local/bin/doxybook2 to give doxybook2 permissions. Run doxybook2 --help to ensure that this works properly. If permission is still denied, navigate to System Preferences \u0026gt; Security \u0026amp; Privacy \u0026gt; General. You should see a message at the bottom that says: \u0026ldquo;doxybook2 was blocked from use because it is not from an identified developer.\u0026rdquo; Click Allow Anyway.\nUpdated docs can be built by running cd scripts and ./update-docs.sh. After a successful build, navigate to /misc/docs-hugo/ and run hugo server to create a local host of the website. Access the website using local host to ensure the correct results.\n"},{"id":6,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/","title":"armamexc","section":"Namespaces","content":" armamexc # arma/mex interface using Matlab C API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; T m2T_scalar(const mxArray * matlab_scalar)\nConvert Matlab mxArray to scalar of type T. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)\nConvert matlab matrix to armadillo. template \u0026lt;typename T \u0026gt; mxArray * a2m_mat(arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat)\nConvert armadillo to matlab matrix. template \u0026lt;typename T \u0026gt; mxArray * a2m_vec(arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec)\nConvert armadillo to matlab vector. Detailed Description # Utilities for arma/mex interface using Matlab C API\nFunction Details # m2T_scalar # template \u0026lt;class T \u0026gt; inline T m2T_scalar( const mxArray * matlab_scalar ) Parameters:\nmatlab_scalar matlab scalar Template Parameters:\nT type Return: scalar of type T\nm2a_mat # template \u0026lt;class T \u0026gt; inline arma::Mat\u0026lt; T \u0026gt; m2a_mat( const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true ) Parameters:\nmatlab_mat matlab matrix copy_aux_mem [optional] whether to copy auxiliary memory strict [optional] strictly enforce the above Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_mat( arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat ) Parameters:\narma_mat armadillo matrix Return: matlab matrix\na2m_vec # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_vec( arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec ) Parameters:\narma_vec armadillo vector Return: matlab vector\nUpdated on 31 March 2025 at 16:03:55 EDT\n"},{"id":7,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/","title":"armamexcpp","section":"Namespaces","content":" armamexcpp # arma/mex interface using Matlab C++ API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat(matlab::data::CellArray \u0026amp; matlab_cell)\nConvert matlab cell array to vector of armadillo matrices. template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array)\nConvert matlab matrix to a vector of scalars. template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo vector. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat(const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec(const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab vector. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec(const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert vector of scalar T to matlab matrix. Detailed Description # utilities for arma/mex interface using Matlab C++ API\nFunction Details # m2a_cellmat # template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat( matlab::data::CellArray \u0026amp; matlab_cell ) Parameters:\nmatlab_cell matlab cell Template Parameters:\nT type Return: vector of armadillo matrices of type T\nm2s_vec # template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: vector of type T\nm2a_vec # template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: armadillo vector of type T\nm2a_mat # template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab matrix Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat( const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_mat arma matrix factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\na2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec( const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_vec armadillo vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\ns2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec( const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\nstd_vec standard vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\nUpdated on 31 March 2025 at 16:03:55 EDT\n"},{"id":8,"href":"/lds-ctrl-est/docs/terminology/control-estimation/","title":"C\u0026E","section":"LDS C+E Documentation","content":" Control \u0026amp; Estimation # The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.\nState estimation # In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by \\[\\widehat{\\mathbf{x}}_{t|t-1} = \\mathbf{A}\\widehat{\\mathbf{x}}_{t-1|t-1} \u0026#43; \\mathbf{B} u_{t-1} \u0026#43; \\mathbf{m}_{t-1} \\;,\\] \\[\\widehat{\\mathbf{x}}_{t|t} = \\widehat{\\mathbf{x}}_{t|t-1} \u0026#43; \\mathbf{K}^{\\rm e}_t \\left(\\mathbf{z}_t - \\widehat{\\mathbf{y}}_{t|t-1}\\right)\\;,\\] where \\( \\hat{\\left(\\cdot\\right)}_{t|j} \\) indicates an estimate at time \\( t \\) given data up to time \\( j \\) inclusive, \\( \\mathbf{K}^{\\rm e} \\) is the estimator gain, and\n\\[ \\widehat{\\mathbf{y}}_{t|t-1} = h\\left( \\widehat{\\mathbf{x}}_{t|t-1} \\right) \\; .\\] In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.\nIn the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.\nAdaptive estimation of process disturbance # Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.\nWhen parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk \\[\\mathbf{m}_{t} = \\mathbf{m}_{t-1} \u0026#43; \\mathbf{w}^m_{t-1} \\;,\\] where \\( \\mathbf{w}^m \\sim \\mathcal{N}\\left(0, \\mathbf{Q}_m\\right)\\) . Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.\nControl # Given the estimated state, the controller updates the inputs to the system according to the following law: \\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] where \\( \\left( \\cdot \\right)^{\\rm ref} \\) correspond to reference/target signals and \\( \\mathbf{K}^c_x \\) is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.\nIf users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as\n\\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right) - \\mathbf{K}^c_{\\rm inty} \\sum_{j=1}^{t}\\left( \\widehat{\\mathbf{y}}_j - \\mathbf{y}^{\\rm ref}_j \\right) \\;.\\] An additional option available to users is a control law that updates the change in u,\n\\[\\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\left(\\mathbf{u}_{t-1} - \\mathbf{u}^{\\rm ref}_{t-1} \\right) - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] \\[\\mathbf{u}_{t} = \\mathbf{u}_{t-1} \u0026#43; \\Delta\\mathbf{u}_{t} \\; .\\] Notice that this takes the form of a first-order difference equation for updating control (i.e., \\( \\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\mathbf{u}_{t-1} \u0026#43; \\epsilon_{t-1} \\) ), effectively low-pass filtering the input depending on the characteristics of \\( \\mathbf{K}^c_u \\) . This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.\nIntegral action and the \\( \\Delta \\mathbf{u} \\) control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR\u0026rsquo;d to use in combination.\nCalculating reference state-control from output # In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce \\( \\mathbf{x}^{\\rm ref} \\) and \\( \\mathbf{u}^{\\rm ref} \\) . Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.\nModel Predictive Control # Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.\nIn the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.\n"},{"id":9,"href":"/lds-ctrl-est/docs/api/classes/","title":"Classes","section":"LDS C+E Documentation","content":" Classes # lds::Controller\nlds::EM\nlds::Fit LDS Fit Type.\nlds::SSID\nlds::SwitchedController SwitchedController Type.\nlds::System Linear Dynamical System Type.\nlds::UniformMatrixList\nlds::UniformSystemList\nlds::UniformVectorList\nlds::gaussian::Controller Gaussian-observation Controller Type.\nlds::gaussian::Fit GLDS Fit Type.\nlds::gaussian::FitEM GLDS E-M Fit Type.\nlds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.\nlds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.\nlds::gaussian::System Gaussian LDS Type.\nlds::poisson::Controller PLDS Controller Type.\nlds::poisson::Fit PLDS Fit Type.\nlds::poisson::FitEM PLDS E-M Fit Type.\nlds::poisson::FitSSID Subspace Identification (SSID) for PLDS.\nlds::poisson::SwitchedController Poisson-observation SwitchedController Type.\nlds::poisson::System Poisson System type.\nUpdated on 31 March 2025 at 16:03:56 EDT\n"},{"id":10,"href":"/lds-ctrl-est/docs/api/modules/group__control__masks/","title":"Control Mode Bit Masks","section":"Modules","content":" Control Mode Bit Masks # provides fill types for constructing new armadillo vectors, matrices More\u0026hellip; Attributes # Name const std::size_t kControlTypeDeltaU control designed to penalize change in input const std::size_t kControlTypeIntY control using integral action const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m Detailed Description # Control mode bit masks. These can be bit-wise OR\u0026rsquo;d to use in combination.\nAttribute Details # kControlTypeDeltaU # static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY # static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM # static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\nUpdated on 31 March 2025 at 16:03:56 EDT\n"},{"id":11,"href":"/lds-ctrl-est/docs/api/modules/group__defaults/","title":"Defaults","section":"Modules","content":" Defaults # More\u0026hellip; Attributes # Name const data_t kDefaultP0 default state estimate covar const data_t kDefaultQ0 default process noise covar const data_t kDefaultR0 default output noise covar Detailed Description # Default values for common variables (e.g., default diagonal elements of covariances)\nAttribute Details # kDefaultP0 # static const data_t kDefaultP0 = 1e-6; kDefaultQ0 # static const data_t kDefaultQ0 = 1e-6; kDefaultR0 # static const data_t kDefaultR0 = 1e-2; Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":12,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/","title":"eg_glds_ctrl.cpp","section":"Examples","content":" eg_glds_ctrl.cpp # Example GLDS Control ```cpp\n//===\u0026ndash; eg_glds_ctrl.cpp - Example GLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS Control ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\n// output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false;\n// Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err\n// setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]);\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; }\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_ctrl.cpp_ ------------------------------- Updated on 31 March 2025 at 16:03:56 EDT "},{"id":13,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/","title":"eg_glds_du_plds_ctrl.cpp","section":"Examples","content":" eg_glds_du_plds_ctrl.cpp # Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u). ```cpp\n//===\u0026ndash; eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt);\n// to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU;\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_du_plds_ctrl.cpp_ ------------------------------- Updated on 31 March 2025 at 16:03:56 EDT "},{"id":14,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/","title":"eg_plds_ctrl.cpp","section":"Examples","content":" eg_plds_ctrl.cpp # Example PLDS Control ```cpp\n//===\u0026ndash; eg_plds_ctrl.cpp - Example PLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Control ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt);\n// Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err\n// Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\nsize_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\nVector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled\u0026hellip; lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system);\n// for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type);\n// set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty);\n// to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0;\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros);\n// set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y();\nx_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x();\nm_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_ctrl.cpp_ ------------------------------- Updated on 31 March 2025 at 16:03:56 EDT "},{"id":15,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/","title":"eg_plds_est.cpp","section":"Examples","content":" eg_plds_est.cpp # Example PLDS Estimation ```cpp\n//===\u0026ndash; eg_plds_est.cpp - Example PLDS Estimation \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0);\nint main() { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.\n// construct ground truth system\u0026hellip; lds::poisson::System system_true(n_u, n_x, n_y, dt);\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset();\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt);\n// Can copy parameters from another system object system_estimator = system_true;\n// wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est);\n// set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true;\n// set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;estimator:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; system_estimator.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));\n// create matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros);\n// states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\nMatrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simlation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1));\n// Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simlation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows;\nif ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026ldquo;Q must be n x n.\u0026rdquo;); }\nMatrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); }\nreturn x; }\n_Filename: eg_plds_est.cpp_ ------------------------------- Updated on 31 March 2025 at 16:03:56 EDT "},{"id":16,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/","title":"eg_plds_switched_ctrl.cpp","section":"Examples","content":" eg_plds_switched_ctrl.cpp # Example Switched PLDS Control ```cpp\n//===\u0026ndash; eg_plds_switched_ctrl.cpp - Example Switched PLDS Control \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026quot;;\n// whether to do switched control bool do_switch_ctrl = true;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt);\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices. data_t scale_sys_b = 2;\nMatrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));\ncontrolled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions\n// reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);\n// Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system);\n// set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys1:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys2:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x));\nswitched_controller.set_y_ref(y_ref0);\nstd::vectorlds::poisson::System systems_vec(3, lds::poisson::System()); lds::UniformSystemListlds::poisson::System systems(std::move(systems_vec));\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;switched_controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; switched_controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Fake measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros);\n// create Matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);\n// modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones);\n// set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } }\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace)); mode.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;mode\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_switched_ctrl.cpp_ ------------------------------- Updated on 31 March 2025 at 16:03:56 EDT "},{"id":17,"href":"/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/","title":"examples","section":"Files","content":" examples # Files # Name examples/eg_glds_ctrl.cpp examples/eg_glds_du_plds_ctrl.cpp examples/eg_plds_ctrl.cpp examples/eg_plds_est.cpp examples/eg_plds_switched_ctrl.cpp Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":18,"href":"/lds-ctrl-est/docs/api/examples/","title":"Examples","section":"LDS C+E Documentation","content":" Examples # eg_glds_ctrl.cpp Example GLDS Control.\neg_glds_du_plds_ctrl.cpp Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).\neg_plds_ctrl.cpp Example PLDS Control.\neg_plds_est.cpp Example PLDS Estimation.\neg_plds_switched_ctrl.cpp Example Switched PLDS Control.\nUpdated on 31 March 2025 at 16:03:56 EDT\n"},{"id":19,"href":"/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/","title":"examples/eg_glds_ctrl.cpp","section":"Files","content":" examples/eg_glds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":20,"href":"/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/","title":"examples/eg_glds_du_plds_ctrl.cpp","section":"Files","content":" examples/eg_glds_du_plds_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU; // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":21,"href":"/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/","title":"examples/eg_plds_ctrl.cpp","section":"Files","content":" examples/eg_plds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err // Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled... lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type); // set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":22,"href":"/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/","title":"examples/eg_plds_est.cpp","section":"Files","content":" examples/eg_plds_est.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name Matrix random_walk(size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0) int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # random_walk # Matrix random_walk( size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0 ) main # int main() Source code # //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. // construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state // Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. // turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; } Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":23,"href":"/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/","title":"examples/eg_plds_switched_ctrl.cpp","section":"Files","content":" examples/eg_plds_switched_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Source code # //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 // simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions // reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; } Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":24,"href":"/lds-ctrl-est/docs/api/files/","title":"Files","section":"LDS C+E Documentation","content":" Files # examples/eg_glds_ctrl.cpp\nexamples/eg_glds_du_plds_ctrl.cpp\nexamples/eg_plds_ctrl.cpp\nexamples/eg_plds_est.cpp\nexamples/eg_plds_switched_ctrl.cpp\nldsCtrlEst_h/lds.h lds namespace\nldsCtrlEst_h/lds_ctrl.h Controller.\nldsCtrlEst_h/lds_fit.h LDS base fit type.\nldsCtrlEst_h/lds_fit_em.h subspace identification\nldsCtrlEst_h/lds_fit_ssid.h subspace identification\nldsCtrlEst_h/lds_gaussian.h glds namespace\nldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.\nldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.\nldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.\nldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.\nldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.\nldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.\nldsCtrlEst_h/lds_poisson.h plds namespace\nldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.\nldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.\nldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.\nldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.\nldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.\nldsCtrlEst_h/lds_poisson_sys.h PLDS base type.\nldsCtrlEst_h/lds_sctrl.h SwitchedController type.\nldsCtrlEst_h/lds_sys.h LDS base type.\nldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.\nldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.\nldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.\nldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)\nldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)\nsrc/lds.cpp misc lds namespace functions\nsrc/lds_gaussian_sys.cpp GLDS base type.\nsrc/lds_poisson_sys.cpp PLDS base type.\nsrc/lds_sys.cpp LDS base type.\nsrc/lds_uniform_vecs.cpp Uniformly sized vectors.\nUpdated on 31 March 2025 at 16:03:56 EDT\n"},{"id":25,"href":"/lds-ctrl-est/docs/tutorials/eg_glds_control/","title":"GLDS Control","section":"LDS C+E Examples","content":" GLDS Control Tutorial # This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\] \\[\ry_{t} = x_t\r\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.\n/// Going to simulate a switching disturbance (m) acting on system size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_y).fill(m_low); Finally, assign the parameters using corresponding set-methods.\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); Creating the controller # Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.\nFor the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.\n// make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nWith the controller constructed, control variables may be set.\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.\nThe control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller\u0026rsquo;s online estimates of the output, state, and disturbance are given in purple.\n"},{"id":26,"href":"/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/","title":"include","section":"Files","content":" include # Directories # Name ldsCtrlEst_h Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":27,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds/","title":"lds","section":"Namespaces","content":" lds # Linear Dynamical Systems (LDS) namespace. Namespaces # Name lds::gaussian Linear Dynamical Systems with Gaussian observations. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::Controller class lds::EM class lds::Fit LDS Fit Type. class lds::SSID class lds::SwitchedController SwitchedController Type. class lds::System Linear Dynamical System Type. class lds::UniformMatrixList class lds::UniformSystemList class lds::UniformVectorList Types # Name enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}\nweighting options for SSID enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2} using double data_t using arma::Col\u0026lt; data_t \u0026gt; Vector using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Cube\u0026lt; data_t \u0026gt; Cube using arma::subview\u0026lt; data_t \u0026gt; View Functions # Name void Limit(std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub) void Limit(Vector \u0026amp; x, data_t lb, data_t ub) void Limit(Matrix \u0026amp; x, data_t lb, data_t ub) void Reassign(Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Vector in place void Reassign(Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Matrix in place void ForceSymPD(Matrix \u0026amp; X)\nforces matrix to be symmetric positive-definite void ForceSymMinEig(Matrix \u0026amp; X, data_t eig_min =0)\nforces matrix to be symmetric and have a minimum eigenvalue void lq(Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X)\nLQ decomposition. Matrix calcCov(const Matrix \u0026amp; A, const Matrix \u0026amp; B)\nCalculate covariance matrix. Attributes # Name const data_t kInf Some useful numbers. const data_t kPi Type Details # SSIDWt # Enumerator Value Description kSSIDNone None. kSSIDMOESP MOESP (AKA \u0026ldquo;robust method\u0026rdquo; in van Overschee 1996) kSSIDCVA CVA \u0026ldquo;Canonical Variate Analysis\u0026rdquo;. Weighting options for singular value decomposition performed during subspace identification (SSID)\nReference:\nvan Overschee, de Moor. 1996. Subspace Identification for Linear Systems.\nMatrixListFreeDim # Enumerator Value Description kMatFreeDimNone neither dim free to be hetero in mat list kMatFreeDim1 allow 1st dim of mats in list to be hetero kMatFreeDim2 allow 2nd dim of mats in list to be hetero data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nVector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Cube # using lds::Cube = arma::Cube\u0026lt;data_t\u0026gt;; View # using lds::View = arma::subview\u0026lt;data_t\u0026gt;; Function Details # Limit # inline void Limit( std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Vector \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Matrix \u0026amp; x, data_t lb, data_t ub ) Reassign # inline void Reassign( Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Vector other other Vector parenthetical optional description provided by caller to ease debugging Reassign # inline void Reassign( Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Matrix other other Matrix parenthetical optional description provided by caller to ease debugging ForceSymPD # void ForceSymPD( Matrix \u0026amp; X ) Parameters:\nX mutated matrix ForceSymMinEig # void ForceSymMinEig( Matrix \u0026amp; X, data_t eig_min =0 ) Parameters:\nX mutated matrix eig_min [optional] minimum eigen value lq # void lq( Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X ) Parameters:\nL lower triangle matrix Qt orthonormal matrix (transposed cf QR decomp) X matrix being decomposed calcCov # Matrix calcCov( const Matrix \u0026amp; A, const Matrix \u0026amp; B ) Parameters:\nA some matrix B some other matrix Return: covariance\nAttribute Details # kInf # static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); kPi # static const data_t kPi = arma::datum::pi; Updated on 31 March 2025 at 16:03:55 EDT\n"},{"id":28,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/","title":"lds::Controller","section":"Classes","content":" lds::Controller # More\u0026hellip;\nInherited by lds::SwitchedController\u0026lt; System \u0026gt;, lds::gaussian::Controller, lds::poisson::Controller\nPublic Functions # Name Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes # Name System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::Controller; Public Function Details # Controller # Controller() =default Controller # inline Controller( const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Controller # inline Controller( System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Control # inline const Vector \u0026amp; Control( const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step). This is the most flexible option, but requires user to have set the controller\u0026rsquo;s y_ref, x_ref, and u_ref variables.\nControlOutputReference # inline const Vector \u0026amp; ControlOutputReference( const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_estimation [optional] whether to update state estimate (if false, effectively open-loop control) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.\nsys # inline const System \u0026amp; sys() const Kc # inline const Matrix \u0026amp; Kc() const Kc_inty # inline const Matrix \u0026amp; Kc_inty() const Kc_u # inline const Matrix \u0026amp; Kc_u() const g_design # inline const Vector \u0026amp; g_design() const u_ref # inline const Vector \u0026amp; u_ref() const x_ref # inline const Vector \u0026amp; x_ref() const y_ref # inline const Vector \u0026amp; y_ref() const control_type # inline size_t control_type() const tau_awu # inline data_t tau_awu() const u_lb # inline data_t u_lb() const u_ub # inline data_t u_ub() const set_sys # inline void set_sys( const System \u0026amp; sys ) set_g_design # inline void set_g_design( const Vector \u0026amp; g_design ) set_u_ref # inline void set_u_ref( const Vector \u0026amp; u_ref ) set_x_ref # inline void set_x_ref( const Vector \u0026amp; x_ref ) set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) Reimplemented by: lds::gaussian::Controller::set_y_ref, lds::gaussian::SwitchedController::set_y_ref, lds::poisson::Controller::set_y_ref, lds::poisson::SwitchedController::set_y_ref\nset_Kc # inline void set_Kc( const Matrix \u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const Matrix \u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const Matrix \u0026amp; Kc_u ) set_tau_awu # inline void set_tau_awu( data_t tau ) set_control_type # inline void set_control_type( size_t control_type ) Parameters:\ncontrol_type control type bit mask Template Parameters:\nSystem type derived from lds::System set_u_lb # inline void set_u_lb( data_t u_lb ) Parameters:\nu_lb control lower bound set_u_ub # inline void set_u_ub( data_t u_ub ) Parameters:\nu_ub control upper bound Reset # inline void Reset() Print # inline void Print() Protected Attribute Details # sys_ # System sys_; u_ # Vector u_; u_return_ # Vector u_return_; g_design_ # Vector g_design_; u_ref_ # Vector u_ref_; u_ref_prev_ # Vector u_ref_prev_; x_ref_ # Vector x_ref_; y_ref_ # Vector y_ref_; cx_ref_ # Vector cx_ref_; Kc_ # Matrix Kc_; Kc_u_ # Matrix Kc_u_; Kc_inty_ # Matrix Kc_inty_; du_ref_ # Vector du_ref_; dv_ref_ # Vector dv_ref_; v_ref_ # Vector v_ref_; dv_ # Vector dv_; v_ # Vector v_; int_e_ # Vector int_e_; int_e_awu_adjust_ # Vector int_e_awu_adjust_; u_sat_ # Vector u_sat_; do_control_prev_ # bool do_control_prev_ = false; do_lock_control_prev_ # bool do_lock_control_prev_ = false; u_saturated_ # bool u_saturated_ = false; u_lb_ # data_t u_lb_ {}; u_ub_ # data_t u_ub_ {}; tau_awu_ # data_t tau_awu_ {}; k_awu_ # data_t k_awu_ = 0; t_since_control_onset_ # data_t t_since_control_onset_ = 0; control_type_ # size_t control_type_ {}; Updated on 31 March 2025 at 16:03:55 EDT\n"},{"id":29,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/","title":"lds::EM","section":"Classes","content":" lds::EM # More\u0026hellip;\nInherited by lds::gaussian::FitEM, lds::poisson::FitEM\nPublic Functions # Name EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions # Name void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() virtual void MaximizeOutput() =0 virtual void MaximizeMeasurement() =0 void Smooth(bool force_common_initial)\nget smoothed estimates virtual void RecurseKe(Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t) =0\nrecursively update estimator gain Ke void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::EM; Public Function Details # EM # EM() =default EM # EM( size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nn_x number of states dt sample period u_train input training data z_train measurement training data EM # EM( const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nfit0 initial fit u_train input training data z_train measurement training data ~EM # virtual ~EM() =default Run # const Fit \u0026amp; Run( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2 ) Parameters:\ncalc_dynamics [optional] whether to calculate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law max_iter max number of iterations tol convergence tolerance (max fractional abs change) Return: Fit\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nx # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const y # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const sum_E_x_t_x_t # inline const Matrix \u0026amp; sum_E_x_t_x_t() const sum_E_xu_tm1_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const sum_E_xu_t_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const n_t_tot # inline size_t n_t_tot() theta # inline const Vector \u0026amp; theta() const Protected Function Details # Expectation # void Expectation( bool force_common_initial =false ) Parameters:\nforce_common_initial whether to force common initial condition for all trials Maximization # void Maximization( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false ) Parameters:\ncalc_dynamics [optional] whether to caclulate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law MaximizeDynamics # void MaximizeDynamics() MaximizeQ # void MaximizeQ() MaximizeInitial # void MaximizeInitial() MaximizeOutput # virtual void MaximizeOutput() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput\nMaximizeMeasurement # virtual void MaximizeMeasurement() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement\nSmooth # void Smooth( bool force_common_initial ) Parameters:\nforce_common_initial whether to force common initial conditions RecurseKe # virtual void RecurseKe( Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t ) =0 Parameters:\nKe estimator gain P_pre cov of predicted state est. P_post cov of postior sate est. t time Reimplemented by: lds::gaussian::FitEM::RecurseKe, lds::poisson::FitEM::RecurseKe\nReset # void Reset() InitVars # void InitVars() UpdateTheta # Vector UpdateTheta() Return: parameter list\nProtected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; x_ # std::vector\u0026lt; Matrix \u0026gt; x_; P_ # std::vector\u0026lt; Cube \u0026gt; P_; P_t_tm1_ # std::vector\u0026lt; Cube \u0026gt; P_t_tm1_; y_ # std::vector\u0026lt; Matrix \u0026gt; y_; diag_y_ # Matrix diag_y_; sum_E_x_t_x_t_ # Matrix sum_E_x_t_x_t_; sum_E_xu_tm1_xu_tm1_ # Matrix sum_E_xu_tm1_xu_tm1_; sum_E_xu_t_xu_tm1_ # Matrix sum_E_xu_t_xu_tm1_; fit_ # Fit fit_; theta_ # Vector theta_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; Updated on 31 March 2025 at 16:03:55 EDT\n"},{"id":30,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/","title":"lds::Fit","section":"Classes","content":" lds::Fit # LDS Fit Type. #include \u0026lt;lds_fit.h\u0026gt;\nInherited by lds::gaussian::Fit, lds::poisson::Fit\nPublic Functions # Name Fit() =default\nConstructs a new Fit. Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias virtual const Matrix \u0026amp; R() const =0 void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance virtual void set_R(const Matrix \u0026amp; R) =0\nsets output noise covariance (if any) void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) =0\noutput function Protected Attributes # Name data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period ~Fit # virtual ~Fit() =default n_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const m # inline const Vector \u0026amp; m() const Q # inline const Matrix \u0026amp; Q() const x0 # inline const Vector \u0026amp; x0() const P0 # inline const Matrix \u0026amp; P0() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const R # virtual const Matrix \u0026amp; R() const =0 Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R\nset_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_g # inline void set_g( const Vector \u0026amp; g ) set_m # inline void set_m( const Vector \u0026amp; m ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # virtual void set_R( const Matrix \u0026amp; R ) =0 Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R\nset_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) f # inline View f( Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t ) Parameters:\nx state estimate (over time) u input (over time) t time index Return: view of updated state\nf # inline View f( Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t ) Parameters:\nx_pre predicted state est. x_post posterior state est. u input (over time) t time index Return: view of predicted state\nh # virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) =0 Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplemented by: lds::gaussian::Fit::h, lds::poisson::Fit::h\nProtected Attribute Details # dt_ # data_t dt_ {}; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; m_ # Vector m_; Q_ # Matrix Q_; C_ # Matrix C_; d_ # Vector d_; R_ # Matrix R_; x0_ # Vector x0_; P0_ # Matrix P0_; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; Updated on 31 March 2025 at 16:03:55 EDT\n"},{"id":31,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/","title":"lds::gaussian","section":"Namespaces","content":" lds::gaussian # Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. class lds::gaussian::Fit GLDS Fit Type. class lds::gaussian::FitEM GLDS E-M Fit Type. class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. class lds::gaussian::System Gaussian LDS Type. Updated on 31 March 2025 at 16:03:55 EDT\n"},{"id":32,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/","title":"lds::gaussian::Controller","section":"Classes","content":" lds::gaussian::Controller # Gaussian-observation Controller Type. #include \u0026lt;lds_gaussian_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 31 March 2025 at 16:03:55 EDT\n"},{"id":33,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/","title":"lds::gaussian::Fit","section":"Classes","content":" lds::gaussian::Fit # GLDS Fit Type. #include \u0026lt;lds_gaussian_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual const Matrix \u0026amp; R() const override\ngets measurement noise covariance virtual void set_R(const Matrix \u0026amp; R) override\nsets measurement noise covariance virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period R # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nh # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nUpdated on 31 March 2025 at 16:03:55 EDT\n"},{"id":34,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/","title":"lds::gaussian::FitEM","section":"Classes","content":" lds::gaussian::FitEM # GLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_gaussian_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::gaussian::FitEM; This type is used in the process of fitting GLDS models by expectation-maximization (EM). Updated on 31 March 2025 at 16:03:55 EDT\n"},{"id":35,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/","title":"lds::gaussian::FitSSID","section":"Classes","content":" lds::gaussian::FitSSID # Subspace Identification (SSID) for GLDS. #include \u0026lt;lds_gaussian_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 31 March 2025 at 16:03:55 EDT\n"},{"id":36,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/","title":"lds::gaussian::SwitchedController","section":"Classes","content":" lds::gaussian::SwitchedController # Gaussian-observation SwitchedController Type. #include \u0026lt;lds_gaussian_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 31 March 2025 at 16:03:55 EDT\n"},{"id":37,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/","title":"lds::gaussian::System","section":"Classes","content":" lds::gaussian::System # Gaussian LDS Type. #include \u0026lt;lds_gaussian_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)\nConstructs a new Gaussian System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. const Matrix \u0026amp; R() const\nGet output noise covariance. void set_Q(const Matrix \u0026amp; Q) void set_R(const Matrix \u0026amp; R)\nSet output noise covariance. void set_Ke(const Matrix \u0026amp; Ke)\nSet estimator gain. void set_Ke_m(const Matrix \u0026amp; Ke_m)\nSet disturbance estimator gain. void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively update estimator gain. Protected Attributes # Name Matrix R_ covariance of output noise bool do_recurse_Ke_ whether to recursively calculate estimator gain Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0 ) Parameters:\nn_u number of inputs (u) n_x number of states (x) n_y number of outputs (y) dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) r0 [optional] initial diagonal elements of output noise covariance (R) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nR # inline const Matrix \u0026amp; R() const set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # inline void set_R( const Matrix \u0026amp; R ) set_Ke # inline void set_Ke( const Matrix \u0026amp; Ke ) set_Ke_m # inline void set_Ke_m( const Matrix \u0026amp; Ke_m ) Print # void Print() Protected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nProtected Attribute Details # R_ # Matrix R_; do_recurse_Ke_ # bool do_recurse_Ke_ {}; Updated on 31 March 2025 at 16:03:55 EDT\n"},{"id":38,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/","title":"lds::poisson","section":"Namespaces","content":" lds::poisson # Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. class lds::poisson::Fit PLDS Fit Type. class lds::poisson::FitEM PLDS E-M Fit Type. class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. class lds::poisson::System Poisson System type. Attributes # Name std::random_device rd random device for simulating poisson data std::mt19937 rng random number generator for simulating poisson data Attribute Details # rd # static std::random_device rd; rng # static std::mt19937 rng = std::mt19937( rd()); Updated on 31 March 2025 at 16:03:55 EDT\n"},{"id":39,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/","title":"lds::poisson::Controller","section":"Classes","content":" lds::poisson::Controller # PLDS Controller Type. #include \u0026lt;lds_poisson_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 31 March 2025 at 16:03:55 EDT\n"},{"id":40,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/","title":"lds::poisson::Fit","section":"Classes","content":" lds::poisson::Fit # PLDS Fit Type. #include \u0026lt;lds_poisson_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function virtual void set_R(const Matrix \u0026amp; R) override\nsets output noise covariance (if any) virtual const Matrix \u0026amp; R() const override Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # inline Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period h # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nR # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nUpdated on 31 March 2025 at 16:03:55 EDT\n"},{"id":41,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/","title":"lds::poisson::FitEM","section":"Classes","content":" lds::poisson::FitEM # PLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_poisson_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::poisson::FitEM; This type is used in the process of fitting PLDS models by expectation-maximization (EM). Updated on 31 March 2025 at 16:03:55 EDT\n"},{"id":42,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/","title":"lds::poisson::FitSSID","section":"Classes","content":" lds::poisson::FitSSID # Subspace Identification (SSID) for PLDS. #include \u0026lt;lds_poisson_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 31 March 2025 at 16:03:55 EDT\n"},{"id":43,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/","title":"lds::poisson::SwitchedController","section":"Classes","content":" lds::poisson::SwitchedController # Poisson-observation SwitchedController Type. #include \u0026lt;lds_poisson_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 31 March 2025 at 16:03:56 EDT\n"},{"id":44,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/","title":"lds::poisson::System","section":"Classes","content":" lds::poisson::System # Poisson System type. #include \u0026lt;lds_poisson_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nConstructs a new Poisson System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively recalculate estimator gain (Ke) Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nProtected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nRecursively recalculate estimator gain (Ke).\nReferences:\nSmith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.\nEden UT, \u0026hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.\nUpdated on 31 March 2025 at 16:03:56 EDT\n"},{"id":45,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/","title":"lds::SSID","section":"Classes","content":" lds::SSID # More\u0026hellip;\nInherited by lds::gaussian::FitSSID, lds::poisson::FitSSID\nPublic Functions # Name SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions # Name void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. virtual void DecomposeData() =0\nDecompose data to lower-triangular matrix (used in Solve) void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::SSID; Public Function Details # SSID # SSID() =default SSID # SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf) ) Parameters:\nn_x number of states n_h size of block-hankel data matrix dt sample period u_train input training data z_train measurement training data d output bias Run # std::tuple\u0026lt; Fit, Vector \u0026gt; Run( SSIDWt ssid_wt ) Parameters:\nssid_wt weight for singular value decomp Return: tuple (Fit, singular values)\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nProtected Function Details # CalcD # void CalcD( data_t t_silence =0.1, data_t thresh_silence =0.001 ) Parameters:\nt_silence threshold on period of time that qualifies as \u0026ldquo;silence\u0026rdquo; thresh_silence threshold on input amplitude u that qualifies as \u0026ldquo;silence\u0026rdquo; CreateHankelDataMat # void CreateHankelDataMat() Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.\nDecomposeData # virtual void DecomposeData() =0 Reimplemented by: lds::gaussian::FitSSID::DecomposeData, lds::poisson::FitSSID::DecomposeData\nCalcSVD # void CalcSVD( SSIDWt wt ) Parameters:\nssid_wt weight for SVD Solve # void Solve( data_t wt_dc ) Parameters:\nwt_dc weight placed on getting correct DC I/O gain RecomputeExtObs # void RecomputeExtObs() Protected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; D_ # Matrix D_; fit_ # Fit fit_; g_dc_ # Matrix g_dc_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_h_ # size_t n_h_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; L_ # Matrix L_; s_ # Vector s_; ext_obs_t_ # Matrix ext_obs_t_; Updated on 31 March 2025 at 16:03:55 EDT\n"},{"id":46,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/","title":"lds::SwitchedController","section":"Classes","content":" lds::SwitchedController # SwitchedController Type. More\u0026hellip;\n#include \u0026lt;lds_sctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nInherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController\nPublic Functions # Name SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes # Name std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::SwitchedController; Public Function Details # SwitchedController # SwitchedController() =default SwitchedController # inline SwitchedController( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask SwitchedController # inline SwitchedController( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Switch # inline void Switch( size_t idx, bool do_force_switch =false ) Parameters:\nidx index do_force_switch whether to force a system switch even if already there. set_Kc # inline void set_Kc( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc ) set_Kc # inline void set_Kc( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty ) set_Kc_inty # inline void set_Kc_inty( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u ) set_Kc_u # inline void set_Kc_u( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u ) set_g_design # inline void set_g_design( const UniformVectorList \u0026amp; g ) set_g_design # inline void set_g_design( UniformVectorList \u0026amp;\u0026amp; g ) Protected Attribute Details # systems_ # std::vector\u0026lt; System \u0026gt; systems_; n_sys_ # size_t n_sys_ {}; idx_ # size_t idx_ {}; Kc_list_ # UniformMatrixList Kc_list_; Kc_inty_list_ # UniformMatrixList Kc_inty_list_; Kc_u_list_ # UniformMatrixList Kc_u_list_; g_design_list_ # UniformVectorList g_design_list_; Updated on 31 March 2025 at 16:03:55 EDT\n"},{"id":47,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_system/","title":"lds::System","section":"Classes","content":" lds::System # Linear Dynamical System Type. #include \u0026lt;lds_sys.h\u0026gt;\nInherited by lds::gaussian::System, lds::poisson::System\nPublic Functions # Name System() =default\nConstructs a new System. System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nconstructs a new System virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) =0\nsimulates system (single time step) void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function virtual void h() =0\nsystem output function virtual Vector h_(Vector x) =0\nsystem output function (stateless) size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void RecurseKe() =0\nRecursively recalculate estimator gain (Ke) void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes # Name bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes # Name std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 diagonal elements for state estimate covariance q0 diagonal elements for process noise covariance ~System # inline virtual ~System() Filter # void Filter( const Vector \u0026amp; u_tm1, const Vector \u0026amp; z ) Parameters:\nu_tm1 input at t-minus-1 z_t current measurement Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.\nSimulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) =0 Parameters:\nu_tm1 input at time t-1 Return: simulated measurement at time t\nReimplemented by: lds::gaussian::System::Simulate, lds::poisson::System::Simulate\nf # inline void f( const Vector \u0026amp; u, bool do_add_noise =false ) Parameters:\nu input do_add_noise whether to add simulated process noise h # virtual void h() =0 Reimplemented by: lds::gaussian::System::h, lds::poisson::System::h\nh_ # virtual Vector h_( Vector x ) =0 Parameters:\nx_t state at time t Return: predicted state at time t + 1\nReimplemented by: lds::gaussian::System::h_, lds::poisson::System::h_\nn_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const x # inline const Vector \u0026amp; x() const P # inline const Matrix \u0026amp; P() const m # inline const Vector \u0026amp; m() const P_m # inline const Matrix \u0026amp; P_m() const cx # inline const Vector \u0026amp; cx() const y # inline const Vector \u0026amp; y() const x0 # inline const Vector \u0026amp; x0() const m0 # inline const Vector \u0026amp; m0() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const Ke # inline const Matrix \u0026amp; Ke() const Ke_m # inline const Matrix \u0026amp; Ke_m() const Q # inline const Matrix \u0026amp; Q() Q_m # inline const Matrix \u0026amp; Q_m() P0 # inline const Matrix \u0026amp; P0() P0_m # inline const Matrix \u0026amp; P0_m() set_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_m # inline void set_m( const Vector \u0026amp; m, bool do_force_assign =false ) set_g # inline void set_g( const Vector \u0026amp; g ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_Q_m # inline void set_Q_m( const Matrix \u0026amp; Q_m ) set_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_P0_m # inline void set_P0_m( const Matrix \u0026amp; P0_m ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) set_x # inline void set_x( const Vector \u0026amp; x ) Reset # void Reset() nstep_pred_block # std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block( UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1 ) Print # void Print() Protected Function Details # RecurseKe # virtual void RecurseKe() =0 Reimplemented by: lds::gaussian::System::RecurseKe, lds::poisson::System::RecurseKe\nInitVars # void InitVars( data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Public Attribute Details # do_adapt_m # bool do_adapt_m {}; Protected Attribute Details # n_x_ # std::size_t n_x_ {}; n_u_ # std::size_t n_u_ {}; n_y_ # std::size_t n_y_ {}; dt_ # data_t dt_ {}; x_ # Vector x_; P_ # Matrix P_; m_ # Vector m_; P_m_ # Matrix P_m_; cx_ # Vector cx_; y_ # Vector y_; z_ # Vector z_; x0_ # Vector x0_; P0_ # Matrix P0_; m0_ # Vector m0_; P0_m_ # Matrix P0_m_; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; Q_ # Matrix Q_; Q_m_ # Matrix Q_m_; C_ # Matrix C_; d_ # Vector d_; Ke_ # Matrix Ke_; Ke_m_ # Matrix Ke_m_; Updated on 31 March 2025 at 16:03:55 EDT\n"},{"id":48,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/","title":"lds::UniformMatrixList","section":"Classes","content":" lds::UniformMatrixList # More\u0026hellip;\nInherits from std::vector\u0026lt; Matrix \u0026gt;\nPublic Functions # Name UniformMatrixList() =default\nConstructs a new UniformMatrixList. UniformMatrixList(const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent. UniformMatrixList(std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent. UniformMatrixList(std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent. UniformMatrixList(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nConstructs a new UniformMatrixList (copy). UniformMatrixList(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nConstructs a new UniformMatrixList (move). ~UniformMatrixList() =default\nDestroys the object. const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim(size_t n =0) const\ngets dimensions of uniformly sized matrices size_t size()\nsize of container const Matrix \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Matrix \u0026amp; that, size_t n)\nswaps input matrix with n^th matrix of list UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nassigns the contents (copy) UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nassigns the contents (move) void append(const Matrix \u0026amp; mat)\nappends a matrix to the list Detailed Description # template \u0026lt;MatrixListFreeDim D =kMatFreeDimNone\u0026gt; class lds::UniformMatrixList; Public Function Details # UniformMatrixList # UniformMatrixList() =default UniformMatrixList # explicit UniformMatrixList( const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # explicit UniformMatrixList( std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList UniformMatrixList # UniformMatrixList( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList ~UniformMatrixList # ~UniformMatrixList() =default dim # inline const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim( size_t n =0 ) const Parameters:\nn [optional] index in list of matrices Return: dimensions\nsize # inline size_t size() at # inline const Matrix \u0026amp; at( size_t n ) Swap # inline void Swap( Matrix \u0026amp; that, size_t n ) Parameters:\nthat input matrix n index where the matrix is moved operator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\noperator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\nappend # void append( const Matrix \u0026amp; mat ) Parameters:\nmat input matrix Updated on 31 March 2025 at 16:03:55 EDT\n"},{"id":49,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/","title":"lds::UniformSystemList","section":"Classes","content":" lds::UniformSystemList # More\u0026hellip;\nInherits from std::vector\u0026lt; System \u0026gt;\nPublic Functions # Name UniformSystemList() =default\nConstructs a new UniformSystemList. UniformSystemList(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by copying existing vector of System if dimensions consistent. UniformSystemList(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by moving existing vector of System if dimensions consistent. UniformSystemList(std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList from initializer_list of System if dimensions consistent. UniformSystemList(const UniformSystemList \u0026amp; that)\nConstructs a new UniformSystemList (copy). UniformSystemList(UniformSystemList \u0026amp;\u0026amp; that)\nConstructs a new UniformSystemList (move). ~UniformSystemList() =default\nDestroys the object. const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const\ngets dimensions of the uniformly sized systems size_t size()\nsize of container const System \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(System \u0026amp; that, size_t n)\nswaps input system with n^th system of list UniformSystemList \u0026amp; operator=(const UniformSystemList \u0026amp; that)\nassigns the contents (copy) UniformSystemList \u0026amp; operator=(UniformSystemList \u0026amp;\u0026amp; that)\nassigns the contents (move) Detailed Description # template \u0026lt;typename System \u0026gt; class lds::UniformSystemList; Public Function Details # UniformSystemList # UniformSystemList() =default UniformSystemList # explicit UniformSystemList( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # explicit UniformSystemList( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList UniformSystemList # UniformSystemList( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList ~UniformSystemList # ~UniformSystemList() =default dim # inline const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const size # inline size_t size() at # inline const System \u0026amp; at( size_t n ) Swap # inline void Swap( System \u0026amp; that, size_t n ) Parameters:\nthat input system n index where the system is moved operator= # inline UniformSystemList \u0026amp; operator=( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\noperator= # inline UniformSystemList \u0026amp; operator=( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\nUpdated on 31 March 2025 at 16:03:55 EDT\n"},{"id":50,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/","title":"lds::UniformVectorList","section":"Classes","content":" lds::UniformVectorList # Inherits from std::vector\u0026lt; Vector \u0026gt;\nPublic Functions # Name UniformVectorList() =default\nConstructs a new UniformVectorList. UniformVectorList(const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent. UniformVectorList(std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent. UniformVectorList(std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0)\nConstructs a new UniformVectorList from initializer_list of Vector if dimensions consistent. UniformVectorList(const UniformVectorList \u0026amp; that)\nConstructs a new UniformVectorList (copy) UniformVectorList(UniformVectorList \u0026amp;\u0026amp; that)\nConstructs a new UniformVectorList (move) ~UniformVectorList() =default\nDestroys the object. size_t dim() const\ngets dimensions of the uniformly sized matrices size_t size()\nsize of container const Vector \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Vector \u0026amp; that, size_t n)\nswaps input matrix with n^th vector of list UniformVectorList \u0026amp; operator=(const UniformVectorList \u0026amp; that)\nassigns the contents (copy) UniformVectorList \u0026amp; operator=(UniformVectorList \u0026amp;\u0026amp; that)\nassigns the contents (move) Public Function Details # UniformVectorList # UniformVectorList() =default UniformVectorList # explicit UniformVectorList( const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dims dimension UniformVectorList # explicit UniformVectorList( std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList UniformVectorList # UniformVectorList( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList ~UniformVectorList # ~UniformVectorList() =default dim # inline size_t dim() const size # inline size_t size() at # inline const Vector \u0026amp; at( size_t n ) Swap # inline void Swap( Vector \u0026amp; that, size_t n ) Parameters:\nthat input vector n index where the vector is moved operator= # inline UniformVectorList \u0026amp; operator=( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\noperator= # inline UniformVectorList \u0026amp; operator=( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\nUpdated on 31 March 2025 at 16:03:55 EDT\n"},{"id":51,"href":"/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/","title":"ldsCtrlEst_h","section":"Files","content":" ldsCtrlEst_h # Files # Name ldsCtrlEst_h/lds.h lds namespace ldsCtrlEst_h/lds_ctrl.h Controller. ldsCtrlEst_h/lds_fit.h LDS base fit type. ldsCtrlEst_h/lds_fit_em.h subspace identification ldsCtrlEst_h/lds_fit_ssid.h subspace identification ldsCtrlEst_h/lds_gaussian.h glds namespace ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller. ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type. ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type. ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type. ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type. ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type. ldsCtrlEst_h/lds_poisson.h plds namespace ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type. ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type. ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type. ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type. ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type. ldsCtrlEst_h/lds_poisson_sys.h PLDS base type. ldsCtrlEst_h/lds_sctrl.h SwitchedController type. ldsCtrlEst_h/lds_sys.h LDS base type. ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices. ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems. ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors. ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":52,"href":"/lds-ctrl-est/docs/api/files/lds__ctrl_8h/","title":"ldsCtrlEst_h/lds_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_ctrl.h # Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Controller Detailed Description # This file declares the type for control of a linear dynamical system (lds::Controller).\nSource code # //===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_CTRL_H #define LDSCTRLEST_LDS_CTRL_H // namespace #include \u0026#34;lds.h\u0026#34; // system type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class Controller { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); public: Controller() = default; Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); const Vector\u0026amp; Control(const Vector\u0026amp; z, bool do_control = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); const Vector\u0026amp; ControlOutputReference(const Vector\u0026amp; z, bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); // get methods: const System\u0026amp; sys() const { return sys_; }; const Matrix\u0026amp; Kc() const { return Kc_; }; const Matrix\u0026amp; Kc_inty() const { return Kc_inty_; }; const Matrix\u0026amp; Kc_u() const { return Kc_u_; }; const Vector\u0026amp; g_design() const { return g_design_; }; const Vector\u0026amp; u_ref() const { return u_ref_; }; const Vector\u0026amp; x_ref() const { return x_ref_; }; const Vector\u0026amp; y_ref() const { return y_ref_; }; size_t control_type() const { return control_type_; }; data_t tau_awu() const { return tau_awu_; }; data_t u_lb() const { return u_lb_; }; data_t u_ub() const { return u_ub_; }; // set methods void set_sys(const System\u0026amp; sys) { bool does_match = sys_.n_u() == sys.n_u(); does_match = does_match \u0026amp;\u0026amp; (sys_.n_x() == sys.n_x()); does_match = does_match \u0026amp;\u0026amp; (sys_.n_y() == sys.n_y()); if (does_match) { sys_ = sys; } else { throw std::runtime_error( \u0026#34;new system argument to `set_sys` does not match dimensionality of \u0026#34; \u0026#34;existing system\u0026#34;); } }; void set_g_design(const Vector\u0026amp; g_design) { Reassign(g_design_, g_design); }; void set_u_ref(const Vector\u0026amp; u_ref) { Reassign(u_ref_, u_ref); }; void set_x_ref(const Vector\u0026amp; x_ref) { Reassign(x_ref_, x_ref); cx_ref_ = sys_.C() * x_ref_; }; // y_ref needs to be handled differently depending on output fn. // (need to populate cx_ref_ too, which depends on output fn) virtual void set_y_ref(const Vector\u0026amp; y_ref) { Reassign(y_ref_, y_ref); }; void set_Kc(const Matrix\u0026amp; Kc) { Reassign(Kc_, Kc); }; void set_Kc_inty(const Matrix\u0026amp; Kc_inty) { Reassign(Kc_inty_, Kc_inty); }; void set_Kc_u(const Matrix\u0026amp; Kc_u) { Reassign(Kc_u_, Kc_u); }; void set_tau_awu(data_t tau) { tau_awu_ = tau; k_awu_ = sys_.dt() / tau_awu_; }; void set_control_type(size_t control_type); // There is no reason u_lb/ub should not be public, but making set methods // anyway. void set_u_lb(data_t u_lb) { u_lb_ = u_lb; }; void set_u_ub(data_t u_ub) { u_ub_ = u_ub; }; void Reset() { sys_.Reset(); u_ref_.zeros(); u_ref_prev_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); u_saturated_ = false; t_since_control_onset_ = 0.0; }; void Print() { sys_.Print(); std::cout \u0026lt;\u0026lt; \u0026#34;g_design : \u0026#34; \u0026lt;\u0026lt; g_design_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_lb : \u0026#34; \u0026lt;\u0026lt; u_lb_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_ub : \u0026#34; \u0026lt;\u0026lt; u_ub_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }; protected: System sys_; Vector u_; Vector u_return_; Vector g_design_; // reference signals Vector u_ref_; // create no set method for this: Vector u_ref_prev_; Vector x_ref_; Vector y_ref_; Vector cx_ref_; // Controller gains Matrix Kc_; Matrix Kc_u_; Matrix Kc_inty_; // control after g inversion // do not need set methods for these. Vector du_ref_; Vector dv_ref_; Vector v_ref_; Vector dv_; Vector v_; // integral error // do not need set method for this Vector int_e_; Vector int_e_awu_adjust_; Vector u_sat_; bool do_control_prev_ = false; bool do_lock_control_prev_ = false; // whether the g of system has become inverted from what you think it is // (gain_ref) bool u_saturated_ = false; // should be safe to have references here bc nothing needs to be done // (like reset vars) when it changes... data_t u_lb_{}; data_t u_ub_{}; data_t tau_awu_{}; data_t k_awu_ = 0; data_t t_since_control_onset_ = 0; size_t control_type_{}; private: void CalcControl(bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); void CalcSteadyStateSetPoint(); void AntiWindup(); void InitVars(size_t control_type); }; // Implement the above: template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(sys), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(std::move(sys)), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::set_control_type(size_t control_type) { if (control_type_ == control_type) { return; } // creating a blank slate... control_type_ = 0; Kc_inty_.zeros(0, 0); Kc_u_.zeros(0, 0); int_e_.zeros(0, 0); int_e_awu_adjust_.zeros(0, 0); // controller was designed to minimize integral error if (control_type \u0026amp; kControlTypeIntY) { Kc_inty_.zeros(sys_.n_u(), sys_.n_y()); int_e_.zeros(sys_.n_y()); int_e_awu_adjust_.zeros(sys_.n_u()); control_type_ = control_type_ | kControlTypeIntY; } // controller was designed to minimize deltaU // (i.e. state augmented with u) if (control_type \u0026amp; kControlTypeDeltaU) { Kc_u_.zeros(sys_.n_u(), sys_.n_u()); control_type_ = control_type_ | kControlTypeDeltaU; } // whether to adapt set point calculate with (re-estimated) process // disturbance (m) if (control_type \u0026amp; kControlTypeAdaptM) { if (sys_.do_adapt_m) // only if adapting m... { control_type_ = control_type_ | kControlTypeAdaptM; } } } // set_control_type template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::Control( const Vector\u0026amp; z, bool do_control, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement sys_.Filter(u_, z); bool do_estimation = true; // always have estimator on in this case // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::ControlOutputReference( const Vector\u0026amp; z, bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement if (do_estimation) { sys_.Filter(u_, z); } else { sys_.f(u_); } // calculate the set point // solves for u_ref and x_ref when output is at y_ref at steady state. if (do_control) { CalcSteadyStateSetPoint(); } // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcControl(bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { if (do_control \u0026amp;\u0026amp; do_estimation) { if (!do_control_prev_) { if (do_reset_at_control_onset) { Reset(); } t_since_control_onset_ = 0.0; } else { t_since_control_onset_ += sys_.dt(); } // enforce softstart on control vars. if (sigma_soft_start \u0026gt; 0) { // half-Gaussian soft-start scaling factor data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) / (2 * pow(sigma_soft_start, 2))); u_ref_ *= soft_start_sf; // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too // x_ref_ *= soft_start_sf; // cx_ref_ *= soft_start_sf; // y_ref_ *= soft_start_sf; } if (!do_lock_control) { // first do u -\u0026gt; v change of vars. (v = g.*u) // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver // control voltage u[=]V) v_ref_ = g_design_ % u_ref_; // Given FB, calc. the change in control if (control_type_ \u0026amp; kControlTypeDeltaU) { // if control designed to minimize not u but deltaU (i.e. state aug with // u): // TODO(mfbolus): Commented out for now. See note below. // du_ref_ = u_ref_ - u_ref_prev_; // dv_ref_ = g_design_ % du_ref_; // TODO(mfbolus): Assuming users want *smooth* control signals if using // kControlTypeDeltaU, it should be the case that dv_ref_ is --\u0026gt; 0. May // want to revisit, but I am going to force it to be zero unless a // situation arises that argues for keeping the above. dv_ref_.zeros(); dv_ = dv_ref_; // nominally-optimal. dv_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error dv_ -= Kc_u_ * (v_ - v_ref_); // penalty on amp u (rel to ref) if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if(!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error dv_ -= Kc_inty_ * int_e_; // control for integrated error } // update the control v_ += dv_; } else { v_ = v_ref_; // nominally-optimal. v_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if (!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error v_ -= Kc_inty_ * int_e_; // control for integrated error } } // convert back to control voltage u[=]V u_ = v_ / sys_.g(); } // else do nothing until lock is low } else { // if not control // feed through u_ref in open loop u_ = u_ref_ % g_design_ / sys_.g(); v_ = sys_.g() % u_; u_ref_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); } // ends do_control // enforce box constraints (and antiwindup) AntiWindup(); // add noise to input? // The value for u that is *returned* to user after addition of any noise, // while keeping controller/estimator blind to this addition. u_return_ = u_; if ((sigma_u_noise \u0026gt; 0.0) \u0026amp;\u0026amp; (do_control \u0026amp;\u0026amp; !do_lock_control)) { u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn); Limit(u_return_, u_lb_, u_ub_); }; // For next time step: u_ref_prev_ = u_ref_; do_control_prev_ = do_control; do_lock_control_prev_ = do_lock_control; } // CalcControl template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcSteadyStateSetPoint() { // Linearly-constrained least squares (ls). // // _reference: // Boyd \u0026amp; Vandenberghe (2018) Introduction to Applied Linear Algebra // Matrix a_ls = join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros)); Vector b_ls = cx_ref_; Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye), sys_.B() * arma::diagmat(sys_.g())); Vector d_ls = -sys_.m0(); if (control_type_ \u0026amp; kControlTypeAdaptM) { d_ls = -sys_.m(); // adapt setpoint calc with disturbance? } Matrix a_ls_t = a_ls.t(); // TODO(mfbolus): not sure why but causes seg // fault if I do not do this. Matrix phi_ls = join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()), join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros))); // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse: Matrix inv_phi = pinv(phi_ls); Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls); x_ref_ = xulam.subvec(0, sys_.n_x() - 1); u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1); cx_ref_ = sys_.C() * x_ref_; } // CalcSteadyStateSetPoint template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::AntiWindup() { u_saturated_ = false; u_sat_ = u_; // limit u and flag whether saturated for (size_t k = 0; k \u0026lt; u_.n_elem; k++) { if (u_[k] \u0026lt; u_lb_) { u_sat_[k] = u_lb_; u_saturated_ = true; } if (u_[k] \u0026gt; u_ub_) { u_sat_[k] = u_ub_; u_saturated_ = true; } } if ((control_type_ \u0026amp; kControlTypeIntY) \u0026amp;\u0026amp; (tau_awu_ \u0026lt; lds::kInf)) { // one-step back-calculation (calculate intE for u=u_sat) // (Astroem, Rundqwist 1989 warn against using this...) // int_e_awu_adjust_ = // solve(Kc_inty_, (u_ - u_sat_)); // pinv(Kc_inty) * (u-uSat); // gradual: see Astroem, Rundqwist 1989 // this is a fudge for doing MIMO gradual // n.b., went ahead and multiplied 1/T by dt so don\u0026#39;t have to do that here. int_e_awu_adjust_ = k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_); // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_); int_e_ += int_e_awu_adjust_; } // set u to saturated version u_ = u_sat_; } template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::InitVars(size_t control_type) { // initialize to default values u_ref_ = Vector(sys_.n_u(), fill::zeros); u_ref_prev_ = Vector(sys_.n_u(), fill::zeros); x_ref_ = Vector(sys_.n_x(), fill::zeros); y_ref_ = Vector(sys_.n_y(), fill::zeros); cx_ref_ = Vector(sys_.n_y(), fill::zeros); u_ = Vector(sys_.n_u(), fill::zeros); u_return_ = Vector(sys_.n_u(), fill::zeros); u_sat_ = Vector(sys_.n_u(), fill::zeros); // Might not need all these, so zero elements until later. Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros); Kc_u_ = Matrix(0, 0, fill::zeros); Kc_inty_ = Matrix(0, 0, fill::zeros); g_design_ = sys_.g(); // by default, same as model dv_ = Vector(sys_.n_u(), fill::zeros); v_ = Vector(sys_.n_u(), fill::zeros); du_ref_ = Vector(sys_.n_u(), fill::zeros); dv_ref_ = Vector(sys_.n_u(), fill::zeros); v_ref_ = Vector(sys_.n_u(), fill::zeros); int_e_ = Vector(0, fill::zeros); int_e_awu_adjust_ = Vector(0, fill::zeros); set_control_type(control_type); } } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":53,"href":"/lds-ctrl-est/docs/api/files/lds__fit__em_8h/","title":"ldsCtrlEst_h/lds_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_em.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::EM Detailed Description # This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).\nSource code # //===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_EMAX_H #define LDSCTRLEST_LDS_EMAX_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class EM { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: EM() = default; EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); virtual ~EM() = default; const Fit\u0026amp; Run(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = true, bool calc_output = true, bool calc_measurement = true, size_t max_iter = 100, data_t tol = 1e-2); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); // auto tuple = std::make_tuple(u_, z_); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; x() const { return x_; }; const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; y() const { return y_; }; const Matrix\u0026amp; sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; }; const Matrix\u0026amp; sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; }; const Matrix\u0026amp; sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; }; size_t n_t_tot() { return n_t_tot_; } const Vector\u0026amp; theta() const { return theta_; }; protected: void Expectation(bool force_common_initial = false); void Maximization(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = false, bool calc_output = false, bool calc_measurement = false); void MaximizeDynamics(); void MaximizeQ(); void MaximizeInitial(); virtual void MaximizeOutput() = 0; virtual void MaximizeMeasurement() = 0; void Smooth(bool force_common_initial); virtual void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) = 0; void Reset(); void InitVars(); Vector UpdateTheta(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; std::vector\u0026lt;Matrix\u0026gt; x_; std::vector\u0026lt;Cube\u0026gt; P_; std::vector\u0026lt;Cube\u0026gt; P_t_tm1_; std::vector\u0026lt;Matrix\u0026gt; y_; Matrix diag_y_; // expectations calculated in E-step Matrix sum_E_x_t_x_t_; Matrix sum_E_xu_tm1_xu_tm1_; Matrix sum_E_xu_t_xu_tm1_; Fit fit_; Vector theta_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; }; template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; fit_ = Fit(n_u_, n_x, n_y_, dt); u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { // make sure fit dims match I/O data if (fit0.n_u() != u_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and input training data have inconsistent dimensions\u0026#34;); } if (fit0.n_y() != z_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and output training data have inconsistent dimensions\u0026#34;); } fit_ = fit0; u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::InitVars() { // check input/output data dimensions are consistent if (z_.size() != u_.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_.at(trial).n_cols != u_.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } n_u_ = fit_.n_u(); n_x_ = fit_.n_x(); n_y_ = fit_.n_y(); dt_ = fit_.dt(); x_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); P_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); P_t_tm1_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); y_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros); P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros); } diag_y_ = Matrix(n_y_, n_y_, fill::zeros); // covariances in expectation step sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); } template \u0026lt;typename Fit\u0026gt; const Fit\u0026amp; EM\u0026lt;Fit\u0026gt;::Run(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement, size_t max_iter, data_t tol) { Reset(); // to initial conditions size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_; Vector theta(n_params); Vector theta_new(n_params); data_t max_dtheta = 1; // if solving for initial conditions, allow them be varied. // otherwise, freeze at provided values. bool force_common_initial = !calc_init; // go until parameter convergence for (size_t l = 0; l \u0026lt; max_iter; l++) { theta_ = UpdateTheta(); std::cout \u0026lt;\u0026lt; \u0026#34;Iteration \u0026#34; \u0026lt;\u0026lt; l + 1 \u0026lt;\u0026lt; \u0026#34;/\u0026#34; \u0026lt;\u0026lt; max_iter \u0026lt;\u0026lt; \u0026#34; ...\\n\u0026#34;; Expectation(force_common_initial); Maximization(calc_dynamics, calc_Q, calc_init, calc_output, calc_measurement); // check convergence theta_new = UpdateTheta(); Vector dtheta = abs(theta_new - theta_) / abs(theta_); // some parameters could be zero... arma::uvec ubi_finite = find_finite(dtheta); max_dtheta = max(dtheta.elem(ubi_finite)); std::cout \u0026lt;\u0026lt; \u0026#34;max dtheta: \u0026#34; \u0026lt;\u0026lt; max_dtheta \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; if (max_dtheta \u0026lt; tol) { std::cout \u0026lt;\u0026lt; \u0026#34;Converged.\\n\u0026#34;; break; } std::cout \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } return fit_; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Smooth(bool force_common_initial) { Matrix k_e(n_x_, n_y_); // estimator gain Cube k_backfilt; // back-filtering gains // TODO(mfbolus): this loop could be made parallel for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { Matrix x_pre(n_x_, n_t_[trial], fill::zeros); Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros); Matrix x_post(n_x_, n_t_[trial], fill::zeros); Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros); if (force_common_initial) // forces all trials to have same initial // conditions. { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); } y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); // This *should not* be necessary but make sure P is symmetric. ForceSymPD(P_[trial].slice(0)); x_pre.col(0) = x_[trial].col(0); p_pre.slice(0) = P_[trial].slice(0); x_post.col(0) = x_[trial].col(0); p_post.slice(0) = P_[trial].slice(0); // filter for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // predict fit_.f(x_pre, x_post, u_.at(trial), t); fit_.h(y_[trial], x_pre, t); diag_y_.diag() = y_[trial].col(t); // TODO(mfbolus): change if parallel // update --\u0026gt; posterior estimation RecurseKe(k_e, p_pre, p_post, t); x_post.col(t) = x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t)); y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d(); } // backfilter -\u0026gt; Smoothed estimate // Reference: // Shumway et Stoffer (1982) ForceSymPD(p_post.slice(n_t_[trial] - 1)); k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1); P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 0; t--) { // TODO(mfmbolus): should not be necessary to force symm positive def ForceSymPD(p_pre.slice(t)); ForceSymPD(p_post.slice(t - 1)); ForceSymPD(P_[trial].slice(t)); k_backfilt.slice(t - 1) = p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t)); x_[trial].col(t - 1) = x_post.col(t - 1) + k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t)); P_[trial].slice(t - 1) = p_post.slice(t - 1) + k_backfilt.slice(t - 1) * (P_[trial].slice(t) - p_pre.slice(t)) * k_backfilt.slice(t - 1).t(); } // do the same for P_t_tm1 Matrix id(n_x_, n_x_, fill::eye); P_t_tm1_[trial].slice(n_t_[trial] - 1) = (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 1; t--) { P_t_tm1_[trial].slice(t - 1) = p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() + k_backfilt.slice(t - 1) * (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) * k_backfilt.slice(t - 2).t(); } // finally, get smoothed estimate of output for (size_t t = 0; t \u0026lt; n_t_[trial]; t++) { fit_.h(y_[trial], x_[trial], t); } // samps loop } // trial loop } // Smooth // template \u0026lt;typename Fit\u0026gt; // void EM\u0026lt;Fit\u0026gt;::RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) { // // predict covar // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q(); // // update Ke // Ke = P_pre.slice(t) * fit_.C().t() * // inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R()); // // update cov // // Reference: Ghahramani et Hinton (1996) // P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t); // // // n.b. for poisson : // // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + // fit_.Q(); // // // update cov // // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ * // // fit_.C()); // // // update Ke // // Ke = P_post.slice(t) * fit_.C(); // } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Expectation(bool force_common_initial) { // calculate the mean/cov of state needed for maximizing E[pr(z|theta)] Smooth(force_common_initial); // now get the various forms of sum(E[xx\u0026#39;]) needed // n.b. Going to start at t=1 rather than 0 bc most max terms need that. // so really \u0026#34;n_t_tot_\u0026#34; is (n_t_tot_-1) n_t_tot_ = 0; sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); Vector xu_tm1(n_x_ + n_u_, fill::zeros); Vector xu_t(n_x_ + n_u_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // ------------ sum_E_x_t_x_t ------------ sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t(); sum_E_x_t_x_t_ += P_[trial].slice(t); // ------------ sum_E_xu_tm1_xu_tm1 ------------ xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1)); sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t(); sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t - 1); // ------------ sum_E_xu_t_xu_tm1 ------------ xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t)); sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t(); sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_t_tm1_[trial].slice(t); n_t_tot_ += 1; } // time } // trial } // Expectation template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement) { if (calc_output) { MaximizeOutput(); } if (calc_measurement) { MaximizeMeasurement(); } if (calc_dynamics) { MaximizeDynamics(); } if (calc_Q) { MaximizeQ(); } if (calc_init) { MaximizeInitial(); } } // Maximization template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeDynamics() { // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) * inv_sympd(sum_E_xu_tm1_xu_tm1_); fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1)); std::cout \u0026lt;\u0026lt; \u0026#34;A_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.A()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.B()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeQ() { // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 * // inv_sympd(sum_E_xu_tm1_xu_tm1_) * // sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // this way is same as above iff dynamics were just updated: // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix ab = arma::join_horiz(fit_.A(), fit_.B()); // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // From scratch method: // Q is covariance of the error between state and dynamics-predicted state // (aka process noise) // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})\u0026#39;] // t-1 terms: View sum_e_x_tm1_x_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_u_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1); View sum_e_x_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); // t, t-1 terms: View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_x_t_u_tm1 = sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); Matrix q = sum_E_x_t_x_t_; q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t(); q -= sum_e_x_t_x_tm1 * fit_.A().t(); q -= fit_.A() * sum_e_x_t_x_tm1.t(); // input-related terms: q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t(); q -= sum_e_x_t_u_tm1 * fit_.B().t(); q -= fit_.B() * sum_e_x_t_u_tm1.t(); q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t(); q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t(); q /= n_t_tot_; fit_.set_Q(q); std::cout \u0026lt;\u0026lt; \u0026#34;Q_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.Q()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // std::cout \u0026lt;\u0026lt; \u0026#34;Q_new: \\n\u0026#34; \u0026lt;\u0026lt; fit_.Q() \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeInitial() { Vector x0 = fit_.x0(); x0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { x0 += x_[trial].col(0); } x0 /= z_.size(); std::cout \u0026lt;\u0026lt; \u0026#34;x0_new[0]: \u0026#34; \u0026lt;\u0026lt; x0[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // always recalc P0 even if the initial state is fixed (at zero, for // example) Matrix e_var(n_x_, n_x_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t(); } e_var /= z_.size(); // go ahead and subtract x0*x0\u0026#39; so don\u0026#39;t have to below. e_var -= x0 * x0.t(); // To get P0, going to get initial P_ per trial and average. // (which might be wrong, but need a single number) Matrix p0 = fit_.P0(); p0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { p0 += (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var; } p0 /= z_.size(); fit_.set_P0(p0); std::cout \u0026lt;\u0026lt; \u0026#34;P0_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.P0()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeOutput() { // solve for C+d: Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros); Vector x1(n_x_ + 1, fill::zeros); x1[n_x_] = 1.0; // augment with one to solve for bias Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { x1.subvec(0, n_x_ - 1) = x_[trial].col(t); sum_zx += z_.at(trial).col(t) * x1.t(); sum_e_x1_x1 += x1 * x1.t(); sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t); } } Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1); fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1)); fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_))); std::cout \u0026lt;\u0026lt; \u0026#34;C_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.C()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.d()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeMeasurement() { // Solve for measurement noise covar size_t n_t_tot = 0; // Ghahgramani, Hinton 1996: Matrix sum_zz(n_y_, n_y_, fill::zeros); Matrix sum_yz(n_y_, n_y_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t(); // Use Cnew: sum_yz += (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t(); n_t_tot += 1; } } fit_.set_R((sum_zz - sum_yz) / n_t_tot); std::cout \u0026lt;\u0026lt; \u0026#34;R_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.R()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Reset() { // reset to initial conditions for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); } } template \u0026lt;typename Fit\u0026gt; Vector EM\u0026lt;Fit\u0026gt;::UpdateTheta() { // TODO(mfbolus): This should include n_y_ more params for d. size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_; if (fit_.R().n_elem \u0026gt; 0) { n_params += n_y_ * n_y_; } Vector theta(n_params); size_t idx_start = 0; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B()); idx_start += n_x_ * n_u_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0()); idx_start += n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C()); idx_start += n_y_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d()); idx_start += n_y_; if (fit_.R().n_elem \u0026gt; 0) { theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R()); } return theta; } } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":54,"href":"/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_ssid.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SSID Detailed Description # This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)\u0026lt;Fit\u0026gt;).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_FIT_SSID_H #define LDSCTRLEST_LDS_FIT_SSID_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class SSID { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: SSID() = default; SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d = Vector(1).fill(-kInf)); std::tuple\u0026lt;Fit, Vector\u0026gt; Run(SSIDWt ssid_wt); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } protected: void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001); void CreateHankelDataMat(); virtual void DecomposeData() = 0; void CalcSVD(SSIDWt wt); void Solve(data_t wt_dc); void RecomputeExtObs(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; Matrix D_; Fit fit_; Matrix g_dc_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_h_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; Matrix L_; Vector s_; Matrix ext_obs_t_; }; template \u0026lt;typename Fit\u0026gt; SSID\u0026lt;Fit\u0026gt;::SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d) { // check input/output data dimensions are consistent if (z_train.size() != u_train.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_train.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_train.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } dt_ = dt; n_x_ = n_x; n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; n_h_ = n_h; // dimensionality check for eventual block-hankel data matrix size_t len = n_t_tot_ - 2 * n_h_ + 1; if (len \u0026lt; (2 * n_h_ * (n_u_ + n_y_))) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;Dataset problem! More rows than columns in block-hankel data \u0026#34; \u0026#34;matrix: 2*(n_u+n_y)*n_h \u0026gt; data-length! Need higher data-length or \u0026#34; \u0026#34;lower n_h.\u0026#34;; throw std::runtime_error(ss.str()); } fit_ = Fit(n_u_, n_x_, n_y_, dt_); u_ = std::move(u_train); z_ = std::move(z_train); if (!d.is_finite() || (d.n_rows != n_y_)) { // TODO(mfbolus): implement least-square solution for impulse response with // a second input of ones. Data-driven way of accounting for offset *not* // driven by an input. // // For now, calculate output bias (d) as the // output wherever the stimulus has not been on for some amount of time. // convolve u with rectangle and take all samples. This is a reasonable // approach, since often when autonomous systems are fit (i.e., systems with // no input), they will subtract off the mean of the output. This // essentially amounts to setting output bias to the mean of the output when // there is no stimulation. data_t t_silence = 0.1; data_t thresh_silence = 0.001; CalcD(t_silence, thresh_silence); } else { fit_.set_d(d); } } template \u0026lt;typename Fit\u0026gt; std::tuple\u0026lt;Fit, Vector\u0026gt; SSID\u0026lt;Fit\u0026gt;::Run(SSIDWt ssid_wt) { // the weight on minimizing dc I/O gain only works for gaussian, // and hopefully not necessary with appropriate dataset. data_t wt_dc = 0; // std::cout \u0026lt;\u0026lt; \u0026#34;creating hankel mat\\n\u0026#34;; CreateHankelDataMat(); // std::cout \u0026lt;\u0026lt; \u0026#34;decomposing data\\n\u0026#34;; DecomposeData(); // std::cout \u0026lt;\u0026lt; \u0026#34;calculating svd\\n\u0026#34;; CalcSVD(ssid_wt); // std::cout \u0026lt;\u0026lt; \u0026#34;solving for params\\n\u0026#34;; Solve(wt_dc); // std::cout \u0026lt;\u0026lt; \u0026#34;fin\\n\u0026#34;; return std::make_tuple(fit_, s_); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcD(data_t t_silence, data_t thresh_silence) { Vector d(z_.at(0).n_rows, fill::zeros); Vector win(static_cast\u0026lt;size_t\u0026gt;(t_silence / dt_), fill::ones); Vector sum_z_silence(n_y_, fill::zeros); size_t n_silence(0); for (size_t trial = 0; trial \u0026lt; u_.size(); trial++) { // find silent samples // start by convolving with Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0)); Vector u_conv = conv(sum_u, win, \u0026#34;same\u0026#34;); // get only the samples that are silent... arma::uvec ubi_silence = find(u_conv \u0026lt;= thresh_silence); if (ubi_silence.n_elem \u0026gt; 0) { sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1); n_silence += ubi_silence.n_elem; } } if (n_silence \u0026gt; 0) { d = sum_z_silence / n_silence; } fit_.set_d(d); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat() { // temporary copy of data Matrix z(n_y_, n_t_tot_, fill::zeros); Matrix u(n_u_, n_t_tot_, fill::zeros); size_t so_far(0); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial); u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial); so_far += n_t_.at(trial); } // remove output bias z.each_col() -= fit_.d(); // calculate I/O gain @ DC while data in convenient form g_dc_ = z * pinv(u); // std::cout \u0026lt;\u0026lt; \u0026#34;G0_data = \u0026#34; \u0026lt;\u0026lt; g_dc_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // create hankel data matrix size_t len = z.n_cols - 2 * n_h_ + 1; // data length in hankel mat // block-hankel data matrix D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros); // past input auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1); // future input auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1); // past output auto y_p = D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1); // future output auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, len - 1); size_t idx = 0; for (size_t k = 0; k \u0026lt; len; k++) { idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } } D_ /= sqrt(static_cast\u0026lt;data_t\u0026gt;(len)); } // template \u0026lt;typename Fit\u0026gt; // void SSID\u0026lt;Fit\u0026gt;::DecomposeData() { // // do LQ decomp instead of calculating covariance expensive way // // Note that \u0026#34;R\u0026#34; in van Overschee is lower-triangular (L), not \u0026#34;R\u0026#34; in QR // // decomp. Very confusing. // Matrix q_t; // lq(L_, q_t, D_); // // van Overschee zeros out the other elements. // L_ = trimatl(L_); // } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcSVD(SSIDWt wt) { // submats that will be needed: auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1); auto R_23_13 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1); auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_) - 1); auto R_44 = L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14); auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1); auto Luf = Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1); auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); // aka: R_f Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1); // from van Overschee subid.m: // Rf = R((2*m+l)*i+1:2*(m+l)*i,:); % Future outputs Matrix U; Matrix V; switch (wt) { case kSSIDNone: { // No weighting. (what van Overschee calls \u0026#34;N4SID\u0026#34;) Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14; arma::svd(U, s_, V, O_k_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDMOESP: { // MOESP weighting // This is what they use in the \u0026#34;robust\u0026#34; algorithm van Overschee, de Moor // 1996 Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); svd(U, s_, V, O_k_ortho_Uf_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDCVA: { // CVA weighting // See van Overschee\u0026#39;s matlab code (subid.m): // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); Matrix inv_w1; Matrix qt1; lq(inv_w1, qt1, R_56_16); // lq decomp of R_f (future output data) inv_w1 = trimatl(inv_w1); inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1); Matrix w_o_w = arma::solve( inv_w1, O_k_ortho_Uf_sans_Qt); // alternatively // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt svd(U, s_, V, w_o_w, \u0026#34;std\u0026#34;); U = inv_w1 * U; break; } } // Truncate to model order (heart of ssid method) auto s_hat = s_.subvec(0, n_x_ - 1); Matrix diag_sqrt_s = diagmat(sqrt(s_hat)); auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1); // get extended observability and controllability mats ext_obs_t_ = u_hat * diag_sqrt_s; // extended observability matrix } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::Solve(data_t wt_dc) { // required submats auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_56_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); // Solve for params using appropriate algorithm: // robust deterministic/stochastic algorithm in van Overschee 1996 // algorithm that the authors say \u0026#34;works\u0026#34; in practice. auto ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix // This is what textbook (1996) says: // // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15); // // HOWEVER, do not know why but have to fill the last place with zeros like // authors\u0026#39; matlab implementation (see `subid.m`) // Otherwise, get ridiculous covariances (although A,C estimates are close to // same...) Matrix Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Matrix S = Tl * pinv(Tr); // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure // stability. fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1)); Matrix ext_obs_t_p1 = join_vert( ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1), Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros)); fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1); // At this point, van Overschee \u0026amp; de Moor suggest re-calculating ext_obs_t_, // ext_obs_tm1 from (A, C) because it was just an approximation. This is RecomputeExtObs(); ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); S = Tl * pinv(Tr); Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_); Matrix Mcurly = pinv(ext_obs_tm1); Matrix Pcurly = Tl - Lcurly * R_56_15; Vector Pvec = vectorise(Pcurly); Matrix Qcurly = R_23_15; // Identify [D; B], assuming D=0 and ensuring DC gain is correct Matrix sum_QcurlyT_kron_Ncurly( (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_), fill::zeros); Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols, fill::eye); eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1, eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1; // van Overschee (1996) p. 126 Matrix N1_Tl = -Lcurly; N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) += join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly); N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) += Matrix(n_y_, n_y_, fill::eye); Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros); Matrix N_k; for (size_t k = 0; k \u0026lt; n_h_; k++) { auto Qcurly_k = Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1); Nk_Tl.zeros(); Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) = N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1); N_k = Nk_Tl * eye_ext_obs_tm1; sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k); } Matrix err_vec; if (wt_dc \u0026gt; 0) { // Constraints enforced by weighted least squares // // Reference: // // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly // Excited Industrial Systems. Conference in Decision and Control. // constraint 1: assume D=0 --\u0026gt; remove the components for Dvec (this is // actually a hard constraint in that it ignores D) Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly; sum_QcurlyT_kron_Ncurly = Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_); size_t kkk = 0; for (size_t k = 1; k \u0026lt; (n_u_ + 1); k++) { size_t start_idx = k * (n_y_ + n_x_) - n_x_; for (size_t kk = 0; kk \u0026lt; n_x_; kk++) { sum_QcurlyT_kron_Ncurly.col(kkk) = sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk); kkk++; } } // constraint 2: Make sure DC I/O gain is correct Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A()); Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_)); Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0); Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 = join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0); // WEIGHTED LS // Important in practice because I care a lot about at least getting the DC // gain correct. Put x weight on minimizing error at DC, relative to others Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye); // Make weight on minimizing DC error immense so at least that // should be nailed. size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows; size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows; size_t stop_row = w.n_rows - 1; size_t stop_col = w.n_cols - 1; // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale // weight with data length? w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc; Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * sum_QcurlyT_kron_Ncurly_b_to_g0) * sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec; fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_)); // Calculate residuals and their cov. // Because I\u0026#39;ve added constraints, I need to re-calculate the right term // with b_vec instead of how van Overschee do in final algorithm. err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec; } else { // default way: *no* constraint on G0 or D=0 Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec; // TODO(mfbolus) n.b., this gets thrown away... // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_); fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_)); err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec; } // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols); // TODO(mfbolus): Something is wrong with the error calculation above. // Use the way van overschee does it in `subid.m` // WARNING: this ignores any above constraints, so Q, R will be approximate... Matrix err = Tl - S * Tr; Matrix cov_err = err * err.t(); fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1)); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::RecomputeExtObs() { ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C(); for (size_t k = 2; k \u0026lt; (n_h_ + 1); k++) { ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) = ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1, ext_obs_t_.n_cols - 1) * fit_.A(); } } } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":55,"href":"/lds-ctrl-est/docs/api/files/lds__fit_8h/","title":"ldsCtrlEst_h/lds_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_fit.h # LDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Fit LDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.\nSource code # //===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDS_FIT_HPP #define LDS_FIT_HPP // namespace #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); virtual ~Fit() = default; // get methods size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; Q() const { return Q_; }; const Vector\u0026amp; x0() const { return x0_; }; const Matrix\u0026amp; P0() const { return P0_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; // gets measurement noise virtual const Matrix\u0026amp; R() const = 0; // set methods (e.g., seeding initial fit values) void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_m(const Vector\u0026amp; m) { Reassign(m_, m); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); ForceSymPD(Q_); }; virtual void set_R(const Matrix\u0026amp; R) = 0; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); ForceSymPD(P0_); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; View f(Matrix\u0026amp; x, const Matrix\u0026amp; u, size_t t) { x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x.col(t); }; View f(Matrix\u0026amp; x_pre, const Matrix\u0026amp; x_post, const Matrix\u0026amp; u, size_t t) { x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x_pre.col(t); }; virtual View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) = 0; protected: data_t dt_{}; // Dynamics Matrix A_; Matrix B_; Vector g_; Vector m_; Matrix Q_; // Output Matrix C_; Vector d_; Matrix R_; // initial conditions Vector x0_; Matrix P0_; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; }; } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":56,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_ctrl.h # GLDS Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. Detailed Description # This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_gaussian_sys.h\u0026#34; // controller #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace gaussian { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); cx_ref_ = y_ref - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; }; } // namespace gaussian } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":57,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_em.h # GLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitEM GLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; }; } // namespace gaussian } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":58,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_ssid.h # GLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; using SSID\u0026lt;Fit\u0026gt;::Run; private: using SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat; using SSID\u0026lt;Fit\u0026gt;::CalcSVD; using SSID\u0026lt;Fit\u0026gt;::Solve; void DecomposeData() override; void SolveVanOverschee(); }; } // namespace gaussian } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":59,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit.h # GLDS fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Fit GLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // fit type #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace gaussian { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); const Matrix\u0026amp; R() const override { return R_; }; void set_R(const Matrix\u0026amp; R) override { Reassign(R_, R); ForceSymPD(R_); }; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = C_ * x.col(t) + d_; return y.col(t); }; }; }; // namespace gaussian } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":60,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sctrl.h # GLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H // controller type #include \u0026#34;lds_gaussian_ctrl.h\u0026#34; // switched controller #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace gaussian { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); cx_ref_ = y_ref - sys_.d(); } // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; }; // SwitchedController } // namespace gaussian } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":61,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/","title":"ldsCtrlEst_h/lds_gaussian_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sys.h # GLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::System Gaussian LDS Type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H #define LDSCTRLEST_LDS_GAUSSIAN_SYS_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; namespace lds { namespace gaussian { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0, data_t r0 = kDefaultR0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; // get methods const Matrix\u0026amp; R() const { return R_; }; // set methods void set_Q(const Matrix\u0026amp; Q) { lds::System::set_Q(Q); do_recurse_Ke_ = true; } void set_R(const Matrix\u0026amp; R) { Reassign(R_, R); do_recurse_Ke_ = true; }; void set_Ke(const Matrix\u0026amp; Ke) { Reassign(Ke_, Ke); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void set_Ke_m(const Matrix\u0026amp; Ke_m) { Reassign(Ke_m_, Ke_m); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void Print(); protected: void h() override { cx_ = C_ * x_; y_ = cx_ + d_; }; Vector h_(Vector x) override { return C_ * x + d_; }; void RecurseKe() override; // Gaussian-output-specific Matrix R_; bool do_recurse_Ke_{}; }; // System } // namespace gaussian } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":62,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian_8h/","title":"ldsCtrlEst_h/lds_gaussian.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian.h # glds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_H #define LDSCTRLEST_LDS_GAUSSIAN_H // namespace #include \u0026#34;lds.h\u0026#34; namespace lds { namespace gaussian { // insert any Gaussian-specific things here... } // namespace gaussian } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":63,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_ctrl.h # PLDS controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. Detailed Description # This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_CTRL_H #define LDSCTRLEST_LDS_POISSON_CTRL_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system type #include \u0026#34;lds_poisson_sys.h\u0026#34; // control type #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace poisson { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); lds::Limit(y_ref_, kYRefLb, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; private: constexpr static const data_t kYRefLb = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":64,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_em.h # PLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitEM PLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\n[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H #define LDSCTRLEST_LDS_POISSON_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override{}; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; data_t NewtonSolveC(); void AnalyticalSolveD(); }; } // namespace poisson } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":65,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_ssid.h # PLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. Detailed Description # This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/)).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H #define LDSCTRLEST_LDS_POISSON_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; private: void DecomposeData() override; void CalcCov(); void PoissonToGaussianMoments(); Matrix cov_; }; } // namespace poisson } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":66,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/","title":"ldsCtrlEst_h/lds_poisson_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit.h # PLDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Fit PLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_H #define LDSCTRLEST_LDS_POISSON_FIT_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // fit #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace poisson { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) : lds::Fit(n_u, n_x, n_y, dt){}; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = exp(C_ * x.col(t) + d_); return y.col(t); }; void set_R(const Matrix\u0026amp; R) override { std::cerr \u0026lt;\u0026lt; \u0026#34;WARNING: Cannot set R (R[0] = \u0026#34; \u0026lt;\u0026lt; R.at(0) \u0026lt;\u0026lt; \u0026#34;). No Gaussian measurement noise in Poisson observation model.\\n\u0026#34;; }; const Matrix\u0026amp; R() const override { return R_; }; }; }; // namespace poisson } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":67,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sctrl.h # PLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #include \u0026#34;lds_poisson_ctrl.h\u0026#34; #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace poisson { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); lds::Limit(y_ref_, kYRefLB, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; private: constexpr static data_t kYRefLB = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":68,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/","title":"ldsCtrlEst_h/lds_poisson_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sys.h # PLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::System Poisson System type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SYS_H #define LDSCTRLEST_LDS_POISSON_SYS_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; // needed for Poisson random number generation #include \u0026lt;random\u0026gt; namespace lds { namespace poisson { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; protected: void h() override { cx_ = C_ * x_; y_ = exp(cx_ + d_); diag_y_.diag() = y_; }; Vector h_(Vector x) override { return exp(C_ * x + d_); }; void RecurseKe() override; private: // Poisson-output-specific Matrix diag_y_; std::poisson_distribution\u0026lt;size_t\u0026gt; pd_; }; // System } // namespace poisson } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":69,"href":"/lds-ctrl-est/docs/api/files/lds__poisson_8h/","title":"ldsCtrlEst_h/lds_poisson.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson.h # plds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_H #define LDSCTRLEST_LDS_POISSON_H #include \u0026#34;lds.h\u0026#34; namespace lds { namespace poisson { // TODO(mfbolus): Not sure if defining these as static here makes the most // sense. Is there a downside to letting multiple poisson System objects share a // common random number generator? static std::random_device rd; static std::mt19937 rng = std::mt19937( rd()); } // namespace poisson } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":70,"href":"/lds-ctrl-est/docs/api/files/lds__sctrl_8h/","title":"ldsCtrlEst_h/lds_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_sctrl.h # SwitchedController type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SwitchedController SwitchedController Type. Detailed Description # This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SCTRL_H #define LDSCTRLEST_LDS_SCTRL_H #include \u0026#34;lds_ctrl.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; #include \u0026#34;lds_uniform_vecs.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class SwitchedController : public Controller\u0026lt;System\u0026gt; { public: SwitchedController() = default; SwitchedController(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); SwitchedController(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); void Switch(size_t idx, bool do_force_switch = false); void set_Kc(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc) { Kc_list_ = Kc; Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc) { Kc_list_ = std::move(Kc); Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_inty) { Kc_inty_list_ = Kc_inty; Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_inty) { Kc_inty_list_ = std::move(Kc_inty); Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_u) { Kc_u_list_ = Kc_u; Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_u) { Kc_u_list_ = std::move(Kc_u); Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(const UniformVectorList\u0026amp; g) { g_design_list_ = g; g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(UniformVectorList\u0026amp;\u0026amp; g) { g_design_list_ = std::move(g); g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; protected: std::vector\u0026lt;System\u0026gt; systems_; size_t n_sys_{}; size_t idx_{}; // controller gains could be different for each UniformMatrixList\u0026lt;\u0026gt; Kc_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_inty_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_u_list_; // design-phase input gain could also be different UniformVectorList g_design_list_; // TODO(mfbolus): not sure why I need to do this. using Controller\u0026lt;System\u0026gt;::Kc_; using Controller\u0026lt;System\u0026gt;::Kc_inty_; using Controller\u0026lt;System\u0026gt;::Kc_u_; using Controller\u0026lt;System\u0026gt;::g_design_; using Controller\u0026lt;System\u0026gt;::sys_; // using Controller\u0026lt;System\u0026gt;::u_ref_; // using Controller\u0026lt;System\u0026gt;::x_ref_; // using Controller\u0026lt;System\u0026gt;::y_ref_; // using Controller\u0026lt;System\u0026gt;::control_type_; private: void InitVars(); using lds::Controller\u0026lt;System\u0026gt;::set_sys; // using Controller\u0026lt;System\u0026gt;::set_Kc; // using Controller\u0026lt;System\u0026gt;::set_Kc_inty; // using Controller\u0026lt;System\u0026gt;::set_Kc_u; // using Controller\u0026lt;System\u0026gt;::set_g_design; }; template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(systems.at(0), u_lb, u_ub, control_type), systems_(systems) { InitVars(); } template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(System(systems.at(0).n_u(), systems.at(0).n_x(), systems.at(0).n_y(), systems.at(0).dt()), u_lb, u_ub, control_type), systems_(std::move(systems)) { InitVars(); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::InitVars() { n_sys_ = systems_.size(); sys_ = systems_.at(0); Kc_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_)); Kc_inty_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); Kc_u_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); g_design_list_ = UniformVectorList(std::vector\u0026lt;Vector\u0026gt;(n_sys_, g_design_)); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::Switch(size_t idx, bool do_force_switch) { if ((idx == idx_) \u0026amp;\u0026amp; !do_force_switch) { return; // already there. } // put old up and get new one out systems_.at(idx_) = std::move(sys_); sys_ = std::move(systems_.at(idx)); // set the state of this system to that of the previous system // TODO(mfbolus): This will only work as intended if state matrix is the same. // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp. sys_.set_m(systems_.at(idx_).m(), true); sys_.set_x(systems_.at(idx_).x()); // swap controller gains Kc_list_.Swap(Kc_, idx_); Kc_list_.Swap(Kc_, idx); if (control_type_ \u0026amp; kControlTypeIntY) { Kc_inty_list_.Swap(Kc_inty_, idx_); Kc_inty_list_.Swap(Kc_inty_, idx); } if (control_type_ \u0026amp; kControlTypeDeltaU) { Kc_u_list_.Swap(Kc_u_, idx_); Kc_u_list_.Swap(Kc_u_, idx); } g_design_list_.Swap(g_design_, idx_); g_design_list_.Swap(g_design_, idx); idx_ = idx; } // Switch } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":71,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8h/","title":"ldsCtrlEst_h/lds_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_sys.h # LDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::System Linear Dynamical System Type. Detailed Description # This file declares and partially defines the base type for linear dynamical systems ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SYS_H #define LDSCTRLEST_LDS_SYS_H #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class System { public: System() = default; System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); virtual ~System() {} void Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z); virtual const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) = 0; void f(const Vector\u0026amp; u, bool do_add_noise = false) { x_ = A_ * x_ + B_ * (g_ % u) + m_; if (do_add_noise) { x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_); } }; virtual void h() = 0; virtual Vector h_(Vector x) = 0; size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Vector\u0026amp; x() const { return x_; }; const Matrix\u0026amp; P() const { return P_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; P_m() const { return P_m_; }; const Vector\u0026amp; cx() const { return cx_; }; const Vector\u0026amp; y() const { return y_; }; const Vector\u0026amp; x0() const { return x0_; }; const Vector\u0026amp; m0() const { return m0_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; const Matrix\u0026amp; Ke() const { return Ke_; }; const Matrix\u0026amp; Ke_m() const { return Ke_m_; }; const Matrix\u0026amp; Q() { return Q_; }; const Matrix\u0026amp; Q_m() { return Q_m_; }; const Matrix\u0026amp; P0() { return P0_; }; const Matrix\u0026amp; P0_m() { return P0_m_; }; void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_m(const Vector\u0026amp; m, bool do_force_assign = false) { Reassign(m0_, m); if ((!do_adapt_m) || do_force_assign) { Reassign(m_, m); } }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); }; void set_Q_m(const Matrix\u0026amp; Q_m) { Reassign(Q_m_, Q_m); }; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); }; void set_P0_m(const Matrix\u0026amp; P0_m) { Reassign(P0_m_, P0_m); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; void set_x(const Vector\u0026amp; x) { Reassign(x_, x); h(); }; void Reset(); std::vector\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; nstep_pred_block( UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z, size_t n_pred = 1); void Print(); // safe to leave this public and non-const bool do_adapt_m{}; protected: virtual void RecurseKe() = 0; void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); std::size_t n_x_{}; std::size_t n_u_{}; std::size_t n_y_{}; data_t dt_{}; // Signals: Vector x_; Matrix P_; Vector m_; Matrix P_m_; Vector cx_; Vector y_; Vector z_; // Parameters: Vector x0_; Matrix P0_; Vector m0_; Matrix P0_m_; Matrix A_; Matrix B_; Vector g_; Matrix Q_; Matrix Q_m_; Matrix C_; Vector d_; Matrix Ke_; Matrix Ke_m_; }; // System } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":72,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/","title":"ldsCtrlEst_h/lds_uniform_mats.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_mats.h # List of uniformly sized matrices. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformMatrixList Detailed Description # This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H #define LDSCTRLEST_LDS_UNIFORM_MATS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { template \u0026lt;MatrixListFreeDim D = kMatFreeDimNone\u0026gt; class UniformMatrixList : public std::vector\u0026lt;Matrix\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Matrix\u0026gt;::vector; // don\u0026#39;t allow push_back to be used since it doesn\u0026#39;t check dims using std::vector\u0026lt;Matrix\u0026gt;::push_back; public: using std::vector\u0026lt;Matrix\u0026gt;::operator=; using std::vector\u0026lt;Matrix\u0026gt;::operator[]; using std::vector\u0026lt;Matrix\u0026gt;::begin; using std::vector\u0026lt;Matrix\u0026gt;::end; using std::vector\u0026lt;Matrix\u0026gt;::size; using std::vector\u0026lt;Matrix\u0026gt;::at; UniformMatrixList() = default; explicit UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); explicit UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; ~UniformMatrixList() = default; const std::array\u0026lt;size_t, 2\u0026gt;\u0026amp; dim(size_t n = 0) const { return dim_.at(n); } size_t size() { return std::vector\u0026lt;Matrix\u0026gt;::size(); }; const Matrix\u0026amp; at(size_t n) { return std::vector\u0026lt;Matrix\u0026gt;::at(n); }; void Swap(Matrix\u0026amp; that, size_t n); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; void append(const Matrix\u0026amp; mat); private: void CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim); std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt; dim_; }; template \u0026lt;MatrixListFreeDim D\u0026gt; inline void UniformMatrixList\u0026lt;D\u0026gt;::Swap(Matrix\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.n_cols); } if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap // not moving, since it causes memory issues. // so this method isn\u0026#39;t a memory-saver as designed for now Matrix tmp = (*this)[n]; (*this)[n] = that; that = tmp; if (D == kMatFreeDim1) { this-\u0026gt;dim_[n][0] = (*this)[n].n_rows; } if (D == kMatFreeDim2) { this-\u0026gt;dim_[n][1] = (*this)[n].n_cols; } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::append(const Matrix\u0026amp; mat) { std::array\u0026lt;size_t, 2\u0026gt; dim({mat.n_rows, mat.n_cols}); CheckDimensions(dim); std::vector\u0026lt;Matrix\u0026gt;::push_back(mat); dim_.push_back(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) { // make sure dim_ vector is initialized if (dim_.empty()) { dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(that.size(), {0, 0}); } // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices\u0026#34;; throw std::runtime_error(ss.str()); } // if dimensions a not zero and do not match, skip move with error message. bool dims_nonzero = true; for (auto d : dim_) { if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; d[0] \u0026lt; 1) { dims_nonzero = false; break; } if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; d[1] \u0026lt; 1) { dims_nonzero = false; break; } } if (dims_nonzero) { bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); } if (!does_match) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrices of size \u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; dim_[k] = that.dim(k); } return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. bool dims_nonzero = true; for (auto d : dim_) { // if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; (d[0] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; (d[1] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // } // // if (dims_nonzero) { // bool does_match = true; // if (!(D == kMatFreeDim1)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); // } // // if (!(D == kMatFreeDim2)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); // } // // if (!does_match) { // this-\u0026gt;at(0).print(\u0026#34;this[0] = \u0026#34;); // that.at(0).print(\u0026#34;that[0] = \u0026#34;); // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformMatrixList element of size (\u0026#34; \u0026lt;\u0026lt; // that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols \u0026lt;\u0026lt; \u0026#34;) for an // element of size (\u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34;). // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Matrix\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(std::move(mats)) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) : vector(that) { (*this) = that; } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { std::array\u0026lt;size_t, 2\u0026gt; dim_k({this-\u0026gt;at(k).n_rows, this-\u0026gt;at(k).n_cols}); dim_.push_back(dim_k); } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim) { // change behavior based on free dim D if ((dim[0] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim1)) { dim[0] = this-\u0026gt;at(0).n_rows; } if ((dim[1] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim2)) { dim[1] = this-\u0026gt;at(0).n_cols; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Matrix\u0026amp; mat : *this) { if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_rows == dim[0]); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_cols == dim[1]); } if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(this-\u0026gt;size(), dim); for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { dim_[k][0] = (*this)[k].n_rows; dim_[k][1] = (*this)[k].n_cols; } } } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":73,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/","title":"ldsCtrlEst_h/lds_uniform_systems.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_systems.h # List of uniformly sized Systems. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformSystemList Detailed Description # This file provides a container for uniformly sized Systems.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector // namespace #include \u0026#34;lds.h\u0026#34; // System type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class UniformSystemList : public std::vector\u0026lt;System\u0026gt; { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;System\u0026gt;::vector; using std::vector\u0026lt;System\u0026gt;::operator=; using std::vector\u0026lt;System\u0026gt;::operator[]; using std::vector\u0026lt;System\u0026gt;::at; using std::vector\u0026lt;System\u0026gt;::begin; using std::vector\u0026lt;System\u0026gt;::end; using std::vector\u0026lt;System\u0026gt;::size; public: UniformSystemList() = default; explicit UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); explicit UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(const UniformSystemList\u0026amp; that); UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept; ~UniformSystemList() = default; const std::array\u0026lt;size_t, 3\u0026gt;\u0026amp; dim() const { return dim_; } size_t size() { return std::vector\u0026lt;System\u0026gt;::size(); }; const System\u0026amp; at(size_t n) { return std::vector\u0026lt;System\u0026gt;::at(n); }; void Swap(System\u0026amp; that, size_t n); UniformSystemList\u0026amp; operator=(const UniformSystemList\u0026amp; that); UniformSystemList\u0026amp; operator=(UniformSystemList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim); std::array\u0026lt;size_t, 3\u0026gt; dim_{}; }; template \u0026lt;typename System\u0026gt; inline void UniformSystemList\u0026lt;System\u0026gt;::Swap(System\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformSystemList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = (dim_[0] == that.n_u()) \u0026amp;\u0026amp; (dim_[1] == that.n_x()) \u0026amp;\u0026amp; (dim_[2] == that.n_y()); if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformSystemList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap System tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( const UniformSystemList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_[0] + dim_[1] + dim_[2]) { std::array\u0026lt;size_t, 3\u0026gt; other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign systems of size \u0026#34; \u0026lt;\u0026lt; dim_[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2] \u0026lt;\u0026lt; \u0026#34; with systems of size \u0026#34; \u0026lt;\u0026lt; other_dim[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other_dim[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2]; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( UniformSystemList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. if (dim_[0] + dim_[1] + dim_[2]) { // bool does_match = (dim_[0] == that.at(0).n_u()) \u0026amp;\u0026amp; // (dim_[1] == that.at(0).n_x()) \u0026amp;\u0026amp; // (dim_[2] == that.at(0).n_y()); // if (!does_match) { // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformSystemList element for an element of \u0026#34; // \u0026#34;different size. Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;System\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(std::move(systems)) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList( std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const UniformSystemList\u0026amp; that) : std::vector\u0026lt;System\u0026gt;(that) { (*this) = that; } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept : std::vector\u0026lt;System\u0026gt;(std::move(that)) { this-\u0026gt;dim_[0] = this-\u0026gt;at(0).n_u(); this-\u0026gt;dim_[1] = this-\u0026gt;at(0).n_x(); this-\u0026gt;dim_[2] = this-\u0026gt;at(0).n_y(); } template \u0026lt;typename System\u0026gt; void UniformSystemList\u0026lt;System\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim) { if (dim[0] + dim[1] + dim[2]) { dim_ = dim; } else { dim_[0] = this-\u0026gt;at(0).n_u(); dim_[1] = this-\u0026gt;at(0).n_x(); dim_[2] = this-\u0026gt;at(0).n_y(); } // make sure dimensiolaties are all uniform bool does_match(true); for (const System\u0026amp; sys : *this) { does_match = does_match \u0026amp;\u0026amp; (sys.n_u() == dim_[0]); does_match = does_match \u0026amp;\u0026amp; (sys.n_x() == dim_[1]); does_match = does_match \u0026amp;\u0026amp; (sys.n_y() == dim_[2]); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input systems are not uniform.\u0026#34;); } } } } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":74,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/","title":"ldsCtrlEst_h/lds_uniform_vecs.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_vecs.h # List of uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformVectorList Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H #define LDSCTRLEST_LDS_UNIFORM_VECS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { class UniformVectorList : public std::vector\u0026lt;Vector\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Vector\u0026gt;::vector; using std::vector\u0026lt;Vector\u0026gt;::operator=; using std::vector\u0026lt;Vector\u0026gt;::operator[]; using std::vector\u0026lt;Vector\u0026gt;::at; using std::vector\u0026lt;Vector\u0026gt;::begin; using std::vector\u0026lt;Vector\u0026gt;::end; using std::vector\u0026lt;Vector\u0026gt;::size; public: UniformVectorList() = default; explicit UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim = 0); explicit UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim = 0); UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim = 0); UniformVectorList(const UniformVectorList\u0026amp; that); UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept; ~UniformVectorList() = default; size_t dim() const { return dim_; } size_t size() { return std::vector\u0026lt;Vector\u0026gt;::size(); }; const Vector\u0026amp; at(size_t n) { return std::vector\u0026lt;Vector\u0026gt;::at(n); }; void Swap(Vector\u0026amp; that, size_t n); UniformVectorList\u0026amp; operator=(const UniformVectorList\u0026amp; that); UniformVectorList\u0026amp; operator=(UniformVectorList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(size_t dim); size_t dim_{}; }; inline void UniformVectorList::Swap(Vector\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = dim_ == that.n_elem; if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap Vector tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( const UniformVectorList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_) { size_t other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ \u0026lt;\u0026lt; \u0026#34; with vectors of size \u0026#34; \u0026lt;\u0026lt; other_dim; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( UniformVectorList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors. Skipping.\\n\u0026#34;; // return (*this); // } // // if (dim_) { // size_t other_dim(that.dim()); // if (dim_ != other_dim) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ // \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; other_dim \u0026lt;\u0026lt; \u0026#34;. // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Vector\u0026gt;::operator=(std::move(that)); return (*this); } } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":75,"href":"/lds-ctrl-est/docs/api/files/lds_8h/","title":"ldsCtrlEst_h/lds.h","section":"Files","content":" ldsCtrlEst_h/lds.h # lds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)) observations.\nSource code # //===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_H #define LDSCTRLEST_LDS_H // #ifndef LDSCTRLEST // #include \u0026lt;ldsCtrlEst\u0026gt; // #endif #include \u0026lt;armadillo\u0026gt; namespace lds { using data_t = double; // may change to float (but breaks mex functions) using Vector = arma::Col\u0026lt;data_t\u0026gt;; using Matrix = arma::Mat\u0026lt;data_t\u0026gt;; using Cube = arma::Cube\u0026lt;data_t\u0026gt;; using View = arma::subview\u0026lt;data_t\u0026gt;; namespace fill = arma::fill; static const std::size_t kControlTypeDeltaU = 0x1; static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); static const data_t kPi = arma::datum::pi; static const data_t kDefaultP0 = 1e-6; static const data_t kDefaultQ0 = 1e-6; static const data_t kDefaultR0 = 1e-2; enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA }; enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2 }; // TODO(mfbolus): for SwitchedController, may want systems to have differing // numbers of states. Use this enum as template parameter? // enum SystemListFreeDim { // kSysFreeDimNone, // kSysFreeDimX ///\u0026lt; allow state dim (x) of systems in list to be hetero // }; // place hard limits on contents of vecors/mats void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub); void Limit(Vector\u0026amp; x, data_t lb, data_t ub); void Limit(Matrix\u0026amp; x, data_t lb, data_t ub); // in-place assign that errs if there are dimension mismatches: void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); // TODO(mfbolus): this is a fudge, but for some reason, cov mats often going // numerically asymm. void ForceSymPD(Matrix\u0026amp; X); void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min = 0); void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X); Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B); inline void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Vector\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Matrix\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if (other.n_elem != some.n_elem) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vector of size \u0026#34; \u0026lt;\u0026lt; some.n_elem \u0026lt;\u0026lt; \u0026#34; with vector of size \u0026#34; \u0026lt;\u0026lt; other.n_elem \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } inline void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrix of size \u0026#34; \u0026lt;\u0026lt; some.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; some.n_cols \u0026lt;\u0026lt; \u0026#34; with matrix of size \u0026#34; \u0026lt;\u0026lt; other.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other.n_cols \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } } // namespace lds #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":76,"href":"/lds-ctrl-est/docs/api/files/mex__c__util_8h/","title":"ldsCtrlEst_h/mex_c_util.h","section":"Files","content":" ldsCtrlEst_h/mex_c_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) More\u0026hellip;\nNamespaces # Name armamexc arma/mex interface using Matlab C API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab/Octave\u0026rsquo;s C mex API.\nSource code # //===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXC_UTIL_H #define LDSCTRLEST_MEXC_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.h\u0026#34; // // If Matlab_FOUND, include matrix.h. // // (Octave does not need/have it.) // #ifdef Matlab_FOUND // #include \u0026#34;matrix.h\u0026#34; // #endif namespace armamexc { template \u0026lt;class T\u0026gt; inline auto m2T_scalar(const mxArray *matlab_scalar) -\u0026gt; T { if (mxGetData(matlab_scalar)) { return static_cast\u0026lt;T\u0026gt;(mxGetScalar(matlab_scalar)); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return 0; } template \u0026lt;class T\u0026gt; inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false, bool strict = true) -\u0026gt; arma::Mat\u0026lt;T\u0026gt; { if (mxGetData(matlab_mat)) { const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat); if (n_dim == 2) { return arma::Mat\u0026lt;T\u0026gt;(static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)), mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem, strict); } mexErrMsgTxt(\u0026#34;Number of dimensions must be 2.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } // TODO(mfbolus): make these templated. template \u0026lt;typename T\u0026gt; inline auto a2m_mat(arma::Mat\u0026lt;T\u0026gt; const \u0026amp;arma_mat) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_mat.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Mat.\u0026#34;); return nullptr; } template \u0026lt;typename T\u0026gt; inline auto a2m_vec(arma::Col\u0026lt;T\u0026gt; const \u0026amp;arma_vec) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_vec.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Col.\u0026#34;); return nullptr; } } // namespace armamexc #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":77,"href":"/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/","title":"ldsCtrlEst_h/mex_cpp_util.h","section":"Files","content":" ldsCtrlEst_h/mex_cpp_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) More\u0026hellip;\nNamespaces # Name armamexcpp arma/mex interface using Matlab C++ API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab\u0026rsquo;s C++ mex API.\nSource code # //===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXCPP_UTIL_H #define LDSCTRLEST_MEXCPP_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.hpp\u0026#34; #include \u0026#34;mexAdapter.hpp\u0026#34; namespace armamexcpp { template \u0026lt;class T\u0026gt; std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; m2a_cellmat(matlab::data::CellArray\u0026amp; matlab_cell) { size_t n_cells = matlab_cell.getNumberOfElements(); std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; arma_mat(n_cells, arma::Mat\u0026lt;T\u0026gt;(1, 1, arma::fill::zeros)); for (size_t k = 0; k \u0026lt; n_cells; k++) { matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = matlab_cell[k]; auto dims = matlab_mat.getDimensions(); arma_mat[k] = arma::Mat\u0026lt;T\u0026gt;(matlab_mat.release().get(), dims[0], dims[1]); } return arma_mat; }; template \u0026lt;class T\u0026gt; std::vector\u0026lt;T\u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt;T\u0026gt;\u0026amp; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); T* ptr = matlab_array.release().get(); std::vector\u0026lt;T\u0026gt; vec(ptr, ptr + n_elem); return vec; }; template \u0026lt;class T\u0026gt; arma::Col\u0026lt;T\u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); // T* ptr = matlab_array.release().get(); // arma::Col\u0026lt;T\u0026gt; vec(ptr, n_elem); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? arma::Col\u0026lt;T\u0026gt; vec(n_elem, arma::fill::zeros); for (size_t k = 0; k \u0026lt; n_elem; k++) { vec[k] = matlab_array[k]; } return vec; }; template \u0026lt;class T\u0026gt; arma::Mat\u0026lt;T\u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { // ArrayDimensions == std::vector\u0026lt;size_t\u0026gt; auto dims = matlab_array.getDimensions(); // T* ptr = matlab_array.release().get(); // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false) // arma::Mat\u0026lt;T\u0026gt; mat(ptr, dims[0], dims[1]); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? // // armadillo and matlab both use column-major ordering, so this should work: size_t n_elem = dims[0] * dims[1]; arma::Mat\u0026lt;T\u0026gt; mat(dims[0], dims[1], arma::fill::zeros); size_t k(0); for (auto m: matlab_array) { mat[k] = m; k++; } return mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_mat(const arma::Mat\u0026lt;T\u0026gt;\u0026amp; arma_mat, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(), arma_mat.memptr() + arma_mat.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_vec(const arma::Col\u0026lt;T\u0026gt;\u0026amp; arma_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;({arma_vec.n_elem, 1}, arma_vec.memptr(), arma_vec.memptr() + arma_vec.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; s2m_vec(const std::vector\u0026lt;T\u0026gt;\u0026amp; std_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size()); return matlab_mat; }; } // namespace armamexcpp #endif Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":78,"href":"/lds-ctrl-est/docs/terminology/model/","title":"Models","section":"LDS C+E Documentation","content":" Model Definitions # This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form: \\[\\mathbf{x}_{t\u0026#43;1} = f\\left( \\mathbf{x}_{t}, \\mathbf{v}_{t} \\right) = \\mathbf{A} \\mathbf{x}_{t} \u0026#43; \\mathbf{B} \\mathbf{v}_{t} \u0026#43; \\mathbf{m}_{t} \u0026#43; \\mathbf{w}_{t}\\] \\[\\mathbf{y}_{t} = h\\left( \\mathbf{x}_{t} \\right)\\] t : time index x : system state v = g%u : input (e.g., in physical units used for model fit) u : control signal sent to actuator (e.g., in Volts) y : system output m : process disturbance w ~ N(0, Q) : process noise/disturbance A : state matrix B : input coupling matrix g : input gain (e.g., for converting to control signal actuator voltage) n.b., assumes this conversion is linear Q : process noise covariance % : element-wise multiplication LDS with Gaussian Observations # For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.\n\\[\\mathbf{y}_{t} = \\mathbf{C} \\mathbf{x}_{t} \u0026#43; \\mathbf{d}\\] \\[\\mathbf{z}_{t} \\sim \\mathcal{N}\\left(\\mathbf{y}_{t} , \\mathbf{R} \\right)\\] z : measurement C : output matrix d : output bias R : measurement noise covariance LDS with Poisson Observations # For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.\n\\[y_{t}^{i} = \\exp \\left(\\mathbf{c}^i \\mathbf{x}_{t} \u0026#43; d^i\\right)\\] \\[z_{t}^i \\sim \\rm{Poisson} \\left(y_{t}^i \\right)\\] i : output index z : measurement (count data) c : i^th row of output matrix (C) d : output bias Model Predictive Control (MPC) # Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.\nIn the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.\n"},{"id":79,"href":"/lds-ctrl-est/docs/api/modules/","title":"Modules","section":"LDS C+E Documentation","content":" Modules # Control Mode Bit Masks provides fill types for constructing new armadillo vectors, matrices\nDefaults\nUpdated on 31 March 2025 at 16:03:56 EDT\n"},{"id":80,"href":"/lds-ctrl-est/docs/api/namespaces/","title":"Namespaces","section":"LDS C+E Documentation","content":" Namespaces # armamexc arma/mex interface using Matlab C API\narmamexcpp arma/mex interface using Matlab C++ API\nlds::gaussian Linear Dynamical Systems with Gaussian observations.\nlds::poisson Linear Dynamical Systems with Poisson observations.\nstd\nUpdated on 31 March 2025 at 16:03:56 EDT\n"},{"id":81,"href":"/lds-ctrl-est/docs/api/pages/","title":"Pages","section":"LDS C+E Documentation","content":" Pages # Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":82,"href":"/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/","title":"PLDS State Estimation","section":"LDS C+E Examples","content":" PLDS State Estimation Tutorial # This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[x_{t\u0026#43;1} = x_t \u0026#43; w_t\\] \\[y_{t} = \\exp\\left(x_t\\right)\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state Finally, assign the parameters using corresponding set-methods.\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); Creating the estimator # Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. To ensure robust estimates, adaptively re-estimate the process disturbance.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); Simulating estimation # In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.\n// Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.\nWith this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.\n"},{"id":83,"href":"/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/","title":"PLDS Switched Control","section":"LDS C+E Examples","content":" PLDS Switched Control Tutorial # This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.\nIn the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating the simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); The system\u0026rsquo;s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 Initially, the system will be in \u0026ldquo;mode\u0026rdquo; 1, where B = b1.\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.\nCreating the controller # Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.\nSimilar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.\nMoreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.\nPutting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying systems: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nNow that the SwitchedController is instantiated, assign its parameters.\n// Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).\nThe control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.\n// Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system\u0026rsquo;s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.\nExample simulation result # Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller\u0026rsquo;s online estimates of the output and state are shown in purple.\nNote that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.\n"},{"id":84,"href":"/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/","title":"src","section":"Files","content":" src # Files # Name src/lds.cpp misc lds namespace functions src/lds_gaussian_sys.cpp GLDS base type. src/lds_poisson_sys.cpp PLDS base type. src/lds_sys.cpp LDS base type. src/lds_uniform_vecs.cpp Uniformly sized vectors. Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":85,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/","title":"src/lds_gaussian_sys.cpp","section":"Files","content":" src/lds_gaussian_sys.cpp # GLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_gaussian_sys.h\u0026gt; lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0, data_t r0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { R_.zeros(n_y, n_y); R_.diag().fill(r0); do_recurse_Ke_=true; }; // recursively estimate Ke void lds::gaussian::System::RecurseKe() { if (!do_recurse_Ke_) { return; } // predict covariance P_ = A_ * P_ * A_.t() + Q_; // calc Kalman gain Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_); // update covariance // Reference: Ghahramani et Hinton (1996) P_ = P_ - Ke_ * C_ * P_; if (do_adapt_m) { P_m_ += Q_m_; // A_m = I (i.e., random walk) Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_); P_m_ = P_m_ - Ke_m_ * C_ * P_m_; } } // Simulate const lds::Vector\u0026amp; lds::gaussian::System::Simulate(const Vector\u0026amp; u_tm1){ f(u_tm1, true);//simulate dynamics with noise added h();//output z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure return z_; } void lds::gaussian::System::Print() { lds::System::Print(); std::cout \u0026lt;\u0026lt; \u0026#34;R: \\n\u0026#34; \u0026lt;\u0026lt; R_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":86,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/","title":"src/lds_poisson_sys.cpp","section":"Files","content":" src/lds_poisson_sys.cpp # PLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_poisson_sys.h\u0026gt; lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { diag_y_ = diagmat(y_); pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(0); }; // Correct: Given measurement (z) and current input (u), update estimate of the // state, covar, output. // // see Eden et al. 2004 void lds::poisson::System::RecurseKe() { // predict covariance P_ = A_ * P_ * A_.t() + Q_; // update cov P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_); Ke_ = P_ * C_.t(); if (do_adapt_m) { P_m_ += Q_m_; // predict (A_m = I) P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_); // update Ke_m_ = P_m_ * C_.t(); } } // Simulate Measurement: z ~ Poisson(y) const lds::Vector\u0026amp; lds::poisson::System::Simulate(const Vector\u0026amp; u_tm1) { f(u_tm1, true); // simulate dynamics with noise added h(); // output z_.zeros(); for (std::size_t k = 0; k \u0026lt; n_y_; k++) { // construct a Poisson distribution object with mean y[k] pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(y_[k]); // pull random sample from this distribution z_[k] = pd_(rng); } return z_; } // ******************* SYS_T ******************* Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":87,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8cpp/","title":"src/lds_sys.cpp","section":"Files","content":" src/lds_sys.cpp # LDS base type. More\u0026hellip;\nDetailed Description # This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- lds_sys.cpp - LDS -------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_sys.h\u0026gt; #include \u0026lt;vector\u0026gt; lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) { InitVars(p0, q0); } void lds::System::InitVars(data_t p0, data_t q0) { // initial conditions. x0_ = Vector(n_x_, fill::zeros); // includes bias (nY) and g (nU) P0_ = p0 * Matrix(n_x_, n_x_, fill::eye); m0_ = x0_; P0_m_ = P0_; // signals x_ = x0_; P_ = P0_; m_ = m0_; P_m_ = P0_m_; y_ = Vector(n_y_, fill::zeros); cx_ = Vector(n_y_, fill::zeros); z_ = Vector(n_y_, fill::zeros); // By default, random walk where each state is independent // In this way, provides independent estimates of rate per channel of output. A_ = Matrix(n_x_, n_x_, fill::eye); B_ = Matrix(n_x_, n_u_, fill::zeros); g_ = Vector(n_u_, fill::ones); Q_ = q0 * Matrix(n_x_, n_x_, fill::eye); Q_m_ = Q_; C_ = Matrix(n_y_, n_x_, fill::eye); // each state will map to an output by d_ = Vector(n_y_, fill::zeros); Ke_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain. Ke_m_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain for m adaptation. do_adapt_m = false; } // Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state // and update estimate of the state, covar, output using Kalman filter void lds::System::Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z_t) { // predict mean f(u_tm1); // dynamics h(); // output // recursively calculate esimator gains (or just keep existing values) // (also predicts+updates estimate covariance) RecurseKe(); // update x_ += Ke_ * (z_t - y_); if (do_adapt_m) { m_ += Ke_m_ * (z_t - y_); // adaptively estimating disturbance } // With new state, estimate output. h(); // --\u0026gt; posterior } void lds::System::Reset() { // reset to initial conditions x_ = x0_; // mean P_ = P0_; // cov of state estimate m_ = m0_; // process disturbance P_m_ = P0_m_; // cov of disturbance estimate h(); } std::vector\u0026lt;lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt;\u0026gt; lds::System::nstep_pred_block(lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; u, lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; z, size_t n_pred) { lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_filt; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_pred; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; y_pred; for (size_t k = 0; k \u0026lt; u.size(); k++) { Reset(); size_t n_t = arma::size(u[k])[1]; Matrix x_filt_k(n_x_, n_t, fill::zeros); Matrix x_pred_k(n_x_, n_t - n_pred, fill::zeros); Matrix y_pred_k(n_y_, n_t - n_pred, fill::zeros); for (size_t t = 0; t \u0026lt; n_t - n_pred; t++) { Vector x_pred_ahead = x_; for (size_t t_u = t; t_u \u0026lt; t + n_pred; t_u++) { x_pred_ahead = A_ * x_pred_ahead + B_ * u[k].col(t_u); } x_pred_k.col(t) = x_pred_ahead; y_pred_k.col(t) = h_(x_pred_ahead); if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; // given previous measurment } for (size_t t = n_t - n_pred; t \u0026lt; n_t; t++) { if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; } x_filt.append(x_filt_k); x_pred.append(x_pred_k); y_pred.append(y_pred_k); } return {x_filt, x_pred, y_pred}; } void lds::System::Print() { std::cout \u0026lt;\u0026lt; \u0026#34;\\n ********** SYSTEM ********** \\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;x: \\n\u0026#34; \u0026lt;\u0026lt; x_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;P: \\n\u0026#34; \u0026lt;\u0026lt; P_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;A: \\n\u0026#34; \u0026lt;\u0026lt; A_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B: \\n\u0026#34; \u0026lt;\u0026lt; B_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;g: \\n\u0026#34; \u0026lt;\u0026lt; g_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;m: \\n\u0026#34; \u0026lt;\u0026lt; m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q: \\n\u0026#34; \u0026lt;\u0026lt; Q_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q_m: \\n\u0026#34; \u0026lt;\u0026lt; Q_m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d: \\n\u0026#34; \u0026lt;\u0026lt; d_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;C: \\n\u0026#34; \u0026lt;\u0026lt; C_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;y: \\n\u0026#34; \u0026lt;\u0026lt; y_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } //******************* SYS_T ******************* Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":88,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/","title":"src/lds_uniform_vecs.cpp","section":"Files","content":" src/lds_uniform_vecs.cpp # Uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_uniform_vecs.h\u0026gt; namespace lds { UniformVectorList::UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); } UniformVectorList::UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim) : vector(std::move(vecs)) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(const UniformVectorList\u0026amp; that) : vector(that) { (*this) = that; } UniformVectorList::UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { this-\u0026gt;dim_ = this-\u0026gt;at(0).n_elem; } void UniformVectorList::CheckDimensions(size_t dim) { if (dim) { dim_ = dim; } else { dim_ = this-\u0026gt;at(0).n_elem; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Vector\u0026amp; vec : *this) { does_match = does_match \u0026amp;\u0026amp; (vec.n_elem == dim_); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } } } // namespace lds Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":89,"href":"/lds-ctrl-est/docs/api/files/lds_8cpp/","title":"src/lds.cpp","section":"Files","content":" src/lds.cpp # misc lds namespace functions More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file implements miscellaneous lds namespace functions not bound to a class.\nSource code # //===-- lds.cpp - LDS -----------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds.h\u0026gt; // insert any necessary function definitions here. namespace lds { void ForceSymPD(Matrix\u0026amp; X) { if (X.is_sympd() || !X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; // for eigenval decomp bool did_succeed(true); Vector d; Matrix u; // see first method (which may not be ideal): // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/ size_t k(1); bool is_sympd = X.is_sympd(); Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye); while (!is_sympd) { if (k \u0026gt; 100) { did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); data_t min_eig = arma::min(d); std::cerr \u0026lt;\u0026lt; \u0026#34;After multiple iterations, min eigen val = \u0026#34; \u0026lt;\u0026lt; min_eig \u0026lt;\u0026lt; \u0026#34;.\\n\u0026#34;; throw std::runtime_error( \u0026#34;Failed to make matrix symmetric positive definite.\u0026#34;); return; } // Limit(d, arma::eps(0), kInf); // force to be positive... // Matrix d_diag = arma::diagmat(d); // X = u * d_diag * u.t(); did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymPD failed.\u0026#34;); } data_t min_eig = arma::min(d); X += id * abs(min_eig) + arma::datum::eps; // make sure symm: X = (X + X.t()) / 2; // double check eigenvals positive after symmetrizing: arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); min_eig = arma::min(d); is_sympd = min_eig \u0026gt; 0; k++; } } void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min) { if (!X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; bool did_succeed(true); Vector d; Matrix u; did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymMinEig failed.\u0026#34;); } Limit(d, eig_min + arma::eps(eig_min), kInf); // enforce lower bound Matrix d_diag = arma::diagmat(d); X = u * d_diag * u.t(); // double check symmetric X = (X + X.t()) / 2; } void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X) { bool did_succeed(true); did_succeed = arma::qr_econ(Qt, L, X.t()); if (!did_succeed) { throw std::runtime_error(\u0026#34;LQ decomposition failed.\u0026#34;); } arma::inplace_trans(L); arma::inplace_trans(Qt); } Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B) { // subtract out mean auto m_a = arma::mean(A, 1); Matrix a0 = A; a0.each_col() -= m_a; auto m_b = arma::mean(B, 1); Matrix b0 = B; b0.each_col() -= m_b; Matrix cov = a0 * b0.t() / a0.n_cols; return cov; } } // namespace lds Updated on 31 March 2025 at 16:03:56 EDT\n"},{"id":90,"href":"/lds-ctrl-est/docs/api/namespaces/namespacestd/","title":"std","section":"Namespaces","content":" std # Updated on 31 March 2025 at 16:03:56 EDT\n"}]
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+[{"id":0,"href":"/lds-ctrl-est/docs/","title":"LDS C+E Documentation","section":"LDS Control \u0026 Estimation","content":" LDS Control \u0026amp; Estimation Documentation # "},{"id":1,"href":"/lds-ctrl-est/docs/tutorials/","title":"LDS C+E Examples","section":"LDS C+E Documentation","content":" Examples # "},{"id":2,"href":"/lds-ctrl-est/acknowledgements/","title":"Acknowledgements","section":"LDS Control \u0026 Estimation","content":" Acknowledgements # Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.\n"},{"id":3,"href":"/lds-ctrl-est/docs/getting-started/getting-started/","title":"Getting Started","section":"LDS C+E Documentation","content":" Getting Started # This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.\nldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.\nTested Configurations # Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the RelWithDebInfo build type in the CMake configure command (-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo):\nUbuntu 18.04 with GCC 7.5 compiler macOS 11 (Big Sur) with Apple Clang 12 compiler Windows 10 with Visual Studio 16.11 (2019 release) and Clang 12 compiler That being said, if you want to debug a build for a single platform, here are some things you can try:\nUse different compilers (or even different versions of a single compiler) Use different versions of vcpkg (which you can control by checking out a different commit in the vcpkg submodule) Mac Pre-requisities # Xcode Command Line Tools will get you clang, gcc, make, and git:\nxcode-select --install Homebrew is \u0026ldquo;The Missing Package Manager for macOS\u0026rdquo; which will make installing lots of things easy. Install like this:\n/bin/bash -c \u0026#34;$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\u0026#34; You can then use it to install CMake, gfortran, and pkg-config:\nbrew install cmake gfortran pkg-config Linux Pre-requisites # You\u0026rsquo;ll need Git, CMake, GCC, gfortran, etc.\nsudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential ninja-build Windows Installation # Look here for Windows-specific instructions.\nDownloading the Library # First, clone the repository along with submodules:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est\rgit submodule update --init Compilation + Installation # Now generate the cache and build using your IDE or from the command line as follows.\nmkdir build \u0026amp;\u0026amp; cd build\rcmake ..\rcmake --build . The first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.\nIf you want to use vcpkg set up somewhere besides this repo\u0026rsquo;s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE\u0026rsquo;s settings.\nYou can verify the build is working by running ctest from the build folder, which runs all the example scripts.\nOptions # This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\nLDSCTRLEST_BUILD_EXAMPLES : [default=ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake to configure/build the library.\nFor basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build cmake --build #build the project sudo make install #[optional] installs to default location (OS-specific) To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location cmake --build #build the project make install #install to /your/install/prefix To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\nLD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.\nPython bindings package ldsctrlest # With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython3_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn\u0026rsquo;t use an undesired version. That environment needs to have NumPy installed.\ncmake --build . --target python_modules The bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory (pip install pytest matplotlib first if you need to).\nSee python/ldsctrlest/README.md for usage details.\nAlso, beware that a single build will probably not work for both the standalone library and the Python package, since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.\nCommon issues # \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.\nexport CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix vcpkg fails on configuration Try running ./bootstrap-vcpkg from the vcpkg folder and try again. If that doesn\u0026rsquo;t work, try updating vcpkg to a newer version (in the source control tab, click on the commit hash by the vcpkg repo then select from the dropdown) and running boostsrap-vcpkg again. You can also try upgrading your system (e.g., apt update, apt upgrade).\nCould not find Python3 (missing: Python3_NumPy_INCLUDE_DIRS NumPy)\nMake sure NumPy is installed in the Python environment you specified. If CMake still can\u0026rsquo;t find it, you may need to tell CMake exactly where to find it by adding an argument to the configure command: -DPython3_NumPy_INCLUDE_DIR=.... You can find that location like this: python -c 'import numpy; print(numpy.get_include())'\n"},{"id":4,"href":"/lds-ctrl-est/docs/getting-started/windows/","title":"Windows","section":"LDS C+E Documentation","content":" Windows Installation # Windows Pre-requisites # Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:\nSet-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time iwr get.scoop.sh | Invoke-Expression Install Git and CMake if you don\u0026rsquo;t already have them:\nscoop install git cmake If that didn\u0026rsquo;t work, follow more detailed instructions here.\nThe easiest way to compile C++ project on Windows is with Visual Studio\u0026rsquo;s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on \u0026ldquo;Desktop development with C++.\u0026rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the \u0026ldquo;Installation details\u0026rdquo; sidebar under optional features.\nAnd the easiest way to use Visual Studio\u0026rsquo;s build tools is with VS Code, along with the CMake Tools extension. Install them and you should be ready to go.\nDownloading the Library # First, clone the repository, either from VS Code or the command line:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est You\u0026rsquo;ll need to initialize the submodules from the command line after the repo is cloned:\ngit submodule update --init Installation # When you open the folder in VS Code, you will like be prompted by the CMake Tools extension to configure the project. Make sure you select the kit (you\u0026rsquo;ll be prompted when you configure\u0026ndash;else there\u0026rsquo;s an icon in the bar on the bottom of the window or type Ctrl+Shift+P, then \u0026ldquo;cmake select kit\u0026rdquo;). Choose Clang [latest version] with GNU CLI ... amd64 assuming you are running a 64-bit OS. (MSVC may work okay too if you don\u0026rsquo;t need to build Python bindings.)\nFollow along with the \u0026ldquo;Getting Started\u0026rdquo; instructions, but where you see config options specified as -DLDSCTREST_BUILD_STATIC=OFF or -DPython3_ROOT_DIR=..., you will enter those in settings: open with Ctrl+,, click \u0026ldquo;workspace\u0026rdquo;, then search for \u0026ldquo;CMake: Configure Args\u0026rdquo; and enter each of your desired arguments as a separate item.\nTo configure, use Ctrl+Shift+P and search for the \u0026ldquo;CMake: Configure\u0026rdquo; command. To build, click the \u0026ldquo;Build\u0026rdquo; button on the bottom bar. Then click the \u0026ldquo;CTest\u0026rdquo; button to run the example scripts.\nConsiderations # Development on Windows has been more prone to bugs than on Unix systems, so if you encounter many problems, consider switching—WSL (Windows Subsystem for Linux) is a good option for Windows users who don\u0026rsquo;t want to work on a different machine.\nCompilation has been successfully tested in VS Code using the following kit, using the \u0026ldquo;RelWithDebInfo\u0026rdquo; config:\nClang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64) Troubleshooting # The build appears to work, but tests fail with code 0xc0000135 OR \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; Have you installed the library? In VS Code, use Shift+F7 to build a specific target, in this case INSTALL. If that doesn\u0026rsquo;t solve your problem, you will likely need to add the build or install folder to your PATH environment variable, which you can do using the settings GUI (search for \u0026ldquo;Edit the system environment variables\u0026rdquo;).\nOn Windows, \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted. Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":5,"href":"/lds-ctrl-est/issues-contributing/","title":"Issues Contributing","section":"LDS Control \u0026 Estimation","content":" Reporting Issues # If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.\nContributing # We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.\nWhen editing any documentation/guides, please use the markdown docs in misc/docs-hugo instead of directly editing the HTML docs. Updated docs can be built by running cd scripts and ./update-docs.sh. This may require having hugo, graphviz and doxygen installed through brew, as well as doxybook2 installed through a git clone.\nClone the doxybook2 repository online and place the executable in your usr/local/bin folder or another bin folder. Run sudo chmod +x /usr/local/bin/doxybook2 to give doxybook2 permissions. Run doxybook2 --help to ensure that this works properly. If permission is still denied, navigate to System Preferences \u0026gt; Security \u0026amp; Privacy \u0026gt; General. You should see a message at the bottom that says: \u0026ldquo;doxybook2 was blocked from use because it is not from an identified developer.\u0026rdquo; Click Allow Anyway.\n"},{"id":6,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/","title":"armamexc","section":"Namespaces","content":" armamexc # arma/mex interface using Matlab C API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; T m2T_scalar(const mxArray * matlab_scalar)\nConvert Matlab mxArray to scalar of type T. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)\nConvert matlab matrix to armadillo. template \u0026lt;typename T \u0026gt; mxArray * a2m_mat(arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat)\nConvert armadillo to matlab matrix. template \u0026lt;typename T \u0026gt; mxArray * a2m_vec(arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec)\nConvert armadillo to matlab vector. Detailed Description # Utilities for arma/mex interface using Matlab C API\nFunction Details # m2T_scalar # template \u0026lt;class T \u0026gt; inline T m2T_scalar( const mxArray * matlab_scalar ) Parameters:\nmatlab_scalar matlab scalar Template Parameters:\nT type Return: scalar of type T\nm2a_mat # template \u0026lt;class T \u0026gt; inline arma::Mat\u0026lt; T \u0026gt; m2a_mat( const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true ) Parameters:\nmatlab_mat matlab matrix copy_aux_mem [optional] whether to copy auxiliary memory strict [optional] strictly enforce the above Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_mat( arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat ) Parameters:\narma_mat armadillo matrix Return: matlab matrix\na2m_vec # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_vec( arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec ) Parameters:\narma_vec armadillo vector Return: matlab vector\nUpdated on 5 March 2025 at 21:33:49 EST\n"},{"id":7,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/","title":"armamexcpp","section":"Namespaces","content":" armamexcpp # arma/mex interface using Matlab C++ API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat(matlab::data::CellArray \u0026amp; matlab_cell)\nConvert matlab cell array to vector of armadillo matrices. template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array)\nConvert matlab matrix to a vector of scalars. template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo vector. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat(const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec(const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab vector. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec(const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert vector of scalar T to matlab matrix. Detailed Description # utilities for arma/mex interface using Matlab C++ API\nFunction Details # m2a_cellmat # template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat( matlab::data::CellArray \u0026amp; matlab_cell ) Parameters:\nmatlab_cell matlab cell Template Parameters:\nT type Return: vector of armadillo matrices of type T\nm2s_vec # template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: vector of type T\nm2a_vec # template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: armadillo vector of type T\nm2a_mat # template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab matrix Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat( const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_mat arma matrix factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\na2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec( const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_vec armadillo vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\ns2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec( const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\nstd_vec standard vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\nUpdated on 5 March 2025 at 21:33:49 EST\n"},{"id":8,"href":"/lds-ctrl-est/docs/terminology/control-estimation/","title":"C\u0026E","section":"LDS C+E Documentation","content":" Control \u0026amp; Estimation # The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.\nState estimation # In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by \\[\\widehat{\\mathbf{x}}_{t|t-1} = \\mathbf{A}\\widehat{\\mathbf{x}}_{t-1|t-1} \u0026#43; \\mathbf{B} u_{t-1} \u0026#43; \\mathbf{m}_{t-1} \\;,\\] \\[\\widehat{\\mathbf{x}}_{t|t} = \\widehat{\\mathbf{x}}_{t|t-1} \u0026#43; \\mathbf{K}^{\\rm e}_t \\left(\\mathbf{z}_t - \\widehat{\\mathbf{y}}_{t|t-1}\\right)\\;,\\] where \\( \\hat{\\left(\\cdot\\right)}_{t|j} \\) indicates an estimate at time \\( t \\) given data up to time \\( j \\) inclusive, \\( \\mathbf{K}^{\\rm e} \\) is the estimator gain, and\n\\[ \\widehat{\\mathbf{y}}_{t|t-1} = h\\left( \\widehat{\\mathbf{x}}_{t|t-1} \\right) \\; .\\] In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.\nIn the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.\nAdaptive estimation of process disturbance # Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.\nWhen parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk \\[\\mathbf{m}_{t} = \\mathbf{m}_{t-1} \u0026#43; \\mathbf{w}^m_{t-1} \\;,\\] where \\( \\mathbf{w}^m \\sim \\mathcal{N}\\left(0, \\mathbf{Q}_m\\right)\\) . Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.\nControl # Given the estimated state, the controller updates the inputs to the system according to the following law: \\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] where \\( \\left( \\cdot \\right)^{\\rm ref} \\) correspond to reference/target signals and \\( \\mathbf{K}^c_x \\) is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.\nIf users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as\n\\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right) - \\mathbf{K}^c_{\\rm inty} \\sum_{j=1}^{t}\\left( \\widehat{\\mathbf{y}}_j - \\mathbf{y}^{\\rm ref}_j \\right) \\;.\\] An additional option available to users is a control law that updates the change in u,\n\\[\\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\left(\\mathbf{u}_{t-1} - \\mathbf{u}^{\\rm ref}_{t-1} \\right) - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] \\[\\mathbf{u}_{t} = \\mathbf{u}_{t-1} \u0026#43; \\Delta\\mathbf{u}_{t} \\; .\\] Notice that this takes the form of a first-order difference equation for updating control (i.e., \\( \\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\mathbf{u}_{t-1} \u0026#43; \\epsilon_{t-1} \\) ), effectively low-pass filtering the input depending on the characteristics of \\( \\mathbf{K}^c_u \\) . This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.\nIntegral action and the \\( \\Delta \\mathbf{u} \\) control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR\u0026rsquo;d to use in combination.\nCalculating reference state-control from output # In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce \\( \\mathbf{x}^{\\rm ref} \\) and \\( \\mathbf{u}^{\\rm ref} \\) . Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.\nModel Predictive Control # Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.\nIn the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.\n"},{"id":9,"href":"/lds-ctrl-est/docs/api/classes/","title":"Classes","section":"LDS C+E Documentation","content":" Classes # lds::Controller\nlds::EM\nlds::Fit LDS Fit Type.\nlds::SSID\nlds::SwitchedController SwitchedController Type.\nlds::System Linear Dynamical System Type.\nlds::UniformMatrixList\nlds::UniformSystemList\nlds::UniformVectorList\nlds::gaussian::Controller Gaussian-observation Controller Type.\nlds::gaussian::Fit GLDS Fit Type.\nlds::gaussian::FitEM GLDS E-M Fit Type.\nlds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.\nlds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.\nlds::gaussian::System Gaussian LDS Type.\nlds::poisson::Controller PLDS Controller Type.\nlds::poisson::Fit PLDS Fit Type.\nlds::poisson::FitEM PLDS E-M Fit Type.\nlds::poisson::FitSSID Subspace Identification (SSID) for PLDS.\nlds::poisson::SwitchedController Poisson-observation SwitchedController Type.\nlds::poisson::System Poisson System type.\nUpdated on 5 March 2025 at 21:33:49 EST\n"},{"id":10,"href":"/lds-ctrl-est/docs/api/modules/group__control__masks/","title":"Control Mode Bit Masks","section":"Modules","content":" Control Mode Bit Masks # provides fill types for constructing new armadillo vectors, matrices More\u0026hellip; Attributes # Name const std::size_t kControlTypeDeltaU control designed to penalize change in input const std::size_t kControlTypeIntY control using integral action const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m Detailed Description # Control mode bit masks. These can be bit-wise OR\u0026rsquo;d to use in combination.\nAttribute Details # kControlTypeDeltaU # static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY # static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM # static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\nUpdated on 5 March 2025 at 21:33:49 EST\n"},{"id":11,"href":"/lds-ctrl-est/docs/api/modules/group__defaults/","title":"Defaults","section":"Modules","content":" Defaults # More\u0026hellip; Attributes # Name const data_t kDefaultP0 default state estimate covar const data_t kDefaultQ0 default process noise covar const data_t kDefaultR0 default output noise covar Detailed Description # Default values for common variables (e.g., default diagonal elements of covariances)\nAttribute Details # kDefaultP0 # static const data_t kDefaultP0 = 1e-6; kDefaultQ0 # static const data_t kDefaultQ0 = 1e-6; kDefaultR0 # static const data_t kDefaultR0 = 1e-2; Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":12,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/","title":"eg_glds_ctrl.cpp","section":"Examples","content":" eg_glds_ctrl.cpp # Example GLDS Control ```cpp\n//===\u0026ndash; eg_glds_ctrl.cpp - Example GLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS Control ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\n// output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false;\n// Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err\n// setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]);\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; }\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 21:33:49 EST "},{"id":13,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/","title":"eg_glds_du_plds_ctrl.cpp","section":"Examples","content":" eg_glds_du_plds_ctrl.cpp # Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u). ```cpp\n//===\u0026ndash; eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt);\n// to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU;\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_du_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 21:33:49 EST "},{"id":14,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/","title":"eg_plds_ctrl.cpp","section":"Examples","content":" eg_plds_ctrl.cpp # Example PLDS Control ```cpp\n//===\u0026ndash; eg_plds_ctrl.cpp - Example PLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Control ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt);\n// Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err\n// Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\nsize_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\nVector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled\u0026hellip; lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system);\n// for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type);\n// set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty);\n// to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0;\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros);\n// set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y();\nx_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x();\nm_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 21:33:49 EST "},{"id":15,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/","title":"eg_plds_est.cpp","section":"Examples","content":" eg_plds_est.cpp # Example PLDS Estimation ```cpp\n//===\u0026ndash; eg_plds_est.cpp - Example PLDS Estimation \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0);\nint main() { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.\n// construct ground truth system\u0026hellip; lds::poisson::System system_true(n_u, n_x, n_y, dt);\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset();\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt);\n// Can copy parameters from another system object system_estimator = system_true;\n// wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est);\n// set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true;\n// set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;estimator:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; system_estimator.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));\n// create matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros);\n// states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\nMatrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simlation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1));\n// Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simlation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows;\nif ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026ldquo;Q must be n x n.\u0026rdquo;); }\nMatrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); }\nreturn x; }\n_Filename: eg_plds_est.cpp_ ------------------------------- Updated on 5 March 2025 at 21:33:49 EST "},{"id":16,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/","title":"eg_plds_switched_ctrl.cpp","section":"Examples","content":" eg_plds_switched_ctrl.cpp # Example Switched PLDS Control ```cpp\n//===\u0026ndash; eg_plds_switched_ctrl.cpp - Example Switched PLDS Control \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026quot;;\n// whether to do switched control bool do_switch_ctrl = true;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt);\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices. data_t scale_sys_b = 2;\nMatrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));\ncontrolled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions\n// reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);\n// Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system);\n// set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys1:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys2:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x));\nswitched_controller.set_y_ref(y_ref0);\nstd::vectorlds::poisson::System systems_vec(3, lds::poisson::System()); lds::UniformSystemListlds::poisson::System systems(std::move(systems_vec));\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;switched_controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; switched_controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Fake measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros);\n// create Matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);\n// modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones);\n// set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } }\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace)); mode.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;mode\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_switched_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 21:33:49 EST "},{"id":17,"href":"/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/","title":"examples","section":"Files","content":" examples # Files # Name examples/eg_glds_ctrl.cpp examples/eg_glds_du_plds_ctrl.cpp examples/eg_plds_ctrl.cpp examples/eg_plds_est.cpp examples/eg_plds_switched_ctrl.cpp Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":18,"href":"/lds-ctrl-est/docs/api/examples/","title":"Examples","section":"LDS C+E Documentation","content":" Examples # eg_glds_ctrl.cpp Example GLDS Control.\neg_glds_du_plds_ctrl.cpp Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).\neg_plds_ctrl.cpp Example PLDS Control.\neg_plds_est.cpp Example PLDS Estimation.\neg_plds_switched_ctrl.cpp Example Switched PLDS Control.\nUpdated on 5 March 2025 at 21:33:49 EST\n"},{"id":19,"href":"/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/","title":"examples/eg_glds_ctrl.cpp","section":"Files","content":" examples/eg_glds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":20,"href":"/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/","title":"examples/eg_glds_du_plds_ctrl.cpp","section":"Files","content":" examples/eg_glds_du_plds_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU; // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":21,"href":"/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/","title":"examples/eg_plds_ctrl.cpp","section":"Files","content":" examples/eg_plds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err // Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled... lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type); // set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":22,"href":"/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/","title":"examples/eg_plds_est.cpp","section":"Files","content":" examples/eg_plds_est.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name Matrix random_walk(size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0) int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # random_walk # Matrix random_walk( size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0 ) main # int main() Source code # //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. // construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state // Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. // turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; } Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":23,"href":"/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/","title":"examples/eg_plds_switched_ctrl.cpp","section":"Files","content":" examples/eg_plds_switched_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Source code # //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 // simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions // reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":24,"href":"/lds-ctrl-est/docs/api/files/","title":"Files","section":"LDS C+E Documentation","content":" Files # examples/eg_glds_ctrl.cpp\nexamples/eg_glds_du_plds_ctrl.cpp\nexamples/eg_plds_ctrl.cpp\nexamples/eg_plds_est.cpp\nexamples/eg_plds_switched_ctrl.cpp\nldsCtrlEst_h/lds.h lds namespace\nldsCtrlEst_h/lds_ctrl.h Controller.\nldsCtrlEst_h/lds_fit.h LDS base fit type.\nldsCtrlEst_h/lds_fit_em.h subspace identification\nldsCtrlEst_h/lds_fit_ssid.h subspace identification\nldsCtrlEst_h/lds_gaussian.h glds namespace\nldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.\nldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.\nldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.\nldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.\nldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.\nldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.\nldsCtrlEst_h/lds_poisson.h plds namespace\nldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.\nldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.\nldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.\nldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.\nldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.\nldsCtrlEst_h/lds_poisson_sys.h PLDS base type.\nldsCtrlEst_h/lds_sctrl.h SwitchedController type.\nldsCtrlEst_h/lds_sys.h LDS base type.\nldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.\nldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.\nldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.\nldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)\nldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)\nsrc/lds.cpp misc lds namespace functions\nsrc/lds_gaussian_sys.cpp GLDS base type.\nsrc/lds_poisson_sys.cpp PLDS base type.\nsrc/lds_sys.cpp LDS base type.\nsrc/lds_uniform_vecs.cpp Uniformly sized vectors.\nUpdated on 5 March 2025 at 21:33:49 EST\n"},{"id":25,"href":"/lds-ctrl-est/docs/tutorials/eg_glds_control/","title":"GLDS Control","section":"LDS C+E Examples","content":" GLDS Control Tutorial # This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\] \\[\ry_{t} = x_t\r\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.\n/// Going to simulate a switching disturbance (m) acting on system size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_y).fill(m_low); Finally, assign the parameters using corresponding set-methods.\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); Creating the controller # Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.\nFor the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.\n// make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nWith the controller constructed, control variables may be set.\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.\nThe control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller\u0026rsquo;s online estimates of the output, state, and disturbance are given in purple.\n"},{"id":26,"href":"/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/","title":"include","section":"Files","content":" include # Directories # Name ldsCtrlEst_h Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":27,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds/","title":"lds","section":"Namespaces","content":" lds # Linear Dynamical Systems (LDS) namespace. Namespaces # Name lds::gaussian Linear Dynamical Systems with Gaussian observations. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::Controller class lds::EM class lds::Fit LDS Fit Type. class lds::SSID class lds::SwitchedController SwitchedController Type. class lds::System Linear Dynamical System Type. class lds::UniformMatrixList class lds::UniformSystemList class lds::UniformVectorList Types # Name enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}\nweighting options for SSID enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2} using double data_t using arma::Col\u0026lt; data_t \u0026gt; Vector using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Cube\u0026lt; data_t \u0026gt; Cube using arma::subview\u0026lt; data_t \u0026gt; View Functions # Name void Limit(std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub) void Limit(Vector \u0026amp; x, data_t lb, data_t ub) void Limit(Matrix \u0026amp; x, data_t lb, data_t ub) void Reassign(Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Vector in place void Reassign(Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Matrix in place void ForceSymPD(Matrix \u0026amp; X)\nforces matrix to be symmetric positive-definite void ForceSymMinEig(Matrix \u0026amp; X, data_t eig_min =0)\nforces matrix to be symmetric and have a minimum eigenvalue void lq(Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X)\nLQ decomposition. Matrix calcCov(const Matrix \u0026amp; A, const Matrix \u0026amp; B)\nCalculate covariance matrix. Attributes # Name const data_t kInf Some useful numbers. const data_t kPi Type Details # SSIDWt # Enumerator Value Description kSSIDNone None. kSSIDMOESP MOESP (AKA \u0026ldquo;robust method\u0026rdquo; in van Overschee 1996) kSSIDCVA CVA \u0026ldquo;Canonical Variate Analysis\u0026rdquo;. Weighting options for singular value decomposition performed during subspace identification (SSID)\nReference:\nvan Overschee, de Moor. 1996. Subspace Identification for Linear Systems.\nMatrixListFreeDim # Enumerator Value Description kMatFreeDimNone neither dim free to be hetero in mat list kMatFreeDim1 allow 1st dim of mats in list to be hetero kMatFreeDim2 allow 2nd dim of mats in list to be hetero data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nVector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Cube # using lds::Cube = arma::Cube\u0026lt;data_t\u0026gt;; View # using lds::View = arma::subview\u0026lt;data_t\u0026gt;; Function Details # Limit # inline void Limit( std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Vector \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Matrix \u0026amp; x, data_t lb, data_t ub ) Reassign # inline void Reassign( Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Vector other other Vector parenthetical optional description provided by caller to ease debugging Reassign # inline void Reassign( Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Matrix other other Matrix parenthetical optional description provided by caller to ease debugging ForceSymPD # void ForceSymPD( Matrix \u0026amp; X ) Parameters:\nX mutated matrix ForceSymMinEig # void ForceSymMinEig( Matrix \u0026amp; X, data_t eig_min =0 ) Parameters:\nX mutated matrix eig_min [optional] minimum eigen value lq # void lq( Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X ) Parameters:\nL lower triangle matrix Qt orthonormal matrix (transposed cf QR decomp) X matrix being decomposed calcCov # Matrix calcCov( const Matrix \u0026amp; A, const Matrix \u0026amp; B ) Parameters:\nA some matrix B some other matrix Return: covariance\nAttribute Details # kInf # static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); kPi # static const data_t kPi = arma::datum::pi; Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":28,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/","title":"lds::Controller","section":"Classes","content":" lds::Controller # More\u0026hellip;\nInherited by lds::SwitchedController\u0026lt; System \u0026gt;, lds::gaussian::Controller, lds::poisson::Controller\nPublic Functions # Name Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes # Name System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::Controller; Public Function Details # Controller # Controller() =default Controller # inline Controller( const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Controller # inline Controller( System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Control # inline const Vector \u0026amp; Control( const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step). This is the most flexible option, but requires user to have set the controller\u0026rsquo;s y_ref, x_ref, and u_ref variables.\nControlOutputReference # inline const Vector \u0026amp; ControlOutputReference( const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_estimation [optional] whether to update state estimate (if false, effectively open-loop control) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.\nsys # inline const System \u0026amp; sys() const Kc # inline const Matrix \u0026amp; Kc() const Kc_inty # inline const Matrix \u0026amp; Kc_inty() const Kc_u # inline const Matrix \u0026amp; Kc_u() const g_design # inline const Vector \u0026amp; g_design() const u_ref # inline const Vector \u0026amp; u_ref() const x_ref # inline const Vector \u0026amp; x_ref() const y_ref # inline const Vector \u0026amp; y_ref() const control_type # inline size_t control_type() const tau_awu # inline data_t tau_awu() const u_lb # inline data_t u_lb() const u_ub # inline data_t u_ub() const set_sys # inline void set_sys( const System \u0026amp; sys ) set_g_design # inline void set_g_design( const Vector \u0026amp; g_design ) set_u_ref # inline void set_u_ref( const Vector \u0026amp; u_ref ) set_x_ref # inline void set_x_ref( const Vector \u0026amp; x_ref ) set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) Reimplemented by: lds::gaussian::Controller::set_y_ref, lds::gaussian::SwitchedController::set_y_ref, lds::poisson::Controller::set_y_ref, lds::poisson::SwitchedController::set_y_ref\nset_Kc # inline void set_Kc( const Matrix \u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const Matrix \u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const Matrix \u0026amp; Kc_u ) set_tau_awu # inline void set_tau_awu( data_t tau ) set_control_type # inline void set_control_type( size_t control_type ) Parameters:\ncontrol_type control type bit mask Template Parameters:\nSystem type derived from lds::System set_u_lb # inline void set_u_lb( data_t u_lb ) Parameters:\nu_lb control lower bound set_u_ub # inline void set_u_ub( data_t u_ub ) Parameters:\nu_ub control upper bound Reset # inline void Reset() Print # inline void Print() Protected Attribute Details # sys_ # System sys_; u_ # Vector u_; u_return_ # Vector u_return_; g_design_ # Vector g_design_; u_ref_ # Vector u_ref_; u_ref_prev_ # Vector u_ref_prev_; x_ref_ # Vector x_ref_; y_ref_ # Vector y_ref_; cx_ref_ # Vector cx_ref_; Kc_ # Matrix Kc_; Kc_u_ # Matrix Kc_u_; Kc_inty_ # Matrix Kc_inty_; du_ref_ # Vector du_ref_; dv_ref_ # Vector dv_ref_; v_ref_ # Vector v_ref_; dv_ # Vector dv_; v_ # Vector v_; int_e_ # Vector int_e_; int_e_awu_adjust_ # Vector int_e_awu_adjust_; u_sat_ # Vector u_sat_; do_control_prev_ # bool do_control_prev_ = false; do_lock_control_prev_ # bool do_lock_control_prev_ = false; u_saturated_ # bool u_saturated_ = false; u_lb_ # data_t u_lb_ {}; u_ub_ # data_t u_ub_ {}; tau_awu_ # data_t tau_awu_ {}; k_awu_ # data_t k_awu_ = 0; t_since_control_onset_ # data_t t_since_control_onset_ = 0; control_type_ # size_t control_type_ {}; Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":29,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/","title":"lds::EM","section":"Classes","content":" lds::EM # More\u0026hellip;\nInherited by lds::gaussian::FitEM, lds::poisson::FitEM\nPublic Functions # Name EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions # Name void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() virtual void MaximizeOutput() =0 virtual void MaximizeMeasurement() =0 void Smooth(bool force_common_initial)\nget smoothed estimates virtual void RecurseKe(Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t) =0\nrecursively update estimator gain Ke void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::EM; Public Function Details # EM # EM() =default EM # EM( size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nn_x number of states dt sample period u_train input training data z_train measurement training data EM # EM( const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nfit0 initial fit u_train input training data z_train measurement training data ~EM # virtual ~EM() =default Run # const Fit \u0026amp; Run( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2 ) Parameters:\ncalc_dynamics [optional] whether to calculate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law max_iter max number of iterations tol convergence tolerance (max fractional abs change) Return: Fit\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nx # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const y # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const sum_E_x_t_x_t # inline const Matrix \u0026amp; sum_E_x_t_x_t() const sum_E_xu_tm1_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const sum_E_xu_t_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const n_t_tot # inline size_t n_t_tot() theta # inline const Vector \u0026amp; theta() const Protected Function Details # Expectation # void Expectation( bool force_common_initial =false ) Parameters:\nforce_common_initial whether to force common initial condition for all trials Maximization # void Maximization( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false ) Parameters:\ncalc_dynamics [optional] whether to caclulate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law MaximizeDynamics # void MaximizeDynamics() MaximizeQ # void MaximizeQ() MaximizeInitial # void MaximizeInitial() MaximizeOutput # virtual void MaximizeOutput() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput\nMaximizeMeasurement # virtual void MaximizeMeasurement() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement\nSmooth # void Smooth( bool force_common_initial ) Parameters:\nforce_common_initial whether to force common initial conditions RecurseKe # virtual void RecurseKe( Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t ) =0 Parameters:\nKe estimator gain P_pre cov of predicted state est. P_post cov of postior sate est. t time Reimplemented by: lds::gaussian::FitEM::RecurseKe, lds::poisson::FitEM::RecurseKe\nReset # void Reset() InitVars # void InitVars() UpdateTheta # Vector UpdateTheta() Return: parameter list\nProtected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; x_ # std::vector\u0026lt; Matrix \u0026gt; x_; P_ # std::vector\u0026lt; Cube \u0026gt; P_; P_t_tm1_ # std::vector\u0026lt; Cube \u0026gt; P_t_tm1_; y_ # std::vector\u0026lt; Matrix \u0026gt; y_; diag_y_ # Matrix diag_y_; sum_E_x_t_x_t_ # Matrix sum_E_x_t_x_t_; sum_E_xu_tm1_xu_tm1_ # Matrix sum_E_xu_tm1_xu_tm1_; sum_E_xu_t_xu_tm1_ # Matrix sum_E_xu_t_xu_tm1_; fit_ # Fit fit_; theta_ # Vector theta_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":30,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/","title":"lds::Fit","section":"Classes","content":" lds::Fit # LDS Fit Type. #include \u0026lt;lds_fit.h\u0026gt;\nInherited by lds::gaussian::Fit, lds::poisson::Fit\nPublic Functions # Name Fit() =default\nConstructs a new Fit. Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias virtual const Matrix \u0026amp; R() const =0 void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance virtual void set_R(const Matrix \u0026amp; R) =0\nsets output noise covariance (if any) void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) =0\noutput function Protected Attributes # Name data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period ~Fit # virtual ~Fit() =default n_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const m # inline const Vector \u0026amp; m() const Q # inline const Matrix \u0026amp; Q() const x0 # inline const Vector \u0026amp; x0() const P0 # inline const Matrix \u0026amp; P0() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const R # virtual const Matrix \u0026amp; R() const =0 Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R\nset_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_g # inline void set_g( const Vector \u0026amp; g ) set_m # inline void set_m( const Vector \u0026amp; m ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # virtual void set_R( const Matrix \u0026amp; R ) =0 Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R\nset_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) f # inline View f( Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t ) Parameters:\nx state estimate (over time) u input (over time) t time index Return: view of updated state\nf # inline View f( Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t ) Parameters:\nx_pre predicted state est. x_post posterior state est. u input (over time) t time index Return: view of predicted state\nh # virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) =0 Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplemented by: lds::gaussian::Fit::h, lds::poisson::Fit::h\nProtected Attribute Details # dt_ # data_t dt_ {}; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; m_ # Vector m_; Q_ # Matrix Q_; C_ # Matrix C_; d_ # Vector d_; R_ # Matrix R_; x0_ # Vector x0_; P0_ # Matrix P0_; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":31,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/","title":"lds::gaussian","section":"Namespaces","content":" lds::gaussian # Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. class lds::gaussian::Fit GLDS Fit Type. class lds::gaussian::FitEM GLDS E-M Fit Type. class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. class lds::gaussian::System Gaussian LDS Type. Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":32,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/","title":"lds::gaussian::Controller","section":"Classes","content":" lds::gaussian::Controller # Gaussian-observation Controller Type. #include \u0026lt;lds_gaussian_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 21:33:49 EST\n"},{"id":33,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/","title":"lds::gaussian::Fit","section":"Classes","content":" lds::gaussian::Fit # GLDS Fit Type. #include \u0026lt;lds_gaussian_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual const Matrix \u0026amp; R() const override\ngets measurement noise covariance virtual void set_R(const Matrix \u0026amp; R) override\nsets measurement noise covariance virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period R # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nh # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nUpdated on 5 March 2025 at 21:33:49 EST\n"},{"id":34,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/","title":"lds::gaussian::FitEM","section":"Classes","content":" lds::gaussian::FitEM # GLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_gaussian_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::gaussian::FitEM; This type is used in the process of fitting GLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":35,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/","title":"lds::gaussian::FitSSID","section":"Classes","content":" lds::gaussian::FitSSID # Subspace Identification (SSID) for GLDS. #include \u0026lt;lds_gaussian_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":36,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/","title":"lds::gaussian::SwitchedController","section":"Classes","content":" lds::gaussian::SwitchedController # Gaussian-observation SwitchedController Type. #include \u0026lt;lds_gaussian_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 21:33:49 EST\n"},{"id":37,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/","title":"lds::gaussian::System","section":"Classes","content":" lds::gaussian::System # Gaussian LDS Type. #include \u0026lt;lds_gaussian_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)\nConstructs a new Gaussian System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. const Matrix \u0026amp; R() const\nGet output noise covariance. void set_Q(const Matrix \u0026amp; Q) void set_R(const Matrix \u0026amp; R)\nSet output noise covariance. void set_Ke(const Matrix \u0026amp; Ke)\nSet estimator gain. void set_Ke_m(const Matrix \u0026amp; Ke_m)\nSet disturbance estimator gain. void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively update estimator gain. Protected Attributes # Name Matrix R_ covariance of output noise bool do_recurse_Ke_ whether to recursively calculate estimator gain Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0 ) Parameters:\nn_u number of inputs (u) n_x number of states (x) n_y number of outputs (y) dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) r0 [optional] initial diagonal elements of output noise covariance (R) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nR # inline const Matrix \u0026amp; R() const set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # inline void set_R( const Matrix \u0026amp; R ) set_Ke # inline void set_Ke( const Matrix \u0026amp; Ke ) set_Ke_m # inline void set_Ke_m( const Matrix \u0026amp; Ke_m ) Print # void Print() Protected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nProtected Attribute Details # R_ # Matrix R_; do_recurse_Ke_ # bool do_recurse_Ke_ {}; Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":38,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/","title":"lds::poisson","section":"Namespaces","content":" lds::poisson # Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. class lds::poisson::Fit PLDS Fit Type. class lds::poisson::FitEM PLDS E-M Fit Type. class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. class lds::poisson::System Poisson System type. Attributes # Name std::random_device rd random device for simulating poisson data std::mt19937 rng random number generator for simulating poisson data Attribute Details # rd # static std::random_device rd; rng # static std::mt19937 rng = std::mt19937( rd()); Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":39,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/","title":"lds::poisson::Controller","section":"Classes","content":" lds::poisson::Controller # PLDS Controller Type. #include \u0026lt;lds_poisson_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 21:33:49 EST\n"},{"id":40,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/","title":"lds::poisson::Fit","section":"Classes","content":" lds::poisson::Fit # PLDS Fit Type. #include \u0026lt;lds_poisson_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function virtual void set_R(const Matrix \u0026amp; R) override\nsets output noise covariance (if any) virtual const Matrix \u0026amp; R() const override Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # inline Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period h # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nR # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nUpdated on 5 March 2025 at 21:33:49 EST\n"},{"id":41,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/","title":"lds::poisson::FitEM","section":"Classes","content":" lds::poisson::FitEM # PLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_poisson_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::poisson::FitEM; This type is used in the process of fitting PLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":42,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/","title":"lds::poisson::FitSSID","section":"Classes","content":" lds::poisson::FitSSID # Subspace Identification (SSID) for PLDS. #include \u0026lt;lds_poisson_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":43,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/","title":"lds::poisson::SwitchedController","section":"Classes","content":" lds::poisson::SwitchedController # Poisson-observation SwitchedController Type. #include \u0026lt;lds_poisson_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 21:33:49 EST\n"},{"id":44,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/","title":"lds::poisson::System","section":"Classes","content":" lds::poisson::System # Poisson System type. #include \u0026lt;lds_poisson_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nConstructs a new Poisson System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively recalculate estimator gain (Ke) Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nProtected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nRecursively recalculate estimator gain (Ke).\nReferences:\nSmith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.\nEden UT, \u0026hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.\nUpdated on 5 March 2025 at 21:33:49 EST\n"},{"id":45,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/","title":"lds::SSID","section":"Classes","content":" lds::SSID # More\u0026hellip;\nInherited by lds::gaussian::FitSSID, lds::poisson::FitSSID\nPublic Functions # Name SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions # Name void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. virtual void DecomposeData() =0\nDecompose data to lower-triangular matrix (used in Solve) void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::SSID; Public Function Details # SSID # SSID() =default SSID # SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf) ) Parameters:\nn_x number of states n_h size of block-hankel data matrix dt sample period u_train input training data z_train measurement training data d output bias Run # std::tuple\u0026lt; Fit, Vector \u0026gt; Run( SSIDWt ssid_wt ) Parameters:\nssid_wt weight for singular value decomp Return: tuple (Fit, singular values)\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nProtected Function Details # CalcD # void CalcD( data_t t_silence =0.1, data_t thresh_silence =0.001 ) Parameters:\nt_silence threshold on period of time that qualifies as \u0026ldquo;silence\u0026rdquo; thresh_silence threshold on input amplitude u that qualifies as \u0026ldquo;silence\u0026rdquo; CreateHankelDataMat # void CreateHankelDataMat() Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.\nDecomposeData # virtual void DecomposeData() =0 Reimplemented by: lds::gaussian::FitSSID::DecomposeData, lds::poisson::FitSSID::DecomposeData\nCalcSVD # void CalcSVD( SSIDWt wt ) Parameters:\nssid_wt weight for SVD Solve # void Solve( data_t wt_dc ) Parameters:\nwt_dc weight placed on getting correct DC I/O gain RecomputeExtObs # void RecomputeExtObs() Protected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; D_ # Matrix D_; fit_ # Fit fit_; g_dc_ # Matrix g_dc_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_h_ # size_t n_h_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; L_ # Matrix L_; s_ # Vector s_; ext_obs_t_ # Matrix ext_obs_t_; Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":46,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/","title":"lds::SwitchedController","section":"Classes","content":" lds::SwitchedController # SwitchedController Type. More\u0026hellip;\n#include \u0026lt;lds_sctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nInherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController\nPublic Functions # Name SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes # Name std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::SwitchedController; Public Function Details # SwitchedController # SwitchedController() =default SwitchedController # inline SwitchedController( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask SwitchedController # inline SwitchedController( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Switch # inline void Switch( size_t idx, bool do_force_switch =false ) Parameters:\nidx index do_force_switch whether to force a system switch even if already there. set_Kc # inline void set_Kc( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc ) set_Kc # inline void set_Kc( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty ) set_Kc_inty # inline void set_Kc_inty( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u ) set_Kc_u # inline void set_Kc_u( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u ) set_g_design # inline void set_g_design( const UniformVectorList \u0026amp; g ) set_g_design # inline void set_g_design( UniformVectorList \u0026amp;\u0026amp; g ) Protected Attribute Details # systems_ # std::vector\u0026lt; System \u0026gt; systems_; n_sys_ # size_t n_sys_ {}; idx_ # size_t idx_ {}; Kc_list_ # UniformMatrixList Kc_list_; Kc_inty_list_ # UniformMatrixList Kc_inty_list_; Kc_u_list_ # UniformMatrixList Kc_u_list_; g_design_list_ # UniformVectorList g_design_list_; Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":47,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_system/","title":"lds::System","section":"Classes","content":" lds::System # Linear Dynamical System Type. #include \u0026lt;lds_sys.h\u0026gt;\nInherited by lds::gaussian::System, lds::poisson::System\nPublic Functions # Name System() =default\nConstructs a new System. System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nconstructs a new System virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) =0\nsimulates system (single time step) void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function virtual void h() =0\nsystem output function virtual Vector h_(Vector x) =0\nsystem output function (stateless) size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void RecurseKe() =0\nRecursively recalculate estimator gain (Ke) void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes # Name bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes # Name std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 diagonal elements for state estimate covariance q0 diagonal elements for process noise covariance ~System # inline virtual ~System() Filter # void Filter( const Vector \u0026amp; u_tm1, const Vector \u0026amp; z ) Parameters:\nu_tm1 input at t-minus-1 z_t current measurement Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.\nSimulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) =0 Parameters:\nu_tm1 input at time t-1 Return: simulated measurement at time t\nReimplemented by: lds::gaussian::System::Simulate, lds::poisson::System::Simulate\nf # inline void f( const Vector \u0026amp; u, bool do_add_noise =false ) Parameters:\nu input do_add_noise whether to add simulated process noise h # virtual void h() =0 Reimplemented by: lds::gaussian::System::h, lds::poisson::System::h\nh_ # virtual Vector h_( Vector x ) =0 Parameters:\nx_t state at time t Return: predicted state at time t + 1\nReimplemented by: lds::gaussian::System::h_, lds::poisson::System::h_\nn_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const x # inline const Vector \u0026amp; x() const P # inline const Matrix \u0026amp; P() const m # inline const Vector \u0026amp; m() const P_m # inline const Matrix \u0026amp; P_m() const cx # inline const Vector \u0026amp; cx() const y # inline const Vector \u0026amp; y() const x0 # inline const Vector \u0026amp; x0() const m0 # inline const Vector \u0026amp; m0() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const Ke # inline const Matrix \u0026amp; Ke() const Ke_m # inline const Matrix \u0026amp; Ke_m() const Q # inline const Matrix \u0026amp; Q() Q_m # inline const Matrix \u0026amp; Q_m() P0 # inline const Matrix \u0026amp; P0() P0_m # inline const Matrix \u0026amp; P0_m() set_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_m # inline void set_m( const Vector \u0026amp; m, bool do_force_assign =false ) set_g # inline void set_g( const Vector \u0026amp; g ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_Q_m # inline void set_Q_m( const Matrix \u0026amp; Q_m ) set_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_P0_m # inline void set_P0_m( const Matrix \u0026amp; P0_m ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) set_x # inline void set_x( const Vector \u0026amp; x ) Reset # void Reset() nstep_pred_block # std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block( UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1 ) Print # void Print() Protected Function Details # RecurseKe # virtual void RecurseKe() =0 Reimplemented by: lds::gaussian::System::RecurseKe, lds::poisson::System::RecurseKe\nInitVars # void InitVars( data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Public Attribute Details # do_adapt_m # bool do_adapt_m {}; Protected Attribute Details # n_x_ # std::size_t n_x_ {}; n_u_ # std::size_t n_u_ {}; n_y_ # std::size_t n_y_ {}; dt_ # data_t dt_ {}; x_ # Vector x_; P_ # Matrix P_; m_ # Vector m_; P_m_ # Matrix P_m_; cx_ # Vector cx_; y_ # Vector y_; z_ # Vector z_; x0_ # Vector x0_; P0_ # Matrix P0_; m0_ # Vector m0_; P0_m_ # Matrix P0_m_; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; Q_ # Matrix Q_; Q_m_ # Matrix Q_m_; C_ # Matrix C_; d_ # Vector d_; Ke_ # Matrix Ke_; Ke_m_ # Matrix Ke_m_; Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":48,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/","title":"lds::UniformMatrixList","section":"Classes","content":" lds::UniformMatrixList # More\u0026hellip;\nInherits from std::vector\u0026lt; Matrix \u0026gt;\nPublic Functions # Name UniformMatrixList() =default\nConstructs a new UniformMatrixList. UniformMatrixList(const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent. UniformMatrixList(std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent. UniformMatrixList(std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent. UniformMatrixList(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nConstructs a new UniformMatrixList (copy). UniformMatrixList(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nConstructs a new UniformMatrixList (move). ~UniformMatrixList() =default\nDestroys the object. const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim(size_t n =0) const\ngets dimensions of uniformly sized matrices size_t size()\nsize of container const Matrix \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Matrix \u0026amp; that, size_t n)\nswaps input matrix with n^th matrix of list UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nassigns the contents (copy) UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nassigns the contents (move) void append(const Matrix \u0026amp; mat)\nappends a matrix to the list Detailed Description # template \u0026lt;MatrixListFreeDim D =kMatFreeDimNone\u0026gt; class lds::UniformMatrixList; Public Function Details # UniformMatrixList # UniformMatrixList() =default UniformMatrixList # explicit UniformMatrixList( const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # explicit UniformMatrixList( std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList UniformMatrixList # UniformMatrixList( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList ~UniformMatrixList # ~UniformMatrixList() =default dim # inline const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim( size_t n =0 ) const Parameters:\nn [optional] index in list of matrices Return: dimensions\nsize # inline size_t size() at # inline const Matrix \u0026amp; at( size_t n ) Swap # inline void Swap( Matrix \u0026amp; that, size_t n ) Parameters:\nthat input matrix n index where the matrix is moved operator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\noperator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\nappend # void append( const Matrix \u0026amp; mat ) Parameters:\nmat input matrix Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":49,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/","title":"lds::UniformSystemList","section":"Classes","content":" lds::UniformSystemList # More\u0026hellip;\nInherits from std::vector\u0026lt; System \u0026gt;\nPublic Functions # Name UniformSystemList() =default\nConstructs a new UniformSystemList. UniformSystemList(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by copying existing vector of System if dimensions consistent. UniformSystemList(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by moving existing vector of System if dimensions consistent. UniformSystemList(std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList from initializer_list of System if dimensions consistent. UniformSystemList(const UniformSystemList \u0026amp; that)\nConstructs a new UniformSystemList (copy). UniformSystemList(UniformSystemList \u0026amp;\u0026amp; that)\nConstructs a new UniformSystemList (move). ~UniformSystemList() =default\nDestroys the object. const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const\ngets dimensions of the uniformly sized systems size_t size()\nsize of container const System \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(System \u0026amp; that, size_t n)\nswaps input system with n^th system of list UniformSystemList \u0026amp; operator=(const UniformSystemList \u0026amp; that)\nassigns the contents (copy) UniformSystemList \u0026amp; operator=(UniformSystemList \u0026amp;\u0026amp; that)\nassigns the contents (move) Detailed Description # template \u0026lt;typename System \u0026gt; class lds::UniformSystemList; Public Function Details # UniformSystemList # UniformSystemList() =default UniformSystemList # explicit UniformSystemList( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # explicit UniformSystemList( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList UniformSystemList # UniformSystemList( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList ~UniformSystemList # ~UniformSystemList() =default dim # inline const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const size # inline size_t size() at # inline const System \u0026amp; at( size_t n ) Swap # inline void Swap( System \u0026amp; that, size_t n ) Parameters:\nthat input system n index where the system is moved operator= # inline UniformSystemList \u0026amp; operator=( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\noperator= # inline UniformSystemList \u0026amp; operator=( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\nUpdated on 5 March 2025 at 21:33:49 EST\n"},{"id":50,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/","title":"lds::UniformVectorList","section":"Classes","content":" lds::UniformVectorList # Inherits from std::vector\u0026lt; Vector \u0026gt;\nPublic Functions # Name UniformVectorList() =default\nConstructs a new UniformVectorList. UniformVectorList(const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent. UniformVectorList(std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent. UniformVectorList(std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0)\nConstructs a new UniformVectorList from initializer_list of Vector if dimensions consistent. UniformVectorList(const UniformVectorList \u0026amp; that)\nConstructs a new UniformVectorList (copy) UniformVectorList(UniformVectorList \u0026amp;\u0026amp; that)\nConstructs a new UniformVectorList (move) ~UniformVectorList() =default\nDestroys the object. size_t dim() const\ngets dimensions of the uniformly sized matrices size_t size()\nsize of container const Vector \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Vector \u0026amp; that, size_t n)\nswaps input matrix with n^th vector of list UniformVectorList \u0026amp; operator=(const UniformVectorList \u0026amp; that)\nassigns the contents (copy) UniformVectorList \u0026amp; operator=(UniformVectorList \u0026amp;\u0026amp; that)\nassigns the contents (move) Public Function Details # UniformVectorList # UniformVectorList() =default UniformVectorList # explicit UniformVectorList( const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dims dimension UniformVectorList # explicit UniformVectorList( std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList UniformVectorList # UniformVectorList( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList ~UniformVectorList # ~UniformVectorList() =default dim # inline size_t dim() const size # inline size_t size() at # inline const Vector \u0026amp; at( size_t n ) Swap # inline void Swap( Vector \u0026amp; that, size_t n ) Parameters:\nthat input vector n index where the vector is moved operator= # inline UniformVectorList \u0026amp; operator=( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\noperator= # inline UniformVectorList \u0026amp; operator=( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\nUpdated on 5 March 2025 at 21:33:49 EST\n"},{"id":51,"href":"/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/","title":"ldsCtrlEst_h","section":"Files","content":" ldsCtrlEst_h # Files # Name ldsCtrlEst_h/lds.h lds namespace ldsCtrlEst_h/lds_ctrl.h Controller. ldsCtrlEst_h/lds_fit.h LDS base fit type. ldsCtrlEst_h/lds_fit_em.h subspace identification ldsCtrlEst_h/lds_fit_ssid.h subspace identification ldsCtrlEst_h/lds_gaussian.h glds namespace ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller. ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type. ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type. ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type. ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type. ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type. ldsCtrlEst_h/lds_poisson.h plds namespace ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type. ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type. ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type. ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type. ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type. ldsCtrlEst_h/lds_poisson_sys.h PLDS base type. ldsCtrlEst_h/lds_sctrl.h SwitchedController type. ldsCtrlEst_h/lds_sys.h LDS base type. ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices. ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems. ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors. ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":52,"href":"/lds-ctrl-est/docs/api/files/lds__ctrl_8h/","title":"ldsCtrlEst_h/lds_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_ctrl.h # Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Controller Detailed Description # This file declares the type for control of a linear dynamical system (lds::Controller).\nSource code # //===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_CTRL_H #define LDSCTRLEST_LDS_CTRL_H // namespace #include \u0026#34;lds.h\u0026#34; // system type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class Controller { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); public: Controller() = default; Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); const Vector\u0026amp; Control(const Vector\u0026amp; z, bool do_control = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); const Vector\u0026amp; ControlOutputReference(const Vector\u0026amp; z, bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); // get methods: const System\u0026amp; sys() const { return sys_; }; const Matrix\u0026amp; Kc() const { return Kc_; }; const Matrix\u0026amp; Kc_inty() const { return Kc_inty_; }; const Matrix\u0026amp; Kc_u() const { return Kc_u_; }; const Vector\u0026amp; g_design() const { return g_design_; }; const Vector\u0026amp; u_ref() const { return u_ref_; }; const Vector\u0026amp; x_ref() const { return x_ref_; }; const Vector\u0026amp; y_ref() const { return y_ref_; }; size_t control_type() const { return control_type_; }; data_t tau_awu() const { return tau_awu_; }; data_t u_lb() const { return u_lb_; }; data_t u_ub() const { return u_ub_; }; // set methods void set_sys(const System\u0026amp; sys) { bool does_match = sys_.n_u() == sys.n_u(); does_match = does_match \u0026amp;\u0026amp; (sys_.n_x() == sys.n_x()); does_match = does_match \u0026amp;\u0026amp; (sys_.n_y() == sys.n_y()); if (does_match) { sys_ = sys; } else { throw std::runtime_error( \u0026#34;new system argument to `set_sys` does not match dimensionality of \u0026#34; \u0026#34;existing system\u0026#34;); } }; void set_g_design(const Vector\u0026amp; g_design) { Reassign(g_design_, g_design); }; void set_u_ref(const Vector\u0026amp; u_ref) { Reassign(u_ref_, u_ref); }; void set_x_ref(const Vector\u0026amp; x_ref) { Reassign(x_ref_, x_ref); cx_ref_ = sys_.C() * x_ref_; }; // y_ref needs to be handled differently depending on output fn. // (need to populate cx_ref_ too, which depends on output fn) virtual void set_y_ref(const Vector\u0026amp; y_ref) { Reassign(y_ref_, y_ref); }; void set_Kc(const Matrix\u0026amp; Kc) { Reassign(Kc_, Kc); }; void set_Kc_inty(const Matrix\u0026amp; Kc_inty) { Reassign(Kc_inty_, Kc_inty); }; void set_Kc_u(const Matrix\u0026amp; Kc_u) { Reassign(Kc_u_, Kc_u); }; void set_tau_awu(data_t tau) { tau_awu_ = tau; k_awu_ = sys_.dt() / tau_awu_; }; void set_control_type(size_t control_type); // There is no reason u_lb/ub should not be public, but making set methods // anyway. void set_u_lb(data_t u_lb) { u_lb_ = u_lb; }; void set_u_ub(data_t u_ub) { u_ub_ = u_ub; }; void Reset() { sys_.Reset(); u_ref_.zeros(); u_ref_prev_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); u_saturated_ = false; t_since_control_onset_ = 0.0; }; void Print() { sys_.Print(); std::cout \u0026lt;\u0026lt; \u0026#34;g_design : \u0026#34; \u0026lt;\u0026lt; g_design_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_lb : \u0026#34; \u0026lt;\u0026lt; u_lb_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_ub : \u0026#34; \u0026lt;\u0026lt; u_ub_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }; protected: System sys_; Vector u_; Vector u_return_; Vector g_design_; // reference signals Vector u_ref_; // create no set method for this: Vector u_ref_prev_; Vector x_ref_; Vector y_ref_; Vector cx_ref_; // Controller gains Matrix Kc_; Matrix Kc_u_; Matrix Kc_inty_; // control after g inversion // do not need set methods for these. Vector du_ref_; Vector dv_ref_; Vector v_ref_; Vector dv_; Vector v_; // integral error // do not need set method for this Vector int_e_; Vector int_e_awu_adjust_; Vector u_sat_; bool do_control_prev_ = false; bool do_lock_control_prev_ = false; // whether the g of system has become inverted from what you think it is // (gain_ref) bool u_saturated_ = false; // should be safe to have references here bc nothing needs to be done // (like reset vars) when it changes... data_t u_lb_{}; data_t u_ub_{}; data_t tau_awu_{}; data_t k_awu_ = 0; data_t t_since_control_onset_ = 0; size_t control_type_{}; private: void CalcControl(bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); void CalcSteadyStateSetPoint(); void AntiWindup(); void InitVars(size_t control_type); }; // Implement the above: template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(sys), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(std::move(sys)), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::set_control_type(size_t control_type) { if (control_type_ == control_type) { return; } // creating a blank slate... control_type_ = 0; Kc_inty_.zeros(0, 0); Kc_u_.zeros(0, 0); int_e_.zeros(0, 0); int_e_awu_adjust_.zeros(0, 0); // controller was designed to minimize integral error if (control_type \u0026amp; kControlTypeIntY) { Kc_inty_.zeros(sys_.n_u(), sys_.n_y()); int_e_.zeros(sys_.n_y()); int_e_awu_adjust_.zeros(sys_.n_u()); control_type_ = control_type_ | kControlTypeIntY; } // controller was designed to minimize deltaU // (i.e. state augmented with u) if (control_type \u0026amp; kControlTypeDeltaU) { Kc_u_.zeros(sys_.n_u(), sys_.n_u()); control_type_ = control_type_ | kControlTypeDeltaU; } // whether to adapt set point calculate with (re-estimated) process // disturbance (m) if (control_type \u0026amp; kControlTypeAdaptM) { if (sys_.do_adapt_m) // only if adapting m... { control_type_ = control_type_ | kControlTypeAdaptM; } } } // set_control_type template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::Control( const Vector\u0026amp; z, bool do_control, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement sys_.Filter(u_, z); bool do_estimation = true; // always have estimator on in this case // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::ControlOutputReference( const Vector\u0026amp; z, bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement if (do_estimation) { sys_.Filter(u_, z); } else { sys_.f(u_); } // calculate the set point // solves for u_ref and x_ref when output is at y_ref at steady state. if (do_control) { CalcSteadyStateSetPoint(); } // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcControl(bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { if (do_control \u0026amp;\u0026amp; do_estimation) { if (!do_control_prev_) { if (do_reset_at_control_onset) { Reset(); } t_since_control_onset_ = 0.0; } else { t_since_control_onset_ += sys_.dt(); } // enforce softstart on control vars. if (sigma_soft_start \u0026gt; 0) { // half-Gaussian soft-start scaling factor data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) / (2 * pow(sigma_soft_start, 2))); u_ref_ *= soft_start_sf; // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too // x_ref_ *= soft_start_sf; // cx_ref_ *= soft_start_sf; // y_ref_ *= soft_start_sf; } if (!do_lock_control) { // first do u -\u0026gt; v change of vars. (v = g.*u) // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver // control voltage u[=]V) v_ref_ = g_design_ % u_ref_; // Given FB, calc. the change in control if (control_type_ \u0026amp; kControlTypeDeltaU) { // if control designed to minimize not u but deltaU (i.e. state aug with // u): // TODO(mfbolus): Commented out for now. See note below. // du_ref_ = u_ref_ - u_ref_prev_; // dv_ref_ = g_design_ % du_ref_; // TODO(mfbolus): Assuming users want *smooth* control signals if using // kControlTypeDeltaU, it should be the case that dv_ref_ is --\u0026gt; 0. May // want to revisit, but I am going to force it to be zero unless a // situation arises that argues for keeping the above. dv_ref_.zeros(); dv_ = dv_ref_; // nominally-optimal. dv_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error dv_ -= Kc_u_ * (v_ - v_ref_); // penalty on amp u (rel to ref) if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if(!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error dv_ -= Kc_inty_ * int_e_; // control for integrated error } // update the control v_ += dv_; } else { v_ = v_ref_; // nominally-optimal. v_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if (!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error v_ -= Kc_inty_ * int_e_; // control for integrated error } } // convert back to control voltage u[=]V u_ = v_ / sys_.g(); } // else do nothing until lock is low } else { // if not control // feed through u_ref in open loop u_ = u_ref_ % g_design_ / sys_.g(); v_ = sys_.g() % u_; u_ref_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); } // ends do_control // enforce box constraints (and antiwindup) AntiWindup(); // add noise to input? // The value for u that is *returned* to user after addition of any noise, // while keeping controller/estimator blind to this addition. u_return_ = u_; if ((sigma_u_noise \u0026gt; 0.0) \u0026amp;\u0026amp; (do_control \u0026amp;\u0026amp; !do_lock_control)) { u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn); Limit(u_return_, u_lb_, u_ub_); }; // For next time step: u_ref_prev_ = u_ref_; do_control_prev_ = do_control; do_lock_control_prev_ = do_lock_control; } // CalcControl template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcSteadyStateSetPoint() { // Linearly-constrained least squares (ls). // // _reference: // Boyd \u0026amp; Vandenberghe (2018) Introduction to Applied Linear Algebra // Matrix a_ls = join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros)); Vector b_ls = cx_ref_; Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye), sys_.B() * arma::diagmat(sys_.g())); Vector d_ls = -sys_.m0(); if (control_type_ \u0026amp; kControlTypeAdaptM) { d_ls = -sys_.m(); // adapt setpoint calc with disturbance? } Matrix a_ls_t = a_ls.t(); // TODO(mfbolus): not sure why but causes seg // fault if I do not do this. Matrix phi_ls = join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()), join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros))); // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse: Matrix inv_phi = pinv(phi_ls); Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls); x_ref_ = xulam.subvec(0, sys_.n_x() - 1); u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1); cx_ref_ = sys_.C() * x_ref_; } // CalcSteadyStateSetPoint template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::AntiWindup() { u_saturated_ = false; u_sat_ = u_; // limit u and flag whether saturated for (size_t k = 0; k \u0026lt; u_.n_elem; k++) { if (u_[k] \u0026lt; u_lb_) { u_sat_[k] = u_lb_; u_saturated_ = true; } if (u_[k] \u0026gt; u_ub_) { u_sat_[k] = u_ub_; u_saturated_ = true; } } if ((control_type_ \u0026amp; kControlTypeIntY) \u0026amp;\u0026amp; (tau_awu_ \u0026lt; lds::kInf)) { // one-step back-calculation (calculate intE for u=u_sat) // (Astroem, Rundqwist 1989 warn against using this...) // int_e_awu_adjust_ = // solve(Kc_inty_, (u_ - u_sat_)); // pinv(Kc_inty) * (u-uSat); // gradual: see Astroem, Rundqwist 1989 // this is a fudge for doing MIMO gradual // n.b., went ahead and multiplied 1/T by dt so don\u0026#39;t have to do that here. int_e_awu_adjust_ = k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_); // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_); int_e_ += int_e_awu_adjust_; } // set u to saturated version u_ = u_sat_; } template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::InitVars(size_t control_type) { // initialize to default values u_ref_ = Vector(sys_.n_u(), fill::zeros); u_ref_prev_ = Vector(sys_.n_u(), fill::zeros); x_ref_ = Vector(sys_.n_x(), fill::zeros); y_ref_ = Vector(sys_.n_y(), fill::zeros); cx_ref_ = Vector(sys_.n_y(), fill::zeros); u_ = Vector(sys_.n_u(), fill::zeros); u_return_ = Vector(sys_.n_u(), fill::zeros); u_sat_ = Vector(sys_.n_u(), fill::zeros); // Might not need all these, so zero elements until later. Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros); Kc_u_ = Matrix(0, 0, fill::zeros); Kc_inty_ = Matrix(0, 0, fill::zeros); g_design_ = sys_.g(); // by default, same as model dv_ = Vector(sys_.n_u(), fill::zeros); v_ = Vector(sys_.n_u(), fill::zeros); du_ref_ = Vector(sys_.n_u(), fill::zeros); dv_ref_ = Vector(sys_.n_u(), fill::zeros); v_ref_ = Vector(sys_.n_u(), fill::zeros); int_e_ = Vector(0, fill::zeros); int_e_awu_adjust_ = Vector(0, fill::zeros); set_control_type(control_type); } } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":53,"href":"/lds-ctrl-est/docs/api/files/lds__fit__em_8h/","title":"ldsCtrlEst_h/lds_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_em.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::EM Detailed Description # This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).\nSource code # //===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_EMAX_H #define LDSCTRLEST_LDS_EMAX_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class EM { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: EM() = default; EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); virtual ~EM() = default; const Fit\u0026amp; Run(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = true, bool calc_output = true, bool calc_measurement = true, size_t max_iter = 100, data_t tol = 1e-2); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); // auto tuple = std::make_tuple(u_, z_); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; x() const { return x_; }; const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; y() const { return y_; }; const Matrix\u0026amp; sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; }; const Matrix\u0026amp; sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; }; const Matrix\u0026amp; sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; }; size_t n_t_tot() { return n_t_tot_; } const Vector\u0026amp; theta() const { return theta_; }; protected: void Expectation(bool force_common_initial = false); void Maximization(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = false, bool calc_output = false, bool calc_measurement = false); void MaximizeDynamics(); void MaximizeQ(); void MaximizeInitial(); virtual void MaximizeOutput() = 0; virtual void MaximizeMeasurement() = 0; void Smooth(bool force_common_initial); virtual void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) = 0; void Reset(); void InitVars(); Vector UpdateTheta(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; std::vector\u0026lt;Matrix\u0026gt; x_; std::vector\u0026lt;Cube\u0026gt; P_; std::vector\u0026lt;Cube\u0026gt; P_t_tm1_; std::vector\u0026lt;Matrix\u0026gt; y_; Matrix diag_y_; // expectations calculated in E-step Matrix sum_E_x_t_x_t_; Matrix sum_E_xu_tm1_xu_tm1_; Matrix sum_E_xu_t_xu_tm1_; Fit fit_; Vector theta_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; }; template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; fit_ = Fit(n_u_, n_x, n_y_, dt); u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { // make sure fit dims match I/O data if (fit0.n_u() != u_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and input training data have inconsistent dimensions\u0026#34;); } if (fit0.n_y() != z_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and output training data have inconsistent dimensions\u0026#34;); } fit_ = fit0; u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::InitVars() { // check input/output data dimensions are consistent if (z_.size() != u_.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_.at(trial).n_cols != u_.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } n_u_ = fit_.n_u(); n_x_ = fit_.n_x(); n_y_ = fit_.n_y(); dt_ = fit_.dt(); x_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); P_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); P_t_tm1_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); y_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros); P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros); } diag_y_ = Matrix(n_y_, n_y_, fill::zeros); // covariances in expectation step sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); } template \u0026lt;typename Fit\u0026gt; const Fit\u0026amp; EM\u0026lt;Fit\u0026gt;::Run(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement, size_t max_iter, data_t tol) { Reset(); // to initial conditions size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_; Vector theta(n_params); Vector theta_new(n_params); data_t max_dtheta = 1; // if solving for initial conditions, allow them be varied. // otherwise, freeze at provided values. bool force_common_initial = !calc_init; // go until parameter convergence for (size_t l = 0; l \u0026lt; max_iter; l++) { theta_ = UpdateTheta(); std::cout \u0026lt;\u0026lt; \u0026#34;Iteration \u0026#34; \u0026lt;\u0026lt; l + 1 \u0026lt;\u0026lt; \u0026#34;/\u0026#34; \u0026lt;\u0026lt; max_iter \u0026lt;\u0026lt; \u0026#34; ...\\n\u0026#34;; Expectation(force_common_initial); Maximization(calc_dynamics, calc_Q, calc_init, calc_output, calc_measurement); // check convergence theta_new = UpdateTheta(); Vector dtheta = abs(theta_new - theta_) / abs(theta_); // some parameters could be zero... arma::uvec ubi_finite = find_finite(dtheta); max_dtheta = max(dtheta.elem(ubi_finite)); std::cout \u0026lt;\u0026lt; \u0026#34;max dtheta: \u0026#34; \u0026lt;\u0026lt; max_dtheta \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; if (max_dtheta \u0026lt; tol) { std::cout \u0026lt;\u0026lt; \u0026#34;Converged.\\n\u0026#34;; break; } std::cout \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } return fit_; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Smooth(bool force_common_initial) { Matrix k_e(n_x_, n_y_); // estimator gain Cube k_backfilt; // back-filtering gains // TODO(mfbolus): this loop could be made parallel for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { Matrix x_pre(n_x_, n_t_[trial], fill::zeros); Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros); Matrix x_post(n_x_, n_t_[trial], fill::zeros); Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros); if (force_common_initial) // forces all trials to have same initial // conditions. { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); } y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); // This *should not* be necessary but make sure P is symmetric. ForceSymPD(P_[trial].slice(0)); x_pre.col(0) = x_[trial].col(0); p_pre.slice(0) = P_[trial].slice(0); x_post.col(0) = x_[trial].col(0); p_post.slice(0) = P_[trial].slice(0); // filter for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // predict fit_.f(x_pre, x_post, u_.at(trial), t); fit_.h(y_[trial], x_pre, t); diag_y_.diag() = y_[trial].col(t); // TODO(mfbolus): change if parallel // update --\u0026gt; posterior estimation RecurseKe(k_e, p_pre, p_post, t); x_post.col(t) = x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t)); y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d(); } // backfilter -\u0026gt; Smoothed estimate // Reference: // Shumway et Stoffer (1982) ForceSymPD(p_post.slice(n_t_[trial] - 1)); k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1); P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 0; t--) { // TODO(mfmbolus): should not be necessary to force symm positive def ForceSymPD(p_pre.slice(t)); ForceSymPD(p_post.slice(t - 1)); ForceSymPD(P_[trial].slice(t)); k_backfilt.slice(t - 1) = p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t)); x_[trial].col(t - 1) = x_post.col(t - 1) + k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t)); P_[trial].slice(t - 1) = p_post.slice(t - 1) + k_backfilt.slice(t - 1) * (P_[trial].slice(t) - p_pre.slice(t)) * k_backfilt.slice(t - 1).t(); } // do the same for P_t_tm1 Matrix id(n_x_, n_x_, fill::eye); P_t_tm1_[trial].slice(n_t_[trial] - 1) = (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 1; t--) { P_t_tm1_[trial].slice(t - 1) = p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() + k_backfilt.slice(t - 1) * (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) * k_backfilt.slice(t - 2).t(); } // finally, get smoothed estimate of output for (size_t t = 0; t \u0026lt; n_t_[trial]; t++) { fit_.h(y_[trial], x_[trial], t); } // samps loop } // trial loop } // Smooth // template \u0026lt;typename Fit\u0026gt; // void EM\u0026lt;Fit\u0026gt;::RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) { // // predict covar // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q(); // // update Ke // Ke = P_pre.slice(t) * fit_.C().t() * // inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R()); // // update cov // // Reference: Ghahramani et Hinton (1996) // P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t); // // // n.b. for poisson : // // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + // fit_.Q(); // // // update cov // // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ * // // fit_.C()); // // // update Ke // // Ke = P_post.slice(t) * fit_.C(); // } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Expectation(bool force_common_initial) { // calculate the mean/cov of state needed for maximizing E[pr(z|theta)] Smooth(force_common_initial); // now get the various forms of sum(E[xx\u0026#39;]) needed // n.b. Going to start at t=1 rather than 0 bc most max terms need that. // so really \u0026#34;n_t_tot_\u0026#34; is (n_t_tot_-1) n_t_tot_ = 0; sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); Vector xu_tm1(n_x_ + n_u_, fill::zeros); Vector xu_t(n_x_ + n_u_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // ------------ sum_E_x_t_x_t ------------ sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t(); sum_E_x_t_x_t_ += P_[trial].slice(t); // ------------ sum_E_xu_tm1_xu_tm1 ------------ xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1)); sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t(); sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t - 1); // ------------ sum_E_xu_t_xu_tm1 ------------ xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t)); sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t(); sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_t_tm1_[trial].slice(t); n_t_tot_ += 1; } // time } // trial } // Expectation template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement) { if (calc_output) { MaximizeOutput(); } if (calc_measurement) { MaximizeMeasurement(); } if (calc_dynamics) { MaximizeDynamics(); } if (calc_Q) { MaximizeQ(); } if (calc_init) { MaximizeInitial(); } } // Maximization template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeDynamics() { // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) * inv_sympd(sum_E_xu_tm1_xu_tm1_); fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1)); std::cout \u0026lt;\u0026lt; \u0026#34;A_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.A()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.B()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeQ() { // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 * // inv_sympd(sum_E_xu_tm1_xu_tm1_) * // sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // this way is same as above iff dynamics were just updated: // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix ab = arma::join_horiz(fit_.A(), fit_.B()); // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // From scratch method: // Q is covariance of the error between state and dynamics-predicted state // (aka process noise) // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})\u0026#39;] // t-1 terms: View sum_e_x_tm1_x_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_u_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1); View sum_e_x_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); // t, t-1 terms: View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_x_t_u_tm1 = sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); Matrix q = sum_E_x_t_x_t_; q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t(); q -= sum_e_x_t_x_tm1 * fit_.A().t(); q -= fit_.A() * sum_e_x_t_x_tm1.t(); // input-related terms: q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t(); q -= sum_e_x_t_u_tm1 * fit_.B().t(); q -= fit_.B() * sum_e_x_t_u_tm1.t(); q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t(); q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t(); q /= n_t_tot_; fit_.set_Q(q); std::cout \u0026lt;\u0026lt; \u0026#34;Q_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.Q()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // std::cout \u0026lt;\u0026lt; \u0026#34;Q_new: \\n\u0026#34; \u0026lt;\u0026lt; fit_.Q() \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeInitial() { Vector x0 = fit_.x0(); x0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { x0 += x_[trial].col(0); } x0 /= z_.size(); std::cout \u0026lt;\u0026lt; \u0026#34;x0_new[0]: \u0026#34; \u0026lt;\u0026lt; x0[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // always recalc P0 even if the initial state is fixed (at zero, for // example) Matrix e_var(n_x_, n_x_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t(); } e_var /= z_.size(); // go ahead and subtract x0*x0\u0026#39; so don\u0026#39;t have to below. e_var -= x0 * x0.t(); // To get P0, going to get initial P_ per trial and average. // (which might be wrong, but need a single number) Matrix p0 = fit_.P0(); p0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { p0 += (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var; } p0 /= z_.size(); fit_.set_P0(p0); std::cout \u0026lt;\u0026lt; \u0026#34;P0_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.P0()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeOutput() { // solve for C+d: Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros); Vector x1(n_x_ + 1, fill::zeros); x1[n_x_] = 1.0; // augment with one to solve for bias Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { x1.subvec(0, n_x_ - 1) = x_[trial].col(t); sum_zx += z_.at(trial).col(t) * x1.t(); sum_e_x1_x1 += x1 * x1.t(); sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t); } } Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1); fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1)); fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_))); std::cout \u0026lt;\u0026lt; \u0026#34;C_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.C()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.d()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeMeasurement() { // Solve for measurement noise covar size_t n_t_tot = 0; // Ghahgramani, Hinton 1996: Matrix sum_zz(n_y_, n_y_, fill::zeros); Matrix sum_yz(n_y_, n_y_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t(); // Use Cnew: sum_yz += (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t(); n_t_tot += 1; } } fit_.set_R((sum_zz - sum_yz) / n_t_tot); std::cout \u0026lt;\u0026lt; \u0026#34;R_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.R()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Reset() { // reset to initial conditions for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); } } template \u0026lt;typename Fit\u0026gt; Vector EM\u0026lt;Fit\u0026gt;::UpdateTheta() { // TODO(mfbolus): This should include n_y_ more params for d. size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_; if (fit_.R().n_elem \u0026gt; 0) { n_params += n_y_ * n_y_; } Vector theta(n_params); size_t idx_start = 0; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B()); idx_start += n_x_ * n_u_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0()); idx_start += n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C()); idx_start += n_y_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d()); idx_start += n_y_; if (fit_.R().n_elem \u0026gt; 0) { theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R()); } return theta; } } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":54,"href":"/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_ssid.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SSID Detailed Description # This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)\u0026lt;Fit\u0026gt;).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_FIT_SSID_H #define LDSCTRLEST_LDS_FIT_SSID_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class SSID { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: SSID() = default; SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d = Vector(1).fill(-kInf)); std::tuple\u0026lt;Fit, Vector\u0026gt; Run(SSIDWt ssid_wt); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } protected: void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001); void CreateHankelDataMat(); virtual void DecomposeData() = 0; void CalcSVD(SSIDWt wt); void Solve(data_t wt_dc); void RecomputeExtObs(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; Matrix D_; Fit fit_; Matrix g_dc_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_h_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; Matrix L_; Vector s_; Matrix ext_obs_t_; }; template \u0026lt;typename Fit\u0026gt; SSID\u0026lt;Fit\u0026gt;::SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d) { // check input/output data dimensions are consistent if (z_train.size() != u_train.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_train.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_train.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } dt_ = dt; n_x_ = n_x; n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; n_h_ = n_h; // dimensionality check for eventual block-hankel data matrix size_t len = n_t_tot_ - 2 * n_h_ + 1; if (len \u0026lt; (2 * n_h_ * (n_u_ + n_y_))) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;Dataset problem! More rows than columns in block-hankel data \u0026#34; \u0026#34;matrix: 2*(n_u+n_y)*n_h \u0026gt; data-length! Need higher data-length or \u0026#34; \u0026#34;lower n_h.\u0026#34;; throw std::runtime_error(ss.str()); } fit_ = Fit(n_u_, n_x_, n_y_, dt_); u_ = std::move(u_train); z_ = std::move(z_train); if (!d.is_finite() || (d.n_rows != n_y_)) { // TODO(mfbolus): implement least-square solution for impulse response with // a second input of ones. Data-driven way of accounting for offset *not* // driven by an input. // // For now, calculate output bias (d) as the // output wherever the stimulus has not been on for some amount of time. // convolve u with rectangle and take all samples. This is a reasonable // approach, since often when autonomous systems are fit (i.e., systems with // no input), they will subtract off the mean of the output. This // essentially amounts to setting output bias to the mean of the output when // there is no stimulation. data_t t_silence = 0.1; data_t thresh_silence = 0.001; CalcD(t_silence, thresh_silence); } else { fit_.set_d(d); } } template \u0026lt;typename Fit\u0026gt; std::tuple\u0026lt;Fit, Vector\u0026gt; SSID\u0026lt;Fit\u0026gt;::Run(SSIDWt ssid_wt) { // the weight on minimizing dc I/O gain only works for gaussian, // and hopefully not necessary with appropriate dataset. data_t wt_dc = 0; // std::cout \u0026lt;\u0026lt; \u0026#34;creating hankel mat\\n\u0026#34;; CreateHankelDataMat(); // std::cout \u0026lt;\u0026lt; \u0026#34;decomposing data\\n\u0026#34;; DecomposeData(); // std::cout \u0026lt;\u0026lt; \u0026#34;calculating svd\\n\u0026#34;; CalcSVD(ssid_wt); // std::cout \u0026lt;\u0026lt; \u0026#34;solving for params\\n\u0026#34;; Solve(wt_dc); // std::cout \u0026lt;\u0026lt; \u0026#34;fin\\n\u0026#34;; return std::make_tuple(fit_, s_); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcD(data_t t_silence, data_t thresh_silence) { Vector d(z_.at(0).n_rows, fill::zeros); Vector win(static_cast\u0026lt;size_t\u0026gt;(t_silence / dt_), fill::ones); Vector sum_z_silence(n_y_, fill::zeros); size_t n_silence(0); for (size_t trial = 0; trial \u0026lt; u_.size(); trial++) { // find silent samples // start by convolving with Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0)); Vector u_conv = conv(sum_u, win, \u0026#34;same\u0026#34;); // get only the samples that are silent... arma::uvec ubi_silence = find(u_conv \u0026lt;= thresh_silence); if (ubi_silence.n_elem \u0026gt; 0) { sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1); n_silence += ubi_silence.n_elem; } } if (n_silence \u0026gt; 0) { d = sum_z_silence / n_silence; } fit_.set_d(d); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat() { // temporary copy of data Matrix z(n_y_, n_t_tot_, fill::zeros); Matrix u(n_u_, n_t_tot_, fill::zeros); size_t so_far(0); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial); u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial); so_far += n_t_.at(trial); } // remove output bias z.each_col() -= fit_.d(); // calculate I/O gain @ DC while data in convenient form g_dc_ = z * pinv(u); // std::cout \u0026lt;\u0026lt; \u0026#34;G0_data = \u0026#34; \u0026lt;\u0026lt; g_dc_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // create hankel data matrix size_t len = z.n_cols - 2 * n_h_ + 1; // data length in hankel mat // block-hankel data matrix D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros); // past input auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1); // future input auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1); // past output auto y_p = D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1); // future output auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, len - 1); size_t idx = 0; for (size_t k = 0; k \u0026lt; len; k++) { idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } } D_ /= sqrt(static_cast\u0026lt;data_t\u0026gt;(len)); } // template \u0026lt;typename Fit\u0026gt; // void SSID\u0026lt;Fit\u0026gt;::DecomposeData() { // // do LQ decomp instead of calculating covariance expensive way // // Note that \u0026#34;R\u0026#34; in van Overschee is lower-triangular (L), not \u0026#34;R\u0026#34; in QR // // decomp. Very confusing. // Matrix q_t; // lq(L_, q_t, D_); // // van Overschee zeros out the other elements. // L_ = trimatl(L_); // } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcSVD(SSIDWt wt) { // submats that will be needed: auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1); auto R_23_13 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1); auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_) - 1); auto R_44 = L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14); auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1); auto Luf = Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1); auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); // aka: R_f Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1); // from van Overschee subid.m: // Rf = R((2*m+l)*i+1:2*(m+l)*i,:); % Future outputs Matrix U; Matrix V; switch (wt) { case kSSIDNone: { // No weighting. (what van Overschee calls \u0026#34;N4SID\u0026#34;) Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14; arma::svd(U, s_, V, O_k_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDMOESP: { // MOESP weighting // This is what they use in the \u0026#34;robust\u0026#34; algorithm van Overschee, de Moor // 1996 Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); svd(U, s_, V, O_k_ortho_Uf_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDCVA: { // CVA weighting // See van Overschee\u0026#39;s matlab code (subid.m): // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); Matrix inv_w1; Matrix qt1; lq(inv_w1, qt1, R_56_16); // lq decomp of R_f (future output data) inv_w1 = trimatl(inv_w1); inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1); Matrix w_o_w = arma::solve( inv_w1, O_k_ortho_Uf_sans_Qt); // alternatively // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt svd(U, s_, V, w_o_w, \u0026#34;std\u0026#34;); U = inv_w1 * U; break; } } // Truncate to model order (heart of ssid method) auto s_hat = s_.subvec(0, n_x_ - 1); Matrix diag_sqrt_s = diagmat(sqrt(s_hat)); auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1); // get extended observability and controllability mats ext_obs_t_ = u_hat * diag_sqrt_s; // extended observability matrix } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::Solve(data_t wt_dc) { // required submats auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_56_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); // Solve for params using appropriate algorithm: // robust deterministic/stochastic algorithm in van Overschee 1996 // algorithm that the authors say \u0026#34;works\u0026#34; in practice. auto ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix // This is what textbook (1996) says: // // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15); // // HOWEVER, do not know why but have to fill the last place with zeros like // authors\u0026#39; matlab implementation (see `subid.m`) // Otherwise, get ridiculous covariances (although A,C estimates are close to // same...) Matrix Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Matrix S = Tl * pinv(Tr); // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure // stability. fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1)); Matrix ext_obs_t_p1 = join_vert( ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1), Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros)); fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1); // At this point, van Overschee \u0026amp; de Moor suggest re-calculating ext_obs_t_, // ext_obs_tm1 from (A, C) because it was just an approximation. This is RecomputeExtObs(); ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); S = Tl * pinv(Tr); Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_); Matrix Mcurly = pinv(ext_obs_tm1); Matrix Pcurly = Tl - Lcurly * R_56_15; Vector Pvec = vectorise(Pcurly); Matrix Qcurly = R_23_15; // Identify [D; B], assuming D=0 and ensuring DC gain is correct Matrix sum_QcurlyT_kron_Ncurly( (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_), fill::zeros); Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols, fill::eye); eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1, eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1; // van Overschee (1996) p. 126 Matrix N1_Tl = -Lcurly; N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) += join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly); N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) += Matrix(n_y_, n_y_, fill::eye); Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros); Matrix N_k; for (size_t k = 0; k \u0026lt; n_h_; k++) { auto Qcurly_k = Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1); Nk_Tl.zeros(); Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) = N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1); N_k = Nk_Tl * eye_ext_obs_tm1; sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k); } Matrix err_vec; if (wt_dc \u0026gt; 0) { // Constraints enforced by weighted least squares // // Reference: // // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly // Excited Industrial Systems. Conference in Decision and Control. // constraint 1: assume D=0 --\u0026gt; remove the components for Dvec (this is // actually a hard constraint in that it ignores D) Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly; sum_QcurlyT_kron_Ncurly = Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_); size_t kkk = 0; for (size_t k = 1; k \u0026lt; (n_u_ + 1); k++) { size_t start_idx = k * (n_y_ + n_x_) - n_x_; for (size_t kk = 0; kk \u0026lt; n_x_; kk++) { sum_QcurlyT_kron_Ncurly.col(kkk) = sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk); kkk++; } } // constraint 2: Make sure DC I/O gain is correct Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A()); Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_)); Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0); Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 = join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0); // WEIGHTED LS // Important in practice because I care a lot about at least getting the DC // gain correct. Put x weight on minimizing error at DC, relative to others Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye); // Make weight on minimizing DC error immense so at least that // should be nailed. size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows; size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows; size_t stop_row = w.n_rows - 1; size_t stop_col = w.n_cols - 1; // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale // weight with data length? w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc; Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * sum_QcurlyT_kron_Ncurly_b_to_g0) * sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec; fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_)); // Calculate residuals and their cov. // Because I\u0026#39;ve added constraints, I need to re-calculate the right term // with b_vec instead of how van Overschee do in final algorithm. err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec; } else { // default way: *no* constraint on G0 or D=0 Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec; // TODO(mfbolus) n.b., this gets thrown away... // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_); fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_)); err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec; } // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols); // TODO(mfbolus): Something is wrong with the error calculation above. // Use the way van overschee does it in `subid.m` // WARNING: this ignores any above constraints, so Q, R will be approximate... Matrix err = Tl - S * Tr; Matrix cov_err = err * err.t(); fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1)); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::RecomputeExtObs() { ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C(); for (size_t k = 2; k \u0026lt; (n_h_ + 1); k++) { ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) = ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1, ext_obs_t_.n_cols - 1) * fit_.A(); } } } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":55,"href":"/lds-ctrl-est/docs/api/files/lds__fit_8h/","title":"ldsCtrlEst_h/lds_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_fit.h # LDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Fit LDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.\nSource code # //===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDS_FIT_HPP #define LDS_FIT_HPP // namespace #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); virtual ~Fit() = default; // get methods size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; Q() const { return Q_; }; const Vector\u0026amp; x0() const { return x0_; }; const Matrix\u0026amp; P0() const { return P0_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; // gets measurement noise virtual const Matrix\u0026amp; R() const = 0; // set methods (e.g., seeding initial fit values) void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_m(const Vector\u0026amp; m) { Reassign(m_, m); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); ForceSymPD(Q_); }; virtual void set_R(const Matrix\u0026amp; R) = 0; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); ForceSymPD(P0_); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; View f(Matrix\u0026amp; x, const Matrix\u0026amp; u, size_t t) { x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x.col(t); }; View f(Matrix\u0026amp; x_pre, const Matrix\u0026amp; x_post, const Matrix\u0026amp; u, size_t t) { x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x_pre.col(t); }; virtual View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) = 0; protected: data_t dt_{}; // Dynamics Matrix A_; Matrix B_; Vector g_; Vector m_; Matrix Q_; // Output Matrix C_; Vector d_; Matrix R_; // initial conditions Vector x0_; Matrix P0_; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; }; } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":56,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_ctrl.h # GLDS Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. Detailed Description # This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_gaussian_sys.h\u0026#34; // controller #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace gaussian { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); cx_ref_ = y_ref - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":57,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_em.h # GLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitEM GLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":58,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_ssid.h # GLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; using SSID\u0026lt;Fit\u0026gt;::Run; private: using SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat; using SSID\u0026lt;Fit\u0026gt;::CalcSVD; using SSID\u0026lt;Fit\u0026gt;::Solve; void DecomposeData() override; void SolveVanOverschee(); }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":59,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit.h # GLDS fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Fit GLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // fit type #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace gaussian { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); const Matrix\u0026amp; R() const override { return R_; }; void set_R(const Matrix\u0026amp; R) override { Reassign(R_, R); ForceSymPD(R_); }; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = C_ * x.col(t) + d_; return y.col(t); }; }; }; // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":60,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sctrl.h # GLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H // controller type #include \u0026#34;lds_gaussian_ctrl.h\u0026#34; // switched controller #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace gaussian { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); cx_ref_ = y_ref - sys_.d(); } // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; }; // SwitchedController } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":61,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/","title":"ldsCtrlEst_h/lds_gaussian_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sys.h # GLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::System Gaussian LDS Type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H #define LDSCTRLEST_LDS_GAUSSIAN_SYS_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; namespace lds { namespace gaussian { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0, data_t r0 = kDefaultR0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; // get methods const Matrix\u0026amp; R() const { return R_; }; // set methods void set_Q(const Matrix\u0026amp; Q) { lds::System::set_Q(Q); do_recurse_Ke_ = true; } void set_R(const Matrix\u0026amp; R) { Reassign(R_, R); do_recurse_Ke_ = true; }; void set_Ke(const Matrix\u0026amp; Ke) { Reassign(Ke_, Ke); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void set_Ke_m(const Matrix\u0026amp; Ke_m) { Reassign(Ke_m_, Ke_m); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void Print(); protected: void h() override { cx_ = C_ * x_; y_ = cx_ + d_; }; Vector h_(Vector x) override { return C_ * x + d_; }; void RecurseKe() override; // Gaussian-output-specific Matrix R_; bool do_recurse_Ke_{}; }; // System } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":62,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian_8h/","title":"ldsCtrlEst_h/lds_gaussian.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian.h # glds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_H #define LDSCTRLEST_LDS_GAUSSIAN_H // namespace #include \u0026#34;lds.h\u0026#34; namespace lds { namespace gaussian { // insert any Gaussian-specific things here... } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":63,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_ctrl.h # PLDS controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. Detailed Description # This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_CTRL_H #define LDSCTRLEST_LDS_POISSON_CTRL_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system type #include \u0026#34;lds_poisson_sys.h\u0026#34; // control type #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace poisson { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); lds::Limit(y_ref_, kYRefLb, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; private: constexpr static const data_t kYRefLb = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":64,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_em.h # PLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitEM PLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\n[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H #define LDSCTRLEST_LDS_POISSON_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override{}; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; data_t NewtonSolveC(); void AnalyticalSolveD(); }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":65,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_ssid.h # PLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. Detailed Description # This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/)).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H #define LDSCTRLEST_LDS_POISSON_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; private: void DecomposeData() override; void CalcCov(); void PoissonToGaussianMoments(); Matrix cov_; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":66,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/","title":"ldsCtrlEst_h/lds_poisson_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit.h # PLDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Fit PLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_H #define LDSCTRLEST_LDS_POISSON_FIT_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // fit #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace poisson { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) : lds::Fit(n_u, n_x, n_y, dt){}; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = exp(C_ * x.col(t) + d_); return y.col(t); }; void set_R(const Matrix\u0026amp; R) override { std::cerr \u0026lt;\u0026lt; \u0026#34;WARNING: Cannot set R (R[0] = \u0026#34; \u0026lt;\u0026lt; R.at(0) \u0026lt;\u0026lt; \u0026#34;). No Gaussian measurement noise in Poisson observation model.\\n\u0026#34;; }; const Matrix\u0026amp; R() const override { return R_; }; }; }; // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":67,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sctrl.h # PLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #include \u0026#34;lds_poisson_ctrl.h\u0026#34; #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace poisson { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); lds::Limit(y_ref_, kYRefLB, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; private: constexpr static data_t kYRefLB = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":68,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/","title":"ldsCtrlEst_h/lds_poisson_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sys.h # PLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::System Poisson System type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SYS_H #define LDSCTRLEST_LDS_POISSON_SYS_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; // needed for Poisson random number generation #include \u0026lt;random\u0026gt; namespace lds { namespace poisson { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; protected: void h() override { cx_ = C_ * x_; y_ = exp(cx_ + d_); diag_y_.diag() = y_; }; Vector h_(Vector x) override { return exp(C_ * x + d_); }; void RecurseKe() override; private: // Poisson-output-specific Matrix diag_y_; std::poisson_distribution\u0026lt;size_t\u0026gt; pd_; }; // System } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":69,"href":"/lds-ctrl-est/docs/api/files/lds__poisson_8h/","title":"ldsCtrlEst_h/lds_poisson.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson.h # plds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_H #define LDSCTRLEST_LDS_POISSON_H #include \u0026#34;lds.h\u0026#34; namespace lds { namespace poisson { // TODO(mfbolus): Not sure if defining these as static here makes the most // sense. Is there a downside to letting multiple poisson System objects share a // common random number generator? static std::random_device rd; static std::mt19937 rng = std::mt19937( rd()); } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":70,"href":"/lds-ctrl-est/docs/api/files/lds__sctrl_8h/","title":"ldsCtrlEst_h/lds_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_sctrl.h # SwitchedController type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SwitchedController SwitchedController Type. Detailed Description # This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SCTRL_H #define LDSCTRLEST_LDS_SCTRL_H #include \u0026#34;lds_ctrl.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; #include \u0026#34;lds_uniform_vecs.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class SwitchedController : public Controller\u0026lt;System\u0026gt; { public: SwitchedController() = default; SwitchedController(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); SwitchedController(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); void Switch(size_t idx, bool do_force_switch = false); void set_Kc(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc) { Kc_list_ = Kc; Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc) { Kc_list_ = std::move(Kc); Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_inty) { Kc_inty_list_ = Kc_inty; Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_inty) { Kc_inty_list_ = std::move(Kc_inty); Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_u) { Kc_u_list_ = Kc_u; Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_u) { Kc_u_list_ = std::move(Kc_u); Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(const UniformVectorList\u0026amp; g) { g_design_list_ = g; g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(UniformVectorList\u0026amp;\u0026amp; g) { g_design_list_ = std::move(g); g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; protected: std::vector\u0026lt;System\u0026gt; systems_; size_t n_sys_{}; size_t idx_{}; // controller gains could be different for each UniformMatrixList\u0026lt;\u0026gt; Kc_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_inty_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_u_list_; // design-phase input gain could also be different UniformVectorList g_design_list_; // TODO(mfbolus): not sure why I need to do this. using Controller\u0026lt;System\u0026gt;::Kc_; using Controller\u0026lt;System\u0026gt;::Kc_inty_; using Controller\u0026lt;System\u0026gt;::Kc_u_; using Controller\u0026lt;System\u0026gt;::g_design_; using Controller\u0026lt;System\u0026gt;::sys_; // using Controller\u0026lt;System\u0026gt;::u_ref_; // using Controller\u0026lt;System\u0026gt;::x_ref_; // using Controller\u0026lt;System\u0026gt;::y_ref_; // using Controller\u0026lt;System\u0026gt;::control_type_; private: void InitVars(); using lds::Controller\u0026lt;System\u0026gt;::set_sys; // using Controller\u0026lt;System\u0026gt;::set_Kc; // using Controller\u0026lt;System\u0026gt;::set_Kc_inty; // using Controller\u0026lt;System\u0026gt;::set_Kc_u; // using Controller\u0026lt;System\u0026gt;::set_g_design; }; template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(systems.at(0), u_lb, u_ub, control_type), systems_(systems) { InitVars(); } template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(System(systems.at(0).n_u(), systems.at(0).n_x(), systems.at(0).n_y(), systems.at(0).dt()), u_lb, u_ub, control_type), systems_(std::move(systems)) { InitVars(); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::InitVars() { n_sys_ = systems_.size(); sys_ = systems_.at(0); Kc_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_)); Kc_inty_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); Kc_u_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); g_design_list_ = UniformVectorList(std::vector\u0026lt;Vector\u0026gt;(n_sys_, g_design_)); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::Switch(size_t idx, bool do_force_switch) { if ((idx == idx_) \u0026amp;\u0026amp; !do_force_switch) { return; // already there. } // put old up and get new one out systems_.at(idx_) = std::move(sys_); sys_ = std::move(systems_.at(idx)); // set the state of this system to that of the previous system // TODO(mfbolus): This will only work as intended if state matrix is the same. // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp. sys_.set_m(systems_.at(idx_).m(), true); sys_.set_x(systems_.at(idx_).x()); // swap controller gains Kc_list_.Swap(Kc_, idx_); Kc_list_.Swap(Kc_, idx); if (control_type_ \u0026amp; kControlTypeIntY) { Kc_inty_list_.Swap(Kc_inty_, idx_); Kc_inty_list_.Swap(Kc_inty_, idx); } if (control_type_ \u0026amp; kControlTypeDeltaU) { Kc_u_list_.Swap(Kc_u_, idx_); Kc_u_list_.Swap(Kc_u_, idx); } g_design_list_.Swap(g_design_, idx_); g_design_list_.Swap(g_design_, idx); idx_ = idx; } // Switch } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":71,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8h/","title":"ldsCtrlEst_h/lds_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_sys.h # LDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::System Linear Dynamical System Type. Detailed Description # This file declares and partially defines the base type for linear dynamical systems ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SYS_H #define LDSCTRLEST_LDS_SYS_H #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class System { public: System() = default; System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); virtual ~System() {} void Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z); virtual const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) = 0; void f(const Vector\u0026amp; u, bool do_add_noise = false) { x_ = A_ * x_ + B_ * (g_ % u) + m_; if (do_add_noise) { x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_); } }; virtual void h() = 0; virtual Vector h_(Vector x) = 0; size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Vector\u0026amp; x() const { return x_; }; const Matrix\u0026amp; P() const { return P_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; P_m() const { return P_m_; }; const Vector\u0026amp; cx() const { return cx_; }; const Vector\u0026amp; y() const { return y_; }; const Vector\u0026amp; x0() const { return x0_; }; const Vector\u0026amp; m0() const { return m0_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; const Matrix\u0026amp; Ke() const { return Ke_; }; const Matrix\u0026amp; Ke_m() const { return Ke_m_; }; const Matrix\u0026amp; Q() { return Q_; }; const Matrix\u0026amp; Q_m() { return Q_m_; }; const Matrix\u0026amp; P0() { return P0_; }; const Matrix\u0026amp; P0_m() { return P0_m_; }; void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_m(const Vector\u0026amp; m, bool do_force_assign = false) { Reassign(m0_, m); if ((!do_adapt_m) || do_force_assign) { Reassign(m_, m); } }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); }; void set_Q_m(const Matrix\u0026amp; Q_m) { Reassign(Q_m_, Q_m); }; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); }; void set_P0_m(const Matrix\u0026amp; P0_m) { Reassign(P0_m_, P0_m); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; void set_x(const Vector\u0026amp; x) { Reassign(x_, x); h(); }; void Reset(); std::vector\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; nstep_pred_block( UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z, size_t n_pred = 1); void Print(); // safe to leave this public and non-const bool do_adapt_m{}; protected: virtual void RecurseKe() = 0; void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); std::size_t n_x_{}; std::size_t n_u_{}; std::size_t n_y_{}; data_t dt_{}; // Signals: Vector x_; Matrix P_; Vector m_; Matrix P_m_; Vector cx_; Vector y_; Vector z_; // Parameters: Vector x0_; Matrix P0_; Vector m0_; Matrix P0_m_; Matrix A_; Matrix B_; Vector g_; Matrix Q_; Matrix Q_m_; Matrix C_; Vector d_; Matrix Ke_; Matrix Ke_m_; }; // System } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":72,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/","title":"ldsCtrlEst_h/lds_uniform_mats.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_mats.h # List of uniformly sized matrices. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformMatrixList Detailed Description # This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H #define LDSCTRLEST_LDS_UNIFORM_MATS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { template \u0026lt;MatrixListFreeDim D = kMatFreeDimNone\u0026gt; class UniformMatrixList : public std::vector\u0026lt;Matrix\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Matrix\u0026gt;::vector; // don\u0026#39;t allow push_back to be used since it doesn\u0026#39;t check dims using std::vector\u0026lt;Matrix\u0026gt;::push_back; public: using std::vector\u0026lt;Matrix\u0026gt;::operator=; using std::vector\u0026lt;Matrix\u0026gt;::operator[]; using std::vector\u0026lt;Matrix\u0026gt;::begin; using std::vector\u0026lt;Matrix\u0026gt;::end; using std::vector\u0026lt;Matrix\u0026gt;::size; using std::vector\u0026lt;Matrix\u0026gt;::at; UniformMatrixList() = default; explicit UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); explicit UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; ~UniformMatrixList() = default; const std::array\u0026lt;size_t, 2\u0026gt;\u0026amp; dim(size_t n = 0) const { return dim_.at(n); } size_t size() { return std::vector\u0026lt;Matrix\u0026gt;::size(); }; const Matrix\u0026amp; at(size_t n) { return std::vector\u0026lt;Matrix\u0026gt;::at(n); }; void Swap(Matrix\u0026amp; that, size_t n); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; void append(const Matrix\u0026amp; mat); private: void CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim); std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt; dim_; }; template \u0026lt;MatrixListFreeDim D\u0026gt; inline void UniformMatrixList\u0026lt;D\u0026gt;::Swap(Matrix\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.n_cols); } if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap // not moving, since it causes memory issues. // so this method isn\u0026#39;t a memory-saver as designed for now Matrix tmp = (*this)[n]; (*this)[n] = that; that = tmp; if (D == kMatFreeDim1) { this-\u0026gt;dim_[n][0] = (*this)[n].n_rows; } if (D == kMatFreeDim2) { this-\u0026gt;dim_[n][1] = (*this)[n].n_cols; } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::append(const Matrix\u0026amp; mat) { std::array\u0026lt;size_t, 2\u0026gt; dim({mat.n_rows, mat.n_cols}); CheckDimensions(dim); std::vector\u0026lt;Matrix\u0026gt;::push_back(mat); dim_.push_back(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) { // make sure dim_ vector is initialized if (dim_.empty()) { dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(that.size(), {0, 0}); } // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices\u0026#34;; throw std::runtime_error(ss.str()); } // if dimensions a not zero and do not match, skip move with error message. bool dims_nonzero = true; for (auto d : dim_) { if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; d[0] \u0026lt; 1) { dims_nonzero = false; break; } if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; d[1] \u0026lt; 1) { dims_nonzero = false; break; } } if (dims_nonzero) { bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); } if (!does_match) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrices of size \u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; dim_[k] = that.dim(k); } return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. bool dims_nonzero = true; for (auto d : dim_) { // if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; (d[0] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; (d[1] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // } // // if (dims_nonzero) { // bool does_match = true; // if (!(D == kMatFreeDim1)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); // } // // if (!(D == kMatFreeDim2)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); // } // // if (!does_match) { // this-\u0026gt;at(0).print(\u0026#34;this[0] = \u0026#34;); // that.at(0).print(\u0026#34;that[0] = \u0026#34;); // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformMatrixList element of size (\u0026#34; \u0026lt;\u0026lt; // that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols \u0026lt;\u0026lt; \u0026#34;) for an // element of size (\u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34;). // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Matrix\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(std::move(mats)) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) : vector(that) { (*this) = that; } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { std::array\u0026lt;size_t, 2\u0026gt; dim_k({this-\u0026gt;at(k).n_rows, this-\u0026gt;at(k).n_cols}); dim_.push_back(dim_k); } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim) { // change behavior based on free dim D if ((dim[0] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim1)) { dim[0] = this-\u0026gt;at(0).n_rows; } if ((dim[1] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim2)) { dim[1] = this-\u0026gt;at(0).n_cols; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Matrix\u0026amp; mat : *this) { if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_rows == dim[0]); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_cols == dim[1]); } if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(this-\u0026gt;size(), dim); for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { dim_[k][0] = (*this)[k].n_rows; dim_[k][1] = (*this)[k].n_cols; } } } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":73,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/","title":"ldsCtrlEst_h/lds_uniform_systems.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_systems.h # List of uniformly sized Systems. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformSystemList Detailed Description # This file provides a container for uniformly sized Systems.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector // namespace #include \u0026#34;lds.h\u0026#34; // System type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class UniformSystemList : public std::vector\u0026lt;System\u0026gt; { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;System\u0026gt;::vector; using std::vector\u0026lt;System\u0026gt;::operator=; using std::vector\u0026lt;System\u0026gt;::operator[]; using std::vector\u0026lt;System\u0026gt;::at; using std::vector\u0026lt;System\u0026gt;::begin; using std::vector\u0026lt;System\u0026gt;::end; using std::vector\u0026lt;System\u0026gt;::size; public: UniformSystemList() = default; explicit UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); explicit UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(const UniformSystemList\u0026amp; that); UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept; ~UniformSystemList() = default; const std::array\u0026lt;size_t, 3\u0026gt;\u0026amp; dim() const { return dim_; } size_t size() { return std::vector\u0026lt;System\u0026gt;::size(); }; const System\u0026amp; at(size_t n) { return std::vector\u0026lt;System\u0026gt;::at(n); }; void Swap(System\u0026amp; that, size_t n); UniformSystemList\u0026amp; operator=(const UniformSystemList\u0026amp; that); UniformSystemList\u0026amp; operator=(UniformSystemList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim); std::array\u0026lt;size_t, 3\u0026gt; dim_{}; }; template \u0026lt;typename System\u0026gt; inline void UniformSystemList\u0026lt;System\u0026gt;::Swap(System\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformSystemList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = (dim_[0] == that.n_u()) \u0026amp;\u0026amp; (dim_[1] == that.n_x()) \u0026amp;\u0026amp; (dim_[2] == that.n_y()); if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformSystemList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap System tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( const UniformSystemList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_[0] + dim_[1] + dim_[2]) { std::array\u0026lt;size_t, 3\u0026gt; other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign systems of size \u0026#34; \u0026lt;\u0026lt; dim_[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2] \u0026lt;\u0026lt; \u0026#34; with systems of size \u0026#34; \u0026lt;\u0026lt; other_dim[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other_dim[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2]; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( UniformSystemList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. if (dim_[0] + dim_[1] + dim_[2]) { // bool does_match = (dim_[0] == that.at(0).n_u()) \u0026amp;\u0026amp; // (dim_[1] == that.at(0).n_x()) \u0026amp;\u0026amp; // (dim_[2] == that.at(0).n_y()); // if (!does_match) { // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformSystemList element for an element of \u0026#34; // \u0026#34;different size. Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;System\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(std::move(systems)) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList( std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const UniformSystemList\u0026amp; that) : std::vector\u0026lt;System\u0026gt;(that) { (*this) = that; } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept : std::vector\u0026lt;System\u0026gt;(std::move(that)) { this-\u0026gt;dim_[0] = this-\u0026gt;at(0).n_u(); this-\u0026gt;dim_[1] = this-\u0026gt;at(0).n_x(); this-\u0026gt;dim_[2] = this-\u0026gt;at(0).n_y(); } template \u0026lt;typename System\u0026gt; void UniformSystemList\u0026lt;System\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim) { if (dim[0] + dim[1] + dim[2]) { dim_ = dim; } else { dim_[0] = this-\u0026gt;at(0).n_u(); dim_[1] = this-\u0026gt;at(0).n_x(); dim_[2] = this-\u0026gt;at(0).n_y(); } // make sure dimensiolaties are all uniform bool does_match(true); for (const System\u0026amp; sys : *this) { does_match = does_match \u0026amp;\u0026amp; (sys.n_u() == dim_[0]); does_match = does_match \u0026amp;\u0026amp; (sys.n_x() == dim_[1]); does_match = does_match \u0026amp;\u0026amp; (sys.n_y() == dim_[2]); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input systems are not uniform.\u0026#34;); } } } } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":74,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/","title":"ldsCtrlEst_h/lds_uniform_vecs.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_vecs.h # List of uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformVectorList Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H #define LDSCTRLEST_LDS_UNIFORM_VECS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { class UniformVectorList : public std::vector\u0026lt;Vector\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Vector\u0026gt;::vector; using std::vector\u0026lt;Vector\u0026gt;::operator=; using std::vector\u0026lt;Vector\u0026gt;::operator[]; using std::vector\u0026lt;Vector\u0026gt;::at; using std::vector\u0026lt;Vector\u0026gt;::begin; using std::vector\u0026lt;Vector\u0026gt;::end; using std::vector\u0026lt;Vector\u0026gt;::size; public: UniformVectorList() = default; explicit UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim = 0); explicit UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim = 0); UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim = 0); UniformVectorList(const UniformVectorList\u0026amp; that); UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept; ~UniformVectorList() = default; size_t dim() const { return dim_; } size_t size() { return std::vector\u0026lt;Vector\u0026gt;::size(); }; const Vector\u0026amp; at(size_t n) { return std::vector\u0026lt;Vector\u0026gt;::at(n); }; void Swap(Vector\u0026amp; that, size_t n); UniformVectorList\u0026amp; operator=(const UniformVectorList\u0026amp; that); UniformVectorList\u0026amp; operator=(UniformVectorList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(size_t dim); size_t dim_{}; }; inline void UniformVectorList::Swap(Vector\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = dim_ == that.n_elem; if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap Vector tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( const UniformVectorList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_) { size_t other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ \u0026lt;\u0026lt; \u0026#34; with vectors of size \u0026#34; \u0026lt;\u0026lt; other_dim; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( UniformVectorList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors. Skipping.\\n\u0026#34;; // return (*this); // } // // if (dim_) { // size_t other_dim(that.dim()); // if (dim_ != other_dim) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ // \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; other_dim \u0026lt;\u0026lt; \u0026#34;. // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Vector\u0026gt;::operator=(std::move(that)); return (*this); } } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":75,"href":"/lds-ctrl-est/docs/api/files/lds_8h/","title":"ldsCtrlEst_h/lds.h","section":"Files","content":" ldsCtrlEst_h/lds.h # lds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)) observations.\nSource code # //===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_H #define LDSCTRLEST_LDS_H // #ifndef LDSCTRLEST // #include \u0026lt;ldsCtrlEst\u0026gt; // #endif #include \u0026lt;armadillo\u0026gt; namespace lds { using data_t = double; // may change to float (but breaks mex functions) using Vector = arma::Col\u0026lt;data_t\u0026gt;; using Matrix = arma::Mat\u0026lt;data_t\u0026gt;; using Cube = arma::Cube\u0026lt;data_t\u0026gt;; using View = arma::subview\u0026lt;data_t\u0026gt;; namespace fill = arma::fill; static const std::size_t kControlTypeDeltaU = 0x1; static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); static const data_t kPi = arma::datum::pi; static const data_t kDefaultP0 = 1e-6; static const data_t kDefaultQ0 = 1e-6; static const data_t kDefaultR0 = 1e-2; enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA }; enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2 }; // TODO(mfbolus): for SwitchedController, may want systems to have differing // numbers of states. Use this enum as template parameter? // enum SystemListFreeDim { // kSysFreeDimNone, // kSysFreeDimX ///\u0026lt; allow state dim (x) of systems in list to be hetero // }; // place hard limits on contents of vecors/mats void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub); void Limit(Vector\u0026amp; x, data_t lb, data_t ub); void Limit(Matrix\u0026amp; x, data_t lb, data_t ub); // in-place assign that errs if there are dimension mismatches: void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); // TODO(mfbolus): this is a fudge, but for some reason, cov mats often going // numerically asymm. void ForceSymPD(Matrix\u0026amp; X); void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min = 0); void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X); Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B); inline void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Vector\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Matrix\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if (other.n_elem != some.n_elem) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vector of size \u0026#34; \u0026lt;\u0026lt; some.n_elem \u0026lt;\u0026lt; \u0026#34; with vector of size \u0026#34; \u0026lt;\u0026lt; other.n_elem \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } inline void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrix of size \u0026#34; \u0026lt;\u0026lt; some.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; some.n_cols \u0026lt;\u0026lt; \u0026#34; with matrix of size \u0026#34; \u0026lt;\u0026lt; other.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other.n_cols \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } } // namespace lds #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":76,"href":"/lds-ctrl-est/docs/api/files/mex__c__util_8h/","title":"ldsCtrlEst_h/mex_c_util.h","section":"Files","content":" ldsCtrlEst_h/mex_c_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) More\u0026hellip;\nNamespaces # Name armamexc arma/mex interface using Matlab C API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab/Octave\u0026rsquo;s C mex API.\nSource code # //===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXC_UTIL_H #define LDSCTRLEST_MEXC_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.h\u0026#34; // // If Matlab_FOUND, include matrix.h. // // (Octave does not need/have it.) // #ifdef Matlab_FOUND // #include \u0026#34;matrix.h\u0026#34; // #endif namespace armamexc { template \u0026lt;class T\u0026gt; inline auto m2T_scalar(const mxArray *matlab_scalar) -\u0026gt; T { if (mxGetData(matlab_scalar)) { return static_cast\u0026lt;T\u0026gt;(mxGetScalar(matlab_scalar)); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return 0; } template \u0026lt;class T\u0026gt; inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false, bool strict = true) -\u0026gt; arma::Mat\u0026lt;T\u0026gt; { if (mxGetData(matlab_mat)) { const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat); if (n_dim == 2) { return arma::Mat\u0026lt;T\u0026gt;(static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)), mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem, strict); } mexErrMsgTxt(\u0026#34;Number of dimensions must be 2.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } // TODO(mfbolus): make these templated. template \u0026lt;typename T\u0026gt; inline auto a2m_mat(arma::Mat\u0026lt;T\u0026gt; const \u0026amp;arma_mat) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_mat.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Mat.\u0026#34;); return nullptr; } template \u0026lt;typename T\u0026gt; inline auto a2m_vec(arma::Col\u0026lt;T\u0026gt; const \u0026amp;arma_vec) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_vec.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Col.\u0026#34;); return nullptr; } } // namespace armamexc #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":77,"href":"/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/","title":"ldsCtrlEst_h/mex_cpp_util.h","section":"Files","content":" ldsCtrlEst_h/mex_cpp_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) More\u0026hellip;\nNamespaces # Name armamexcpp arma/mex interface using Matlab C++ API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab\u0026rsquo;s C++ mex API.\nSource code # //===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXCPP_UTIL_H #define LDSCTRLEST_MEXCPP_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.hpp\u0026#34; #include \u0026#34;mexAdapter.hpp\u0026#34; namespace armamexcpp { template \u0026lt;class T\u0026gt; std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; m2a_cellmat(matlab::data::CellArray\u0026amp; matlab_cell) { size_t n_cells = matlab_cell.getNumberOfElements(); std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; arma_mat(n_cells, arma::Mat\u0026lt;T\u0026gt;(1, 1, arma::fill::zeros)); for (size_t k = 0; k \u0026lt; n_cells; k++) { matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = matlab_cell[k]; auto dims = matlab_mat.getDimensions(); arma_mat[k] = arma::Mat\u0026lt;T\u0026gt;(matlab_mat.release().get(), dims[0], dims[1]); } return arma_mat; }; template \u0026lt;class T\u0026gt; std::vector\u0026lt;T\u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt;T\u0026gt;\u0026amp; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); T* ptr = matlab_array.release().get(); std::vector\u0026lt;T\u0026gt; vec(ptr, ptr + n_elem); return vec; }; template \u0026lt;class T\u0026gt; arma::Col\u0026lt;T\u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); // T* ptr = matlab_array.release().get(); // arma::Col\u0026lt;T\u0026gt; vec(ptr, n_elem); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? arma::Col\u0026lt;T\u0026gt; vec(n_elem, arma::fill::zeros); for (size_t k = 0; k \u0026lt; n_elem; k++) { vec[k] = matlab_array[k]; } return vec; }; template \u0026lt;class T\u0026gt; arma::Mat\u0026lt;T\u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { // ArrayDimensions == std::vector\u0026lt;size_t\u0026gt; auto dims = matlab_array.getDimensions(); // T* ptr = matlab_array.release().get(); // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false) // arma::Mat\u0026lt;T\u0026gt; mat(ptr, dims[0], dims[1]); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? // // armadillo and matlab both use column-major ordering, so this should work: size_t n_elem = dims[0] * dims[1]; arma::Mat\u0026lt;T\u0026gt; mat(dims[0], dims[1], arma::fill::zeros); size_t k(0); for (auto m: matlab_array) { mat[k] = m; k++; } return mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_mat(const arma::Mat\u0026lt;T\u0026gt;\u0026amp; arma_mat, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(), arma_mat.memptr() + arma_mat.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_vec(const arma::Col\u0026lt;T\u0026gt;\u0026amp; arma_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;({arma_vec.n_elem, 1}, arma_vec.memptr(), arma_vec.memptr() + arma_vec.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; s2m_vec(const std::vector\u0026lt;T\u0026gt;\u0026amp; std_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size()); return matlab_mat; }; } // namespace armamexcpp #endif Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":78,"href":"/lds-ctrl-est/docs/terminology/model/","title":"Models","section":"LDS C+E Documentation","content":" Model Definitions # This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form: \\[\\mathbf{x}_{t\u0026#43;1} = f\\left( \\mathbf{x}_{t}, \\mathbf{v}_{t} \\right) = \\mathbf{A} \\mathbf{x}_{t} \u0026#43; \\mathbf{B} \\mathbf{v}_{t} \u0026#43; \\mathbf{m}_{t} \u0026#43; \\mathbf{w}_{t}\\] \\[\\mathbf{y}_{t} = h\\left( \\mathbf{x}_{t} \\right)\\] t : time index x : system state v = g%u : input (e.g., in physical units used for model fit) u : control signal sent to actuator (e.g., in Volts) y : system output m : process disturbance w ~ N(0, Q) : process noise/disturbance A : state matrix B : input coupling matrix g : input gain (e.g., for converting to control signal actuator voltage) n.b., assumes this conversion is linear Q : process noise covariance % : element-wise multiplication LDS with Gaussian Observations # For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.\n\\[\\mathbf{y}_{t} = \\mathbf{C} \\mathbf{x}_{t} \u0026#43; \\mathbf{d}\\] \\[\\mathbf{z}_{t} \\sim \\mathcal{N}\\left(\\mathbf{y}_{t} , \\mathbf{R} \\right)\\] z : measurement C : output matrix d : output bias R : measurement noise covariance LDS with Poisson Observations # For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.\n\\[y_{t}^{i} = \\exp \\left(\\mathbf{c}^i \\mathbf{x}_{t} \u0026#43; d^i\\right)\\] \\[z_{t}^i \\sim \\rm{Poisson} \\left(y_{t}^i \\right)\\] i : output index z : measurement (count data) c : i^th row of output matrix (C) d : output bias Model Predictive Control (MPC) # Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.\nIn the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.\n"},{"id":79,"href":"/lds-ctrl-est/docs/api/modules/","title":"Modules","section":"LDS C+E Documentation","content":" Modules # Control Mode Bit Masks provides fill types for constructing new armadillo vectors, matrices\nDefaults\nUpdated on 5 March 2025 at 21:33:49 EST\n"},{"id":80,"href":"/lds-ctrl-est/docs/api/namespaces/","title":"Namespaces","section":"LDS C+E Documentation","content":" Namespaces # armamexc arma/mex interface using Matlab C API\narmamexcpp arma/mex interface using Matlab C++ API\nlds::gaussian Linear Dynamical Systems with Gaussian observations.\nlds::poisson Linear Dynamical Systems with Poisson observations.\nstd\nUpdated on 5 March 2025 at 21:33:49 EST\n"},{"id":81,"href":"/lds-ctrl-est/docs/api/pages/","title":"Pages","section":"LDS C+E Documentation","content":" Pages # Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":82,"href":"/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/","title":"PLDS State Estimation","section":"LDS C+E Examples","content":" PLDS State Estimation Tutorial # This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[x_{t\u0026#43;1} = x_t \u0026#43; w_t\\] \\[y_{t} = \\exp\\left(x_t\\right)\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state Finally, assign the parameters using corresponding set-methods.\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); Creating the estimator # Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. To ensure robust estimates, adaptively re-estimate the process disturbance.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); Simulating estimation # In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.\n// Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.\nWith this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.\n"},{"id":83,"href":"/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/","title":"PLDS Switched Control","section":"LDS C+E Examples","content":" PLDS Switched Control Tutorial # This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.\nIn the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating the simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); The system\u0026rsquo;s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 Initially, the system will be in \u0026ldquo;mode\u0026rdquo; 1, where B = b1.\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.\nCreating the controller # Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.\nSimilar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.\nMoreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.\nPutting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying systems: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nNow that the SwitchedController is instantiated, assign its parameters.\n// Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).\nThe control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.\n// Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system\u0026rsquo;s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.\nExample simulation result # Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller\u0026rsquo;s online estimates of the output and state are shown in purple.\nNote that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.\n"},{"id":84,"href":"/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/","title":"src","section":"Files","content":" src # Files # Name src/lds.cpp misc lds namespace functions src/lds_gaussian_sys.cpp GLDS base type. src/lds_poisson_sys.cpp PLDS base type. src/lds_sys.cpp LDS base type. src/lds_uniform_vecs.cpp Uniformly sized vectors. Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":85,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/","title":"src/lds_gaussian_sys.cpp","section":"Files","content":" src/lds_gaussian_sys.cpp # GLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_gaussian_sys.h\u0026gt; lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0, data_t r0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { R_.zeros(n_y, n_y); R_.diag().fill(r0); do_recurse_Ke_=true; }; // recursively estimate Ke void lds::gaussian::System::RecurseKe() { if (!do_recurse_Ke_) { return; } // predict covariance P_ = A_ * P_ * A_.t() + Q_; // calc Kalman gain Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_); // update covariance // Reference: Ghahramani et Hinton (1996) P_ = P_ - Ke_ * C_ * P_; if (do_adapt_m) { P_m_ += Q_m_; // A_m = I (i.e., random walk) Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_); P_m_ = P_m_ - Ke_m_ * C_ * P_m_; } } // Simulate const lds::Vector\u0026amp; lds::gaussian::System::Simulate(const Vector\u0026amp; u_tm1){ f(u_tm1, true);//simulate dynamics with noise added h();//output z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure return z_; } void lds::gaussian::System::Print() { lds::System::Print(); std::cout \u0026lt;\u0026lt; \u0026#34;R: \\n\u0026#34; \u0026lt;\u0026lt; R_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":86,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/","title":"src/lds_poisson_sys.cpp","section":"Files","content":" src/lds_poisson_sys.cpp # PLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_poisson_sys.h\u0026gt; lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { diag_y_ = diagmat(y_); pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(0); }; // Correct: Given measurement (z) and current input (u), update estimate of the // state, covar, output. // // see Eden et al. 2004 void lds::poisson::System::RecurseKe() { // predict covariance P_ = A_ * P_ * A_.t() + Q_; // update cov P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_); Ke_ = P_ * C_.t(); if (do_adapt_m) { P_m_ += Q_m_; // predict (A_m = I) P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_); // update Ke_m_ = P_m_ * C_.t(); } } // Simulate Measurement: z ~ Poisson(y) const lds::Vector\u0026amp; lds::poisson::System::Simulate(const Vector\u0026amp; u_tm1) { f(u_tm1, true); // simulate dynamics with noise added h(); // output z_.zeros(); for (std::size_t k = 0; k \u0026lt; n_y_; k++) { // construct a Poisson distribution object with mean y[k] pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(y_[k]); // pull random sample from this distribution z_[k] = pd_(rng); } return z_; } // ******************* SYS_T ******************* Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":87,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8cpp/","title":"src/lds_sys.cpp","section":"Files","content":" src/lds_sys.cpp # LDS base type. More\u0026hellip;\nDetailed Description # This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- lds_sys.cpp - LDS -------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_sys.h\u0026gt; #include \u0026lt;vector\u0026gt; lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) { InitVars(p0, q0); } void lds::System::InitVars(data_t p0, data_t q0) { // initial conditions. x0_ = Vector(n_x_, fill::zeros); // includes bias (nY) and g (nU) P0_ = p0 * Matrix(n_x_, n_x_, fill::eye); m0_ = x0_; P0_m_ = P0_; // signals x_ = x0_; P_ = P0_; m_ = m0_; P_m_ = P0_m_; y_ = Vector(n_y_, fill::zeros); cx_ = Vector(n_y_, fill::zeros); z_ = Vector(n_y_, fill::zeros); // By default, random walk where each state is independent // In this way, provides independent estimates of rate per channel of output. A_ = Matrix(n_x_, n_x_, fill::eye); B_ = Matrix(n_x_, n_u_, fill::zeros); g_ = Vector(n_u_, fill::ones); Q_ = q0 * Matrix(n_x_, n_x_, fill::eye); Q_m_ = Q_; C_ = Matrix(n_y_, n_x_, fill::eye); // each state will map to an output by d_ = Vector(n_y_, fill::zeros); Ke_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain. Ke_m_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain for m adaptation. do_adapt_m = false; } // Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state // and update estimate of the state, covar, output using Kalman filter void lds::System::Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z_t) { // predict mean f(u_tm1); // dynamics h(); // output // recursively calculate esimator gains (or just keep existing values) // (also predicts+updates estimate covariance) RecurseKe(); // update x_ += Ke_ * (z_t - y_); if (do_adapt_m) { m_ += Ke_m_ * (z_t - y_); // adaptively estimating disturbance } // With new state, estimate output. h(); // --\u0026gt; posterior } void lds::System::Reset() { // reset to initial conditions x_ = x0_; // mean P_ = P0_; // cov of state estimate m_ = m0_; // process disturbance P_m_ = P0_m_; // cov of disturbance estimate h(); } std::vector\u0026lt;lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt;\u0026gt; lds::System::nstep_pred_block(lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; u, lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; z, size_t n_pred) { lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_filt; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_pred; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; y_pred; for (size_t k = 0; k \u0026lt; u.size(); k++) { Reset(); size_t n_t = arma::size(u[k])[1]; Matrix x_filt_k(n_x_, n_t, fill::zeros); Matrix x_pred_k(n_x_, n_t - n_pred, fill::zeros); Matrix y_pred_k(n_y_, n_t - n_pred, fill::zeros); for (size_t t = 0; t \u0026lt; n_t - n_pred; t++) { Vector x_pred_ahead = x_; for (size_t t_u = t; t_u \u0026lt; t + n_pred; t_u++) { x_pred_ahead = A_ * x_pred_ahead + B_ * u[k].col(t_u); } x_pred_k.col(t) = x_pred_ahead; y_pred_k.col(t) = h_(x_pred_ahead); if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; // given previous measurment } for (size_t t = n_t - n_pred; t \u0026lt; n_t; t++) { if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; } x_filt.append(x_filt_k); x_pred.append(x_pred_k); y_pred.append(y_pred_k); } return {x_filt, x_pred, y_pred}; } void lds::System::Print() { std::cout \u0026lt;\u0026lt; \u0026#34;\\n ********** SYSTEM ********** \\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;x: \\n\u0026#34; \u0026lt;\u0026lt; x_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;P: \\n\u0026#34; \u0026lt;\u0026lt; P_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;A: \\n\u0026#34; \u0026lt;\u0026lt; A_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B: \\n\u0026#34; \u0026lt;\u0026lt; B_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;g: \\n\u0026#34; \u0026lt;\u0026lt; g_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;m: \\n\u0026#34; \u0026lt;\u0026lt; m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q: \\n\u0026#34; \u0026lt;\u0026lt; Q_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q_m: \\n\u0026#34; \u0026lt;\u0026lt; Q_m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d: \\n\u0026#34; \u0026lt;\u0026lt; d_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;C: \\n\u0026#34; \u0026lt;\u0026lt; C_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;y: \\n\u0026#34; \u0026lt;\u0026lt; y_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } //******************* SYS_T ******************* Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":88,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/","title":"src/lds_uniform_vecs.cpp","section":"Files","content":" src/lds_uniform_vecs.cpp # Uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_uniform_vecs.h\u0026gt; namespace lds { UniformVectorList::UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); } UniformVectorList::UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim) : vector(std::move(vecs)) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(const UniformVectorList\u0026amp; that) : vector(that) { (*this) = that; } UniformVectorList::UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { this-\u0026gt;dim_ = this-\u0026gt;at(0).n_elem; } void UniformVectorList::CheckDimensions(size_t dim) { if (dim) { dim_ = dim; } else { dim_ = this-\u0026gt;at(0).n_elem; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Vector\u0026amp; vec : *this) { does_match = does_match \u0026amp;\u0026amp; (vec.n_elem == dim_); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } } } // namespace lds Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":89,"href":"/lds-ctrl-est/docs/api/files/lds_8cpp/","title":"src/lds.cpp","section":"Files","content":" src/lds.cpp # misc lds namespace functions More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file implements miscellaneous lds namespace functions not bound to a class.\nSource code # //===-- lds.cpp - LDS -----------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds.h\u0026gt; // insert any necessary function definitions here. namespace lds { void ForceSymPD(Matrix\u0026amp; X) { if (X.is_sympd() || !X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; // for eigenval decomp bool did_succeed(true); Vector d; Matrix u; // see first method (which may not be ideal): // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/ size_t k(1); bool is_sympd = X.is_sympd(); Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye); while (!is_sympd) { if (k \u0026gt; 100) { did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); data_t min_eig = arma::min(d); std::cerr \u0026lt;\u0026lt; \u0026#34;After multiple iterations, min eigen val = \u0026#34; \u0026lt;\u0026lt; min_eig \u0026lt;\u0026lt; \u0026#34;.\\n\u0026#34;; throw std::runtime_error( \u0026#34;Failed to make matrix symmetric positive definite.\u0026#34;); return; } // Limit(d, arma::eps(0), kInf); // force to be positive... // Matrix d_diag = arma::diagmat(d); // X = u * d_diag * u.t(); did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymPD failed.\u0026#34;); } data_t min_eig = arma::min(d); X += id * abs(min_eig) + arma::datum::eps; // make sure symm: X = (X + X.t()) / 2; // double check eigenvals positive after symmetrizing: arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); min_eig = arma::min(d); is_sympd = min_eig \u0026gt; 0; k++; } } void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min) { if (!X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; bool did_succeed(true); Vector d; Matrix u; did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymMinEig failed.\u0026#34;); } Limit(d, eig_min + arma::eps(eig_min), kInf); // enforce lower bound Matrix d_diag = arma::diagmat(d); X = u * d_diag * u.t(); // double check symmetric X = (X + X.t()) / 2; } void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X) { bool did_succeed(true); did_succeed = arma::qr_econ(Qt, L, X.t()); if (!did_succeed) { throw std::runtime_error(\u0026#34;LQ decomposition failed.\u0026#34;); } arma::inplace_trans(L); arma::inplace_trans(Qt); } Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B) { // subtract out mean auto m_a = arma::mean(A, 1); Matrix a0 = A; a0.each_col() -= m_a; auto m_b = arma::mean(B, 1); Matrix b0 = B; b0.each_col() -= m_b; Matrix cov = a0 * b0.t() / a0.n_cols; return cov; } } // namespace lds Updated on 5 March 2025 at 21:33:49 EST\n"},{"id":90,"href":"/lds-ctrl-est/docs/api/namespaces/namespacestd/","title":"std","section":"Namespaces","content":" std # Updated on 5 March 2025 at 21:33:49 EST\n"}]
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+[{"id":0,"href":"/lds-ctrl-est/docs/","title":"LDS C+E Documentation","section":"LDS Control \u0026 Estimation","content":" LDS Control \u0026amp; Estimation Documentation # "},{"id":1,"href":"/lds-ctrl-est/docs/tutorials/","title":"LDS C+E Examples","section":"LDS C+E Documentation","content":" Examples # "},{"id":2,"href":"/lds-ctrl-est/acknowledgements/","title":"Acknowledgements","section":"LDS Control \u0026 Estimation","content":" Acknowledgements # Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.\n"},{"id":3,"href":"/lds-ctrl-est/docs/getting-started/getting-started/","title":"Getting Started","section":"LDS C+E Documentation","content":" Getting Started # This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.\nldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.\nTested Configurations # Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the RelWithDebInfo build type in the CMake configure command (-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo):\nUbuntu 18.04 with GCC 7.5 compiler macOS 11 (Big Sur) with Apple Clang 12 compiler Windows 10 with Visual Studio 16.11 (2019 release) and Clang 12 compiler That being said, if you want to debug a build for a single platform, here are some things you can try:\nUse different compilers (or even different versions of a single compiler) Use different versions of vcpkg (which you can control by checking out a different commit in the vcpkg submodule) Mac Pre-requisities # Xcode Command Line Tools will get you clang, gcc, make, and git:\nxcode-select --install Homebrew is \u0026ldquo;The Missing Package Manager for macOS\u0026rdquo; which will make installing lots of things easy. Install like this:\n/bin/bash -c \u0026#34;$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\u0026#34; You can then use it to install CMake, gfortran, and pkg-config:\nbrew install cmake gfortran pkg-config Linux Pre-requisites # You\u0026rsquo;ll need Git, CMake, GCC, gfortran, etc.\nsudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential ninja-build Windows Installation # Look here for Windows-specific instructions.\nDownloading the Library # First, clone the repository along with submodules:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est\rgit submodule update --init Compilation + Installation # Now generate the cache and build using your IDE or from the command line as follows.\nmkdir build \u0026amp;\u0026amp; cd build\rcmake ..\rcmake --build . The first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.\nIf you want to use vcpkg set up somewhere besides this repo\u0026rsquo;s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE\u0026rsquo;s settings.\nYou can verify the build is working by running ctest from the build folder, which runs all the example scripts.\nOptions # This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\nLDSCTRLEST_BUILD_EXAMPLES : [default=ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake to configure/build the library.\nFor basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build cmake --build #build the project sudo make install #[optional] installs to default location (OS-specific) To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location cmake --build #build the project make install #install to /your/install/prefix To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\nLD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.\nPython bindings package ldsctrlest # With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython3_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn\u0026rsquo;t use an undesired version. That environment needs to have NumPy installed.\ncmake --build . --target python_modules The bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory (pip install pytest matplotlib first if you need to).\nSee python/ldsctrlest/README.md for usage details.\nAlso, beware that a single build will probably not work for both the standalone library and the Python package, since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.\nCommon issues # \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.\nexport CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix vcpkg fails on configuration Try running ./bootstrap-vcpkg from the vcpkg folder and try again. If that doesn\u0026rsquo;t work, try updating vcpkg to a newer version (in the source control tab, click on the commit hash by the vcpkg repo then select from the dropdown) and running boostsrap-vcpkg again. You can also try upgrading your system (e.g., apt update, apt upgrade).\nCould not find Python3 (missing: Python3_NumPy_INCLUDE_DIRS NumPy)\nMake sure NumPy is installed in the Python environment you specified. If CMake still can\u0026rsquo;t find it, you may need to tell CMake exactly where to find it by adding an argument to the configure command: -DPython3_NumPy_INCLUDE_DIR=.... You can find that location like this: python -c 'import numpy; print(numpy.get_include())'\n"},{"id":4,"href":"/lds-ctrl-est/docs/getting-started/windows/","title":"Windows","section":"LDS C+E Documentation","content":" Windows Installation # Windows Pre-requisites # Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:\nSet-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time iwr get.scoop.sh | Invoke-Expression Install Git and CMake if you don\u0026rsquo;t already have them:\nscoop install git cmake If that didn\u0026rsquo;t work, follow more detailed instructions here.\nThe easiest way to compile C++ project on Windows is with Visual Studio\u0026rsquo;s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on \u0026ldquo;Desktop development with C++.\u0026rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the \u0026ldquo;Installation details\u0026rdquo; sidebar under optional features.\nAnd the easiest way to use Visual Studio\u0026rsquo;s build tools is with VS Code, along with the CMake Tools extension. Install them and you should be ready to go.\nDownloading the Library # First, clone the repository, either from VS Code or the command line:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est You\u0026rsquo;ll need to initialize the submodules from the command line after the repo is cloned:\ngit submodule update --init Installation # When you open the folder in VS Code, you will like be prompted by the CMake Tools extension to configure the project. Make sure you select the kit (you\u0026rsquo;ll be prompted when you configure\u0026ndash;else there\u0026rsquo;s an icon in the bar on the bottom of the window or type Ctrl+Shift+P, then \u0026ldquo;cmake select kit\u0026rdquo;). Choose Clang [latest version] with GNU CLI ... amd64 assuming you are running a 64-bit OS. (MSVC may work okay too if you don\u0026rsquo;t need to build Python bindings.)\nFollow along with the \u0026ldquo;Getting Started\u0026rdquo; instructions, but where you see config options specified as -DLDSCTREST_BUILD_STATIC=OFF or -DPython3_ROOT_DIR=..., you will enter those in settings: open with Ctrl+,, click \u0026ldquo;workspace\u0026rdquo;, then search for \u0026ldquo;CMake: Configure Args\u0026rdquo; and enter each of your desired arguments as a separate item.\nTo configure, use Ctrl+Shift+P and search for the \u0026ldquo;CMake: Configure\u0026rdquo; command. To build, click the \u0026ldquo;Build\u0026rdquo; button on the bottom bar. Then click the \u0026ldquo;CTest\u0026rdquo; button to run the example scripts.\nConsiderations # Development on Windows has been more prone to bugs than on Unix systems, so if you encounter many problems, consider switching—WSL (Windows Subsystem for Linux) is a good option for Windows users who don\u0026rsquo;t want to work on a different machine.\nCompilation has been successfully tested in VS Code using the following kit, using the \u0026ldquo;RelWithDebInfo\u0026rdquo; config:\nClang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64) Troubleshooting # The build appears to work, but tests fail with code 0xc0000135 OR \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; Have you installed the library? In VS Code, use Shift+F7 to build a specific target, in this case INSTALL. If that doesn\u0026rsquo;t solve your problem, you will likely need to add the build or install folder to your PATH environment variable, which you can do using the settings GUI (search for \u0026ldquo;Edit the system environment variables\u0026rdquo;).\nOn Windows, \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted. Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":5,"href":"/lds-ctrl-est/issues-contributing/","title":"Issues Contributing","section":"LDS Control \u0026 Estimation","content":" Reporting Issues # If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.\nContributing # We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.\n"},{"id":6,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/","title":"armamexc","section":"Namespaces","content":" armamexc # arma/mex interface using Matlab C API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; T m2T_scalar(const mxArray * matlab_scalar)\nConvert Matlab mxArray to scalar of type T. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)\nConvert matlab matrix to armadillo. template \u0026lt;typename T \u0026gt; mxArray * a2m_mat(arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat)\nConvert armadillo to matlab matrix. template \u0026lt;typename T \u0026gt; mxArray * a2m_vec(arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec)\nConvert armadillo to matlab vector. Detailed Description # Utilities for arma/mex interface using Matlab C API\nFunction Details # m2T_scalar # template \u0026lt;class T \u0026gt; inline T m2T_scalar( const mxArray * matlab_scalar ) Parameters:\nmatlab_scalar matlab scalar Template Parameters:\nT type Return: scalar of type T\nm2a_mat # template \u0026lt;class T \u0026gt; inline arma::Mat\u0026lt; T \u0026gt; m2a_mat( const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true ) Parameters:\nmatlab_mat matlab matrix copy_aux_mem [optional] whether to copy auxiliary memory strict [optional] strictly enforce the above Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_mat( arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat ) Parameters:\narma_mat armadillo matrix Return: matlab matrix\na2m_vec # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_vec( arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec ) Parameters:\narma_vec armadillo vector Return: matlab vector\nUpdated on 5 March 2025 at 21:01:32 EST\n"},{"id":7,"href":"/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/","title":"armamexcpp","section":"Namespaces","content":" armamexcpp # arma/mex interface using Matlab C++ API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat(matlab::data::CellArray \u0026amp; matlab_cell)\nConvert matlab cell array to vector of armadillo matrices. template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array)\nConvert matlab matrix to a vector of scalars. template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo vector. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat(const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec(const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab vector. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec(const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert vector of scalar T to matlab matrix. Detailed Description # utilities for arma/mex interface using Matlab C++ API\nFunction Details # m2a_cellmat # template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat( matlab::data::CellArray \u0026amp; matlab_cell ) Parameters:\nmatlab_cell matlab cell Template Parameters:\nT type Return: vector of armadillo matrices of type T\nm2s_vec # template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: vector of type T\nm2a_vec # template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: armadillo vector of type T\nm2a_mat # template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab matrix Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat( const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_mat arma matrix factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\na2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec( const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_vec armadillo vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\ns2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec( const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\nstd_vec standard vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\nUpdated on 5 March 2025 at 21:01:32 EST\n"},{"id":8,"href":"/lds-ctrl-est/docs/terminology/control-estimation/","title":"C\u0026E","section":"LDS C+E Documentation","content":" Control \u0026amp; Estimation # The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.\nState estimation # In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by \\[\\widehat{\\mathbf{x}}_{t|t-1} = \\mathbf{A}\\widehat{\\mathbf{x}}_{t-1|t-1} \u0026#43; \\mathbf{B} u_{t-1} \u0026#43; \\mathbf{m}_{t-1} \\;,\\] \\[\\widehat{\\mathbf{x}}_{t|t} = \\widehat{\\mathbf{x}}_{t|t-1} \u0026#43; \\mathbf{K}^{\\rm e}_t \\left(\\mathbf{z}_t - \\widehat{\\mathbf{y}}_{t|t-1}\\right)\\;,\\] where \\( \\hat{\\left(\\cdot\\right)}_{t|j} \\) indicates an estimate at time \\( t \\) given data up to time \\( j \\) inclusive, \\( \\mathbf{K}^{\\rm e} \\) is the estimator gain, and\n\\[ \\widehat{\\mathbf{y}}_{t|t-1} = h\\left( \\widehat{\\mathbf{x}}_{t|t-1} \\right) \\; .\\] In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.\nIn the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.\nAdaptive estimation of process disturbance # Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.\nWhen parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk \\[\\mathbf{m}_{t} = \\mathbf{m}_{t-1} \u0026#43; \\mathbf{w}^m_{t-1} \\;,\\] where \\( \\mathbf{w}^m \\sim \\mathcal{N}\\left(0, \\mathbf{Q}_m\\right)\\) . Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.\nControl # Given the estimated state, the controller updates the inputs to the system according to the following law: \\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] where \\( \\left( \\cdot \\right)^{\\rm ref} \\) correspond to reference/target signals and \\( \\mathbf{K}^c_x \\) is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.\nIf users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as\n\\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right) - \\mathbf{K}^c_{\\rm inty} \\sum_{j=1}^{t}\\left( \\widehat{\\mathbf{y}}_j - \\mathbf{y}^{\\rm ref}_j \\right) \\;.\\] An additional option available to users is a control law that updates the change in u,\n\\[\\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\left(\\mathbf{u}_{t-1} - \\mathbf{u}^{\\rm ref}_{t-1} \\right) - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] \\[\\mathbf{u}_{t} = \\mathbf{u}_{t-1} \u0026#43; \\Delta\\mathbf{u}_{t} \\; .\\] Notice that this takes the form of a first-order difference equation for updating control (i.e., \\( \\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\mathbf{u}_{t-1} \u0026#43; \\epsilon_{t-1} \\) ), effectively low-pass filtering the input depending on the characteristics of \\( \\mathbf{K}^c_u \\) . This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.\nIntegral action and the \\( \\Delta \\mathbf{u} \\) control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR\u0026rsquo;d to use in combination.\nCalculating reference state-control from output # In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce \\( \\mathbf{x}^{\\rm ref} \\) and \\( \\mathbf{u}^{\\rm ref} \\) . Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.\n"},{"id":9,"href":"/lds-ctrl-est/docs/api/classes/","title":"Classes","section":"LDS C+E Documentation","content":" Classes # lds::Controller\nlds::EM\nlds::Fit LDS Fit Type.\nlds::SSID\nlds::SwitchedController SwitchedController Type.\nlds::System Linear Dynamical System Type.\nlds::UniformMatrixList\nlds::UniformSystemList\nlds::UniformVectorList\nlds::gaussian::Controller Gaussian-observation Controller Type.\nlds::gaussian::Fit GLDS Fit Type.\nlds::gaussian::FitEM GLDS E-M Fit Type.\nlds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.\nlds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.\nlds::gaussian::System Gaussian LDS Type.\nlds::poisson::Controller PLDS Controller Type.\nlds::poisson::Fit PLDS Fit Type.\nlds::poisson::FitEM PLDS E-M Fit Type.\nlds::poisson::FitSSID Subspace Identification (SSID) for PLDS.\nlds::poisson::SwitchedController Poisson-observation SwitchedController Type.\nlds::poisson::System Poisson System type.\nUpdated on 5 March 2025 at 21:01:32 EST\n"},{"id":10,"href":"/lds-ctrl-est/docs/api/modules/group__control__masks/","title":"Control Mode Bit Masks","section":"Modules","content":" Control Mode Bit Masks # provides fill types for constructing new armadillo vectors, matrices More\u0026hellip; Attributes # Name const std::size_t kControlTypeDeltaU control designed to penalize change in input const std::size_t kControlTypeIntY control using integral action const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m Detailed Description # Control mode bit masks. These can be bit-wise OR\u0026rsquo;d to use in combination.\nAttribute Details # kControlTypeDeltaU # static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY # static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM # static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\nUpdated on 5 March 2025 at 21:01:32 EST\n"},{"id":11,"href":"/lds-ctrl-est/docs/api/modules/group__defaults/","title":"Defaults","section":"Modules","content":" Defaults # More\u0026hellip; Attributes # Name const data_t kDefaultP0 default state estimate covar const data_t kDefaultQ0 default process noise covar const data_t kDefaultR0 default output noise covar Detailed Description # Default values for common variables (e.g., default diagonal elements of covariances)\nAttribute Details # kDefaultP0 # static const data_t kDefaultP0 = 1e-6; kDefaultQ0 # static const data_t kDefaultQ0 = 1e-6; kDefaultR0 # static const data_t kDefaultR0 = 1e-2; Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":12,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/","title":"eg_glds_ctrl.cpp","section":"Examples","content":" eg_glds_ctrl.cpp # Example GLDS Control ```cpp\n//===\u0026ndash; eg_glds_ctrl.cpp - Example GLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS Control ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\n// output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false;\n// Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err\n// setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]);\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; }\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 21:01:32 EST "},{"id":13,"href":"/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/","title":"eg_glds_du_plds_ctrl.cpp","section":"Examples","content":" eg_glds_du_plds_ctrl.cpp # Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u). ```cpp\n//===\u0026ndash; eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt);\n// to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU;\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_du_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 21:01:32 EST "},{"id":14,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/","title":"eg_plds_ctrl.cpp","section":"Examples","content":" eg_plds_ctrl.cpp # Example PLDS Control ```cpp\n//===\u0026ndash; eg_plds_ctrl.cpp - Example PLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Control ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt);\n// Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err\n// Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\nsize_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\nVector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled\u0026hellip; lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system);\n// for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type);\n// set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty);\n// to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0;\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros);\n// set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y();\nx_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x();\nm_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 21:01:32 EST "},{"id":15,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/","title":"eg_plds_est.cpp","section":"Examples","content":" eg_plds_est.cpp # Example PLDS Estimation ```cpp\n//===\u0026ndash; eg_plds_est.cpp - Example PLDS Estimation \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0);\nint main() { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.\n// construct ground truth system\u0026hellip; lds::poisson::System system_true(n_u, n_x, n_y, dt);\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset();\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt);\n// Can copy parameters from another system object system_estimator = system_true;\n// wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est);\n// set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true;\n// set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;estimator:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; system_estimator.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));\n// create matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros);\n// states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\nMatrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simlation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1));\n// Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simlation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows;\nif ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026ldquo;Q must be n x n.\u0026rdquo;); }\nMatrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); }\nreturn x; }\n_Filename: eg_plds_est.cpp_ ------------------------------- Updated on 5 March 2025 at 21:01:32 EST "},{"id":16,"href":"/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/","title":"eg_plds_switched_ctrl.cpp","section":"Examples","content":" eg_plds_switched_ctrl.cpp # Example Switched PLDS Control ```cpp\n//===\u0026ndash; eg_plds_switched_ctrl.cpp - Example Switched PLDS Control \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026quot;;\n// whether to do switched control bool do_switch_ctrl = true;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt);\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices. data_t scale_sys_b = 2;\nMatrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));\ncontrolled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions\n// reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);\n// Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system);\n// set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys1:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys2:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x));\nswitched_controller.set_y_ref(y_ref0);\nstd::vectorlds::poisson::System systems_vec(3, lds::poisson::System()); lds::UniformSystemListlds::poisson::System systems(std::move(systems_vec));\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;switched_controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; switched_controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Fake measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros);\n// create Matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);\n// modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones);\n// set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } }\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace)); mode.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;mode\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_switched_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 21:01:32 EST "},{"id":17,"href":"/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/","title":"examples","section":"Files","content":" examples # Files # Name examples/eg_glds_ctrl.cpp examples/eg_glds_du_plds_ctrl.cpp examples/eg_plds_ctrl.cpp examples/eg_plds_est.cpp examples/eg_plds_switched_ctrl.cpp Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":18,"href":"/lds-ctrl-est/docs/api/examples/","title":"Examples","section":"LDS C+E Documentation","content":" Examples # eg_glds_ctrl.cpp Example GLDS Control.\neg_glds_du_plds_ctrl.cpp Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).\neg_plds_ctrl.cpp Example PLDS Control.\neg_plds_est.cpp Example PLDS Estimation.\neg_plds_switched_ctrl.cpp Example Switched PLDS Control.\nUpdated on 5 March 2025 at 21:01:32 EST\n"},{"id":19,"href":"/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/","title":"examples/eg_glds_ctrl.cpp","section":"Files","content":" examples/eg_glds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":20,"href":"/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/","title":"examples/eg_glds_du_plds_ctrl.cpp","section":"Files","content":" examples/eg_glds_du_plds_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU; // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":21,"href":"/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/","title":"examples/eg_plds_ctrl.cpp","section":"Files","content":" examples/eg_plds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err // Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled... lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type); // set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":22,"href":"/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/","title":"examples/eg_plds_est.cpp","section":"Files","content":" examples/eg_plds_est.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name Matrix random_walk(size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0) int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # random_walk # Matrix random_walk( size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0 ) main # int main() Source code # //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. // construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state // Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. // turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; } Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":23,"href":"/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/","title":"examples/eg_plds_switched_ctrl.cpp","section":"Files","content":" examples/eg_plds_switched_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Source code # //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 // simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions // reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":24,"href":"/lds-ctrl-est/docs/api/files/","title":"Files","section":"LDS C+E Documentation","content":" Files # examples/eg_glds_ctrl.cpp\nexamples/eg_glds_du_plds_ctrl.cpp\nexamples/eg_plds_ctrl.cpp\nexamples/eg_plds_est.cpp\nexamples/eg_plds_switched_ctrl.cpp\nldsCtrlEst_h/lds.h lds namespace\nldsCtrlEst_h/lds_ctrl.h Controller.\nldsCtrlEst_h/lds_fit.h LDS base fit type.\nldsCtrlEst_h/lds_fit_em.h subspace identification\nldsCtrlEst_h/lds_fit_ssid.h subspace identification\nldsCtrlEst_h/lds_gaussian.h glds namespace\nldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.\nldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.\nldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.\nldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.\nldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.\nldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.\nldsCtrlEst_h/lds_poisson.h plds namespace\nldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.\nldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.\nldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.\nldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.\nldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.\nldsCtrlEst_h/lds_poisson_sys.h PLDS base type.\nldsCtrlEst_h/lds_sctrl.h SwitchedController type.\nldsCtrlEst_h/lds_sys.h LDS base type.\nldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.\nldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.\nldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.\nldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)\nldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)\nsrc/lds.cpp misc lds namespace functions\nsrc/lds_gaussian_sys.cpp GLDS base type.\nsrc/lds_poisson_sys.cpp PLDS base type.\nsrc/lds_sys.cpp LDS base type.\nsrc/lds_uniform_vecs.cpp Uniformly sized vectors.\nUpdated on 5 March 2025 at 21:01:32 EST\n"},{"id":25,"href":"/lds-ctrl-est/docs/tutorials/eg_glds_control/","title":"GLDS Control","section":"LDS C+E Examples","content":" GLDS Control Tutorial # This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\] \\[\ry_{t} = x_t\r\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.\n/// Going to simulate a switching disturbance (m) acting on system size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_y).fill(m_low); Finally, assign the parameters using corresponding set-methods.\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); Creating the controller # Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.\nFor the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.\n// make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nWith the controller constructed, control variables may be set.\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.\nThe control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller\u0026rsquo;s online estimates of the output, state, and disturbance are given in purple.\n"},{"id":26,"href":"/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/","title":"include","section":"Files","content":" include # Directories # Name ldsCtrlEst_h Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":27,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds/","title":"lds","section":"Namespaces","content":" lds # Linear Dynamical Systems (LDS) namespace. Namespaces # Name lds::gaussian Linear Dynamical Systems with Gaussian observations. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::Controller class lds::EM class lds::Fit LDS Fit Type. class lds::SSID class lds::SwitchedController SwitchedController Type. class lds::System Linear Dynamical System Type. class lds::UniformMatrixList class lds::UniformSystemList class lds::UniformVectorList Types # Name enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}\nweighting options for SSID enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2} using double data_t using arma::Col\u0026lt; data_t \u0026gt; Vector using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Cube\u0026lt; data_t \u0026gt; Cube using arma::subview\u0026lt; data_t \u0026gt; View Functions # Name void Limit(std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub) void Limit(Vector \u0026amp; x, data_t lb, data_t ub) void Limit(Matrix \u0026amp; x, data_t lb, data_t ub) void Reassign(Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Vector in place void Reassign(Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Matrix in place void ForceSymPD(Matrix \u0026amp; X)\nforces matrix to be symmetric positive-definite void ForceSymMinEig(Matrix \u0026amp; X, data_t eig_min =0)\nforces matrix to be symmetric and have a minimum eigenvalue void lq(Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X)\nLQ decomposition. Matrix calcCov(const Matrix \u0026amp; A, const Matrix \u0026amp; B)\nCalculate covariance matrix. Attributes # Name const data_t kInf Some useful numbers. const data_t kPi Type Details # SSIDWt # Enumerator Value Description kSSIDNone None. kSSIDMOESP MOESP (AKA \u0026ldquo;robust method\u0026rdquo; in van Overschee 1996) kSSIDCVA CVA \u0026ldquo;Canonical Variate Analysis\u0026rdquo;. Weighting options for singular value decomposition performed during subspace identification (SSID)\nReference:\nvan Overschee, de Moor. 1996. Subspace Identification for Linear Systems.\nMatrixListFreeDim # Enumerator Value Description kMatFreeDimNone neither dim free to be hetero in mat list kMatFreeDim1 allow 1st dim of mats in list to be hetero kMatFreeDim2 allow 2nd dim of mats in list to be hetero data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nVector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Cube # using lds::Cube = arma::Cube\u0026lt;data_t\u0026gt;; View # using lds::View = arma::subview\u0026lt;data_t\u0026gt;; Function Details # Limit # inline void Limit( std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Vector \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Matrix \u0026amp; x, data_t lb, data_t ub ) Reassign # inline void Reassign( Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Vector other other Vector parenthetical optional description provided by caller to ease debugging Reassign # inline void Reassign( Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Matrix other other Matrix parenthetical optional description provided by caller to ease debugging ForceSymPD # void ForceSymPD( Matrix \u0026amp; X ) Parameters:\nX mutated matrix ForceSymMinEig # void ForceSymMinEig( Matrix \u0026amp; X, data_t eig_min =0 ) Parameters:\nX mutated matrix eig_min [optional] minimum eigen value lq # void lq( Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X ) Parameters:\nL lower triangle matrix Qt orthonormal matrix (transposed cf QR decomp) X matrix being decomposed calcCov # Matrix calcCov( const Matrix \u0026amp; A, const Matrix \u0026amp; B ) Parameters:\nA some matrix B some other matrix Return: covariance\nAttribute Details # kInf # static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); kPi # static const data_t kPi = arma::datum::pi; Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":28,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/","title":"lds::Controller","section":"Classes","content":" lds::Controller # More\u0026hellip;\nInherited by lds::SwitchedController\u0026lt; System \u0026gt;, lds::gaussian::Controller, lds::poisson::Controller\nPublic Functions # Name Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes # Name System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::Controller; Public Function Details # Controller # Controller() =default Controller # inline Controller( const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Controller # inline Controller( System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Control # inline const Vector \u0026amp; Control( const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step). This is the most flexible option, but requires user to have set the controller\u0026rsquo;s y_ref, x_ref, and u_ref variables.\nControlOutputReference # inline const Vector \u0026amp; ControlOutputReference( const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_estimation [optional] whether to update state estimate (if false, effectively open-loop control) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.\nsys # inline const System \u0026amp; sys() const Kc # inline const Matrix \u0026amp; Kc() const Kc_inty # inline const Matrix \u0026amp; Kc_inty() const Kc_u # inline const Matrix \u0026amp; Kc_u() const g_design # inline const Vector \u0026amp; g_design() const u_ref # inline const Vector \u0026amp; u_ref() const x_ref # inline const Vector \u0026amp; x_ref() const y_ref # inline const Vector \u0026amp; y_ref() const control_type # inline size_t control_type() const tau_awu # inline data_t tau_awu() const u_lb # inline data_t u_lb() const u_ub # inline data_t u_ub() const set_sys # inline void set_sys( const System \u0026amp; sys ) set_g_design # inline void set_g_design( const Vector \u0026amp; g_design ) set_u_ref # inline void set_u_ref( const Vector \u0026amp; u_ref ) set_x_ref # inline void set_x_ref( const Vector \u0026amp; x_ref ) set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) Reimplemented by: lds::gaussian::Controller::set_y_ref, lds::gaussian::SwitchedController::set_y_ref, lds::poisson::Controller::set_y_ref, lds::poisson::SwitchedController::set_y_ref\nset_Kc # inline void set_Kc( const Matrix \u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const Matrix \u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const Matrix \u0026amp; Kc_u ) set_tau_awu # inline void set_tau_awu( data_t tau ) set_control_type # inline void set_control_type( size_t control_type ) Parameters:\ncontrol_type control type bit mask Template Parameters:\nSystem type derived from lds::System set_u_lb # inline void set_u_lb( data_t u_lb ) Parameters:\nu_lb control lower bound set_u_ub # inline void set_u_ub( data_t u_ub ) Parameters:\nu_ub control upper bound Reset # inline void Reset() Print # inline void Print() Protected Attribute Details # sys_ # System sys_; u_ # Vector u_; u_return_ # Vector u_return_; g_design_ # Vector g_design_; u_ref_ # Vector u_ref_; u_ref_prev_ # Vector u_ref_prev_; x_ref_ # Vector x_ref_; y_ref_ # Vector y_ref_; cx_ref_ # Vector cx_ref_; Kc_ # Matrix Kc_; Kc_u_ # Matrix Kc_u_; Kc_inty_ # Matrix Kc_inty_; du_ref_ # Vector du_ref_; dv_ref_ # Vector dv_ref_; v_ref_ # Vector v_ref_; dv_ # Vector dv_; v_ # Vector v_; int_e_ # Vector int_e_; int_e_awu_adjust_ # Vector int_e_awu_adjust_; u_sat_ # Vector u_sat_; do_control_prev_ # bool do_control_prev_ = false; do_lock_control_prev_ # bool do_lock_control_prev_ = false; u_saturated_ # bool u_saturated_ = false; u_lb_ # data_t u_lb_ {}; u_ub_ # data_t u_ub_ {}; tau_awu_ # data_t tau_awu_ {}; k_awu_ # data_t k_awu_ = 0; t_since_control_onset_ # data_t t_since_control_onset_ = 0; control_type_ # size_t control_type_ {}; Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":29,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/","title":"lds::EM","section":"Classes","content":" lds::EM # More\u0026hellip;\nInherited by lds::gaussian::FitEM, lds::poisson::FitEM\nPublic Functions # Name EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions # Name void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() virtual void MaximizeOutput() =0 virtual void MaximizeMeasurement() =0 void Smooth(bool force_common_initial)\nget smoothed estimates virtual void RecurseKe(Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t) =0\nrecursively update estimator gain Ke void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::EM; Public Function Details # EM # EM() =default EM # EM( size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nn_x number of states dt sample period u_train input training data z_train measurement training data EM # EM( const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nfit0 initial fit u_train input training data z_train measurement training data ~EM # virtual ~EM() =default Run # const Fit \u0026amp; Run( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2 ) Parameters:\ncalc_dynamics [optional] whether to calculate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law max_iter max number of iterations tol convergence tolerance (max fractional abs change) Return: Fit\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nx # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const y # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const sum_E_x_t_x_t # inline const Matrix \u0026amp; sum_E_x_t_x_t() const sum_E_xu_tm1_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const sum_E_xu_t_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const n_t_tot # inline size_t n_t_tot() theta # inline const Vector \u0026amp; theta() const Protected Function Details # Expectation # void Expectation( bool force_common_initial =false ) Parameters:\nforce_common_initial whether to force common initial condition for all trials Maximization # void Maximization( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false ) Parameters:\ncalc_dynamics [optional] whether to caclulate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law MaximizeDynamics # void MaximizeDynamics() MaximizeQ # void MaximizeQ() MaximizeInitial # void MaximizeInitial() MaximizeOutput # virtual void MaximizeOutput() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput\nMaximizeMeasurement # virtual void MaximizeMeasurement() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement\nSmooth # void Smooth( bool force_common_initial ) Parameters:\nforce_common_initial whether to force common initial conditions RecurseKe # virtual void RecurseKe( Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t ) =0 Parameters:\nKe estimator gain P_pre cov of predicted state est. P_post cov of postior sate est. t time Reimplemented by: lds::gaussian::FitEM::RecurseKe, lds::poisson::FitEM::RecurseKe\nReset # void Reset() InitVars # void InitVars() UpdateTheta # Vector UpdateTheta() Return: parameter list\nProtected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; x_ # std::vector\u0026lt; Matrix \u0026gt; x_; P_ # std::vector\u0026lt; Cube \u0026gt; P_; P_t_tm1_ # std::vector\u0026lt; Cube \u0026gt; P_t_tm1_; y_ # std::vector\u0026lt; Matrix \u0026gt; y_; diag_y_ # Matrix diag_y_; sum_E_x_t_x_t_ # Matrix sum_E_x_t_x_t_; sum_E_xu_tm1_xu_tm1_ # Matrix sum_E_xu_tm1_xu_tm1_; sum_E_xu_t_xu_tm1_ # Matrix sum_E_xu_t_xu_tm1_; fit_ # Fit fit_; theta_ # Vector theta_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":30,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/","title":"lds::Fit","section":"Classes","content":" lds::Fit # LDS Fit Type. #include \u0026lt;lds_fit.h\u0026gt;\nInherited by lds::gaussian::Fit, lds::poisson::Fit\nPublic Functions # Name Fit() =default\nConstructs a new Fit. Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias virtual const Matrix \u0026amp; R() const =0 void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance virtual void set_R(const Matrix \u0026amp; R) =0\nsets output noise covariance (if any) void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) =0\noutput function Protected Attributes # Name data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period ~Fit # virtual ~Fit() =default n_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const m # inline const Vector \u0026amp; m() const Q # inline const Matrix \u0026amp; Q() const x0 # inline const Vector \u0026amp; x0() const P0 # inline const Matrix \u0026amp; P0() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const R # virtual const Matrix \u0026amp; R() const =0 Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R\nset_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_g # inline void set_g( const Vector \u0026amp; g ) set_m # inline void set_m( const Vector \u0026amp; m ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # virtual void set_R( const Matrix \u0026amp; R ) =0 Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R\nset_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) f # inline View f( Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t ) Parameters:\nx state estimate (over time) u input (over time) t time index Return: view of updated state\nf # inline View f( Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t ) Parameters:\nx_pre predicted state est. x_post posterior state est. u input (over time) t time index Return: view of predicted state\nh # virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) =0 Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplemented by: lds::gaussian::Fit::h, lds::poisson::Fit::h\nProtected Attribute Details # dt_ # data_t dt_ {}; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; m_ # Vector m_; Q_ # Matrix Q_; C_ # Matrix C_; d_ # Vector d_; R_ # Matrix R_; x0_ # Vector x0_; P0_ # Matrix P0_; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":31,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/","title":"lds::gaussian","section":"Namespaces","content":" lds::gaussian # Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. class lds::gaussian::Fit GLDS Fit Type. class lds::gaussian::FitEM GLDS E-M Fit Type. class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. class lds::gaussian::System Gaussian LDS Type. Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":32,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/","title":"lds::gaussian::Controller","section":"Classes","content":" lds::gaussian::Controller # Gaussian-observation Controller Type. #include \u0026lt;lds_gaussian_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 21:01:32 EST\n"},{"id":33,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/","title":"lds::gaussian::Fit","section":"Classes","content":" lds::gaussian::Fit # GLDS Fit Type. #include \u0026lt;lds_gaussian_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual const Matrix \u0026amp; R() const override\ngets measurement noise covariance virtual void set_R(const Matrix \u0026amp; R) override\nsets measurement noise covariance virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period R # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nh # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nUpdated on 5 March 2025 at 21:01:32 EST\n"},{"id":34,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/","title":"lds::gaussian::FitEM","section":"Classes","content":" lds::gaussian::FitEM # GLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_gaussian_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::gaussian::FitEM; This type is used in the process of fitting GLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":35,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/","title":"lds::gaussian::FitSSID","section":"Classes","content":" lds::gaussian::FitSSID # Subspace Identification (SSID) for GLDS. #include \u0026lt;lds_gaussian_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":36,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/","title":"lds::gaussian::SwitchedController","section":"Classes","content":" lds::gaussian::SwitchedController # Gaussian-observation SwitchedController Type. #include \u0026lt;lds_gaussian_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 21:01:32 EST\n"},{"id":37,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/","title":"lds::gaussian::System","section":"Classes","content":" lds::gaussian::System # Gaussian LDS Type. #include \u0026lt;lds_gaussian_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)\nConstructs a new Gaussian System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. const Matrix \u0026amp; R() const\nGet output noise covariance. void set_Q(const Matrix \u0026amp; Q) void set_R(const Matrix \u0026amp; R)\nSet output noise covariance. void set_Ke(const Matrix \u0026amp; Ke)\nSet estimator gain. void set_Ke_m(const Matrix \u0026amp; Ke_m)\nSet disturbance estimator gain. void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively update estimator gain. Protected Attributes # Name Matrix R_ covariance of output noise bool do_recurse_Ke_ whether to recursively calculate estimator gain Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0 ) Parameters:\nn_u number of inputs (u) n_x number of states (x) n_y number of outputs (y) dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) r0 [optional] initial diagonal elements of output noise covariance (R) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nR # inline const Matrix \u0026amp; R() const set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # inline void set_R( const Matrix \u0026amp; R ) set_Ke # inline void set_Ke( const Matrix \u0026amp; Ke ) set_Ke_m # inline void set_Ke_m( const Matrix \u0026amp; Ke_m ) Print # void Print() Protected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nProtected Attribute Details # R_ # Matrix R_; do_recurse_Ke_ # bool do_recurse_Ke_ {}; Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":38,"href":"/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/","title":"lds::poisson","section":"Namespaces","content":" lds::poisson # Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. class lds::poisson::Fit PLDS Fit Type. class lds::poisson::FitEM PLDS E-M Fit Type. class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. class lds::poisson::System Poisson System type. Attributes # Name std::random_device rd random device for simulating poisson data std::mt19937 rng random number generator for simulating poisson data Attribute Details # rd # static std::random_device rd; rng # static std::mt19937 rng = std::mt19937( rd()); Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":39,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/","title":"lds::poisson::Controller","section":"Classes","content":" lds::poisson::Controller # PLDS Controller Type. #include \u0026lt;lds_poisson_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 21:01:32 EST\n"},{"id":40,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/","title":"lds::poisson::Fit","section":"Classes","content":" lds::poisson::Fit # PLDS Fit Type. #include \u0026lt;lds_poisson_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function virtual void set_R(const Matrix \u0026amp; R) override\nsets output noise covariance (if any) virtual const Matrix \u0026amp; R() const override Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # inline Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period h # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nR # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nUpdated on 5 March 2025 at 21:01:32 EST\n"},{"id":41,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/","title":"lds::poisson::FitEM","section":"Classes","content":" lds::poisson::FitEM # PLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_poisson_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::poisson::FitEM; This type is used in the process of fitting PLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":42,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/","title":"lds::poisson::FitSSID","section":"Classes","content":" lds::poisson::FitSSID # Subspace Identification (SSID) for PLDS. #include \u0026lt;lds_poisson_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":43,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/","title":"lds::poisson::SwitchedController","section":"Classes","content":" lds::poisson::SwitchedController # Poisson-observation SwitchedController Type. #include \u0026lt;lds_poisson_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 21:01:32 EST\n"},{"id":44,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/","title":"lds::poisson::System","section":"Classes","content":" lds::poisson::System # Poisson System type. #include \u0026lt;lds_poisson_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nConstructs a new Poisson System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively recalculate estimator gain (Ke) Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nProtected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nRecursively recalculate estimator gain (Ke).\nReferences:\nSmith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.\nEden UT, \u0026hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.\nUpdated on 5 March 2025 at 21:01:32 EST\n"},{"id":45,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/","title":"lds::SSID","section":"Classes","content":" lds::SSID # More\u0026hellip;\nInherited by lds::gaussian::FitSSID, lds::poisson::FitSSID\nPublic Functions # Name SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions # Name void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. virtual void DecomposeData() =0\nDecompose data to lower-triangular matrix (used in Solve) void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::SSID; Public Function Details # SSID # SSID() =default SSID # SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf) ) Parameters:\nn_x number of states n_h size of block-hankel data matrix dt sample period u_train input training data z_train measurement training data d output bias Run # std::tuple\u0026lt; Fit, Vector \u0026gt; Run( SSIDWt ssid_wt ) Parameters:\nssid_wt weight for singular value decomp Return: tuple (Fit, singular values)\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nProtected Function Details # CalcD # void CalcD( data_t t_silence =0.1, data_t thresh_silence =0.001 ) Parameters:\nt_silence threshold on period of time that qualifies as \u0026ldquo;silence\u0026rdquo; thresh_silence threshold on input amplitude u that qualifies as \u0026ldquo;silence\u0026rdquo; CreateHankelDataMat # void CreateHankelDataMat() Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.\nDecomposeData # virtual void DecomposeData() =0 Reimplemented by: lds::gaussian::FitSSID::DecomposeData, lds::poisson::FitSSID::DecomposeData\nCalcSVD # void CalcSVD( SSIDWt wt ) Parameters:\nssid_wt weight for SVD Solve # void Solve( data_t wt_dc ) Parameters:\nwt_dc weight placed on getting correct DC I/O gain RecomputeExtObs # void RecomputeExtObs() Protected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; D_ # Matrix D_; fit_ # Fit fit_; g_dc_ # Matrix g_dc_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_h_ # size_t n_h_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; L_ # Matrix L_; s_ # Vector s_; ext_obs_t_ # Matrix ext_obs_t_; Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":46,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/","title":"lds::SwitchedController","section":"Classes","content":" lds::SwitchedController # SwitchedController Type. More\u0026hellip;\n#include \u0026lt;lds_sctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nInherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController\nPublic Functions # Name SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes # Name std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::SwitchedController; Public Function Details # SwitchedController # SwitchedController() =default SwitchedController # inline SwitchedController( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask SwitchedController # inline SwitchedController( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Switch # inline void Switch( size_t idx, bool do_force_switch =false ) Parameters:\nidx index do_force_switch whether to force a system switch even if already there. set_Kc # inline void set_Kc( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc ) set_Kc # inline void set_Kc( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty ) set_Kc_inty # inline void set_Kc_inty( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u ) set_Kc_u # inline void set_Kc_u( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u ) set_g_design # inline void set_g_design( const UniformVectorList \u0026amp; g ) set_g_design # inline void set_g_design( UniformVectorList \u0026amp;\u0026amp; g ) Protected Attribute Details # systems_ # std::vector\u0026lt; System \u0026gt; systems_; n_sys_ # size_t n_sys_ {}; idx_ # size_t idx_ {}; Kc_list_ # UniformMatrixList Kc_list_; Kc_inty_list_ # UniformMatrixList Kc_inty_list_; Kc_u_list_ # UniformMatrixList Kc_u_list_; g_design_list_ # UniformVectorList g_design_list_; Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":47,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_system/","title":"lds::System","section":"Classes","content":" lds::System # Linear Dynamical System Type. #include \u0026lt;lds_sys.h\u0026gt;\nInherited by lds::gaussian::System, lds::poisson::System\nPublic Functions # Name System() =default\nConstructs a new System. System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nconstructs a new System virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) =0\nsimulates system (single time step) void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function virtual void h() =0\nsystem output function virtual Vector h_(Vector x) =0\nsystem output function (stateless) size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void RecurseKe() =0\nRecursively recalculate estimator gain (Ke) void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes # Name bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes # Name std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 diagonal elements for state estimate covariance q0 diagonal elements for process noise covariance ~System # inline virtual ~System() Filter # void Filter( const Vector \u0026amp; u_tm1, const Vector \u0026amp; z ) Parameters:\nu_tm1 input at t-minus-1 z_t current measurement Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.\nSimulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) =0 Parameters:\nu_tm1 input at time t-1 Return: simulated measurement at time t\nReimplemented by: lds::gaussian::System::Simulate, lds::poisson::System::Simulate\nf # inline void f( const Vector \u0026amp; u, bool do_add_noise =false ) Parameters:\nu input do_add_noise whether to add simulated process noise h # virtual void h() =0 Reimplemented by: lds::gaussian::System::h, lds::poisson::System::h\nh_ # virtual Vector h_( Vector x ) =0 Parameters:\nx_t state at time t Return: predicted state at time t + 1\nReimplemented by: lds::gaussian::System::h_, lds::poisson::System::h_\nn_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const x # inline const Vector \u0026amp; x() const P # inline const Matrix \u0026amp; P() const m # inline const Vector \u0026amp; m() const P_m # inline const Matrix \u0026amp; P_m() const cx # inline const Vector \u0026amp; cx() const y # inline const Vector \u0026amp; y() const x0 # inline const Vector \u0026amp; x0() const m0 # inline const Vector \u0026amp; m0() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const Ke # inline const Matrix \u0026amp; Ke() const Ke_m # inline const Matrix \u0026amp; Ke_m() const Q # inline const Matrix \u0026amp; Q() Q_m # inline const Matrix \u0026amp; Q_m() P0 # inline const Matrix \u0026amp; P0() P0_m # inline const Matrix \u0026amp; P0_m() set_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_m # inline void set_m( const Vector \u0026amp; m, bool do_force_assign =false ) set_g # inline void set_g( const Vector \u0026amp; g ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_Q_m # inline void set_Q_m( const Matrix \u0026amp; Q_m ) set_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_P0_m # inline void set_P0_m( const Matrix \u0026amp; P0_m ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) set_x # inline void set_x( const Vector \u0026amp; x ) Reset # void Reset() nstep_pred_block # std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block( UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1 ) Print # void Print() Protected Function Details # RecurseKe # virtual void RecurseKe() =0 Reimplemented by: lds::gaussian::System::RecurseKe, lds::poisson::System::RecurseKe\nInitVars # void InitVars( data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Public Attribute Details # do_adapt_m # bool do_adapt_m {}; Protected Attribute Details # n_x_ # std::size_t n_x_ {}; n_u_ # std::size_t n_u_ {}; n_y_ # std::size_t n_y_ {}; dt_ # data_t dt_ {}; x_ # Vector x_; P_ # Matrix P_; m_ # Vector m_; P_m_ # Matrix P_m_; cx_ # Vector cx_; y_ # Vector y_; z_ # Vector z_; x0_ # Vector x0_; P0_ # Matrix P0_; m0_ # Vector m0_; P0_m_ # Matrix P0_m_; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; Q_ # Matrix Q_; Q_m_ # Matrix Q_m_; C_ # Matrix C_; d_ # Vector d_; Ke_ # Matrix Ke_; Ke_m_ # Matrix Ke_m_; Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":48,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/","title":"lds::UniformMatrixList","section":"Classes","content":" lds::UniformMatrixList # More\u0026hellip;\nInherits from std::vector\u0026lt; Matrix \u0026gt;\nPublic Functions # Name UniformMatrixList() =default\nConstructs a new UniformMatrixList. UniformMatrixList(const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent. UniformMatrixList(std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent. UniformMatrixList(std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent. UniformMatrixList(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nConstructs a new UniformMatrixList (copy). UniformMatrixList(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nConstructs a new UniformMatrixList (move). ~UniformMatrixList() =default\nDestroys the object. const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim(size_t n =0) const\ngets dimensions of uniformly sized matrices size_t size()\nsize of container const Matrix \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Matrix \u0026amp; that, size_t n)\nswaps input matrix with n^th matrix of list UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nassigns the contents (copy) UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nassigns the contents (move) void append(const Matrix \u0026amp; mat)\nappends a matrix to the list Detailed Description # template \u0026lt;MatrixListFreeDim D =kMatFreeDimNone\u0026gt; class lds::UniformMatrixList; Public Function Details # UniformMatrixList # UniformMatrixList() =default UniformMatrixList # explicit UniformMatrixList( const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # explicit UniformMatrixList( std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList UniformMatrixList # UniformMatrixList( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList ~UniformMatrixList # ~UniformMatrixList() =default dim # inline const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim( size_t n =0 ) const Parameters:\nn [optional] index in list of matrices Return: dimensions\nsize # inline size_t size() at # inline const Matrix \u0026amp; at( size_t n ) Swap # inline void Swap( Matrix \u0026amp; that, size_t n ) Parameters:\nthat input matrix n index where the matrix is moved operator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\noperator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\nappend # void append( const Matrix \u0026amp; mat ) Parameters:\nmat input matrix Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":49,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/","title":"lds::UniformSystemList","section":"Classes","content":" lds::UniformSystemList # More\u0026hellip;\nInherits from std::vector\u0026lt; System \u0026gt;\nPublic Functions # Name UniformSystemList() =default\nConstructs a new UniformSystemList. UniformSystemList(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by copying existing vector of System if dimensions consistent. UniformSystemList(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by moving existing vector of System if dimensions consistent. UniformSystemList(std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList from initializer_list of System if dimensions consistent. UniformSystemList(const UniformSystemList \u0026amp; that)\nConstructs a new UniformSystemList (copy). UniformSystemList(UniformSystemList \u0026amp;\u0026amp; that)\nConstructs a new UniformSystemList (move). ~UniformSystemList() =default\nDestroys the object. const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const\ngets dimensions of the uniformly sized systems size_t size()\nsize of container const System \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(System \u0026amp; that, size_t n)\nswaps input system with n^th system of list UniformSystemList \u0026amp; operator=(const UniformSystemList \u0026amp; that)\nassigns the contents (copy) UniformSystemList \u0026amp; operator=(UniformSystemList \u0026amp;\u0026amp; that)\nassigns the contents (move) Detailed Description # template \u0026lt;typename System \u0026gt; class lds::UniformSystemList; Public Function Details # UniformSystemList # UniformSystemList() =default UniformSystemList # explicit UniformSystemList( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # explicit UniformSystemList( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList UniformSystemList # UniformSystemList( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList ~UniformSystemList # ~UniformSystemList() =default dim # inline const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const size # inline size_t size() at # inline const System \u0026amp; at( size_t n ) Swap # inline void Swap( System \u0026amp; that, size_t n ) Parameters:\nthat input system n index where the system is moved operator= # inline UniformSystemList \u0026amp; operator=( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\noperator= # inline UniformSystemList \u0026amp; operator=( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\nUpdated on 5 March 2025 at 21:01:32 EST\n"},{"id":50,"href":"/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/","title":"lds::UniformVectorList","section":"Classes","content":" lds::UniformVectorList # Inherits from std::vector\u0026lt; Vector \u0026gt;\nPublic Functions # Name UniformVectorList() =default\nConstructs a new UniformVectorList. UniformVectorList(const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent. UniformVectorList(std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent. UniformVectorList(std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0)\nConstructs a new UniformVectorList from initializer_list of Vector if dimensions consistent. UniformVectorList(const UniformVectorList \u0026amp; that)\nConstructs a new UniformVectorList (copy) UniformVectorList(UniformVectorList \u0026amp;\u0026amp; that)\nConstructs a new UniformVectorList (move) ~UniformVectorList() =default\nDestroys the object. size_t dim() const\ngets dimensions of the uniformly sized matrices size_t size()\nsize of container const Vector \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Vector \u0026amp; that, size_t n)\nswaps input matrix with n^th vector of list UniformVectorList \u0026amp; operator=(const UniformVectorList \u0026amp; that)\nassigns the contents (copy) UniformVectorList \u0026amp; operator=(UniformVectorList \u0026amp;\u0026amp; that)\nassigns the contents (move) Public Function Details # UniformVectorList # UniformVectorList() =default UniformVectorList # explicit UniformVectorList( const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dims dimension UniformVectorList # explicit UniformVectorList( std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList UniformVectorList # UniformVectorList( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList ~UniformVectorList # ~UniformVectorList() =default dim # inline size_t dim() const size # inline size_t size() at # inline const Vector \u0026amp; at( size_t n ) Swap # inline void Swap( Vector \u0026amp; that, size_t n ) Parameters:\nthat input vector n index where the vector is moved operator= # inline UniformVectorList \u0026amp; operator=( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\noperator= # inline UniformVectorList \u0026amp; operator=( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\nUpdated on 5 March 2025 at 21:01:32 EST\n"},{"id":51,"href":"/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/","title":"ldsCtrlEst_h","section":"Files","content":" ldsCtrlEst_h # Files # Name ldsCtrlEst_h/lds.h lds namespace ldsCtrlEst_h/lds_ctrl.h Controller. ldsCtrlEst_h/lds_fit.h LDS base fit type. ldsCtrlEst_h/lds_fit_em.h subspace identification ldsCtrlEst_h/lds_fit_ssid.h subspace identification ldsCtrlEst_h/lds_gaussian.h glds namespace ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller. ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type. ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type. ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type. ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type. ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type. ldsCtrlEst_h/lds_poisson.h plds namespace ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type. ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type. ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type. ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type. ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type. ldsCtrlEst_h/lds_poisson_sys.h PLDS base type. ldsCtrlEst_h/lds_sctrl.h SwitchedController type. ldsCtrlEst_h/lds_sys.h LDS base type. ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices. ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems. ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors. ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":52,"href":"/lds-ctrl-est/docs/api/files/lds__ctrl_8h/","title":"ldsCtrlEst_h/lds_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_ctrl.h # Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Controller Detailed Description # This file declares the type for control of a linear dynamical system (lds::Controller).\nSource code # //===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_CTRL_H #define LDSCTRLEST_LDS_CTRL_H // namespace #include \u0026#34;lds.h\u0026#34; // system type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class Controller { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); public: Controller() = default; Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); const Vector\u0026amp; Control(const Vector\u0026amp; z, bool do_control = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); const Vector\u0026amp; ControlOutputReference(const Vector\u0026amp; z, bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); // get methods: const System\u0026amp; sys() const { return sys_; }; const Matrix\u0026amp; Kc() const { return Kc_; }; const Matrix\u0026amp; Kc_inty() const { return Kc_inty_; }; const Matrix\u0026amp; Kc_u() const { return Kc_u_; }; const Vector\u0026amp; g_design() const { return g_design_; }; const Vector\u0026amp; u_ref() const { return u_ref_; }; const Vector\u0026amp; x_ref() const { return x_ref_; }; const Vector\u0026amp; y_ref() const { return y_ref_; }; size_t control_type() const { return control_type_; }; data_t tau_awu() const { return tau_awu_; }; data_t u_lb() const { return u_lb_; }; data_t u_ub() const { return u_ub_; }; // set methods void set_sys(const System\u0026amp; sys) { bool does_match = sys_.n_u() == sys.n_u(); does_match = does_match \u0026amp;\u0026amp; (sys_.n_x() == sys.n_x()); does_match = does_match \u0026amp;\u0026amp; (sys_.n_y() == sys.n_y()); if (does_match) { sys_ = sys; } else { throw std::runtime_error( \u0026#34;new system argument to `set_sys` does not match dimensionality of \u0026#34; \u0026#34;existing system\u0026#34;); } }; void set_g_design(const Vector\u0026amp; g_design) { Reassign(g_design_, g_design); }; void set_u_ref(const Vector\u0026amp; u_ref) { Reassign(u_ref_, u_ref); }; void set_x_ref(const Vector\u0026amp; x_ref) { Reassign(x_ref_, x_ref); cx_ref_ = sys_.C() * x_ref_; }; // y_ref needs to be handled differently depending on output fn. // (need to populate cx_ref_ too, which depends on output fn) virtual void set_y_ref(const Vector\u0026amp; y_ref) { Reassign(y_ref_, y_ref); }; void set_Kc(const Matrix\u0026amp; Kc) { Reassign(Kc_, Kc); }; void set_Kc_inty(const Matrix\u0026amp; Kc_inty) { Reassign(Kc_inty_, Kc_inty); }; void set_Kc_u(const Matrix\u0026amp; Kc_u) { Reassign(Kc_u_, Kc_u); }; void set_tau_awu(data_t tau) { tau_awu_ = tau; k_awu_ = sys_.dt() / tau_awu_; }; void set_control_type(size_t control_type); // There is no reason u_lb/ub should not be public, but making set methods // anyway. void set_u_lb(data_t u_lb) { u_lb_ = u_lb; }; void set_u_ub(data_t u_ub) { u_ub_ = u_ub; }; void Reset() { sys_.Reset(); u_ref_.zeros(); u_ref_prev_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); u_saturated_ = false; t_since_control_onset_ = 0.0; }; void Print() { sys_.Print(); std::cout \u0026lt;\u0026lt; \u0026#34;g_design : \u0026#34; \u0026lt;\u0026lt; g_design_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_lb : \u0026#34; \u0026lt;\u0026lt; u_lb_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_ub : \u0026#34; \u0026lt;\u0026lt; u_ub_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }; protected: System sys_; Vector u_; Vector u_return_; Vector g_design_; // reference signals Vector u_ref_; // create no set method for this: Vector u_ref_prev_; Vector x_ref_; Vector y_ref_; Vector cx_ref_; // Controller gains Matrix Kc_; Matrix Kc_u_; Matrix Kc_inty_; // control after g inversion // do not need set methods for these. Vector du_ref_; Vector dv_ref_; Vector v_ref_; Vector dv_; Vector v_; // integral error // do not need set method for this Vector int_e_; Vector int_e_awu_adjust_; Vector u_sat_; bool do_control_prev_ = false; bool do_lock_control_prev_ = false; // whether the g of system has become inverted from what you think it is // (gain_ref) bool u_saturated_ = false; // should be safe to have references here bc nothing needs to be done // (like reset vars) when it changes... data_t u_lb_{}; data_t u_ub_{}; data_t tau_awu_{}; data_t k_awu_ = 0; data_t t_since_control_onset_ = 0; size_t control_type_{}; private: void CalcControl(bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); void CalcSteadyStateSetPoint(); void AntiWindup(); void InitVars(size_t control_type); }; // Implement the above: template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(sys), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(std::move(sys)), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::set_control_type(size_t control_type) { if (control_type_ == control_type) { return; } // creating a blank slate... control_type_ = 0; Kc_inty_.zeros(0, 0); Kc_u_.zeros(0, 0); int_e_.zeros(0, 0); int_e_awu_adjust_.zeros(0, 0); // controller was designed to minimize integral error if (control_type \u0026amp; kControlTypeIntY) { Kc_inty_.zeros(sys_.n_u(), sys_.n_y()); int_e_.zeros(sys_.n_y()); int_e_awu_adjust_.zeros(sys_.n_u()); control_type_ = control_type_ | kControlTypeIntY; } // controller was designed to minimize deltaU // (i.e. state augmented with u) if (control_type \u0026amp; kControlTypeDeltaU) { Kc_u_.zeros(sys_.n_u(), sys_.n_u()); control_type_ = control_type_ | kControlTypeDeltaU; } // whether to adapt set point calculate with (re-estimated) process // disturbance (m) if (control_type \u0026amp; kControlTypeAdaptM) { if (sys_.do_adapt_m) // only if adapting m... { control_type_ = control_type_ | kControlTypeAdaptM; } } } // set_control_type template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::Control( const Vector\u0026amp; z, bool do_control, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement sys_.Filter(u_, z); bool do_estimation = true; // always have estimator on in this case // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::ControlOutputReference( const Vector\u0026amp; z, bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement if (do_estimation) { sys_.Filter(u_, z); } else { sys_.f(u_); } // calculate the set point // solves for u_ref and x_ref when output is at y_ref at steady state. if (do_control) { CalcSteadyStateSetPoint(); } // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcControl(bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { if (do_control \u0026amp;\u0026amp; do_estimation) { if (!do_control_prev_) { if (do_reset_at_control_onset) { Reset(); } t_since_control_onset_ = 0.0; } else { t_since_control_onset_ += sys_.dt(); } // enforce softstart on control vars. if (sigma_soft_start \u0026gt; 0) { // half-Gaussian soft-start scaling factor data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) / (2 * pow(sigma_soft_start, 2))); u_ref_ *= soft_start_sf; // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too // x_ref_ *= soft_start_sf; // cx_ref_ *= soft_start_sf; // y_ref_ *= soft_start_sf; } if (!do_lock_control) { // first do u -\u0026gt; v change of vars. (v = g.*u) // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver // control voltage u[=]V) v_ref_ = g_design_ % u_ref_; // Given FB, calc. the change in control if (control_type_ \u0026amp; kControlTypeDeltaU) { // if control designed to minimize not u but deltaU (i.e. state aug with // u): // TODO(mfbolus): Commented out for now. See note below. // du_ref_ = u_ref_ - u_ref_prev_; // dv_ref_ = g_design_ % du_ref_; // TODO(mfbolus): Assuming users want *smooth* control signals if using // kControlTypeDeltaU, it should be the case that dv_ref_ is --\u0026gt; 0. May // want to revisit, but I am going to force it to be zero unless a // situation arises that argues for keeping the above. dv_ref_.zeros(); dv_ = dv_ref_; // nominally-optimal. dv_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error dv_ -= Kc_u_ * (v_ - v_ref_); // penalty on amp u (rel to ref) if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if(!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error dv_ -= Kc_inty_ * int_e_; // control for integrated error } // update the control v_ += dv_; } else { v_ = v_ref_; // nominally-optimal. v_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if (!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error v_ -= Kc_inty_ * int_e_; // control for integrated error } } // convert back to control voltage u[=]V u_ = v_ / sys_.g(); } // else do nothing until lock is low } else { // if not control // feed through u_ref in open loop u_ = u_ref_ % g_design_ / sys_.g(); v_ = sys_.g() % u_; u_ref_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); } // ends do_control // enforce box constraints (and antiwindup) AntiWindup(); // add noise to input? // The value for u that is *returned* to user after addition of any noise, // while keeping controller/estimator blind to this addition. u_return_ = u_; if ((sigma_u_noise \u0026gt; 0.0) \u0026amp;\u0026amp; (do_control \u0026amp;\u0026amp; !do_lock_control)) { u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn); Limit(u_return_, u_lb_, u_ub_); }; // For next time step: u_ref_prev_ = u_ref_; do_control_prev_ = do_control; do_lock_control_prev_ = do_lock_control; } // CalcControl template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcSteadyStateSetPoint() { // Linearly-constrained least squares (ls). // // _reference: // Boyd \u0026amp; Vandenberghe (2018) Introduction to Applied Linear Algebra // Matrix a_ls = join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros)); Vector b_ls = cx_ref_; Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye), sys_.B() * arma::diagmat(sys_.g())); Vector d_ls = -sys_.m0(); if (control_type_ \u0026amp; kControlTypeAdaptM) { d_ls = -sys_.m(); // adapt setpoint calc with disturbance? } Matrix a_ls_t = a_ls.t(); // TODO(mfbolus): not sure why but causes seg // fault if I do not do this. Matrix phi_ls = join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()), join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros))); // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse: Matrix inv_phi = pinv(phi_ls); Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls); x_ref_ = xulam.subvec(0, sys_.n_x() - 1); u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1); cx_ref_ = sys_.C() * x_ref_; } // CalcSteadyStateSetPoint template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::AntiWindup() { u_saturated_ = false; u_sat_ = u_; // limit u and flag whether saturated for (size_t k = 0; k \u0026lt; u_.n_elem; k++) { if (u_[k] \u0026lt; u_lb_) { u_sat_[k] = u_lb_; u_saturated_ = true; } if (u_[k] \u0026gt; u_ub_) { u_sat_[k] = u_ub_; u_saturated_ = true; } } if ((control_type_ \u0026amp; kControlTypeIntY) \u0026amp;\u0026amp; (tau_awu_ \u0026lt; lds::kInf)) { // one-step back-calculation (calculate intE for u=u_sat) // (Astroem, Rundqwist 1989 warn against using this...) // int_e_awu_adjust_ = // solve(Kc_inty_, (u_ - u_sat_)); // pinv(Kc_inty) * (u-uSat); // gradual: see Astroem, Rundqwist 1989 // this is a fudge for doing MIMO gradual // n.b., went ahead and multiplied 1/T by dt so don\u0026#39;t have to do that here. int_e_awu_adjust_ = k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_); // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_); int_e_ += int_e_awu_adjust_; } // set u to saturated version u_ = u_sat_; } template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::InitVars(size_t control_type) { // initialize to default values u_ref_ = Vector(sys_.n_u(), fill::zeros); u_ref_prev_ = Vector(sys_.n_u(), fill::zeros); x_ref_ = Vector(sys_.n_x(), fill::zeros); y_ref_ = Vector(sys_.n_y(), fill::zeros); cx_ref_ = Vector(sys_.n_y(), fill::zeros); u_ = Vector(sys_.n_u(), fill::zeros); u_return_ = Vector(sys_.n_u(), fill::zeros); u_sat_ = Vector(sys_.n_u(), fill::zeros); // Might not need all these, so zero elements until later. Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros); Kc_u_ = Matrix(0, 0, fill::zeros); Kc_inty_ = Matrix(0, 0, fill::zeros); g_design_ = sys_.g(); // by default, same as model dv_ = Vector(sys_.n_u(), fill::zeros); v_ = Vector(sys_.n_u(), fill::zeros); du_ref_ = Vector(sys_.n_u(), fill::zeros); dv_ref_ = Vector(sys_.n_u(), fill::zeros); v_ref_ = Vector(sys_.n_u(), fill::zeros); int_e_ = Vector(0, fill::zeros); int_e_awu_adjust_ = Vector(0, fill::zeros); set_control_type(control_type); } } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":53,"href":"/lds-ctrl-est/docs/api/files/lds__fit__em_8h/","title":"ldsCtrlEst_h/lds_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_em.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::EM Detailed Description # This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).\nSource code # //===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_EMAX_H #define LDSCTRLEST_LDS_EMAX_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class EM { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: EM() = default; EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); virtual ~EM() = default; const Fit\u0026amp; Run(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = true, bool calc_output = true, bool calc_measurement = true, size_t max_iter = 100, data_t tol = 1e-2); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); // auto tuple = std::make_tuple(u_, z_); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; x() const { return x_; }; const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; y() const { return y_; }; const Matrix\u0026amp; sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; }; const Matrix\u0026amp; sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; }; const Matrix\u0026amp; sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; }; size_t n_t_tot() { return n_t_tot_; } const Vector\u0026amp; theta() const { return theta_; }; protected: void Expectation(bool force_common_initial = false); void Maximization(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = false, bool calc_output = false, bool calc_measurement = false); void MaximizeDynamics(); void MaximizeQ(); void MaximizeInitial(); virtual void MaximizeOutput() = 0; virtual void MaximizeMeasurement() = 0; void Smooth(bool force_common_initial); virtual void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) = 0; void Reset(); void InitVars(); Vector UpdateTheta(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; std::vector\u0026lt;Matrix\u0026gt; x_; std::vector\u0026lt;Cube\u0026gt; P_; std::vector\u0026lt;Cube\u0026gt; P_t_tm1_; std::vector\u0026lt;Matrix\u0026gt; y_; Matrix diag_y_; // expectations calculated in E-step Matrix sum_E_x_t_x_t_; Matrix sum_E_xu_tm1_xu_tm1_; Matrix sum_E_xu_t_xu_tm1_; Fit fit_; Vector theta_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; }; template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; fit_ = Fit(n_u_, n_x, n_y_, dt); u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { // make sure fit dims match I/O data if (fit0.n_u() != u_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and input training data have inconsistent dimensions\u0026#34;); } if (fit0.n_y() != z_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and output training data have inconsistent dimensions\u0026#34;); } fit_ = fit0; u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::InitVars() { // check input/output data dimensions are consistent if (z_.size() != u_.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_.at(trial).n_cols != u_.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } n_u_ = fit_.n_u(); n_x_ = fit_.n_x(); n_y_ = fit_.n_y(); dt_ = fit_.dt(); x_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); P_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); P_t_tm1_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); y_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros); P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros); } diag_y_ = Matrix(n_y_, n_y_, fill::zeros); // covariances in expectation step sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); } template \u0026lt;typename Fit\u0026gt; const Fit\u0026amp; EM\u0026lt;Fit\u0026gt;::Run(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement, size_t max_iter, data_t tol) { Reset(); // to initial conditions size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_; Vector theta(n_params); Vector theta_new(n_params); data_t max_dtheta = 1; // if solving for initial conditions, allow them be varied. // otherwise, freeze at provided values. bool force_common_initial = !calc_init; // go until parameter convergence for (size_t l = 0; l \u0026lt; max_iter; l++) { theta_ = UpdateTheta(); std::cout \u0026lt;\u0026lt; \u0026#34;Iteration \u0026#34; \u0026lt;\u0026lt; l + 1 \u0026lt;\u0026lt; \u0026#34;/\u0026#34; \u0026lt;\u0026lt; max_iter \u0026lt;\u0026lt; \u0026#34; ...\\n\u0026#34;; Expectation(force_common_initial); Maximization(calc_dynamics, calc_Q, calc_init, calc_output, calc_measurement); // check convergence theta_new = UpdateTheta(); Vector dtheta = abs(theta_new - theta_) / abs(theta_); // some parameters could be zero... arma::uvec ubi_finite = find_finite(dtheta); max_dtheta = max(dtheta.elem(ubi_finite)); std::cout \u0026lt;\u0026lt; \u0026#34;max dtheta: \u0026#34; \u0026lt;\u0026lt; max_dtheta \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; if (max_dtheta \u0026lt; tol) { std::cout \u0026lt;\u0026lt; \u0026#34;Converged.\\n\u0026#34;; break; } std::cout \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } return fit_; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Smooth(bool force_common_initial) { Matrix k_e(n_x_, n_y_); // estimator gain Cube k_backfilt; // back-filtering gains // TODO(mfbolus): this loop could be made parallel for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { Matrix x_pre(n_x_, n_t_[trial], fill::zeros); Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros); Matrix x_post(n_x_, n_t_[trial], fill::zeros); Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros); if (force_common_initial) // forces all trials to have same initial // conditions. { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); } y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); // This *should not* be necessary but make sure P is symmetric. ForceSymPD(P_[trial].slice(0)); x_pre.col(0) = x_[trial].col(0); p_pre.slice(0) = P_[trial].slice(0); x_post.col(0) = x_[trial].col(0); p_post.slice(0) = P_[trial].slice(0); // filter for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // predict fit_.f(x_pre, x_post, u_.at(trial), t); fit_.h(y_[trial], x_pre, t); diag_y_.diag() = y_[trial].col(t); // TODO(mfbolus): change if parallel // update --\u0026gt; posterior estimation RecurseKe(k_e, p_pre, p_post, t); x_post.col(t) = x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t)); y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d(); } // backfilter -\u0026gt; Smoothed estimate // Reference: // Shumway et Stoffer (1982) ForceSymPD(p_post.slice(n_t_[trial] - 1)); k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1); P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 0; t--) { // TODO(mfmbolus): should not be necessary to force symm positive def ForceSymPD(p_pre.slice(t)); ForceSymPD(p_post.slice(t - 1)); ForceSymPD(P_[trial].slice(t)); k_backfilt.slice(t - 1) = p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t)); x_[trial].col(t - 1) = x_post.col(t - 1) + k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t)); P_[trial].slice(t - 1) = p_post.slice(t - 1) + k_backfilt.slice(t - 1) * (P_[trial].slice(t) - p_pre.slice(t)) * k_backfilt.slice(t - 1).t(); } // do the same for P_t_tm1 Matrix id(n_x_, n_x_, fill::eye); P_t_tm1_[trial].slice(n_t_[trial] - 1) = (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 1; t--) { P_t_tm1_[trial].slice(t - 1) = p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() + k_backfilt.slice(t - 1) * (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) * k_backfilt.slice(t - 2).t(); } // finally, get smoothed estimate of output for (size_t t = 0; t \u0026lt; n_t_[trial]; t++) { fit_.h(y_[trial], x_[trial], t); } // samps loop } // trial loop } // Smooth // template \u0026lt;typename Fit\u0026gt; // void EM\u0026lt;Fit\u0026gt;::RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) { // // predict covar // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q(); // // update Ke // Ke = P_pre.slice(t) * fit_.C().t() * // inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R()); // // update cov // // Reference: Ghahramani et Hinton (1996) // P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t); // // // n.b. for poisson : // // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + // fit_.Q(); // // // update cov // // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ * // // fit_.C()); // // // update Ke // // Ke = P_post.slice(t) * fit_.C(); // } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Expectation(bool force_common_initial) { // calculate the mean/cov of state needed for maximizing E[pr(z|theta)] Smooth(force_common_initial); // now get the various forms of sum(E[xx\u0026#39;]) needed // n.b. Going to start at t=1 rather than 0 bc most max terms need that. // so really \u0026#34;n_t_tot_\u0026#34; is (n_t_tot_-1) n_t_tot_ = 0; sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); Vector xu_tm1(n_x_ + n_u_, fill::zeros); Vector xu_t(n_x_ + n_u_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // ------------ sum_E_x_t_x_t ------------ sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t(); sum_E_x_t_x_t_ += P_[trial].slice(t); // ------------ sum_E_xu_tm1_xu_tm1 ------------ xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1)); sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t(); sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t - 1); // ------------ sum_E_xu_t_xu_tm1 ------------ xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t)); sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t(); sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_t_tm1_[trial].slice(t); n_t_tot_ += 1; } // time } // trial } // Expectation template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement) { if (calc_output) { MaximizeOutput(); } if (calc_measurement) { MaximizeMeasurement(); } if (calc_dynamics) { MaximizeDynamics(); } if (calc_Q) { MaximizeQ(); } if (calc_init) { MaximizeInitial(); } } // Maximization template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeDynamics() { // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) * inv_sympd(sum_E_xu_tm1_xu_tm1_); fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1)); std::cout \u0026lt;\u0026lt; \u0026#34;A_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.A()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.B()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeQ() { // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 * // inv_sympd(sum_E_xu_tm1_xu_tm1_) * // sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // this way is same as above iff dynamics were just updated: // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix ab = arma::join_horiz(fit_.A(), fit_.B()); // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // From scratch method: // Q is covariance of the error between state and dynamics-predicted state // (aka process noise) // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})\u0026#39;] // t-1 terms: View sum_e_x_tm1_x_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_u_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1); View sum_e_x_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); // t, t-1 terms: View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_x_t_u_tm1 = sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); Matrix q = sum_E_x_t_x_t_; q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t(); q -= sum_e_x_t_x_tm1 * fit_.A().t(); q -= fit_.A() * sum_e_x_t_x_tm1.t(); // input-related terms: q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t(); q -= sum_e_x_t_u_tm1 * fit_.B().t(); q -= fit_.B() * sum_e_x_t_u_tm1.t(); q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t(); q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t(); q /= n_t_tot_; fit_.set_Q(q); std::cout \u0026lt;\u0026lt; \u0026#34;Q_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.Q()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // std::cout \u0026lt;\u0026lt; \u0026#34;Q_new: \\n\u0026#34; \u0026lt;\u0026lt; fit_.Q() \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeInitial() { Vector x0 = fit_.x0(); x0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { x0 += x_[trial].col(0); } x0 /= z_.size(); std::cout \u0026lt;\u0026lt; \u0026#34;x0_new[0]: \u0026#34; \u0026lt;\u0026lt; x0[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // always recalc P0 even if the initial state is fixed (at zero, for // example) Matrix e_var(n_x_, n_x_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t(); } e_var /= z_.size(); // go ahead and subtract x0*x0\u0026#39; so don\u0026#39;t have to below. e_var -= x0 * x0.t(); // To get P0, going to get initial P_ per trial and average. // (which might be wrong, but need a single number) Matrix p0 = fit_.P0(); p0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { p0 += (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var; } p0 /= z_.size(); fit_.set_P0(p0); std::cout \u0026lt;\u0026lt; \u0026#34;P0_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.P0()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeOutput() { // solve for C+d: Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros); Vector x1(n_x_ + 1, fill::zeros); x1[n_x_] = 1.0; // augment with one to solve for bias Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { x1.subvec(0, n_x_ - 1) = x_[trial].col(t); sum_zx += z_.at(trial).col(t) * x1.t(); sum_e_x1_x1 += x1 * x1.t(); sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t); } } Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1); fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1)); fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_))); std::cout \u0026lt;\u0026lt; \u0026#34;C_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.C()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.d()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeMeasurement() { // Solve for measurement noise covar size_t n_t_tot = 0; // Ghahgramani, Hinton 1996: Matrix sum_zz(n_y_, n_y_, fill::zeros); Matrix sum_yz(n_y_, n_y_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t(); // Use Cnew: sum_yz += (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t(); n_t_tot += 1; } } fit_.set_R((sum_zz - sum_yz) / n_t_tot); std::cout \u0026lt;\u0026lt; \u0026#34;R_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.R()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Reset() { // reset to initial conditions for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); } } template \u0026lt;typename Fit\u0026gt; Vector EM\u0026lt;Fit\u0026gt;::UpdateTheta() { // TODO(mfbolus): This should include n_y_ more params for d. size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_; if (fit_.R().n_elem \u0026gt; 0) { n_params += n_y_ * n_y_; } Vector theta(n_params); size_t idx_start = 0; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B()); idx_start += n_x_ * n_u_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0()); idx_start += n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C()); idx_start += n_y_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d()); idx_start += n_y_; if (fit_.R().n_elem \u0026gt; 0) { theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R()); } return theta; } } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":54,"href":"/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_ssid.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SSID Detailed Description # This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/)\u0026lt;Fit\u0026gt;).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_FIT_SSID_H #define LDSCTRLEST_LDS_FIT_SSID_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class SSID { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: SSID() = default; SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d = Vector(1).fill(-kInf)); std::tuple\u0026lt;Fit, Vector\u0026gt; Run(SSIDWt ssid_wt); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } protected: void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001); void CreateHankelDataMat(); virtual void DecomposeData() = 0; void CalcSVD(SSIDWt wt); void Solve(data_t wt_dc); void RecomputeExtObs(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; Matrix D_; Fit fit_; Matrix g_dc_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_h_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; Matrix L_; Vector s_; Matrix ext_obs_t_; }; template \u0026lt;typename Fit\u0026gt; SSID\u0026lt;Fit\u0026gt;::SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d) { // check input/output data dimensions are consistent if (z_train.size() != u_train.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_train.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_train.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } dt_ = dt; n_x_ = n_x; n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; n_h_ = n_h; // dimensionality check for eventual block-hankel data matrix size_t len = n_t_tot_ - 2 * n_h_ + 1; if (len \u0026lt; (2 * n_h_ * (n_u_ + n_y_))) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;Dataset problem! More rows than columns in block-hankel data \u0026#34; \u0026#34;matrix: 2*(n_u+n_y)*n_h \u0026gt; data-length! Need higher data-length or \u0026#34; \u0026#34;lower n_h.\u0026#34;; throw std::runtime_error(ss.str()); } fit_ = Fit(n_u_, n_x_, n_y_, dt_); u_ = std::move(u_train); z_ = std::move(z_train); if (!d.is_finite() || (d.n_rows != n_y_)) { // TODO(mfbolus): implement least-square solution for impulse response with // a second input of ones. Data-driven way of accounting for offset *not* // driven by an input. // // For now, calculate output bias (d) as the // output wherever the stimulus has not been on for some amount of time. // convolve u with rectangle and take all samples. This is a reasonable // approach, since often when autonomous systems are fit (i.e., systems with // no input), they will subtract off the mean of the output. This // essentially amounts to setting output bias to the mean of the output when // there is no stimulation. data_t t_silence = 0.1; data_t thresh_silence = 0.001; CalcD(t_silence, thresh_silence); } else { fit_.set_d(d); } } template \u0026lt;typename Fit\u0026gt; std::tuple\u0026lt;Fit, Vector\u0026gt; SSID\u0026lt;Fit\u0026gt;::Run(SSIDWt ssid_wt) { // the weight on minimizing dc I/O gain only works for gaussian, // and hopefully not necessary with appropriate dataset. data_t wt_dc = 0; // std::cout \u0026lt;\u0026lt; \u0026#34;creating hankel mat\\n\u0026#34;; CreateHankelDataMat(); // std::cout \u0026lt;\u0026lt; \u0026#34;decomposing data\\n\u0026#34;; DecomposeData(); // std::cout \u0026lt;\u0026lt; \u0026#34;calculating svd\\n\u0026#34;; CalcSVD(ssid_wt); // std::cout \u0026lt;\u0026lt; \u0026#34;solving for params\\n\u0026#34;; Solve(wt_dc); // std::cout \u0026lt;\u0026lt; \u0026#34;fin\\n\u0026#34;; return std::make_tuple(fit_, s_); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcD(data_t t_silence, data_t thresh_silence) { Vector d(z_.at(0).n_rows, fill::zeros); Vector win(static_cast\u0026lt;size_t\u0026gt;(t_silence / dt_), fill::ones); Vector sum_z_silence(n_y_, fill::zeros); size_t n_silence(0); for (size_t trial = 0; trial \u0026lt; u_.size(); trial++) { // find silent samples // start by convolving with Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0)); Vector u_conv = conv(sum_u, win, \u0026#34;same\u0026#34;); // get only the samples that are silent... arma::uvec ubi_silence = find(u_conv \u0026lt;= thresh_silence); if (ubi_silence.n_elem \u0026gt; 0) { sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1); n_silence += ubi_silence.n_elem; } } if (n_silence \u0026gt; 0) { d = sum_z_silence / n_silence; } fit_.set_d(d); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat() { // temporary copy of data Matrix z(n_y_, n_t_tot_, fill::zeros); Matrix u(n_u_, n_t_tot_, fill::zeros); size_t so_far(0); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial); u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial); so_far += n_t_.at(trial); } // remove output bias z.each_col() -= fit_.d(); // calculate I/O gain @ DC while data in convenient form g_dc_ = z * pinv(u); // std::cout \u0026lt;\u0026lt; \u0026#34;G0_data = \u0026#34; \u0026lt;\u0026lt; g_dc_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // create hankel data matrix size_t len = z.n_cols - 2 * n_h_ + 1; // data length in hankel mat // block-hankel data matrix D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros); // past input auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1); // future input auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1); // past output auto y_p = D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1); // future output auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, len - 1); size_t idx = 0; for (size_t k = 0; k \u0026lt; len; k++) { idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } } D_ /= sqrt(static_cast\u0026lt;data_t\u0026gt;(len)); } // template \u0026lt;typename Fit\u0026gt; // void SSID\u0026lt;Fit\u0026gt;::DecomposeData() { // // do LQ decomp instead of calculating covariance expensive way // // Note that \u0026#34;R\u0026#34; in van Overschee is lower-triangular (L), not \u0026#34;R\u0026#34; in QR // // decomp. Very confusing. // Matrix q_t; // lq(L_, q_t, D_); // // van Overschee zeros out the other elements. // L_ = trimatl(L_); // } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcSVD(SSIDWt wt) { // submats that will be needed: auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1); auto R_23_13 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1); auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_) - 1); auto R_44 = L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14); auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1); auto Luf = Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1); auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); // aka: R_f Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1); // from van Overschee subid.m: // Rf = R((2*m+l)*i+1:2*(m+l)*i,:); % Future outputs Matrix U; Matrix V; switch (wt) { case kSSIDNone: { // No weighting. (what van Overschee calls \u0026#34;N4SID\u0026#34;) Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14; arma::svd(U, s_, V, O_k_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDMOESP: { // MOESP weighting // This is what they use in the \u0026#34;robust\u0026#34; algorithm van Overschee, de Moor // 1996 Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); svd(U, s_, V, O_k_ortho_Uf_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDCVA: { // CVA weighting // See van Overschee\u0026#39;s matlab code (subid.m): // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); Matrix inv_w1; Matrix qt1; lq(inv_w1, qt1, R_56_16); // lq decomp of R_f (future output data) inv_w1 = trimatl(inv_w1); inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1); Matrix w_o_w = arma::solve( inv_w1, O_k_ortho_Uf_sans_Qt); // alternatively // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt svd(U, s_, V, w_o_w, \u0026#34;std\u0026#34;); U = inv_w1 * U; break; } } // Truncate to model order (heart of ssid method) auto s_hat = s_.subvec(0, n_x_ - 1); Matrix diag_sqrt_s = diagmat(sqrt(s_hat)); auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1); // get extended observability and controllability mats ext_obs_t_ = u_hat * diag_sqrt_s; // extended observability matrix } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::Solve(data_t wt_dc) { // required submats auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_56_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); // Solve for params using appropriate algorithm: // robust deterministic/stochastic algorithm in van Overschee 1996 // algorithm that the authors say \u0026#34;works\u0026#34; in practice. auto ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix // This is what textbook (1996) says: // // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15); // // HOWEVER, do not know why but have to fill the last place with zeros like // authors\u0026#39; matlab implementation (see `subid.m`) // Otherwise, get ridiculous covariances (although A,C estimates are close to // same...) Matrix Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Matrix S = Tl * pinv(Tr); // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure // stability. fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1)); Matrix ext_obs_t_p1 = join_vert( ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1), Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros)); fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1); // At this point, van Overschee \u0026amp; de Moor suggest re-calculating ext_obs_t_, // ext_obs_tm1 from (A, C) because it was just an approximation. This is RecomputeExtObs(); ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); S = Tl * pinv(Tr); Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_); Matrix Mcurly = pinv(ext_obs_tm1); Matrix Pcurly = Tl - Lcurly * R_56_15; Vector Pvec = vectorise(Pcurly); Matrix Qcurly = R_23_15; // Identify [D; B], assuming D=0 and ensuring DC gain is correct Matrix sum_QcurlyT_kron_Ncurly( (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_), fill::zeros); Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols, fill::eye); eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1, eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1; // van Overschee (1996) p. 126 Matrix N1_Tl = -Lcurly; N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) += join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly); N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) += Matrix(n_y_, n_y_, fill::eye); Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros); Matrix N_k; for (size_t k = 0; k \u0026lt; n_h_; k++) { auto Qcurly_k = Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1); Nk_Tl.zeros(); Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) = N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1); N_k = Nk_Tl * eye_ext_obs_tm1; sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k); } Matrix err_vec; if (wt_dc \u0026gt; 0) { // Constraints enforced by weighted least squares // // Reference: // // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly // Excited Industrial Systems. Conference in Decision and Control. // constraint 1: assume D=0 --\u0026gt; remove the components for Dvec (this is // actually a hard constraint in that it ignores D) Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly; sum_QcurlyT_kron_Ncurly = Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_); size_t kkk = 0; for (size_t k = 1; k \u0026lt; (n_u_ + 1); k++) { size_t start_idx = k * (n_y_ + n_x_) - n_x_; for (size_t kk = 0; kk \u0026lt; n_x_; kk++) { sum_QcurlyT_kron_Ncurly.col(kkk) = sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk); kkk++; } } // constraint 2: Make sure DC I/O gain is correct Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A()); Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_)); Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0); Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 = join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0); // WEIGHTED LS // Important in practice because I care a lot about at least getting the DC // gain correct. Put x weight on minimizing error at DC, relative to others Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye); // Make weight on minimizing DC error immense so at least that // should be nailed. size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows; size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows; size_t stop_row = w.n_rows - 1; size_t stop_col = w.n_cols - 1; // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale // weight with data length? w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc; Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * sum_QcurlyT_kron_Ncurly_b_to_g0) * sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec; fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_)); // Calculate residuals and their cov. // Because I\u0026#39;ve added constraints, I need to re-calculate the right term // with b_vec instead of how van Overschee do in final algorithm. err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec; } else { // default way: *no* constraint on G0 or D=0 Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec; // TODO(mfbolus) n.b., this gets thrown away... // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_); fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_)); err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec; } // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols); // TODO(mfbolus): Something is wrong with the error calculation above. // Use the way van overschee does it in `subid.m` // WARNING: this ignores any above constraints, so Q, R will be approximate... Matrix err = Tl - S * Tr; Matrix cov_err = err * err.t(); fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1)); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::RecomputeExtObs() { ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C(); for (size_t k = 2; k \u0026lt; (n_h_ + 1); k++) { ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) = ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1, ext_obs_t_.n_cols - 1) * fit_.A(); } } } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":55,"href":"/lds-ctrl-est/docs/api/files/lds__fit_8h/","title":"ldsCtrlEst_h/lds_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_fit.h # LDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Fit LDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.\nSource code # //===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDS_FIT_HPP #define LDS_FIT_HPP // namespace #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); virtual ~Fit() = default; // get methods size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; Q() const { return Q_; }; const Vector\u0026amp; x0() const { return x0_; }; const Matrix\u0026amp; P0() const { return P0_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; // gets measurement noise virtual const Matrix\u0026amp; R() const = 0; // set methods (e.g., seeding initial fit values) void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_m(const Vector\u0026amp; m) { Reassign(m_, m); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); ForceSymPD(Q_); }; virtual void set_R(const Matrix\u0026amp; R) = 0; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); ForceSymPD(P0_); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; View f(Matrix\u0026amp; x, const Matrix\u0026amp; u, size_t t) { x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x.col(t); }; View f(Matrix\u0026amp; x_pre, const Matrix\u0026amp; x_post, const Matrix\u0026amp; u, size_t t) { x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x_pre.col(t); }; virtual View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) = 0; protected: data_t dt_{}; // Dynamics Matrix A_; Matrix B_; Vector g_; Vector m_; Matrix Q_; // Output Matrix C_; Vector d_; Matrix R_; // initial conditions Vector x0_; Matrix P0_; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; }; } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":56,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_ctrl.h # GLDS Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. Detailed Description # This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_gaussian_sys.h\u0026#34; // controller #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace gaussian { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); cx_ref_ = y_ref - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":57,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_em.h # GLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitEM GLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":58,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_ssid.h # GLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; using SSID\u0026lt;Fit\u0026gt;::Run; private: using SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat; using SSID\u0026lt;Fit\u0026gt;::CalcSVD; using SSID\u0026lt;Fit\u0026gt;::Solve; void DecomposeData() override; void SolveVanOverschee(); }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":59,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit.h # GLDS fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Fit GLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // fit type #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace gaussian { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); const Matrix\u0026amp; R() const override { return R_; }; void set_R(const Matrix\u0026amp; R) override { Reassign(R_, R); ForceSymPD(R_); }; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = C_ * x.col(t) + d_; return y.col(t); }; }; }; // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":60,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sctrl.h # GLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H // controller type #include \u0026#34;lds_gaussian_ctrl.h\u0026#34; // switched controller #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace gaussian { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); cx_ref_ = y_ref - sys_.d(); } // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; }; // SwitchedController } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":61,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/","title":"ldsCtrlEst_h/lds_gaussian_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sys.h # GLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::System Gaussian LDS Type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H #define LDSCTRLEST_LDS_GAUSSIAN_SYS_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; namespace lds { namespace gaussian { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0, data_t r0 = kDefaultR0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; // get methods const Matrix\u0026amp; R() const { return R_; }; // set methods void set_Q(const Matrix\u0026amp; Q) { lds::System::set_Q(Q); do_recurse_Ke_ = true; } void set_R(const Matrix\u0026amp; R) { Reassign(R_, R); do_recurse_Ke_ = true; }; void set_Ke(const Matrix\u0026amp; Ke) { Reassign(Ke_, Ke); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void set_Ke_m(const Matrix\u0026amp; Ke_m) { Reassign(Ke_m_, Ke_m); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void Print(); protected: void h() override { cx_ = C_ * x_; y_ = cx_ + d_; }; Vector h_(Vector x) override { return C_ * x + d_; }; void RecurseKe() override; // Gaussian-output-specific Matrix R_; bool do_recurse_Ke_{}; }; // System } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":62,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian_8h/","title":"ldsCtrlEst_h/lds_gaussian.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian.h # glds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_H #define LDSCTRLEST_LDS_GAUSSIAN_H // namespace #include \u0026#34;lds.h\u0026#34; namespace lds { namespace gaussian { // insert any Gaussian-specific things here... } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":63,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_ctrl.h # PLDS controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. Detailed Description # This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_CTRL_H #define LDSCTRLEST_LDS_POISSON_CTRL_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system type #include \u0026#34;lds_poisson_sys.h\u0026#34; // control type #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace poisson { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); lds::Limit(y_ref_, kYRefLb, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; private: constexpr static const data_t kYRefLb = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":64,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_em.h # PLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitEM PLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\n[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H #define LDSCTRLEST_LDS_POISSON_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override{}; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; data_t NewtonSolveC(); void AnalyticalSolveD(); }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":65,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_ssid.h # PLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. Detailed Description # This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/)).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H #define LDSCTRLEST_LDS_POISSON_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; private: void DecomposeData() override; void CalcCov(); void PoissonToGaussianMoments(); Matrix cov_; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":66,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/","title":"ldsCtrlEst_h/lds_poisson_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit.h # PLDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Fit PLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_H #define LDSCTRLEST_LDS_POISSON_FIT_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // fit #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace poisson { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) : lds::Fit(n_u, n_x, n_y, dt){}; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = exp(C_ * x.col(t) + d_); return y.col(t); }; void set_R(const Matrix\u0026amp; R) override { std::cerr \u0026lt;\u0026lt; \u0026#34;WARNING: Cannot set R (R[0] = \u0026#34; \u0026lt;\u0026lt; R.at(0) \u0026lt;\u0026lt; \u0026#34;). No Gaussian measurement noise in Poisson observation model.\\n\u0026#34;; }; const Matrix\u0026amp; R() const override { return R_; }; }; }; // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":67,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sctrl.h # PLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #include \u0026#34;lds_poisson_ctrl.h\u0026#34; #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace poisson { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); lds::Limit(y_ref_, kYRefLB, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; private: constexpr static data_t kYRefLB = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":68,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/","title":"ldsCtrlEst_h/lds_poisson_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sys.h # PLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::System Poisson System type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SYS_H #define LDSCTRLEST_LDS_POISSON_SYS_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; // needed for Poisson random number generation #include \u0026lt;random\u0026gt; namespace lds { namespace poisson { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; protected: void h() override { cx_ = C_ * x_; y_ = exp(cx_ + d_); diag_y_.diag() = y_; }; Vector h_(Vector x) override { return exp(C_ * x + d_); }; void RecurseKe() override; private: // Poisson-output-specific Matrix diag_y_; std::poisson_distribution\u0026lt;size_t\u0026gt; pd_; }; // System } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":69,"href":"/lds-ctrl-est/docs/api/files/lds__poisson_8h/","title":"ldsCtrlEst_h/lds_poisson.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson.h # plds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_H #define LDSCTRLEST_LDS_POISSON_H #include \u0026#34;lds.h\u0026#34; namespace lds { namespace poisson { // TODO(mfbolus): Not sure if defining these as static here makes the most // sense. Is there a downside to letting multiple poisson System objects share a // common random number generator? static std::random_device rd; static std::mt19937 rng = std::mt19937( rd()); } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":70,"href":"/lds-ctrl-est/docs/api/files/lds__sctrl_8h/","title":"ldsCtrlEst_h/lds_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_sctrl.h # SwitchedController type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SwitchedController SwitchedController Type. Detailed Description # This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SCTRL_H #define LDSCTRLEST_LDS_SCTRL_H #include \u0026#34;lds_ctrl.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; #include \u0026#34;lds_uniform_vecs.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class SwitchedController : public Controller\u0026lt;System\u0026gt; { public: SwitchedController() = default; SwitchedController(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); SwitchedController(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); void Switch(size_t idx, bool do_force_switch = false); void set_Kc(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc) { Kc_list_ = Kc; Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc) { Kc_list_ = std::move(Kc); Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_inty) { Kc_inty_list_ = Kc_inty; Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_inty) { Kc_inty_list_ = std::move(Kc_inty); Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_u) { Kc_u_list_ = Kc_u; Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_u) { Kc_u_list_ = std::move(Kc_u); Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(const UniformVectorList\u0026amp; g) { g_design_list_ = g; g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(UniformVectorList\u0026amp;\u0026amp; g) { g_design_list_ = std::move(g); g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; protected: std::vector\u0026lt;System\u0026gt; systems_; size_t n_sys_{}; size_t idx_{}; // controller gains could be different for each UniformMatrixList\u0026lt;\u0026gt; Kc_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_inty_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_u_list_; // design-phase input gain could also be different UniformVectorList g_design_list_; // TODO(mfbolus): not sure why I need to do this. using Controller\u0026lt;System\u0026gt;::Kc_; using Controller\u0026lt;System\u0026gt;::Kc_inty_; using Controller\u0026lt;System\u0026gt;::Kc_u_; using Controller\u0026lt;System\u0026gt;::g_design_; using Controller\u0026lt;System\u0026gt;::sys_; // using Controller\u0026lt;System\u0026gt;::u_ref_; // using Controller\u0026lt;System\u0026gt;::x_ref_; // using Controller\u0026lt;System\u0026gt;::y_ref_; // using Controller\u0026lt;System\u0026gt;::control_type_; private: void InitVars(); using lds::Controller\u0026lt;System\u0026gt;::set_sys; // using Controller\u0026lt;System\u0026gt;::set_Kc; // using Controller\u0026lt;System\u0026gt;::set_Kc_inty; // using Controller\u0026lt;System\u0026gt;::set_Kc_u; // using Controller\u0026lt;System\u0026gt;::set_g_design; }; template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(systems.at(0), u_lb, u_ub, control_type), systems_(systems) { InitVars(); } template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(System(systems.at(0).n_u(), systems.at(0).n_x(), systems.at(0).n_y(), systems.at(0).dt()), u_lb, u_ub, control_type), systems_(std::move(systems)) { InitVars(); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::InitVars() { n_sys_ = systems_.size(); sys_ = systems_.at(0); Kc_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_)); Kc_inty_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); Kc_u_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); g_design_list_ = UniformVectorList(std::vector\u0026lt;Vector\u0026gt;(n_sys_, g_design_)); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::Switch(size_t idx, bool do_force_switch) { if ((idx == idx_) \u0026amp;\u0026amp; !do_force_switch) { return; // already there. } // put old up and get new one out systems_.at(idx_) = std::move(sys_); sys_ = std::move(systems_.at(idx)); // set the state of this system to that of the previous system // TODO(mfbolus): This will only work as intended if state matrix is the same. // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp. sys_.set_m(systems_.at(idx_).m(), true); sys_.set_x(systems_.at(idx_).x()); // swap controller gains Kc_list_.Swap(Kc_, idx_); Kc_list_.Swap(Kc_, idx); if (control_type_ \u0026amp; kControlTypeIntY) { Kc_inty_list_.Swap(Kc_inty_, idx_); Kc_inty_list_.Swap(Kc_inty_, idx); } if (control_type_ \u0026amp; kControlTypeDeltaU) { Kc_u_list_.Swap(Kc_u_, idx_); Kc_u_list_.Swap(Kc_u_, idx); } g_design_list_.Swap(g_design_, idx_); g_design_list_.Swap(g_design_, idx); idx_ = idx; } // Switch } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":71,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8h/","title":"ldsCtrlEst_h/lds_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_sys.h # LDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::System Linear Dynamical System Type. Detailed Description # This file declares and partially defines the base type for linear dynamical systems ([lds::System](/lds-ctrl-est/docs/api/classes/classlds_1_1_system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SYS_H #define LDSCTRLEST_LDS_SYS_H #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class System { public: System() = default; System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); virtual ~System() {} void Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z); virtual const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) = 0; void f(const Vector\u0026amp; u, bool do_add_noise = false) { x_ = A_ * x_ + B_ * (g_ % u) + m_; if (do_add_noise) { x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_); } }; virtual void h() = 0; virtual Vector h_(Vector x) = 0; size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Vector\u0026amp; x() const { return x_; }; const Matrix\u0026amp; P() const { return P_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; P_m() const { return P_m_; }; const Vector\u0026amp; cx() const { return cx_; }; const Vector\u0026amp; y() const { return y_; }; const Vector\u0026amp; x0() const { return x0_; }; const Vector\u0026amp; m0() const { return m0_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; const Matrix\u0026amp; Ke() const { return Ke_; }; const Matrix\u0026amp; Ke_m() const { return Ke_m_; }; const Matrix\u0026amp; Q() { return Q_; }; const Matrix\u0026amp; Q_m() { return Q_m_; }; const Matrix\u0026amp; P0() { return P0_; }; const Matrix\u0026amp; P0_m() { return P0_m_; }; void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_m(const Vector\u0026amp; m, bool do_force_assign = false) { Reassign(m0_, m); if ((!do_adapt_m) || do_force_assign) { Reassign(m_, m); } }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); }; void set_Q_m(const Matrix\u0026amp; Q_m) { Reassign(Q_m_, Q_m); }; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); }; void set_P0_m(const Matrix\u0026amp; P0_m) { Reassign(P0_m_, P0_m); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; void set_x(const Vector\u0026amp; x) { Reassign(x_, x); h(); }; void Reset(); std::vector\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; nstep_pred_block( UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z, size_t n_pred = 1); void Print(); // safe to leave this public and non-const bool do_adapt_m{}; protected: virtual void RecurseKe() = 0; void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); std::size_t n_x_{}; std::size_t n_u_{}; std::size_t n_y_{}; data_t dt_{}; // Signals: Vector x_; Matrix P_; Vector m_; Matrix P_m_; Vector cx_; Vector y_; Vector z_; // Parameters: Vector x0_; Matrix P0_; Vector m0_; Matrix P0_m_; Matrix A_; Matrix B_; Vector g_; Matrix Q_; Matrix Q_m_; Matrix C_; Vector d_; Matrix Ke_; Matrix Ke_m_; }; // System } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":72,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/","title":"ldsCtrlEst_h/lds_uniform_mats.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_mats.h # List of uniformly sized matrices. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformMatrixList Detailed Description # This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H #define LDSCTRLEST_LDS_UNIFORM_MATS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { template \u0026lt;MatrixListFreeDim D = kMatFreeDimNone\u0026gt; class UniformMatrixList : public std::vector\u0026lt;Matrix\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Matrix\u0026gt;::vector; // don\u0026#39;t allow push_back to be used since it doesn\u0026#39;t check dims using std::vector\u0026lt;Matrix\u0026gt;::push_back; public: using std::vector\u0026lt;Matrix\u0026gt;::operator=; using std::vector\u0026lt;Matrix\u0026gt;::operator[]; using std::vector\u0026lt;Matrix\u0026gt;::begin; using std::vector\u0026lt;Matrix\u0026gt;::end; using std::vector\u0026lt;Matrix\u0026gt;::size; using std::vector\u0026lt;Matrix\u0026gt;::at; UniformMatrixList() = default; explicit UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); explicit UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; ~UniformMatrixList() = default; const std::array\u0026lt;size_t, 2\u0026gt;\u0026amp; dim(size_t n = 0) const { return dim_.at(n); } size_t size() { return std::vector\u0026lt;Matrix\u0026gt;::size(); }; const Matrix\u0026amp; at(size_t n) { return std::vector\u0026lt;Matrix\u0026gt;::at(n); }; void Swap(Matrix\u0026amp; that, size_t n); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; void append(const Matrix\u0026amp; mat); private: void CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim); std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt; dim_; }; template \u0026lt;MatrixListFreeDim D\u0026gt; inline void UniformMatrixList\u0026lt;D\u0026gt;::Swap(Matrix\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.n_cols); } if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap // not moving, since it causes memory issues. // so this method isn\u0026#39;t a memory-saver as designed for now Matrix tmp = (*this)[n]; (*this)[n] = that; that = tmp; if (D == kMatFreeDim1) { this-\u0026gt;dim_[n][0] = (*this)[n].n_rows; } if (D == kMatFreeDim2) { this-\u0026gt;dim_[n][1] = (*this)[n].n_cols; } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::append(const Matrix\u0026amp; mat) { std::array\u0026lt;size_t, 2\u0026gt; dim({mat.n_rows, mat.n_cols}); CheckDimensions(dim); std::vector\u0026lt;Matrix\u0026gt;::push_back(mat); dim_.push_back(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) { // make sure dim_ vector is initialized if (dim_.empty()) { dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(that.size(), {0, 0}); } // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices\u0026#34;; throw std::runtime_error(ss.str()); } // if dimensions a not zero and do not match, skip move with error message. bool dims_nonzero = true; for (auto d : dim_) { if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; d[0] \u0026lt; 1) { dims_nonzero = false; break; } if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; d[1] \u0026lt; 1) { dims_nonzero = false; break; } } if (dims_nonzero) { bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); } if (!does_match) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrices of size \u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; dim_[k] = that.dim(k); } return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. bool dims_nonzero = true; for (auto d : dim_) { // if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; (d[0] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; (d[1] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // } // // if (dims_nonzero) { // bool does_match = true; // if (!(D == kMatFreeDim1)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); // } // // if (!(D == kMatFreeDim2)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); // } // // if (!does_match) { // this-\u0026gt;at(0).print(\u0026#34;this[0] = \u0026#34;); // that.at(0).print(\u0026#34;that[0] = \u0026#34;); // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformMatrixList element of size (\u0026#34; \u0026lt;\u0026lt; // that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols \u0026lt;\u0026lt; \u0026#34;) for an // element of size (\u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34;). // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Matrix\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(std::move(mats)) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) : vector(that) { (*this) = that; } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { std::array\u0026lt;size_t, 2\u0026gt; dim_k({this-\u0026gt;at(k).n_rows, this-\u0026gt;at(k).n_cols}); dim_.push_back(dim_k); } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim) { // change behavior based on free dim D if ((dim[0] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim1)) { dim[0] = this-\u0026gt;at(0).n_rows; } if ((dim[1] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim2)) { dim[1] = this-\u0026gt;at(0).n_cols; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Matrix\u0026amp; mat : *this) { if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_rows == dim[0]); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_cols == dim[1]); } if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(this-\u0026gt;size(), dim); for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { dim_[k][0] = (*this)[k].n_rows; dim_[k][1] = (*this)[k].n_cols; } } } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":73,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/","title":"ldsCtrlEst_h/lds_uniform_systems.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_systems.h # List of uniformly sized Systems. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformSystemList Detailed Description # This file provides a container for uniformly sized Systems.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector // namespace #include \u0026#34;lds.h\u0026#34; // System type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class UniformSystemList : public std::vector\u0026lt;System\u0026gt; { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;System\u0026gt;::vector; using std::vector\u0026lt;System\u0026gt;::operator=; using std::vector\u0026lt;System\u0026gt;::operator[]; using std::vector\u0026lt;System\u0026gt;::at; using std::vector\u0026lt;System\u0026gt;::begin; using std::vector\u0026lt;System\u0026gt;::end; using std::vector\u0026lt;System\u0026gt;::size; public: UniformSystemList() = default; explicit UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); explicit UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(const UniformSystemList\u0026amp; that); UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept; ~UniformSystemList() = default; const std::array\u0026lt;size_t, 3\u0026gt;\u0026amp; dim() const { return dim_; } size_t size() { return std::vector\u0026lt;System\u0026gt;::size(); }; const System\u0026amp; at(size_t n) { return std::vector\u0026lt;System\u0026gt;::at(n); }; void Swap(System\u0026amp; that, size_t n); UniformSystemList\u0026amp; operator=(const UniformSystemList\u0026amp; that); UniformSystemList\u0026amp; operator=(UniformSystemList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim); std::array\u0026lt;size_t, 3\u0026gt; dim_{}; }; template \u0026lt;typename System\u0026gt; inline void UniformSystemList\u0026lt;System\u0026gt;::Swap(System\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformSystemList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = (dim_[0] == that.n_u()) \u0026amp;\u0026amp; (dim_[1] == that.n_x()) \u0026amp;\u0026amp; (dim_[2] == that.n_y()); if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformSystemList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap System tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( const UniformSystemList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_[0] + dim_[1] + dim_[2]) { std::array\u0026lt;size_t, 3\u0026gt; other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign systems of size \u0026#34; \u0026lt;\u0026lt; dim_[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2] \u0026lt;\u0026lt; \u0026#34; with systems of size \u0026#34; \u0026lt;\u0026lt; other_dim[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other_dim[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2]; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( UniformSystemList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. if (dim_[0] + dim_[1] + dim_[2]) { // bool does_match = (dim_[0] == that.at(0).n_u()) \u0026amp;\u0026amp; // (dim_[1] == that.at(0).n_x()) \u0026amp;\u0026amp; // (dim_[2] == that.at(0).n_y()); // if (!does_match) { // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformSystemList element for an element of \u0026#34; // \u0026#34;different size. Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;System\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(std::move(systems)) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList( std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const UniformSystemList\u0026amp; that) : std::vector\u0026lt;System\u0026gt;(that) { (*this) = that; } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept : std::vector\u0026lt;System\u0026gt;(std::move(that)) { this-\u0026gt;dim_[0] = this-\u0026gt;at(0).n_u(); this-\u0026gt;dim_[1] = this-\u0026gt;at(0).n_x(); this-\u0026gt;dim_[2] = this-\u0026gt;at(0).n_y(); } template \u0026lt;typename System\u0026gt; void UniformSystemList\u0026lt;System\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim) { if (dim[0] + dim[1] + dim[2]) { dim_ = dim; } else { dim_[0] = this-\u0026gt;at(0).n_u(); dim_[1] = this-\u0026gt;at(0).n_x(); dim_[2] = this-\u0026gt;at(0).n_y(); } // make sure dimensiolaties are all uniform bool does_match(true); for (const System\u0026amp; sys : *this) { does_match = does_match \u0026amp;\u0026amp; (sys.n_u() == dim_[0]); does_match = does_match \u0026amp;\u0026amp; (sys.n_x() == dim_[1]); does_match = does_match \u0026amp;\u0026amp; (sys.n_y() == dim_[2]); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input systems are not uniform.\u0026#34;); } } } } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":74,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/","title":"ldsCtrlEst_h/lds_uniform_vecs.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_vecs.h # List of uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformVectorList Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H #define LDSCTRLEST_LDS_UNIFORM_VECS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { class UniformVectorList : public std::vector\u0026lt;Vector\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Vector\u0026gt;::vector; using std::vector\u0026lt;Vector\u0026gt;::operator=; using std::vector\u0026lt;Vector\u0026gt;::operator[]; using std::vector\u0026lt;Vector\u0026gt;::at; using std::vector\u0026lt;Vector\u0026gt;::begin; using std::vector\u0026lt;Vector\u0026gt;::end; using std::vector\u0026lt;Vector\u0026gt;::size; public: UniformVectorList() = default; explicit UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim = 0); explicit UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim = 0); UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim = 0); UniformVectorList(const UniformVectorList\u0026amp; that); UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept; ~UniformVectorList() = default; size_t dim() const { return dim_; } size_t size() { return std::vector\u0026lt;Vector\u0026gt;::size(); }; const Vector\u0026amp; at(size_t n) { return std::vector\u0026lt;Vector\u0026gt;::at(n); }; void Swap(Vector\u0026amp; that, size_t n); UniformVectorList\u0026amp; operator=(const UniformVectorList\u0026amp; that); UniformVectorList\u0026amp; operator=(UniformVectorList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(size_t dim); size_t dim_{}; }; inline void UniformVectorList::Swap(Vector\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = dim_ == that.n_elem; if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap Vector tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( const UniformVectorList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_) { size_t other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ \u0026lt;\u0026lt; \u0026#34; with vectors of size \u0026#34; \u0026lt;\u0026lt; other_dim; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( UniformVectorList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors. Skipping.\\n\u0026#34;; // return (*this); // } // // if (dim_) { // size_t other_dim(that.dim()); // if (dim_ != other_dim) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ // \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; other_dim \u0026lt;\u0026lt; \u0026#34;. // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Vector\u0026gt;::operator=(std::move(that)); return (*this); } } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":75,"href":"/lds-ctrl-est/docs/api/files/lds_8h/","title":"ldsCtrlEst_h/lds.h","section":"Files","content":" ldsCtrlEst_h/lds.h # lds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/)) observations.\nSource code # //===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_H #define LDSCTRLEST_LDS_H // #ifndef LDSCTRLEST // #include \u0026lt;ldsCtrlEst\u0026gt; // #endif #include \u0026lt;armadillo\u0026gt; namespace lds { using data_t = double; // may change to float (but breaks mex functions) using Vector = arma::Col\u0026lt;data_t\u0026gt;; using Matrix = arma::Mat\u0026lt;data_t\u0026gt;; using Cube = arma::Cube\u0026lt;data_t\u0026gt;; using View = arma::subview\u0026lt;data_t\u0026gt;; namespace fill = arma::fill; static const std::size_t kControlTypeDeltaU = 0x1; static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); static const data_t kPi = arma::datum::pi; static const data_t kDefaultP0 = 1e-6; static const data_t kDefaultQ0 = 1e-6; static const data_t kDefaultR0 = 1e-2; enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA }; enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2 }; // TODO(mfbolus): for SwitchedController, may want systems to have differing // numbers of states. Use this enum as template parameter? // enum SystemListFreeDim { // kSysFreeDimNone, // kSysFreeDimX ///\u0026lt; allow state dim (x) of systems in list to be hetero // }; // place hard limits on contents of vecors/mats void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub); void Limit(Vector\u0026amp; x, data_t lb, data_t ub); void Limit(Matrix\u0026amp; x, data_t lb, data_t ub); // in-place assign that errs if there are dimension mismatches: void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); // TODO(mfbolus): this is a fudge, but for some reason, cov mats often going // numerically asymm. void ForceSymPD(Matrix\u0026amp; X); void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min = 0); void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X); Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B); inline void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Vector\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Matrix\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if (other.n_elem != some.n_elem) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vector of size \u0026#34; \u0026lt;\u0026lt; some.n_elem \u0026lt;\u0026lt; \u0026#34; with vector of size \u0026#34; \u0026lt;\u0026lt; other.n_elem \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } inline void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrix of size \u0026#34; \u0026lt;\u0026lt; some.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; some.n_cols \u0026lt;\u0026lt; \u0026#34; with matrix of size \u0026#34; \u0026lt;\u0026lt; other.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other.n_cols \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } } // namespace lds #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":76,"href":"/lds-ctrl-est/docs/api/files/mex__c__util_8h/","title":"ldsCtrlEst_h/mex_c_util.h","section":"Files","content":" ldsCtrlEst_h/mex_c_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) More\u0026hellip;\nNamespaces # Name armamexc arma/mex interface using Matlab C API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab/Octave\u0026rsquo;s C mex API.\nSource code # //===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXC_UTIL_H #define LDSCTRLEST_MEXC_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.h\u0026#34; // // If Matlab_FOUND, include matrix.h. // // (Octave does not need/have it.) // #ifdef Matlab_FOUND // #include \u0026#34;matrix.h\u0026#34; // #endif namespace armamexc { template \u0026lt;class T\u0026gt; inline auto m2T_scalar(const mxArray *matlab_scalar) -\u0026gt; T { if (mxGetData(matlab_scalar)) { return static_cast\u0026lt;T\u0026gt;(mxGetScalar(matlab_scalar)); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return 0; } template \u0026lt;class T\u0026gt; inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false, bool strict = true) -\u0026gt; arma::Mat\u0026lt;T\u0026gt; { if (mxGetData(matlab_mat)) { const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat); if (n_dim == 2) { return arma::Mat\u0026lt;T\u0026gt;(static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)), mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem, strict); } mexErrMsgTxt(\u0026#34;Number of dimensions must be 2.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } // TODO(mfbolus): make these templated. template \u0026lt;typename T\u0026gt; inline auto a2m_mat(arma::Mat\u0026lt;T\u0026gt; const \u0026amp;arma_mat) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_mat.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Mat.\u0026#34;); return nullptr; } template \u0026lt;typename T\u0026gt; inline auto a2m_vec(arma::Col\u0026lt;T\u0026gt; const \u0026amp;arma_vec) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_vec.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Col.\u0026#34;); return nullptr; } } // namespace armamexc #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":77,"href":"/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/","title":"ldsCtrlEst_h/mex_cpp_util.h","section":"Files","content":" ldsCtrlEst_h/mex_cpp_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) More\u0026hellip;\nNamespaces # Name armamexcpp arma/mex interface using Matlab C++ API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab\u0026rsquo;s C++ mex API.\nSource code # //===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXCPP_UTIL_H #define LDSCTRLEST_MEXCPP_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.hpp\u0026#34; #include \u0026#34;mexAdapter.hpp\u0026#34; namespace armamexcpp { template \u0026lt;class T\u0026gt; std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; m2a_cellmat(matlab::data::CellArray\u0026amp; matlab_cell) { size_t n_cells = matlab_cell.getNumberOfElements(); std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; arma_mat(n_cells, arma::Mat\u0026lt;T\u0026gt;(1, 1, arma::fill::zeros)); for (size_t k = 0; k \u0026lt; n_cells; k++) { matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = matlab_cell[k]; auto dims = matlab_mat.getDimensions(); arma_mat[k] = arma::Mat\u0026lt;T\u0026gt;(matlab_mat.release().get(), dims[0], dims[1]); } return arma_mat; }; template \u0026lt;class T\u0026gt; std::vector\u0026lt;T\u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt;T\u0026gt;\u0026amp; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); T* ptr = matlab_array.release().get(); std::vector\u0026lt;T\u0026gt; vec(ptr, ptr + n_elem); return vec; }; template \u0026lt;class T\u0026gt; arma::Col\u0026lt;T\u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); // T* ptr = matlab_array.release().get(); // arma::Col\u0026lt;T\u0026gt; vec(ptr, n_elem); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? arma::Col\u0026lt;T\u0026gt; vec(n_elem, arma::fill::zeros); for (size_t k = 0; k \u0026lt; n_elem; k++) { vec[k] = matlab_array[k]; } return vec; }; template \u0026lt;class T\u0026gt; arma::Mat\u0026lt;T\u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { // ArrayDimensions == std::vector\u0026lt;size_t\u0026gt; auto dims = matlab_array.getDimensions(); // T* ptr = matlab_array.release().get(); // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false) // arma::Mat\u0026lt;T\u0026gt; mat(ptr, dims[0], dims[1]); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? // // armadillo and matlab both use column-major ordering, so this should work: size_t n_elem = dims[0] * dims[1]; arma::Mat\u0026lt;T\u0026gt; mat(dims[0], dims[1], arma::fill::zeros); size_t k(0); for (auto m: matlab_array) { mat[k] = m; k++; } return mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_mat(const arma::Mat\u0026lt;T\u0026gt;\u0026amp; arma_mat, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(), arma_mat.memptr() + arma_mat.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_vec(const arma::Col\u0026lt;T\u0026gt;\u0026amp; arma_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;({arma_vec.n_elem, 1}, arma_vec.memptr(), arma_vec.memptr() + arma_vec.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; s2m_vec(const std::vector\u0026lt;T\u0026gt;\u0026amp; std_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size()); return matlab_mat; }; } // namespace armamexcpp #endif Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":78,"href":"/lds-ctrl-est/docs/terminology/model/","title":"Models","section":"LDS C+E Documentation","content":" Model Definitions # This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form: \\[\\mathbf{x}_{t\u0026#43;1} = f\\left( \\mathbf{x}_{t}, \\mathbf{v}_{t} \\right) = \\mathbf{A} \\mathbf{x}_{t} \u0026#43; \\mathbf{B} \\mathbf{v}_{t} \u0026#43; \\mathbf{m}_{t} \u0026#43; \\mathbf{w}_{t}\\] \\[\\mathbf{y}_{t} = h\\left( \\mathbf{x}_{t} \\right)\\] t : time index x : system state v = g%u : input (e.g., in physical units used for model fit) u : control signal sent to actuator (e.g., in Volts) y : system output m : process disturbance w ~ N(0, Q) : process noise/disturbance A : state matrix B : input coupling matrix g : input gain (e.g., for converting to control signal actuator voltage) n.b., assumes this conversion is linear Q : process noise covariance % : element-wise multiplication LDS with Gaussian Observations # For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.\n\\[\\mathbf{y}_{t} = \\mathbf{C} \\mathbf{x}_{t} \u0026#43; \\mathbf{d}\\] \\[\\mathbf{z}_{t} \\sim \\mathcal{N}\\left(\\mathbf{y}_{t} , \\mathbf{R} \\right)\\] z : measurement C : output matrix d : output bias R : measurement noise covariance LDS with Poisson Observations # For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.\n\\[y_{t}^{i} = \\exp \\left(\\mathbf{c}^i \\mathbf{x}_{t} \u0026#43; d^i\\right)\\] \\[z_{t}^i \\sim \\rm{Poisson} \\left(y_{t}^i \\right)\\] i : output index z : measurement (count data) c : i^th row of output matrix (C) d : output bias Model Predictive Control (MPC) # Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.\nIn the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.\n"},{"id":79,"href":"/lds-ctrl-est/docs/api/modules/","title":"Modules","section":"LDS C+E Documentation","content":" Modules # Control Mode Bit Masks provides fill types for constructing new armadillo vectors, matrices\nDefaults\nUpdated on 5 March 2025 at 21:01:32 EST\n"},{"id":80,"href":"/lds-ctrl-est/docs/api/namespaces/","title":"Namespaces","section":"LDS C+E Documentation","content":" Namespaces # armamexc arma/mex interface using Matlab C API\narmamexcpp arma/mex interface using Matlab C++ API\nlds::gaussian Linear Dynamical Systems with Gaussian observations.\nlds::poisson Linear Dynamical Systems with Poisson observations.\nstd\nUpdated on 5 March 2025 at 21:01:32 EST\n"},{"id":81,"href":"/lds-ctrl-est/docs/api/pages/","title":"Pages","section":"LDS C+E Documentation","content":" Pages # Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":82,"href":"/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/","title":"PLDS State Estimation","section":"LDS C+E Examples","content":" PLDS State Estimation Tutorial # This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[x_{t\u0026#43;1} = x_t \u0026#43; w_t\\] \\[y_{t} = \\exp\\left(x_t\\right)\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state Finally, assign the parameters using corresponding set-methods.\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); Creating the estimator # Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. To ensure robust estimates, adaptively re-estimate the process disturbance.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); Simulating estimation # In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.\n// Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.\nWith this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.\n"},{"id":83,"href":"/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/","title":"PLDS Switched Control","section":"LDS C+E Examples","content":" PLDS Switched Control Tutorial # This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.\nIn the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating the simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); The system\u0026rsquo;s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 Initially, the system will be in \u0026ldquo;mode\u0026rdquo; 1, where B = b1.\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.\nCreating the controller # Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.\nSimilar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.\nMoreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.\nPutting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying systems: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nNow that the SwitchedController is instantiated, assign its parameters.\n// Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).\nThe control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.\n// Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system\u0026rsquo;s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.\nExample simulation result # Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller\u0026rsquo;s online estimates of the output and state are shown in purple.\nNote that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.\n"},{"id":84,"href":"/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/","title":"src","section":"Files","content":" src # Files # Name src/lds.cpp misc lds namespace functions src/lds_gaussian_sys.cpp GLDS base type. src/lds_poisson_sys.cpp PLDS base type. src/lds_sys.cpp LDS base type. src/lds_uniform_vecs.cpp Uniformly sized vectors. Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":85,"href":"/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/","title":"src/lds_gaussian_sys.cpp","section":"Files","content":" src/lds_gaussian_sys.cpp # GLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_gaussian_sys.h\u0026gt; lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0, data_t r0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { R_.zeros(n_y, n_y); R_.diag().fill(r0); do_recurse_Ke_=true; }; // recursively estimate Ke void lds::gaussian::System::RecurseKe() { if (!do_recurse_Ke_) { return; } // predict covariance P_ = A_ * P_ * A_.t() + Q_; // calc Kalman gain Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_); // update covariance // Reference: Ghahramani et Hinton (1996) P_ = P_ - Ke_ * C_ * P_; if (do_adapt_m) { P_m_ += Q_m_; // A_m = I (i.e., random walk) Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_); P_m_ = P_m_ - Ke_m_ * C_ * P_m_; } } // Simulate const lds::Vector\u0026amp; lds::gaussian::System::Simulate(const Vector\u0026amp; u_tm1){ f(u_tm1, true);//simulate dynamics with noise added h();//output z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure return z_; } void lds::gaussian::System::Print() { lds::System::Print(); std::cout \u0026lt;\u0026lt; \u0026#34;R: \\n\u0026#34; \u0026lt;\u0026lt; R_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":86,"href":"/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/","title":"src/lds_poisson_sys.cpp","section":"Files","content":" src/lds_poisson_sys.cpp # PLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_poisson_sys.h\u0026gt; lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { diag_y_ = diagmat(y_); pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(0); }; // Correct: Given measurement (z) and current input (u), update estimate of the // state, covar, output. // // see Eden et al. 2004 void lds::poisson::System::RecurseKe() { // predict covariance P_ = A_ * P_ * A_.t() + Q_; // update cov P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_); Ke_ = P_ * C_.t(); if (do_adapt_m) { P_m_ += Q_m_; // predict (A_m = I) P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_); // update Ke_m_ = P_m_ * C_.t(); } } // Simulate Measurement: z ~ Poisson(y) const lds::Vector\u0026amp; lds::poisson::System::Simulate(const Vector\u0026amp; u_tm1) { f(u_tm1, true); // simulate dynamics with noise added h(); // output z_.zeros(); for (std::size_t k = 0; k \u0026lt; n_y_; k++) { // construct a Poisson distribution object with mean y[k] pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(y_[k]); // pull random sample from this distribution z_[k] = pd_(rng); } return z_; } // ******************* SYS_T ******************* Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":87,"href":"/lds-ctrl-est/docs/api/files/lds__sys_8cpp/","title":"src/lds_sys.cpp","section":"Files","content":" src/lds_sys.cpp # LDS base type. More\u0026hellip;\nDetailed Description # This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- lds_sys.cpp - LDS -------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_sys.h\u0026gt; #include \u0026lt;vector\u0026gt; lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) { InitVars(p0, q0); } void lds::System::InitVars(data_t p0, data_t q0) { // initial conditions. x0_ = Vector(n_x_, fill::zeros); // includes bias (nY) and g (nU) P0_ = p0 * Matrix(n_x_, n_x_, fill::eye); m0_ = x0_; P0_m_ = P0_; // signals x_ = x0_; P_ = P0_; m_ = m0_; P_m_ = P0_m_; y_ = Vector(n_y_, fill::zeros); cx_ = Vector(n_y_, fill::zeros); z_ = Vector(n_y_, fill::zeros); // By default, random walk where each state is independent // In this way, provides independent estimates of rate per channel of output. A_ = Matrix(n_x_, n_x_, fill::eye); B_ = Matrix(n_x_, n_u_, fill::zeros); g_ = Vector(n_u_, fill::ones); Q_ = q0 * Matrix(n_x_, n_x_, fill::eye); Q_m_ = Q_; C_ = Matrix(n_y_, n_x_, fill::eye); // each state will map to an output by d_ = Vector(n_y_, fill::zeros); Ke_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain. Ke_m_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain for m adaptation. do_adapt_m = false; } // Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state // and update estimate of the state, covar, output using Kalman filter void lds::System::Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z_t) { // predict mean f(u_tm1); // dynamics h(); // output // recursively calculate esimator gains (or just keep existing values) // (also predicts+updates estimate covariance) RecurseKe(); // update x_ += Ke_ * (z_t - y_); if (do_adapt_m) { m_ += Ke_m_ * (z_t - y_); // adaptively estimating disturbance } // With new state, estimate output. h(); // --\u0026gt; posterior } void lds::System::Reset() { // reset to initial conditions x_ = x0_; // mean P_ = P0_; // cov of state estimate m_ = m0_; // process disturbance P_m_ = P0_m_; // cov of disturbance estimate h(); } std::vector\u0026lt;lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt;\u0026gt; lds::System::nstep_pred_block(lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; u, lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; z, size_t n_pred) { lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_filt; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_pred; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; y_pred; for (size_t k = 0; k \u0026lt; u.size(); k++) { Reset(); size_t n_t = arma::size(u[k])[1]; Matrix x_filt_k(n_x_, n_t, fill::zeros); Matrix x_pred_k(n_x_, n_t - n_pred, fill::zeros); Matrix y_pred_k(n_y_, n_t - n_pred, fill::zeros); for (size_t t = 0; t \u0026lt; n_t - n_pred; t++) { Vector x_pred_ahead = x_; for (size_t t_u = t; t_u \u0026lt; t + n_pred; t_u++) { x_pred_ahead = A_ * x_pred_ahead + B_ * u[k].col(t_u); } x_pred_k.col(t) = x_pred_ahead; y_pred_k.col(t) = h_(x_pred_ahead); if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; // given previous measurment } for (size_t t = n_t - n_pred; t \u0026lt; n_t; t++) { if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; } x_filt.append(x_filt_k); x_pred.append(x_pred_k); y_pred.append(y_pred_k); } return {x_filt, x_pred, y_pred}; } void lds::System::Print() { std::cout \u0026lt;\u0026lt; \u0026#34;\\n ********** SYSTEM ********** \\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;x: \\n\u0026#34; \u0026lt;\u0026lt; x_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;P: \\n\u0026#34; \u0026lt;\u0026lt; P_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;A: \\n\u0026#34; \u0026lt;\u0026lt; A_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B: \\n\u0026#34; \u0026lt;\u0026lt; B_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;g: \\n\u0026#34; \u0026lt;\u0026lt; g_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;m: \\n\u0026#34; \u0026lt;\u0026lt; m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q: \\n\u0026#34; \u0026lt;\u0026lt; Q_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q_m: \\n\u0026#34; \u0026lt;\u0026lt; Q_m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d: \\n\u0026#34; \u0026lt;\u0026lt; d_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;C: \\n\u0026#34; \u0026lt;\u0026lt; C_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;y: \\n\u0026#34; \u0026lt;\u0026lt; y_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } //******************* SYS_T ******************* Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":88,"href":"/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/","title":"src/lds_uniform_vecs.cpp","section":"Files","content":" src/lds_uniform_vecs.cpp # Uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_uniform_vecs.h\u0026gt; namespace lds { UniformVectorList::UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); } UniformVectorList::UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim) : vector(std::move(vecs)) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(const UniformVectorList\u0026amp; that) : vector(that) { (*this) = that; } UniformVectorList::UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { this-\u0026gt;dim_ = this-\u0026gt;at(0).n_elem; } void UniformVectorList::CheckDimensions(size_t dim) { if (dim) { dim_ = dim; } else { dim_ = this-\u0026gt;at(0).n_elem; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Vector\u0026amp; vec : *this) { does_match = does_match \u0026amp;\u0026amp; (vec.n_elem == dim_); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } } } // namespace lds Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":89,"href":"/lds-ctrl-est/docs/api/files/lds_8cpp/","title":"src/lds.cpp","section":"Files","content":" src/lds.cpp # misc lds namespace functions More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file implements miscellaneous lds namespace functions not bound to a class.\nSource code # //===-- lds.cpp - LDS -----------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds.h\u0026gt; // insert any necessary function definitions here. namespace lds { void ForceSymPD(Matrix\u0026amp; X) { if (X.is_sympd() || !X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; // for eigenval decomp bool did_succeed(true); Vector d; Matrix u; // see first method (which may not be ideal): // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/ size_t k(1); bool is_sympd = X.is_sympd(); Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye); while (!is_sympd) { if (k \u0026gt; 100) { did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); data_t min_eig = arma::min(d); std::cerr \u0026lt;\u0026lt; \u0026#34;After multiple iterations, min eigen val = \u0026#34; \u0026lt;\u0026lt; min_eig \u0026lt;\u0026lt; \u0026#34;.\\n\u0026#34;; throw std::runtime_error( \u0026#34;Failed to make matrix symmetric positive definite.\u0026#34;); return; } // Limit(d, arma::eps(0), kInf); // force to be positive... // Matrix d_diag = arma::diagmat(d); // X = u * d_diag * u.t(); did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymPD failed.\u0026#34;); } data_t min_eig = arma::min(d); X += id * abs(min_eig) + arma::datum::eps; // make sure symm: X = (X + X.t()) / 2; // double check eigenvals positive after symmetrizing: arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); min_eig = arma::min(d); is_sympd = min_eig \u0026gt; 0; k++; } } void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min) { if (!X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; bool did_succeed(true); Vector d; Matrix u; did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymMinEig failed.\u0026#34;); } Limit(d, eig_min + arma::eps(eig_min), kInf); // enforce lower bound Matrix d_diag = arma::diagmat(d); X = u * d_diag * u.t(); // double check symmetric X = (X + X.t()) / 2; } void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X) { bool did_succeed(true); did_succeed = arma::qr_econ(Qt, L, X.t()); if (!did_succeed) { throw std::runtime_error(\u0026#34;LQ decomposition failed.\u0026#34;); } arma::inplace_trans(L); arma::inplace_trans(Qt); } Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B) { // subtract out mean auto m_a = arma::mean(A, 1); Matrix a0 = A; a0.each_col() -= m_a; auto m_b = arma::mean(B, 1); Matrix b0 = B; b0.each_col() -= m_b; Matrix cov = a0 * b0.t() / a0.n_cols; return cov; } } // namespace lds Updated on 5 March 2025 at 21:01:32 EST\n"},{"id":90,"href":"/lds-ctrl-est/docs/api/namespaces/namespacestd/","title":"std","section":"Namespaces","content":" std # Updated on 5 March 2025 at 21:01:32 EST\n"}]
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+[{"id":0,"href":"/docs/","title":"LDS C+E Documentation","section":"LDS Control \u0026 Estimation","content":" LDS Control \u0026amp; Estimation Documentation # "},{"id":1,"href":"/docs/tutorials/","title":"LDS C+E Examples","section":"LDS C+E Documentation","content":" Examples # "},{"id":2,"href":"/acknowledgements/","title":"Acknowledgements","section":"LDS Control \u0026 Estimation","content":" Acknowledgements # Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.\n"},{"id":3,"href":"/docs/getting-started/getting-started/","title":"Getting Started","section":"LDS C+E Documentation","content":" Getting Started # This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.\nldsCtrlEst requires Armadillo for linear algebra as well as HDF5 for saving output. vcpkg is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.\nTested Configurations # Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the RelWithDebInfo build type in the CMake configure command (-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo):\nUbuntu 18.04 with GCC 7.5 compiler macOS 11 (Big Sur) with Apple Clang 12 compiler Windows 10 with Visual Studio 16.11 (2019 release) and Clang 12 compiler That being said, if you want to debug a build for a single platform, here are some things you can try:\nUse different compilers (or even different versions of a single compiler) Use different versions of vcpkg (which you can control by checking out a different commit in the vcpkg submodule) Mac Pre-requisities # Xcode Command Line Tools will get you clang, gcc, make, and git:\nxcode-select --install Homebrew is \u0026ldquo;The Missing Package Manager for macOS\u0026rdquo; which will make installing lots of things easy. Install like this:\n/bin/bash -c \u0026#34;$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\u0026#34; You can then use it to install CMake, gfortran, and pkg-config:\nbrew install cmake gfortran pkg-config Linux Pre-requisites # You\u0026rsquo;ll need Git, CMake, GCC, gfortran, etc.\nsudo apt install git cmake pkg-config gfortran curl zip unzip tar build-essential ninja-build Windows Installation # Look here for Windows-specific instructions.\nDownloading the Library # First, clone the repository along with submodules:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est\rgit submodule update --init Compilation + Installation # Now generate the cache and build using your IDE or from the command line as follows.\nmkdir build \u0026amp;\u0026amp; cd build\rcmake ..\rcmake --build . The first time, vcpkg will automatically install dependencies into [build directory]/vcpkg_installed/, which will likely take about 10-20 minutes.\nIf you want to use vcpkg set up somewhere besides this repo\u0026rsquo;s submodule, add -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake to the cmake command directly or through your IDE\u0026rsquo;s settings.\nYou can verify the build is working by running ctest from the build folder, which runs all the example scripts.\nOptions # This project is configured/compiled/installed by way of CMake and (on Unix-based operating systems) GNU Make. For configuration with CMake, there are three available options.\nLDSCTRLEST_BUILD_EXAMPLES : [default=ON] whether to build example programs located under examples/ in the source tree LDSCTRLEST_BUILD_FIT : [default=ON] whether to build the auxiliary fitting portion of the source code that is not pertinent to control implementation LDSCTRLEST_BUILD_STATIC : [default=ON] whether to statically link against OpenBLAS and create a static ldsCtrlEst library for future use n.b., If both options 2 and 3 are enabled, Matlab/Octave mex functions will be compiled for exposing some of the fitting functionality to Matlab/Octave, assuming these programs are installed.\nBelow are example usages of cmake to configure/build the library.\nFor basic project build \u0026amp; install\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake .. #configure build cmake --build #build the project sudo make install #[optional] installs to default location (OS-specific) To set the install prefix\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DCMAKE_INSTALL_PREFIX=/your/install/prefix .. #configure build with chosen install location cmake --build #build the project make install #install to /your/install/prefix To build the bare bones project, excluding fit code and Matlab mex code.\ncd /path/to/repository mkdir build \u0026amp;\u0026amp; cd build cmake -DLDSCTRLEST_BUILD_FIT=0 .. #configure not to build the fitting portion of library make #build the project n.b., If you choose not to install the library or install it to the non-default location, ensure you have updated the following environment variables on Unix-based operating systems.\nLD_LIBRARY_PATH: search path for dynamically loaded libraries PKG_CONFIG_PATH: search path for pkg-config tool On Windows, you may need to add the build location to the PATH environment variable for the library to be used elsewhere.\nPython bindings package ldsctrlest # With the LDSCTRLEST_BUILD_PYTHON setting (off by default) and the pybind11 submodule initialized, you can build Python bindings. You will probably want to specify the installation of Python to use by adding a -DPython3_ROOT_DIR=[path/to/install/dir] argument to the CMake cache generation command (the first one) so CMake doesn\u0026rsquo;t use an undesired version. That environment needs to have NumPy installed.\ncmake --build . --target python_modules The bindings need to be generated just once per Python version. Once the build is complete, navigate to the [build location]/python folder and run pip install . to make it importable anywhere for your current environment. The file structure only works correctly for this if you use a single-config generator like Ninja or Make, though. You can verify the installation was successful by running pytest from the build/python directory (pip install pytest matplotlib first if you need to).\nSee python/ldsctrlest/README.md for usage details.\nAlso, beware that a single build will probably not work for both the standalone library and the Python package, since the conversion between NumPy and Armadillo alters the way Armadillo allocates memory. In this case you may want to build once with -DLDSCTRLEST_BUILD_PYTHON=ON, install the package, then again with -DLDSCTRLEST_BUILD_PYTHON=OFF for the pure C++ build to work correctly.\nCommon issues # \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; If cmake and/or pkg-config cannot find the required configuration files for your project to link against ldsCtrlEst, make sure that these utilities know to look for them in the non-default location where you installed the library. For cmake this means adding your chosen install prefix to the environment variable CMAKE_PREFIX_PATH. Similarly, for pkg-config you need to add your/install/prefix/lib/pkgconfig to its search path, PKG_CONFIG_PATH. Assuming a Unix shell whose login startup file is ~/.profile and ldsCtrlEst was installed using prefix your/install/prefix, add the following to .profile.\nexport CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:/your/install/prefix export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/your/install/prefix vcpkg fails on configuration Try running ./bootstrap-vcpkg from the vcpkg folder and try again. If that doesn\u0026rsquo;t work, try updating vcpkg to a newer version (in the source control tab, click on the commit hash by the vcpkg repo then select from the dropdown) and running boostsrap-vcpkg again. You can also try upgrading your system (e.g., apt update, apt upgrade).\nCould not find Python3 (missing: Python3_NumPy_INCLUDE_DIRS NumPy)\nMake sure NumPy is installed in the Python environment you specified. If CMake still can\u0026rsquo;t find it, you may need to tell CMake exactly where to find it by adding an argument to the configure command: -DPython3_NumPy_INCLUDE_DIR=.... You can find that location like this: python -c 'import numpy; print(numpy.get_include())'\n"},{"id":4,"href":"/docs/getting-started/windows/","title":"Windows","section":"LDS C+E Documentation","content":" Windows Installation # Windows Pre-requisites # Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:\nSet-ExecutionPolicy RemoteSigned -Scope CurrentUser # Optional: Needed to run a remote script the first time iwr get.scoop.sh | Invoke-Expression Install Git and CMake if you don\u0026rsquo;t already have them:\nscoop install git cmake If that didn\u0026rsquo;t work, follow more detailed instructions here.\nThe easiest way to compile C++ project on Windows is with Visual Studio\u0026rsquo;s build tools, which you can download here (or here for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on \u0026ldquo;Desktop development with C++.\u0026rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the \u0026ldquo;Installation details\u0026rdquo; sidebar under optional features.\nAnd the easiest way to use Visual Studio\u0026rsquo;s build tools is with VS Code, along with the CMake Tools extension. Install them and you should be ready to go.\nDownloading the Library # First, clone the repository, either from VS Code or the command line:\ngit clone https://github.com/cloctools/lds-ctrl-est.git cd lds-ctrl-est You\u0026rsquo;ll need to initialize the submodules from the command line after the repo is cloned:\ngit submodule update --init Installation # When you open the folder in VS Code, you will like be prompted by the CMake Tools extension to configure the project. Make sure you select the kit (you\u0026rsquo;ll be prompted when you configure\u0026ndash;else there\u0026rsquo;s an icon in the bar on the bottom of the window or type Ctrl+Shift+P, then \u0026ldquo;cmake select kit\u0026rdquo;). Choose Clang [latest version] with GNU CLI ... amd64 assuming you are running a 64-bit OS. (MSVC may work okay too if you don\u0026rsquo;t need to build Python bindings.)\nFollow along with the \u0026ldquo;Getting Started\u0026rdquo; instructions, but where you see config options specified as -DLDSCTREST_BUILD_STATIC=OFF or -DPython3_ROOT_DIR=..., you will enter those in settings: open with Ctrl+,, click \u0026ldquo;workspace\u0026rdquo;, then search for \u0026ldquo;CMake: Configure Args\u0026rdquo; and enter each of your desired arguments as a separate item.\nTo configure, use Ctrl+Shift+P and search for the \u0026ldquo;CMake: Configure\u0026rdquo; command. To build, click the \u0026ldquo;Build\u0026rdquo; button on the bottom bar. Then click the \u0026ldquo;CTest\u0026rdquo; button to run the example scripts.\nConsiderations # Development on Windows has been more prone to bugs than on Unix systems, so if you encounter many problems, consider switching—WSL (Windows Subsystem for Linux) is a good option for Windows users who don\u0026rsquo;t want to work on a different machine.\nCompilation has been successfully tested in VS Code using the following kit, using the \u0026ldquo;RelWithDebInfo\u0026rdquo; config:\nClang 12.0.0 (GNU CLI) for MSVC 16.11.31702.278 (Visual Studio Community 2019 Release - amd64) Troubleshooting # The build appears to work, but tests fail with code 0xc0000135 OR \u0026ldquo;I have built the library and installed it in a non-default location. In building my own project linking against ldsCtrlEst, cmake or pkg-config cannot find the library or its configuration information.\u0026rdquo; Have you installed the library? In VS Code, use Shift+F7 to build a specific target, in this case INSTALL. If that doesn\u0026rsquo;t solve your problem, you will likely need to add the build or install folder to your PATH environment variable, which you can do using the settings GUI (search for \u0026ldquo;Edit the system environment variables\u0026rdquo;).\nOn Windows, \u0026ldquo;Generate CMake Cache\u0026rdquo; step errs because creating symbolic links is not permitted. Certain source files are sym-linked to the build/install directories during configuration with cmake. As such, your user in Windows must be permitted to do so. Make sure that your user is listed next to Control Panel -\u0026gt; Administrative Tools -\u0026gt; Local Policies -\u0026gt; User Rights Assignment -\u0026gt; Create Symbolic Links.\n"},{"id":5,"href":"/issues-contributing/","title":"Issues Contributing","section":"LDS Control \u0026 Estimation","content":" Reporting Issues # If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.\nContributing # We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.\n"},{"id":6,"href":"/docs/api/namespaces/namespacearmamexc/","title":"armamexc","section":"Namespaces","content":" armamexc # arma/mex interface using Matlab C API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; T m2T_scalar(const mxArray * matlab_scalar)\nConvert Matlab mxArray to scalar of type T. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true)\nConvert matlab matrix to armadillo. template \u0026lt;typename T \u0026gt; mxArray * a2m_mat(arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat)\nConvert armadillo to matlab matrix. template \u0026lt;typename T \u0026gt; mxArray * a2m_vec(arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec)\nConvert armadillo to matlab vector. Detailed Description # Utilities for arma/mex interface using Matlab C API\nFunction Details # m2T_scalar # template \u0026lt;class T \u0026gt; inline T m2T_scalar( const mxArray * matlab_scalar ) Parameters:\nmatlab_scalar matlab scalar Template Parameters:\nT type Return: scalar of type T\nm2a_mat # template \u0026lt;class T \u0026gt; inline arma::Mat\u0026lt; T \u0026gt; m2a_mat( const mxArray * matlab_mat, bool copy_aux_mem =false, bool strict =true ) Parameters:\nmatlab_mat matlab matrix copy_aux_mem [optional] whether to copy auxiliary memory strict [optional] strictly enforce the above Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_mat( arma::Mat\u0026lt; T \u0026gt; const \u0026amp; arma_mat ) Parameters:\narma_mat armadillo matrix Return: matlab matrix\na2m_vec # template \u0026lt;typename T \u0026gt; inline mxArray * a2m_vec( arma::Col\u0026lt; T \u0026gt; const \u0026amp; arma_vec ) Parameters:\narma_vec armadillo vector Return: matlab vector\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":7,"href":"/docs/api/namespaces/namespacearmamexcpp/","title":"armamexcpp","section":"Namespaces","content":" armamexcpp # arma/mex interface using Matlab C++ API More\u0026hellip; Functions # Name template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat(matlab::data::CellArray \u0026amp; matlab_cell)\nConvert matlab cell array to vector of armadillo matrices. template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array)\nConvert matlab matrix to a vector of scalars. template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo vector. template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array)\nConvert matlab to armadillo matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat(const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab matrix. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec(const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert armadillo to matlab vector. template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec(const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory)\nConvert vector of scalar T to matlab matrix. Detailed Description # utilities for arma/mex interface using Matlab C++ API\nFunction Details # m2a_cellmat # template \u0026lt;class T \u0026gt; std::vector\u0026lt; arma::Mat\u0026lt; T \u0026gt; \u0026gt; m2a_cellmat( matlab::data::CellArray \u0026amp; matlab_cell ) Parameters:\nmatlab_cell matlab cell Template Parameters:\nT type Return: vector of armadillo matrices of type T\nm2s_vec # template \u0026lt;class T \u0026gt; std::vector\u0026lt; T \u0026gt; m2s_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; \u0026amp; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: vector of type T\nm2a_vec # template \u0026lt;class T \u0026gt; arma::Col\u0026lt; T \u0026gt; m2a_vec( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab array Template Parameters:\nT type Return: armadillo vector of type T\nm2a_mat # template \u0026lt;class T \u0026gt; arma::Mat\u0026lt; T \u0026gt; m2a_mat( matlab::data::TypedArray\u0026lt; T \u0026gt; matlab_array ) Parameters:\nmatlab_array matlab matrix Template Parameters:\nT type Return: armadillo matrix of type T\na2m_mat # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_mat( const arma::Mat\u0026lt; T \u0026gt; \u0026amp; arma_mat, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_mat arma matrix factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\na2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; a2m_vec( const arma::Col\u0026lt; T \u0026gt; \u0026amp; arma_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\narma_vec armadillo vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\ns2m_vec # template \u0026lt;class T \u0026gt; matlab::data::TypedArray\u0026lt; T \u0026gt; s2m_vec( const std::vector\u0026lt; T \u0026gt; \u0026amp; std_vec, matlab::data::ArrayFactory \u0026amp; factory ) Parameters:\nstd_vec standard vector factory matlab \u0026ldquo;array factory\u0026rdquo; Template Parameters:\nT type Return: matlab matrix\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":8,"href":"/docs/terminology/control-estimation/","title":"C\u0026E","section":"LDS C+E Documentation","content":" Control \u0026amp; Estimation # The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., filtering). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.\nState estimation # In general, the filtering performed to estimate the underlying state proceeds recursively by first using the model dynamics to predict the state change at the next time step, followed by updating this prediction when a new measurement is available. For a LDS, this two-step process can be summarized by \\[\\widehat{\\mathbf{x}}_{t|t-1} = \\mathbf{A}\\widehat{\\mathbf{x}}_{t-1|t-1} \u0026#43; \\mathbf{B} u_{t-1} \u0026#43; \\mathbf{m}_{t-1} \\;,\\] \\[\\widehat{\\mathbf{x}}_{t|t} = \\widehat{\\mathbf{x}}_{t|t-1} \u0026#43; \\mathbf{K}^{\\rm e}_t \\left(\\mathbf{z}_t - \\widehat{\\mathbf{y}}_{t|t-1}\\right)\\;,\\] where \\( \\hat{\\left(\\cdot\\right)}_{t|j} \\) indicates an estimate at time \\( t \\) given data up to time \\( j \\) inclusive, \\( \\mathbf{K}^{\\rm e} \\) is the estimator gain, and\n\\[ \\widehat{\\mathbf{y}}_{t|t-1} = h\\left( \\widehat{\\mathbf{x}}_{t|t-1} \\right) \\; .\\] In the case of GLDS models, the estimator gain (called Ke in library) is calculated recursively by Kalman filtering, which requires knowledge of the process noise and measurement noise covariances (Q, R) in addition to the system matrices. For time-invariant GLDS models, the infinite horizon solution is often used, so this gain need not be time-varying. Users may instead set its pre-determined value with the lds::gaussian::System::set_Ke mutator.\nIn the case of PLDS models, there is an analogue of the Kalman filter developed for dynamical systems with point-process observations (Eden et al. 2004). This nonlinear filter recursively updates Ke at each time step and requires an estimate of the process noise covariance (Q) as well.\nAdaptive estimation of process disturbance # Both the Kalman filter and point-process analogue are model-based; therefore, their performance can be sensitive to model mismatch, whether this be imperfect model fitting or true drifts in system behavior. A practical approach to improving robustness is parameter adaptation. To that end, this library provides dual state-parameter estimation. Specifically, an additive process disturbance (m) is adaptively re-estimated when the lds::System::do_adapt_m property is set to true. This effectively provides integral action on minimizing state estimation error that could either be due to model mismatch or a true disturbance.\nWhen parameter adaptation is enabled, this process disturbance is assumed to vary stochastically on a random walk \\[\\mathbf{m}_{t} = \\mathbf{m}_{t-1} \u0026#43; \\mathbf{w}^m_{t-1} \\;,\\] where \\( \\mathbf{w}^m \\sim \\mathcal{N}\\left(0, \\mathbf{Q}_m\\right)\\) . Kalman filtering or the point-process analogue are then used to estimate this disturbance in parallel with the state.\nControl # Given the estimated state, the controller updates the inputs to the system according to the following law: \\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] where \\( \\left( \\cdot \\right)^{\\rm ref} \\) correspond to reference/target signals and \\( \\mathbf{K}^c_x \\) is the state feedback controller gain. Recall that these controller gains are assumed to have been designed before the experiment using, for example, LQR.\nIf users are employing integral action for more robust tracking at DC and did not use the approach of augmenting the state vector and system matrices accordingly, there is an option to include the integral term as\n\\[\\mathbf{u}_{t} = \\mathbf{u}^{\\rm ref}_t - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right) - \\mathbf{K}^c_{\\rm inty} \\sum_{j=1}^{t}\\left( \\widehat{\\mathbf{y}}_j - \\mathbf{y}^{\\rm ref}_j \\right) \\;.\\] An additional option available to users is a control law that updates the change in u,\n\\[\\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\left(\\mathbf{u}_{t-1} - \\mathbf{u}^{\\rm ref}_{t-1} \\right) - \\mathbf{K}^c_x \\left( \\widehat{\\mathbf{x}}_t - \\mathbf{x}^{\\rm ref}_t\\right)\\;,\\] \\[\\mathbf{u}_{t} = \\mathbf{u}_{t-1} \u0026#43; \\Delta\\mathbf{u}_{t} \\; .\\] Notice that this takes the form of a first-order difference equation for updating control (i.e., \\( \\Delta\\mathbf{u}_{t} = -\\mathbf{K}^c_u \\mathbf{u}_{t-1} \u0026#43; \\epsilon_{t-1} \\) ), effectively low-pass filtering the input depending on the characteristics of \\( \\mathbf{K}^c_u \\) . This can be useful in cases where users have designed the controller gains by LQR to minimize not the amplitude of the input, but the change in input, by augmenting the state vector with the input during LQR design.\nIntegral action and the \\( \\Delta \\mathbf{u} \\) control law can be combined. The library keeps track of the controller type by way of bit masks which can be bit-wise OR\u0026rsquo;d to use in combination.\nCalculating reference state-control from output # In cases where an output reference is supplied and the goal is to track either a static or slowly varying output, users do not have to produce \\( \\mathbf{x}^{\\rm ref} \\) and \\( \\mathbf{u}^{\\rm ref} \\) . Methods are provided for calculating the state and control that would be required to reach the reference output at steady state (lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference). This is achieved by linearly-constrained least squares. For single-output systems, it results in an exact solution; however, for multi-output problems it provides a least squares comprimise across outputs.\n"},{"id":9,"href":"/docs/api/classes/","title":"Classes","section":"LDS C+E Documentation","content":" Classes # lds::Controller\nlds::EM\nlds::Fit LDS Fit Type.\nlds::SSID\nlds::SwitchedController SwitchedController Type.\nlds::System Linear Dynamical System Type.\nlds::UniformMatrixList\nlds::UniformSystemList\nlds::UniformVectorList\nlds::gaussian::Controller Gaussian-observation Controller Type.\nlds::gaussian::Fit GLDS Fit Type.\nlds::gaussian::FitEM GLDS E-M Fit Type.\nlds::gaussian::FitSSID Subspace Identification (SSID) for GLDS.\nlds::gaussian::SwitchedController Gaussian-observation SwitchedController Type.\nlds::gaussian::System Gaussian LDS Type.\nlds::poisson::Controller PLDS Controller Type.\nlds::poisson::Fit PLDS Fit Type.\nlds::poisson::FitEM PLDS E-M Fit Type.\nlds::poisson::FitSSID Subspace Identification (SSID) for PLDS.\nlds::poisson::SwitchedController Poisson-observation SwitchedController Type.\nlds::poisson::System Poisson System type.\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":10,"href":"/docs/api/modules/group__control__masks/","title":"Control Mode Bit Masks","section":"Modules","content":" Control Mode Bit Masks # provides fill types for constructing new armadillo vectors, matrices More\u0026hellip; Attributes # Name const std::size_t kControlTypeDeltaU control designed to penalize change in input const std::size_t kControlTypeIntY control using integral action const std::size_t kControlTypeAdaptM adapt control setpoint with re-estimated disturbance m Detailed Description # Control mode bit masks. These can be bit-wise OR\u0026rsquo;d to use in combination.\nAttribute Details # kControlTypeDeltaU # static const std::size_t kControlTypeDeltaU = 0x1; Control was designed to penalize change in input (i.e., the state was augmented with input u)\nkControlTypeIntY # static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; Control using integral action (i.e., the state was augmented with output y during design)\nkControlTypeAdaptM # static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; Adapt control setpoint adapted with re-estimated process disturbance m.\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":11,"href":"/docs/api/modules/group__defaults/","title":"Defaults","section":"Modules","content":" Defaults # More\u0026hellip; Attributes # Name const data_t kDefaultP0 default state estimate covar const data_t kDefaultQ0 default process noise covar const data_t kDefaultR0 default output noise covar Detailed Description # Default values for common variables (e.g., default diagonal elements of covariances)\nAttribute Details # kDefaultP0 # static const data_t kDefaultP0 = 1e-6; kDefaultQ0 # static const data_t kDefaultQ0 = 1e-6; kDefaultR0 # static const data_t kDefaultR0 = 1e-2; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":12,"href":"/docs/api/examples/eg_glds_ctrl_8cpp-example/","title":"eg_glds_ctrl.cpp","section":"Examples","content":" eg_glds_ctrl.cpp # Example GLDS Control ```cpp\n//===\u0026ndash; eg_glds_ctrl.cpp - Example GLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS Control ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\n// output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4;\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false;\n// Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err\n// setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]);\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; }\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:35:01 EST "},{"id":13,"href":"/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/","title":"eg_glds_du_plds_ctrl.cpp","section":"Examples","content":" eg_glds_du_plds_ctrl.cpp # Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u). ```cpp\n//===\u0026ndash; eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026quot;;\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt);\n// construct ground truth system to be controlled\u0026hellip; // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0);\nsize_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\n// initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// make a controller lds::gaussian::Controller controller; { // Create incorrect model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50;\n// let's assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); }\n// Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt);\n// to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp\n// set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU;\n// set controller type controller.set_control_type(control_type);\n// Let\u0026rsquo;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10);\n// Set params. // n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances. controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;control system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// set up variables for simulation // create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0];\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y();\nx_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x();\nm_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\ncout \u0026laquo; \u0026ldquo;Saving simulation data to disk.\\n\u0026rdquo;;\n// saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_glds_du_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\ncout \u0026laquo; \u0026ldquo;fin.\\n\u0026rdquo;; return 0; }\n_Filename: eg_glds_du_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:35:01 EST "},{"id":14,"href":"/docs/api/examples/eg_plds_ctrl_8cpp-example/","title":"eg_plds_ctrl.cpp","section":"Examples","content":" eg_plds_ctrl.cpp # Example PLDS Control ```cpp\n//===\u0026ndash; eg_plds_ctrl.cpp - Example PLDS Control \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Control ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt);\n// Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err\n// Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);\nsize_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi;\nVector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled\u0026hellip; lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset();\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controlled_system:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controlled_system.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system);\n// for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type);\n// set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty);\n// to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// create Matrix to save outputs in\u0026hellip; Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0;\n// Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros);\n// outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros);\n// *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros);\n// set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y();\nx_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x();\nm_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true);\n// e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:35:01 EST "},{"id":15,"href":"/docs/api/examples/eg_plds_est_8cpp-example/","title":"eg_plds_est.cpp","section":"Examples","content":" eg_plds_est.cpp # Example PLDS Estimation ```cpp\n//===\u0026ndash; eg_plds_est.cpp - Example PLDS Estimation \u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout;\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0);\nint main() { cout \u0026laquo; \u0026quot; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026quot;;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation.\n// construct ground truth system\u0026hellip; lds::poisson::System system_true(n_u, n_x, n_y, dt);\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset();\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt);\n// Can copy parameters from another system object system_estimator = system_true;\n// wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est);\n// set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true;\n// set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m);\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;estimator:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; system_estimator.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros));\n// create matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros);\n// states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros);\nMatrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros);\n// initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simlation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1));\n// Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simlation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); m_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); m_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;m_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_est.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace));\nreturn 0; }\n// for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows;\nif ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026ldquo;Q must be n x n.\u0026rdquo;); }\nMatrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); }\nreturn x; }\n_Filename: eg_plds_est.cpp_ ------------------------------- Updated on 5 March 2025 at 16:35:01 EST "},{"id":16,"href":"/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/","title":"eg_plds_switched_ctrl.cpp","section":"Examples","content":" eg_plds_switched_ctrl.cpp # Example Switched PLDS Control ```cpp\n//===\u0026ndash; eg_plds_switched_ctrl.cpp - Example Switched PLDS Control \u0026mdash;===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026ldquo;License\u0026rdquo;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026ldquo;AS IS\u0026rdquo; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===// //===\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;\u0026mdash;-===//\n#include using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout;\nauto main() -\u0026gt; int { cout \u0026laquo; \u0026quot; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026quot;;\n// whether to do switched control bool do_switch_ctrl = true;\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1;\n// no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt);\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt);\n// Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices. data_t scale_sys_b = 2;\nMatrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt));\ncontrolled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions\n// reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt);\n// Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b);\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system);\n// set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys1:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;sys2:\\n\u0026quot;; cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026quot;.....................................\\n\u0026quot;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x));\nswitched_controller.set_y_ref(y_ref0);\nstd::vectorlds::poisson::System systems_vec(3, lds::poisson::System()); lds::UniformSystemListlds::poisson::System systems(std::move(systems_vec));\ncout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;switched_controller:\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;; switched_controller.Print(); cout \u0026laquo; \u0026ldquo;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\\n\u0026rdquo;;\n// Fake measurements Matrix z(n_y, n_t, arma::fill::zeros);\n// Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros);\n// create Matrix to save outputs in\u0026hellip; Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]);\n// modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones);\n// set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x();\ncout \u0026laquo; \u0026ldquo;Starting \u0026quot; \u0026laquo; n_t * dt \u0026laquo; \u0026quot; sec simulation \u0026hellip; \\n\u0026rdquo;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } }\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); }\nauto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026laquo; \u0026ldquo;Finished simulation in \u0026quot; \u0026laquo; sim_time_ms.count() \u0026laquo; \u0026quot; ms.\\n\u0026rdquo;; cout \u0026laquo; \u0026ldquo;(app. \u0026quot; \u0026laquo; (sim_time_ms.count() / n_t) * 1e3 \u0026laquo; \u0026quot; us/time-step)\\n\u0026rdquo;;\n// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;\nauto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;dt\u0026rdquo;)); y_ref.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_ref\u0026rdquo;, replace)); u.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;u\u0026rdquo;, replace)); z.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;z\u0026rdquo;, replace)); x_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_true\u0026rdquo;, replace)); y_true.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_true\u0026rdquo;, replace)); x_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;x_hat\u0026rdquo;, replace)); y_hat.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;y_hat\u0026rdquo;, replace)); mode.save(arma::hdf5_name(\u0026ldquo;eg_plds_switched_ctrl.h5\u0026rdquo;, \u0026ldquo;mode\u0026rdquo;, replace));\nreturn 0; }\n_Filename: eg_plds_switched_ctrl.cpp_ ------------------------------- Updated on 5 March 2025 at 16:35:01 EST "},{"id":17,"href":"/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/","title":"examples","section":"Files","content":" examples # Files # Name examples/eg_glds_ctrl.cpp examples/eg_glds_du_plds_ctrl.cpp examples/eg_plds_ctrl.cpp examples/eg_plds_est.cpp examples/eg_plds_switched_ctrl.cpp Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":18,"href":"/docs/api/examples/","title":"Examples","section":"LDS C+E Documentation","content":" Examples # eg_glds_ctrl.cpp Example GLDS Control.\neg_glds_du_plds_ctrl.cpp Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).\neg_plds_ctrl.cpp Example PLDS Control.\neg_plds_est.cpp Example PLDS Estimation.\neg_plds_switched_ctrl.cpp Example Switched PLDS Control.\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":19,"href":"/docs/api/files/eg__glds__ctrl_8cpp/","title":"examples/eg_glds_ctrl.cpp","section":"Files","content":" examples/eg_glds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_ctrl.cpp - Example GLDS Control ---------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS Control ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // set initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":20,"href":"/docs/api/files/eg__glds__du__plds__ctrl_8cpp/","title":"examples/eg_glds_du_plds_ctrl.cpp","section":"Files","content":" examples/eg_glds_du_plds_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_glds_du_plds_ctrl.cpp - Example GLDS Delta u Control of PLDS ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Gaussian LDS du Control of PLDS ********** \\n\\n\u0026#34;; // Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); // construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2.5e-2); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 0; // 1e-3; // probability of going from low to high disturb. data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_x).fill(m_low); Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_x0(x0_true); controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 50; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // process noise covariance Matrix q_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-8; // output noise covariance Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * 1e-2; lds::gaussian::System controller_system(n_u, n_x, n_y, dt); controller_system.set_A(a_true); controller_system.set_B(b_controller); controller_system.set_g(g_true); controller_system.set_m(m_controller); controller_system.set_Q(q_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-8; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } // Control variables: // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); // to design for this example, augmented state with control and made the input // du; cost on output q_y = 1, on integral output = 1e2, on u = 0, on du = // 1e-2. Matrix k_x = Matrix(n_u, n_x).fill(2.44); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(97.4); // gains on integrated err Matrix k_u = Matrix(n_u, n_u).fill(5.23e-2); // gains on input amp // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; // update change in control (LP filters control) control_type = control_type | lds::kControlTypeDeltaU; // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(10); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_Kc_u(k_u); controller.set_g_design(g_design); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;control system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // set up variables for simulation // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::ones) * y_ref0[0]; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // get initial val y_hat.submat(0, 0, n_y - 1, 0) = controller.sys().y(); y_true.submat(0, 0, n_y - 1, 0) = controlled_system.y(); x_hat.submat(0, 0, n_x - 1, 0) = controller.sys().x(); x_true.submat(0, 0, n_x - 1, 0) = controlled_system.x(); m_hat.submat(0, 0, n_x - 1, 0) = controller.sys().m(); m_true.submat(0, 0, n_x - 1, 0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // input Vector u_tm1(u.colptr(t - 1), u.n_rows, false, true); // Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); // save the signals y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;Saving simulation data to disk.\\n\u0026#34;; // saved variables: dt, lambdaHat, xHat, mHat, z, u, lambdaRef, lambdaTrue, // xTrue, mTrue saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_glds_du_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); cout \u0026lt;\u0026lt; \u0026#34;fin.\\n\u0026#34;; return 0; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":21,"href":"/docs/api/files/eg__plds__ctrl_8cpp/","title":"examples/eg_plds_ctrl.cpp","section":"Files","content":" examples/eg_plds_ctrl.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # main # int main() Going to simulate a switching disturbance (m) acting on system\nSource code # //===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Control ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(10.0 / dt); // Control variables: _reference/target output, controller gains // n.b., Can either use Vector (arma::Col) or std::vector Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt; Matrix k_x = Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) + 10; // gains on integrated output err // Set control type bit mask, so controller knows what to do size_t control_type = lds::kControlTypeIntY; // integral action // Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = 0.986; Matrix b_true(n_x, n_u, arma::fill::zeros); b_true[0] = 0.054; Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt); size_t which_m = 0; data_t m_low = log(1 * dt) * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; data_t m_high = log(20 * dt) * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; Vector m0_true = Vector(n_x, arma::fill::ones) * m_low; // construct ground truth system to be controlled... lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_x0(x0_true); // reset to initial conditions controlled_system.Reset(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controlled_system:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controlled_system.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Create the controller lds::poisson::Controller controller; { // Create model used for control. lds::poisson::System controller_system(controlled_system); // for this example, assume model correct, except disturbance Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low; Vector x0_controller = arma::log(y_ref0); controller_system.set_m(m0_controller); controller_system.set_x0(x0_controller); controller_system.Reset(); //reset to new init condition // adaptively re-estimate process disturbance (m) controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5; controller_system.set_Q_m(q_m); data_t u_lb = 0.0; data_t u_ub = 5.0; controller = std::move( lds::poisson::Controller(std::move(controller_system), u_lb, u_ub)); } // set controller type controller.set_control_type(control_type); // set controller gains controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); // to protect against integral windup when output is consistently above // target: data_t tau_awu(0.1); controller.set_tau_awu(tau_awu); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // create Matrix to save outputs in... Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros); y_ref.each_col() += y_ref0; // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // simulated control signal ([=] V) Matrix u(n_u, n_t, arma::fill::zeros); // outputs, states and gain/disturbance params // *_hat indicates online estimates Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_y, n_t, arma::fill::zeros); // *_true indicates ground truth (system being controlled) Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_y, n_t, arma::fill::zeros); // set initial val y_hat.col(0) = controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = controller.sys().x(); x_true.col(0) = controlled_system.x(); m_hat.col(0) = controller.sys().m(); m_true.col(0) = controlled_system.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // simulate a stochastically switched disturbance Vector chance = arma::randu\u0026lt;Vector\u0026gt;(1); if (which_m == 0) // low disturbance { if (chance[0] \u0026lt; pr_lo2hi) { // switches low -\u0026gt; high disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_high); which_m = 1; } } else { // high disturbance if (chance[0] \u0026lt; pr_hi2lo) { // swithces high -\u0026gt; low disturbance m0_true = std::vector\u0026lt;data_t\u0026gt;(n_x, m_low); which_m = 0; } } controlled_system.set_m(m0_true); // e.g., use sinusoidal reference data_t f = 0.5; // freq [=] Hz Vector t_vec = Vector(n_y, arma::fill::ones) * t; y_ref.col(t) += y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4); // Simulate the true system. z.col(t)=controlled_system.Simulate(u.col(t-1)); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Notably, it does this in the // log-linear space (i.e., log(y)). // // Therefore, it is only applicable to regulation problems or cases where // reference trajectory changes slowly compared to system dynamics. controller.set_y_ref(y_ref.col(t)); u.col(t)=controller.ControlOutputReference(z.col(t)); y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); m_true.col(t) = controlled_system.m(); y_hat.col(t) = controller.sys().y(); x_hat.col(t) = controller.sys().x(); m_hat.col(t) = controller.sys().m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":22,"href":"/docs/api/files/eg__plds__est_8cpp/","title":"examples/eg_plds_est.cpp","section":"Files","content":" examples/eg_plds_est.cpp # Types # Name using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector using double data_t Functions # Name Matrix random_walk(size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0) int main() Type Details # Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nFunction Details # random_walk # Matrix random_walk( size_t n_t, const Matrix \u0026amp; Q, const Vector \u0026amp; x0 ) main # int main() Source code # //===-- eg_plds_est.cpp - Example PLDS Estimation -------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0); int main() { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Poisson LDS Estimation ********** \\n\\n\u0026#34;; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. // construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); // Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state // Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); // Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. // turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;estimator:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; system_estimator.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Set up simulation : // Simulated measurements Matrix z(n_y, n_t, arma::fill::zeros); // Stimulus (generate random stimulus) Matrix q_u = Matrix(n_u, n_u, arma::fill::eye) * 1e-3; // cov of random walk Matrix u = random_walk(n_t, q_u, Vector(n_u, arma::fill::zeros)); // create matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); // states and disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix m_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix m_true(n_x, n_t, arma::fill::zeros); // initial conditions y_hat.col(0) = system_estimator.y(); y_true.col(0) = system_true.y(); x_hat.col(0) = system_estimator.x(); x_true.col(0) = system_true.x(); m_hat.col(0) = system_estimator.m(); m_true.col(0) = system_true.m(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simlation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); // save signals y_hat.col(t) = system_estimator.y(); y_true.col(t) = system_true.y(); x_true.col(t) = system_true.x(); m_true.col(t) = system_true.m(); x_hat.col(t) = system_estimator.x(); m_hat.col(t) = system_estimator.m(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simlation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;dt\u0026#34;)); u.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); m_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); m_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;m_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_est.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); return 0; } // for generating random input Matrix random_walk(size_t n_t, const Matrix\u0026amp; Q, const Vector\u0026amp; x0) { size_t n = Q.n_rows; if ((n != Q.n_cols) || (Q.n_cols != Q.n_rows)) { throw std::logic_error(\u0026#34;Q must be `n` x `n`.\u0026#34;); } Matrix x(n, n_t, arma::fill::zeros); x.col(0) = x0; for (size_t t = 1; t \u0026lt; n_t; t++) { x.col(t) = x.col(t - 1) + arma::mvnrnd(Vector(n, arma::fill::zeros), Q); } return x; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":23,"href":"/docs/api/files/eg__plds__switched__ctrl_8cpp/","title":"examples/eg_plds_switched_ctrl.cpp","section":"Files","content":" examples/eg_plds_switched_ctrl.cpp # Types # Name using double data_t using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Col\u0026lt; data_t \u0026gt; Vector Functions # Name int main() Type Details # data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nMatrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Vector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Function Details # main # int main() Source code # //===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst\u0026gt; using lds::data_t; using lds::Matrix; using lds::Vector; using std::cout; auto main() -\u0026gt; int { cout \u0026lt;\u0026lt; \u0026#34; ********** Example Switched Poisson LDS Control ********** \\n\\n\u0026#34;; // whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); // for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 // simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions // reference Vector y_ref0 = Vector(n_y, arma::fill::zeros).fill(25.0 * dt); // Let underlying system 1 be more sensitive than system 2 Matrix b2 = Matrix(n_x, n_u).fill(b1[0] / scale_sys_b); // create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys1:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys1.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;sys2:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; sys2.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying system s: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } // Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); std::vector\u0026lt;lds::poisson::System\u0026gt; systems_vec(3, lds::poisson::System()); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems(std::move(systems_vec)); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;switched_controller:\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; switched_controller.Print(); cout \u0026lt;\u0026lt; \u0026#34;.....................................\\n\u0026#34;; // Fake measurements Matrix z(n_y, n_t, arma::fill::zeros); // Will later contain control. Matrix u(n_u, n_t, arma::fill::zeros); // create Matrix to save outputs in... Matrix y_hat(n_y, n_t, arma::fill::zeros); Matrix y_true(n_y, n_t, arma::fill::zeros); Matrix y_ref = Matrix(n_y, n_t).fill(y_ref0[0]); // modes and gain/disturbance params Matrix x_hat(n_x, n_t, arma::fill::zeros); Matrix x_true(n_x, n_t, arma::fill::zeros); Matrix mode(1, n_t, arma::fill::ones); // set initial val y_hat.col(0) = switched_controller.sys().y(); y_true.col(0) = controlled_system.y(); x_hat.col(0) = switched_controller.sys().x(); x_true.col(0) = controlled_system.x(); cout \u0026lt;\u0026lt; \u0026#34;Starting \u0026#34; \u0026lt;\u0026lt; n_t * dt \u0026lt;\u0026lt; \u0026#34; sec simulation ... \\n\u0026#34;; auto start = std::chrono::high_resolution_clock::now(); for (size_t t = 1; t \u0026lt; n_t; t++) { // Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); mode.col(t) = which_mode; y_ref.col(t) = y_ref0; y_true.col(t) = controlled_system.y(); x_true.col(t) = controlled_system.x(); y_hat.col(t) = switched_controller.sys().y(); x_hat.col(t) = switched_controller.sys().x(); } auto finish = std::chrono::high_resolution_clock::now(); std::chrono::duration\u0026lt;data_t, std::milli\u0026gt; sim_time_ms = finish - start; cout \u0026lt;\u0026lt; \u0026#34;Finished simulation in \u0026#34; \u0026lt;\u0026lt; sim_time_ms.count() \u0026lt;\u0026lt; \u0026#34; ms.\\n\u0026#34;; cout \u0026lt;\u0026lt; \u0026#34;(app. \u0026#34; \u0026lt;\u0026lt; (sim_time_ms.count() / n_t) * 1e3 \u0026lt;\u0026lt; \u0026#34; us/time-step)\\n\u0026#34;; // saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true, // x_true, m_true saving with hdf5 via armadillo arma::hdf5_opts::opts replace = arma::hdf5_opts::replace; auto dt_vec = Vector(1).fill(dt); dt_vec.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;dt\u0026#34;)); y_ref.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_ref\u0026#34;, replace)); u.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;u\u0026#34;, replace)); z.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;z\u0026#34;, replace)); x_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_true\u0026#34;, replace)); y_true.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_true\u0026#34;, replace)); x_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;x_hat\u0026#34;, replace)); y_hat.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;y_hat\u0026#34;, replace)); mode.save(arma::hdf5_name(\u0026#34;eg_plds_switched_ctrl.h5\u0026#34;, \u0026#34;mode\u0026#34;, replace)); return 0; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":24,"href":"/docs/api/files/","title":"Files","section":"LDS C+E Documentation","content":" Files # examples/eg_glds_ctrl.cpp\nexamples/eg_glds_du_plds_ctrl.cpp\nexamples/eg_plds_ctrl.cpp\nexamples/eg_plds_est.cpp\nexamples/eg_plds_switched_ctrl.cpp\nldsCtrlEst_h/lds.h lds namespace\nldsCtrlEst_h/lds_ctrl.h Controller.\nldsCtrlEst_h/lds_fit.h LDS base fit type.\nldsCtrlEst_h/lds_fit_em.h subspace identification\nldsCtrlEst_h/lds_fit_ssid.h subspace identification\nldsCtrlEst_h/lds_gaussian.h glds namespace\nldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller.\nldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type.\nldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type.\nldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type.\nldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type.\nldsCtrlEst_h/lds_gaussian_sys.h GLDS base type.\nldsCtrlEst_h/lds_poisson.h plds namespace\nldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type.\nldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type.\nldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type.\nldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type.\nldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type.\nldsCtrlEst_h/lds_poisson_sys.h PLDS base type.\nldsCtrlEst_h/lds_sctrl.h SwitchedController type.\nldsCtrlEst_h/lds_sys.h LDS base type.\nldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices.\nldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems.\nldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors.\nldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API)\nldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API)\nsrc/lds.cpp misc lds namespace functions\nsrc/lds_gaussian_sys.cpp GLDS base type.\nsrc/lds_poisson_sys.cpp PLDS base type.\nsrc/lds_sys.cpp LDS base type.\nsrc/lds_uniform_vecs.cpp Uniformly sized vectors.\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":25,"href":"/docs/tutorials/eg_glds_control/","title":"GLDS Control","section":"LDS C+E Examples","content":" GLDS Control Tutorial # This tutorial shows how to use this library to control a system with a Gaussian LDS controller (lds::gaussian::Controller). In place of a physical system, a GLDS model (lds::gaussian::System) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 5 seconds.\n// Make 1st-order SISO system, sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(5.0 / dt); When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system to be controlled... // initializes to random walk model with top-most n_y state observed lds::gaussian::System controlled_system(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[\rx_{t\u0026#43;1} = x_t \u0026#43; w_t\r\\] \\[\ry_{t} = x_t\r\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Ground-truth parameters for the controlled system // (stand-in for physical system to be controlled) Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.01); Matrix b_true = Matrix(n_x, n_u).fill(2e-4); // control signal to model input unit conversion e.g., V -\u0026gt; mW/mm2: Vector g_true = Vector(n_y).fill(10.0); // output noise covariance Matrix r_true = Matrix(n_y, n_y, arma::fill::eye) * 1e-4; As mentioned above, this example will feature a stochastic disturbance. More specifically, a process disturbance will randomly change between two values.\n/// Going to simulate a switching disturbance (m) acting on system size_t which_m = 0; // whether low or high disturbance (0, 1) data_t m_low = 5 * dt * (1 - a_true[0]); data_t pr_lo2hi = 1e-3; // probability of going from low to high disturb. data_t m_high = 20 * dt * (1 - a_true[0]); data_t pr_hi2lo = pr_lo2hi; // initially let m be low Vector m0_true = Vector(n_y).fill(m_low); Finally, assign the parameters using corresponding set-methods.\n// Assign params. controlled_system.set_A(a_true); controlled_system.set_B(b_true); controlled_system.set_m(m0_true); controlled_system.set_g(g_true); controlled_system.set_R(r_true); Creating the controller # Now, create the controller. This requires first constructing the system model that the control uses for estimating state feedback and updating the control signal. A controller is then constructed from this lds::gaussian::System object and upper/lower bounds on the control signal (u_lb, u_ub below), past which the control saturates. Here, the control signal is command voltage sent to an analog driver (e.g., for an LED). Its limits are 0 to 5 V. If your actuator does not saturate somehow, simply set the lower and upper bounds to -lds::kInf and lds::kInf, respectively. Simple saturation is currently the only actuator model in this library.\nFor the sake of this simulation, the system model input matrix is set to an incorrect value. We also assume that the controller feedback gains were designed with an actuator whose conversion factor from volts to physical units (e.g., mW/mm2 optical intensity) differed from the actuator being used in the current experiment.\n// make a controller lds::gaussian::Controller controller; { // Create **incorrect** model used for control. // (e.g., imperfect model fitting) Matrix b_controller = b_true / 2; // let\u0026#39;s assume zero process disturbance initially // (will be re-estimating) Vector m_controller = Vector(n_x, arma::fill::zeros); // for this demo, just use arbitrary default R Matrix r_controller = Matrix(n_y, n_y, arma::fill::eye) * lds::kDefaultR0; lds::gaussian::System controller_system(controlled_system); controller_system.set_B(b_controller); controller_system.set_m(m_controller); controller_system.set_R(r_controller); controller_system.Reset(); // reset to new m // going to adaptively re-estimate the disturbance controller_system.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise // acting on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; controller_system.set_Q_m(q_m); // create controller // lower and upper bounds on control signal (e.g., in Volts) data_t u_lb = 0.0; // [=] V data_t u_ub = 5.0; // [=] V controller = std::move( lds::gaussian::Controller(std::move(controller_system), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nWith the controller constructed, control variables may be set.\n// Control variables: // if following enabled, adapts set point with re-estimated process // disturbance n.b., should not need integral action if this is enabled as the // adaptive estimator minimizes DC error bool do_adaptive_set_point = false; // Reference/target output, controller gains Vector y_ref0 = Vector(n_y).fill(20.0 * dt); Matrix k_x = Matrix(n_u, n_x).fill(100); // gains on state error Matrix k_inty = Matrix(n_u, n_y).fill(1e3); // gains on integrated err // setting initial state to target to avoid error at onset: Vector x0 = Vector(n_x).fill(y_ref0[0]); // set up controller type bit mask so controller knows how to proceed size_t control_type = 0; if (do_adaptive_set_point) { // adapt set point with estimated disturbance control_type = control_type | lds::kControlTypeAdaptM; } else { // use integral action to minimize DC error control_type = control_type | lds::kControlTypeIntY; } // set controller type controller.set_control_type(control_type); // Let\u0026#39;s say these controller gains were designed assuming g was 9 V/(mW/mm2): Vector g_design = Vector(n_u).fill(9); // Set params. // **n.b. using arbitrary defaults for Q, R in this example. Really, these // should be set by users, as they tune characteristics of Kalman filter. // Users can also choose not to recursively calculate the estimator gain and // supply it (setKe) instead of covariances.** controller.set_y_ref(y_ref0); controller.set_Kc(k_x); controller.set_Kc_inty(k_inty); controller.set_g_design(g_design); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output and supply the current measurement z. It then calculates the solution for the state/input required to track the reference output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system.\nThe control loop is carried out here in a simple for-loop, where a the controlled system is simulated, a measurement taken, and the control signal updated.\n// Simulate the true system. z.col(t) = controlled_system.Simulate(u_tm1); // This method uses a steady-state solution to control problem to calculate // x_ref, u_ref from reference output y_ref. Therefore, it is only // applicable to regulation problems or cases where reference trajectory // changes slowly compared to system dynamics. u.col(t) = controller.ControlOutputReference(z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the control signal. The controller\u0026rsquo;s online estimates of the output, state, and disturbance are given in purple.\n"},{"id":26,"href":"/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/","title":"include","section":"Files","content":" include # Directories # Name ldsCtrlEst_h Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":27,"href":"/docs/api/namespaces/namespacelds/","title":"lds","section":"Namespaces","content":" lds # Linear Dynamical Systems (LDS) namespace. Namespaces # Name lds::gaussian Linear Dynamical Systems with Gaussian observations. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::Controller class lds::EM class lds::Fit LDS Fit Type. class lds::SSID class lds::SwitchedController SwitchedController Type. class lds::System Linear Dynamical System Type. class lds::UniformMatrixList class lds::UniformSystemList class lds::UniformVectorList Types # Name enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA}\nweighting options for SSID enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2} using double data_t using arma::Col\u0026lt; data_t \u0026gt; Vector using arma::Mat\u0026lt; data_t \u0026gt; Matrix using arma::Cube\u0026lt; data_t \u0026gt; Cube using arma::subview\u0026lt; data_t \u0026gt; View Functions # Name void Limit(std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub) void Limit(Vector \u0026amp; x, data_t lb, data_t ub) void Limit(Matrix \u0026amp; x, data_t lb, data_t ub) void Reassign(Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Vector in place void Reassign(Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026ldquo;Reassign\u0026rdquo;)\nreassigns contents of some Matrix in place void ForceSymPD(Matrix \u0026amp; X)\nforces matrix to be symmetric positive-definite void ForceSymMinEig(Matrix \u0026amp; X, data_t eig_min =0)\nforces matrix to be symmetric and have a minimum eigenvalue void lq(Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X)\nLQ decomposition. Matrix calcCov(const Matrix \u0026amp; A, const Matrix \u0026amp; B)\nCalculate covariance matrix. Attributes # Name const data_t kInf Some useful numbers. const data_t kPi Type Details # SSIDWt # Enumerator Value Description kSSIDNone None. kSSIDMOESP MOESP (AKA \u0026ldquo;robust method\u0026rdquo; in van Overschee 1996) kSSIDCVA CVA \u0026ldquo;Canonical Variate Analysis\u0026rdquo;. Weighting options for singular value decomposition performed during subspace identification (SSID)\nReference:\nvan Overschee, de Moor. 1996. Subspace Identification for Linear Systems.\nMatrixListFreeDim # Enumerator Value Description kMatFreeDimNone neither dim free to be hetero in mat list kMatFreeDim1 allow 1st dim of mats in list to be hetero kMatFreeDim2 allow 2nd dim of mats in list to be hetero data_t # using lds::data_t = double; Type of all data in library. If need 32b, change double to float. This could be potentially useful for large scale problems where there are memory constraints.\nVector # using lds::Vector = arma::Col\u0026lt;data_t\u0026gt;; Matrix # using lds::Matrix = arma::Mat\u0026lt;data_t\u0026gt;; Cube # using lds::Cube = arma::Cube\u0026lt;data_t\u0026gt;; View # using lds::View = arma::subview\u0026lt;data_t\u0026gt;; Function Details # Limit # inline void Limit( std::vector\u0026lt; data_t \u0026gt; \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Vector \u0026amp; x, data_t lb, data_t ub ) Limit # inline void Limit( Matrix \u0026amp; x, data_t lb, data_t ub ) Reassign # inline void Reassign( Vector \u0026amp; some, const Vector \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Vector other other Vector parenthetical optional description provided by caller to ease debugging Reassign # inline void Reassign( Matrix \u0026amp; some, const Matrix \u0026amp; other, const std::string \u0026amp; parenthetical =\u0026#34;Reassign\u0026#34; ) Parameters:\nsome some Matrix other other Matrix parenthetical optional description provided by caller to ease debugging ForceSymPD # void ForceSymPD( Matrix \u0026amp; X ) Parameters:\nX mutated matrix ForceSymMinEig # void ForceSymMinEig( Matrix \u0026amp; X, data_t eig_min =0 ) Parameters:\nX mutated matrix eig_min [optional] minimum eigen value lq # void lq( Matrix \u0026amp; L, Matrix \u0026amp; Qt, const Matrix \u0026amp; X ) Parameters:\nL lower triangle matrix Qt orthonormal matrix (transposed cf QR decomp) X matrix being decomposed calcCov # Matrix calcCov( const Matrix \u0026amp; A, const Matrix \u0026amp; B ) Parameters:\nA some matrix B some other matrix Return: covariance\nAttribute Details # kInf # static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); kPi # static const data_t kPi = arma::datum::pi; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":28,"href":"/docs/api/classes/classlds_1_1_controller/","title":"lds::Controller","section":"Classes","content":" lds::Controller # More\u0026hellip;\nInherited by lds::SwitchedController\u0026lt; System \u0026gt;, lds::gaussian::Controller, lds::poisson::Controller\nPublic Functions # Name Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes # Name System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::Controller; Public Function Details # Controller # Controller() =default Controller # inline Controller( const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Controller # inline Controller( System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsys System (derived from lds::System) u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Template Parameters:\nSystem type derived from lds::System Control # inline const Vector \u0026amp; Control( const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step). This is the most flexible option, but requires user to have set the controller\u0026rsquo;s y_ref, x_ref, and u_ref variables.\nControlOutputReference # inline const Vector \u0026amp; ControlOutputReference( const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true ) Parameters:\nz measurement do_control [optional] whether to update control (true) or simply feed through u_ref (false) do_estimation [optional] whether to update state estimate (if false, effectively open-loop control) do_lock_control [optional] whether to lock control at its current value sigma_soft_start [optional] standard deviation (sigma) of a Gaussian soft-start to control (do_control from false to true) sigma_u_noise [optional] standard deviation (sigma) of Gaussian noise added on top of control signal do_reset_at_control_onset [optional] whether to reset controller at control epoch onset (i.e., do_control from false to true) Return: updated control signal\nUpdates the control signal (single-step), given previously-set y_ref. This method calculates the rest of the set point (u_ref, x_ref) that is required to for the system to be at y_ref at steady state. This is accomplished by linearly-constrained least-squares. For a single-output system, the solution should be exact within control saturation limits. For a multi-output system, it provides the least-squares comprimise across the outputs.\nsys # inline const System \u0026amp; sys() const Kc # inline const Matrix \u0026amp; Kc() const Kc_inty # inline const Matrix \u0026amp; Kc_inty() const Kc_u # inline const Matrix \u0026amp; Kc_u() const g_design # inline const Vector \u0026amp; g_design() const u_ref # inline const Vector \u0026amp; u_ref() const x_ref # inline const Vector \u0026amp; x_ref() const y_ref # inline const Vector \u0026amp; y_ref() const control_type # inline size_t control_type() const tau_awu # inline data_t tau_awu() const u_lb # inline data_t u_lb() const u_ub # inline data_t u_ub() const set_sys # inline void set_sys( const System \u0026amp; sys ) set_g_design # inline void set_g_design( const Vector \u0026amp; g_design ) set_u_ref # inline void set_u_ref( const Vector \u0026amp; u_ref ) set_x_ref # inline void set_x_ref( const Vector \u0026amp; x_ref ) set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) Reimplemented by: lds::gaussian::Controller::set_y_ref, lds::gaussian::SwitchedController::set_y_ref, lds::poisson::Controller::set_y_ref, lds::poisson::SwitchedController::set_y_ref\nset_Kc # inline void set_Kc( const Matrix \u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const Matrix \u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const Matrix \u0026amp; Kc_u ) set_tau_awu # inline void set_tau_awu( data_t tau ) set_control_type # inline void set_control_type( size_t control_type ) Parameters:\ncontrol_type control type bit mask Template Parameters:\nSystem type derived from lds::System set_u_lb # inline void set_u_lb( data_t u_lb ) Parameters:\nu_lb control lower bound set_u_ub # inline void set_u_ub( data_t u_ub ) Parameters:\nu_ub control upper bound Reset # inline void Reset() Print # inline void Print() Protected Attribute Details # sys_ # System sys_; u_ # Vector u_; u_return_ # Vector u_return_; g_design_ # Vector g_design_; u_ref_ # Vector u_ref_; u_ref_prev_ # Vector u_ref_prev_; x_ref_ # Vector x_ref_; y_ref_ # Vector y_ref_; cx_ref_ # Vector cx_ref_; Kc_ # Matrix Kc_; Kc_u_ # Matrix Kc_u_; Kc_inty_ # Matrix Kc_inty_; du_ref_ # Vector du_ref_; dv_ref_ # Vector dv_ref_; v_ref_ # Vector v_ref_; dv_ # Vector dv_; v_ # Vector v_; int_e_ # Vector int_e_; int_e_awu_adjust_ # Vector int_e_awu_adjust_; u_sat_ # Vector u_sat_; do_control_prev_ # bool do_control_prev_ = false; do_lock_control_prev_ # bool do_lock_control_prev_ = false; u_saturated_ # bool u_saturated_ = false; u_lb_ # data_t u_lb_ {}; u_ub_ # data_t u_ub_ {}; tau_awu_ # data_t tau_awu_ {}; k_awu_ # data_t k_awu_ = 0; t_since_control_onset_ # data_t t_since_control_onset_ = 0; control_type_ # size_t control_type_ {}; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":29,"href":"/docs/api/classes/classlds_1_1_e_m/","title":"lds::EM","section":"Classes","content":" lds::EM # More\u0026hellip;\nInherited by lds::gaussian::FitEM, lds::poisson::FitEM\nPublic Functions # Name EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions # Name void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() virtual void MaximizeOutput() =0 virtual void MaximizeMeasurement() =0 void Smooth(bool force_common_initial)\nget smoothed estimates virtual void RecurseKe(Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t) =0\nrecursively update estimator gain Ke void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::EM; Public Function Details # EM # EM() =default EM # EM( size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nn_x number of states dt sample period u_train input training data z_train measurement training data EM # EM( const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train ) Parameters:\nfit0 initial fit u_train input training data z_train measurement training data ~EM # virtual ~EM() =default Run # const Fit \u0026amp; Run( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2 ) Parameters:\ncalc_dynamics [optional] whether to calculate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law max_iter max number of iterations tol convergence tolerance (max fractional abs change) Return: Fit\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nx # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const y # inline const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const sum_E_x_t_x_t # inline const Matrix \u0026amp; sum_E_x_t_x_t() const sum_E_xu_tm1_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const sum_E_xu_t_xu_tm1 # inline const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const n_t_tot # inline size_t n_t_tot() theta # inline const Vector \u0026amp; theta() const Protected Function Details # Expectation # void Expectation( bool force_common_initial =false ) Parameters:\nforce_common_initial whether to force common initial condition for all trials Maximization # void Maximization( bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false ) Parameters:\ncalc_dynamics [optional] whether to caclulate dynamics (A, B) calc_Q [optional] whether to calculate process noise covariance calc_init [optional] whether to calculate initial conditions calc_output [optional] whether to calculate output function calc_measurement [optional] whether to calculate parameters for measurement/observation law MaximizeDynamics # void MaximizeDynamics() MaximizeQ # void MaximizeQ() MaximizeInitial # void MaximizeInitial() MaximizeOutput # virtual void MaximizeOutput() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeOutput, lds::poisson::FitEM::MaximizeOutput\nMaximizeMeasurement # virtual void MaximizeMeasurement() =0 Reimplemented by: lds::gaussian::FitEM::MaximizeMeasurement, lds::poisson::FitEM::MaximizeMeasurement\nSmooth # void Smooth( bool force_common_initial ) Parameters:\nforce_common_initial whether to force common initial conditions RecurseKe # virtual void RecurseKe( Matrix \u0026amp; Ke, Cube \u0026amp; P_pre, Cube \u0026amp; P_post, size_t t ) =0 Parameters:\nKe estimator gain P_pre cov of predicted state est. P_post cov of postior sate est. t time Reimplemented by: lds::gaussian::FitEM::RecurseKe, lds::poisson::FitEM::RecurseKe\nReset # void Reset() InitVars # void InitVars() UpdateTheta # Vector UpdateTheta() Return: parameter list\nProtected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; x_ # std::vector\u0026lt; Matrix \u0026gt; x_; P_ # std::vector\u0026lt; Cube \u0026gt; P_; P_t_tm1_ # std::vector\u0026lt; Cube \u0026gt; P_t_tm1_; y_ # std::vector\u0026lt; Matrix \u0026gt; y_; diag_y_ # Matrix diag_y_; sum_E_x_t_x_t_ # Matrix sum_E_x_t_x_t_; sum_E_xu_tm1_xu_tm1_ # Matrix sum_E_xu_tm1_xu_tm1_; sum_E_xu_t_xu_tm1_ # Matrix sum_E_xu_t_xu_tm1_; fit_ # Fit fit_; theta_ # Vector theta_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":30,"href":"/docs/api/classes/classlds_1_1_fit/","title":"lds::Fit","section":"Classes","content":" lds::Fit # LDS Fit Type. #include \u0026lt;lds_fit.h\u0026gt;\nInherited by lds::gaussian::Fit, lds::poisson::Fit\nPublic Functions # Name Fit() =default\nConstructs a new Fit. Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias virtual const Matrix \u0026amp; R() const =0 void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance virtual void set_R(const Matrix \u0026amp; R) =0\nsets output noise covariance (if any) void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) =0\noutput function Protected Attributes # Name data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period ~Fit # virtual ~Fit() =default n_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const m # inline const Vector \u0026amp; m() const Q # inline const Matrix \u0026amp; Q() const x0 # inline const Vector \u0026amp; x0() const P0 # inline const Matrix \u0026amp; P0() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const R # virtual const Matrix \u0026amp; R() const =0 Reimplemented by: lds::gaussian::Fit::R, lds::poisson::Fit::R\nset_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_g # inline void set_g( const Vector \u0026amp; g ) set_m # inline void set_m( const Vector \u0026amp; m ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # virtual void set_R( const Matrix \u0026amp; R ) =0 Reimplemented by: lds::gaussian::Fit::set_R, lds::poisson::Fit::set_R\nset_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) f # inline View f( Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t ) Parameters:\nx state estimate (over time) u input (over time) t time index Return: view of updated state\nf # inline View f( Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t ) Parameters:\nx_pre predicted state est. x_post posterior state est. u input (over time) t time index Return: view of predicted state\nh # virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) =0 Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplemented by: lds::gaussian::Fit::h, lds::poisson::Fit::h\nProtected Attribute Details # dt_ # data_t dt_ {}; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; m_ # Vector m_; Q_ # Matrix Q_; C_ # Matrix C_; d_ # Vector d_; R_ # Matrix R_; x0_ # Vector x0_; P0_ # Matrix P0_; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":31,"href":"/docs/api/namespaces/namespacelds_1_1gaussian/","title":"lds::gaussian","section":"Namespaces","content":" lds::gaussian # Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. class lds::gaussian::Fit GLDS Fit Type. class lds::gaussian::FitEM GLDS E-M Fit Type. class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. class lds::gaussian::System Gaussian LDS Type. Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":32,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_controller/","title":"lds::gaussian::Controller","section":"Classes","content":" lds::gaussian::Controller # Gaussian-observation Controller Type. #include \u0026lt;lds_gaussian_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":33,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_fit/","title":"lds::gaussian::Fit","section":"Classes","content":" lds::gaussian::Fit # GLDS Fit Type. #include \u0026lt;lds_gaussian_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual const Matrix \u0026amp; R() const override\ngets measurement noise covariance virtual void set_R(const Matrix \u0026amp; R) override\nsets measurement noise covariance virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period R # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nh # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":34,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/","title":"lds::gaussian::FitEM","section":"Classes","content":" lds::gaussian::FitEM # GLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_gaussian_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::gaussian::FitEM; This type is used in the process of fitting GLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":35,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/","title":"lds::gaussian::FitSSID","section":"Classes","content":" lds::gaussian::FitSSID # Subspace Identification (SSID) for GLDS. #include \u0026lt;lds_gaussian_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":36,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/","title":"lds::gaussian::SwitchedController","section":"Classes","content":" lds::gaussian::SwitchedController # Gaussian-observation SwitchedController Type. #include \u0026lt;lds_gaussian_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nsets reference output Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":37,"href":"/docs/api/classes/classlds_1_1gaussian_1_1_system/","title":"lds::gaussian::System","section":"Classes","content":" lds::gaussian::System # Gaussian LDS Type. #include \u0026lt;lds_gaussian_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0)\nConstructs a new Gaussian System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. const Matrix \u0026amp; R() const\nGet output noise covariance. void set_Q(const Matrix \u0026amp; Q) void set_R(const Matrix \u0026amp; R)\nSet output noise covariance. void set_Ke(const Matrix \u0026amp; Ke)\nSet estimator gain. void set_Ke_m(const Matrix \u0026amp; Ke_m)\nSet disturbance estimator gain. void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively update estimator gain. Protected Attributes # Name Matrix R_ covariance of output noise bool do_recurse_Ke_ whether to recursively calculate estimator gain Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0, data_t r0 =kDefaultR0 ) Parameters:\nn_u number of inputs (u) n_x number of states (x) n_y number of outputs (y) dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) r0 [optional] initial diagonal elements of output noise covariance (R) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nR # inline const Matrix \u0026amp; R() const set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_R # inline void set_R( const Matrix \u0026amp; R ) set_Ke # inline void set_Ke( const Matrix \u0026amp; Ke ) set_Ke_m # inline void set_Ke_m( const Matrix \u0026amp; Ke_m ) Print # void Print() Protected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nProtected Attribute Details # R_ # Matrix R_; do_recurse_Ke_ # bool do_recurse_Ke_ {}; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":38,"href":"/docs/api/namespaces/namespacelds_1_1poisson/","title":"lds::poisson","section":"Namespaces","content":" lds::poisson # Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. class lds::poisson::Fit PLDS Fit Type. class lds::poisson::FitEM PLDS E-M Fit Type. class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. class lds::poisson::System Poisson System type. Attributes # Name std::random_device rd random device for simulating poisson data std::mt19937 rng random number generator for simulating poisson data Attribute Details # rd # static std::random_device rd; rng # static std::mt19937 rng = std::mt19937( rd()); Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":39,"href":"/docs/api/classes/classlds_1_1poisson_1_1_controller/","title":"lds::poisson::Controller","section":"Classes","content":" lds::poisson::Controller # PLDS Controller Type. #include \u0026lt;lds_poisson_ctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":40,"href":"/docs/api/classes/classlds_1_1poisson_1_1_fit/","title":"lds::poisson::Fit","section":"Classes","content":" lds::poisson::Fit # PLDS Fit Type. #include \u0026lt;lds_poisson_fit.h\u0026gt;\nInherits from lds::Fit\nPublic Functions # Name Fit() =default Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt)\nConstructs a new Fit. virtual View h(Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t) override\noutput function virtual void set_R(const Matrix \u0026amp; R) override\nsets output noise covariance (if any) virtual const Matrix \u0026amp; R() const override Additional inherited members # Public Functions inherited from lds::Fit\nName virtual ~Fit() =default size_t n_u() const\ngets number of inputs size_t n_x() const\ngets number of states size_t n_y() const\ngets number of outputs data_t dt() const\ngets sample period const Matrix \u0026amp; A() const\ngets state matrix const Matrix \u0026amp; B() const\ngets input matrix const Vector \u0026amp; g() const\ngets input gain const Vector \u0026amp; m() const\ngets process disturbance const Matrix \u0026amp; Q() const\ngets process noise covariance const Vector \u0026amp; x0() const\ngets initial state estimate const Matrix \u0026amp; P0() const\ngets covariance of initial state estimate const Matrix \u0026amp; C() const\ngets output matrix const Vector \u0026amp; d() const\ngets output bias void set_A(const Matrix \u0026amp; A)\nsets state matrix void set_B(const Matrix \u0026amp; B)\nsets input matrix void set_g(const Vector \u0026amp; g)\nsets input gain/conversion factor void set_m(const Vector \u0026amp; m)\nsets process disturbance void set_Q(const Matrix \u0026amp; Q)\nsets process noise covariance void set_x0(const Vector \u0026amp; x0)\nsets initial state estimate void set_P0(const Matrix \u0026amp; P0)\nsets initial state estimate covariance void set_C(const Matrix \u0026amp; C)\nsets output matrix void set_d(const Vector \u0026amp; d)\nsets output bias View f(Matrix \u0026amp; x, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function View f(Matrix \u0026amp; x_pre, const Matrix \u0026amp; x_post, const Matrix \u0026amp; u, size_t t)\nsystem dynamics function Protected Attributes inherited from lds::Fit\nName data_t dt_ sample period Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Vector m_ process noise mean Matrix Q_ process noise cov Matrix C_ output matrix Vector d_ output bias Matrix R_ measurement noise Vector x0_ initial state Matrix P0_ initial covar size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs Public Function Details # Fit # Fit() =default Fit # inline Fit( size_t n_u, size_t n_x, size_t n_y, data_t dt ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period h # inline virtual View h( Matrix \u0026amp; y, const Matrix \u0026amp; x, size_t t ) override Parameters:\ny output estimate (over time) x state estimate (over time) t time index Return: output\nReimplements: lds::Fit::h\nset_R # inline virtual void set_R( const Matrix \u0026amp; R ) override Reimplements: lds::Fit::set_R\nR # inline virtual const Matrix \u0026amp; R() const override Reimplements: lds::Fit::R\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":41,"href":"/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/","title":"lds::poisson::FitEM","section":"Classes","content":" lds::poisson::FitEM # PLDS E-M Fit Type. More\u0026hellip;\n#include \u0026lt;lds_poisson_fit_em.h\u0026gt;\nInherits from lds::EM\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName EM() =default\nConstructs a new EMFit type. EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. EM(const Fit \u0026amp; fit0, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train)\nConstructs a new EMFit type. virtual ~EM() =default const Fit \u0026amp; Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2)\nRuns fitting by Expectation(E)-Maximization(M) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the input/output data to caller. const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; x() const\ngets estimated state (over time) const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; y() const\ngets estimated output (over time) const Matrix \u0026amp; sum_E_x_t_x_t() const\ngets state-input covariance const Matrix \u0026amp; sum_E_xu_tm1_xu_tm1() const\ngets state-input covariance (t-minus-1) const Matrix \u0026amp; sum_E_xu_t_xu_tm1() const\ngets single lag state-input covariance size_t n_t_tot()\ntotal number of time samples const Vector \u0026amp; theta() const\ngets parameters updated in M step Protected Functions inherited from lds::EM\u0026lt; Fit \u0026gt;\nName void Expectation(bool force_common_initial =false)\nExpectation step. void Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)\nMaximization step. void MaximizeDynamics() void MaximizeQ() void MaximizeInitial() void Smooth(bool force_common_initial)\nget smoothed estimates void Reset()\nreset to initial conditions void InitVars()\nInitializes the variables. Vector UpdateTheta()\nupdates parameter list, theta Protected Attributes inherited from lds::EM\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data std::vector\u0026lt; Matrix \u0026gt; x_ state estimate std::vector\u0026lt; Cube \u0026gt; P_ state estimate cov std::vector\u0026lt; Cube \u0026gt; P_t_tm1_ single-lag state covariance std::vector\u0026lt; Matrix \u0026gt; y_ output estimate Matrix diag_y_ Matrix sum_E_x_t_x_t_ state covariance (current time) Matrix sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1) Matrix sum_E_xu_t_xu_tm1_ single lag state-input covariance Fit fit_ Vector theta_ data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Detailed Description # class lds::poisson::FitEM; This type is used in the process of fitting PLDS models by expectation-maximization (EM). Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":42,"href":"/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/","title":"lds::poisson::FitSSID","section":"Classes","content":" lds::poisson::FitSSID # Subspace Identification (SSID) for PLDS. #include \u0026lt;lds_poisson_fit_ssid.h\u0026gt;\nInherits from lds::SSID\u0026lt; Fit \u0026gt;\nAdditional inherited members # Public Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes inherited from lds::SSID\u0026lt; Fit \u0026gt;\nName UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":43,"href":"/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/","title":"lds::poisson::SwitchedController","section":"Classes","content":" lds::poisson::SwitchedController # Poisson-observation SwitchedController Type. #include \u0026lt;lds_poisson_sctrl.h\u0026gt;\nInherits from lds::SwitchedController\u0026lt; System \u0026gt;, lds::Controller\u0026lt; System \u0026gt;\nPublic Functions # Name virtual void set_y_ref(const Vector \u0026amp; y_ref) override\nSet reference output. Additional inherited members # Public Functions inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes inherited from lds::SwitchedController\u0026lt; System \u0026gt;\nName std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_g_design(const Vector \u0026amp; g_design)\nSet input gain used in controller design (g_design) void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) void set_Kc(const Matrix \u0026amp; Kc)\nSet state controller gain. void set_Kc_inty(const Matrix \u0026amp; Kc_inty)\nSet integral controller gain. void set_Kc_u(const Matrix \u0026amp; Kc_u)\nSet input controller gain. void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Public Function Details # set_y_ref # inline virtual void set_y_ref( const Vector \u0026amp; y_ref ) override Reimplements: lds::Controller::set_y_ref\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":44,"href":"/docs/api/classes/classlds_1_1poisson_1_1_system/","title":"lds::poisson::System","section":"Classes","content":" lds::poisson::System # Poisson System type. #include \u0026lt;lds_poisson_sys.h\u0026gt;\nInherits from lds::System\nPublic Functions # Name System() =default\nConstructs a new System. System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nConstructs a new Poisson System. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) override\nSimulate system measurement. Protected Functions # Name virtual void h() override\nSystem output function. virtual Vector h_(Vector x) override\nSystem output function: stateless. virtual void RecurseKe() override\nRecursively recalculate estimator gain (Ke) Additional inherited members # Public Functions inherited from lds::System\nName virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions inherited from lds::System\nName void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes inherited from lds::System\nName bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes inherited from lds::System\nName std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 [optional] initial diagonal elements of state estimate covariance (P) q0 [optional] initial diagonal elements of process noise covariance (Q) Simulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) override Parameters:\nu_tm1 input at t-1 Return: z measurement\nReimplements: lds::System::Simulate\nSimulate system and produce measurement\nProtected Function Details # h # inline virtual void h() override Reimplements: lds::System::h\nh_ # inline virtual Vector h_( Vector x ) override Reimplements: lds::System::h_\nRecurseKe # virtual void RecurseKe() override Reimplements: lds::System::RecurseKe\nRecursively recalculate estimator gain (Ke).\nReferences:\nSmith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.\nEden UT, \u0026hellip;, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":45,"href":"/docs/api/classes/classlds_1_1_s_s_i_d/","title":"lds::SSID","section":"Classes","content":" lds::SSID # More\u0026hellip;\nInherited by lds::gaussian::FitSSID, lds::poisson::FitSSID\nPublic Functions # Name SSID() =default\nConstructs a new SSIDFit type. SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf))\nConstructs a new SSIDFit type. std::tuple\u0026lt; Fit, Vector \u0026gt; Run(SSIDWt ssid_wt)\nRuns fitting by subspace identification (SSID) std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData()\nReturns the I/O data to caller. Protected Functions # Name void CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001)\nUsing periods of silence in inputs (u), calculates the output \\ bias (d) void CreateHankelDataMat()\nCreates the block-hankel I/O data matrix. virtual void DecomposeData() =0\nDecompose data to lower-triangular matrix (used in Solve) void CalcSVD(SSIDWt wt)\nperforms the singular value decomposition (SVD) void Solve(data_t wt_dc)\nsolves for LDS parameters void RecomputeExtObs()\nrecompute extended observability matrix from estimates of A, C Protected Attributes # Name UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_ input training data UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_ measurement training data Matrix D_ block-Hankel I/O data matrix Fit fit_ fit Matrix g_dc_ I/O gain @ DC. data_t dt_ sample period size_t n_u_ number of inputs size_t n_x_ number of states size_t n_y_ number of outputs size_t n_h_ size_t n_trials_ number of input/output data sequences std::vector\u0026lt; size_t \u0026gt; n_t_ number of time steps size_t n_t_tot_ total number of time steps across trials Matrix L_ lower triangle decomp of covariance matrix Vector s_ singular values Matrix ext_obs_t_ extended observability matrix Detailed Description # template \u0026lt;typename Fit \u0026gt; class lds::SSID; Public Function Details # SSID # SSID() =default SSID # SSID( size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026amp;\u0026amp; z_train, const Vector \u0026amp; d =Vector(1).fill(-kInf) ) Parameters:\nn_x number of states n_h size of block-hankel data matrix dt sample period u_train input training data z_train measurement training data d output bias Run # std::tuple\u0026lt; Fit, Vector \u0026gt; Run( SSIDWt ssid_wt ) Parameters:\nssid_wt weight for singular value decomp Return: tuple (Fit, singular values)\nReturnData # inline std::tuple\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt;, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; ReturnData() Return: tuple(input data, output data)\nProtected Function Details # CalcD # void CalcD( data_t t_silence =0.1, data_t thresh_silence =0.001 ) Parameters:\nt_silence threshold on period of time that qualifies as \u0026ldquo;silence\u0026rdquo; thresh_silence threshold on input amplitude u that qualifies as \u0026ldquo;silence\u0026rdquo; CreateHankelDataMat # void CreateHankelDataMat() Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.\nDecomposeData # virtual void DecomposeData() =0 Reimplemented by: lds::gaussian::FitSSID::DecomposeData, lds::poisson::FitSSID::DecomposeData\nCalcSVD # void CalcSVD( SSIDWt wt ) Parameters:\nssid_wt weight for SVD Solve # void Solve( data_t wt_dc ) Parameters:\nwt_dc weight placed on getting correct DC I/O gain RecomputeExtObs # void RecomputeExtObs() Protected Attribute Details # u_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u_; z_ # UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z_; D_ # Matrix D_; fit_ # Fit fit_; g_dc_ # Matrix g_dc_; dt_ # data_t dt_ {}; n_u_ # size_t n_u_ {}; n_x_ # size_t n_x_ {}; n_y_ # size_t n_y_ {}; n_h_ # size_t n_h_ {}; n_trials_ # size_t n_trials_ {}; n_t_ # std::vector\u0026lt; size_t \u0026gt; n_t_; n_t_tot_ # size_t n_t_tot_ {}; L_ # Matrix L_; s_ # Vector s_; ext_obs_t_ # Matrix ext_obs_t_; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":46,"href":"/docs/api/classes/classlds_1_1_switched_controller/","title":"lds::SwitchedController","section":"Classes","content":" lds::SwitchedController # SwitchedController Type. More\u0026hellip;\n#include \u0026lt;lds_sctrl.h\u0026gt;\nInherits from lds::Controller\u0026lt; System \u0026gt;\nInherited by lds::gaussian::SwitchedController, lds::poisson::SwitchedController\nPublic Functions # Name SwitchedController() =default\nConstructs a new SwitchedController. SwitchedController(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController. SwitchedController(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new SwitchedController (moves systems). void Switch(size_t idx, bool do_force_switch =false)\nSwitch to a different sub-system/controller. void set_Kc(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc)\nsets state feedback gains void set_Kc(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc)\nsets state feedback gains (moving) void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty)\nsets integral feedback gains void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty)\nsets integral feedback gains (moving) void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u)\nsets input feedback gains void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u)\nsets input feedback gains (moving) void set_g_design(const UniformVectorList \u0026amp; g)\nsets input gain used during controller design void set_g_design(UniformVectorList \u0026amp;\u0026amp; g)\nsets input gain used during controller design (moving) Protected Attributes # Name std::vector\u0026lt; System \u0026gt; systems_ underlying sub-systems which are switched between size_t n_sys_ number of systems size_t idx_ current system/controller index. UniformMatrixList Kc_list_ UniformMatrixList Kc_inty_list_ UniformMatrixList Kc_u_list_ UniformVectorList g_design_list_ Additional inherited members # Public Functions inherited from lds::Controller\u0026lt; System \u0026gt;\nName Controller() =default\nConstructs a new Controller. Controller(const System \u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller. Controller(System \u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type =0)\nConstructs a new Controller by moving the system object. const Vector \u0026amp; Control(const Vector \u0026amp; z, bool do_control =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal (single-step) const Vector \u0026amp; ControlOutputReference(const Vector \u0026amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, data_t sigma_soft_start =0, data_t sigma_u_noise =0, bool do_reset_at_control_onset =true)\nupdates control signal, given previously-set (single-step) const System \u0026amp; sys() const const Matrix \u0026amp; Kc() const\nGet state feedback controller gain. const Matrix \u0026amp; Kc_inty() const\nGet integral controller gain. const Matrix \u0026amp; Kc_u() const\nGet input feedback controller gain. const Vector \u0026amp; g_design() const\nGet input gain used in controller design. const Vector \u0026amp; u_ref() const\nGet reference input. const Vector \u0026amp; x_ref() const\nGet reference state. const Vector \u0026amp; y_ref() const\nGet reference output. size_t control_type() const\nGet controller type. data_t tau_awu() const\nGet time constant of anti-integral-windup. data_t u_lb() const\nGet control lower bound. data_t u_ub() const\nGet control upper bound. void set_sys(const System \u0026amp; sys)\nSet system. void set_u_ref(const Vector \u0026amp; u_ref)\nSet reference input (u_ref) void set_x_ref(const Vector \u0026amp; x_ref)\nSet reference state (x_ref) virtual void set_y_ref(const Vector \u0026amp; y_ref)\nSet reference output (y_ref) void set_tau_awu(data_t tau)\nSet time constant of anti-integral-windup. void set_control_type(size_t control_type)\nSets the control type. void set_u_lb(data_t u_lb)\nsets control lower bound void set_u_ub(data_t u_ub)\nSets control upper bound. void Reset()\nreset system and control variables. void Print()\nprints variables to stdout Protected Attributes inherited from lds::Controller\u0026lt; System \u0026gt;\nName System sys_ underlying LDS Vector u_ control signal Vector u_return_ control signal that is returned to user Vector g_design_ input gain of the system used for controller design Vector u_ref_ reference input Vector u_ref_prev_ reference input at previous time step Vector x_ref_ reference state Vector y_ref_ reference output Vector cx_ref_ Matrix Kc_ state controller gain Matrix Kc_u_ input controller gain (optional when control updates \\deltaU) Matrix Kc_inty_ integral controller gain Vector du_ref_ Vector dv_ref_ Vector v_ref_ Vector dv_ Vector v_ Control after g inversion (e.g., control in physical units) Vector int_e_ integrated error Vector int_e_awu_adjust_ anti-windup adjustment to intE Vector u_sat_ control signal after saturation (for antiWindup) bool do_control_prev_ bool do_lock_control_prev_ bool u_saturated_ whether control signal has reached saturation limits data_t u_lb_ lower bound on control data_t u_ub_ upper bound on control data_t tau_awu_ antiwindup time constant data_t k_awu_ data_t t_since_control_onset_ time since control epoch onset size_t control_type_ controller type Detailed Description # template \u0026lt;typename System \u0026gt; class lds::SwitchedController; Public Function Details # SwitchedController # SwitchedController() =default SwitchedController # inline SwitchedController( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask SwitchedController # inline SwitchedController( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type =0 ) Parameters:\nsystems vector of sub-systems u_lb lower bound on control (u) u_ub upper bound on control (u) control_type [optional] control type bit mask Switch # inline void Switch( size_t idx, bool do_force_switch =false ) Parameters:\nidx index do_force_switch whether to force a system switch even if already there. set_Kc # inline void set_Kc( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc ) set_Kc # inline void set_Kc( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc ) set_Kc_inty # inline void set_Kc_inty( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_inty ) set_Kc_inty # inline void set_Kc_inty( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_inty ) set_Kc_u # inline void set_Kc_u( const UniformMatrixList\u0026lt;\u0026gt; \u0026amp; Kc_u ) set_Kc_u # inline void set_Kc_u( UniformMatrixList\u0026lt;\u0026gt; \u0026amp;\u0026amp; Kc_u ) set_g_design # inline void set_g_design( const UniformVectorList \u0026amp; g ) set_g_design # inline void set_g_design( UniformVectorList \u0026amp;\u0026amp; g ) Protected Attribute Details # systems_ # std::vector\u0026lt; System \u0026gt; systems_; n_sys_ # size_t n_sys_ {}; idx_ # size_t idx_ {}; Kc_list_ # UniformMatrixList Kc_list_; Kc_inty_list_ # UniformMatrixList Kc_inty_list_; Kc_u_list_ # UniformMatrixList Kc_u_list_; g_design_list_ # UniformVectorList g_design_list_; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":47,"href":"/docs/api/classes/classlds_1_1_system/","title":"lds::System","section":"Classes","content":" lds::System # Linear Dynamical System Type. #include \u0026lt;lds_sys.h\u0026gt;\nInherited by lds::gaussian::System, lds::poisson::System\nPublic Functions # Name System() =default\nConstructs a new System. System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)\nconstructs a new System virtual ~System() void Filter(const Vector \u0026amp; u_tm1, const Vector \u0026amp; z)\nFilter data to produce causal state estimates. virtual const Vector \u0026amp; Simulate(const Vector \u0026amp; u_tm1) =0\nsimulates system (single time step) void f(const Vector \u0026amp; u, bool do_add_noise =false)\nsystem dynamics function virtual void h() =0\nsystem output function virtual Vector h_(Vector x) =0\nsystem output function (stateless) size_t n_u() const\nGet number of inputs. size_t n_x() const\nGet number of states. size_t n_y() const\nGet number of outputs. data_t dt() const\nGet sample period. const Vector \u0026amp; x() const\nGet current state. const Matrix \u0026amp; P() const\nGet covariance of state estimate. const Vector \u0026amp; m() const\nGet current process disturbance/bias. const Matrix \u0026amp; P_m() const\nGet covariance of process disturbance estimate. const Vector \u0026amp; cx() const\nGet C*x. const Vector \u0026amp; y() const\nGet output. const Vector \u0026amp; x0() const\nGet initial state. const Vector \u0026amp; m0() const\nGet initial disturbance. const Matrix \u0026amp; A() const\nGet state matrix. const Matrix \u0026amp; B() const\nGet input matrix. const Vector \u0026amp; g() const\nGet input gain/conversion factor. const Matrix \u0026amp; C() const\nGet output matrix. const Vector \u0026amp; d() const\nGet output bias. const Matrix \u0026amp; Ke() const\nGet estimator gain. const Matrix \u0026amp; Ke_m() const\nGet estimator gain for process disturbance (m) const Matrix \u0026amp; Q()\nGet process noise covariance. const Matrix \u0026amp; Q_m()\nGet process noise covariance of disturbance evoluation. const Matrix \u0026amp; P0()\nGet covariance of initial state. const Matrix \u0026amp; P0_m()\nGet covariance of initial process disturbance. void set_A(const Matrix \u0026amp; A)\nSet state matrix. void set_B(const Matrix \u0026amp; B)\nSet input matrix. void set_m(const Vector \u0026amp; m, bool do_force_assign =false)\nSet process disturbance. void set_g(const Vector \u0026amp; g)\nSet input gain. void set_Q(const Matrix \u0026amp; Q)\nSet process noise covariance. void set_Q_m(const Matrix \u0026amp; Q_m)\nSet process noise covariance of disturbance evoluation. void set_x0(const Vector \u0026amp; x0)\nSet initial state. void set_P0(const Matrix \u0026amp; P0)\nSet covariance of initial state. void set_P0_m(const Matrix \u0026amp; P0_m)\nSet covariance of initial process disturbance. void set_C(const Matrix \u0026amp; C)\nSet output matrix. void set_d(const Vector \u0026amp; d)\nSet output bias. void set_x(const Vector \u0026amp; x)\nSet state of system. void Reset()\nReset system variables. std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block(UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1) void Print()\nPrint system variables to stdout. Protected Functions # Name virtual void RecurseKe() =0\nRecursively recalculate estimator gain (Ke) void InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Public Attributes # Name bool do_adapt_m whether to adaptively estimate disturbance m Protected Attributes # Name std::size_t n_x_ number of states std::size_t n_u_ number of inputs std::size_t n_y_ number of outputs data_t dt_ sample period Vector x_ state Matrix P_ covariance of state estimate Vector m_ process disturbance Matrix P_m_ covariance of disturbance estimate Vector cx_ C*x. Vector y_ output Vector z_ measurement Vector x0_ initial state Matrix P0_ covariance of initial state estimate Vector m0_ initial process disturbance Matrix P0_m_ covariance of initial disturbance est. Matrix A_ state matrix Matrix B_ input matrix Vector g_ input gain Matrix Q_ covariance of process noise Matrix Q_m_ covariance of disturbance random walk Matrix C_ output matrix Vector d_ output bias Matrix Ke_ estimator gain Matrix Ke_m_ estimator gain for process disturbance Public Function Details # System # System() =default System # System( size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Parameters:\nn_u number of inputs n_x number of states n_y number of outputs dt sample period p0 diagonal elements for state estimate covariance q0 diagonal elements for process noise covariance ~System # inline virtual ~System() Filter # void Filter( const Vector \u0026amp; u_tm1, const Vector \u0026amp; z ) Parameters:\nu_tm1 input at t-minus-1 z_t current measurement Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.\nSimulate # virtual const Vector \u0026amp; Simulate( const Vector \u0026amp; u_tm1 ) =0 Parameters:\nu_tm1 input at time t-1 Return: simulated measurement at time t\nReimplemented by: lds::gaussian::System::Simulate, lds::poisson::System::Simulate\nf # inline void f( const Vector \u0026amp; u, bool do_add_noise =false ) Parameters:\nu input do_add_noise whether to add simulated process noise h # virtual void h() =0 Reimplemented by: lds::gaussian::System::h, lds::poisson::System::h\nh_ # virtual Vector h_( Vector x ) =0 Parameters:\nx_t state at time t Return: predicted state at time t + 1\nReimplemented by: lds::gaussian::System::h_, lds::poisson::System::h_\nn_u # inline size_t n_u() const n_x # inline size_t n_x() const n_y # inline size_t n_y() const dt # inline data_t dt() const x # inline const Vector \u0026amp; x() const P # inline const Matrix \u0026amp; P() const m # inline const Vector \u0026amp; m() const P_m # inline const Matrix \u0026amp; P_m() const cx # inline const Vector \u0026amp; cx() const y # inline const Vector \u0026amp; y() const x0 # inline const Vector \u0026amp; x0() const m0 # inline const Vector \u0026amp; m0() const A # inline const Matrix \u0026amp; A() const B # inline const Matrix \u0026amp; B() const g # inline const Vector \u0026amp; g() const C # inline const Matrix \u0026amp; C() const d # inline const Vector \u0026amp; d() const Ke # inline const Matrix \u0026amp; Ke() const Ke_m # inline const Matrix \u0026amp; Ke_m() const Q # inline const Matrix \u0026amp; Q() Q_m # inline const Matrix \u0026amp; Q_m() P0 # inline const Matrix \u0026amp; P0() P0_m # inline const Matrix \u0026amp; P0_m() set_A # inline void set_A( const Matrix \u0026amp; A ) set_B # inline void set_B( const Matrix \u0026amp; B ) set_m # inline void set_m( const Vector \u0026amp; m, bool do_force_assign =false ) set_g # inline void set_g( const Vector \u0026amp; g ) set_Q # inline void set_Q( const Matrix \u0026amp; Q ) set_Q_m # inline void set_Q_m( const Matrix \u0026amp; Q_m ) set_x0 # inline void set_x0( const Vector \u0026amp; x0 ) set_P0 # inline void set_P0( const Matrix \u0026amp; P0 ) set_P0_m # inline void set_P0_m( const Matrix \u0026amp; P0_m ) set_C # inline void set_C( const Matrix \u0026amp; C ) set_d # inline void set_d( const Vector \u0026amp; d ) set_x # inline void set_x( const Vector \u0026amp; x ) Reset # void Reset() nstep_pred_block # std::vector\u0026lt; UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; \u0026gt; nstep_pred_block( UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; u, UniformMatrixList\u0026lt; kMatFreeDim2 \u0026gt; z, size_t n_pred =1 ) Print # void Print() Protected Function Details # RecurseKe # virtual void RecurseKe() =0 Reimplemented by: lds::gaussian::System::RecurseKe, lds::poisson::System::RecurseKe\nInitVars # void InitVars( data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0 ) Public Attribute Details # do_adapt_m # bool do_adapt_m {}; Protected Attribute Details # n_x_ # std::size_t n_x_ {}; n_u_ # std::size_t n_u_ {}; n_y_ # std::size_t n_y_ {}; dt_ # data_t dt_ {}; x_ # Vector x_; P_ # Matrix P_; m_ # Vector m_; P_m_ # Matrix P_m_; cx_ # Vector cx_; y_ # Vector y_; z_ # Vector z_; x0_ # Vector x0_; P0_ # Matrix P0_; m0_ # Vector m0_; P0_m_ # Matrix P0_m_; A_ # Matrix A_; B_ # Matrix B_; g_ # Vector g_; Q_ # Matrix Q_; Q_m_ # Matrix Q_m_; C_ # Matrix C_; d_ # Vector d_; Ke_ # Matrix Ke_; Ke_m_ # Matrix Ke_m_; Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":48,"href":"/docs/api/classes/classlds_1_1_uniform_matrix_list/","title":"lds::UniformMatrixList","section":"Classes","content":" lds::UniformMatrixList # More\u0026hellip;\nInherits from std::vector\u0026lt; Matrix \u0026gt;\nPublic Functions # Name UniformMatrixList() =default\nConstructs a new UniformMatrixList. UniformMatrixList(const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by copying existing vector of Matrix if dimensions consistent. UniformMatrixList(std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList by moving existing vector of Matrix if dimensions consistent. UniformMatrixList(std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0})\nConstructs a new UniformMatrixList from initializer_list of Matrix if dimensions consistent. UniformMatrixList(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nConstructs a new UniformMatrixList (copy). UniformMatrixList(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nConstructs a new UniformMatrixList (move). ~UniformMatrixList() =default\nDestroys the object. const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim(size_t n =0) const\ngets dimensions of uniformly sized matrices size_t size()\nsize of container const Matrix \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Matrix \u0026amp; that, size_t n)\nswaps input matrix with n^th matrix of list UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that)\nassigns the contents (copy) UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=(UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that)\nassigns the contents (move) void append(const Matrix \u0026amp; mat)\nappends a matrix to the list Detailed Description # template \u0026lt;MatrixListFreeDim D =kMatFreeDimNone\u0026gt; class lds::UniformMatrixList; Public Function Details # UniformMatrixList # UniformMatrixList() =default UniformMatrixList # explicit UniformMatrixList( const std::vector\u0026lt; Matrix \u0026gt; \u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # explicit UniformMatrixList( std::vector\u0026lt; Matrix \u0026gt; \u0026amp;\u0026amp; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( std::initializer_list\u0026lt; Matrix \u0026gt; mats, std::array\u0026lt; size_t, 2 \u0026gt; dim ={0, 0} ) Parameters:\nmats input matrices dim dimensions UniformMatrixList # UniformMatrixList( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList UniformMatrixList # UniformMatrixList( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList ~UniformMatrixList # ~UniformMatrixList() =default dim # inline const std::array\u0026lt; size_t, 2 \u0026gt; \u0026amp; dim( size_t n =0 ) const Parameters:\nn [optional] index in list of matrices Return: dimensions\nsize # inline size_t size() at # inline const Matrix \u0026amp; at( size_t n ) Swap # inline void Swap( Matrix \u0026amp; that, size_t n ) Parameters:\nthat input matrix n index where the matrix is moved operator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( const UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\noperator= # inline UniformMatrixList\u0026lt; D \u0026gt; \u0026amp; operator=( UniformMatrixList\u0026lt; D \u0026gt; \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformMatrixList Return: reference to object\nappend # void append( const Matrix \u0026amp; mat ) Parameters:\nmat input matrix Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":49,"href":"/docs/api/classes/classlds_1_1_uniform_system_list/","title":"lds::UniformSystemList","section":"Classes","content":" lds::UniformSystemList # More\u0026hellip;\nInherits from std::vector\u0026lt; System \u0026gt;\nPublic Functions # Name UniformSystemList() =default\nConstructs a new UniformSystemList. UniformSystemList(const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by copying existing vector of System if dimensions consistent. UniformSystemList(std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList by moving existing vector of System if dimensions consistent. UniformSystemList(std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0})\nConstructs a new UniformSystemList from initializer_list of System if dimensions consistent. UniformSystemList(const UniformSystemList \u0026amp; that)\nConstructs a new UniformSystemList (copy). UniformSystemList(UniformSystemList \u0026amp;\u0026amp; that)\nConstructs a new UniformSystemList (move). ~UniformSystemList() =default\nDestroys the object. const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const\ngets dimensions of the uniformly sized systems size_t size()\nsize of container const System \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(System \u0026amp; that, size_t n)\nswaps input system with n^th system of list UniformSystemList \u0026amp; operator=(const UniformSystemList \u0026amp; that)\nassigns the contents (copy) UniformSystemList \u0026amp; operator=(UniformSystemList \u0026amp;\u0026amp; that)\nassigns the contents (move) Detailed Description # template \u0026lt;typename System \u0026gt; class lds::UniformSystemList; Public Function Details # UniformSystemList # UniformSystemList() =default UniformSystemList # explicit UniformSystemList( const std::vector\u0026lt; System \u0026gt; \u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # explicit UniformSystemList( std::vector\u0026lt; System \u0026gt; \u0026amp;\u0026amp; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( std::initializer_list\u0026lt; System \u0026gt; systems, std::array\u0026lt; size_t, 3 \u0026gt; dim ={0, 0, 0} ) Parameters:\nsystems input systems dim dimensions (n_u, n_x, n_y) UniformSystemList # UniformSystemList( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList UniformSystemList # UniformSystemList( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList ~UniformSystemList # ~UniformSystemList() =default dim # inline const std::array\u0026lt; size_t, 3 \u0026gt; \u0026amp; dim() const size # inline size_t size() at # inline const System \u0026amp; at( size_t n ) Swap # inline void Swap( System \u0026amp; that, size_t n ) Parameters:\nthat input system n index where the system is moved operator= # inline UniformSystemList \u0026amp; operator=( const UniformSystemList \u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\noperator= # inline UniformSystemList \u0026amp; operator=( UniformSystemList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformSystemList Return: reference to object\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":50,"href":"/docs/api/classes/classlds_1_1_uniform_vector_list/","title":"lds::UniformVectorList","section":"Classes","content":" lds::UniformVectorList # Inherits from std::vector\u0026lt; Vector \u0026gt;\nPublic Functions # Name UniformVectorList() =default\nConstructs a new UniformVectorList. UniformVectorList(const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by copying existing vector of Vector if dimensions consistent. UniformVectorList(std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0)\nConstructs a new UniformVectorList by moving existing vector of Vector if dimensions consistent. UniformVectorList(std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0)\nConstructs a new UniformVectorList from initializer_list of Vector if dimensions consistent. UniformVectorList(const UniformVectorList \u0026amp; that)\nConstructs a new UniformVectorList (copy) UniformVectorList(UniformVectorList \u0026amp;\u0026amp; that)\nConstructs a new UniformVectorList (move) ~UniformVectorList() =default\nDestroys the object. size_t dim() const\ngets dimensions of the uniformly sized matrices size_t size()\nsize of container const Vector \u0026amp; at(size_t n)\ngets reference to n^th element void Swap(Vector \u0026amp; that, size_t n)\nswaps input matrix with n^th vector of list UniformVectorList \u0026amp; operator=(const UniformVectorList \u0026amp; that)\nassigns the contents (copy) UniformVectorList \u0026amp; operator=(UniformVectorList \u0026amp;\u0026amp; that)\nassigns the contents (move) Public Function Details # UniformVectorList # UniformVectorList() =default UniformVectorList # explicit UniformVectorList( const std::vector\u0026lt; Vector \u0026gt; \u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dims dimension UniformVectorList # explicit UniformVectorList( std::vector\u0026lt; Vector \u0026gt; \u0026amp;\u0026amp; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( std::initializer_list\u0026lt; Vector \u0026gt; vecs, size_t dim =0 ) Parameters:\nvecs input vectors dim dimension UniformVectorList # UniformVectorList( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList UniformVectorList # UniformVectorList( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList ~UniformVectorList # ~UniformVectorList() =default dim # inline size_t dim() const size # inline size_t size() at # inline const Vector \u0026amp; at( size_t n ) Swap # inline void Swap( Vector \u0026amp; that, size_t n ) Parameters:\nthat input vector n index where the vector is moved operator= # inline UniformVectorList \u0026amp; operator=( const UniformVectorList \u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\noperator= # inline UniformVectorList \u0026amp; operator=( UniformVectorList \u0026amp;\u0026amp; that ) Parameters:\nthat another UniformVectorList Return: reference to object\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":51,"href":"/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/","title":"ldsCtrlEst_h","section":"Files","content":" ldsCtrlEst_h # Files # Name ldsCtrlEst_h/lds.h lds namespace ldsCtrlEst_h/lds_ctrl.h Controller. ldsCtrlEst_h/lds_fit.h LDS base fit type. ldsCtrlEst_h/lds_fit_em.h subspace identification ldsCtrlEst_h/lds_fit_ssid.h subspace identification ldsCtrlEst_h/lds_gaussian.h glds namespace ldsCtrlEst_h/lds_gaussian_ctrl.h GLDS Controller. ldsCtrlEst_h/lds_gaussian_fit.h GLDS fit type. ldsCtrlEst_h/lds_gaussian_fit_em.h GLDS E-M fit type. ldsCtrlEst_h/lds_gaussian_fit_ssid.h GLDS SSID fit type. ldsCtrlEst_h/lds_gaussian_sctrl.h GLDS switched controller type. ldsCtrlEst_h/lds_gaussian_sys.h GLDS base type. ldsCtrlEst_h/lds_poisson.h plds namespace ldsCtrlEst_h/lds_poisson_ctrl.h PLDS controller type. ldsCtrlEst_h/lds_poisson_fit.h PLDS base fit type. ldsCtrlEst_h/lds_poisson_fit_em.h PLDS E-M fit type. ldsCtrlEst_h/lds_poisson_fit_ssid.h PLDS SSID fit type. ldsCtrlEst_h/lds_poisson_sctrl.h PLDS switched controller type. ldsCtrlEst_h/lds_poisson_sys.h PLDS base type. ldsCtrlEst_h/lds_sctrl.h SwitchedController type. ldsCtrlEst_h/lds_sys.h LDS base type. ldsCtrlEst_h/lds_uniform_mats.h List of uniformly sized matrices. ldsCtrlEst_h/lds_uniform_systems.h List of uniformly sized Systems. ldsCtrlEst_h/lds_uniform_vecs.h List of uniformly sized vectors. ldsCtrlEst_h/mex_c_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) ldsCtrlEst_h/mex_cpp_util.h arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":52,"href":"/docs/api/files/lds__ctrl_8h/","title":"ldsCtrlEst_h/lds_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_ctrl.h # Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Controller Detailed Description # This file declares the type for control of a linear dynamical system (lds::Controller).\nSource code # //===-- ldsCtrlEst_h/lds_control.h - Controller -----------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_CTRL_H #define LDSCTRLEST_LDS_CTRL_H // namespace #include \u0026#34;lds.h\u0026#34; // system type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class Controller { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); public: Controller() = default; Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type = 0); const Vector\u0026amp; Control(const Vector\u0026amp; z, bool do_control = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); const Vector\u0026amp; ControlOutputReference(const Vector\u0026amp; z, bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); // get methods: const System\u0026amp; sys() const { return sys_; }; const Matrix\u0026amp; Kc() const { return Kc_; }; const Matrix\u0026amp; Kc_inty() const { return Kc_inty_; }; const Matrix\u0026amp; Kc_u() const { return Kc_u_; }; const Vector\u0026amp; g_design() const { return g_design_; }; const Vector\u0026amp; u_ref() const { return u_ref_; }; const Vector\u0026amp; x_ref() const { return x_ref_; }; const Vector\u0026amp; y_ref() const { return y_ref_; }; size_t control_type() const { return control_type_; }; data_t tau_awu() const { return tau_awu_; }; data_t u_lb() const { return u_lb_; }; data_t u_ub() const { return u_ub_; }; // set methods void set_sys(const System\u0026amp; sys) { bool does_match = sys_.n_u() == sys.n_u(); does_match = does_match \u0026amp;\u0026amp; (sys_.n_x() == sys.n_x()); does_match = does_match \u0026amp;\u0026amp; (sys_.n_y() == sys.n_y()); if (does_match) { sys_ = sys; } else { throw std::runtime_error( \u0026#34;new system argument to `set_sys` does not match dimensionality of \u0026#34; \u0026#34;existing system\u0026#34;); } }; void set_g_design(const Vector\u0026amp; g_design) { Reassign(g_design_, g_design); }; void set_u_ref(const Vector\u0026amp; u_ref) { Reassign(u_ref_, u_ref); }; void set_x_ref(const Vector\u0026amp; x_ref) { Reassign(x_ref_, x_ref); cx_ref_ = sys_.C() * x_ref_; }; // y_ref needs to be handled differently depending on output fn. // (need to populate cx_ref_ too, which depends on output fn) virtual void set_y_ref(const Vector\u0026amp; y_ref) { Reassign(y_ref_, y_ref); }; void set_Kc(const Matrix\u0026amp; Kc) { Reassign(Kc_, Kc); }; void set_Kc_inty(const Matrix\u0026amp; Kc_inty) { Reassign(Kc_inty_, Kc_inty); }; void set_Kc_u(const Matrix\u0026amp; Kc_u) { Reassign(Kc_u_, Kc_u); }; void set_tau_awu(data_t tau) { tau_awu_ = tau; k_awu_ = sys_.dt() / tau_awu_; }; void set_control_type(size_t control_type); // There is no reason u_lb/ub should not be public, but making set methods // anyway. void set_u_lb(data_t u_lb) { u_lb_ = u_lb; }; void set_u_ub(data_t u_ub) { u_ub_ = u_ub; }; void Reset() { sys_.Reset(); u_ref_.zeros(); u_ref_prev_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); u_saturated_ = false; t_since_control_onset_ = 0.0; }; void Print() { sys_.Print(); std::cout \u0026lt;\u0026lt; \u0026#34;g_design : \u0026#34; \u0026lt;\u0026lt; g_design_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_lb : \u0026#34; \u0026lt;\u0026lt; u_lb_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;u_ub : \u0026#34; \u0026lt;\u0026lt; u_ub_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; }; protected: System sys_; Vector u_; Vector u_return_; Vector g_design_; // reference signals Vector u_ref_; // create no set method for this: Vector u_ref_prev_; Vector x_ref_; Vector y_ref_; Vector cx_ref_; // Controller gains Matrix Kc_; Matrix Kc_u_; Matrix Kc_inty_; // control after g inversion // do not need set methods for these. Vector du_ref_; Vector dv_ref_; Vector v_ref_; Vector dv_; Vector v_; // integral error // do not need set method for this Vector int_e_; Vector int_e_awu_adjust_; Vector u_sat_; bool do_control_prev_ = false; bool do_lock_control_prev_ = false; // whether the g of system has become inverted from what you think it is // (gain_ref) bool u_saturated_ = false; // should be safe to have references here bc nothing needs to be done // (like reset vars) when it changes... data_t u_lb_{}; data_t u_ub_{}; data_t tau_awu_{}; data_t k_awu_ = 0; data_t t_since_control_onset_ = 0; size_t control_type_{}; private: void CalcControl(bool do_control = true, bool do_estimation = true, bool do_lock_control = false, data_t sigma_soft_start = 0, data_t sigma_u_noise = 0, bool do_reset_at_control_onset = true); void CalcSteadyStateSetPoint(); void AntiWindup(); void InitVars(size_t control_type); }; // Implement the above: template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(const System\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(sys), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline Controller\u0026lt;System\u0026gt;::Controller(System\u0026amp;\u0026amp; sys, data_t u_lb, data_t u_ub, size_t control_type) : sys_(std::move(sys)), u_lb_(u_lb), u_ub_(u_ub), tau_awu_(lds::kInf) { InitVars(control_type); } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::set_control_type(size_t control_type) { if (control_type_ == control_type) { return; } // creating a blank slate... control_type_ = 0; Kc_inty_.zeros(0, 0); Kc_u_.zeros(0, 0); int_e_.zeros(0, 0); int_e_awu_adjust_.zeros(0, 0); // controller was designed to minimize integral error if (control_type \u0026amp; kControlTypeIntY) { Kc_inty_.zeros(sys_.n_u(), sys_.n_y()); int_e_.zeros(sys_.n_y()); int_e_awu_adjust_.zeros(sys_.n_u()); control_type_ = control_type_ | kControlTypeIntY; } // controller was designed to minimize deltaU // (i.e. state augmented with u) if (control_type \u0026amp; kControlTypeDeltaU) { Kc_u_.zeros(sys_.n_u(), sys_.n_u()); control_type_ = control_type_ | kControlTypeDeltaU; } // whether to adapt set point calculate with (re-estimated) process // disturbance (m) if (control_type \u0026amp; kControlTypeAdaptM) { if (sys_.do_adapt_m) // only if adapting m... { control_type_ = control_type_ | kControlTypeAdaptM; } } } // set_control_type template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::Control( const Vector\u0026amp; z, bool do_control, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement sys_.Filter(u_, z); bool do_estimation = true; // always have estimator on in this case // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline const Vector\u0026amp; Controller\u0026lt;System\u0026gt;::ControlOutputReference( const Vector\u0026amp; z, bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { // update state estimates, given latest measurement if (do_estimation) { sys_.Filter(u_, z); } else { sys_.f(u_); } // calculate the set point // solves for u_ref and x_ref when output is at y_ref at steady state. if (do_control) { CalcSteadyStateSetPoint(); } // calculate control signal CalcControl(do_control, do_estimation, do_lock_control, sigma_soft_start, sigma_u_noise, do_reset_at_control_onset); return u_return_; } template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcControl(bool do_control, bool do_estimation, bool do_lock_control, data_t sigma_soft_start, data_t sigma_u_noise, bool do_reset_at_control_onset) { if (do_control \u0026amp;\u0026amp; do_estimation) { if (!do_control_prev_) { if (do_reset_at_control_onset) { Reset(); } t_since_control_onset_ = 0.0; } else { t_since_control_onset_ += sys_.dt(); } // enforce softstart on control vars. if (sigma_soft_start \u0026gt; 0) { // half-Gaussian soft-start scaling factor data_t soft_start_sf = 1 - exp(-pow(t_since_control_onset_, 2) / (2 * pow(sigma_soft_start, 2))); u_ref_ *= soft_start_sf; // TODO(mfbolus): May be appropriate to soft-start x_ref, y_ref too // x_ref_ *= soft_start_sf; // cx_ref_ *= soft_start_sf; // y_ref_ *= soft_start_sf; } if (!do_lock_control) { // first do u -\u0026gt; v change of vars. (v = g.*u) // e.g., convert into physical units (e.g., v[=] mW/mm2 rather than driver // control voltage u[=]V) v_ref_ = g_design_ % u_ref_; // Given FB, calc. the change in control if (control_type_ \u0026amp; kControlTypeDeltaU) { // if control designed to minimize not u but deltaU (i.e. state aug with // u): // TODO(mfbolus): Commented out for now. See note below. // du_ref_ = u_ref_ - u_ref_prev_; // dv_ref_ = g_design_ % du_ref_; // TODO(mfbolus): Assuming users want *smooth* control signals if using // kControlTypeDeltaU, it should be the case that dv_ref_ is --\u0026gt; 0. May // want to revisit, but I am going to force it to be zero unless a // situation arises that argues for keeping the above. dv_ref_.zeros(); dv_ = dv_ref_; // nominally-optimal. dv_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error dv_ -= Kc_u_ * (v_ - v_ref_); // penalty on amp u (rel to ref) if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if(!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error dv_ -= Kc_inty_ * int_e_; // control for integrated error } // update the control v_ += dv_; } else { v_ = v_ref_; // nominally-optimal. v_ -= Kc_ * (sys_.x() - x_ref_); // instantaneous state error if (control_type_ \u0026amp; kControlTypeIntY) { // TODO(mfbolus): one approach to protection against integral windup // would be to not integrate error when control signal saturated: // if (!uSaturated) int_e_ += (sys_.cx() - cx_ref_) * sys_.dt(); // integrated error v_ -= Kc_inty_ * int_e_; // control for integrated error } } // convert back to control voltage u[=]V u_ = v_ / sys_.g(); } // else do nothing until lock is low } else { // if not control // feed through u_ref in open loop u_ = u_ref_ % g_design_ / sys_.g(); v_ = sys_.g() % u_; u_ref_.zeros(); int_e_.zeros(); int_e_awu_adjust_.zeros(); u_sat_.zeros(); } // ends do_control // enforce box constraints (and antiwindup) AntiWindup(); // add noise to input? // The value for u that is *returned* to user after addition of any noise, // while keeping controller/estimator blind to this addition. u_return_ = u_; if ((sigma_u_noise \u0026gt; 0.0) \u0026amp;\u0026amp; (do_control \u0026amp;\u0026amp; !do_lock_control)) { u_return_ += sigma_u_noise * Vector(sys_.n_u(), fill::randn); Limit(u_return_, u_lb_, u_ub_); }; // For next time step: u_ref_prev_ = u_ref_; do_control_prev_ = do_control; do_lock_control_prev_ = do_lock_control; } // CalcControl template \u0026lt;typename System\u0026gt; inline void Controller\u0026lt;System\u0026gt;::CalcSteadyStateSetPoint() { // Linearly-constrained least squares (ls). // // _reference: // Boyd \u0026amp; Vandenberghe (2018) Introduction to Applied Linear Algebra // Matrix a_ls = join_horiz(sys_.C(), Matrix(sys_.n_y(), sys_.n_u(), fill::zeros)); Vector b_ls = cx_ref_; Matrix c_ls = join_horiz(sys_.A() - Matrix(sys_.n_x(), sys_.n_x(), fill::eye), sys_.B() * arma::diagmat(sys_.g())); Vector d_ls = -sys_.m0(); if (control_type_ \u0026amp; kControlTypeAdaptM) { d_ls = -sys_.m(); // adapt setpoint calc with disturbance? } Matrix a_ls_t = a_ls.t(); // TODO(mfbolus): not sure why but causes seg // fault if I do not do this. Matrix phi_ls = join_vert(join_horiz(2 * a_ls_t * a_ls, c_ls.t()), join_horiz(c_ls, Matrix(sys_.n_x(), sys_.n_x(), fill::zeros))); // TODO(mfbolus): should be actual inverse, rather than pseudo-inverse: Matrix inv_phi = pinv(phi_ls); Vector xulam = inv_phi * join_vert(2 * a_ls_t * b_ls, d_ls); x_ref_ = xulam.subvec(0, sys_.n_x() - 1); u_ref_ = xulam.subvec(sys_.n_x(), sys_.n_x() + sys_.n_u() - 1); cx_ref_ = sys_.C() * x_ref_; } // CalcSteadyStateSetPoint template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::AntiWindup() { u_saturated_ = false; u_sat_ = u_; // limit u and flag whether saturated for (size_t k = 0; k \u0026lt; u_.n_elem; k++) { if (u_[k] \u0026lt; u_lb_) { u_sat_[k] = u_lb_; u_saturated_ = true; } if (u_[k] \u0026gt; u_ub_) { u_sat_[k] = u_ub_; u_saturated_ = true; } } if ((control_type_ \u0026amp; kControlTypeIntY) \u0026amp;\u0026amp; (tau_awu_ \u0026lt; lds::kInf)) { // one-step back-calculation (calculate intE for u=u_sat) // (Astroem, Rundqwist 1989 warn against using this...) // int_e_awu_adjust_ = // solve(Kc_inty_, (u_ - u_sat_)); // pinv(Kc_inty) * (u-uSat); // gradual: see Astroem, Rundqwist 1989 // this is a fudge for doing MIMO gradual // n.b., went ahead and multiplied 1/T by dt so don\u0026#39;t have to do that here. int_e_awu_adjust_ = k_awu_ * (sign(Kc_inty_).t() / sys_.n_u()) * (u_ - u_sat_); // int_e_awu_adjust_ = k_awu_ * (u_-u_sat_); int_e_ += int_e_awu_adjust_; } // set u to saturated version u_ = u_sat_; } template \u0026lt;typename System\u0026gt; void Controller\u0026lt;System\u0026gt;::InitVars(size_t control_type) { // initialize to default values u_ref_ = Vector(sys_.n_u(), fill::zeros); u_ref_prev_ = Vector(sys_.n_u(), fill::zeros); x_ref_ = Vector(sys_.n_x(), fill::zeros); y_ref_ = Vector(sys_.n_y(), fill::zeros); cx_ref_ = Vector(sys_.n_y(), fill::zeros); u_ = Vector(sys_.n_u(), fill::zeros); u_return_ = Vector(sys_.n_u(), fill::zeros); u_sat_ = Vector(sys_.n_u(), fill::zeros); // Might not need all these, so zero elements until later. Kc_ = Matrix(sys_.n_u(), sys_.n_x(), fill::zeros); Kc_u_ = Matrix(0, 0, fill::zeros); Kc_inty_ = Matrix(0, 0, fill::zeros); g_design_ = sys_.g(); // by default, same as model dv_ = Vector(sys_.n_u(), fill::zeros); v_ = Vector(sys_.n_u(), fill::zeros); du_ref_ = Vector(sys_.n_u(), fill::zeros); dv_ref_ = Vector(sys_.n_u(), fill::zeros); v_ref_ = Vector(sys_.n_u(), fill::zeros); int_e_ = Vector(0, fill::zeros); int_e_awu_adjust_ = Vector(0, fill::zeros); set_control_type(control_type); } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":53,"href":"/docs/api/files/lds__fit__em_8h/","title":"ldsCtrlEst_h/lds_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_em.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::EM Detailed Description # This file declares the type for fitting a linear dynamical system by expectation-maximization (lds::EM).\nSource code # //===-- ldsCtrlEst_h/lds_fit_em.h - EM Fit ----------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_EMAX_H #define LDSCTRLEST_LDS_EMAX_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class EM { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: EM() = default; EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train); virtual ~EM() = default; const Fit\u0026amp; Run(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = true, bool calc_output = true, bool calc_measurement = true, size_t max_iter = 100, data_t tol = 1e-2); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); // auto tuple = std::make_tuple(u_, z_); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; x() const { return x_; }; const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; y() const { return y_; }; const Matrix\u0026amp; sum_E_x_t_x_t() const { return sum_E_x_t_x_t_; }; const Matrix\u0026amp; sum_E_xu_tm1_xu_tm1() const { return sum_E_xu_tm1_xu_tm1_; }; const Matrix\u0026amp; sum_E_xu_t_xu_tm1() const { return sum_E_xu_t_xu_tm1_; }; size_t n_t_tot() { return n_t_tot_; } const Vector\u0026amp; theta() const { return theta_; }; protected: void Expectation(bool force_common_initial = false); void Maximization(bool calc_dynamics = true, bool calc_Q = true, bool calc_init = false, bool calc_output = false, bool calc_measurement = false); void MaximizeDynamics(); void MaximizeQ(); void MaximizeInitial(); virtual void MaximizeOutput() = 0; virtual void MaximizeMeasurement() = 0; void Smooth(bool force_common_initial); virtual void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) = 0; void Reset(); void InitVars(); Vector UpdateTheta(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; std::vector\u0026lt;Matrix\u0026gt; x_; std::vector\u0026lt;Cube\u0026gt; P_; std::vector\u0026lt;Cube\u0026gt; P_t_tm1_; std::vector\u0026lt;Matrix\u0026gt; y_; Matrix diag_y_; // expectations calculated in E-step Matrix sum_E_x_t_x_t_; Matrix sum_E_xu_tm1_xu_tm1_; Matrix sum_E_xu_t_xu_tm1_; Fit fit_; Vector theta_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; }; template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(size_t n_x, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; fit_ = Fit(n_u_, n_x, n_y_, dt); u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; EM\u0026lt;Fit\u0026gt;::EM(const Fit\u0026amp; fit0, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train) { // make sure fit dims match I/O data if (fit0.n_u() != u_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and input training data have inconsistent dimensions\u0026#34;); } if (fit0.n_y() != z_train.at(0).n_rows) { throw std::runtime_error( \u0026#34;Initial fit and output training data have inconsistent dimensions\u0026#34;); } fit_ = fit0; u_ = std::move(u_train); z_ = std::move(z_train); InitVars(); } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::InitVars() { // check input/output data dimensions are consistent if (z_.size() != u_.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_.at(trial).n_cols != u_.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } n_u_ = fit_.n_u(); n_x_ = fit_.n_x(); n_y_ = fit_.n_y(); dt_ = fit_.dt(); x_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); P_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); P_t_tm1_ = std::vector\u0026lt;Cube\u0026gt;(n_trials_); y_ = std::vector\u0026lt;Matrix\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial] = Matrix(n_x_, n_t_[trial], fill::zeros); P_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); P_t_tm1_[trial] = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); y_[trial] = Matrix(n_y_, n_t_[trial], fill::zeros); } diag_y_ = Matrix(n_y_, n_y_, fill::zeros); // covariances in expectation step sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); } template \u0026lt;typename Fit\u0026gt; const Fit\u0026amp; EM\u0026lt;Fit\u0026gt;::Run(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement, size_t max_iter, data_t tol) { Reset(); // to initial conditions size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_ * n_y_; Vector theta(n_params); Vector theta_new(n_params); data_t max_dtheta = 1; // if solving for initial conditions, allow them be varied. // otherwise, freeze at provided values. bool force_common_initial = !calc_init; // go until parameter convergence for (size_t l = 0; l \u0026lt; max_iter; l++) { theta_ = UpdateTheta(); std::cout \u0026lt;\u0026lt; \u0026#34;Iteration \u0026#34; \u0026lt;\u0026lt; l + 1 \u0026lt;\u0026lt; \u0026#34;/\u0026#34; \u0026lt;\u0026lt; max_iter \u0026lt;\u0026lt; \u0026#34; ...\\n\u0026#34;; Expectation(force_common_initial); Maximization(calc_dynamics, calc_Q, calc_init, calc_output, calc_measurement); // check convergence theta_new = UpdateTheta(); Vector dtheta = abs(theta_new - theta_) / abs(theta_); // some parameters could be zero... arma::uvec ubi_finite = find_finite(dtheta); max_dtheta = max(dtheta.elem(ubi_finite)); std::cout \u0026lt;\u0026lt; \u0026#34;max dtheta: \u0026#34; \u0026lt;\u0026lt; max_dtheta \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; if (max_dtheta \u0026lt; tol) { std::cout \u0026lt;\u0026lt; \u0026#34;Converged.\\n\u0026#34;; break; } std::cout \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } return fit_; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Smooth(bool force_common_initial) { Matrix k_e(n_x_, n_y_); // estimator gain Cube k_backfilt; // back-filtering gains // TODO(mfbolus): this loop could be made parallel for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { Matrix x_pre(n_x_, n_t_[trial], fill::zeros); Cube p_pre(n_x_, n_x_, n_t_[trial], fill::zeros); Matrix x_post(n_x_, n_t_[trial], fill::zeros); Cube p_post(n_x_, n_x_, n_t_[trial], fill::zeros); if (force_common_initial) // forces all trials to have same initial // conditions. { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); } y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); // This *should not* be necessary but make sure P is symmetric. ForceSymPD(P_[trial].slice(0)); x_pre.col(0) = x_[trial].col(0); p_pre.slice(0) = P_[trial].slice(0); x_post.col(0) = x_[trial].col(0); p_post.slice(0) = P_[trial].slice(0); // filter for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // predict fit_.f(x_pre, x_post, u_.at(trial), t); fit_.h(y_[trial], x_pre, t); diag_y_.diag() = y_[trial].col(t); // TODO(mfbolus): change if parallel // update --\u0026gt; posterior estimation RecurseKe(k_e, p_pre, p_post, t); x_post.col(t) = x_pre.col(t) + k_e * (z_.at(trial).col(t) - y_[trial].col(t)); y_[trial].col(t) = fit_.C() * x_post.col(t) + fit_.d(); } // backfilter -\u0026gt; Smoothed estimate // Reference: // Shumway et Stoffer (1982) ForceSymPD(p_post.slice(n_t_[trial] - 1)); k_backfilt = Cube(n_x_, n_x_, n_t_[trial], fill::zeros); x_[trial].col(n_t_[trial] - 1) = x_post.col(n_t_[trial] - 1); P_[trial].slice(n_t_[trial] - 1) = p_post.slice(n_t_[trial] - 1); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 0; t--) { // TODO(mfmbolus): should not be necessary to force symm positive def ForceSymPD(p_pre.slice(t)); ForceSymPD(p_post.slice(t - 1)); ForceSymPD(P_[trial].slice(t)); k_backfilt.slice(t - 1) = p_post.slice(t - 1) * fit_.A().t() * inv_sympd(p_pre.slice(t)); x_[trial].col(t - 1) = x_post.col(t - 1) + k_backfilt.slice(t - 1) * (x_[trial].col(t) - x_pre.col(t)); P_[trial].slice(t - 1) = p_post.slice(t - 1) + k_backfilt.slice(t - 1) * (P_[trial].slice(t) - p_pre.slice(t)) * k_backfilt.slice(t - 1).t(); } // do the same for P_t_tm1 Matrix id(n_x_, n_x_, fill::eye); P_t_tm1_[trial].slice(n_t_[trial] - 1) = (id - k_e * fit_.C()) * fit_.A() * p_post.slice(n_t_[trial] - 2); for (size_t t = (n_t_[trial] - 1); t \u0026gt; 1; t--) { P_t_tm1_[trial].slice(t - 1) = p_post.slice(t - 1) * k_backfilt.slice(t - 2).t() + k_backfilt.slice(t - 1) * (P_t_tm1_[trial].slice(t) - fit_.A() * p_post.slice(t - 1)) * k_backfilt.slice(t - 2).t(); } // finally, get smoothed estimate of output for (size_t t = 0; t \u0026lt; n_t_[trial]; t++) { fit_.h(y_[trial], x_[trial], t); } // samps loop } // trial loop } // Smooth // template \u0026lt;typename Fit\u0026gt; // void EM\u0026lt;Fit\u0026gt;::RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) { // // predict covar // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + fit_.Q(); // // update Ke // Ke = P_pre.slice(t) * fit_.C().t() * // inv_sympd(fit_.C() * P_pre.slice(t) * fit_.C().t() + fit_.R()); // // update cov // // Reference: Ghahramani et Hinton (1996) // P_post.slice(t) = P_pre.slice(t) - Ke * fit_.C() * P_pre.slice(t); // // // n.b. for poisson : // // P_pre.slice(t) = fit_.A() * P_post.slice(t - 1) * fit_.A().t() + // fit_.Q(); // // // update cov // // P_post.slice(t) = pinv(pinv(P_pre.slice(t)) + fit_.C().t() * diag_y_ * // // fit_.C()); // // // update Ke // // Ke = P_post.slice(t) * fit_.C(); // } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Expectation(bool force_common_initial) { // calculate the mean/cov of state needed for maximizing E[pr(z|theta)] Smooth(force_common_initial); // now get the various forms of sum(E[xx\u0026#39;]) needed // n.b. Going to start at t=1 rather than 0 bc most max terms need that. // so really \u0026#34;n_t_tot_\u0026#34; is (n_t_tot_-1) n_t_tot_ = 0; sum_E_x_t_x_t_ = Matrix(n_x_, n_x_, fill::zeros); sum_E_xu_tm1_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); sum_E_xu_t_xu_tm1_ = Matrix(n_x_ + n_u_, n_x_ + n_u_, fill::zeros); Vector xu_tm1(n_x_ + n_u_, fill::zeros); Vector xu_t(n_x_ + n_u_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { // ------------ sum_E_x_t_x_t ------------ sum_E_x_t_x_t_ += x_[trial].col(t) * x_[trial].col(t).t(); sum_E_x_t_x_t_ += P_[trial].slice(t); // ------------ sum_E_xu_tm1_xu_tm1 ------------ xu_tm1 = join_vert(x_[trial].col(t - 1), u_.at(trial).col(t - 1)); sum_E_xu_tm1_xu_tm1_ += xu_tm1 * xu_tm1.t(); sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t - 1); // ------------ sum_E_xu_t_xu_tm1 ------------ xu_t = join_vert(x_[trial].col(t), u_.at(trial).col(t)); sum_E_xu_t_xu_tm1_ += xu_t * xu_tm1.t(); sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_t_tm1_[trial].slice(t); n_t_tot_ += 1; } // time } // trial } // Expectation template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Maximization(bool calc_dynamics, bool calc_Q, bool calc_init, bool calc_output, bool calc_measurement) { if (calc_output) { MaximizeOutput(); } if (calc_measurement) { MaximizeMeasurement(); } if (calc_dynamics) { MaximizeDynamics(); } if (calc_Q) { MaximizeQ(); } if (calc_init) { MaximizeInitial(); } } // Maximization template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeDynamics() { // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) Matrix ab = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1) * inv_sympd(sum_E_xu_tm1_xu_tm1_); fit_.set_A(ab.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_B(ab.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1)); std::cout \u0026lt;\u0026lt; \u0026#34;A_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.A()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.B()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeQ() { // // Shumway, Stoffer (1982); Ghahgramani, Hinton (1996) // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix q = sum_E_x_t_x_t_ - sum_e_x_t_xu_tm1 * // inv_sympd(sum_E_xu_tm1_xu_tm1_) * // sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // this way is same as above iff dynamics were just updated: // View sum_e_x_t_xu_tm1 = // sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ + n_u_ - 1); // Matrix ab = arma::join_horiz(fit_.A(), fit_.B()); // Matrix q = sum_E_x_t_x_t_ - ab * sum_e_x_t_xu_tm1.t(); // q /= n_t_tot_; // From scratch method: // Q is covariance of the error between state and dynamics-predicted state // (aka process noise) // Q* = E[(x_t - Ax_{t-1} - Bu_{t-1})*(x_t - Ax_{t-1} - Bu_{t-1})\u0026#39;] // t-1 terms: View sum_e_x_tm1_x_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_u_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(n_x_, n_x_, n_x_ + n_u_ - 1, n_x_ + n_u_ - 1); View sum_e_x_tm1_u_tm1 = sum_E_xu_tm1_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); // t, t-1 terms: View sum_e_x_t_x_tm1 = sum_E_xu_t_xu_tm1_.submat(0, 0, n_x_ - 1, n_x_ - 1); View sum_e_x_t_u_tm1 = sum_E_xu_t_xu_tm1_.submat(0, n_x_, n_x_ - 1, n_x_ + n_u_ - 1); Matrix q = sum_E_x_t_x_t_; q += fit_.A() * sum_e_x_tm1_x_tm1 * fit_.A().t(); q -= sum_e_x_t_x_tm1 * fit_.A().t(); q -= fit_.A() * sum_e_x_t_x_tm1.t(); // input-related terms: q += fit_.B() * sum_e_u_tm1_u_tm1 * fit_.B().t(); q -= sum_e_x_t_u_tm1 * fit_.B().t(); q -= fit_.B() * sum_e_x_t_u_tm1.t(); q += fit_.A() * sum_e_x_tm1_u_tm1 * fit_.B().t(); q += fit_.B() * sum_e_x_tm1_u_tm1.t() * fit_.A().t(); q /= n_t_tot_; fit_.set_Q(q); std::cout \u0026lt;\u0026lt; \u0026#34;Q_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.Q()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // std::cout \u0026lt;\u0026lt; \u0026#34;Q_new: \\n\u0026#34; \u0026lt;\u0026lt; fit_.Q() \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeInitial() { Vector x0 = fit_.x0(); x0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { x0 += x_[trial].col(0); } x0 /= z_.size(); std::cout \u0026lt;\u0026lt; \u0026#34;x0_new[0]: \u0026#34; \u0026lt;\u0026lt; x0[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // always recalc P0 even if the initial state is fixed (at zero, for // example) Matrix e_var(n_x_, n_x_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { e_var += (x_[trial].col(0) - x0) * (x_[trial].col(0) - x0).t(); } e_var /= z_.size(); // go ahead and subtract x0*x0\u0026#39; so don\u0026#39;t have to below. e_var -= x0 * x0.t(); // To get P0, going to get initial P_ per trial and average. // (which might be wrong, but need a single number) Matrix p0 = fit_.P0(); p0.zeros(); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { p0 += (x_[trial].col(0) * x_[trial].col(0).t()) + P_[trial].slice(0) + e_var; } p0 /= z_.size(); fit_.set_P0(p0); std::cout \u0026lt;\u0026lt; \u0026#34;P0_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.P0()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeOutput() { // solve for C+d: Matrix sum_zx(n_y_, n_x_ + 1, fill::zeros); Vector x1(n_x_ + 1, fill::zeros); x1[n_x_] = 1.0; // augment with one to solve for bias Matrix sum_e_x1_x1(n_x_ + 1, n_x_ + 1, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { x1.subvec(0, n_x_ - 1) = x_[trial].col(t); sum_zx += z_.at(trial).col(t) * x1.t(); sum_e_x1_x1 += x1 * x1.t(); sum_e_x1_x1.submat(0, 0, n_x_ - 1, n_x_ - 1) += P_[trial].slice(t); } } Matrix cd = sum_zx * inv_sympd(sum_e_x1_x1); fit_.set_C(cd.submat(0, 0, n_y_ - 1, n_x_ - 1)); fit_.set_d(vectorise(cd.submat(0, n_x_, n_y_ - 1, n_x_))); std::cout \u0026lt;\u0026lt; \u0026#34;C_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.C()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.d()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::MaximizeMeasurement() { // Solve for measurement noise covar size_t n_t_tot = 0; // Ghahgramani, Hinton 1996: Matrix sum_zz(n_y_, n_y_, fill::zeros); Matrix sum_yz(n_y_, n_y_, fill::zeros); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { for (size_t t = 1; t \u0026lt; n_t_[trial]; t++) { sum_zz += z_.at(trial).col(t) * z_.at(trial).col(t).t(); // Use Cnew: sum_yz += (fit_.C() * x_[trial].col(t) + fit_.d()) * z_.at(trial).col(t).t(); n_t_tot += 1; } } fit_.set_R((sum_zz - sum_yz) / n_t_tot); std::cout \u0026lt;\u0026lt; \u0026#34;R_new[0]: \u0026#34; \u0026lt;\u0026lt; fit_.R()[0] \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } template \u0026lt;typename Fit\u0026gt; void EM\u0026lt;Fit\u0026gt;::Reset() { // reset to initial conditions for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { x_[trial].col(0) = fit_.x0(); P_[trial].slice(0) = fit_.P0(); y_[trial].col(0) = fit_.C() * x_[trial].col(0) + fit_.d(); } } template \u0026lt;typename Fit\u0026gt; Vector EM\u0026lt;Fit\u0026gt;::UpdateTheta() { // TODO(mfbolus): This should include n_y_ more params for d. size_t n_params = 3 * n_x_ * n_x_ + n_x_ * n_u_ + n_x_ + n_y_ * n_x_ + n_y_; if (fit_.R().n_elem \u0026gt; 0) { n_params += n_y_ * n_y_; } Vector theta(n_params); size_t idx_start = 0; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.A()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_u_ - 1) = vectorise(fit_.B()); idx_start += n_x_ * n_u_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.Q()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_x_ - 1) = vectorise(fit_.x0()); idx_start += n_x_; theta.subvec(idx_start, idx_start + n_x_ * n_x_ - 1) = vectorise(fit_.P0()); idx_start += n_x_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ * n_x_ - 1) = vectorise(fit_.C()); idx_start += n_y_ * n_x_; theta.subvec(idx_start, idx_start + n_y_ - 1) = vectorise(fit_.d()); idx_start += n_y_; if (fit_.R().n_elem \u0026gt; 0) { theta.subvec(idx_start, idx_start + n_y_ * n_y_ - 1) = vectorise(fit_.R()); } return theta; } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":54,"href":"/docs/api/files/lds__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_fit_ssid.h # subspace identification More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SSID Detailed Description # This file declares and partially defines a template type by which LDS models are fit by a subspace identification (SSID) algorithm ([lds::SSID](/docs/api/classes/classlds_1_1_s_s_i_d/)\u0026lt;Fit\u0026gt;).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_fit_ssid.h - SSID Fit ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_FIT_SSID_H #define LDSCTRLEST_LDS_FIT_SSID_H #include \u0026#34;lds_fit.h\u0026#34; namespace lds { template \u0026lt;typename Fit\u0026gt; class SSID { static_assert(std::is_base_of\u0026lt;lds::Fit, Fit\u0026gt;::value, \u0026#34;Fit must be derived from lds::Fit type.\u0026#34;); public: SSID() = default; SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d = Vector(1).fill(-kInf)); std::tuple\u0026lt;Fit, Vector\u0026gt; Run(SSIDWt ssid_wt); std::tuple\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; ReturnData() { auto tuple = std::make_tuple(std::move(u_), std::move(z_)); u_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); z_ = UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;(); return tuple; } protected: void CalcD(data_t t_silence = 0.1, data_t thresh_silence = 0.001); void CreateHankelDataMat(); virtual void DecomposeData() = 0; void CalcSVD(SSIDWt wt); void Solve(data_t wt_dc); void RecomputeExtObs(); // input/output training data UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u_; UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z_; Matrix D_; Fit fit_; Matrix g_dc_; data_t dt_{}; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; size_t n_h_{}; size_t n_trials_{}; std::vector\u0026lt;size_t\u0026gt; n_t_; size_t n_t_tot_{}; Matrix L_; Vector s_; Matrix ext_obs_t_; }; template \u0026lt;typename Fit\u0026gt; SSID\u0026lt;Fit\u0026gt;::SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; u_train, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026amp;\u0026amp; z_train, const Vector\u0026amp; d) { // check input/output data dimensions are consistent if (z_train.size() != u_train.size()) { throw std::runtime_error( \u0026#34;I/O training data have different number of trials.\u0026#34;); } n_trials_ = u_train.size(); n_t_tot_ = 0; n_t_ = std::vector\u0026lt;size_t\u0026gt;(n_trials_); for (size_t trial = 0; trial \u0026lt; n_trials_; trial++) { if (z_train.at(trial).n_cols != u_train.at(trial).n_cols) { throw std::runtime_error( \u0026#34;I/O training data have different number of time steps.\u0026#34;); } n_t_[trial] = u_train.at(trial).n_cols; n_t_tot_ += n_t_[trial]; } dt_ = dt; n_x_ = n_x; n_u_ = u_train.at(0).n_rows; n_y_ = z_train.at(0).n_rows; n_h_ = n_h; // dimensionality check for eventual block-hankel data matrix size_t len = n_t_tot_ - 2 * n_h_ + 1; if (len \u0026lt; (2 * n_h_ * (n_u_ + n_y_))) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;Dataset problem! More rows than columns in block-hankel data \u0026#34; \u0026#34;matrix: 2*(n_u+n_y)*n_h \u0026gt; data-length! Need higher data-length or \u0026#34; \u0026#34;lower n_h.\u0026#34;; throw std::runtime_error(ss.str()); } fit_ = Fit(n_u_, n_x_, n_y_, dt_); u_ = std::move(u_train); z_ = std::move(z_train); if (!d.is_finite() || (d.n_rows != n_y_)) { // TODO(mfbolus): implement least-square solution for impulse response with // a second input of ones. Data-driven way of accounting for offset *not* // driven by an input. // // For now, calculate output bias (d) as the // output wherever the stimulus has not been on for some amount of time. // convolve u with rectangle and take all samples. This is a reasonable // approach, since often when autonomous systems are fit (i.e., systems with // no input), they will subtract off the mean of the output. This // essentially amounts to setting output bias to the mean of the output when // there is no stimulation. data_t t_silence = 0.1; data_t thresh_silence = 0.001; CalcD(t_silence, thresh_silence); } else { fit_.set_d(d); } } template \u0026lt;typename Fit\u0026gt; std::tuple\u0026lt;Fit, Vector\u0026gt; SSID\u0026lt;Fit\u0026gt;::Run(SSIDWt ssid_wt) { // the weight on minimizing dc I/O gain only works for gaussian, // and hopefully not necessary with appropriate dataset. data_t wt_dc = 0; // std::cout \u0026lt;\u0026lt; \u0026#34;creating hankel mat\\n\u0026#34;; CreateHankelDataMat(); // std::cout \u0026lt;\u0026lt; \u0026#34;decomposing data\\n\u0026#34;; DecomposeData(); // std::cout \u0026lt;\u0026lt; \u0026#34;calculating svd\\n\u0026#34;; CalcSVD(ssid_wt); // std::cout \u0026lt;\u0026lt; \u0026#34;solving for params\\n\u0026#34;; Solve(wt_dc); // std::cout \u0026lt;\u0026lt; \u0026#34;fin\\n\u0026#34;; return std::make_tuple(fit_, s_); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcD(data_t t_silence, data_t thresh_silence) { Vector d(z_.at(0).n_rows, fill::zeros); Vector win(static_cast\u0026lt;size_t\u0026gt;(t_silence / dt_), fill::ones); Vector sum_z_silence(n_y_, fill::zeros); size_t n_silence(0); for (size_t trial = 0; trial \u0026lt; u_.size(); trial++) { // find silent samples // start by convolving with Vector sum_u = vectorise(sum(abs(u_.at(trial)), 0)); Vector u_conv = conv(sum_u, win, \u0026#34;same\u0026#34;); // get only the samples that are silent... arma::uvec ubi_silence = find(u_conv \u0026lt;= thresh_silence); if (ubi_silence.n_elem \u0026gt; 0) { sum_z_silence += arma::sum(z_.at(trial).cols(ubi_silence), 1); n_silence += ubi_silence.n_elem; } } if (n_silence \u0026gt; 0) { d = sum_z_silence / n_silence; } fit_.set_d(d); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat() { // temporary copy of data Matrix z(n_y_, n_t_tot_, fill::zeros); Matrix u(n_u_, n_t_tot_, fill::zeros); size_t so_far(0); for (size_t trial = 0; trial \u0026lt; z_.size(); trial++) { z.submat(0, so_far, n_y_ - 1, so_far + n_t_.at(trial) - 1) = z_.at(trial); u.submat(0, so_far, n_u_ - 1, so_far + n_t_.at(trial) - 1) = u_.at(trial); so_far += n_t_.at(trial); } // remove output bias z.each_col() -= fit_.d(); // calculate I/O gain @ DC while data in convenient form g_dc_ = z * pinv(u); // std::cout \u0026lt;\u0026lt; \u0026#34;G0_data = \u0026#34; \u0026lt;\u0026lt; g_dc_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; // create hankel data matrix size_t len = z.n_cols - 2 * n_h_ + 1; // data length in hankel mat // block-hankel data matrix D_ = Matrix(2 * n_h_ * (n_u_ + n_y_), len, fill::zeros); // past input auto u_p = D_.submat(0, 0, n_h_ * n_u_ - 1, len - 1); // future input auto u_f = D_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, len - 1); // past output auto y_p = D_.submat(2 * n_h_ * n_u_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, len - 1); // future output auto y_f = D_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, len - 1); size_t idx = 0; for (size_t k = 0; k \u0026lt; len; k++) { idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { u_p.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { u_f.col(k).subvec(idx, idx + n_u_ - 1) = u.col(kk); idx += n_u_; } idx = 0; for (size_t kk = k; kk \u0026lt; (n_h_ + k); kk++) { y_p.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } idx = 0; for (size_t kk = (n_h_ + k); kk \u0026lt; (2 * n_h_ + k); kk++) { y_f.col(k).subvec(idx, idx + n_y_ - 1) = z.col(kk); idx += n_y_; } } D_ /= sqrt(static_cast\u0026lt;data_t\u0026gt;(len)); } // template \u0026lt;typename Fit\u0026gt; // void SSID\u0026lt;Fit\u0026gt;::DecomposeData() { // // do LQ decomp instead of calculating covariance expensive way // // Note that \u0026#34;R\u0026#34; in van Overschee is lower-triangular (L), not \u0026#34;R\u0026#34; in QR // // decomp. Very confusing. // Matrix q_t; // lq(L_, q_t, D_); // // van Overschee zeros out the other elements. // L_ = trimatl(L_); // } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::CalcSVD(SSIDWt wt) { // submats that will be needed: auto R_14_14 = L_.submat(0, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_14 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_11_13 = L_.submat(0, 0, n_h_ * n_u_ - 1, n_h_ * (2 * n_u_) - 1); auto R_23_13 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, 2 * n_h_ * n_u_ - 1); auto R_44_14 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_44_13 = L_.submat(2 * n_u_ * n_h_, 0, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_) - 1); auto R_44 = L_.submat(2 * n_u_ * n_h_, 2 * n_u_ * n_h_, n_h_ * (2 * n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); Matrix Lup_Luf_Lyp = R_56_14 * pinv(R_14_14); auto Lup = Lup_Luf_Lyp.submat(0, 0, n_h_ * n_y_ - 1, n_h_ * n_u_ - 1); auto Luf = Lup_Luf_Lyp.submat(0, n_h_ * n_u_, n_h_ * n_y_ - 1, 2 * n_h_ * n_u_ - 1); auto Lyp = Lup_Luf_Lyp.submat(0, 2 * n_h_ * n_u_, n_h_ * n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) - 1); // aka: R_f Matrix R_56_16 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, L_.n_cols - 1); // from van Overschee subid.m: // Rf = R((2*m+l)*i+1:2*(m+l)*i,:); % Future outputs Matrix U; Matrix V; switch (wt) { case kSSIDNone: { // No weighting. (what van Overschee calls \u0026#34;N4SID\u0026#34;) Matrix O_k_sans_Qt = Lup * R_11_14 + Lyp * R_44_14; arma::svd(U, s_, V, O_k_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDMOESP: { // MOESP weighting // This is what they use in the \u0026#34;robust\u0026#34; algorithm van Overschee, de Moor // 1996 Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); svd(U, s_, V, O_k_ortho_Uf_sans_Qt, \u0026#34;std\u0026#34;); } break; case kSSIDCVA: { // CVA weighting // See van Overschee\u0026#39;s matlab code (subid.m): // https://www.mathworks.com/matlabcentral/fileexchange/2290-subspace-identification-for-linear-systems Matrix Pi = Matrix(2 * n_h_ * n_u_, 2 * n_h_ * n_u_, fill::eye) - R_23_13.t() * inv(R_23_13 * R_23_13.t()) * R_23_13; Matrix O_k_ortho_Uf_sans_Qt = join_horiz((Lup * R_11_13 + Lyp * R_44_13) * Pi, Lyp * R_44); Matrix inv_w1; Matrix qt1; lq(inv_w1, qt1, R_56_16); // lq decomp of R_f (future output data) inv_w1 = trimatl(inv_w1); inv_w1 = inv_w1.submat(0, 0, n_y_ * n_h_ - 1, n_y_ * n_h_ - 1); Matrix w_o_w = arma::solve( inv_w1, O_k_ortho_Uf_sans_Qt); // alternatively // pinv(inv_W1)*O_k_ortho_Uf_sans_Qt svd(U, s_, V, w_o_w, \u0026#34;std\u0026#34;); U = inv_w1 * U; break; } } // Truncate to model order (heart of ssid method) auto s_hat = s_.subvec(0, n_x_ - 1); Matrix diag_sqrt_s = diagmat(sqrt(s_hat)); auto u_hat = U.submat(0, 0, U.n_rows - 1, n_x_ - 1); // get extended observability and controllability mats ext_obs_t_ = u_hat * diag_sqrt_s; // extended observability matrix } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::Solve(data_t wt_dc) { // required submats auto R_56_14 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + 2 * n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) - 1); auto R_23_15 = L_.submat(n_h_ * n_u_, 0, 2 * n_h_ * n_u_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_66_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_) + n_y_, 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_55_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); auto R_56_15 = L_.submat(n_h_ * (2 * n_u_ + n_y_), 0, 2 * n_h_ * (n_u_ + n_y_) - 1, n_h_ * (2 * n_u_ + n_y_) + n_y_ - 1); // Solve for params using appropriate algorithm: // robust deterministic/stochastic algorithm in van Overschee 1996 // algorithm that the authors say \u0026#34;works\u0026#34; in practice. auto ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix // This is what textbook (1996) says: // // Matrix Tr = join_vert(pinv(ext_obs_t_) * R_56_15, R_23_15); // // HOWEVER, do not know why but have to fill the last place with zeros like // authors\u0026#39; matlab implementation (see `subid.m`) // Otherwise, get ridiculous covariances (although A,C estimates are close to // same...) Matrix Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); Matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Matrix S = Tl * pinv(Tr); // Use alternative in van Overschee 1996, p. 129. Apparently, should ensure // stability. fit_.set_C(ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1)); Matrix ext_obs_t_p1 = join_vert( ext_obs_t_.submat(n_y_, 0, ext_obs_t_.n_rows - 1, ext_obs_t_.n_cols - 1), Matrix(n_y_, ext_obs_t_.n_cols, fill::zeros)); fit_.set_A(pinv(ext_obs_t_) * ext_obs_t_p1); // At this point, van Overschee \u0026amp; de Moor suggest re-calculating ext_obs_t_, // ext_obs_tm1 from (A, C) because it was just an approximation. This is RecomputeExtObs(); ext_obs_tm1 = ext_obs_t_.submat( 0, 0, ext_obs_t_.n_rows - 1 - n_y_, ext_obs_t_.n_cols - 1); // extended observability matrix Tl = join_vert(pinv(ext_obs_tm1) * R_66_15, R_55_15); Tr = join_vert( join_horiz(pinv(ext_obs_t_) * R_56_14, Matrix(n_x_, n_y_, fill::zeros)), R_23_15); S = Tl * pinv(Tr); Matrix Lcurly = S.submat(0, 0, n_x_ + n_y_ - 1, n_x_ - 1) * pinv(ext_obs_t_); Matrix Mcurly = pinv(ext_obs_tm1); Matrix Pcurly = Tl - Lcurly * R_56_15; Vector Pvec = vectorise(Pcurly); Matrix Qcurly = R_23_15; // Identify [D; B], assuming D=0 and ensuring DC gain is correct Matrix sum_QcurlyT_kron_Ncurly( (n_h_ * (2 * n_u_ + n_y_) + n_y_) * (n_y_ + n_x_), n_u_ * (n_y_ + n_x_), fill::zeros); Matrix eye_ext_obs_tm1(n_y_ + ext_obs_tm1.n_rows, n_y_ + ext_obs_tm1.n_cols, fill::eye); eye_ext_obs_tm1.submat(n_y_, n_y_, eye_ext_obs_tm1.n_rows - 1, eye_ext_obs_tm1.n_cols - 1) = ext_obs_tm1; // van Overschee (1996) p. 126 Matrix N1_Tl = -Lcurly; N1_Tl.submat(0, 0, n_x_ - 1, N1_Tl.n_cols - 1) += join_horiz(Matrix(n_x_, n_y_, fill::zeros), Mcurly); N1_Tl.submat(n_x_, 0, n_x_ + n_y_ - 1, n_y_ - 1) += Matrix(n_y_, n_y_, fill::eye); Matrix Nk_Tl(N1_Tl.n_rows, N1_Tl.n_cols, fill::zeros); Matrix N_k; for (size_t k = 0; k \u0026lt; n_h_; k++) { auto Qcurly_k = Qcurly.submat(n_u_ * k, 0, n_u_ * (k + 1) - 1, Qcurly.n_cols - 1); Nk_Tl.zeros(); Nk_Tl.submat(0, 0, n_x_ + n_y_ - 1, Nk_Tl.n_cols - k * n_y_ - 1) = N1_Tl.submat(0, k * n_y_, N1_Tl.n_rows - 1, N1_Tl.n_cols - 1); N_k = Nk_Tl * eye_ext_obs_tm1; sum_QcurlyT_kron_Ncurly += kron(Qcurly_k.t(), N_k); } Matrix err_vec; if (wt_dc \u0026gt; 0) { // Constraints enforced by weighted least squares // // Reference: // // Privara S, ..., Ferkl L_. (2010) Subspace Identification of Poorly // Excited Industrial Systems. Conference in Decision and Control. // constraint 1: assume D=0 --\u0026gt; remove the components for Dvec (this is // actually a hard constraint in that it ignores D) Matrix sum_QcurlyT_kron_Ncurly_db = sum_QcurlyT_kron_Ncurly; sum_QcurlyT_kron_Ncurly = Matrix(sum_QcurlyT_kron_Ncurly_db.n_rows, n_x_ * n_u_); size_t kkk = 0; for (size_t k = 1; k \u0026lt; (n_u_ + 1); k++) { size_t start_idx = k * (n_y_ + n_x_) - n_x_; for (size_t kk = 0; kk \u0026lt; n_x_; kk++) { sum_QcurlyT_kron_Ncurly.col(kkk) = sum_QcurlyT_kron_Ncurly_db.col(start_idx + kk); kkk++; } } // constraint 2: Make sure DC I/O gain is correct Matrix b_to_g0 = fit_.C() * inv(Matrix(n_x_, n_x_, fill::eye) - fit_.A()); Matrix Pvec_Gvec = join_vert(Pvec, vectorise(g_dc_)); Matrix eye_kron_b_to_g0 = kron(Matrix(n_u_, n_u_, fill::eye), b_to_g0); Matrix sum_QcurlyT_kron_Ncurly_b_to_g0 = join_vert(sum_QcurlyT_kron_Ncurly, eye_kron_b_to_g0); // WEIGHTED LS // Important in practice because I care a lot about at least getting the DC // gain correct. Put x weight on minimizing error at DC, relative to others Matrix w(sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, sum_QcurlyT_kron_Ncurly_b_to_g0.n_rows, fill::eye); // Make weight on minimizing DC error immense so at least that // should be nailed. size_t start_row = sum_QcurlyT_kron_Ncurly.n_rows; size_t start_col = sum_QcurlyT_kron_Ncurly.n_rows; size_t stop_row = w.n_rows - 1; size_t stop_col = w.n_cols - 1; // w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc*N;// scale // weight with data length? w.submat(start_row, start_col, stop_row, stop_col) *= wt_dc; Vector b_vec = inv(sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * sum_QcurlyT_kron_Ncurly_b_to_g0) * sum_QcurlyT_kron_Ncurly_b_to_g0.t() * w * Pvec_Gvec; fit_.set_B(Matrix(b_vec.memptr(), n_x_, n_u_)); // Calculate residuals and their cov. // Because I\u0026#39;ve added constraints, I need to re-calculate the right term // with b_vec instead of how van Overschee do in final algorithm. err_vec = Pvec - sum_QcurlyT_kron_Ncurly * b_vec; } else { // default way: *no* constraint on G0 or D=0 Vector db_vec = pinv(sum_QcurlyT_kron_Ncurly) * Pvec; // TODO(mfbolus) n.b., this gets thrown away... // Matrix D = Matrix(db_vec.memptr(), n_y_, n_u_); fit_.set_B(Matrix(db_vec.memptr() + (n_u_ * n_y_), n_x_, n_u_)); err_vec = Pvec - sum_QcurlyT_kron_Ncurly * db_vec; } // Matrix err = Matrix(err_vec.memptr(), Pcurly.n_rows, Pcurly.n_cols); // TODO(mfbolus): Something is wrong with the error calculation above. // Use the way van overschee does it in `subid.m` // WARNING: this ignores any above constraints, so Q, R will be approximate... Matrix err = Tl - S * Tr; Matrix cov_err = err * err.t(); fit_.set_Q(cov_err.submat(0, 0, n_x_ - 1, n_x_ - 1)); fit_.set_R(cov_err.submat(n_x_, n_x_, n_x_ + n_y_ - 1, n_x_ + n_y_ - 1)); } template \u0026lt;typename Fit\u0026gt; void SSID\u0026lt;Fit\u0026gt;::RecomputeExtObs() { ext_obs_t_.submat(0, 0, n_y_ - 1, ext_obs_t_.n_cols - 1) = fit_.C(); for (size_t k = 2; k \u0026lt; (n_h_ + 1); k++) { ext_obs_t_.submat((k - 1) * n_y_, 0, k * n_y_ - 1, ext_obs_t_.n_cols - 1) = ext_obs_t_.submat((k - 2) * n_y_, 0, (k - 1) * n_y_ - 1, ext_obs_t_.n_cols - 1) * fit_.A(); } } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":55,"href":"/docs/api/files/lds__fit_8h/","title":"ldsCtrlEst_h/lds_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_fit.h # LDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::Fit LDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a linear dynamical system. It is expounded upon by variants with Gaussian and Poisson observation assumptions for fitting.\nSource code # //===-- ldsCtrlEst_h/lds_fit.h - Fit Type for LDS ---------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDS_FIT_HPP #define LDS_FIT_HPP // namespace #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); virtual ~Fit() = default; // get methods size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; Q() const { return Q_; }; const Vector\u0026amp; x0() const { return x0_; }; const Matrix\u0026amp; P0() const { return P0_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; // gets measurement noise virtual const Matrix\u0026amp; R() const = 0; // set methods (e.g., seeding initial fit values) void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_m(const Vector\u0026amp; m) { Reassign(m_, m); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); ForceSymPD(Q_); }; virtual void set_R(const Matrix\u0026amp; R) = 0; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); ForceSymPD(P0_); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; View f(Matrix\u0026amp; x, const Matrix\u0026amp; u, size_t t) { x.col(t) = A_ * x.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x.col(t); }; View f(Matrix\u0026amp; x_pre, const Matrix\u0026amp; x_post, const Matrix\u0026amp; u, size_t t) { x_pre.col(t) = A_ * x_post.col(t - 1) + B_ * (g_ % u.col(t - 1)) + m_; return x_pre.col(t); }; virtual View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) = 0; protected: data_t dt_{}; // Dynamics Matrix A_; Matrix B_; Vector g_; Vector m_; Matrix Q_; // Output Matrix C_; Vector d_; Matrix R_; // initial conditions Vector x0_; Matrix P0_; size_t n_u_{}; size_t n_x_{}; size_t n_y_{}; }; } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":56,"href":"/docs/api/files/lds__gaussian__ctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_ctrl.h # GLDS Controller. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Controller Gaussian-observation Controller Type. Detailed Description # This file declares and partially defines the type for control of a gaussian-observation linear dynamical system (lds::gaussian::Controller). It inherits functionality from the underlying GLDS model type (lds::gaussian::System), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_ctrl.h - GLDS Controller ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_CTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_CTRL_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_gaussian_sys.h\u0026#34; // controller #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace gaussian { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); cx_ref_ = y_ref - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":57,"href":"/docs/api/files/lds__gaussian__fit__em_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_em.h # GLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitEM GLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_em.h - GLDS Fit (EM) ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":58,"href":"/docs/api/files/lds__gaussian__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit_ssid.h # GLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::FitSSID Subspace Identification (SSID) for GLDS. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by a subspace identification (SSID) algorithm (lds::gaussian::ssidFit_t).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer.\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit_ssid.h - GLDS Fit (SSID) --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_gaussian_fit.h\u0026#34; namespace lds { namespace gaussian { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; using SSID\u0026lt;Fit\u0026gt;::Run; private: using SSID\u0026lt;Fit\u0026gt;::CreateHankelDataMat; using SSID\u0026lt;Fit\u0026gt;::CalcSVD; using SSID\u0026lt;Fit\u0026gt;::Solve; void DecomposeData() override; void SolveVanOverschee(); }; } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":59,"href":"/docs/api/files/lds__gaussian__fit_8h/","title":"ldsCtrlEst_h/lds_gaussian_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_fit.h # GLDS fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::Fit GLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_fit.h - Fit Type for GLDS -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_FIT_H #define LDSCTRLEST_LDS_GAUSSIAN_FIT_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // fit type #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace gaussian { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt); const Matrix\u0026amp; R() const override { return R_; }; void set_R(const Matrix\u0026amp; R) override { Reassign(R_, R); ForceSymPD(R_); }; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = C_ * x.col(t) + d_; return y.col(t); }; }; }; // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":60,"href":"/docs/api/files/lds__gaussian__sctrl_8h/","title":"ldsCtrlEst_h/lds_gaussian_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sctrl.h # GLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::SwitchedController Gaussian-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sctrl.h - Switched Controller -*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H #define LDSCTRLEST_LDS_GAUSSIAN_SCTRL_H // controller type #include \u0026#34;lds_gaussian_ctrl.h\u0026#34; // switched controller #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace gaussian { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); cx_ref_ = y_ref - sys_.d(); } // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; }; // SwitchedController } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":61,"href":"/docs/api/files/lds__gaussian__sys_8h/","title":"ldsCtrlEst_h/lds_gaussian_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian_sys.h # GLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Classes # Name class lds::gaussian::System Gaussian LDS Type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems ([lds::gaussian::System](/docs/api/classes/classlds_1_1gaussian_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian_sys.h - GLDS ------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_SYS_H #define LDSCTRLEST_LDS_GAUSSIAN_SYS_H // namespace #include \u0026#34;lds_gaussian.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; namespace lds { namespace gaussian { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0, data_t r0 = kDefaultR0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; // get methods const Matrix\u0026amp; R() const { return R_; }; // set methods void set_Q(const Matrix\u0026amp; Q) { lds::System::set_Q(Q); do_recurse_Ke_ = true; } void set_R(const Matrix\u0026amp; R) { Reassign(R_, R); do_recurse_Ke_ = true; }; void set_Ke(const Matrix\u0026amp; Ke) { Reassign(Ke_, Ke); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void set_Ke_m(const Matrix\u0026amp; Ke_m) { Reassign(Ke_m_, Ke_m); // if users have set Ke, they must not want to calculate it online. do_recurse_Ke_ = false; }; void Print(); protected: void h() override { cx_ = C_ * x_; y_ = cx_ + d_; }; Vector h_(Vector x) override { return C_ * x + d_; }; void RecurseKe() override; // Gaussian-output-specific Matrix R_; bool do_recurse_Ke_{}; }; // System } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":62,"href":"/docs/api/files/lds__gaussian_8h/","title":"ldsCtrlEst_h/lds_gaussian.h","section":"Files","content":" ldsCtrlEst_h/lds_gaussian.h # glds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::gaussian Linear Dynamical Systems with Gaussian observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Gaussian observations ([lds::gaussian](/docs/api/namespaces/namespacelds_1_1gaussian/)).\nSource code # //===-- ldsCtrlEst_h/lds_gaussian.h - LDS with Gaussian Output --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_GAUSSIAN_H #define LDSCTRLEST_LDS_GAUSSIAN_H // namespace #include \u0026#34;lds.h\u0026#34; namespace lds { namespace gaussian { // insert any Gaussian-specific things here... } // namespace gaussian } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":63,"href":"/docs/api/files/lds__poisson__ctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_ctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_ctrl.h # PLDS controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Controller PLDS Controller Type. Detailed Description # This file declares and partially defines the type for feedback control of a Poisson-output linear dynamical system ([lds::poisson::Controller](/docs/api/classes/classlds_1_1poisson_1_1_controller/)). It inherits functionality from the underlying PLDS model type ([lds::poisson::System](/docs/api/classes/classlds_1_1poisson_1_1_system/)), including state estimation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_ctrl.h - PLDS Controller -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_CTRL_H #define LDSCTRLEST_LDS_POISSON_CTRL_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system type #include \u0026#34;lds_poisson_sys.h\u0026#34; // control type #include \u0026#34;lds_ctrl.h\u0026#34; namespace lds { namespace poisson { class Controller : public lds::Controller\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_, y_ref); lds::Limit(y_ref_, kYRefLb, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_sys; using lds::Controller\u0026lt;System\u0026gt;::set_g_design; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_Kc; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::set_Kc_u; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; private: constexpr static const data_t kYRefLb = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":64,"href":"/docs/api/files/lds__poisson__fit__em_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_em.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_em.h # PLDS E-M fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitEM PLDS E-M Fit Type. Detailed Description # This file declares and partially defines a type by which Gaussian-output LDS models are fit by the expectation-maximization (EM) algorithm (lds::gaussian::emFit_t).\nReferences: [1] Shumway RH, Stoffer DS. (1982) An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm.Journal of Time Series Analysis 3(2).\n[2] Ghahramani Z, Hinton GE. (1996) Parameter Estimation for Linear Dynamical Systems. Technical Report CRG-TR-96-2.\n[3] Smith A, Brown E. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_em.h - PLDS Fit (EM) -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_EM_H #define LDSCTRLEST_LDS_POISSON_FIT_EM_H #include \u0026#34;lds_fit_em.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitEM : public EM\u0026lt;Fit\u0026gt; { public: using EM\u0026lt;Fit\u0026gt;::EM; private: void MaximizeOutput() override; void MaximizeMeasurement() override{}; void RecurseKe(Matrix\u0026amp; Ke, Cube\u0026amp; P_pre, Cube\u0026amp; P_post, size_t t) override; data_t NewtonSolveC(); void AnalyticalSolveD(); }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":65,"href":"/docs/api/files/lds__poisson__fit__ssid_8h/","title":"ldsCtrlEst_h/lds_poisson_fit_ssid.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit_ssid.h # PLDS SSID fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::FitSSID Subspace Identification (SSID) for PLDS. Detailed Description # This file declares and partially defines a type by which Poisson-output LDS models are fit by a subspace identification (SSID) algorithm ([lds::gaussian::FitSSID](/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/)).\nReferences: [1] van Overschee P, de Moore B. (1996) Subspace Identification for Linear Systems. Boston: Springer. [2] Buesing L, Macke JH, Sahani M. (2012) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 25.\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit_ssid.h - PLDS Fit (SSID) ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_SSID_H #define LDSCTRLEST_LDS_POISSON_FIT_SSID_H #include \u0026#34;lds_fit_ssid.h\u0026#34; #include \u0026#34;lds_poisson_fit.h\u0026#34; namespace lds { namespace poisson { class FitSSID : public SSID\u0026lt;Fit\u0026gt; { public: using SSID\u0026lt;Fit\u0026gt;::SSID; private: void DecomposeData() override; void CalcCov(); void PoissonToGaussianMoments(); Matrix cov_; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":66,"href":"/docs/api/files/lds__poisson__fit_8h/","title":"ldsCtrlEst_h/lds_poisson_fit.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_fit.h # PLDS base fit type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::Fit PLDS Fit Type. Detailed Description # This file declares and partially defines the base fit type for a Gaussian-output linear dynamical system. Models are fit by either subspace identification (SSID) or expectation-maximization (EM).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_fit.h - Fit Type for PLDS ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_FIT_H #define LDSCTRLEST_LDS_POISSON_FIT_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // fit #include \u0026#34;lds_fit.h\u0026#34; namespace lds { namespace poisson { class Fit : public lds::Fit { public: Fit() = default; Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) : lds::Fit(n_u, n_x, n_y, dt){}; View h(Matrix\u0026amp; y, const Matrix\u0026amp; x, size_t t) override { y.col(t) = exp(C_ * x.col(t) + d_); return y.col(t); }; void set_R(const Matrix\u0026amp; R) override { std::cerr \u0026lt;\u0026lt; \u0026#34;WARNING: Cannot set R (R[0] = \u0026#34; \u0026lt;\u0026lt; R.at(0) \u0026lt;\u0026lt; \u0026#34;). No Gaussian measurement noise in Poisson observation model.\\n\u0026#34;; }; const Matrix\u0026amp; R() const override { return R_; }; }; }; // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":67,"href":"/docs/api/files/lds__poisson__sctrl_8h/","title":"ldsCtrlEst_h/lds_poisson_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sctrl.h # PLDS switched controller type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::SwitchedController Poisson-observation SwitchedController Type. Detailed Description # This file declares and partially defines the type for switched feedback control of a system approximated as multiple discrete Poisson-output linear dynamical systems (lds::poisson::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sctrl.h - Switched Controller --*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #define LDSCTRLEST_LDS_POISSON_SWITCHED_CTRL_H #include \u0026#34;lds_poisson_ctrl.h\u0026#34; #include \u0026#34;lds_sctrl.h\u0026#34; namespace lds { namespace poisson { class SwitchedController : public lds::SwitchedController\u0026lt;System\u0026gt; { public: void set_y_ref(const Vector\u0026amp; y_ref) override { Reassign(y_ref_,y_ref); lds::Limit(y_ref_, kYRefLB, lds::kInf); cx_ref_ = log(y_ref_) - sys_.d(); }; // make sure base class template methods available using lds::SwitchedController\u0026lt;System\u0026gt;::SwitchedController; using lds::SwitchedController\u0026lt;System\u0026gt;::Switch; using lds::SwitchedController\u0026lt;System\u0026gt;::Control; using lds::SwitchedController\u0026lt;System\u0026gt;::ControlOutputReference; using lds::SwitchedController\u0026lt;System\u0026gt;::sys; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::set_g_design; using lds::SwitchedController\u0026lt;System\u0026gt;::set_u_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_x_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_y_ref; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_inty; using lds::SwitchedController\u0026lt;System\u0026gt;::set_Kc_u; using lds::SwitchedController\u0026lt;System\u0026gt;::set_tau_awu; using lds::SwitchedController\u0026lt;System\u0026gt;::set_control_type; using lds::SwitchedController\u0026lt;System\u0026gt;::Reset; using lds::SwitchedController\u0026lt;System\u0026gt;::Print; private: constexpr static data_t kYRefLB = 1e-4; }; } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":68,"href":"/docs/api/files/lds__poisson__sys_8h/","title":"ldsCtrlEst_h/lds_poisson_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson_sys.h # PLDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Classes # Name class lds::poisson::System Poisson System type. Detailed Description # This file declares and partially defines the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems ([lds::poisson::System](/docs/api/classes/classlds_1_1poisson_1_1_system/)). It inherits functionality from the underlying linear dynamical system ([lds::System](/docs/api/classes/classlds_1_1_system/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson_sys.h - PLDS -------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_SYS_H #define LDSCTRLEST_LDS_POISSON_SYS_H // namespace #include \u0026#34;lds_poisson.h\u0026#34; // system #include \u0026#34;lds_sys.h\u0026#34; // needed for Poisson random number generation #include \u0026lt;random\u0026gt; namespace lds { namespace poisson { class System : public lds::System { public: System() = default; System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) override; protected: void h() override { cx_ = C_ * x_; y_ = exp(cx_ + d_); diag_y_.diag() = y_; }; Vector h_(Vector x) override { return exp(C_ * x + d_); }; void RecurseKe() override; private: // Poisson-output-specific Matrix diag_y_; std::poisson_distribution\u0026lt;size_t\u0026gt; pd_; }; // System } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":69,"href":"/docs/api/files/lds__poisson_8h/","title":"ldsCtrlEst_h/lds_poisson.h","section":"Files","content":" ldsCtrlEst_h/lds_poisson.h # plds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. lds::poisson Linear Dynamical Systems with Poisson observations. Detailed Description # This file declares and partially defines the namespace for linear dynamical systems with Poisson observations ([lds::poisson](/docs/api/namespaces/namespacelds_1_1poisson/)).\nSource code # //===-- ldsCtrlEst_h/lds_poisson.h - LDS with Poisson Output ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_POISSON_H #define LDSCTRLEST_LDS_POISSON_H #include \u0026#34;lds.h\u0026#34; namespace lds { namespace poisson { // TODO(mfbolus): Not sure if defining these as static here makes the most // sense. Is there a downside to letting multiple poisson System objects share a // common random number generator? static std::random_device rd; static std::mt19937 rng = std::mt19937( rd()); } // namespace poisson } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":70,"href":"/docs/api/files/lds__sctrl_8h/","title":"ldsCtrlEst_h/lds_sctrl.h","section":"Files","content":" ldsCtrlEst_h/lds_sctrl.h # SwitchedController type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::SwitchedController SwitchedController Type. Detailed Description # This file declares the type for switched control of a system approximated as multiple discrete Gaussian-output linear dynamical systems (lds::gaussian::SwitchedController).\nSource code # //===-- ldsCtrlEst_h/lds_sctrl.h - Switched Controller ----------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SCTRL_H #define LDSCTRLEST_LDS_SCTRL_H #include \u0026#34;lds_ctrl.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; #include \u0026#34;lds_uniform_vecs.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class SwitchedController : public Controller\u0026lt;System\u0026gt; { public: SwitchedController() = default; SwitchedController(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); SwitchedController(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type = 0); void Switch(size_t idx, bool do_force_switch = false); void set_Kc(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc) { Kc_list_ = Kc; Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc) { Kc_list_ = std::move(Kc); Kc_ = Kc_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_inty) { Kc_inty_list_ = Kc_inty; Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_inty(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_inty) { Kc_inty_list_ = std::move(Kc_inty); Kc_inty_ = Kc_inty_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(const UniformMatrixList\u0026lt;\u0026gt;\u0026amp; Kc_u) { Kc_u_list_ = Kc_u; Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_Kc_u(UniformMatrixList\u0026lt;\u0026gt;\u0026amp;\u0026amp; Kc_u) { Kc_u_list_ = std::move(Kc_u); Kc_u_ = Kc_u_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(const UniformVectorList\u0026amp; g) { g_design_list_ = g; g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; void set_g_design(UniformVectorList\u0026amp;\u0026amp; g) { g_design_list_ = std::move(g); g_design_ = g_design_list_.at(0); // set to first if (idx_ != 0) { Switch(idx_, true); } }; // make sure base class template methods available using lds::Controller\u0026lt;System\u0026gt;::Controller; using lds::Controller\u0026lt;System\u0026gt;::Control; using lds::Controller\u0026lt;System\u0026gt;::ControlOutputReference; using lds::Controller\u0026lt;System\u0026gt;::sys; using lds::Controller\u0026lt;System\u0026gt;::Kc; using lds::Controller\u0026lt;System\u0026gt;::Kc_inty; using lds::Controller\u0026lt;System\u0026gt;::Kc_u; using lds::Controller\u0026lt;System\u0026gt;::g_design; using lds::Controller\u0026lt;System\u0026gt;::u_ref; using lds::Controller\u0026lt;System\u0026gt;::x_ref; using lds::Controller\u0026lt;System\u0026gt;::y_ref; using lds::Controller\u0026lt;System\u0026gt;::control_type; using lds::Controller\u0026lt;System\u0026gt;::set_u_ref; using lds::Controller\u0026lt;System\u0026gt;::set_x_ref; using lds::Controller\u0026lt;System\u0026gt;::set_y_ref; using lds::Controller\u0026lt;System\u0026gt;::set_tau_awu; using lds::Controller\u0026lt;System\u0026gt;::set_control_type; using lds::Controller\u0026lt;System\u0026gt;::Reset; using lds::Controller\u0026lt;System\u0026gt;::Print; protected: std::vector\u0026lt;System\u0026gt; systems_; size_t n_sys_{}; size_t idx_{}; // controller gains could be different for each UniformMatrixList\u0026lt;\u0026gt; Kc_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_inty_list_; UniformMatrixList\u0026lt;\u0026gt; Kc_u_list_; // design-phase input gain could also be different UniformVectorList g_design_list_; // TODO(mfbolus): not sure why I need to do this. using Controller\u0026lt;System\u0026gt;::Kc_; using Controller\u0026lt;System\u0026gt;::Kc_inty_; using Controller\u0026lt;System\u0026gt;::Kc_u_; using Controller\u0026lt;System\u0026gt;::g_design_; using Controller\u0026lt;System\u0026gt;::sys_; // using Controller\u0026lt;System\u0026gt;::u_ref_; // using Controller\u0026lt;System\u0026gt;::x_ref_; // using Controller\u0026lt;System\u0026gt;::y_ref_; // using Controller\u0026lt;System\u0026gt;::control_type_; private: void InitVars(); using lds::Controller\u0026lt;System\u0026gt;::set_sys; // using Controller\u0026lt;System\u0026gt;::set_Kc; // using Controller\u0026lt;System\u0026gt;::set_Kc_inty; // using Controller\u0026lt;System\u0026gt;::set_Kc_u; // using Controller\u0026lt;System\u0026gt;::set_g_design; }; template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(systems.at(0), u_lb, u_ub, control_type), systems_(systems) { InitVars(); } template \u0026lt;typename System\u0026gt; inline SwitchedController\u0026lt;System\u0026gt;::SwitchedController( std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, data_t u_lb, data_t u_ub, size_t control_type) : Controller\u0026lt;System\u0026gt;(System(systems.at(0).n_u(), systems.at(0).n_x(), systems.at(0).n_y(), systems.at(0).dt()), u_lb, u_ub, control_type), systems_(std::move(systems)) { InitVars(); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::InitVars() { n_sys_ = systems_.size(); sys_ = systems_.at(0); Kc_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_)); Kc_inty_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); Kc_u_list_ = UniformMatrixList\u0026lt;\u0026gt;(std::vector\u0026lt;Matrix\u0026gt;(n_sys_, Kc_inty_)); g_design_list_ = UniformVectorList(std::vector\u0026lt;Vector\u0026gt;(n_sys_, g_design_)); } template \u0026lt;typename System\u0026gt; inline void SwitchedController\u0026lt;System\u0026gt;::Switch(size_t idx, bool do_force_switch) { if ((idx == idx_) \u0026amp;\u0026amp; !do_force_switch) { return; // already there. } // put old up and get new one out systems_.at(idx_) = std::move(sys_); sys_ = std::move(systems_.at(idx)); // set the state of this system to that of the previous system // TODO(mfbolus): This will only work as intended if state matrix is the same. // See example fudge in 0.4 branch src/lds_poisson_sctrl.cpp. sys_.set_m(systems_.at(idx_).m(), true); sys_.set_x(systems_.at(idx_).x()); // swap controller gains Kc_list_.Swap(Kc_, idx_); Kc_list_.Swap(Kc_, idx); if (control_type_ \u0026amp; kControlTypeIntY) { Kc_inty_list_.Swap(Kc_inty_, idx_); Kc_inty_list_.Swap(Kc_inty_, idx); } if (control_type_ \u0026amp; kControlTypeDeltaU) { Kc_u_list_.Swap(Kc_u_, idx_); Kc_u_list_.Swap(Kc_u_, idx); } g_design_list_.Swap(g_design_, idx_); g_design_list_.Swap(g_design_, idx); idx_ = idx; } // Switch } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":71,"href":"/docs/api/files/lds__sys_8h/","title":"ldsCtrlEst_h/lds_sys.h","section":"Files","content":" ldsCtrlEst_h/lds_sys.h # LDS base type. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::System Linear Dynamical System Type. Detailed Description # This file declares and partially defines the base type for linear dynamical systems ([lds::System](/docs/api/classes/classlds_1_1_system/)). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- ldsCtrlEst_h/lds_sys.h - LDS ----------------------------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_SYS_H #define LDSCTRLEST_LDS_SYS_H #include \u0026#34;lds.h\u0026#34; #include \u0026#34;lds_uniform_mats.h\u0026#34; namespace lds { class System { public: System() = default; System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); virtual ~System() {} void Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z); virtual const Vector\u0026amp; Simulate(const Vector\u0026amp; u_tm1) = 0; void f(const Vector\u0026amp; u, bool do_add_noise = false) { x_ = A_ * x_ + B_ * (g_ % u) + m_; if (do_add_noise) { x_ += arma::mvnrnd(Vector(n_x_).fill(0), Q_); } }; virtual void h() = 0; virtual Vector h_(Vector x) = 0; size_t n_u() const { return n_u_; }; size_t n_x() const { return n_x_; }; size_t n_y() const { return n_y_; }; data_t dt() const { return dt_; }; const Vector\u0026amp; x() const { return x_; }; const Matrix\u0026amp; P() const { return P_; }; const Vector\u0026amp; m() const { return m_; }; const Matrix\u0026amp; P_m() const { return P_m_; }; const Vector\u0026amp; cx() const { return cx_; }; const Vector\u0026amp; y() const { return y_; }; const Vector\u0026amp; x0() const { return x0_; }; const Vector\u0026amp; m0() const { return m0_; }; const Matrix\u0026amp; A() const { return A_; }; const Matrix\u0026amp; B() const { return B_; }; const Vector\u0026amp; g() const { return g_; }; const Matrix\u0026amp; C() const { return C_; }; const Vector\u0026amp; d() const { return d_; }; const Matrix\u0026amp; Ke() const { return Ke_; }; const Matrix\u0026amp; Ke_m() const { return Ke_m_; }; const Matrix\u0026amp; Q() { return Q_; }; const Matrix\u0026amp; Q_m() { return Q_m_; }; const Matrix\u0026amp; P0() { return P0_; }; const Matrix\u0026amp; P0_m() { return P0_m_; }; void set_A(const Matrix\u0026amp; A) { Reassign(A_, A); }; void set_B(const Matrix\u0026amp; B) { Reassign(B_, B); }; void set_m(const Vector\u0026amp; m, bool do_force_assign = false) { Reassign(m0_, m); if ((!do_adapt_m) || do_force_assign) { Reassign(m_, m); } }; void set_g(const Vector\u0026amp; g) { Reassign(g_, g); }; void set_Q(const Matrix\u0026amp; Q) { Reassign(Q_, Q); }; void set_Q_m(const Matrix\u0026amp; Q_m) { Reassign(Q_m_, Q_m); }; void set_x0(const Vector\u0026amp; x0) { Reassign(x0_, x0); }; void set_P0(const Matrix\u0026amp; P0) { Reassign(P0_, P0); }; void set_P0_m(const Matrix\u0026amp; P0_m) { Reassign(P0_m_, P0_m); }; void set_C(const Matrix\u0026amp; C) { Reassign(C_, C); }; void set_d(const Vector\u0026amp; d) { Reassign(d_, d); }; void set_x(const Vector\u0026amp; x) { Reassign(x_, x); h(); }; void Reset(); std::vector\u0026lt;UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt;\u0026gt; nstep_pred_block( UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; u, UniformMatrixList\u0026lt;kMatFreeDim2\u0026gt; z, size_t n_pred = 1); void Print(); // safe to leave this public and non-const bool do_adapt_m{}; protected: virtual void RecurseKe() = 0; void InitVars(data_t p0 = kDefaultP0, data_t q0 = kDefaultQ0); std::size_t n_x_{}; std::size_t n_u_{}; std::size_t n_y_{}; data_t dt_{}; // Signals: Vector x_; Matrix P_; Vector m_; Matrix P_m_; Vector cx_; Vector y_; Vector z_; // Parameters: Vector x0_; Matrix P0_; Vector m0_; Matrix P0_m_; Matrix A_; Matrix B_; Vector g_; Matrix Q_; Matrix Q_m_; Matrix C_; Vector d_; Matrix Ke_; Matrix Ke_m_; }; // System } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":72,"href":"/docs/api/files/lds__uniform__mats_8h/","title":"ldsCtrlEst_h/lds_uniform_mats.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_mats.h # List of uniformly sized matrices. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformMatrixList Detailed Description # This file provides a container for uniformly sized matrices. Users may specify one dimension to be free to vary in the list.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_mats.h - Uniform Matrices ------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_MATS_H #define LDSCTRLEST_LDS_UNIFORM_MATS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { template \u0026lt;MatrixListFreeDim D = kMatFreeDimNone\u0026gt; class UniformMatrixList : public std::vector\u0026lt;Matrix\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Matrix\u0026gt;::vector; // don\u0026#39;t allow push_back to be used since it doesn\u0026#39;t check dims using std::vector\u0026lt;Matrix\u0026gt;::push_back; public: using std::vector\u0026lt;Matrix\u0026gt;::operator=; using std::vector\u0026lt;Matrix\u0026gt;::operator[]; using std::vector\u0026lt;Matrix\u0026gt;::begin; using std::vector\u0026lt;Matrix\u0026gt;::end; using std::vector\u0026lt;Matrix\u0026gt;::size; using std::vector\u0026lt;Matrix\u0026gt;::at; UniformMatrixList() = default; explicit UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); explicit UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim = {0, 0}); UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; ~UniformMatrixList() = default; const std::array\u0026lt;size_t, 2\u0026gt;\u0026amp; dim(size_t n = 0) const { return dim_.at(n); } size_t size() { return std::vector\u0026lt;Matrix\u0026gt;::size(); }; const Matrix\u0026amp; at(size_t n) { return std::vector\u0026lt;Matrix\u0026gt;::at(n); }; void Swap(Matrix\u0026amp; that, size_t n); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that); UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; operator=(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept; void append(const Matrix\u0026amp; mat); private: void CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim); std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt; dim_; }; template \u0026lt;MatrixListFreeDim D\u0026gt; inline void UniformMatrixList\u0026lt;D\u0026gt;::Swap(Matrix\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.n_cols); } if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap // not moving, since it causes memory issues. // so this method isn\u0026#39;t a memory-saver as designed for now Matrix tmp = (*this)[n]; (*this)[n] = that; that = tmp; if (D == kMatFreeDim1) { this-\u0026gt;dim_[n][0] = (*this)[n].n_rows; } if (D == kMatFreeDim2) { this-\u0026gt;dim_[n][1] = (*this)[n].n_cols; } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::append(const Matrix\u0026amp; mat) { std::array\u0026lt;size_t, 2\u0026gt; dim({mat.n_rows, mat.n_cols}); CheckDimensions(dim); std::vector\u0026lt;Matrix\u0026gt;::push_back(mat); dim_.push_back(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) { // make sure dim_ vector is initialized if (dim_.empty()) { dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(that.size(), {0, 0}); } // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices\u0026#34;; throw std::runtime_error(ss.str()); } // if dimensions a not zero and do not match, skip move with error message. bool dims_nonzero = true; for (auto d : dim_) { if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; d[0] \u0026lt; 1) { dims_nonzero = false; break; } if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; d[1] \u0026lt; 1) { dims_nonzero = false; break; } } if (dims_nonzero) { bool does_match = true; if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); } if (!does_match) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrices of size \u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; dim_[k] = that.dim(k); } return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; inline UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; UniformMatrixList\u0026lt;D\u0026gt;::operator=( UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; matrices with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; matrices. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. bool dims_nonzero = true; for (auto d : dim_) { // if (!(D == kMatFreeDim1) \u0026amp;\u0026amp; (d[0] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // if (!(D == kMatFreeDim2) \u0026amp;\u0026amp; (d[1] \u0026lt; 1)) { // dims_nonzero = false; // break; // } // } // // if (dims_nonzero) { // bool does_match = true; // if (!(D == kMatFreeDim1)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][0] == that.at(0).n_rows); // } // // if (!(D == kMatFreeDim2)) { // does_match = does_match \u0026amp;\u0026amp; (dim_[0][1] == that.at(0).n_cols); // } // // if (!does_match) { // this-\u0026gt;at(0).print(\u0026#34;this[0] = \u0026#34;); // that.at(0).print(\u0026#34;that[0] = \u0026#34;); // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformMatrixList element of size (\u0026#34; \u0026lt;\u0026lt; // that.at(0).n_rows \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; that.at(0).n_cols \u0026lt;\u0026lt; \u0026#34;) for an // element of size (\u0026#34; \u0026lt;\u0026lt; dim_[0][0] \u0026lt;\u0026lt; \u0026#34;,\u0026#34; \u0026lt;\u0026lt; dim_[0][1] \u0026lt;\u0026lt; \u0026#34;). // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Matrix\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const std::vector\u0026lt;Matrix\u0026gt;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::vector\u0026lt;Matrix\u0026gt;\u0026amp;\u0026amp; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(std::move(mats)) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(std::initializer_list\u0026lt;Matrix\u0026gt; mats, std::array\u0026lt;size_t, 2\u0026gt; dim) : vector(mats) { CheckDimensions(dim); }; template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(const UniformMatrixList\u0026lt;D\u0026gt;\u0026amp; that) : vector(that) { (*this) = that; } template \u0026lt;MatrixListFreeDim D\u0026gt; UniformMatrixList\u0026lt;D\u0026gt;::UniformMatrixList(UniformMatrixList\u0026lt;D\u0026gt;\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { std::array\u0026lt;size_t, 2\u0026gt; dim_k({this-\u0026gt;at(k).n_rows, this-\u0026gt;at(k).n_cols}); dim_.push_back(dim_k); } } template \u0026lt;MatrixListFreeDim D\u0026gt; void UniformMatrixList\u0026lt;D\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 2\u0026gt; dim) { // change behavior based on free dim D if ((dim[0] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim1)) { dim[0] = this-\u0026gt;at(0).n_rows; } if ((dim[1] == 0) \u0026amp;\u0026amp; !(D == kMatFreeDim2)) { dim[1] = this-\u0026gt;at(0).n_cols; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Matrix\u0026amp; mat : *this) { if (!(D == kMatFreeDim1)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_rows == dim[0]); } if (!(D == kMatFreeDim2)) { does_match = does_match \u0026amp;\u0026amp; (mat.n_cols == dim[1]); } if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } dim_ = std::vector\u0026lt;std::array\u0026lt;size_t, 2\u0026gt;\u0026gt;(this-\u0026gt;size(), dim); for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { dim_[k][0] = (*this)[k].n_rows; dim_[k][1] = (*this)[k].n_cols; } } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":73,"href":"/docs/api/files/lds__uniform__systems_8h/","title":"ldsCtrlEst_h/lds_uniform_systems.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_systems.h # List of uniformly sized Systems. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformSystemList Detailed Description # This file provides a container for uniformly sized Systems.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_systems.h - Uniform Systems ----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #define LDSCTRLEST_LDS_UNIFORM_SYSTEMS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector // namespace #include \u0026#34;lds.h\u0026#34; // System type #include \u0026#34;lds_sys.h\u0026#34; namespace lds { template \u0026lt;typename System\u0026gt; class UniformSystemList : public std::vector\u0026lt;System\u0026gt; { static_assert(std::is_base_of\u0026lt;lds::System, System\u0026gt;::value, \u0026#34;System must be derived from lds::System type.\u0026#34;); private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;System\u0026gt;::vector; using std::vector\u0026lt;System\u0026gt;::operator=; using std::vector\u0026lt;System\u0026gt;::operator[]; using std::vector\u0026lt;System\u0026gt;::at; using std::vector\u0026lt;System\u0026gt;::begin; using std::vector\u0026lt;System\u0026gt;::end; using std::vector\u0026lt;System\u0026gt;::size; public: UniformSystemList() = default; explicit UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); explicit UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim = {0, 0, 0}); UniformSystemList(const UniformSystemList\u0026amp; that); UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept; ~UniformSystemList() = default; const std::array\u0026lt;size_t, 3\u0026gt;\u0026amp; dim() const { return dim_; } size_t size() { return std::vector\u0026lt;System\u0026gt;::size(); }; const System\u0026amp; at(size_t n) { return std::vector\u0026lt;System\u0026gt;::at(n); }; void Swap(System\u0026amp; that, size_t n); UniformSystemList\u0026amp; operator=(const UniformSystemList\u0026amp; that); UniformSystemList\u0026amp; operator=(UniformSystemList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim); std::array\u0026lt;size_t, 3\u0026gt; dim_{}; }; template \u0026lt;typename System\u0026gt; inline void UniformSystemList\u0026lt;System\u0026gt;::Swap(System\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformSystemList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = (dim_[0] == that.n_u()) \u0026amp;\u0026amp; (dim_[1] == that.n_x()) \u0026amp;\u0026amp; (dim_[2] == that.n_y()); if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformSystemList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap System tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( const UniformSystemList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_[0] + dim_[1] + dim_[2]) { std::array\u0026lt;size_t, 3\u0026gt; other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign systems of size \u0026#34; \u0026lt;\u0026lt; dim_[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2] \u0026lt;\u0026lt; \u0026#34; with systems of size \u0026#34; \u0026lt;\u0026lt; other_dim[0] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other_dim[1] \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; dim_[2]; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } template \u0026lt;typename System\u0026gt; inline UniformSystemList\u0026lt;System\u0026gt;\u0026amp; UniformSystemList\u0026lt;System\u0026gt;::operator=( UniformSystemList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // // if empty, assume a default constructed object and safe to move // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; systems with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; systems. Skipping.\\n\u0026#34;; // return (*this); // } // // // if dimensions a not zero and do not match, skip move with error // message. if (dim_[0] + dim_[1] + dim_[2]) { // bool does_match = (dim_[0] == that.at(0).n_u()) \u0026amp;\u0026amp; // (dim_[1] == that.at(0).n_x()) \u0026amp;\u0026amp; // (dim_[2] == that.at(0).n_y()); // if (!does_match) { // std::cerr // \u0026lt;\u0026lt; \u0026#34;Cannot move a UniformSystemList element for an element of \u0026#34; // \u0026#34;different size. Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;System\u0026gt;::operator=(std::move(that)); return (*this); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const std::vector\u0026lt;System\u0026gt;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(std::vector\u0026lt;System\u0026gt;\u0026amp;\u0026amp; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(std::move(systems)) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList( std::initializer_list\u0026lt;System\u0026gt; systems, std::array\u0026lt;size_t, 3\u0026gt; dim) : std::vector\u0026lt;System\u0026gt;(systems) { CheckDimensions(dim); }; template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(const UniformSystemList\u0026amp; that) : std::vector\u0026lt;System\u0026gt;(that) { (*this) = that; } template \u0026lt;typename System\u0026gt; UniformSystemList\u0026lt;System\u0026gt;::UniformSystemList(UniformSystemList\u0026amp;\u0026amp; that) noexcept : std::vector\u0026lt;System\u0026gt;(std::move(that)) { this-\u0026gt;dim_[0] = this-\u0026gt;at(0).n_u(); this-\u0026gt;dim_[1] = this-\u0026gt;at(0).n_x(); this-\u0026gt;dim_[2] = this-\u0026gt;at(0).n_y(); } template \u0026lt;typename System\u0026gt; void UniformSystemList\u0026lt;System\u0026gt;::CheckDimensions(std::array\u0026lt;size_t, 3\u0026gt; dim) { if (dim[0] + dim[1] + dim[2]) { dim_ = dim; } else { dim_[0] = this-\u0026gt;at(0).n_u(); dim_[1] = this-\u0026gt;at(0).n_x(); dim_[2] = this-\u0026gt;at(0).n_y(); } // make sure dimensiolaties are all uniform bool does_match(true); for (const System\u0026amp; sys : *this) { does_match = does_match \u0026amp;\u0026amp; (sys.n_u() == dim_[0]); does_match = does_match \u0026amp;\u0026amp; (sys.n_x() == dim_[1]); does_match = does_match \u0026amp;\u0026amp; (sys.n_y() == dim_[2]); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input systems are not uniform.\u0026#34;); } } } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":74,"href":"/docs/api/files/lds__uniform__vecs_8h/","title":"ldsCtrlEst_h/lds_uniform_vecs.h","section":"Files","content":" ldsCtrlEst_h/lds_uniform_vecs.h # List of uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Classes # Name class lds::UniformVectorList Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.h - Uniform Vectors -------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_UNIFORM_VECS_H #define LDSCTRLEST_LDS_UNIFORM_VECS_H #include \u0026lt;array\u0026gt; // std::array #include \u0026lt;vector\u0026gt; // std::vector #include \u0026#34;lds.h\u0026#34; namespace lds { class UniformVectorList : public std::vector\u0026lt;Vector\u0026gt; { private: // TODO(mfbolus): would rather *uncomment* the below for sake of conversion // using std::vector\u0026lt;Vector\u0026gt;::vector; using std::vector\u0026lt;Vector\u0026gt;::operator=; using std::vector\u0026lt;Vector\u0026gt;::operator[]; using std::vector\u0026lt;Vector\u0026gt;::at; using std::vector\u0026lt;Vector\u0026gt;::begin; using std::vector\u0026lt;Vector\u0026gt;::end; using std::vector\u0026lt;Vector\u0026gt;::size; public: UniformVectorList() = default; explicit UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim = 0); explicit UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim = 0); UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim = 0); UniformVectorList(const UniformVectorList\u0026amp; that); UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept; ~UniformVectorList() = default; size_t dim() const { return dim_; } size_t size() { return std::vector\u0026lt;Vector\u0026gt;::size(); }; const Vector\u0026amp; at(size_t n) { return std::vector\u0026lt;Vector\u0026gt;::at(n); }; void Swap(Vector\u0026amp; that, size_t n); UniformVectorList\u0026amp; operator=(const UniformVectorList\u0026amp; that); UniformVectorList\u0026amp; operator=(UniformVectorList\u0026amp;\u0026amp; that) noexcept; private: void CheckDimensions(size_t dim); size_t dim_{}; }; inline void UniformVectorList::Swap(Vector\u0026amp; that, size_t n) { // make sure request in range if (n \u0026gt;= this-\u0026gt;size()) { std::cerr \u0026lt;\u0026lt; \u0026#34;Requested UniformMatrixList element out of bounds. Skipping.\\n\u0026#34;; return; } // check dim bool does_match = dim_ == that.n_elem; if (!does_match) { std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot swap a UniformMatrixList element for an element of \u0026#34; \u0026#34;different size. Skipping.\\n\u0026#34;; return; } // if checks pass, perform swap Vector tmp = std::move((*this)[n]); (*this)[n] = std::move(that); that = std::move(tmp); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( const UniformVectorList\u0026amp; that) { // check dimensions if (!this-\u0026gt;empty()) { if (this-\u0026gt;size() != that.size()) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors\u0026#34;; throw std::runtime_error(ss.str()); } if (dim_) { size_t other_dim(that.dim()); if (dim_ != other_dim) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ \u0026lt;\u0026lt; \u0026#34; with vectors of size \u0026#34; \u0026lt;\u0026lt; other_dim; throw std::runtime_error(ss.str()); } } } for (size_t k = 0; k \u0026lt; this-\u0026gt;size(); k++) { (*this)[k] = that[k]; } return (*this); } inline UniformVectorList\u0026amp; UniformVectorList::operator=( UniformVectorList\u0026amp;\u0026amp; that) noexcept { // // check dimensions // if (!this-\u0026gt;empty()) { // if (this-\u0026gt;size() != that.size()) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign \u0026#34; \u0026lt;\u0026lt; this-\u0026gt;size() \u0026lt;\u0026lt; \u0026#34; vectors with \u0026#34; // \u0026lt;\u0026lt; that.size() \u0026lt;\u0026lt; \u0026#34; vectors. Skipping.\\n\u0026#34;; // return (*this); // } // // if (dim_) { // size_t other_dim(that.dim()); // if (dim_ != other_dim) { // std::cerr \u0026lt;\u0026lt; \u0026#34;Cannot reassign vectors of size \u0026#34; \u0026lt;\u0026lt; dim_ // \u0026lt;\u0026lt; \u0026#34; with matrices of size \u0026#34; \u0026lt;\u0026lt; other_dim \u0026lt;\u0026lt; \u0026#34;. // Skipping.\\n\u0026#34;; // return (*this); // } // } // } dim_ = that.dim_; std::vector\u0026lt;Vector\u0026gt;::operator=(std::move(that)); return (*this); } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":75,"href":"/docs/api/files/lds_8h/","title":"ldsCtrlEst_h/lds.h","section":"Files","content":" ldsCtrlEst_h/lds.h # lds namespace More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file defines the lds namespace, which will be an umbrella for linear dynamical systems with Gaussian ([lds::gaussian](/docs/api/namespaces/namespacelds_1_1gaussian/)) or Poisson ([lds::poisson](/docs/api/namespaces/namespacelds_1_1poisson/)) observations.\nSource code # //===-- ldsCtrlEst_h/lds.h - Linear Dynmical System Namespace ---*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_LDS_H #define LDSCTRLEST_LDS_H // #ifndef LDSCTRLEST // #include \u0026lt;ldsCtrlEst\u0026gt; // #endif #include \u0026lt;armadillo\u0026gt; namespace lds { using data_t = double; // may change to float (but breaks mex functions) using Vector = arma::Col\u0026lt;data_t\u0026gt;; using Matrix = arma::Mat\u0026lt;data_t\u0026gt;; using Cube = arma::Cube\u0026lt;data_t\u0026gt;; using View = arma::subview\u0026lt;data_t\u0026gt;; namespace fill = arma::fill; static const std::size_t kControlTypeDeltaU = 0x1; static const std::size_t kControlTypeIntY = kControlTypeDeltaU \u0026lt;\u0026lt; 1; static const std::size_t kControlTypeAdaptM = kControlTypeDeltaU \u0026lt;\u0026lt; 2; static const data_t kInf = std::numeric_limits\u0026lt;data_t\u0026gt;::infinity(); static const data_t kPi = arma::datum::pi; static const data_t kDefaultP0 = 1e-6; static const data_t kDefaultQ0 = 1e-6; static const data_t kDefaultR0 = 1e-2; enum SSIDWt { kSSIDNone, kSSIDMOESP, kSSIDCVA }; enum MatrixListFreeDim { kMatFreeDimNone, kMatFreeDim1, kMatFreeDim2 }; // TODO(mfbolus): for SwitchedController, may want systems to have differing // numbers of states. Use this enum as template parameter? // enum SystemListFreeDim { // kSysFreeDimNone, // kSysFreeDimX ///\u0026lt; allow state dim (x) of systems in list to be hetero // }; // place hard limits on contents of vecors/mats void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub); void Limit(Vector\u0026amp; x, data_t lb, data_t ub); void Limit(Matrix\u0026amp; x, data_t lb, data_t ub); // in-place assign that errs if there are dimension mismatches: void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical = \u0026#34;Reassign\u0026#34;); // TODO(mfbolus): this is a fudge, but for some reason, cov mats often going // numerically asymm. void ForceSymPD(Matrix\u0026amp; X); void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min = 0); void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X); Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B); inline void Limit(std::vector\u0026lt;data_t\u0026gt;\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Vector\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Limit(Matrix\u0026amp; x, data_t lb, data_t ub) { for (data_t\u0026amp; el : x) { el = el \u0026lt; lb ? lb : el; el = el \u0026gt; ub ? ub : el; } } inline void Reassign(Vector\u0026amp; some, const Vector\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if (other.n_elem != some.n_elem) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign vector of size \u0026#34; \u0026lt;\u0026lt; some.n_elem \u0026lt;\u0026lt; \u0026#34; with vector of size \u0026#34; \u0026lt;\u0026lt; other.n_elem \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } inline void Reassign(Matrix\u0026amp; some, const Matrix\u0026amp; other, const std::string\u0026amp; parenthetical) { // check dimensions if ((other.n_rows != some.n_rows) || (other.n_cols != some.n_cols)) { std::ostringstream ss; ss \u0026lt;\u0026lt; \u0026#34;cannot reassign matrix of size \u0026#34; \u0026lt;\u0026lt; some.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; some.n_cols \u0026lt;\u0026lt; \u0026#34; with matrix of size \u0026#34; \u0026lt;\u0026lt; other.n_rows \u0026lt;\u0026lt; \u0026#34;x\u0026#34; \u0026lt;\u0026lt; other.n_cols \u0026lt;\u0026lt; \u0026#34;(\u0026#34; \u0026lt;\u0026lt; parenthetical \u0026lt;\u0026lt; \u0026#34;)\u0026#34;; throw std::runtime_error(ss.str()); } for (size_t k = 0; k \u0026lt; some.n_elem; k++) { some[k] = other[k]; } } } // namespace lds #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":76,"href":"/docs/api/files/mex__c__util_8h/","title":"ldsCtrlEst_h/mex_c_util.h","section":"Files","content":" ldsCtrlEst_h/mex_c_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C API) More\u0026hellip;\nNamespaces # Name armamexc arma/mex interface using Matlab C API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab/Octave\u0026rsquo;s C mex API.\nSource code # //===-- ldsCtrlEst_h/mex_c_util.h - Mex C API Utilities ---------*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXC_UTIL_H #define LDSCTRLEST_MEXC_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.h\u0026#34; // // If Matlab_FOUND, include matrix.h. // // (Octave does not need/have it.) // #ifdef Matlab_FOUND // #include \u0026#34;matrix.h\u0026#34; // #endif namespace armamexc { template \u0026lt;class T\u0026gt; inline auto m2T_scalar(const mxArray *matlab_scalar) -\u0026gt; T { if (mxGetData(matlab_scalar)) { return static_cast\u0026lt;T\u0026gt;(mxGetScalar(matlab_scalar)); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return 0; } template \u0026lt;class T\u0026gt; inline auto m2a_mat(const mxArray *matlab_mat, bool copy_aux_mem = false, bool strict = true) -\u0026gt; arma::Mat\u0026lt;T\u0026gt; { if (mxGetData(matlab_mat)) { const mwSize n_dim = mxGetNumberOfDimensions(matlab_mat); if (n_dim == 2) { return arma::Mat\u0026lt;T\u0026gt;(static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)), mxGetM(matlab_mat), mxGetN(matlab_mat), copy_aux_mem, strict); } mexErrMsgTxt(\u0026#34;Number of dimensions must be 2.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } mexErrMsgTxt(\u0026#34;No data available.\u0026#34;); return arma::Mat\u0026lt;T\u0026gt;(); } // TODO(mfbolus): make these templated. template \u0026lt;typename T\u0026gt; inline auto a2m_mat(arma::Mat\u0026lt;T\u0026gt; const \u0026amp;arma_mat) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_mat.n_rows, arma_mat.n_cols, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_mat.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_mat.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Mat.\u0026#34;); return nullptr; } template \u0026lt;typename T\u0026gt; inline auto a2m_vec(arma::Col\u0026lt;T\u0026gt; const \u0026amp;arma_vec) -\u0026gt; mxArray * { mxArray *matlab_mat = mxCreateNumericMatrix(arma_vec.n_elem, 1, mxDOUBLE_CLASS, mxREAL); if (matlab_mat) { auto *dst_pointer = static_cast\u0026lt;T *\u0026gt;(mxGetData(matlab_mat)); const auto *src_pointer = const_cast\u0026lt;T *\u0026gt;(arma_vec.memptr()); // TODO(mfbolus): I just want to MOVE the data, not copy. std::memcpy(dst_pointer, src_pointer, sizeof(T) * arma_vec.n_elem); return matlab_mat; } mexErrMsgTxt(\u0026#34;Failed to create matlab mat from arma::Col.\u0026#34;); return nullptr; } } // namespace armamexc #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":77,"href":"/docs/api/files/mex__cpp__util_8h/","title":"ldsCtrlEst_h/mex_cpp_util.h","section":"Files","content":" ldsCtrlEst_h/mex_cpp_util.h # arma \u0026lt;-\u0026gt; mex interoperability utilities (Matlab C++ API) More\u0026hellip;\nNamespaces # Name armamexcpp arma/mex interface using Matlab C++ API Detailed Description # This file defines utility functions for interoperability between armadillo and Matlab\u0026rsquo;s C++ mex API.\nSource code # //===-- ldsCtrlEst_h/mex_cpp_util.h - Mex C++ API Utilities -----*- C++ -*-===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #ifndef LDSCTRLEST_MEXCPP_UTIL_H #define LDSCTRLEST_MEXCPP_UTIL_H #include \u0026lt;ldsCtrlEst\u0026gt; #include \u0026#34;mex.hpp\u0026#34; #include \u0026#34;mexAdapter.hpp\u0026#34; namespace armamexcpp { template \u0026lt;class T\u0026gt; std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; m2a_cellmat(matlab::data::CellArray\u0026amp; matlab_cell) { size_t n_cells = matlab_cell.getNumberOfElements(); std::vector\u0026lt;arma::Mat\u0026lt;T\u0026gt;\u0026gt; arma_mat(n_cells, arma::Mat\u0026lt;T\u0026gt;(1, 1, arma::fill::zeros)); for (size_t k = 0; k \u0026lt; n_cells; k++) { matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = matlab_cell[k]; auto dims = matlab_mat.getDimensions(); arma_mat[k] = arma::Mat\u0026lt;T\u0026gt;(matlab_mat.release().get(), dims[0], dims[1]); } return arma_mat; }; template \u0026lt;class T\u0026gt; std::vector\u0026lt;T\u0026gt; m2s_vec(matlab::data::TypedArray\u0026lt;T\u0026gt;\u0026amp; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); T* ptr = matlab_array.release().get(); std::vector\u0026lt;T\u0026gt; vec(ptr, ptr + n_elem); return vec; }; template \u0026lt;class T\u0026gt; arma::Col\u0026lt;T\u0026gt; m2a_vec(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { size_t n_elem = matlab_array.getNumberOfElements(); // T* ptr = matlab_array.release().get(); // arma::Col\u0026lt;T\u0026gt; vec(ptr, n_elem); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? arma::Col\u0026lt;T\u0026gt; vec(n_elem, arma::fill::zeros); for (size_t k = 0; k \u0026lt; n_elem; k++) { vec[k] = matlab_array[k]; } return vec; }; template \u0026lt;class T\u0026gt; arma::Mat\u0026lt;T\u0026gt; m2a_mat(matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_array) { // ArrayDimensions == std::vector\u0026lt;size_t\u0026gt; auto dims = matlab_array.getDimensions(); // T* ptr = matlab_array.release().get(); // // mat(ptr_aux_mem, n_rows, n_cols, copy_aux_mem = true, strict = false) // arma::Mat\u0026lt;T\u0026gt; mat(ptr, dims[0], dims[1]); //, false); // TODO(mfbolus): for some reason, using the above pointer at times leads to // getting garbage values. matlab array values may be stored in non-contiguous // memory? // // armadillo and matlab both use column-major ordering, so this should work: size_t n_elem = dims[0] * dims[1]; arma::Mat\u0026lt;T\u0026gt; mat(dims[0], dims[1], arma::fill::zeros); size_t k(0); for (auto m: matlab_array) { mat[k] = m; k++; } return mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_mat(const arma::Mat\u0026lt;T\u0026gt;\u0026amp; arma_mat, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {arma_mat.n_rows, arma_mat.n_cols}, arma_mat.memptr(), arma_mat.memptr() + arma_mat.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; a2m_vec(const arma::Col\u0026lt;T\u0026gt;\u0026amp; arma_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;({arma_vec.n_elem, 1}, arma_vec.memptr(), arma_vec.memptr() + arma_vec.n_elem); return matlab_mat; }; template \u0026lt;class T\u0026gt; matlab::data::TypedArray\u0026lt;T\u0026gt; s2m_vec(const std::vector\u0026lt;T\u0026gt;\u0026amp; std_vec, matlab::data::ArrayFactory\u0026amp; factory) { const matlab::data::TypedArray\u0026lt;T\u0026gt; matlab_mat = factory.createArray\u0026lt;T\u0026gt;( {std_vec.size(), 1}, std_vec.data(), std_vec.data() + std_vec.size()); return matlab_mat; }; } // namespace armamexcpp #endif Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":78,"href":"/docs/terminology/model/","title":"Models","section":"LDS C+E Documentation","content":" Model Definitions # This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form: \\[\\mathbf{x}_{t\u0026#43;1} = f\\left( \\mathbf{x}_{t}, \\mathbf{v}_{t} \\right) = \\mathbf{A} \\mathbf{x}_{t} \u0026#43; \\mathbf{B} \\mathbf{v}_{t} \u0026#43; \\mathbf{m}_{t} \u0026#43; \\mathbf{w}_{t}\\] \\[\\mathbf{y}_{t} = h\\left( \\mathbf{x}_{t} \\right)\\] t : time index x : system state v = g%u : input (e.g., in physical units used for model fit) u : control signal sent to actuator (e.g., in Volts) y : system output m : process disturbance w ~ N(0, Q) : process noise/disturbance A : state matrix B : input coupling matrix g : input gain (e.g., for converting to control signal actuator voltage) n.b., assumes this conversion is linear Q : process noise covariance % : element-wise multiplication LDS with Gaussian Observations # For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.\n\\[\\mathbf{y}_{t} = \\mathbf{C} \\mathbf{x}_{t} \u0026#43; \\mathbf{d}\\] \\[\\mathbf{z}_{t} \\sim \\mathcal{N}\\left(\\mathbf{y}_{t} , \\mathbf{R} \\right)\\] z : measurement C : output matrix d : output bias R : measurement noise covariance LDS with Poisson Observations # For linear dynamical systems whose outputs are assumed to be rates underlying measured count data derived from a Poisson distribution (Poisson LDS models), the output function takes the following form. Note an element-wise exponentiation is used to rectify the linear dynamics for the rate of the Poisson process.\n\\[y_{t}^{i} = \\exp \\left(\\mathbf{c}^i \\mathbf{x}_{t} \u0026#43; d^i\\right)\\] \\[z_{t}^i \\sim \\rm{Poisson} \\left(y_{t}^i \\right)\\] i : output index z : measurement (count data) c : i^th row of output matrix (C) d : output bias Model Predictive Control (MPC) # Model Predictive Control (MPC) is an advanced control strategy that utilizes a dynamic model of the system to predict and optimize future behavior over a specified time horizon. At each control step, MPC solves an optimization problem to determine the control inputs that minimize a cost function, which typically includes terms for tracking desired reference trajectories and penalizing excessive control efforts. This approach allows MPC to handle multivariable systems with constraints effectively, making it suitable for complex industrial applications.\nIn the context of linear systems, the optimization problem within MPC can be formulated as a quadratic program. This involves defining a quadratic cost function over the prediction horizon, which balances the trade-off between tracking performance and control effort. The solution to this quadratic program yields the optimal control inputs that drive the system towards the desired state while respecting operational constraints. Tools like the Operator Splitting Quadratic Program (OSQP) solver are often employed to efficiently solve these optimization problems in real-time.\n"},{"id":79,"href":"/docs/api/modules/","title":"Modules","section":"LDS C+E Documentation","content":" Modules # Control Mode Bit Masks provides fill types for constructing new armadillo vectors, matrices\nDefaults\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":80,"href":"/docs/api/namespaces/","title":"Namespaces","section":"LDS C+E Documentation","content":" Namespaces # armamexc arma/mex interface using Matlab C API\narmamexcpp arma/mex interface using Matlab C++ API\nlds::gaussian Linear Dynamical Systems with Gaussian observations.\nlds::poisson Linear Dynamical Systems with Poisson observations.\nstd\nUpdated on 5 March 2025 at 16:35:01 EST\n"},{"id":81,"href":"/docs/api/pages/","title":"Pages","section":"LDS C+E Documentation","content":" Pages # Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":82,"href":"/docs/tutorials/eg_plds_state_estimation/","title":"PLDS State Estimation","section":"LDS C+E Examples","content":" PLDS State Estimation Tutorial # This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (lds::poisson::System) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating a simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; // no. inputs size_t n_x = 1; // no. states size_t n_y = 1; // no. outputs auto n_t = static_cast\u0026lt;size_t\u0026gt;(30 / dt); // no time steps for simulation. When a system is initialized, rather than requiring all parameters be provided at construction, users may create a default system by setting only the dimensions and sample period.\n// construct ground truth system... lds::poisson::System system_true(n_u, n_x, n_y, dt); This default system is a random walk, where the state matrix is identity, the input matrix is zeros, and the top min(n_x, n_y) states are observed at the output. i.e., for this example, \\[x_{t\u0026#43;1} = x_t \u0026#43; w_t\\] \\[y_{t} = \\exp\\left(x_t\\right)\\] where \\( w_{t} \\sim \\mathcal{N}\\left( 0, Q \\right) \\) .\nNow, create non-default parameters for this model.\n// Model parameters Matrix a_true(n_x, n_x, arma::fill::eye); a_true[0] = exp(-dt / 0.075); Matrix b_true = Matrix(n_x, n_u).fill(1e-2); Vector m0_true = Vector(n_x, arma::fill::zeros).fill(-7e-2); // disturbance Vector x0_true = m0_true * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state Finally, assign the parameters using corresponding set-methods.\n// Assign params. system_true.set_A(a_true); system_true.set_B(b_true); system_true.set_x0(x0_true); system_true.set_m(m0_true); system_true.Reset(); Creating the estimator # Now, create the estimator. The system type includes filtering functionality for state estimation, so create another lds::poisson::System. As noted above, the only parameter mismatch in this simulation will be the process disturbance.\n// Construct system for estimation // e.g., will create a model with incorrect disturbance lds::poisson::System system_estimator(n_u, n_x, n_y, dt); // Can copy parameters from another system object system_estimator = system_true; // wrong disturbance Vector m0_est = m0_true * 2; system_estimator.set_m(m0_est); // set new initial conditions Vector x0_est = m0_est * arma::inv(Matrix(n_x, n_x, arma::fill::eye) - a_true); // initial state system_estimator.set_x0(x0_est); system_estimator.Reset(); // reset to initial condition. To ensure robust estimates, adaptively re-estimate the process disturbance.\n// turn on adaptive disturbance estimation system_estimator.do_adapt_m = true; // set adaptation rate by changing covariance of assumed process noise acting // on random-walk evolution of m Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-6; system_estimator.set_Q_m(q_m); Simulating estimation # In this demonstration, random inputs are presented to the system, measurements are taken, and filtering is carried out in a for-loop.\n// Simlate the true system. z.col(t) = system_true.Simulate(u.col(t - 1)); // Filter (predict -\u0026gt; update) system_estimator.Filter(u.col(t - 1), z.col(t)); Example simulation result # Below are example results for this simulation, including outputs, latent states, process disturbance, and the input. The online estimates of the output, state, and disturbance are given in purple.\nWith this parameterization, it takes the estimator approximately 5 seconds to minimize state error. The state and output error distributions for the period after 5 seconds is shown below.\n"},{"id":83,"href":"/docs/tutorials/eg_switched_plds_control/","title":"PLDS Switched Control","section":"LDS C+E Examples","content":" PLDS Switched Control Tutorial # This tutorial shows how to use this library to control a system with a switched PLDS controller (lds::poisson::SwitchedController). This type of controller is applicable in scenarios where a physical system is not accurately captured by a single LDS but has multiple discrete operating modes where the dynamics can be well-approximated as linear.\nIn the example that follows, another PLDS model (lds::poisson::System) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.\nThe full code for this can be found here.\nPreamble # In addition to including the main ldsCtrlEst header, this tutorial will use some shorthand.\n#include \u0026lt;ldsCtrlEst\u0026gt; using lds::Matrix; using lds::Vector; using lds::data_t; using std::cout; Note that lds::Matrix and lds::Vector are typedefs for arma::Mat\u0026lt;data_t\u0026gt; and arma::Col\u0026lt;data_t\u0026gt;, where the data type is double by default. May be changed to float in include/ldsCtrlEst_h/lds.h if there are memory constraints (e.g., large-scale MIMO control problems).\nCreating the simulated system # A first-order single-input/single-output system will be used for the purposes of this demonstration. The simulation will be run at 1 kHz for 30 seconds.\n// whether to do switched control bool do_switch_ctrl = true; // Make SISO system sampled at 1kHz data_t dt = 1e-3; size_t n_u = 1; size_t n_x = 1; size_t n_y = 1; // no time steps for simulation. auto n_t = static_cast\u0026lt;size_t\u0026gt;(30.0 / dt); The system\u0026rsquo;s input matrix (B) will be switched stochastically from one value (b1) to a less sensitive value (b2) according to the following probabilities.\n// for simulating switching size_t which_mode = 1; data_t pr_21 = 1e-3; // prob mode 1 -\u0026gt; 2 data_t pr_12 = pr_21; // prob mode 2 -\u0026gt; 1 Initially, the system will be in \u0026ldquo;mode\u0026rdquo; 1, where B = b1.\n// simulated system being controlled lds::poisson::System controlled_system(n_u, n_x, n_y, dt); // **Assume the system is not well characterized by one LDS, but is well // characterized by two LDS models with different input matrices.** data_t scale_sys_b = 2; Matrix a(n_x, n_x, arma::fill::eye); a[0] = 0.985; Matrix b1 = Matrix(n_x, n_u).fill(0.05); Vector d = Vector(n_y, arma::fill::zeros).fill(log(1 * dt)); controlled_system.set_A(a); controlled_system.set_B(b1); controlled_system.set_d(d); controlled_system.Reset(); // reset to initial conditions See the GLDS Control and PLDS State Estimation tutorials for more detail about creating System objects.\nCreating the controller # Now, create the controller. A switched-system controller (SwitchedController) essentially toggles between the parameters of its subsystems when the controller is told a switch has occured. The first thing the user needs to do is define these subsystems. In this example, there are two Poisson systems (sys1, sys2), which are the same save for their input gains.\nSimilar to a non-switched controller, constructing a SwitchedController requires these system models and upper/lower bounds on control. See the GLDS Control tutorial for more details. In the case of a SwitchedController, it needs a list of systems, using the std::vector container.\nMoreover, when assigning control-related signals such as the feedback controller gains, it is crucial that the list of gains optimized for each operating mode of the system have the same dimensionality. For this reason, this library provides UniformMatrixList and UniformVectorList containers that should be used when setting Kc, Kc_inty, g_design. These containers are std::vectors whose contents are uniformly sized.\nPutting this information together, here is how to create the controller and the list of controller gains optimized for each system operating mode.\n// create switched controller lds::poisson::SwitchedController switched_controller; lds::UniformMatrixList\u0026lt;\u0026gt; k_x; // feedback controller gains { // create switched controller sub-systems // system 1 lds::poisson::System sys1(controlled_system); // set process noise covariance Matrix q_controller = Matrix(n_x, n_x, arma::fill::eye) * 5e-3; sys1.set_Q(q_controller); // adaptively estimate process disturbance (m) // n.b. using arbitrary default value for process noise if enabled. sys1.do_adapt_m = true; // setting initial mode to target to avoid large error at onset: Vector x0_controller = arma::log(y_ref0) - d; sys1.set_x0(x0_controller); sys1.Reset(); // reset to initial conditions // system 2 lds::poisson::System sys2 = sys1; // set parameters sys2.set_B(b2); lds::UniformSystemList\u0026lt;lds::poisson::System\u0026gt; systems({sys1, sys2}); // controller gains for underlying systems: Matrix k_x1(n_u, n_x, arma::fill::ones); Matrix k_x2 = scale_sys_b * k_x1; // system2 is x-times less sensitive. k_x = lds::UniformMatrixList\u0026lt;\u0026gt;({k_x1, k_x2}); data_t u_lb = 0.0; data_t u_ub = 5.0; switched_controller = std::move( lds::poisson::SwitchedController(std::move(systems), u_lb, u_ub)); } Note that the above code block demonstrates how move semantics can be used for assignment/construction. Copy assignment/construction is of course also allowed.\nNow that the SwitchedController is instantiated, assign its parameters.\n// Control variables size_t control_type = 0; // no integral action, etc switched_controller.set_control_type(control_type); switched_controller.set_Kc(std::move(k_x)); switched_controller.set_y_ref(y_ref0); Simulating control # In this demonstration, we will use the ControlOutputReference method which allows users to simply set the reference output event rate (y_ref) and supply the current measurement z. It then calculates the solution for the state/input required to track that output at steady state. In this case, the goal is to regulate the output about a constant reference, so it is appropriate here. This method can also be used for time-varying references as long as this variation is slow compared to the dynamics of the system. Importantly, this method performs control in the linear state space (i.e., taking the logarithm of the reference output).\nThe control loop is carried out here in a simple for-loop, controlled system is simulated along with stochastic mode switches, a measurement taken, and the control signal updated.\n// Let the controlled system stochastically change gain // Assume another algorithm decodes this mode change and signals the // switched_controller Vector chance(1, arma::fill::randu); if (which_mode == 1) // mode1 { if (chance[0] \u0026lt; pr_21) { which_mode = 2; controlled_system.set_B(b2); if (do_switch_ctrl) { switched_controller.Switch(1); } } } else { // mode2 if (chance[0] \u0026lt; pr_12) { which_mode = 1; controlled_system.set_B(b1); if (do_switch_ctrl) { switched_controller.Switch(0); } } } // Simulate the true system. z.col(t) = controlled_system.Simulate(u.col(t - 1)); // perform control u.col(t) = switched_controller.ControlOutputReference(z.col(t)); Note that as the gain of the controlled system changes stochastically, the controller is informed of this change. In practice, a user must decode such changes in the system\u0026rsquo;s operating mode and call the Switch method accordingly. Such a decoder is not currently included in this library.\nExample simulation result # Below are example results for this simulation, including outputs, latent states, mode switches, and the control signal. The controller\u0026rsquo;s online estimates of the output and state are shown in purple.\nNote that every time the operating mode of the system changes (here, a gain changes), the controller immediately adjusts its inputs. In contrast, a non-switched controller with integral action would also compensate but do so in a comparitively sluggish fashion.\n"},{"id":84,"href":"/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/","title":"src","section":"Files","content":" src # Files # Name src/lds.cpp misc lds namespace functions src/lds_gaussian_sys.cpp GLDS base type. src/lds_poisson_sys.cpp PLDS base type. src/lds_sys.cpp LDS base type. src/lds_uniform_vecs.cpp Uniformly sized vectors. Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":85,"href":"/docs/api/files/lds__gaussian__sys_8cpp/","title":"src/lds_gaussian_sys.cpp","section":"Files","content":" src/lds_gaussian_sys.cpp # GLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Gaussian-output linear dynamical systems (lds::gaussian::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_gaussian_sys.cpp - GLDS ---------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_gaussian_sys.h\u0026gt; lds::gaussian::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0, data_t r0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { R_.zeros(n_y, n_y); R_.diag().fill(r0); do_recurse_Ke_=true; }; // recursively estimate Ke void lds::gaussian::System::RecurseKe() { if (!do_recurse_Ke_) { return; } // predict covariance P_ = A_ * P_ * A_.t() + Q_; // calc Kalman gain Ke_ = P_ * C_.t() * inv_sympd(C_ * P_ * C_.t() + R_); // update covariance // Reference: Ghahramani et Hinton (1996) P_ = P_ - Ke_ * C_ * P_; if (do_adapt_m) { P_m_ += Q_m_; // A_m = I (i.e., random walk) Ke_m_ = P_m_ * C_.t() * inv_sympd(C_ * P_m_ * C_.t() + R_); P_m_ = P_m_ - Ke_m_ * C_ * P_m_; } } // Simulate const lds::Vector\u0026amp; lds::gaussian::System::Simulate(const Vector\u0026amp; u_tm1){ f(u_tm1, true);//simulate dynamics with noise added h();//output z_ = y_ + arma::mvnrnd(Vector(n_y_).fill(0), R_);//measure return z_; } void lds::gaussian::System::Print() { lds::System::Print(); std::cout \u0026lt;\u0026lt; \u0026#34;R: \\n\u0026#34; \u0026lt;\u0026lt; R_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":86,"href":"/docs/api/files/lds__poisson__sys_8cpp/","title":"src/lds_poisson_sys.cpp","section":"Files","content":" src/lds_poisson_sys.cpp # PLDS base type. More\u0026hellip;\nDetailed Description # This file implements the type for state estimation (filtering) as well as simulation of Poisson-output linear dynamical systems (lds::poisson::sys_t). It inherits functionality from the underlying linear dynamical system (lds::sys_t).\nSource code # //===-- lds_poisson_sys.cpp - PLDS ----------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_poisson_sys.h\u0026gt; lds::poisson::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : lds::System(n_u, n_x, n_y, dt, p0, q0) { diag_y_ = diagmat(y_); pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(0); }; // Correct: Given measurement (z) and current input (u), update estimate of the // state, covar, output. // // see Eden et al. 2004 void lds::poisson::System::RecurseKe() { // predict covariance P_ = A_ * P_ * A_.t() + Q_; // update cov P_ = pinv(pinv(P_) + C_.t() * diag_y_ * C_); Ke_ = P_ * C_.t(); if (do_adapt_m) { P_m_ += Q_m_; // predict (A_m = I) P_m_ = pinv(pinv(P_m_) + C_.t() * diag_y_ * C_); // update Ke_m_ = P_m_ * C_.t(); } } // Simulate Measurement: z ~ Poisson(y) const lds::Vector\u0026amp; lds::poisson::System::Simulate(const Vector\u0026amp; u_tm1) { f(u_tm1, true); // simulate dynamics with noise added h(); // output z_.zeros(); for (std::size_t k = 0; k \u0026lt; n_y_; k++) { // construct a Poisson distribution object with mean y[k] pd_ = std::poisson_distribution\u0026lt;size_t\u0026gt;(y_[k]); // pull random sample from this distribution z_[k] = pd_(rng); } return z_; } // ******************* SYS_T ******************* Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":87,"href":"/docs/api/files/lds__sys_8cpp/","title":"src/lds_sys.cpp","section":"Files","content":" src/lds_sys.cpp # LDS base type. More\u0026hellip;\nDetailed Description # This file implements the base type for linear dynamical systems (lds::System). Note that this class defines the underlying linear dynamics, but does not have output functions.Gaussian- and Poisson-output variants will be built upon this class.\nSource code # //===-- lds_sys.cpp - LDS -------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_sys.h\u0026gt; #include \u0026lt;vector\u0026gt; lds::System::System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0, data_t q0) : n_u_(n_u), n_x_(n_x), n_y_(n_y), dt_(dt) { InitVars(p0, q0); } void lds::System::InitVars(data_t p0, data_t q0) { // initial conditions. x0_ = Vector(n_x_, fill::zeros); // includes bias (nY) and g (nU) P0_ = p0 * Matrix(n_x_, n_x_, fill::eye); m0_ = x0_; P0_m_ = P0_; // signals x_ = x0_; P_ = P0_; m_ = m0_; P_m_ = P0_m_; y_ = Vector(n_y_, fill::zeros); cx_ = Vector(n_y_, fill::zeros); z_ = Vector(n_y_, fill::zeros); // By default, random walk where each state is independent // In this way, provides independent estimates of rate per channel of output. A_ = Matrix(n_x_, n_x_, fill::eye); B_ = Matrix(n_x_, n_u_, fill::zeros); g_ = Vector(n_u_, fill::ones); Q_ = q0 * Matrix(n_x_, n_x_, fill::eye); Q_m_ = Q_; C_ = Matrix(n_y_, n_x_, fill::eye); // each state will map to an output by d_ = Vector(n_y_, fill::zeros); Ke_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain. Ke_m_ = Matrix(n_x_, n_y_, fill::zeros); // estimator gain for m adaptation. do_adapt_m = false; } // Filter: Given measurement (`z`) and previous input (`u_tm1`), predict state // and update estimate of the state, covar, output using Kalman filter void lds::System::Filter(const Vector\u0026amp; u_tm1, const Vector\u0026amp; z_t) { // predict mean f(u_tm1); // dynamics h(); // output // recursively calculate esimator gains (or just keep existing values) // (also predicts+updates estimate covariance) RecurseKe(); // update x_ += Ke_ * (z_t - y_); if (do_adapt_m) { m_ += Ke_m_ * (z_t - y_); // adaptively estimating disturbance } // With new state, estimate output. h(); // --\u0026gt; posterior } void lds::System::Reset() { // reset to initial conditions x_ = x0_; // mean P_ = P0_; // cov of state estimate m_ = m0_; // process disturbance P_m_ = P0_m_; // cov of disturbance estimate h(); } std::vector\u0026lt;lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt;\u0026gt; lds::System::nstep_pred_block(lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; u, lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; z, size_t n_pred) { lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_filt; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; x_pred; lds::UniformMatrixList\u0026lt;lds::kMatFreeDim2\u0026gt; y_pred; for (size_t k = 0; k \u0026lt; u.size(); k++) { Reset(); size_t n_t = arma::size(u[k])[1]; Matrix x_filt_k(n_x_, n_t, fill::zeros); Matrix x_pred_k(n_x_, n_t - n_pred, fill::zeros); Matrix y_pred_k(n_y_, n_t - n_pred, fill::zeros); for (size_t t = 0; t \u0026lt; n_t - n_pred; t++) { Vector x_pred_ahead = x_; for (size_t t_u = t; t_u \u0026lt; t + n_pred; t_u++) { x_pred_ahead = A_ * x_pred_ahead + B_ * u[k].col(t_u); } x_pred_k.col(t) = x_pred_ahead; y_pred_k.col(t) = h_(x_pred_ahead); if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; // given previous measurment } for (size_t t = n_t - n_pred; t \u0026lt; n_t; t++) { if (t \u0026gt; 0) { Filter(u[k].col(t - 1), z[k].col(t)); } x_filt_k.col(t) = x_; } x_filt.append(x_filt_k); x_pred.append(x_pred_k); y_pred.append(y_pred_k); } return {x_filt, x_pred, y_pred}; } void lds::System::Print() { std::cout \u0026lt;\u0026lt; \u0026#34;\\n ********** SYSTEM ********** \\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;x: \\n\u0026#34; \u0026lt;\u0026lt; x_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;P: \\n\u0026#34; \u0026lt;\u0026lt; P_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;A: \\n\u0026#34; \u0026lt;\u0026lt; A_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;B: \\n\u0026#34; \u0026lt;\u0026lt; B_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;g: \\n\u0026#34; \u0026lt;\u0026lt; g_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;m: \\n\u0026#34; \u0026lt;\u0026lt; m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q: \\n\u0026#34; \u0026lt;\u0026lt; Q_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;Q_m: \\n\u0026#34; \u0026lt;\u0026lt; Q_m_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;d: \\n\u0026#34; \u0026lt;\u0026lt; d_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;C: \\n\u0026#34; \u0026lt;\u0026lt; C_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; std::cout \u0026lt;\u0026lt; \u0026#34;y: \\n\u0026#34; \u0026lt;\u0026lt; y_ \u0026lt;\u0026lt; \u0026#34;\\n\u0026#34;; } //******************* SYS_T ******************* Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":88,"href":"/docs/api/files/lds__uniform__vecs_8cpp/","title":"src/lds_uniform_vecs.cpp","section":"Files","content":" src/lds_uniform_vecs.cpp # Uniformly sized vectors. More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file provides a container for uniformly sized vectors.\nSource code # //===-- ldsCtrlEst_h/lds_uniform_vecs.cpp - Uniform Matrices --------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // Limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds_uniform_vecs.h\u0026gt; namespace lds { UniformVectorList::UniformVectorList(const std::vector\u0026lt;Vector\u0026gt;\u0026amp; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); } UniformVectorList::UniformVectorList(std::vector\u0026lt;Vector\u0026gt;\u0026amp;\u0026amp; vecs, size_t dim) : vector(std::move(vecs)) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(std::initializer_list\u0026lt;Vector\u0026gt; vecs, size_t dim) : vector(vecs) { CheckDimensions(dim); }; UniformVectorList::UniformVectorList(const UniformVectorList\u0026amp; that) : vector(that) { (*this) = that; } UniformVectorList::UniformVectorList(UniformVectorList\u0026amp;\u0026amp; that) noexcept : vector(std::move(that)) { this-\u0026gt;dim_ = this-\u0026gt;at(0).n_elem; } void UniformVectorList::CheckDimensions(size_t dim) { if (dim) { dim_ = dim; } else { dim_ = this-\u0026gt;at(0).n_elem; } // make sure dimensiolaties are all uniform bool does_match(true); for (const Vector\u0026amp; vec : *this) { does_match = does_match \u0026amp;\u0026amp; (vec.n_elem == dim_); if (!does_match) { throw std::runtime_error( \u0026#34;Dimensionality of one or more input matrices are not uniform.\u0026#34;); } } } } // namespace lds Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":89,"href":"/docs/api/files/lds_8cpp/","title":"src/lds.cpp","section":"Files","content":" src/lds.cpp # misc lds namespace functions More\u0026hellip;\nNamespaces # Name lds Linear Dynamical Systems (LDS) namespace. Detailed Description # This file implements miscellaneous lds namespace functions not bound to a class.\nSource code # //===-- lds.cpp - LDS -----------------------------------------------------===// // // Copyright 2021 Michael Bolus // Copyright 2021 Georgia Institute of Technology // // Licensed under the Apache License, Version 2.0 (the \u0026#34;License\u0026#34;); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an \u0026#34;AS IS\u0026#34; BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // //===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===// #include \u0026lt;ldsCtrlEst_h/lds.h\u0026gt; // insert any necessary function definitions here. namespace lds { void ForceSymPD(Matrix\u0026amp; X) { if (X.is_sympd() || !X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; // for eigenval decomp bool did_succeed(true); Vector d; Matrix u; // see first method (which may not be ideal): // https://nhigham.com/2021/02/16/diagonally-perturbing-a-symmetric-matrix-to-make-it-positive-definite/ size_t k(1); bool is_sympd = X.is_sympd(); Matrix id = Matrix(X.n_rows, X.n_cols, fill::eye); while (!is_sympd) { if (k \u0026gt; 100) { did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); data_t min_eig = arma::min(d); std::cerr \u0026lt;\u0026lt; \u0026#34;After multiple iterations, min eigen val = \u0026#34; \u0026lt;\u0026lt; min_eig \u0026lt;\u0026lt; \u0026#34;.\\n\u0026#34;; throw std::runtime_error( \u0026#34;Failed to make matrix symmetric positive definite.\u0026#34;); return; } // Limit(d, arma::eps(0), kInf); // force to be positive... // Matrix d_diag = arma::diagmat(d); // X = u * d_diag * u.t(); did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymPD failed.\u0026#34;); } data_t min_eig = arma::min(d); X += id * abs(min_eig) + arma::datum::eps; // make sure symm: X = (X + X.t()) / 2; // double check eigenvals positive after symmetrizing: arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); min_eig = arma::min(d); is_sympd = min_eig \u0026gt; 0; k++; } } void ForceSymMinEig(Matrix\u0026amp; X, data_t eig_min) { if (!X.is_square()) { return; } // make symmetric X = (X + X.t()) / 2; bool did_succeed(true); Vector d; Matrix u; did_succeed = arma::eig_sym(d, u, X, \u0026#34;std\u0026#34;); if (!did_succeed) { throw std::runtime_error(\u0026#34;ForceSymMinEig failed.\u0026#34;); } Limit(d, eig_min + arma::eps(eig_min), kInf); // enforce lower bound Matrix d_diag = arma::diagmat(d); X = u * d_diag * u.t(); // double check symmetric X = (X + X.t()) / 2; } void lq(Matrix\u0026amp; L, Matrix\u0026amp; Qt, const Matrix\u0026amp; X) { bool did_succeed(true); did_succeed = arma::qr_econ(Qt, L, X.t()); if (!did_succeed) { throw std::runtime_error(\u0026#34;LQ decomposition failed.\u0026#34;); } arma::inplace_trans(L); arma::inplace_trans(Qt); } Matrix calcCov(const Matrix\u0026amp; A, const Matrix\u0026amp; B) { // subtract out mean auto m_a = arma::mean(A, 1); Matrix a0 = A; a0.each_col() -= m_a; auto m_b = arma::mean(B, 1); Matrix b0 = B; b0.each_col() -= m_b; Matrix cov = a0 * b0.t() / a0.n_cols; return cov; } } // namespace lds Updated on 5 March 2025 at 16:35:01 EST\n"},{"id":90,"href":"/docs/api/namespaces/namespacestd/","title":"std","section":"Namespaces","content":" std # Updated on 5 March 2025 at 16:35:01 EST\n"}]
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diff --git a/docs/en.search.min.6bf4a945c37a68b0e35197d5055a3d63892d9f0912768ecfb7f2bcfc918bed5e.js b/docs/en.search.min.6bf4a945c37a68b0e35197d5055a3d63892d9f0912768ecfb7f2bcfc918bed5e.js
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diff --git a/docs/en.search.min.82d18e8d935694d7ff0b8a23dfdbe00d24d52ff0df91c14896a97f26bf4313f9.js b/docs/en.search.min.82d18e8d935694d7ff0b8a23dfdbe00d24d52ff0df91c14896a97f26bf4313f9.js
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diff --git a/docs/en.search.min.880d08e6f5b646cba945c77cc86a9a79febfc27f66dd68f22ef769e3813e6167.js b/docs/en.search.min.880d08e6f5b646cba945c77cc86a9a79febfc27f66dd68f22ef769e3813e6167.js
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diff --git a/docs/fonts/roboto-mono-v13-latin-regular.woff2 b/docs/fonts/roboto-mono-v13-latin-regular.woff2
new file mode 100644
index 0000000000000000000000000000000000000000..ed384d22fd9f566df41a3a37de9f614adc7f630b
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+  ldsCtrlEst: LDS Control &amp; Estimation
+  #
+
+ldsCtrlEst is a C&#43;&#43; library for estimation and control of linear dynamical systems (LDS) with Gaussian or Poisson observations. It is meant to provide the functionality necessary to implement feedback control of linear dynamical systems experimentally. This library was originally developed for the task of controlling neuronal activity using spike count data as feedback and optogenetic inputs for control. However, the methods are generally applicable.">
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+<li><strong>Library Terminology</strong>
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+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
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+<ul>
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+<li><strong>Tutorials</strong>
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+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
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+  <ul>
+    <li><a href="#ldsctrlest-lds-control--estimation"><code>ldsCtrlEst</code>: LDS Control &amp; Estimation</a></li>
+    <li><a href="#project-scope">Project Scope</a></li>
+    <li><a href="#repository-design">Repository Design</a></li>
+    <li><a href="#repository-organization">Repository Organization</a></li>
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+  <article class="markdown"><h1 id="ldsctrlest-lds-control--estimation">
+  <code>ldsCtrlEst</code>: LDS Control &amp; Estimation
+  <a class="anchor" href="#ldsctrlest-lds-control--estimation">#</a>
+</h1>
+<p><code>ldsCtrlEst</code> is a C++ library for estimation and control of linear dynamical systems (LDS) with Gaussian or Poisson observations. It is meant to provide the functionality necessary to implement feedback control of linear dynamical systems experimentally. This library was originally developed for the task of controlling neuronal activity using spike count data as feedback and optogenetic inputs for control. However, the methods are generally applicable.</p>
+<p>This library currently provides three namespaces.</p>
+<ul>
+<li><code>lds</code> : linear dynamical systems (without output/observations)</li>
+<li><code>lds::gaussian</code>: linear dynamical systems with Gaussian observations</li>
+<li><code>lds::poisson</code>: linear dynamical systems with Poisson observations</li>
+</ul>
+<p><em>Future iterations may include an additional namespace for LDS with Bernoulli observations (<code>lds::bernoulli</code>).</em></p>
+<h1 id="project-scope">
+  Project Scope
+  <a class="anchor" href="#project-scope">#</a>
+</h1>
+<p>The goal of this project is to provide necessary functions to implement feedback control of linear dynamical systems experimentally: <em>i.e.</em>, online estimation of state feedback and calculation of control signal updates. Given its intended use in experiments, the library seeks to be <strong>practical</strong> in all things and thus includes optional features such as adaptive estimation of a process disturbance to improve robustness in state estimation and a mechanism for combatting integrator windup with control signal saturation. For cases where the system to be controlled is not adequately modeled as having linear dynamics but has multiple quasi-linear operating modes, a switched control scheme is also implemented. It switches between multiple controllers designed for each operating mode of the physical system as it changes. It also includes options to toggle on/off feedback control and state estimation independently, which can be practically useful when testing the components of the control system. Moreover, to avoid the need for numerical integration of continuous-time models, all state-space models used here are discrete-time.</p>
+<p>Generally, the <code>ldsCtrlEst</code> library does <strong>not</strong> endeavor to provide functionality for things that can be carried out offline/before experiments. For example, it does not design controller gains. Given a model of the system to be controlled, these parameters may be optimized before experimental application in most cases, and there are numerous options available to scientists/engineers in languages such as Matlab and Python for design. An exception to this guiding principle to project scope is the included code for fitting state-space models to data. Currently, this fitting portion of the library is a configurable option, but in future releases this may migrate to a separate project as it is not intended for online use.</p>
+<p>Among other things, this project also does not provide methods for trajectory optimization, linearization of nonlinear models, or other methods related to nonlinear control, with the exception of the nonlinear state estimator for Poisson-output LDS models.</p>
+<h1 id="repository-design">
+  Repository Design
+  <a class="anchor" href="#repository-design">#</a>
+</h1>
+<p><img src="/lds-ctrl-est/classlds_1_1_system__inherit__graph.png" alt="system class hierarchy" /></p>
+<ul>
+<li>All dynamical systems <em>with observations</em> (<code>lds::gaussian::System</code>, <code>lds::poisson::System</code>) are derived from a prototypical linear dynamical system abstract type (<code>lds::System</code>).</li>
+<li>These Gaussian and Poisson system types include user-accessible functions for one-step filtering for one-step simulation, etc.</li>
+</ul>
+<p><img src="/lds-ctrl-est/classlds_1_1_controller__inherit__graph.png" alt="controller class hierarchy" /></p>
+<ul>
+<li>The controller types for Gaussian- and Poisson-output systems (<code>lds::gaussian::Controller</code>, <code>lds::gaussian::SwitchedController</code>, <code>lds::poisson::Controller</code>, <code>lds::poisson::SwitchedController</code>) are derived from an abstract class template (<code>lds::Controller</code>) that is generic over LDS types derived from <code>lds::System</code> (here, <code>lds::gaussian::System</code> and <code>lds::poisson::System</code>). <code>lds::Controller</code> provides functions for one-step updates of the control signal, based on feedback and a target/reference signal. For the common problem of output reference tracking, the controller uses the underlying system model to estimate the control signal required to track the target, effectively providing model-based open-loop control if the estimator is disabled.</li>
+<li>In order to ensure dimensionalities always match internally, every property of a system/controller class is <code>protected</code> or <code>private</code>. Get methods provide read-only references for most signals/parameters. Where appropriate, set methods are defined so users can change hidden parameters if and only if the new parameter has the correct dimensions.</li>
+</ul>
+<h1 id="repository-organization">
+  Repository Organization
+  <a class="anchor" href="#repository-organization">#</a>
+</h1>
+<ul>
+<li>Header files are located under <code>include/ldsEstCtrl_h</code>.</li>
+<li>Source files are located under <code>src/</code> (main source code) and <code>src-fit/</code> (model fitting-related source code).</li>
+<li>Wrappers for exposing functions to Matlab as executables (mex) are located under <code>matlab/</code>. Currently, only fitting functions of the library are exposed for use in Matlab.</li>
+<li>Complimentary Matlab functions for control and estimation are also located under <code>matlab/</code>. They are provided as methods of <code>GLDS</code> and <code>PLDS</code> class definitions.</li>
+<li>Example programs and visualization scripts are located under <code>examples/</code>.</li>
+<li>Example programs that demonstrate how to use ldsCtrlEst in other projects are provided in <code>misc/</code>. See <code>misc/test-cmake-installation</code> for a project that uses <code>cmake</code> to configure your project build and <code>misc/test-pkgconfig-installation</code> which is the same but uses a hand-written Makefile and calls to pkg-config. As the names suggest, building these programs is a simple way to test your installation of ldsCtrlEst.</li>
+</ul>
+</article>
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+    <li><a href="#ldsctrlest-lds-control--estimation"><code>ldsCtrlEst</code>: LDS Control &amp; Estimation</a></li>
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+    <title>LDS Control &amp; Estimation on LDS C&amp;E</title>
+    <link>https://stanley-rozell.github.io/lds-ctrl-est/</link>
+    <description>Recent content in LDS Control &amp; Estimation on LDS C&amp;E</description>
+    <generator>Hugo</generator>
+    <language>en</language>
+    <atom:link href="https://stanley-rozell.github.io/lds-ctrl-est/index.xml" rel="self" type="application/rss+xml" />
+    <item>
+      <title></title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/acknowledgements/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/acknowledgements/</guid>
+      <description>&lt;h1 id=&#34;acknowledgements&#34;&gt;&#xA;  Acknowledgements&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#acknowledgements&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;Development and publication of this library was supported in part by the NIH/NINDS Collaborative Research in Computational Neuroscience (CRCNS)/BRAIN Grant 5R01NS115327-02.&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title></title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/getting-started/getting-started/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/getting-started/getting-started/</guid>
+      <description>&lt;h1 id=&#34;getting-started&#34;&gt;&#xA;  Getting Started&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#getting-started&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;This library uses the cross-platform tool CMake to orchestrate the building and testing process on Linux, MacOS, and Windows.&lt;/p&gt;&#xA;&lt;p&gt;&lt;code&gt;ldsCtrlEst&lt;/code&gt; requires &lt;a href=&#34;http://arma.sourceforge.net/&#34;&gt;Armadillo&lt;/a&gt; for linear algebra as well as &lt;a href=&#34;https://www.hdfgroup.org/downloads/hdf5/&#34;&gt;HDF5&lt;/a&gt; for saving output. &lt;a href=&#34;https://vcpkg.io/&#34;&gt;&lt;code&gt;vcpkg&lt;/code&gt;&lt;/a&gt; is a cross-platform C++ package manager which allows us to easily install and use the dependencies in isolation.&lt;/p&gt;&#xA;&lt;h2 id=&#34;tested-configurations&#34;&gt;&#xA;  Tested Configurations&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#tested-configurations&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;p&gt;Building C++ libraries with complex dependencies can be tricky business—in our experience builds have inexplicably worked in one environment and failed in another. To save you time, sweat, and tears, we suggest you simply use one of the following setups we know work fairly reliably, using the &lt;code&gt;RelWithDebInfo&lt;/code&gt; build type in the CMake configure command (&lt;code&gt;-DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo&lt;/code&gt;):&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title></title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/getting-started/windows/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/getting-started/windows/</guid>
+      <description>&lt;h1 id=&#34;windows-installation&#34;&gt;&#xA;  Windows Installation&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#windows-installation&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;h2 id=&#34;windows-pre-requisites&#34;&gt;&#xA;  Windows Pre-requisites&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#windows-pre-requisites&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;p&gt;Scoop is a very handy tool for easily installing all sorts of command-line applications. Install like this:&lt;/p&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-shell&#34; data-lang=&#34;shell&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;Set-ExecutionPolicy RemoteSigned -Scope CurrentUser &lt;span style=&#34;color:#75715e&#34;&gt;# Optional: Needed to run a remote script the first time&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;iwr get.scoop.sh | Invoke-Expression&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;Install Git and CMake if you don&amp;rsquo;t already have them:&lt;/p&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-shell&#34; data-lang=&#34;shell&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;scoop install git cmake&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;If that didn&amp;rsquo;t work, follow more detailed instructions &lt;a href=&#34;https://github.com/ScoopInstaller/Install#readme&#34;&gt;here&lt;/a&gt;.&lt;/p&gt;&#xA;&lt;p&gt;The easiest way to compile C++ project on Windows is with Visual Studio&amp;rsquo;s build tools, which you can download &lt;a href=&#34;https://visualstudio.microsoft.com/downloads/&#34;&gt;here&lt;/a&gt; (or &lt;a href=&#34;https://visualstudio.microsoft.com/vs/older-downloads/&#34;&gt;here&lt;/a&gt; for the 2019 release which we tested—make sure you get the most recent one, e.g., 16.11 at time of writing). In the installer, click on &amp;ldquo;Desktop development with C++.&amp;rdquo; If you want to build Python bindings, you will need to use the Clang compiler, which you can add on the &amp;ldquo;Installation details&amp;rdquo; sidebar under optional features.&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title></title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/issues-contributing/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/issues-contributing/</guid>
+      <description>&lt;h1 id=&#34;reporting-issues&#34;&gt;&#xA;  Reporting Issues&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#reporting-issues&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please &lt;a href=&#34;https://github.com/stanley-rozell/lds-ctrl-est/issues&#34;&gt;open an issue on GitHub&lt;/a&gt; and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.&lt;/p&gt;&#xA;&lt;h1 id=&#34;contributing&#34;&gt;&#xA;  Contributing&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#contributing&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;We welcome any community contributions to this project. Please fork the repository and if possible use &lt;code&gt;clang-format&lt;/code&gt; and &lt;code&gt;clang-tidy&lt;/code&gt; to conform to the coding format/style of this repository.&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>armamexc</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacearmamexc/</guid>
+      <description>arma/mex interface using Matlab C API</description>
+    </item>
+    <item>
+      <title>armamexcpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacearmamexcpp/</guid>
+      <description>arma/mex interface using Matlab C++ API</description>
+    </item>
+    <item>
+      <title>C&amp;E</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/terminology/control-estimation/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/terminology/control-estimation/</guid>
+      <description>&lt;h1 id=&#34;control--estimation&#34;&gt;&#xA;  Control &amp;amp; Estimation&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#control--estimation&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;The control system provided by this library is comprised of a state estimator and a controller. The estimator is responsible for estimating the latent state of the system, given measurements up to and including the current time (i.e., &lt;em&gt;filtering&lt;/em&gt;). At each time step, the controller then uses the resulting state feedback and an internal model of the system to update the inputs to the process being manipulated.&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>Control Mode Bit Masks</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__control__masks/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__control__masks/</guid>
+      <description>provides fill types for constructing new armadillo vectors, matrices</description>
+    </item>
+    <item>
+      <title>Defaults</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__defaults/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__defaults/</guid>
+      <description>&lt;h1 id=&#34;defaults&#34;&gt;&#xA;  Defaults&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#defaults&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;&lt;br&gt; &lt;br&gt;&lt;a href=&#34;#detailed-description&#34;&gt;More&amp;hellip;&lt;/a&gt;&#xA;&lt;br&gt;&lt;/p&gt;&#xA;&lt;h2 id=&#34;attributes&#34;&gt;&#xA;  Attributes&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#attributes&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const data_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__defaults/#variable-kdefaultp0&#34;&gt;kDefaultP0&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;default state estimate covar&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const data_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__defaults/#variable-kdefaultq0&#34;&gt;kDefaultQ0&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;default process noise covar&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const data_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/modules/group__defaults/#variable-kdefaultr0&#34;&gt;kDefaultR0&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;default output noise covar&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;detailed-description&#34;&gt;&#xA;  Detailed Description&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#detailed-description&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;p&gt;Default values for common variables (e.g., default diagonal elements of covariances)&lt;/p&gt;&#xA;&lt;h2 id=&#34;attribute-details&#34;&gt;&#xA;  Attribute Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#attribute-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;kdefaultp0&#34;&gt;&#xA;  kDefaultP0&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#kdefaultp0&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;static&lt;/span&gt; &lt;span style=&#34;color:#66d9ef&#34;&gt;const&lt;/span&gt; data_t kDefaultP0 &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1e-6&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h3 id=&#34;kdefaultq0&#34;&gt;&#xA;  kDefaultQ0&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#kdefaultq0&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;static&lt;/span&gt; &lt;span style=&#34;color:#66d9ef&#34;&gt;const&lt;/span&gt; data_t kDefaultQ0 &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1e-6&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h3 id=&#34;kdefaultr0&#34;&gt;&#xA;  kDefaultR0&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#kdefaultr0&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;static&lt;/span&gt; &lt;span style=&#34;color:#66d9ef&#34;&gt;const&lt;/span&gt; data_t kDefaultR0 &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1e-2&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;p&gt;Updated on 31 March 2025 at 16:04:30 EDT&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>eg_glds_ctrl.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_glds_ctrl_8cpp-example/</guid>
+      <description>Example GLDS Control.</description>
+    </item>
+    <item>
+      <title>eg_glds_du_plds_ctrl.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_glds_du_plds_ctrl_8cpp-example/</guid>
+      <description>Example GLDS Control of PLDS where change in control (du) is being updated, rather than amplitude (u).</description>
+    </item>
+    <item>
+      <title>eg_plds_ctrl.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_ctrl_8cpp-example/</guid>
+      <description>Example PLDS Control.</description>
+    </item>
+    <item>
+      <title>eg_plds_est.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/</guid>
+      <description>Example PLDS Estimation.</description>
+    </item>
+    <item>
+      <title>eg_plds_switched_ctrl.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_switched_ctrl_8cpp-example/</guid>
+      <description>Example Switched PLDS Control.</description>
+    </item>
+    <item>
+      <title>examples</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_d28a4824dc47e487b107a5db32ef43c4/</guid>
+      <description>&lt;h1 id=&#34;examples&#34;&gt;&#xA;  examples&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#examples&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;h2 id=&#34;files&#34;&gt;&#xA;  Files&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#files&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#file-eg-glds-ctrl.cpp&#34;&gt;examples/eg_glds_ctrl.cpp&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#file-eg-glds-du-plds-ctrl.cpp&#34;&gt;examples/eg_glds_du_plds_ctrl.cpp&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#file-eg-plds-ctrl.cpp&#34;&gt;examples/eg_plds_ctrl.cpp&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#file-eg-plds-est.cpp&#34;&gt;examples/eg_plds_est.cpp&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#file-eg-plds-switched-ctrl.cpp&#34;&gt;examples/eg_plds_switched_ctrl.cpp&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;hr&gt;&#xA;&lt;p&gt;Updated on 31 March 2025 at 16:04:30 EDT&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>examples/eg_glds_ctrl.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/</guid>
+      <description>&lt;h1 id=&#34;exampleseg_glds_ctrlcpp&#34;&gt;&#xA;  examples/eg_glds_ctrl.cpp&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#exampleseg_glds_ctrlcpp&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;h2 id=&#34;types&#34;&gt;&#xA;  Types&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#types&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using arma::Mat&amp;lt; data_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#using-matrix&#34;&gt;Matrix&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using arma::Col&amp;lt; data_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#using-vector&#34;&gt;Vector&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using double&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;functions&#34;&gt;&#xA;  Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;int&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__ctrl_8cpp/#function-main&#34;&gt;main&lt;/a&gt;&lt;/strong&gt;()&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;type-details&#34;&gt;&#xA;  Type Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#type-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;matrix&#34;&gt;&#xA;  Matrix&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#matrix&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Matrix &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Mat&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h3 id=&#34;vector&#34;&gt;&#xA;  Vector&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#vector&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Vector &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Col&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h3 id=&#34;data_t&#34;&gt;&#xA;  data_t&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#data_t&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;data_t &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;double&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;Type of all data in library. If need 32b, change &lt;code&gt;double&lt;/code&gt; to &lt;code&gt;float&lt;/code&gt;. This could be potentially useful for large scale problems where there are memory constraints.&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>examples/eg_glds_du_plds_ctrl.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/</guid>
+      <description>&lt;h1 id=&#34;exampleseg_glds_du_plds_ctrlcpp&#34;&gt;&#xA;  examples/eg_glds_du_plds_ctrl.cpp&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#exampleseg_glds_du_plds_ctrlcpp&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;h2 id=&#34;types&#34;&gt;&#xA;  Types&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#types&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using double&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using arma::Mat&amp;lt; data_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#using-matrix&#34;&gt;Matrix&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using arma::Col&amp;lt; data_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#using-vector&#34;&gt;Vector&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;functions&#34;&gt;&#xA;  Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;int&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__glds__du__plds__ctrl_8cpp/#function-main&#34;&gt;main&lt;/a&gt;&lt;/strong&gt;()&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;type-details&#34;&gt;&#xA;  Type Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#type-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;data_t&#34;&gt;&#xA;  data_t&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#data_t&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;data_t &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;double&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;Type of all data in library. If need 32b, change &lt;code&gt;double&lt;/code&gt; to &lt;code&gt;float&lt;/code&gt;. This could be potentially useful for large scale problems where there are memory constraints.&lt;/p&gt;&#xA;&lt;h3 id=&#34;matrix&#34;&gt;&#xA;  Matrix&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#matrix&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Matrix &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Mat&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h3 id=&#34;vector&#34;&gt;&#xA;  Vector&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#vector&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Vector &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Col&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h2 id=&#34;function-details&#34;&gt;&#xA;  Function Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#function-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;main&#34;&gt;&#xA;  main&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#main&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;int&lt;/span&gt; main()&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;Going to simulate a switching disturbance (m) acting on system&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>examples/eg_plds_ctrl.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/</guid>
+      <description>&lt;h1 id=&#34;exampleseg_plds_ctrlcpp&#34;&gt;&#xA;  examples/eg_plds_ctrl.cpp&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#exampleseg_plds_ctrlcpp&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;h2 id=&#34;types&#34;&gt;&#xA;  Types&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#types&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using arma::Mat&amp;lt; data_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#using-matrix&#34;&gt;Matrix&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using arma::Col&amp;lt; data_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#using-vector&#34;&gt;Vector&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using double&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;functions&#34;&gt;&#xA;  Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;int&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__ctrl_8cpp/#function-main&#34;&gt;main&lt;/a&gt;&lt;/strong&gt;()&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;type-details&#34;&gt;&#xA;  Type Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#type-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;matrix&#34;&gt;&#xA;  Matrix&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#matrix&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Matrix &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Mat&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h3 id=&#34;vector&#34;&gt;&#xA;  Vector&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#vector&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Vector &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Col&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h3 id=&#34;data_t&#34;&gt;&#xA;  data_t&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#data_t&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;data_t &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;double&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;Type of all data in library. If need 32b, change &lt;code&gt;double&lt;/code&gt; to &lt;code&gt;float&lt;/code&gt;. This could be potentially useful for large scale problems where there are memory constraints.&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>examples/eg_plds_est.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/</guid>
+      <description>&lt;h1 id=&#34;exampleseg_plds_estcpp&#34;&gt;&#xA;  examples/eg_plds_est.cpp&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#exampleseg_plds_estcpp&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;h2 id=&#34;types&#34;&gt;&#xA;  Types&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#types&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using arma::Mat&amp;lt; data_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#using-matrix&#34;&gt;Matrix&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using arma::Col&amp;lt; data_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#using-vector&#34;&gt;Vector&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using double&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;functions&#34;&gt;&#xA;  Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#function-random-walk&#34;&gt;random_walk&lt;/a&gt;&lt;/strong&gt;(size_t n_t, const Matrix &amp;amp; Q, const Vector &amp;amp; x0)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;int&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__est_8cpp/#function-main&#34;&gt;main&lt;/a&gt;&lt;/strong&gt;()&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;type-details&#34;&gt;&#xA;  Type Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#type-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;matrix&#34;&gt;&#xA;  Matrix&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#matrix&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Matrix &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Mat&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h3 id=&#34;vector&#34;&gt;&#xA;  Vector&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#vector&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Vector &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Col&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h3 id=&#34;data_t&#34;&gt;&#xA;  data_t&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#data_t&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;data_t &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;double&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;Type of all data in library. If need 32b, change &lt;code&gt;double&lt;/code&gt; to &lt;code&gt;float&lt;/code&gt;. This could be potentially useful for large scale problems where there are memory constraints.&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>examples/eg_plds_switched_ctrl.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/</guid>
+      <description>&lt;h1 id=&#34;exampleseg_plds_switched_ctrlcpp&#34;&gt;&#xA;  examples/eg_plds_switched_ctrl.cpp&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#exampleseg_plds_switched_ctrlcpp&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;h2 id=&#34;types&#34;&gt;&#xA;  Types&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#types&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using double&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using arma::Mat&amp;lt; data_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#using-matrix&#34;&gt;Matrix&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;using arma::Col&amp;lt; data_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#using-vector&#34;&gt;Vector&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;functions&#34;&gt;&#xA;  Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;int&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/eg__plds__switched__ctrl_8cpp/#function-main&#34;&gt;main&lt;/a&gt;&lt;/strong&gt;()&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;type-details&#34;&gt;&#xA;  Type Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#type-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;data_t&#34;&gt;&#xA;  data_t&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#data_t&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;data_t &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;double&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;Type of all data in library. If need 32b, change &lt;code&gt;double&lt;/code&gt; to &lt;code&gt;float&lt;/code&gt;. This could be potentially useful for large scale problems where there are memory constraints.&lt;/p&gt;&#xA;&lt;h3 id=&#34;matrix&#34;&gt;&#xA;  Matrix&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#matrix&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Matrix &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Mat&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h3 id=&#34;vector&#34;&gt;&#xA;  Vector&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#vector&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Vector &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Col&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h2 id=&#34;function-details&#34;&gt;&#xA;  Function Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#function-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;main&#34;&gt;&#xA;  main&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#main&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;int&lt;/span&gt; main()&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h2 id=&#34;source-code&#34;&gt;&#xA;  Source code&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#source-code&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;//===-- eg_plds_switched_ctrl.cpp - Example Switched PLDS Control ---===//&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;//&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;// Copyright 2021 Michael Bolus&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;// Copyright 2021 Georgia Institute of Technology&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;//&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;// Licensed under the Apache License, Version 2.0 (the &amp;#34;License&amp;#34;);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;// you may not use this file except in compliance with the License.&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;// You may obtain a copy of the License at&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;//&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;//     http://www.apache.org/licenses/LICENSE-2.0&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;//&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;// Unless required by applicable law or agreed to in writing, software&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;// distributed under the License is distributed on an &amp;#34;AS IS&amp;#34; BASIS,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;// See the License for the specific language governing permissions and&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;// limitations under the License.&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;//&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;//===----------------------------------------------------------------------===//&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;//===----------------------------------------------------------------------===//&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;#include&lt;/span&gt; &lt;span style=&#34;color:#75715e&#34;&gt;&amp;lt;ldsCtrlEst&amp;gt;&lt;/span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;data_t;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Matrix;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Vector;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;using&lt;/span&gt; std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;cout;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;auto&lt;/span&gt; &lt;span style=&#34;color:#a6e22e&#34;&gt;main&lt;/span&gt;() &lt;span style=&#34;color:#f92672&#34;&gt;-&amp;gt;&lt;/span&gt; &lt;span style=&#34;color:#66d9ef&#34;&gt;int&lt;/span&gt; {&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34; ********** Example Switched Poisson LDS Control ********** &lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// whether to do switched control&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;bool&lt;/span&gt; do_switch_ctrl &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; true;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// Make SISO system sampled at 1kHz&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  data_t dt &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1e-3&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  size_t n_u &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  size_t n_x &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  size_t n_y &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// no time steps for simulation.&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;auto&lt;/span&gt; n_t &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#66d9ef&#34;&gt;static_cast&lt;/span&gt;&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;size_t&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;(&lt;span style=&#34;color:#ae81ff&#34;&gt;30.0&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;/&lt;/span&gt; dt);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// for simulating switching&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  size_t which_mode &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  data_t pr_21 &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1e-3&lt;/span&gt;;   &lt;span style=&#34;color:#75715e&#34;&gt;// prob mode 1 -&amp;gt; 2&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  data_t pr_12 &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; pr_21;  &lt;span style=&#34;color:#75715e&#34;&gt;// prob mode 2 -&amp;gt; 1&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// simulated system being controlled&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;poisson&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;System controlled_system(n_u, n_x, n_y, dt);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// **Assume the system is not well characterized by one LDS, but is well&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// characterized by two LDS models with different input matrices.**&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  data_t scale_sys_b &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;2&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  Matrix a(n_x, n_x, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;eye);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  a[&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;] &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;0.985&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  Matrix b1 &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; Matrix(n_x, n_u).fill(&lt;span style=&#34;color:#ae81ff&#34;&gt;0.05&lt;/span&gt;);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  Vector d &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; Vector(n_y, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;zeros).fill(log(&lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;*&lt;/span&gt; dt));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  controlled_system.set_A(a);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  controlled_system.set_B(b1);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  controlled_system.set_d(d);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  controlled_system.Reset();  &lt;span style=&#34;color:#75715e&#34;&gt;// reset to initial conditions&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// reference&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  Vector y_ref0 &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; Vector(n_y, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;zeros).fill(&lt;span style=&#34;color:#ae81ff&#34;&gt;25.0&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;*&lt;/span&gt; dt);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// Let underlying system 1 be more sensitive than system 2&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  Matrix b2 &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; Matrix(n_x, n_u).fill(b1[&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;] &lt;span style=&#34;color:#f92672&#34;&gt;/&lt;/span&gt; scale_sys_b);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// create switched controller&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;poisson&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;SwitchedController switched_controller;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;UniformMatrixList&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;gt;&lt;/span&gt; k_x;  &lt;span style=&#34;color:#75715e&#34;&gt;// feedback controller gains&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  {&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// create switched controller sub-systems&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// system 1&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;poisson&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;System sys1(controlled_system);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// set process noise covariance&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    Matrix q_controller &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; Matrix(n_x, n_x, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;eye) &lt;span style=&#34;color:#f92672&#34;&gt;*&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;5e-3&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    sys1.set_Q(q_controller);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// adaptively estimate process disturbance (m)&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// n.b. using arbitrary default value for process noise if enabled.&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    sys1.do_adapt_m &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; true;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// setting initial mode to target to avoid large error at onset:&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    Vector x0_controller &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;log(y_ref0) &lt;span style=&#34;color:#f92672&#34;&gt;-&lt;/span&gt; d;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    sys1.set_x0(x0_controller);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    sys1.Reset();  &lt;span style=&#34;color:#75715e&#34;&gt;// reset to initial conditions&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;.....................................&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;sys1:&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;.....................................&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    sys1.Print();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;.....................................&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// system 2&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;poisson&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;System sys2 &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; sys1;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// set parameters&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    sys2.set_B(b2);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;.....................................&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;sys2:&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;.....................................&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    sys2.Print();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;.....................................&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;UniformSystemList&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;poisson&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;System&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; systems({sys1, sys2});&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// controller gains for underlying system s:&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    Matrix k_x1(n_u, n_x, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;ones);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    Matrix k_x2 &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; scale_sys_b &lt;span style=&#34;color:#f92672&#34;&gt;*&lt;/span&gt; k_x1;  &lt;span style=&#34;color:#75715e&#34;&gt;// system2 is x-times less sensitive.&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    k_x &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;UniformMatrixList&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;gt;&lt;/span&gt;({k_x1, k_x2});&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    data_t u_lb &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;0.0&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    data_t u_ub &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;5.0&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    switched_controller &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;move(&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;        lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;poisson&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;SwitchedController(std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;move(systems), u_lb, u_ub));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  }&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// Control variables&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  size_t control_type &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;;  &lt;span style=&#34;color:#75715e&#34;&gt;// no integral action, etc&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  switched_controller.set_control_type(control_type);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  switched_controller.set_Kc(std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;move(k_x));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  switched_controller.set_y_ref(y_ref0);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;vector&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;poisson&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;System&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; systems_vec(&lt;span style=&#34;color:#ae81ff&#34;&gt;3&lt;/span&gt;, lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;poisson&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;System());&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;UniformSystemList&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;lds&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;poisson&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;System&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; systems(std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;move(systems_vec));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;.....................................&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;switched_controller:&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;.....................................&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  switched_controller.Print();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;.....................................&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// Fake measurements&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  Matrix z(n_y, n_t, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;zeros);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// Will later contain control.&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  Matrix u(n_u, n_t, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;zeros);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// create Matrix to save outputs in...&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  Matrix y_hat(n_y, n_t, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;zeros);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  Matrix y_true(n_y, n_t, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;zeros);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  Matrix y_ref &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; Matrix(n_y, n_t).fill(y_ref0[&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;]);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// modes and gain/disturbance params&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  Matrix x_hat(n_x, n_t, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;zeros);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  Matrix x_true(n_x, n_t, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;zeros);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  Matrix mode(&lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;, n_t, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;ones);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// set initial val&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  y_hat.col(&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; switched_controller.sys().y();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  y_true.col(&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; controlled_system.y();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  x_hat.col(&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; switched_controller.sys().x();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  x_true.col(&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; controlled_system.x();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;Starting &amp;#34;&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; n_t &lt;span style=&#34;color:#f92672&#34;&gt;*&lt;/span&gt; dt &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34; sec simulation ... &lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;auto&lt;/span&gt; start &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;chrono&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;high_resolution_clock&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;now();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;for&lt;/span&gt; (size_t t &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;; t &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; n_t; t&lt;span style=&#34;color:#f92672&#34;&gt;++&lt;/span&gt;) {&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// Let the controlled system stochastically change gain&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// Assume another algorithm decodes this mode change and signals the&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// switched_controller&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    Vector chance(&lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;, arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;fill&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;randu);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#66d9ef&#34;&gt;if&lt;/span&gt; (which_mode &lt;span style=&#34;color:#f92672&#34;&gt;==&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;)  &lt;span style=&#34;color:#75715e&#34;&gt;// mode1&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    {&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;      &lt;span style=&#34;color:#66d9ef&#34;&gt;if&lt;/span&gt; (chance[&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;] &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; pr_21) {&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;        which_mode &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;2&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;        controlled_system.set_B(b2);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;        &lt;span style=&#34;color:#66d9ef&#34;&gt;if&lt;/span&gt; (do_switch_ctrl) {&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;          switched_controller.Switch(&lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;        }&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;      }&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    } &lt;span style=&#34;color:#66d9ef&#34;&gt;else&lt;/span&gt; {  &lt;span style=&#34;color:#75715e&#34;&gt;// mode2&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;      &lt;span style=&#34;color:#66d9ef&#34;&gt;if&lt;/span&gt; (chance[&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;] &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; pr_12) {&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;        which_mode &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;        controlled_system.set_B(b1);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;        &lt;span style=&#34;color:#66d9ef&#34;&gt;if&lt;/span&gt; (do_switch_ctrl) {&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;          switched_controller.Switch(&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;        }&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;      }&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    }&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// Simulate the true system.&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    z.col(t) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; controlled_system.Simulate(u.col(t &lt;span style=&#34;color:#f92672&#34;&gt;-&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#75715e&#34;&gt;// perform control&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;    u.col(t) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; switched_controller.ControlOutputReference(z.col(t));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    mode.col(t) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; which_mode;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    y_ref.col(t) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; y_ref0;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    y_true.col(t) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; controlled_system.y();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    x_true.col(t) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; controlled_system.x();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    y_hat.col(t) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; switched_controller.sys().y();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    x_hat.col(t) &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; switched_controller.sys().x();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  }&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;auto&lt;/span&gt; finish &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;chrono&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;high_resolution_clock&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;now();&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;chrono&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;duration&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;data_t, std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;milli&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; sim_time_ms &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; finish &lt;span style=&#34;color:#f92672&#34;&gt;-&lt;/span&gt; start;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;Finished simulation in &amp;#34;&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; sim_time_ms.count() &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34; ms.&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  cout &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;(app. &amp;#34;&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; (sim_time_ms.count() &lt;span style=&#34;color:#f92672&#34;&gt;/&lt;/span&gt; n_t) &lt;span style=&#34;color:#f92672&#34;&gt;*&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;1e3&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&amp;lt;&lt;/span&gt; &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34; us/time-step)&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;\n&lt;/span&gt;&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  &lt;span style=&#34;color:#75715e&#34;&gt;// x_true, m_true saving with hdf5 via armadillo&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#75715e&#34;&gt;&lt;/span&gt;  arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_opts&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;opts replace &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_opts&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;replace;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;auto&lt;/span&gt; dt_vec &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt; Vector(&lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;).fill(dt);&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  dt_vec.save(arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_name(&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;eg_plds_switched_ctrl.h5&amp;#34;&lt;/span&gt;, &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;dt&amp;#34;&lt;/span&gt;));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  y_ref.save(arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_name(&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;eg_plds_switched_ctrl.h5&amp;#34;&lt;/span&gt;, &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;y_ref&amp;#34;&lt;/span&gt;, replace));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  u.save(arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_name(&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;eg_plds_switched_ctrl.h5&amp;#34;&lt;/span&gt;, &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;u&amp;#34;&lt;/span&gt;, replace));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  z.save(arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_name(&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;eg_plds_switched_ctrl.h5&amp;#34;&lt;/span&gt;, &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;z&amp;#34;&lt;/span&gt;, replace));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  x_true.save(arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_name(&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;eg_plds_switched_ctrl.h5&amp;#34;&lt;/span&gt;, &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;x_true&amp;#34;&lt;/span&gt;, replace));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  y_true.save(arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_name(&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;eg_plds_switched_ctrl.h5&amp;#34;&lt;/span&gt;, &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;y_true&amp;#34;&lt;/span&gt;, replace));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  x_hat.save(arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_name(&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;eg_plds_switched_ctrl.h5&amp;#34;&lt;/span&gt;, &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;x_hat&amp;#34;&lt;/span&gt;, replace));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  y_hat.save(arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_name(&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;eg_plds_switched_ctrl.h5&amp;#34;&lt;/span&gt;, &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;y_hat&amp;#34;&lt;/span&gt;, replace));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  mode.save(arma&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;hdf5_name(&lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;eg_plds_switched_ctrl.h5&amp;#34;&lt;/span&gt;, &lt;span style=&#34;color:#e6db74&#34;&gt;&amp;#34;mode&amp;#34;&lt;/span&gt;, replace));&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;  &lt;span style=&#34;color:#66d9ef&#34;&gt;return&lt;/span&gt; &lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;}&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;p&gt;Updated on 31 March 2025 at 16:04:30 EDT&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>GLDS Control</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_glds_control/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_glds_control/</guid>
+      <description>&lt;h1 id=&#34;glds-control-tutorial&#34;&gt;&#xA;  GLDS Control Tutorial&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#glds-control-tutorial&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;This tutorial shows how to use this library to control a system with a Gaussian LDS controller (&lt;code&gt;lds::gaussian::Controller&lt;/code&gt;). In place of a physical system, a GLDS model (&lt;code&gt;lds::gaussian::System&lt;/code&gt;) receives control inputs and simulates measurements for the feedback control loop. The controller is assumed to have an imperfect model of the system being controlled (here, a gain mismatch), and there is a stochastic, unmeasured disturbance acting on the system. A combination of integral action and adaptive estimation of this process disturbance is used to perform control.&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>include</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_d44c64559bbebec7f509842c48db8b23/</guid>
+      <description>&lt;h1 id=&#34;include&#34;&gt;&#xA;  include&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#include&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;h2 id=&#34;directories&#34;&gt;&#xA;  Directories&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#directories&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/#dir-ldsctrlest-h&#34;&gt;ldsCtrlEst_h&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;hr&gt;&#xA;&lt;p&gt;Updated on 31 March 2025 at 16:04:30 EDT&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>lds</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/</guid>
+      <description>Linear Dynamical Systems (LDS) namespace.</description>
+    </item>
+    <item>
+      <title>lds::Controller</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/</guid>
+      <description>&lt;h1 id=&#34;ldscontroller&#34;&gt;&#xA;  lds::Controller&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#ldscontroller&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;&lt;a href=&#34;#detailed-description&#34;&gt;More&amp;hellip;&lt;/a&gt;&lt;/p&gt;&#xA;&lt;p&gt;Inherited by &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/&#34;&gt;lds::SwitchedController&amp;lt; System &amp;gt;&lt;/a&gt;, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/&#34;&gt;lds::gaussian::Controller&lt;/a&gt;, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/&#34;&gt;lds::poisson::Controller&lt;/a&gt;&lt;/p&gt;&#xA;&lt;h2 id=&#34;public-functions&#34;&gt;&#xA;  Public Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller&#34;&gt;Controller&lt;/a&gt;&lt;/strong&gt;() =default&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/&#34;&gt;Controller&lt;/a&gt;.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller&#34;&gt;Controller&lt;/a&gt;&lt;/strong&gt;(const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; &amp;amp; sys, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; u_lb, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; u_ub, size_t control_type =0)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/&#34;&gt;Controller&lt;/a&gt;.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controller&#34;&gt;Controller&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; &amp;amp;&amp;amp; sys, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; u_lb, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; u_ub, size_t control_type =0)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/&#34;&gt;Controller&lt;/a&gt; by moving the system object.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Vector &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control&#34;&gt;Control&lt;/a&gt;&lt;/strong&gt;(const Vector &amp;amp; z, bool do_control =true, bool do_lock_control =false, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; sigma_soft_start =0, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; sigma_u_noise =0, bool do_reset_at_control_onset =true)&lt;br&gt;updates control signal (single-step)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Vector &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-controloutputreference&#34;&gt;ControlOutputReference&lt;/a&gt;&lt;/strong&gt;(const Vector &amp;amp; z, bool do_control =true, bool do_estimation =true, bool do_lock_control =false, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; sigma_soft_start =0, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; sigma_u_noise =0, bool do_reset_at_control_onset =true)&lt;br&gt;updates control signal, given previously-set (single-step)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-sys&#34;&gt;sys&lt;/a&gt;&lt;/strong&gt;() const&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Matrix &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc&#34;&gt;Kc&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get state feedback controller gain.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Matrix &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-inty&#34;&gt;Kc_inty&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get integral controller gain.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Matrix &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-kc-u&#34;&gt;Kc_u&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get input feedback controller gain.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Vector &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-g-design&#34;&gt;g_design&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get input gain used in controller design.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Vector &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ref&#34;&gt;u_ref&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get reference input.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Vector &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-x-ref&#34;&gt;x_ref&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get reference state.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Vector &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-y-ref&#34;&gt;y_ref&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get reference output.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-control-type&#34;&gt;control_type&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get controller type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-tau-awu&#34;&gt;tau_awu&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get time constant of anti-integral-windup.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-lb&#34;&gt;u_lb&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get control lower bound.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-u-ub&#34;&gt;u_ub&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;Get control upper bound.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-sys&#34;&gt;set_sys&lt;/a&gt;&lt;/strong&gt;(const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; &amp;amp; sys)&lt;br&gt;Set system.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-g-design&#34;&gt;set_g_design&lt;/a&gt;&lt;/strong&gt;(const Vector &amp;amp; g_design)&lt;br&gt;Set input gain used in controller design (g_design)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ref&#34;&gt;set_u_ref&lt;/a&gt;&lt;/strong&gt;(const Vector &amp;amp; u_ref)&lt;br&gt;Set reference input (u_ref)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-x-ref&#34;&gt;set_x_ref&lt;/a&gt;&lt;/strong&gt;(const Vector &amp;amp; x_ref)&lt;br&gt;Set reference state (x_ref)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;virtual void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-y-ref&#34;&gt;set_y_ref&lt;/a&gt;&lt;/strong&gt;(const Vector &amp;amp; y_ref)&lt;br&gt;Set reference output (y_ref)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc&#34;&gt;set_Kc&lt;/a&gt;&lt;/strong&gt;(const Matrix &amp;amp; Kc)&lt;br&gt;Set state controller gain.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-inty&#34;&gt;set_Kc_inty&lt;/a&gt;&lt;/strong&gt;(const Matrix &amp;amp; Kc_inty)&lt;br&gt;Set integral controller gain.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-kc-u&#34;&gt;set_Kc_u&lt;/a&gt;&lt;/strong&gt;(const Matrix &amp;amp; Kc_u)&lt;br&gt;Set input controller gain.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-tau-awu&#34;&gt;set_tau_awu&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; tau)&lt;br&gt;Set time constant of anti-integral-windup.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-control-type&#34;&gt;set_control_type&lt;/a&gt;&lt;/strong&gt;(size_t control_type)&lt;br&gt;Sets the control type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-lb&#34;&gt;set_u_lb&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; u_lb)&lt;br&gt;sets control lower bound&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-set-u-ub&#34;&gt;set_u_ub&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; u_ub)&lt;br&gt;Sets control upper bound.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-reset&#34;&gt;Reset&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;reset system and control variables.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#function-print&#34;&gt;Print&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;prints variables to stdout&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;protected-attributes&#34;&gt;&#xA;  Protected Attributes&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#protected-attributes&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-sys-&#34;&gt;sys_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;underlying LDS&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-&#34;&gt;u_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;control signal&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-return-&#34;&gt;u_return_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;control signal that is &lt;em&gt;returned&lt;/em&gt; to user&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-g-design-&#34;&gt;g_design_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;input gain of the system used for controller design&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-&#34;&gt;u_ref_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;reference input&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ref-prev-&#34;&gt;u_ref_prev_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;reference input at previous time step&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-x-ref-&#34;&gt;x_ref_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;reference state&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-y-ref-&#34;&gt;y_ref_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;reference output&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-cx-ref-&#34;&gt;cx_ref_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-&#34;&gt;Kc_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;state controller gain&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-u-&#34;&gt;Kc_u_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;input controller gain (optional when control updates \deltaU)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-kc-inty-&#34;&gt;Kc_inty_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;integral controller gain&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-du-ref-&#34;&gt;du_ref_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-ref-&#34;&gt;dv_ref_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-ref-&#34;&gt;v_ref_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-dv-&#34;&gt;dv_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-v-&#34;&gt;v_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;Control after g inversion (e.g., control in physical units)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-&#34;&gt;int_e_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;integrated error&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-int-e-awu-adjust-&#34;&gt;int_e_awu_adjust_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;anti-windup adjustment to intE&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-sat-&#34;&gt;u_sat_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;control signal after saturation (for antiWindup)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;bool&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-control-prev-&#34;&gt;do_control_prev_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;bool&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-do-lock-control-prev-&#34;&gt;do_lock_control_prev_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;bool&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-saturated-&#34;&gt;u_saturated_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;whether control signal has reached saturation limits&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-lb-&#34;&gt;u_lb_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;lower bound on control&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-u-ub-&#34;&gt;u_ub_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;upper bound on control&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-tau-awu-&#34;&gt;tau_awu_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;antiwindup time constant&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-k-awu-&#34;&gt;k_awu_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-t-since-control-onset-&#34;&gt;t_since_control_onset_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;time since control epoch onset&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_controller/#variable-control-type-&#34;&gt;control_type_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;controller type&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;detailed-description&#34;&gt;&#xA;  Detailed Description&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#detailed-description&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;template&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;typename&lt;/span&gt; System &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;class&lt;/span&gt; &lt;span style=&#34;color:#a6e22e&#34;&gt;lds&lt;/span&gt;&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;Controller;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;hr&gt;&#xA;&lt;h2 id=&#34;public-function-details&#34;&gt;&#xA;  Public Function Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-function-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;controller&#34;&gt;&#xA;  &lt;strong&gt;Controller&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#controller&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;Controller() &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;default&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;h3 id=&#34;controller-1&#34;&gt;&#xA;  &lt;strong&gt;Controller&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#controller-1&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;inline&lt;/span&gt; Controller(&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#66d9ef&#34;&gt;const&lt;/span&gt; System &lt;span style=&#34;color:#f92672&#34;&gt;&amp;amp;&lt;/span&gt; sys,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    data_t u_lb,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    data_t u_ub,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    size_t control_type &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;)&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;&lt;strong&gt;Parameters&lt;/strong&gt;:&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>lds::EM</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/</guid>
+      <description>&lt;h1 id=&#34;ldsem&#34;&gt;&#xA;  lds::EM&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#ldsem&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;&lt;a href=&#34;#detailed-description&#34;&gt;More&amp;hellip;&lt;/a&gt;&lt;/p&gt;&#xA;&lt;p&gt;Inherited by &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/&#34;&gt;lds::gaussian::FitEM&lt;/a&gt;, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/&#34;&gt;lds::poisson::FitEM&lt;/a&gt;&lt;/p&gt;&#xA;&lt;h2 id=&#34;public-functions&#34;&gt;&#xA;  Public Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em&#34;&gt;EM&lt;/a&gt;&lt;/strong&gt;() =default&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/&#34;&gt;EM&lt;/a&gt;&lt;a href=&#34;&#34;&gt;Fit&lt;/a&gt; type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em&#34;&gt;EM&lt;/a&gt;&lt;/strong&gt;(size_t n_x, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; dt, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt; &amp;amp;&amp;amp; u_train, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt; &amp;amp;&amp;amp; z_train)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/&#34;&gt;EM&lt;/a&gt;&lt;a href=&#34;&#34;&gt;Fit&lt;/a&gt; type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-em&#34;&gt;EM&lt;/a&gt;&lt;/strong&gt;(const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/&#34;&gt;Fit&lt;/a&gt; &amp;amp; fit0, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt; &amp;amp;&amp;amp; u_train, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt; &amp;amp;&amp;amp; z_train)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/&#34;&gt;EM&lt;/a&gt;&lt;a href=&#34;&#34;&gt;Fit&lt;/a&gt; type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;virtual&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-~em&#34;&gt;~EM&lt;/a&gt;&lt;/strong&gt;() =default&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/&#34;&gt;Fit&lt;/a&gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-run&#34;&gt;Run&lt;/a&gt;&lt;/strong&gt;(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; tol =1e-2)&lt;br&gt;Runs fitting by Expectation(E)-Maximization(M)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;std::tuple&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt;, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt; &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-returndata&#34;&gt;ReturnData&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;Returns the input/output data to caller.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const std::vector&amp;lt; Matrix &amp;gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-x&#34;&gt;x&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;gets estimated state (over time)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const std::vector&amp;lt; Matrix &amp;gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-y&#34;&gt;y&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;gets estimated output (over time)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Matrix &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-x-t-x-t&#34;&gt;sum_E_x_t_x_t&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;gets state-input covariance&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Matrix &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-xu-tm1-xu-tm1&#34;&gt;sum_E_xu_tm1_xu_tm1&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;gets state-input covariance (t-minus-1)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Matrix &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-sum-e-xu-t-xu-tm1&#34;&gt;sum_E_xu_t_xu_tm1&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;gets single lag state-input covariance&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-n-t-tot&#34;&gt;n_t_tot&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;total number of time samples&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Vector &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-theta&#34;&gt;theta&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;gets parameters updated in M step&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;protected-functions&#34;&gt;&#xA;  Protected Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#protected-functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-expectation&#34;&gt;Expectation&lt;/a&gt;&lt;/strong&gt;(bool force_common_initial =false)&lt;br&gt;Expectation step.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximization&#34;&gt;Maximization&lt;/a&gt;&lt;/strong&gt;(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false)&lt;br&gt;Maximization step.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizedynamics&#34;&gt;MaximizeDynamics&lt;/a&gt;&lt;/strong&gt;()&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizeq&#34;&gt;MaximizeQ&lt;/a&gt;&lt;/strong&gt;()&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizeinitial&#34;&gt;MaximizeInitial&lt;/a&gt;&lt;/strong&gt;()&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;virtual void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizeoutput&#34;&gt;MaximizeOutput&lt;/a&gt;&lt;/strong&gt;() =0&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;virtual void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-maximizemeasurement&#34;&gt;MaximizeMeasurement&lt;/a&gt;&lt;/strong&gt;() =0&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-smooth&#34;&gt;Smooth&lt;/a&gt;&lt;/strong&gt;(bool force_common_initial)&lt;br&gt;get smoothed estimates&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;virtual void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-recurseke&#34;&gt;RecurseKe&lt;/a&gt;&lt;/strong&gt;(Matrix &amp;amp; Ke, Cube &amp;amp; P_pre, Cube &amp;amp; P_post, size_t t) =0&lt;br&gt;recursively update estimator gain Ke&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-reset&#34;&gt;Reset&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;reset to initial conditions&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-initvars&#34;&gt;InitVars&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;Initializes the variables.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#function-updatetheta&#34;&gt;UpdateTheta&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;updates parameter list, theta&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;protected-attributes&#34;&gt;&#xA;  Protected Attributes&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#protected-attributes&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-u-&#34;&gt;u_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;input training data&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-z-&#34;&gt;z_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;measurement training data&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;std::vector&amp;lt; Matrix &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-x-&#34;&gt;x_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;state estimate&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;std::vector&amp;lt; Cube &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-p-&#34;&gt;P_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;state estimate cov&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;std::vector&amp;lt; Cube &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-p-t-tm1-&#34;&gt;P_t_tm1_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;single-lag state covariance&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;std::vector&amp;lt; Matrix &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-y-&#34;&gt;y_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;output estimate&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-diag-y-&#34;&gt;diag_y_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-x-t-x-t-&#34;&gt;sum_E_x_t_x_t_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;state covariance (current time)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-xu-tm1-xu-tm1-&#34;&gt;sum_E_xu_tm1_xu_tm1_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;state-input covariance (t-minus-1)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-sum-e-xu-t-xu-tm1-&#34;&gt;sum_E_xu_t_xu_tm1_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;single lag state-input covariance&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/&#34;&gt;Fit&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-fit-&#34;&gt;fit_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-theta-&#34;&gt;theta_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-dt-&#34;&gt;dt_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;sample period&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-u-&#34;&gt;n_u_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;number of inputs&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-x-&#34;&gt;n_x_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;number of states&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-y-&#34;&gt;n_y_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;number of outputs&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-trials-&#34;&gt;n_trials_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;number of input/output data sequences&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;std::vector&amp;lt; size_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-t-&#34;&gt;n_t_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;number of time steps&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_e_m/#variable-n-t-tot-&#34;&gt;n_t_tot_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;total number of time steps across trials&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;detailed-description&#34;&gt;&#xA;  Detailed Description&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#detailed-description&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;template&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;typename&lt;/span&gt; Fit &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;class&lt;/span&gt; &lt;span style=&#34;color:#a6e22e&#34;&gt;lds&lt;/span&gt;&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;EM;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;hr&gt;&#xA;&lt;h2 id=&#34;public-function-details&#34;&gt;&#xA;  Public Function Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-function-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;em&#34;&gt;&#xA;  &lt;strong&gt;EM&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#em&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;EM() &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;default&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;h3 id=&#34;em-1&#34;&gt;&#xA;  &lt;strong&gt;EM&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#em-1&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;EM(&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    size_t n_x,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    data_t dt,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    UniformMatrixList&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; kMatFreeDim2 &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;amp;&amp;amp;&lt;/span&gt; u_train,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    UniformMatrixList&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; kMatFreeDim2 &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;amp;&amp;amp;&lt;/span&gt; z_train&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;)&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;&lt;strong&gt;Parameters&lt;/strong&gt;:&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>lds::Fit</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/</guid>
+      <description>LDS Fit Type.</description>
+    </item>
+    <item>
+      <title>lds::gaussian</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1gaussian/</guid>
+      <description>Linear Dynamical Systems with Gaussian observations.</description>
+    </item>
+    <item>
+      <title>lds::gaussian::Controller</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_controller/</guid>
+      <description>Gaussian-observation Controller Type.</description>
+    </item>
+    <item>
+      <title>lds::gaussian::Fit</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit/</guid>
+      <description>GLDS Fit Type.</description>
+    </item>
+    <item>
+      <title>lds::gaussian::FitEM</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_e_m/</guid>
+      <description>GLDS E-M Fit Type.</description>
+    </item>
+    <item>
+      <title>lds::gaussian::FitSSID</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/</guid>
+      <description>Subspace Identification (SSID) for GLDS.</description>
+    </item>
+    <item>
+      <title>lds::gaussian::SwitchedController</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_switched_controller/</guid>
+      <description>Gaussian-observation SwitchedController Type.</description>
+    </item>
+    <item>
+      <title>lds::gaussian::System</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_system/</guid>
+      <description>Gaussian LDS Type.</description>
+    </item>
+    <item>
+      <title>lds::poisson</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds_1_1poisson/</guid>
+      <description>Linear Dynamical Systems with Poisson observations.</description>
+    </item>
+    <item>
+      <title>lds::poisson::Controller</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_controller/</guid>
+      <description>PLDS Controller Type.</description>
+    </item>
+    <item>
+      <title>lds::poisson::Fit</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit/</guid>
+      <description>PLDS Fit Type.</description>
+    </item>
+    <item>
+      <title>lds::poisson::FitEM</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_e_m/</guid>
+      <description>PLDS E-M Fit Type.</description>
+    </item>
+    <item>
+      <title>lds::poisson::FitSSID</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/</guid>
+      <description>Subspace Identification (SSID) for PLDS.</description>
+    </item>
+    <item>
+      <title>lds::poisson::SwitchedController</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_switched_controller/</guid>
+      <description>Poisson-observation SwitchedController Type.</description>
+    </item>
+    <item>
+      <title>lds::poisson::System</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_system/</guid>
+      <description>Poisson System type.</description>
+    </item>
+    <item>
+      <title>lds::SSID</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/</guid>
+      <description>&lt;h1 id=&#34;ldsssid&#34;&gt;&#xA;  lds::SSID&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#ldsssid&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;&lt;a href=&#34;#detailed-description&#34;&gt;More&amp;hellip;&lt;/a&gt;&lt;/p&gt;&#xA;&lt;p&gt;Inherited by &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1gaussian_1_1_fit_s_s_i_d/&#34;&gt;lds::gaussian::FitSSID&lt;/a&gt;, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1poisson_1_1_fit_s_s_i_d/&#34;&gt;lds::poisson::FitSSID&lt;/a&gt;&lt;/p&gt;&#xA;&lt;h2 id=&#34;public-functions&#34;&gt;&#xA;  Public Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-ssid&#34;&gt;SSID&lt;/a&gt;&lt;/strong&gt;() =default&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/&#34;&gt;SSID&lt;/a&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/&#34;&gt;Fit&lt;/a&gt; type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-ssid&#34;&gt;SSID&lt;/a&gt;&lt;/strong&gt;(size_t n_x, size_t n_h, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; dt, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt; &amp;amp;&amp;amp; u_train, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt; &amp;amp;&amp;amp; z_train, const Vector &amp;amp; d =Vector(1).fill(-kInf))&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/&#34;&gt;SSID&lt;/a&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/&#34;&gt;Fit&lt;/a&gt; type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;std::tuple&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/&#34;&gt;Fit&lt;/a&gt;, Vector &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-run&#34;&gt;Run&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt&#34;&gt;SSIDWt&lt;/a&gt; ssid_wt)&lt;br&gt;Runs fitting by subspace identification (&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/&#34;&gt;SSID&lt;/a&gt;)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;std::tuple&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt;, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt; &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-returndata&#34;&gt;ReturnData&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;Returns the I/O data to caller.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;protected-functions&#34;&gt;&#xA;  Protected Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#protected-functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-calcd&#34;&gt;CalcD&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; t_silence =0.1, &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; thresh_silence =0.001)&lt;br&gt;Using periods of silence in inputs (u), calculates the output \ bias (d)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-createhankeldatamat&#34;&gt;CreateHankelDataMat&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;Creates the block-hankel I/O data matrix.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;virtual void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-decomposedata&#34;&gt;DecomposeData&lt;/a&gt;&lt;/strong&gt;() =0&lt;br&gt;Decompose data to lower-triangular matrix (used in Solve)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-calcsvd&#34;&gt;CalcSVD&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enum-ssidwt&#34;&gt;SSIDWt&lt;/a&gt; wt)&lt;br&gt;performs the singular value decomposition (SVD)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-solve&#34;&gt;Solve&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt; wt_dc)&lt;br&gt;solves for LDS parameters&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#function-recomputeextobs&#34;&gt;RecomputeExtObs&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;recompute extended observability matrix from estimates of A, C&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;protected-attributes&#34;&gt;&#xA;  Protected Attributes&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#protected-attributes&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-u-&#34;&gt;u_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;input training data&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#enumvalue-kmatfreedim2&#34;&gt;kMatFreeDim2&lt;/a&gt; &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-z-&#34;&gt;z_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;measurement training data&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-d-&#34;&gt;D_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;block-Hankel I/O data matrix&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_fit/&#34;&gt;Fit&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-fit-&#34;&gt;fit_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;fit&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-g-dc-&#34;&gt;g_dc_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;I/O gain @ DC.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacelds/#using-data-t&#34;&gt;data_t&lt;/a&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-dt-&#34;&gt;dt_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;sample period&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-u-&#34;&gt;n_u_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;number of inputs&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-x-&#34;&gt;n_x_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;number of states&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-y-&#34;&gt;n_y_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;number of outputs&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-h-&#34;&gt;n_h_&lt;/a&gt;&lt;/strong&gt;&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-trials-&#34;&gt;n_trials_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;number of input/output data sequences&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;std::vector&amp;lt; size_t &amp;gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-t-&#34;&gt;n_t_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;number of time steps&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-n-t-tot-&#34;&gt;n_t_tot_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;total number of time steps across trials&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-l-&#34;&gt;L_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;lower triangle decomp of covariance matrix&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Vector&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-s-&#34;&gt;s_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;singular values&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Matrix&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s_i_d/#variable-ext-obs-t-&#34;&gt;ext_obs_t_&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;extended observability matrix&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;detailed-description&#34;&gt;&#xA;  Detailed Description&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#detailed-description&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;template&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;typename&lt;/span&gt; Fit &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;class&lt;/span&gt; &lt;span style=&#34;color:#a6e22e&#34;&gt;lds&lt;/span&gt;&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;SSID;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;hr&gt;&#xA;&lt;h2 id=&#34;public-function-details&#34;&gt;&#xA;  Public Function Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-function-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;ssid&#34;&gt;&#xA;  &lt;strong&gt;SSID&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#ssid&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;SSID() &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;default&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;h3 id=&#34;ssid-1&#34;&gt;&#xA;  &lt;strong&gt;SSID&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#ssid-1&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;SSID(&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    size_t n_x,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    size_t n_h,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    data_t dt,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    UniformMatrixList&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; kMatFreeDim2 &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;amp;&amp;amp;&lt;/span&gt; u_train,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    UniformMatrixList&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; kMatFreeDim2 &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;amp;&amp;amp;&lt;/span&gt; z_train,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#66d9ef&#34;&gt;const&lt;/span&gt; Vector &lt;span style=&#34;color:#f92672&#34;&gt;&amp;amp;&lt;/span&gt; d &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;Vector(&lt;span style=&#34;color:#ae81ff&#34;&gt;1&lt;/span&gt;).fill(&lt;span style=&#34;color:#f92672&#34;&gt;-&lt;/span&gt;kInf)&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;)&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;&lt;strong&gt;Parameters&lt;/strong&gt;:&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>lds::SwitchedController</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_switched_controller/</guid>
+      <description>SwitchedController Type.</description>
+    </item>
+    <item>
+      <title>lds::System</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/</guid>
+      <description>Linear Dynamical System Type.</description>
+    </item>
+    <item>
+      <title>lds::UniformMatrixList</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/</guid>
+      <description>&lt;h1 id=&#34;ldsuniformmatrixlist&#34;&gt;&#xA;  lds::UniformMatrixList&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#ldsuniformmatrixlist&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;&lt;a href=&#34;#detailed-description&#34;&gt;More&amp;hellip;&lt;/a&gt;&lt;/p&gt;&#xA;&lt;p&gt;Inherits from std::vector&amp;lt; Matrix &amp;gt;&lt;/p&gt;&#xA;&lt;h2 id=&#34;public-functions&#34;&gt;&#xA;  Public Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist&#34;&gt;UniformMatrixList&lt;/a&gt;&lt;/strong&gt;() =default&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist&#34;&gt;UniformMatrixList&lt;/a&gt;&lt;/strong&gt;(const std::vector&amp;lt; Matrix &amp;gt; &amp;amp; mats, std::array&amp;lt; size_t, 2 &amp;gt; dim ={0, 0})&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt; by copying existing vector of Matrix if dimensions consistent.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist&#34;&gt;UniformMatrixList&lt;/a&gt;&lt;/strong&gt;(std::vector&amp;lt; Matrix &amp;gt; &amp;amp;&amp;amp; mats, std::array&amp;lt; size_t, 2 &amp;gt; dim ={0, 0})&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt; by moving existing vector of Matrix if dimensions consistent.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist&#34;&gt;UniformMatrixList&lt;/a&gt;&lt;/strong&gt;(std::initializer_list&amp;lt; Matrix &amp;gt; mats, std::array&amp;lt; size_t, 2 &amp;gt; dim ={0, 0})&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt; from initializer_list of Matrix if dimensions consistent.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist&#34;&gt;UniformMatrixList&lt;/a&gt;&lt;/strong&gt;(const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; D &amp;gt; &amp;amp; that)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt; (copy).&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-uniformmatrixlist&#34;&gt;UniformMatrixList&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; D &amp;gt; &amp;amp;&amp;amp; that)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt; (move).&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-~uniformmatrixlist&#34;&gt;~UniformMatrixList&lt;/a&gt;&lt;/strong&gt;() =default&lt;br&gt;Destroys the object.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const std::array&amp;lt; size_t, 2 &amp;gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-dim&#34;&gt;dim&lt;/a&gt;&lt;/strong&gt;(size_t n =0) const&lt;br&gt;gets dimensions of uniformly sized matrices&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-size&#34;&gt;size&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;size of container&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Matrix &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-at&#34;&gt;at&lt;/a&gt;&lt;/strong&gt;(size_t n)&lt;br&gt;gets reference to n^th element&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-swap&#34;&gt;Swap&lt;/a&gt;&lt;/strong&gt;(Matrix &amp;amp; that, size_t n)&lt;br&gt;swaps input matrix with n^th matrix of list&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; D &amp;gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-operator=&#34;&gt;operator=&lt;/a&gt;&lt;/strong&gt;(const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; D &amp;gt; &amp;amp; that)&lt;br&gt;assigns the contents (copy)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; D &amp;gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-operator=&#34;&gt;operator=&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/&#34;&gt;UniformMatrixList&lt;/a&gt;&amp;lt; D &amp;gt; &amp;amp;&amp;amp; that)&lt;br&gt;assigns the contents (move)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_matrix_list/#function-append&#34;&gt;append&lt;/a&gt;&lt;/strong&gt;(const Matrix &amp;amp; mat)&lt;br&gt;appends a matrix to the list&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;detailed-description&#34;&gt;&#xA;  Detailed Description&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#detailed-description&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;template&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;MatrixListFreeDim D &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;kMatFreeDimNone&lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;class&lt;/span&gt; &lt;span style=&#34;color:#a6e22e&#34;&gt;lds&lt;/span&gt;&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;UniformMatrixList;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;hr&gt;&#xA;&lt;h2 id=&#34;public-function-details&#34;&gt;&#xA;  Public Function Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-function-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;uniformmatrixlist&#34;&gt;&#xA;  &lt;strong&gt;UniformMatrixList&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#uniformmatrixlist&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;UniformMatrixList() &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;default&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;h3 id=&#34;uniformmatrixlist-1&#34;&gt;&#xA;  &lt;strong&gt;UniformMatrixList&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#uniformmatrixlist-1&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;explicit&lt;/span&gt; UniformMatrixList(&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#66d9ef&#34;&gt;const&lt;/span&gt; std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;vector&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; Matrix &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;amp;&lt;/span&gt; mats,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;array&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; size_t, &lt;span style=&#34;color:#ae81ff&#34;&gt;2&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; dim &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;{&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;, &lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;}&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;)&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;&lt;strong&gt;Parameters&lt;/strong&gt;:&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>lds::UniformSystemList</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/</guid>
+      <description>&lt;h1 id=&#34;ldsuniformsystemlist&#34;&gt;&#xA;  lds::UniformSystemList&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#ldsuniformsystemlist&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;&lt;a href=&#34;#detailed-description&#34;&gt;More&amp;hellip;&lt;/a&gt;&lt;/p&gt;&#xA;&lt;p&gt;Inherits from std::vector&amp;lt; System &amp;gt;&lt;/p&gt;&#xA;&lt;h2 id=&#34;public-functions&#34;&gt;&#xA;  Public Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist&#34;&gt;UniformSystemList&lt;/a&gt;&lt;/strong&gt;() =default&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt;.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist&#34;&gt;UniformSystemList&lt;/a&gt;&lt;/strong&gt;(const std::vector&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; &amp;gt; &amp;amp; systems, std::array&amp;lt; size_t, 3 &amp;gt; dim ={0, 0, 0})&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; by copying existing vector of &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; if dimensions consistent.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist&#34;&gt;UniformSystemList&lt;/a&gt;&lt;/strong&gt;(std::vector&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; &amp;gt; &amp;amp;&amp;amp; systems, std::array&amp;lt; size_t, 3 &amp;gt; dim ={0, 0, 0})&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; by moving existing vector of &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; if dimensions consistent.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist&#34;&gt;UniformSystemList&lt;/a&gt;&lt;/strong&gt;(std::initializer_list&amp;lt; &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; &amp;gt; systems, std::array&amp;lt; size_t, 3 &amp;gt; dim ={0, 0, 0})&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; from initializer_list of &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; if dimensions consistent.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist&#34;&gt;UniformSystemList&lt;/a&gt;&lt;/strong&gt;(const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; &amp;amp; that)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; (copy).&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-uniformsystemlist&#34;&gt;UniformSystemList&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; &amp;amp;&amp;amp; that)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; (move).&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-~uniformsystemlist&#34;&gt;~UniformSystemList&lt;/a&gt;&lt;/strong&gt;() =default&lt;br&gt;Destroys the object.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const std::array&amp;lt; size_t, 3 &amp;gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-dim&#34;&gt;dim&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;gets dimensions of the uniformly sized systems&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-size&#34;&gt;size&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;size of container&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-at&#34;&gt;at&lt;/a&gt;&lt;/strong&gt;(size_t n)&lt;br&gt;gets reference to n^th element&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-swap&#34;&gt;Swap&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_system/&#34;&gt;System&lt;/a&gt; &amp;amp; that, size_t n)&lt;br&gt;swaps input system with n^th system of list&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-operator=&#34;&gt;operator=&lt;/a&gt;&lt;/strong&gt;(const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; &amp;amp; that)&lt;br&gt;assigns the contents (copy)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/#function-operator=&#34;&gt;operator=&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_system_list/&#34;&gt;UniformSystemList&lt;/a&gt; &amp;amp;&amp;amp; that)&lt;br&gt;assigns the contents (move)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h2 id=&#34;detailed-description&#34;&gt;&#xA;  Detailed Description&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#detailed-description&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;template&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;typename&lt;/span&gt; System &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;class&lt;/span&gt; &lt;span style=&#34;color:#a6e22e&#34;&gt;lds&lt;/span&gt;&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;UniformSystemList;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;hr&gt;&#xA;&lt;h2 id=&#34;public-function-details&#34;&gt;&#xA;  Public Function Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-function-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;uniformsystemlist&#34;&gt;&#xA;  &lt;strong&gt;UniformSystemList&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#uniformsystemlist&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;UniformSystemList() &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;default&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;h3 id=&#34;uniformsystemlist-1&#34;&gt;&#xA;  &lt;strong&gt;UniformSystemList&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#uniformsystemlist-1&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;explicit&lt;/span&gt; UniformSystemList(&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#66d9ef&#34;&gt;const&lt;/span&gt; std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;vector&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; System &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;amp;&lt;/span&gt; systems,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;array&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; size_t, &lt;span style=&#34;color:#ae81ff&#34;&gt;3&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; dim &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;{&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;, &lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;, &lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;}&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;)&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;&lt;strong&gt;Parameters&lt;/strong&gt;:&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>lds::UniformVectorList</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/</guid>
+      <description>&lt;h1 id=&#34;ldsuniformvectorlist&#34;&gt;&#xA;  lds::UniformVectorList&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#ldsuniformvectorlist&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;Inherits from std::vector&amp;lt; Vector &amp;gt;&lt;/p&gt;&#xA;&lt;h2 id=&#34;public-functions&#34;&gt;&#xA;  Public Functions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-functions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;&lt;/th&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist&#34;&gt;UniformVectorList&lt;/a&gt;&lt;/strong&gt;() =default&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt;.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist&#34;&gt;UniformVectorList&lt;/a&gt;&lt;/strong&gt;(const std::vector&amp;lt; Vector &amp;gt; &amp;amp; vecs, size_t dim =0)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; by copying existing vector of Vector if dimensions consistent.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist&#34;&gt;UniformVectorList&lt;/a&gt;&lt;/strong&gt;(std::vector&amp;lt; Vector &amp;gt; &amp;amp;&amp;amp; vecs, size_t dim =0)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; by moving existing vector of Vector if dimensions consistent.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist&#34;&gt;UniformVectorList&lt;/a&gt;&lt;/strong&gt;(std::initializer_list&amp;lt; Vector &amp;gt; vecs, size_t dim =0)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; from initializer_list of Vector if dimensions consistent.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist&#34;&gt;UniformVectorList&lt;/a&gt;&lt;/strong&gt;(const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; &amp;amp; that)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; (copy)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-uniformvectorlist&#34;&gt;UniformVectorList&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; &amp;amp;&amp;amp; that)&lt;br&gt;Constructs a new &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; (move)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-~uniformvectorlist&#34;&gt;~UniformVectorList&lt;/a&gt;&lt;/strong&gt;() =default&lt;br&gt;Destroys the object.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-dim&#34;&gt;dim&lt;/a&gt;&lt;/strong&gt;() const&lt;br&gt;gets dimensions of the uniformly sized matrices&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;size_t&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-size&#34;&gt;size&lt;/a&gt;&lt;/strong&gt;()&lt;br&gt;size of container&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;const Vector &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-at&#34;&gt;at&lt;/a&gt;&lt;/strong&gt;(size_t n)&lt;br&gt;gets reference to n^th element&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;void&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-swap&#34;&gt;Swap&lt;/a&gt;&lt;/strong&gt;(Vector &amp;amp; that, size_t n)&lt;br&gt;swaps input matrix with n^th vector of list&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-operator=&#34;&gt;operator=&lt;/a&gt;&lt;/strong&gt;(const &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; &amp;amp; that)&lt;br&gt;assigns the contents (copy)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; &amp;amp;&lt;/td&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/#function-operator=&#34;&gt;operator=&lt;/a&gt;&lt;/strong&gt;(&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/classes/classlds_1_1_uniform_vector_list/&#34;&gt;UniformVectorList&lt;/a&gt; &amp;amp;&amp;amp; that)&lt;br&gt;assigns the contents (move)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;hr&gt;&#xA;&lt;hr&gt;&#xA;&lt;h2 id=&#34;public-function-details&#34;&gt;&#xA;  Public Function Details&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#public-function-details&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;h3 id=&#34;uniformvectorlist&#34;&gt;&#xA;  &lt;strong&gt;UniformVectorList&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#uniformvectorlist&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;UniformVectorList() &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;default&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;hr&gt;&#xA;&lt;h3 id=&#34;uniformvectorlist-1&#34;&gt;&#xA;  &lt;strong&gt;UniformVectorList&lt;/strong&gt;&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#uniformvectorlist-1&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h3&gt;&#xA;&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; style=&#34;color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;&#34;&gt;&lt;code class=&#34;language-cpp&#34; data-lang=&#34;cpp&#34;&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;&lt;span style=&#34;color:#66d9ef&#34;&gt;explicit&lt;/span&gt; UniformVectorList(&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    &lt;span style=&#34;color:#66d9ef&#34;&gt;const&lt;/span&gt; std&lt;span style=&#34;color:#f92672&#34;&gt;::&lt;/span&gt;vector&lt;span style=&#34;color:#f92672&#34;&gt;&amp;lt;&lt;/span&gt; Vector &lt;span style=&#34;color:#f92672&#34;&gt;&amp;gt;&lt;/span&gt; &lt;span style=&#34;color:#f92672&#34;&gt;&amp;amp;&lt;/span&gt; vecs,&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;    size_t dim &lt;span style=&#34;color:#f92672&#34;&gt;=&lt;/span&gt;&lt;span style=&#34;color:#ae81ff&#34;&gt;0&lt;/span&gt;&#xA;&lt;/span&gt;&lt;/span&gt;&lt;span style=&#34;display:flex;&#34;&gt;&lt;span&gt;)&#xA;&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;&lt;strong&gt;Parameters&lt;/strong&gt;:&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_156a98879751e549d6939ca71a62d61f/</guid>
+      <description>&lt;h1 id=&#34;ldsctrlest_h&#34;&gt;&#xA;  ldsCtrlEst_h&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#ldsctrlest_h&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;h2 id=&#34;files&#34;&gt;&#xA;  Files&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#files&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds_8h/#file-lds.h&#34;&gt;ldsCtrlEst_h/lds.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;&lt;code&gt;lds&lt;/code&gt; namespace&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__ctrl_8h/#file-lds-ctrl.h&#34;&gt;ldsCtrlEst_h/lds_ctrl.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;Controller.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit_8h/#file-lds-fit.h&#34;&gt;ldsCtrlEst_h/lds_fit.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;LDS base fit type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit__em_8h/#file-lds-fit-em.h&#34;&gt;ldsCtrlEst_h/lds_fit_em.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;subspace identification&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/#file-lds-fit-ssid.h&#34;&gt;ldsCtrlEst_h/lds_fit_ssid.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;subspace identification&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian_8h/#file-lds-gaussian.h&#34;&gt;ldsCtrlEst_h/lds_gaussian.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;&lt;code&gt;glds&lt;/code&gt; namespace&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/#file-lds-gaussian-ctrl.h&#34;&gt;ldsCtrlEst_h/lds_gaussian_ctrl.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;GLDS Controller.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/#file-lds-gaussian-fit.h&#34;&gt;ldsCtrlEst_h/lds_gaussian_fit.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;GLDS fit type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/#file-lds-gaussian-fit-em.h&#34;&gt;ldsCtrlEst_h/lds_gaussian_fit_em.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;GLDS E-M fit type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/#file-lds-gaussian-fit-ssid.h&#34;&gt;ldsCtrlEst_h/lds_gaussian_fit_ssid.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;GLDS SSID fit type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/#file-lds-gaussian-sctrl.h&#34;&gt;ldsCtrlEst_h/lds_gaussian_sctrl.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;GLDS switched controller type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/#file-lds-gaussian-sys.h&#34;&gt;ldsCtrlEst_h/lds_gaussian_sys.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;GLDS base type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson_8h/#file-lds-poisson.h&#34;&gt;ldsCtrlEst_h/lds_poisson.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;&lt;code&gt;plds&lt;/code&gt; namespace&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/#file-lds-poisson-ctrl.h&#34;&gt;ldsCtrlEst_h/lds_poisson_ctrl.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;PLDS controller type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/#file-lds-poisson-fit.h&#34;&gt;ldsCtrlEst_h/lds_poisson_fit.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;PLDS base fit type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/#file-lds-poisson-fit-em.h&#34;&gt;ldsCtrlEst_h/lds_poisson_fit_em.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;PLDS E-M fit type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/#file-lds-poisson-fit-ssid.h&#34;&gt;ldsCtrlEst_h/lds_poisson_fit_ssid.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;PLDS SSID fit type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/#file-lds-poisson-sctrl.h&#34;&gt;ldsCtrlEst_h/lds_poisson_sctrl.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;PLDS switched controller type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/#file-lds-poisson-sys.h&#34;&gt;ldsCtrlEst_h/lds_poisson_sys.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;PLDS base type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sctrl_8h/#file-lds-sctrl.h&#34;&gt;ldsCtrlEst_h/lds_sctrl.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;SwitchedController type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8h/#file-lds-sys.h&#34;&gt;ldsCtrlEst_h/lds_sys.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;LDS base type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/#file-lds-uniform-mats.h&#34;&gt;ldsCtrlEst_h/lds_uniform_mats.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;List of uniformly sized matrices.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/#file-lds-uniform-systems.h&#34;&gt;ldsCtrlEst_h/lds_uniform_systems.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;List of uniformly sized Systems.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/#file-lds-uniform-vecs.h&#34;&gt;ldsCtrlEst_h/lds_uniform_vecs.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;List of uniformly sized vectors.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/mex__c__util_8h/#file-mex-c-util.h&#34;&gt;ldsCtrlEst_h/mex_c_util.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;arma &amp;lt;-&amp;gt; mex interoperability utilities (Matlab C API)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/#file-mex-cpp-util.h&#34;&gt;ldsCtrlEst_h/mex_cpp_util.h&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;arma &amp;lt;-&amp;gt; mex interoperability utilities (Matlab C++ API)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;hr&gt;&#xA;&lt;p&gt;Updated on 31 March 2025 at 16:04:30 EDT&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_ctrl.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__ctrl_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__ctrl_8h/</guid>
+      <description>Controller.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_fit_em.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit__em_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit__em_8h/</guid>
+      <description>subspace identification</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_fit_ssid.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit__ssid_8h/</guid>
+      <description>subspace identification</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_fit.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__fit_8h/</guid>
+      <description>LDS base fit type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_gaussian_ctrl.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__ctrl_8h/</guid>
+      <description>GLDS Controller.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_gaussian_fit_em.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit__em_8h/</guid>
+      <description>GLDS E-M fit type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_gaussian_fit_ssid.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit__ssid_8h/</guid>
+      <description>GLDS SSID fit type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_gaussian_fit.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__fit_8h/</guid>
+      <description>GLDS fit type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_gaussian_sctrl.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sctrl_8h/</guid>
+      <description>GLDS switched controller type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_gaussian_sys.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8h/</guid>
+      <description>GLDS base type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_gaussian.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian_8h/</guid>
+      <description>glds namespace</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_poisson_ctrl.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__ctrl_8h/</guid>
+      <description>PLDS controller type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_poisson_fit_em.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit__em_8h/</guid>
+      <description>PLDS E-M fit type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_poisson_fit_ssid.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit__ssid_8h/</guid>
+      <description>PLDS SSID fit type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_poisson_fit.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__fit_8h/</guid>
+      <description>PLDS base fit type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_poisson_sctrl.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sctrl_8h/</guid>
+      <description>PLDS switched controller type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_poisson_sys.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sys_8h/</guid>
+      <description>PLDS base type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_poisson.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson_8h/</guid>
+      <description>plds namespace</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_sctrl.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sctrl_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sctrl_8h/</guid>
+      <description>SwitchedController type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_sys.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8h/</guid>
+      <description>LDS base type.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_uniform_mats.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__mats_8h/</guid>
+      <description>List of uniformly sized matrices.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_uniform_systems.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__systems_8h/</guid>
+      <description>List of uniformly sized Systems.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds_uniform_vecs.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8h/</guid>
+      <description>List of uniformly sized vectors.</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/lds.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds_8h/</guid>
+      <description>lds namespace</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/mex_c_util.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/mex__c__util_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/mex__c__util_8h/</guid>
+      <description>arma &amp;lt;-&amp;gt; mex interoperability utilities (Matlab C API)</description>
+    </item>
+    <item>
+      <title>ldsCtrlEst_h/mex_cpp_util.h</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/mex__cpp__util_8h/</guid>
+      <description>arma &amp;lt;-&amp;gt; mex interoperability utilities (Matlab C++ API)</description>
+    </item>
+    <item>
+      <title>Models</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/terminology/model/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/terminology/model/</guid>
+      <description>&lt;h1 id=&#34;model-definitions&#34;&gt;&#xA;  Model Definitions&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#model-definitions&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;This library provides methods for control and estimation of linear dynamical systems (LDS) of the following form:&#xA;&#xA;&lt;link rel=&#34;stylesheet&#34; href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/katex/katex.min.css&#34; /&gt;&#xA;&lt;script defer src=&#34;https://stanley-rozell.github.io/lds-ctrl-est/katex/katex.min.js&#34;&gt;&lt;/script&gt;&#xA;&lt;script defer src=&#34;https://stanley-rozell.github.io/lds-ctrl-est/katex/auto-render.min.js&#34; onload=&#34;renderMathInElement(document.body);&#34;&gt;&lt;/script&gt;&lt;span&gt;&#xA;  \[\mathbf{x}_{t&amp;#43;1} = f\left( \mathbf{x}_{t}, \mathbf{v}_{t} \right) = \mathbf{A} \mathbf{x}_{t} &amp;#43; \mathbf{B} \mathbf{v}_{t} &amp;#43; \mathbf{m}_{t} &amp;#43; \mathbf{w}_{t}\]&#xA;&lt;/span&gt;&#xA;&lt;/p&gt;&#xA;&lt;span&gt;&#xA;  \[\mathbf{y}_{t} = h\left( \mathbf{x}_{t} \right)\]&#xA;&lt;/span&gt;&#xA;&#xA;&lt;pre&gt;&lt;code&gt;t           : time index&#xA;x           : system state&#xA;v = g%u     : input (e.g., in physical units used for model fit)&#xA;u           : control signal sent to actuator (e.g., in Volts)&#xA;y           : system output&#xA;m           : process disturbance&#xA;w ~ N(0, Q) : process noise/disturbance&#xA;&#xA;A           : state matrix&#xA;B           : input coupling matrix&#xA;g           : input gain (e.g., for converting to control signal actuator voltage)&#xA;              n.b., assumes this conversion is linear&#xA;Q           : process noise covariance&#xA;&#xA;%           : element-wise multiplication&#xA;&lt;/code&gt;&lt;/pre&gt;&#xA;&lt;h2 id=&#34;lds-with-gaussian-observations&#34;&gt;&#xA;  LDS with Gaussian Observations&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#lds-with-gaussian-observations&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;p&gt;For linear dynamical systems whose outputs are assumed to be corrupted by additive Gaussian noise before measurement (Gaussian LDS models), the output function takes the following form.&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>PLDS State Estimation</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/</guid>
+      <description>&lt;h1 id=&#34;plds-state-estimation-tutorial&#34;&gt;&#xA;  PLDS State Estimation Tutorial&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#plds-state-estimation-tutorial&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;This tutorial shows how to use this library to estimate the state of an LDS with Poisson observations from input/output data. In place of a physical system, another PLDS model (&lt;code&gt;lds::poisson::System&lt;/code&gt;) receives random inputs and provides measurements for the state estimator. For the sake of example, the only parameter mismatch is assumed to be the process disturbance, which is adaptively re-estimated.&lt;/p&gt;&#xA;&lt;p&gt;The full code for this can be found &lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/examples/eg_plds_est_8cpp-example/&#34;&gt;here&lt;/a&gt;.&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>PLDS Switched Control</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/</guid>
+      <description>&lt;h1 id=&#34;plds-switched-control-tutorial&#34;&gt;&#xA;  PLDS Switched Control Tutorial&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#plds-switched-control-tutorial&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;This tutorial shows how to use this library to control a system with a switched PLDS controller (&lt;code&gt;lds::poisson::SwitchedController&lt;/code&gt;). This type of controller is applicable in scenarios where a physical system is not accurately captured by a &lt;strong&gt;single&lt;/strong&gt; LDS but has &lt;strong&gt;multiple&lt;/strong&gt; discrete operating modes where the dynamics can be well-approximated as linear.&lt;/p&gt;&#xA;&lt;p&gt;In the example that follows, another PLDS model (&lt;code&gt;lds::poisson::System&lt;/code&gt;) is used in place of a physical system. It receives control inputs and provides measurements for the simulated feedback control loop. This system stochastically flips between two input gains. Here, the controller is assumed to have a perfect model of the switching system being controlled. Note that in practice, users would need to have a decoder that estimates operating mode of the physical system being controlled. This library does not currently include operating mode estimation.&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>src</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/dir_68267d1309a1af8e8297ef4c3efbcdba/</guid>
+      <description>&lt;h1 id=&#34;src&#34;&gt;&#xA;  src&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#src&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;h2 id=&#34;files&#34;&gt;&#xA;  Files&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#files&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;Name&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds_8cpp/#file-lds.cpp&#34;&gt;src/lds.cpp&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;misc lds namespace functions&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/#file-lds-gaussian-sys.cpp&#34;&gt;src/lds_gaussian_sys.cpp&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;GLDS base type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/#file-lds-poisson-sys.cpp&#34;&gt;src/lds_poisson_sys.cpp&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;PLDS base type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8cpp/#file-lds-sys.cpp&#34;&gt;src/lds_sys.cpp&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;LDS base type.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;&lt;strong&gt;&lt;a href=&#34;https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/#file-lds-uniform-vecs.cpp&#34;&gt;src/lds_uniform_vecs.cpp&lt;/a&gt;&lt;/strong&gt; &lt;br&gt;Uniformly sized vectors.&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;hr&gt;&#xA;&lt;p&gt;Updated on 31 March 2025 at 16:04:30 EDT&lt;/p&gt;</description>
+    </item>
+    <item>
+      <title>src/lds_gaussian_sys.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__gaussian__sys_8cpp/</guid>
+      <description>GLDS base type.</description>
+    </item>
+    <item>
+      <title>src/lds_poisson_sys.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/</guid>
+      <description>PLDS base type.</description>
+    </item>
+    <item>
+      <title>src/lds_sys.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8cpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8cpp/</guid>
+      <description>LDS base type.</description>
+    </item>
+    <item>
+      <title>src/lds_uniform_vecs.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/</guid>
+      <description>Uniformly sized vectors.</description>
+    </item>
+    <item>
+      <title>src/lds.cpp</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds_8cpp/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds_8cpp/</guid>
+      <description>misc lds namespace functions</description>
+    </item>
+    <item>
+      <title>std</title>
+      <link>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacestd/</link>
+      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+      <guid>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacestd/</guid>
+      <description>&lt;h1 id=&#34;std&#34;&gt;&#xA;  std&#xA;  &lt;a class=&#34;anchor&#34; href=&#34;#std&#34;&gt;#&lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;br&gt;&#xA;&lt;hr&gt;&#xA;&lt;p&gt;Updated on 31 March 2025 at 16:04:30 EDT&lt;/p&gt;</description>
+    </item>
+  </channel>
+</rss>
diff --git a/docs/issues-contributing/index.html b/docs/issues-contributing/index.html
new file mode 100644
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--- /dev/null
+++ b/docs/issues-contributing/index.html
@@ -0,0 +1,263 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="
+  Reporting Issues
+  #
+
+If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.
+
+  Contributing
+  #
+
+We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/issues-contributing/">
+  <meta property="og:site_name" content="LDS C&E">
+  <meta property="og:title" content="LDS C&E">
+  <meta property="og:description" content="Reporting Issues # If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please open an issue on GitHub and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.
+Contributing # We welcome any community contributions to this project. Please fork the repository and if possible use clang-format and clang-tidy to conform to the coding format/style of this repository.">
+  <meta property="og:locale" content="en">
+  <meta property="og:type" content="article">
+<title>Issues Contributing | LDS C&amp;E</title>
+<link rel="manifest" href="/lds-ctrl-est/manifest.json">
+<link rel="icon" href="/lds-ctrl-est/favicon.png" type="image/x-icon">
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+<!--
+Made with Book Theme
+https://github.com/alex-shpak/hugo-book
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+</head>
+<body dir="ltr">
+  <input type="checkbox" class="hidden toggle" id="menu-control" />
+  <input type="checkbox" class="hidden toggle" id="toc-control" />
+  <main class="container flex">
+    <aside class="book-menu">
+      <div class="book-menu-content">
+        
+  <nav>
+<h2 class="book-brand">
+  <a href="/lds-ctrl-est/"><img src="/lds-ctrl-est/ldsctrlest-logo.png" alt="Logo" /><span>LDS C&amp;E</span>
+  </a>
+</h2>
+
+
+<div class="book-search">
+  <input type="text" id="book-search-input" placeholder="Search" aria-label="Search" maxlength="64" data-hotkeys="s/" />
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+</div>
+
+
+
+
+
+
+
+
+
+
+
+  <ul>
+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
+<li><a href="/lds-ctrl-est/docs/api/classes/">Classes</a></li>
+<li><a href="/lds-ctrl-est/docs/api/modules/">Modules</a></li>
+<li><a href="/lds-ctrl-est/docs/api/files/">Files</a></li>
+<li><a href="/lds-ctrl-est/docs/api/examples/">Examples</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/issues-contributing/"class=active><strong>Reporting Issues &amp; Contributing</strong></a></li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
+</ul>
+<br />
+
+
+
+
+
+
+  
+<ul>
+  
+  <li>
+    <a href="https://github.com/stanley-rozell/lds-ctrl-est" target="_blank" rel="noopener">
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+  </label>
+
+  <strong>Issues Contributing</strong>
+
+  <label for="toc-control">
+    
+    <img src="/lds-ctrl-est/svg/toc.svg" class="book-icon" alt="Table of Contents" />
+    
+  </label>
+</div>
+
+
+  
+  <aside class="hidden clearfix">
+    
+  
+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#reporting-issues">Reporting Issues</a></li>
+    <li><a href="#contributing">Contributing</a></li>
+  </ul>
+</nav>
+
+
+
+  </aside>
+  
+ 
+      </header>
+
+      
+      
+  <article class="markdown"><h1 id="reporting-issues">
+  Reporting Issues
+  <a class="anchor" href="#reporting-issues">#</a>
+</h1>
+<p>If you encounter bugs when using this library or have specific feature requests that you believe fall within the stated scope of this project, please <a href="https://github.com/stanley-rozell/lds-ctrl-est/issues">open an issue on GitHub</a> and use an appropriate issue template where possible. You may also fork the repository and submit pull-requests with your suggested changes.</p>
+<h1 id="contributing">
+  Contributing
+  <a class="anchor" href="#contributing">#</a>
+</h1>
+<p>We welcome any community contributions to this project. Please fork the repository and if possible use <code>clang-format</code> and <code>clang-tidy</code> to conform to the coding format/style of this repository.</p>
+<p>When editing any documentation/guides, please use the markdown docs in <code>misc/docs-hugo</code> instead of directly editing the HTML docs.
+This may require having <code>hugo</code>, <code>graphviz</code> and <code>doxygen</code> installed through <code>brew</code>, as well as <code>doxybook2</code> installed through a git clone.</p>
+<p>Clone the <code>doxybook2</code> repository online and place the executable in your <code>usr/local/bin</code> folder or another <code>bin</code> folder.
+Run <code>sudo chmod +x /usr/local/bin/doxybook2</code> to give doxybook2 permissions. Run <code>doxybook2 --help</code> to ensure that this works properly. If permission is still denied, navigate to System Preferences &gt; Security &amp; Privacy &gt; General.
+You should see a message at the bottom that says: &ldquo;doxybook2 was blocked from use because it is not from an identified developer.&rdquo; Click Allow Anyway.</p>
+<p>Updated docs can be built by running <code>cd scripts</code> and <code>./update-docs.sh</code>. After a successful build, navigate to <code>/misc/docs-hugo/</code> and run <code>hugo server</code> to create a local host of the website. Access the website using local host to ensure the correct results.</p>
+</article>
+ 
+      
+
+      <footer class="book-footer">
+        
+  <div class="flex flex-wrap justify-between">
+
+
+
+
+
+</div>
+
+
+
+  <script>(function(){function e(e){const t=window.getSelection(),n=document.createRange();n.selectNodeContents(e),t.removeAllRanges(),t.addRange(n)}document.querySelectorAll("pre code").forEach(t=>{t.addEventListener("click",function(){e(t.parentElement),navigator.clipboard&&navigator.clipboard.writeText(t.parentElement.textContent)})})})()</script>
+
+
+ 
+        
+      </footer>
+
+      
+  
+ 
+
+      <label for="menu-control" class="hidden book-menu-overlay"></label>
+    </div>
+
+    
+    <aside class="book-toc">
+      <div class="book-toc-content">
+        
+  
+<nav id="TableOfContents">
+  <ul>
+    <li><a href="#reporting-issues">Reporting Issues</a></li>
+    <li><a href="#contributing">Contributing</a></li>
+  </ul>
+</nav>
+
+
+ 
+      </div>
+    </aside>
+    
+  </main>
+
+  
+</body>
+</html>
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/katex/auto-render.min.js b/docs/katex/auto-render.min.js
new file mode 100644
index 00000000..3a6d6639
--- /dev/null
+++ b/docs/katex/auto-render.min.js
@@ -0,0 +1 @@
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diff --git a/docs/katex/fonts/KaTeX_Caligraphic-Regular.woff b/docs/katex/fonts/KaTeX_Caligraphic-Regular.woff
new file mode 100644
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t(t,e){this.mode=void 0,this.gullet=void 0,this.settings=void 0,this.leftrightDepth=void 0,this.nextToken=void 0,this.mode="math",this.gullet=new wa(t,e,this.mode),this.settings=e,this.leftrightDepth=0}var e=t.prototype;return e.expect=function(t,e){if(void 0===e&&(e=!0),this.fetch().text!==t)throw new o("Expected '"+t+"', got '"+this.fetch().text+"'",this.fetch());e&&this.consume()},e.consume=function(){this.nextToken=null},e.fetch=function(){return null==this.nextToken&&(this.nextToken=this.gullet.expandNextToken()),this.nextToken},e.switchMode=function(t){this.mode=t,this.gullet.switchMode(t)},e.parse=function(){this.gullet.beginGroup(),this.settings.colorIsTextColor&&this.gullet.macros.set("\\color","\\textcolor");var t=this.parseExpression(!1);return this.expect("EOF"),this.gullet.endGroup(),t},e.parseExpression=function(e,r){for(var a=[];;){"math"===this.mode&&this.consumeSpaces();var n=this.fetch();if(-1!==t.endOfExpression.indexOf(n.text))break;if(r&&n.text===r)break;if(e&&ia[n.text]&&ia[n.text].infix)break;var i=this.parseAtom(r);if(!i)break;a.push(i)}return"text"===this.mode&&this.formLigatures(a),this.handleInfixNodes(a)},e.handleInfixNodes=function(t){for(var e,r=-1,a=0;a<t.length;a++){var n=Vt(t[a],"infix");if(n){if(-1!==r)throw new o("only one infix operator per group",n.token);r=a,e=n.replaceWith}}if(-1!==r&&e){var i,s,h=t.slice(0,r),l=t.slice(r+1);return i=1===h.length&&"ordgroup"===h[0].type?h[0]:{type:"ordgroup",mode:this.mode,body:h},s=1===l.length&&"ordgroup"===l[0].type?l[0]:{type:"ordgroup",mode:this.mode,body:l},["\\\\abovefrac"===e?this.callFunction(e,[i,t[r],s],[]):this.callFunction(e,[i,s],[])]}return t},e.handleSupSubscript=function(e){var r=this.fetch(),a=r.text;this.consume();var n=this.parseGroup(e,!1,t.SUPSUB_GREEDINESS,void 0,void 0,!0);if(!n)throw new o("Expected group after '"+a+"'",r);return n},e.formatUnsupportedCmd=function(t){for(var e=[],r=0;r<t.length;r++)e.push({type:"textord",mode:"text",text:t[r]});var a={type:"text",mode:this.mode,body:e};return{type:"color",mode:this.mode,color:this.settings.errorColor,body:[a]}},e.parseAtom=function(t){var e,r,a=this.parseGroup("atom",!1,null,t);if("text"===this.mode)return a;for(;;){this.consumeSpaces();var n=this.fetch();if("\\limits"===n.text||"\\nolimits"===n.text){var i=Vt(a,"op");if(i){var s="\\limits"===n.text;i.limits=s,i.alwaysHandleSupSub=!0}else{if(!(i=Vt(a,"operatorname"))||!i.alwaysHandleSupSub)throw new o("Limit controls must follow a math operator",n);var h="\\limits"===n.text;i.limits=h}this.consume()}else if("^"===n.text){if(e)throw new o("Double superscript",n);e=this.handleSupSubscript("superscript")}else if("_"===n.text){if(r)throw new o("Double subscript",n);r=this.handleSupSubscript("subscript")}else{if("'"!==n.text)break;if(e)throw new o("Double superscript",n);var l={type:"textord",mode:this.mode,text:"\\prime"},m=[l];for(this.consume();"'"===this.fetch().text;)m.push(l),this.consume();"^"===this.fetch().text&&m.push(this.handleSupSubscript("superscript")),e={type:"ordgroup",mode:this.mode,body:m}}}return e||r?{type:"supsub",mode:this.mode,base:a,sup:e,sub:r}:a},e.parseFunction=function(t,e,r){var a=this.fetch(),n=a.text,i=ia[n];if(!i)return null;if(this.consume(),null!=r&&i.greediness<=r)throw new o("Got function '"+n+"' with no arguments"+(e?" as "+e:""),a);if("text"===this.mode&&!i.allowedInText)throw new o("Can't use function '"+n+"' in text mode",a);if("math"===this.mode&&!1===i.allowedInMath)throw new o("Can't use function '"+n+"' in math mode",a);var s=this.parseArguments(n,i),h=s.args,l=s.optArgs;return this.callFunction(n,h,l,a,t)},e.callFunction=function(t,e,r,a,n){var i={funcName:t,parser:this,token:a,breakOnTokenText:n},s=ia[t];if(s&&s.handler)return s.handler(i,e,r);throw new o("No function handler for 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new file mode 100644
index 00000000..e4b2fdfa
--- /dev/null
+++ b/docs/manifest.json
@@ -0,0 +1,15 @@
+{
+  "name": "LDS C&E",
+  "short_name": "LDS C&E",
+  "start_url": "/",
+  "scope": "/",
+  "display": "standalone",
+  "background_color": "#000000",
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+  "icons": [
+    {
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+      "sizes": "512x512"
+    }
+  ]
+}
diff --git a/docs/mermaid.min.js b/docs/mermaid.min.js
new file mode 100644
index 00000000..48da6d6c
--- /dev/null
+++ b/docs/mermaid.min.js
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n=new Error(t);throw n.hash=e,n}this.trace(t)},parse:function(t){var e=this,n=[0],r=[],i=[null],a=[],o=this.table,s="",c=0,u=0,l=0,h=2,f=1,d=a.slice.call(arguments,1),p=Object.create(this.lexer),g={yy:{}};for(var y in this.yy)Object.prototype.hasOwnProperty.call(this.yy,y)&&(g.yy[y]=this.yy[y]);p.setInput(t,g.yy),g.yy.lexer=p,g.yy.parser=this,void 0===p.yylloc&&(p.yylloc={});var v=p.yylloc;a.push(v);var m=p.options&&p.options.ranges;function b(){var t;return"number"!=typeof(t=r.pop()||p.lex()||f)&&(t instanceof Array&&(t=(r=t).pop()),t=e.symbols_[t]||t),t}"function"==typeof g.yy.parseError?this.parseError=g.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;for(var x,_,k,w,E,T,C,A,S,M={};;){if(k=n[n.length-1],this.defaultActions[k]?w=this.defaultActions[k]:(null==x&&(x=b()),w=o[k]&&o[k][x]),void 0===w||!w.length||!w[0]){var O="";for(T in S=[],o[k])this.terminals_[T]&&T>h&&S.push("'"+this.terminals_[T]+"'");O=p.showPosition?"Parse error on line 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E;C&&(n=n.slice(0,-1*C*2),i=i.slice(0,-1*C),a=a.slice(0,-1*C)),n.push(this.productions_[w[1]][0]),i.push(M.$),a.push(M._$),A=o[n[n.length-2]][n[n.length-1]],n.push(A);break;case 3:return!0}}return!0}},M={EOF:1,parseError:function(t,e){if(!this.yy.parser)throw new Error(t);this.yy.parser.parseError(t,e)},setInput:function(t,e){return this.yy=e||this.yy||{},this._input=t,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var t=this._input[0];return this.yytext+=t,this.yyleng++,this.offset++,this.match+=t,this.matched+=t,t.match(/(?:\r\n?|\n).*/g)?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),t},unput:function(t){var e=t.length,n=t.split(/(?:\r\n?|\n)/g);this._input=t+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-e),this.offset-=e;var r=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),n.length-1&&(this.yylineno-=n.length-1);var i=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:n?(n.length===r.length?this.yylloc.first_column:0)+r[r.length-n.length].length-n[0].length:this.yylloc.first_column-e},this.options.ranges&&(this.yylloc.range=[i[0],i[0]+this.yyleng-e]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){return this.options.backtrack_lexer?(this._backtrack=!0,this):this.parseError("Lexical error on line "+(this.yylineno+1)+". You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},less:function(t){this.unput(this.match.slice(t))},pastInput:function(){var t=this.matched.substr(0,this.matched.length-this.match.length);return(t.length>20?"...":"")+t.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var t=this.match;return t.length<20&&(t+=this._input.substr(0,20-t.length)),(t.substr(0,20)+(t.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var t=this.pastInput(),e=new Array(t.length+1).join("-");return t+this.upcomingInput()+"\n"+e+"^"},test_match:function(t,e){var n,r,i;if(this.options.backtrack_lexer&&(i={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(i.yylloc.range=this.yylloc.range.slice(0))),(r=t[0].match(/(?:\r\n?|\n).*/g))&&(this.yylineno+=r.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:r?r[r.length-1].length-r[r.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+t[0].length},this.yytext+=t[0],this.match+=t[0],this.matches=t,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(t[0].length),this.matched+=t[0],n=this.performAction.call(this,this.yy,this,e,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),n)return n;if(this._backtrack){for(var a in i)this[a]=i[a];return!1}return!1},next:function(){if(this.done)return this.EOF;var t,e,n,r;this._input||(this.done=!0),this._more||(this.yytext="",this.match="");for(var i=this._currentRules(),a=0;a<i.length;a++)if((n=this._input.match(this.rules[i[a]]))&&(!e||n[0].length>e[0].length)){if(e=n,r=a,this.options.backtrack_lexer){if(!1!==(t=this.test_match(n,i[a])))return t;if(this._backtrack){e=!1;continue}return!1}if(!this.options.flex)break}return e?!1!==(t=this.test_match(e,i[r]))&&t:""===this._input?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+". Unrecognized text.\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var t=this.next();return t||this.lex()},begin:function(t){this.conditionStack.push(t)},popState:function(){return this.conditionStack.length-1>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(t){return(t=this.conditionStack.length-1-Math.abs(t||0))>=0?this.conditionStack[t]:"INITIAL"},pushState:function(t){this.begin(t)},stateStackSize:function(){return this.conditionStack.length},options:{"case-insensitive":!0},performAction:function(t,e,n,r){switch(n){case 0:return this.begin("open_directive"),58;case 1:return this.begin("type_directive"),59;case 2:return this.popState(),this.begin("arg_directive"),14;case 3:return this.popState(),this.popState(),61;case 4:return 60;case 5:return 5;case 6:case 7:case 8:case 9:case 10:break;case 11:return this.begin("ID"),16;case 12:return e.yytext=e.yytext.trim(),this.begin("ALIAS"),48;case 13:return this.popState(),this.popState(),this.begin("LINE"),18;case 14:return this.popState(),this.popState(),5;case 15:return this.begin("LINE"),27;case 16:return this.begin("LINE"),29;case 17:return this.begin("LINE"),30;case 18:return this.begin("LINE"),31;case 19:return this.begin("LINE"),36;case 20:return this.begin("LINE"),33;case 21:return this.begin("LINE"),35;case 22:return this.popState(),19;case 23:return 28;case 24:return 43;case 25:return 44;case 26:return 39;case 27:return 37;case 28:return this.begin("ID"),22;case 29:return this.begin("ID"),23;case 30:return 25;case 31:return 7;case 32:return 21;case 33:return 42;case 34:return 5;case 35:return e.yytext=e.yytext.trim(),48;case 36:return 51;case 37:return 52;case 38:return 49;case 39:return 50;case 40:return 53;case 41:return 54;case 42:return 55;case 43:return 56;case 44:return 57;case 45:return 46;case 46:return 47;case 47:return 5;case 48:return"INVALID"}},rules:[/^(?:%%\{)/i,/^(?:((?:(?!\}%%)[^:.])*))/i,/^(?::)/i,/^(?:\}%%)/i,/^(?:((?:(?!\}%%).|\n)*))/i,/^(?:[\n]+)/i,/^(?:\s+)/i,/^(?:((?!\n)\s)+)/i,/^(?:#[^\n]*)/i,/^(?:%(?!\{)[^\n]*)/i,/^(?:[^\}]%%[^\n]*)/i,/^(?:participant\b)/i,/^(?:[^\->:\n,;]+?(?=((?!\n)\s)+as(?!\n)\s|[#\n;]|$))/i,/^(?:as\b)/i,/^(?:(?:))/i,/^(?:loop\b)/i,/^(?:rect\b)/i,/^(?:opt\b)/i,/^(?:alt\b)/i,/^(?:else\b)/i,/^(?:par\b)/i,/^(?:and\b)/i,/^(?:(?:[:]?(?:no)?wrap)?[^#\n;]*)/i,/^(?:end\b)/i,/^(?:left of\b)/i,/^(?:right of\b)/i,/^(?:over\b)/i,/^(?:note\b)/i,/^(?:activate\b)/i,/^(?:deactivate\b)/i,/^(?:title\b)/i,/^(?:sequenceDiagram\b)/i,/^(?:autonumber\b)/i,/^(?:,)/i,/^(?:;)/i,/^(?:[^\+\->:\n,;]+((?!(-x|--x|-\)|--\)))[\-]*[^\+\->:\n,;]+)*)/i,/^(?:->>)/i,/^(?:-->>)/i,/^(?:->)/i,/^(?:-->)/i,/^(?:-[x])/i,/^(?:--[x])/i,/^(?:-[\)])/i,/^(?:--[\)])/i,/^(?::(?:(?:no)?wrap)?[^#\n;]+)/i,/^(?:\+)/i,/^(?:-)/i,/^(?:$)/i,/^(?:.)/i],conditions:{open_directive:{rules:[1,8],inclusive:!1},type_directive:{rules:[2,3,8],inclusive:!1},arg_directive:{rules:[3,4,8],inclusive:!1},ID:{rules:[7,8,12],inclusive:!1},ALIAS:{rules:[7,8,13,14],inclusive:!1},LINE:{rules:[7,8,22],inclusive:!1},INITIAL:{rules:[0,5,6,8,9,10,11,15,16,17,18,19,20,21,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],inclusive:!0}}};function O(){this.yy={}}return S.lexer=M,O.prototype=S,S.Parser=O,new O}();e.parser=i,e.Parser=i.Parser,e.parse=function(){return i.parse.apply(i,arguments)},e.main=function(r){r[1]||(console.log("Usage: "+r[0]+" FILE"),t.exit(1));var i=n(19).readFileSync(n(20).normalize(r[1]),"utf8");return e.parser.parse(i)},n.c[n.s]===r&&e.main(t.argv.slice(1))}).call(this,n(14),n(7)(t))},function(t,e,n){var r=n(198);t.exports={Graph:r.Graph,json:n(301),alg:n(302),version:r.version}},function(t,e,n){var r;try{r={cloneDeep:n(313),constant:n(86),defaults:n(154),each:n(87),filter:n(128),find:n(314),flatten:n(156),forEach:n(126),forIn:n(319),has:n(93),isUndefined:n(139),last:n(320),map:n(140),mapValues:n(321),max:n(322),merge:n(324),min:n(329),minBy:n(330),now:n(331),pick:n(161),range:n(162),reduce:n(142),sortBy:n(338),uniqueId:n(163),values:n(147),zipObject:n(343)}}catch(t){}r||(r=window._),t.exports=r},function(t,e){var n=Array.isArray;t.exports=n},function(t,e,n){
+/**
+ * @license
+ * Copyright (c) 2012-2013 Chris Pettitt
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ * THE SOFTWARE.
+ */
+t.exports={graphlib:n(311),dagre:n(153),intersect:n(368),render:n(370),util:n(12),version:n(382)}},function(t,e){t.exports=function(t){return t.webpackPolyfill||(t.deprecate=function(){},t.paths=[],t.children||(t.children=[]),Object.defineProperty(t,"loaded",{enumerable:!0,get:function(){return t.l}}),Object.defineProperty(t,"id",{enumerable:!0,get:function(){return t.i}}),t.webpackPolyfill=1),t}},function(t,e,n){"use strict";var r=n(4),i=n(17).Graph;function a(t,e,n,i){var a;do{a=r.uniqueId(i)}while(t.hasNode(a));return n.dummy=e,t.setNode(a,n),a}function o(t){return r.max(r.map(t.nodes(),(function(e){var n=t.node(e).rank;if(!r.isUndefined(n))return n})))}t.exports={addDummyNode:a,simplify:function(t){var e=(new i).setGraph(t.graph());return r.forEach(t.nodes(),(function(n){e.setNode(n,t.node(n))})),r.forEach(t.edges(),(function(n){var r=e.edge(n.v,n.w)||{weight:0,minlen:1},i=t.edge(n);e.setEdge(n.v,n.w,{weight:r.weight+i.weight,minlen:Math.max(r.minlen,i.minlen)})})),e},asNonCompoundGraph:function(t){var e=new i({multigraph:t.isMultigraph()}).setGraph(t.graph());return r.forEach(t.nodes(),(function(n){t.children(n).length||e.setNode(n,t.node(n))})),r.forEach(t.edges(),(function(n){e.setEdge(n,t.edge(n))})),e},successorWeights:function(t){var e=r.map(t.nodes(),(function(e){var n={};return r.forEach(t.outEdges(e),(function(e){n[e.w]=(n[e.w]||0)+t.edge(e).weight})),n}));return r.zipObject(t.nodes(),e)},predecessorWeights:function(t){var e=r.map(t.nodes(),(function(e){var n={};return r.forEach(t.inEdges(e),(function(e){n[e.v]=(n[e.v]||0)+t.edge(e).weight})),n}));return r.zipObject(t.nodes(),e)},intersectRect:function(t,e){var n,r,i=t.x,a=t.y,o=e.x-i,s=e.y-a,c=t.width/2,u=t.height/2;if(!o&&!s)throw new Error("Not possible to find intersection inside of the rectangle");Math.abs(s)*c>Math.abs(o)*u?(s<0&&(u=-u),n=u*o/s,r=u):(o<0&&(c=-c),n=c,r=c*s/o);return{x:i+n,y:a+r}},buildLayerMatrix:function(t){var e=r.map(r.range(o(t)+1),(function(){return[]}));return r.forEach(t.nodes(),(function(n){var i=t.node(n),a=i.rank;r.isUndefined(a)||(e[a][i.order]=n)})),e},normalizeRanks:function(t){var e=r.min(r.map(t.nodes(),(function(e){return t.node(e).rank})));r.forEach(t.nodes(),(function(n){var i=t.node(n);r.has(i,"rank")&&(i.rank-=e)}))},removeEmptyRanks:function(t){var e=r.min(r.map(t.nodes(),(function(e){return t.node(e).rank}))),n=[];r.forEach(t.nodes(),(function(r){var i=t.node(r).rank-e;n[i]||(n[i]=[]),n[i].push(r)}));var i=0,a=t.graph().nodeRankFactor;r.forEach(n,(function(e,n){r.isUndefined(e)&&n%a!=0?--i:i&&r.forEach(e,(function(e){t.node(e).rank+=i}))}))},addBorderNode:function(t,e,n,r){var i={width:0,height:0};arguments.length>=4&&(i.rank=n,i.order=r);return a(t,"border",i,e)},maxRank:o,partition:function(t,e){var n={lhs:[],rhs:[]};return r.forEach(t,(function(t){e(t)?n.lhs.push(t):n.rhs.push(t)})),n},time:function(t,e){var n=r.now();try{return e()}finally{console.log(t+" time: "+(r.now()-n)+"ms")}},notime:function(t,e){return e()}}},function(t,e,n){"use strict";Object.defineProperty(e,"__esModule",{value:!0});var r=n(173),i=n(174),a=n(175),o={channel:r.default,lang:i.default,unit:a.default};e.default=o},function(t,e,n){var r;try{r={clone:n(199),constant:n(86),each:n(87),filter:n(128),has:n(93),isArray:n(5),isEmpty:n(276),isFunction:n(37),isUndefined:n(139),keys:n(30),map:n(140),reduce:n(142),size:n(279),transform:n(285),union:n(286),values:n(147)}}catch(t){}r||(r=window._),t.exports=r},function(t,e){t.exports=function(t){var e=typeof t;return null!=t&&("object"==e||"function"==e)}},function(t,e,n){var r=n(43);t.exports={isSubgraph:function(t,e){return!!t.children(e).length},edgeToId:function(t){return 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37:this.$={id1:a[s-2],id2:a[s],relation:a[s-1],relationTitle1:"none",relationTitle2:"none"};break;case 38:this.$={id1:a[s-3],id2:a[s],relation:a[s-1],relationTitle1:a[s-2],relationTitle2:"none"};break;case 39:this.$={id1:a[s-3],id2:a[s],relation:a[s-2],relationTitle1:"none",relationTitle2:a[s-1]};break;case 40:this.$={id1:a[s-4],id2:a[s],relation:a[s-2],relationTitle1:a[s-3],relationTitle2:a[s-1]};break;case 41:this.$={type1:a[s-2],type2:a[s],lineType:a[s-1]};break;case 42:this.$={type1:"none",type2:a[s],lineType:a[s-1]};break;case 43:this.$={type1:a[s-1],type2:"none",lineType:a[s]};break;case 44:this.$={type1:"none",type2:"none",lineType:a[s]};break;case 45:this.$=r.relationType.AGGREGATION;break;case 46:this.$=r.relationType.EXTENSION;break;case 47:this.$=r.relationType.COMPOSITION;break;case 48:this.$=r.relationType.DEPENDENCY;break;case 49:this.$=r.lineType.LINE;break;case 50:this.$=r.lineType.DOTTED_LINE;break;case 51:case 57:this.$=a[s-2],r.setClickEvent(a[s-1],a[s]);break;case 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n=new Error(t);throw n.hash=e,n}this.trace(t)},parse:function(t){var e=this,n=[0],r=[],i=[null],a=[],o=this.table,s="",c=0,u=0,l=0,h=2,f=1,d=a.slice.call(arguments,1),p=Object.create(this.lexer),g={yy:{}};for(var y in this.yy)Object.prototype.hasOwnProperty.call(this.yy,y)&&(g.yy[y]=this.yy[y]);p.setInput(t,g.yy),g.yy.lexer=p,g.yy.parser=this,void 0===p.yylloc&&(p.yylloc={});var v=p.yylloc;a.push(v);var m=p.options&&p.options.ranges;function b(){var t;return"number"!=typeof(t=r.pop()||p.lex()||f)&&(t instanceof Array&&(t=(r=t).pop()),t=e.symbols_[t]||t),t}"function"==typeof g.yy.parseError?this.parseError=g.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;for(var x,_,k,w,E,T,C,A,S,M={};;){if(k=n[n.length-1],this.defaultActions[k]?w=this.defaultActions[k]:(null==x&&(x=b()),w=o[k]&&o[k][x]),void 0===w||!w.length||!w[0]){var O="";for(T in S=[],o[k])this.terminals_[T]&&T>h&&S.push("'"+this.terminals_[T]+"'");O=p.showPosition?"Parse error on line 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e=t.length,n=t.split(/(?:\r\n?|\n)/g);this._input=t+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-e),this.offset-=e;var r=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),n.length-1&&(this.yylineno-=n.length-1);var i=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:n?(n.length===r.length?this.yylloc.first_column:0)+r[r.length-n.length].length-n[0].length:this.yylloc.first_column-e},this.options.ranges&&(this.yylloc.range=[i[0],i[0]+this.yyleng-e]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){return this.options.backtrack_lexer?(this._backtrack=!0,this):this.parseError("Lexical error on line "+(this.yylineno+1)+". 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19:this.$={stmt:"state",id:a[s-3],type:"default",description:a[s-5],doc:a[s-1]};break;case 20:this.$={stmt:"state",id:a[s],type:"fork"};break;case 21:this.$={stmt:"state",id:a[s],type:"join"};break;case 22:this.$={stmt:"state",id:r.getDividerId(),type:"divider"};break;case 23:this.$={stmt:"state",id:a[s-1].trim(),note:{position:a[s-2].trim(),text:a[s].trim()}};break;case 30:case 31:this.$=a[s];break;case 34:r.parseDirective("%%{","open_directive");break;case 35:r.parseDirective(a[s],"type_directive");break;case 36:a[s]=a[s].trim().replace(/'/g,'"'),r.parseDirective(a[s],"arg_directive");break;case 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n=new Error(t);throw n.hash=e,n}this.trace(t)},parse:function(t){var e=this,n=[0],r=[],i=[null],a=[],o=this.table,s="",c=0,u=0,l=0,h=2,f=1,d=a.slice.call(arguments,1),p=Object.create(this.lexer),g={yy:{}};for(var y in this.yy)Object.prototype.hasOwnProperty.call(this.yy,y)&&(g.yy[y]=this.yy[y]);p.setInput(t,g.yy),g.yy.lexer=p,g.yy.parser=this,void 0===p.yylloc&&(p.yylloc={});var v=p.yylloc;a.push(v);var m=p.options&&p.options.ranges;function b(){var t;return"number"!=typeof(t=r.pop()||p.lex()||f)&&(t instanceof Array&&(t=(r=t).pop()),t=e.symbols_[t]||t),t}"function"==typeof g.yy.parseError?this.parseError=g.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;for(var x,_,k,w,E,T,C,A,S,M={};;){if(k=n[n.length-1],this.defaultActions[k]?w=this.defaultActions[k]:(null==x&&(x=b()),w=o[k]&&o[k][x]),void 0===w||!w.length||!w[0]){var O="";for(T in S=[],o[k])this.terminals_[T]&&T>h&&S.push("'"+this.terminals_[T]+"'");O=p.showPosition?"Parse error on line 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e=t.length,n=t.split(/(?:\r\n?|\n)/g);this._input=t+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-e),this.offset-=e;var r=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),n.length-1&&(this.yylineno-=n.length-1);var i=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:n?(n.length===r.length?this.yylloc.first_column:0)+r[r.length-n.length].length-n[0].length:this.yylloc.first_column-e},this.options.ranges&&(this.yylloc.range=[i[0],i[0]+this.yyleng-e]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){return this.options.backtrack_lexer?(this._backtrack=!0,this):this.parseError("Lexical error on line "+(this.yylineno+1)+". 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4:return 41;case 5:break;case 6:console.log("Crap after close");break;case 7:return 5;case 8:case 9:case 10:case 11:break;case 12:return this.pushState("SCALE"),15;case 13:return 16;case 14:this.popState();break;case 15:this.pushState("STATE");break;case 16:return this.popState(),e.yytext=e.yytext.slice(0,-8).trim(),23;case 17:return this.popState(),e.yytext=e.yytext.slice(0,-8).trim(),24;case 18:return this.popState(),e.yytext=e.yytext.slice(0,-8).trim(),23;case 19:return this.popState(),e.yytext=e.yytext.slice(0,-8).trim(),24;case 20:this.begin("STATE_STRING");break;case 21:return this.popState(),this.pushState("STATE_ID"),"AS";case 22:return this.popState(),"ID";case 23:this.popState();break;case 24:return"STATE_DESCR";case 25:return 17;case 26:this.popState();break;case 27:return this.popState(),this.pushState("struct"),18;case 28:return this.popState(),19;case 29:break;case 30:return this.begin("NOTE"),26;case 31:return this.popState(),this.pushState("NOTE_ID"),37;case 32:return this.popState(),this.pushState("NOTE_ID"),38;case 33:this.popState(),this.pushState("FLOATING_NOTE");break;case 34:return this.popState(),this.pushState("FLOATING_NOTE_ID"),"AS";case 35:break;case 36:return"NOTE_TEXT";case 37:return this.popState(),"ID";case 38:return this.popState(),this.pushState("NOTE_TEXT"),22;case 39:return this.popState(),e.yytext=e.yytext.substr(2).trim(),28;case 40:return this.popState(),e.yytext=e.yytext.slice(0,-8).trim(),28;case 41:case 42:return 7;case 43:return 14;case 44:return 36;case 45:return 22;case 46:return e.yytext=e.yytext.trim(),12;case 47:return 13;case 48:return 25;case 49:return 5;case 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64:this.$=a[s-3],r.addVertex(a[s-3],a[s-1],"trapezoid");break;case 65:this.$=a[s-3],r.addVertex(a[s-3],a[s-1],"inv_trapezoid");break;case 66:this.$=a[s-3],r.addVertex(a[s-3],a[s-1],"lean_right");break;case 67:this.$=a[s-3],r.addVertex(a[s-3],a[s-1],"lean_left");break;case 68:this.$=a[s],r.addVertex(a[s]);break;case 69:a[s-1].text=a[s],this.$=a[s-1];break;case 70:case 71:a[s-2].text=a[s-1],this.$=a[s-2];break;case 72:this.$=a[s];break;case 73:var c=r.destructLink(a[s],a[s-2]);this.$={type:c.type,stroke:c.stroke,length:c.length,text:a[s-1]};break;case 74:c=r.destructLink(a[s]);this.$={type:c.type,stroke:c.stroke,length:c.length};break;case 75:this.$=a[s-1];break;case 77:case 91:case 147:this.$=a[s-1]+""+a[s];break;case 92:case 93:this.$=a[s-4],r.addClass(a[s-2],a[s]);break;case 94:this.$=a[s-4],r.setClass(a[s-2],a[s]);break;case 95:case 103:this.$=a[s-1],r.setClickEvent(a[s-1],a[s]);break;case 96:case 104:this.$=a[s-3],r.setClickEvent(a[s-3],a[s-2]),r.setTooltip(a[s-3],a[s]);break;case 97:this.$=a[s-2],r.setClickEvent(a[s-2],a[s-1],a[s]);break;case 98:this.$=a[s-4],r.setClickEvent(a[s-4],a[s-3],a[s-2]),r.setTooltip(a[s-4],a[s]);break;case 99:case 105:this.$=a[s-1],r.setLink(a[s-1],a[s]);break;case 100:case 106:this.$=a[s-3],r.setLink(a[s-3],a[s-2]),r.setTooltip(a[s-3],a[s]);break;case 101:case 107:this.$=a[s-3],r.setLink(a[s-3],a[s-2],a[s]);break;case 102:case 108:this.$=a[s-5],r.setLink(a[s-5],a[s-4],a[s]),r.setTooltip(a[s-5],a[s-2]);break;case 109:this.$=a[s-4],r.addVertex(a[s-2],void 0,void 0,a[s]);break;case 110:case 112:this.$=a[s-4],r.updateLink(a[s-2],a[s]);break;case 111:this.$=a[s-4],r.updateLink([a[s-2]],a[s]);break;case 113:this.$=a[s-8],r.updateLinkInterpolate([a[s-6]],a[s-2]),r.updateLink([a[s-6]],a[s]);break;case 114:this.$=a[s-8],r.updateLinkInterpolate(a[s-6],a[s-2]),r.updateLink(a[s-6],a[s]);break;case 115:this.$=a[s-6],r.updateLinkInterpolate([a[s-4]],a[s]);break;case 116:this.$=a[s-6],r.updateLinkInterpolate(a[s-4],a[s]);break;case 117:case 119:this.$=[a[s]];break;case 118:case 120:a[s-2].push(a[s]),this.$=a[s-2];break;case 122:this.$=a[s-1]+a[s];break;case 144:this.$=a[s];break;case 145:this.$=a[s-1]+""+a[s];break;case 150:this.$="v";break;case 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"+(c+1)+":\n"+p.showPosition()+"\nExpecting "+S.join(", ")+", got '"+(this.terminals_[x]||x)+"'":"Parse error on line "+(c+1)+": Unexpected "+(x==f?"end of input":"'"+(this.terminals_[x]||x)+"'"),this.parseError(O,{text:p.match,token:this.terminals_[x]||x,line:p.yylineno,loc:v,expected:S})}if(w[0]instanceof Array&&w.length>1)throw new Error("Parse Error: multiple actions possible at state: "+k+", token: "+x);switch(w[0]){case 1:n.push(x),i.push(p.yytext),a.push(p.yylloc),n.push(w[1]),x=null,_?(x=_,_=null):(u=p.yyleng,s=p.yytext,c=p.yylineno,v=p.yylloc,l>0&&l--);break;case 2:if(C=this.productions_[w[1]][1],M.$=i[i.length-C],M._$={first_line:a[a.length-(C||1)].first_line,last_line:a[a.length-1].last_line,first_column:a[a.length-(C||1)].first_column,last_column:a[a.length-1].last_column},m&&(M._$.range=[a[a.length-(C||1)].range[0],a[a.length-1].range[1]]),void 0!==(E=this.performAction.apply(M,[s,u,c,g.yy,w[1],i,a].concat(d))))return E;C&&(n=n.slice(0,-1*C*2),i=i.slice(0,-1*C),a=a.slice(0,-1*C)),n.push(this.productions_[w[1]][0]),i.push(M.$),a.push(M._$),A=o[n[n.length-2]][n[n.length-1]],n.push(A);break;case 3:return!0}}return!0}},qt={EOF:1,parseError:function(t,e){if(!this.yy.parser)throw new Error(t);this.yy.parser.parseError(t,e)},setInput:function(t,e){return this.yy=e||this.yy||{},this._input=t,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var t=this._input[0];return this.yytext+=t,this.yyleng++,this.offset++,this.match+=t,this.matched+=t,t.match(/(?:\r\n?|\n).*/g)?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),t},unput:function(t){var e=t.length,n=t.split(/(?:\r\n?|\n)/g);this._input=t+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-e),this.offset-=e;var r=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),n.length-1&&(this.yylineno-=n.length-1);var i=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:n?(n.length===r.length?this.yylloc.first_column:0)+r[r.length-n.length].length-n[0].length:this.yylloc.first_column-e},this.options.ranges&&(this.yylloc.range=[i[0],i[0]+this.yyleng-e]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){return this.options.backtrack_lexer?(this._backtrack=!0,this):this.parseError("Lexical error on line "+(this.yylineno+1)+". You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},less:function(t){this.unput(this.match.slice(t))},pastInput:function(){var t=this.matched.substr(0,this.matched.length-this.match.length);return(t.length>20?"...":"")+t.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var t=this.match;return t.length<20&&(t+=this._input.substr(0,20-t.length)),(t.substr(0,20)+(t.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var t=this.pastInput(),e=new Array(t.length+1).join("-");return t+this.upcomingInput()+"\n"+e+"^"},test_match:function(t,e){var n,r,i;if(this.options.backtrack_lexer&&(i={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(i.yylloc.range=this.yylloc.range.slice(0))),(r=t[0].match(/(?:\r\n?|\n).*/g))&&(this.yylineno+=r.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:r?r[r.length-1].length-r[r.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+t[0].length},this.yytext+=t[0],this.match+=t[0],this.matches=t,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(t[0].length),this.matched+=t[0],n=this.performAction.call(this,this.yy,this,e,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),n)return n;if(this._backtrack){for(var a in i)this[a]=i[a];return!1}return!1},next:function(){if(this.done)return this.EOF;var t,e,n,r;this._input||(this.done=!0),this._more||(this.yytext="",this.match="");for(var i=this._currentRules(),a=0;a<i.length;a++)if((n=this._input.match(this.rules[i[a]]))&&(!e||n[0].length>e[0].length)){if(e=n,r=a,this.options.backtrack_lexer){if(!1!==(t=this.test_match(n,i[a])))return t;if(this._backtrack){e=!1;continue}return!1}if(!this.options.flex)break}return e?!1!==(t=this.test_match(e,i[r]))&&t:""===this._input?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+". Unrecognized text.\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var t=this.next();return t||this.lex()},begin:function(t){this.conditionStack.push(t)},popState:function(){return this.conditionStack.length-1>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(t){return(t=this.conditionStack.length-1-Math.abs(t||0))>=0?this.conditionStack[t]:"INITIAL"},pushState:function(t){this.begin(t)},stateStackSize:function(){return this.conditionStack.length},options:{},performAction:function(t,e,n,r){switch(n){case 0:return this.begin("open_directive"),12;case 1:return this.begin("type_directive"),13;case 2:return this.popState(),this.begin("arg_directive"),10;case 3:return this.popState(),this.popState(),15;case 4:return 14;case 5:case 6:break;case 7:this.begin("string");break;case 8:this.popState();break;case 9:return"STR";case 10:return 75;case 11:return 84;case 12:return 76;case 13:return 93;case 14:return 77;case 15:return 78;case 16:this.begin("href");break;case 17:this.popState();break;case 18:return 89;case 19:this.begin("callbackname");break;case 20:this.popState();break;case 21:this.popState(),this.begin("callbackargs");break;case 22:return 87;case 23:this.popState();break;case 24:return 88;case 25:this.begin("click");break;case 26:this.popState();break;case 27:return 79;case 28:case 29:return t.lex.firstGraph()&&this.begin("dir"),24;case 30:return 38;case 31:return 42;case 32:case 33:case 34:case 35:return 90;case 36:return this.popState(),25;case 37:case 38:case 39:case 40:case 41:case 42:case 43:case 44:case 45:case 46:return this.popState(),26;case 47:return 94;case 48:return 102;case 49:return 47;case 50:return 99;case 51:return 46;case 52:return 20;case 53:return 95;case 54:return 113;case 55:case 56:case 57:return 70;case 58:case 59:case 60:return 69;case 61:return 51;case 62:return 52;case 63:return 53;case 64:return 54;case 65:return 55;case 66:return 56;case 67:return 57;case 68:return 58;case 69:return 100;case 70:return 103;case 71:return 114;case 72:return 111;case 73:return 104;case 74:case 75:return 112;case 76:return 105;case 77:return 61;case 78:return 81;case 79:return"SEP";case 80:return 80;case 81:return 98;case 82:return 63;case 83:return 62;case 84:return 65;case 85:return 64;case 86:return 109;case 87:return 110;case 88:return 71;case 89:return 49;case 90:return 50;case 91:return 40;case 92:return 41;case 93:return 59;case 94:return 60;case 95:return 120;case 96:return 21;case 97:return 22;case 98:return 23}},rules:[/^(?:%%\{)/,/^(?:((?:(?!\}%%)[^:.])*))/,/^(?::)/,/^(?:\}%%)/,/^(?:((?:(?!\}%%).|\n)*))/,/^(?:%%(?!\{)[^\n]*)/,/^(?:[^\}]%%[^\n]*)/,/^(?:["])/,/^(?:["])/,/^(?:[^"]*)/,/^(?:style\b)/,/^(?:default\b)/,/^(?:linkStyle\b)/,/^(?:interpolate\b)/,/^(?:classDef\b)/,/^(?:class\b)/,/^(?:href[\s]+["])/,/^(?:["])/,/^(?:[^"]*)/,/^(?:call[\s]+)/,/^(?:\([\s]*\))/,/^(?:\()/,/^(?:[^(]*)/,/^(?:\))/,/^(?:[^)]*)/,/^(?:click[\s]+)/,/^(?:[\s\n])/,/^(?:[^\s\n]*)/,/^(?:graph\b)/,/^(?:flowchart\b)/,/^(?:subgraph\b)/,/^(?:end\b\s*)/,/^(?:_self\b)/,/^(?:_blank\b)/,/^(?:_parent\b)/,/^(?:_top\b)/,/^(?:(\r?\n)*\s*\n)/,/^(?:\s*LR\b)/,/^(?:\s*RL\b)/,/^(?:\s*TB\b)/,/^(?:\s*BT\b)/,/^(?:\s*TD\b)/,/^(?:\s*BR\b)/,/^(?:\s*<)/,/^(?:\s*>)/,/^(?:\s*\^)/,/^(?:\s*v\b)/,/^(?:[0-9]+)/,/^(?:#)/,/^(?::::)/,/^(?::)/,/^(?:&)/,/^(?:;)/,/^(?:,)/,/^(?:\*)/,/^(?:\s*[xo<]?--+[-xo>]\s*)/,/^(?:\s*[xo<]?==+[=xo>]\s*)/,/^(?:\s*[xo<]?-?\.+-[xo>]?\s*)/,/^(?:\s*[xo<]?--\s*)/,/^(?:\s*[xo<]?==\s*)/,/^(?:\s*[xo<]?-\.\s*)/,/^(?:\(-)/,/^(?:-\))/,/^(?:\(\[)/,/^(?:\]\))/,/^(?:\[\[)/,/^(?:\]\])/,/^(?:\[\()/,/^(?:\)\])/,/^(?:-)/,/^(?:\.)/,/^(?:[\_])/,/^(?:\+)/,/^(?:%)/,/^(?:=)/,/^(?:=)/,/^(?:<)/,/^(?:>)/,/^(?:\^)/,/^(?:\\\|)/,/^(?:v\b)/,/^(?:[A-Za-z]+)/,/^(?:\\\])/,/^(?:\[\/)/,/^(?:\/\])/,/^(?:\[\\)/,/^(?:[!"#$%&'*+,-.`?\\_/])/,/^(?:[\u00AA\u00B5\u00BA\u00C0-\u00D6\u00D8-\u00F6]|[\u00F8-\u02C1\u02C6-\u02D1\u02E0-\u02E4\u02EC\u02EE\u0370-\u0374\u0376\u0377]|[\u037A-\u037D\u0386\u0388-\u038A\u038C\u038E-\u03A1\u03A3-\u03F5]|[\u03F7-\u0481\u048A-\u0527\u0531-\u0556\u0559\u0561-\u0587\u05D0-\u05EA]|[\u05F0-\u05F2\u0620-\u064A\u066E\u066F\u0671-\u06D3\u06D5\u06E5\u06E6\u06EE]|[\u06EF\u06FA-\u06FC\u06FF\u0710\u0712-\u072F\u074D-\u07A5\u07B1\u07CA-\u07EA]|[\u07F4\u07F5\u07FA\u0800-\u0815\u081A\u0824\u0828\u0840-\u0858\u08A0]|[\u08A2-\u08AC\u0904-\u0939\u093D\u0950\u0958-\u0961\u0971-\u0977]|[\u0979-\u097F\u0985-\u098C\u098F\u0990\u0993-\u09A8\u09AA-\u09B0\u09B2]|[\u09B6-\u09B9\u09BD\u09CE\u09DC\u09DD\u09DF-\u09E1\u09F0\u09F1\u0A05-\u0A0A]|[\u0A0F\u0A10\u0A13-\u0A28\u0A2A-\u0A30\u0A32\u0A33\u0A35\u0A36\u0A38\u0A39]|[\u0A59-\u0A5C\u0A5E\u0A72-\u0A74\u0A85-\u0A8D\u0A8F-\u0A91\u0A93-\u0AA8]|[\u0AAA-\u0AB0\u0AB2\u0AB3\u0AB5-\u0AB9\u0ABD\u0AD0\u0AE0\u0AE1\u0B05-\u0B0C]|[\u0B0F\u0B10\u0B13-\u0B28\u0B2A-\u0B30\u0B32\u0B33\u0B35-\u0B39\u0B3D\u0B5C]|[\u0B5D\u0B5F-\u0B61\u0B71\u0B83\u0B85-\u0B8A\u0B8E-\u0B90\u0B92-\u0B95\u0B99]|[\u0B9A\u0B9C\u0B9E\u0B9F\u0BA3\u0BA4\u0BA8-\u0BAA\u0BAE-\u0BB9\u0BD0]|[\u0C05-\u0C0C\u0C0E-\u0C10\u0C12-\u0C28\u0C2A-\u0C33\u0C35-\u0C39\u0C3D]|[\u0C58\u0C59\u0C60\u0C61\u0C85-\u0C8C\u0C8E-\u0C90\u0C92-\u0CA8\u0CAA-\u0CB3]|[\u0CB5-\u0CB9\u0CBD\u0CDE\u0CE0\u0CE1\u0CF1\u0CF2\u0D05-\u0D0C\u0D0E-\u0D10]|[\u0D12-\u0D3A\u0D3D\u0D4E\u0D60\u0D61\u0D7A-\u0D7F\u0D85-\u0D96\u0D9A-\u0DB1]|[\u0DB3-\u0DBB\u0DBD\u0DC0-\u0DC6\u0E01-\u0E30\u0E32\u0E33\u0E40-\u0E46\u0E81]|[\u0E82\u0E84\u0E87\u0E88\u0E8A\u0E8D\u0E94-\u0E97\u0E99-\u0E9F\u0EA1-\u0EA3]|[\u0EA5\u0EA7\u0EAA\u0EAB\u0EAD-\u0EB0\u0EB2\u0EB3\u0EBD\u0EC0-\u0EC4\u0EC6]|[\u0EDC-\u0EDF\u0F00\u0F40-\u0F47\u0F49-\u0F6C\u0F88-\u0F8C\u1000-\u102A]|[\u103F\u1050-\u1055\u105A-\u105D\u1061\u1065\u1066\u106E-\u1070\u1075-\u1081]|[\u108E\u10A0-\u10C5\u10C7\u10CD\u10D0-\u10FA\u10FC-\u1248\u124A-\u124D]|[\u1250-\u1256\u1258\u125A-\u125D\u1260-\u1288\u128A-\u128D\u1290-\u12B0]|[\u12B2-\u12B5\u12B8-\u12BE\u12C0\u12C2-\u12C5\u12C8-\u12D6\u12D8-\u1310]|[\u1312-\u1315\u1318-\u135A\u1380-\u138F\u13A0-\u13F4\u1401-\u166C]|[\u166F-\u167F\u1681-\u169A\u16A0-\u16EA\u1700-\u170C\u170E-\u1711]|[\u1720-\u1731\u1740-\u1751\u1760-\u176C\u176E-\u1770\u1780-\u17B3\u17D7]|[\u17DC\u1820-\u1877\u1880-\u18A8\u18AA\u18B0-\u18F5\u1900-\u191C]|[\u1950-\u196D\u1970-\u1974\u1980-\u19AB\u19C1-\u19C7\u1A00-\u1A16]|[\u1A20-\u1A54\u1AA7\u1B05-\u1B33\u1B45-\u1B4B\u1B83-\u1BA0\u1BAE\u1BAF]|[\u1BBA-\u1BE5\u1C00-\u1C23\u1C4D-\u1C4F\u1C5A-\u1C7D\u1CE9-\u1CEC]|[\u1CEE-\u1CF1\u1CF5\u1CF6\u1D00-\u1DBF\u1E00-\u1F15\u1F18-\u1F1D]|[\u1F20-\u1F45\u1F48-\u1F4D\u1F50-\u1F57\u1F59\u1F5B\u1F5D\u1F5F-\u1F7D]|[\u1F80-\u1FB4\u1FB6-\u1FBC\u1FBE\u1FC2-\u1FC4\u1FC6-\u1FCC\u1FD0-\u1FD3]|[\u1FD6-\u1FDB\u1FE0-\u1FEC\u1FF2-\u1FF4\u1FF6-\u1FFC\u2071\u207F]|[\u2090-\u209C\u2102\u2107\u210A-\u2113\u2115\u2119-\u211D\u2124\u2126\u2128]|[\u212A-\u212D\u212F-\u2139\u213C-\u213F\u2145-\u2149\u214E\u2183\u2184]|[\u2C00-\u2C2E\u2C30-\u2C5E\u2C60-\u2CE4\u2CEB-\u2CEE\u2CF2\u2CF3]|[\u2D00-\u2D25\u2D27\u2D2D\u2D30-\u2D67\u2D6F\u2D80-\u2D96\u2DA0-\u2DA6]|[\u2DA8-\u2DAE\u2DB0-\u2DB6\u2DB8-\u2DBE\u2DC0-\u2DC6\u2DC8-\u2DCE]|[\u2DD0-\u2DD6\u2DD8-\u2DDE\u2E2F\u3005\u3006\u3031-\u3035\u303B\u303C]|[\u3041-\u3096\u309D-\u309F\u30A1-\u30FA\u30FC-\u30FF\u3105-\u312D]|[\u3131-\u318E\u31A0-\u31BA\u31F0-\u31FF\u3400-\u4DB5\u4E00-\u9FCC]|[\uA000-\uA48C\uA4D0-\uA4FD\uA500-\uA60C\uA610-\uA61F\uA62A\uA62B]|[\uA640-\uA66E\uA67F-\uA697\uA6A0-\uA6E5\uA717-\uA71F\uA722-\uA788]|[\uA78B-\uA78E\uA790-\uA793\uA7A0-\uA7AA\uA7F8-\uA801\uA803-\uA805]|[\uA807-\uA80A\uA80C-\uA822\uA840-\uA873\uA882-\uA8B3\uA8F2-\uA8F7\uA8FB]|[\uA90A-\uA925\uA930-\uA946\uA960-\uA97C\uA984-\uA9B2\uA9CF\uAA00-\uAA28]|[\uAA40-\uAA42\uAA44-\uAA4B\uAA60-\uAA76\uAA7A\uAA80-\uAAAF\uAAB1\uAAB5]|[\uAAB6\uAAB9-\uAABD\uAAC0\uAAC2\uAADB-\uAADD\uAAE0-\uAAEA\uAAF2-\uAAF4]|[\uAB01-\uAB06\uAB09-\uAB0E\uAB11-\uAB16\uAB20-\uAB26\uAB28-\uAB2E]|[\uABC0-\uABE2\uAC00-\uD7A3\uD7B0-\uD7C6\uD7CB-\uD7FB\uF900-\uFA6D]|[\uFA70-\uFAD9\uFB00-\uFB06\uFB13-\uFB17\uFB1D\uFB1F-\uFB28\uFB2A-\uFB36]|[\uFB38-\uFB3C\uFB3E\uFB40\uFB41\uFB43\uFB44\uFB46-\uFBB1\uFBD3-\uFD3D]|[\uFD50-\uFD8F\uFD92-\uFDC7\uFDF0-\uFDFB\uFE70-\uFE74\uFE76-\uFEFC]|[\uFF21-\uFF3A\uFF41-\uFF5A\uFF66-\uFFBE\uFFC2-\uFFC7\uFFCA-\uFFCF]|[\uFFD2-\uFFD7\uFFDA-\uFFDC])/,/^(?:\|)/,/^(?:\()/,/^(?:\))/,/^(?:\[)/,/^(?:\])/,/^(?:\{)/,/^(?:\})/,/^(?:")/,/^(?:(\r?\n)+)/,/^(?:\s)/,/^(?:$)/],conditions:{close_directive:{rules:[],inclusive:!1},arg_directive:{rules:[3,4],inclusive:!1},type_directive:{rules:[2,3],inclusive:!1},open_directive:{rules:[1],inclusive:!1},callbackargs:{rules:[23,24],inclusive:!1},callbackname:{rules:[20,21,22],inclusive:!1},href:{rules:[17,18],inclusive:!1},click:{rules:[26,27],inclusive:!1},vertex:{rules:[],inclusive:!1},dir:{rules:[36,37,38,39,40,41,42,43,44,45,46],inclusive:!1},string:{rules:[8,9],inclusive:!1},INITIAL:{rules:[0,5,6,7,10,11,12,13,14,15,16,19,25,28,29,30,31,32,33,34,35,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98],inclusive:!0}}};function Xt(){this.yy={}}return Gt.lexer=qt,Xt.prototype=Gt,Gt.Parser=Xt,new Xt}();e.parser=i,e.Parser=i.Parser,e.parse=function(){return i.parse.apply(i,arguments)},e.main=function(r){r[1]||(console.log("Usage: "+r[0]+" FILE"),t.exit(1));var i=n(19).readFileSync(n(20).normalize(r[1]),"utf8");return e.parser.parse(i)},n.c[n.s]===r&&e.main(t.argv.slice(1))}).call(this,n(14),n(7)(t))},function(t,e,n){(function(t,r){var i=function(){var t=function(t,e,n,r){for(n=n||{},r=t.length;r--;n[t[r]]=e);return n},e=[1,3],n=[1,5],r=[7,9,11,12,13,14,15,16,17,18,20,27,32],i=[1,15],a=[1,16],o=[1,17],s=[1,18],c=[1,19],u=[1,20],l=[1,21],h=[1,23],f=[1,25],d=[1,28],p=[5,7,9,11,12,13,14,15,16,17,18,20,27,32],g={trace:function(){},yy:{},symbols_:{error:2,start:3,directive:4,gantt:5,document:6,EOF:7,line:8,SPACE:9,statement:10,NL:11,dateFormat:12,inclusiveEndDates:13,axisFormat:14,excludes:15,todayMarker:16,title:17,section:18,clickStatement:19,taskTxt:20,taskData:21,openDirective:22,typeDirective:23,closeDirective:24,":":25,argDirective:26,click:27,callbackname:28,callbackargs:29,href:30,clickStatementDebug:31,open_directive:32,type_directive:33,arg_directive:34,close_directive:35,$accept:0,$end:1},terminals_:{2:"error",5:"gantt",7:"EOF",9:"SPACE",11:"NL",12:"dateFormat",13:"inclusiveEndDates",14:"axisFormat",15:"excludes",16:"todayMarker",17:"title",18:"section",20:"taskTxt",21:"taskData",25:":",27:"click",28:"callbackname",29:"callbackargs",30:"href",32:"open_directive",33:"type_directive",34:"arg_directive",35:"close_directive"},productions_:[0,[3,2],[3,3],[6,0],[6,2],[8,2],[8,1],[8,1],[8,1],[10,1],[10,1],[10,1],[10,1],[10,1],[10,1],[10,1],[10,1],[10,2],[10,1],[4,4],[4,6],[19,2],[19,3],[19,3],[19,4],[19,3],[19,4],[19,2],[31,2],[31,3],[31,3],[31,4],[31,3],[31,4],[31,2],[22,1],[23,1],[26,1],[24,1]],performAction:function(t,e,n,r,i,a,o){var s=a.length-1;switch(i){case 2:return a[s-1];case 3:this.$=[];break;case 4:a[s-1].push(a[s]),this.$=a[s-1];break;case 5:case 6:this.$=a[s];break;case 7:case 8:this.$=[];break;case 9:r.setDateFormat(a[s].substr(11)),this.$=a[s].substr(11);break;case 10:r.enableInclusiveEndDates(),this.$=a[s].substr(18);break;case 11:r.setAxisFormat(a[s].substr(11)),this.$=a[s].substr(11);break;case 12:r.setExcludes(a[s].substr(9)),this.$=a[s].substr(9);break;case 13:r.setTodayMarker(a[s].substr(12)),this.$=a[s].substr(12);break;case 14:r.setTitle(a[s].substr(6)),this.$=a[s].substr(6);break;case 15:r.addSection(a[s].substr(8)),this.$=a[s].substr(8);break;case 17:r.addTask(a[s-1],a[s]),this.$="task";break;case 21:this.$=a[s-1],r.setClickEvent(a[s-1],a[s],null);break;case 22:this.$=a[s-2],r.setClickEvent(a[s-2],a[s-1],a[s]);break;case 23:this.$=a[s-2],r.setClickEvent(a[s-2],a[s-1],null),r.setLink(a[s-2],a[s]);break;case 24:this.$=a[s-3],r.setClickEvent(a[s-3],a[s-2],a[s-1]),r.setLink(a[s-3],a[s]);break;case 25:this.$=a[s-2],r.setClickEvent(a[s-2],a[s],null),r.setLink(a[s-2],a[s-1]);break;case 26:this.$=a[s-3],r.setClickEvent(a[s-3],a[s-1],a[s]),r.setLink(a[s-3],a[s-2]);break;case 27:this.$=a[s-1],r.setLink(a[s-1],a[s]);break;case 28:case 34:this.$=a[s-1]+" "+a[s];break;case 29:case 30:case 32:this.$=a[s-2]+" "+a[s-1]+" "+a[s];break;case 31:case 33:this.$=a[s-3]+" "+a[s-2]+" "+a[s-1]+" "+a[s];break;case 35:r.parseDirective("%%{","open_directive");break;case 36:r.parseDirective(a[s],"type_directive");break;case 37:a[s]=a[s].trim().replace(/'/g,'"'),r.parseDirective(a[s],"arg_directive");break;case 38:r.parseDirective("}%%","close_directive","gantt")}},table:[{3:1,4:2,5:e,22:4,32:n},{1:[3]},{3:6,4:2,5:e,22:4,32:n},t(r,[2,3],{6:7}),{23:8,33:[1,9]},{33:[2,35]},{1:[2,1]},{4:24,7:[1,10],8:11,9:[1,12],10:13,11:[1,14],12:i,13:a,14:o,15:s,16:c,17:u,18:l,19:22,20:h,22:4,27:f,32:n},{24:26,25:[1,27],35:d},t([25,35],[2,36]),t(r,[2,8],{1:[2,2]}),t(r,[2,4]),{4:24,10:29,12:i,13:a,14:o,15:s,16:c,17:u,18:l,19:22,20:h,22:4,27:f,32:n},t(r,[2,6]),t(r,[2,7]),t(r,[2,9]),t(r,[2,10]),t(r,[2,11]),t(r,[2,12]),t(r,[2,13]),t(r,[2,14]),t(r,[2,15]),t(r,[2,16]),{21:[1,30]},t(r,[2,18]),{28:[1,31],30:[1,32]},{11:[1,33]},{26:34,34:[1,35]},{11:[2,38]},t(r,[2,5]),t(r,[2,17]),t(r,[2,21],{29:[1,36],30:[1,37]}),t(r,[2,27],{28:[1,38]}),t(p,[2,19]),{24:39,35:d},{35:[2,37]},t(r,[2,22],{30:[1,40]}),t(r,[2,23]),t(r,[2,25],{29:[1,41]}),{11:[1,42]},t(r,[2,24]),t(r,[2,26]),t(p,[2,20])],defaultActions:{5:[2,35],6:[2,1],28:[2,38],35:[2,37]},parseError:function(t,e){if(!e.recoverable){var n=new Error(t);throw n.hash=e,n}this.trace(t)},parse:function(t){var e=this,n=[0],r=[],i=[null],a=[],o=this.table,s="",c=0,u=0,l=0,h=2,f=1,d=a.slice.call(arguments,1),p=Object.create(this.lexer),g={yy:{}};for(var y in this.yy)Object.prototype.hasOwnProperty.call(this.yy,y)&&(g.yy[y]=this.yy[y]);p.setInput(t,g.yy),g.yy.lexer=p,g.yy.parser=this,void 0===p.yylloc&&(p.yylloc={});var v=p.yylloc;a.push(v);var m=p.options&&p.options.ranges;function b(){var t;return"number"!=typeof(t=r.pop()||p.lex()||f)&&(t instanceof Array&&(t=(r=t).pop()),t=e.symbols_[t]||t),t}"function"==typeof g.yy.parseError?this.parseError=g.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;for(var x,_,k,w,E,T,C,A,S,M={};;){if(k=n[n.length-1],this.defaultActions[k]?w=this.defaultActions[k]:(null==x&&(x=b()),w=o[k]&&o[k][x]),void 0===w||!w.length||!w[0]){var O="";for(T in S=[],o[k])this.terminals_[T]&&T>h&&S.push("'"+this.terminals_[T]+"'");O=p.showPosition?"Parse error on line "+(c+1)+":\n"+p.showPosition()+"\nExpecting "+S.join(", ")+", got '"+(this.terminals_[x]||x)+"'":"Parse error on line "+(c+1)+": Unexpected "+(x==f?"end of input":"'"+(this.terminals_[x]||x)+"'"),this.parseError(O,{text:p.match,token:this.terminals_[x]||x,line:p.yylineno,loc:v,expected:S})}if(w[0]instanceof Array&&w.length>1)throw new Error("Parse Error: multiple actions possible at state: "+k+", token: "+x);switch(w[0]){case 1:n.push(x),i.push(p.yytext),a.push(p.yylloc),n.push(w[1]),x=null,_?(x=_,_=null):(u=p.yyleng,s=p.yytext,c=p.yylineno,v=p.yylloc,l>0&&l--);break;case 2:if(C=this.productions_[w[1]][1],M.$=i[i.length-C],M._$={first_line:a[a.length-(C||1)].first_line,last_line:a[a.length-1].last_line,first_column:a[a.length-(C||1)].first_column,last_column:a[a.length-1].last_column},m&&(M._$.range=[a[a.length-(C||1)].range[0],a[a.length-1].range[1]]),void 0!==(E=this.performAction.apply(M,[s,u,c,g.yy,w[1],i,a].concat(d))))return E;C&&(n=n.slice(0,-1*C*2),i=i.slice(0,-1*C),a=a.slice(0,-1*C)),n.push(this.productions_[w[1]][0]),i.push(M.$),a.push(M._$),A=o[n[n.length-2]][n[n.length-1]],n.push(A);break;case 3:return!0}}return!0}},y={EOF:1,parseError:function(t,e){if(!this.yy.parser)throw new Error(t);this.yy.parser.parseError(t,e)},setInput:function(t,e){return this.yy=e||this.yy||{},this._input=t,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var t=this._input[0];return this.yytext+=t,this.yyleng++,this.offset++,this.match+=t,this.matched+=t,t.match(/(?:\r\n?|\n).*/g)?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),t},unput:function(t){var e=t.length,n=t.split(/(?:\r\n?|\n)/g);this._input=t+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-e),this.offset-=e;var r=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),n.length-1&&(this.yylineno-=n.length-1);var i=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:n?(n.length===r.length?this.yylloc.first_column:0)+r[r.length-n.length].length-n[0].length:this.yylloc.first_column-e},this.options.ranges&&(this.yylloc.range=[i[0],i[0]+this.yyleng-e]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){return this.options.backtrack_lexer?(this._backtrack=!0,this):this.parseError("Lexical error on line "+(this.yylineno+1)+". You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},less:function(t){this.unput(this.match.slice(t))},pastInput:function(){var t=this.matched.substr(0,this.matched.length-this.match.length);return(t.length>20?"...":"")+t.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var t=this.match;return t.length<20&&(t+=this._input.substr(0,20-t.length)),(t.substr(0,20)+(t.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var t=this.pastInput(),e=new Array(t.length+1).join("-");return t+this.upcomingInput()+"\n"+e+"^"},test_match:function(t,e){var n,r,i;if(this.options.backtrack_lexer&&(i={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(i.yylloc.range=this.yylloc.range.slice(0))),(r=t[0].match(/(?:\r\n?|\n).*/g))&&(this.yylineno+=r.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:r?r[r.length-1].length-r[r.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+t[0].length},this.yytext+=t[0],this.match+=t[0],this.matches=t,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(t[0].length),this.matched+=t[0],n=this.performAction.call(this,this.yy,this,e,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),n)return n;if(this._backtrack){for(var a in i)this[a]=i[a];return!1}return!1},next:function(){if(this.done)return this.EOF;var t,e,n,r;this._input||(this.done=!0),this._more||(this.yytext="",this.match="");for(var i=this._currentRules(),a=0;a<i.length;a++)if((n=this._input.match(this.rules[i[a]]))&&(!e||n[0].length>e[0].length)){if(e=n,r=a,this.options.backtrack_lexer){if(!1!==(t=this.test_match(n,i[a])))return t;if(this._backtrack){e=!1;continue}return!1}if(!this.options.flex)break}return e?!1!==(t=this.test_match(e,i[r]))&&t:""===this._input?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+". Unrecognized text.\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var t=this.next();return t||this.lex()},begin:function(t){this.conditionStack.push(t)},popState:function(){return this.conditionStack.length-1>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(t){return(t=this.conditionStack.length-1-Math.abs(t||0))>=0?this.conditionStack[t]:"INITIAL"},pushState:function(t){this.begin(t)},stateStackSize:function(){return this.conditionStack.length},options:{"case-insensitive":!0},performAction:function(t,e,n,r){switch(n){case 0:return this.begin("open_directive"),32;case 1:return this.begin("type_directive"),33;case 2:return this.popState(),this.begin("arg_directive"),25;case 3:return this.popState(),this.popState(),35;case 4:return 34;case 5:case 6:case 7:break;case 8:return 11;case 9:case 10:case 11:break;case 12:this.begin("href");break;case 13:this.popState();break;case 14:return 30;case 15:this.begin("callbackname");break;case 16:this.popState();break;case 17:this.popState(),this.begin("callbackargs");break;case 18:return 28;case 19:this.popState();break;case 20:return 29;case 21:this.begin("click");break;case 22:this.popState();break;case 23:return 27;case 24:return 5;case 25:return 12;case 26:return 13;case 27:return 14;case 28:return 15;case 29:return 16;case 30:return"date";case 31:return 17;case 32:return 18;case 33:return 20;case 34:return 21;case 35:return 25;case 36:return 7;case 37:return"INVALID"}},rules:[/^(?:%%\{)/i,/^(?:((?:(?!\}%%)[^:.])*))/i,/^(?::)/i,/^(?:\}%%)/i,/^(?:((?:(?!\}%%).|\n)*))/i,/^(?:%%(?!\{)*[^\n]*)/i,/^(?:[^\}]%%*[^\n]*)/i,/^(?:%%*[^\n]*[\n]*)/i,/^(?:[\n]+)/i,/^(?:\s+)/i,/^(?:#[^\n]*)/i,/^(?:%[^\n]*)/i,/^(?:href[\s]+["])/i,/^(?:["])/i,/^(?:[^"]*)/i,/^(?:call[\s]+)/i,/^(?:\([\s]*\))/i,/^(?:\()/i,/^(?:[^(]*)/i,/^(?:\))/i,/^(?:[^)]*)/i,/^(?:click[\s]+)/i,/^(?:[\s\n])/i,/^(?:[^\s\n]*)/i,/^(?:gantt\b)/i,/^(?:dateFormat\s[^#\n;]+)/i,/^(?:inclusiveEndDates\b)/i,/^(?:axisFormat\s[^#\n;]+)/i,/^(?:excludes\s[^#\n;]+)/i,/^(?:todayMarker\s[^\n;]+)/i,/^(?:\d\d\d\d-\d\d-\d\d\b)/i,/^(?:title\s[^#\n;]+)/i,/^(?:section\s[^#:\n;]+)/i,/^(?:[^#:\n;]+)/i,/^(?::[^#\n;]+)/i,/^(?::)/i,/^(?:$)/i,/^(?:.)/i],conditions:{close_directive:{rules:[],inclusive:!1},arg_directive:{rules:[3,4],inclusive:!1},type_directive:{rules:[2,3],inclusive:!1},open_directive:{rules:[1],inclusive:!1},callbackargs:{rules:[19,20],inclusive:!1},callbackname:{rules:[16,17,18],inclusive:!1},href:{rules:[13,14],inclusive:!1},click:{rules:[22,23],inclusive:!1},INITIAL:{rules:[0,5,6,7,8,9,10,11,12,15,21,24,25,26,27,28,29,30,31,32,33,34,35,36,37],inclusive:!0}}};function v(){this.yy={}}return g.lexer=y,v.prototype=g,g.Parser=v,new v}();e.parser=i,e.Parser=i.Parser,e.parse=function(){return i.parse.apply(i,arguments)},e.main=function(r){r[1]||(console.log("Usage: "+r[0]+" FILE"),t.exit(1));var i=n(19).readFileSync(n(20).normalize(r[1]),"utf8");return e.parser.parse(i)},n.c[n.s]===r&&e.main(t.argv.slice(1))}).call(this,n(14),n(7)(t))},function(t,e,n){(function(t,r){var i=function(){var t=function(t,e,n,r){for(n=n||{},r=t.length;r--;n[t[r]]=e);return 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t=function(t,e,n,r){for(n=n||{},r=t.length;r--;n[t[r]]=e);return n},e=[2,3],n=[1,7],r=[7,12,15,17,19,20,21],i=[7,11,12,15,17,19,20,21],a=[2,20],o=[1,32],s={trace:function(){},yy:{},symbols_:{error:2,start:3,GG:4,":":5,document:6,EOF:7,DIR:8,options:9,body:10,OPT:11,NL:12,line:13,statement:14,COMMIT:15,commit_arg:16,BRANCH:17,ID:18,CHECKOUT:19,MERGE:20,RESET:21,reset_arg:22,STR:23,HEAD:24,reset_parents:25,CARET:26,$accept:0,$end:1},terminals_:{2:"error",4:"GG",5:":",7:"EOF",8:"DIR",11:"OPT",12:"NL",15:"COMMIT",17:"BRANCH",18:"ID",19:"CHECKOUT",20:"MERGE",21:"RESET",23:"STR",24:"HEAD",26:"CARET"},productions_:[0,[3,4],[3,5],[6,0],[6,2],[9,2],[9,1],[10,0],[10,2],[13,2],[13,1],[14,2],[14,2],[14,2],[14,2],[14,2],[16,0],[16,1],[22,2],[22,2],[25,0],[25,2]],performAction:function(t,e,n,r,i,a,o){var s=a.length-1;switch(i){case 1:return a[s-1];case 2:return r.setDirection(a[s-3]),a[s-1];case 4:r.setOptions(a[s-1]),this.$=a[s];break;case 5:a[s-1]+=a[s],this.$=a[s-1];break;case 7:this.$=[];break;case 8:a[s-1].push(a[s]),this.$=a[s-1];break;case 9:this.$=a[s-1];break;case 11:r.commit(a[s]);break;case 12:r.branch(a[s]);break;case 13:r.checkout(a[s]);break;case 14:r.merge(a[s]);break;case 15:r.reset(a[s]);break;case 16:this.$="";break;case 17:this.$=a[s];break;case 18:this.$=a[s-1]+":"+a[s];break;case 19:this.$=a[s-1]+":"+r.count,r.count=0;break;case 20:r.count=0;break;case 21:r.count+=1}},table:[{3:1,4:[1,2]},{1:[3]},{5:[1,3],8:[1,4]},{6:5,7:e,9:6,12:n},{5:[1,8]},{7:[1,9]},t(r,[2,7],{10:10,11:[1,11]}),t(i,[2,6]),{6:12,7:e,9:6,12:n},{1:[2,1]},{7:[2,4],12:[1,15],13:13,14:14,15:[1,16],17:[1,17],19:[1,18],20:[1,19],21:[1,20]},t(i,[2,5]),{7:[1,21]},t(r,[2,8]),{12:[1,22]},t(r,[2,10]),{12:[2,16],16:23,23:[1,24]},{18:[1,25]},{18:[1,26]},{18:[1,27]},{18:[1,30],22:28,24:[1,29]},{1:[2,2]},t(r,[2,9]),{12:[2,11]},{12:[2,17]},{12:[2,12]},{12:[2,13]},{12:[2,14]},{12:[2,15]},{12:a,25:31,26:o},{12:a,25:33,26:o},{12:[2,18]},{12:a,25:34,26:o},{12:[2,19]},{12:[2,21]}],defaultActions:{9:[2,1],21:[2,2],23:[2,11],24:[2,17],25:[2,12],26:[2,13],27:[2,14],28:[2,15],31:[2,18],33:[2,19],34:[2,21]},parseError:function(t,e){if(!e.recoverable){var n=new Error(t);throw n.hash=e,n}this.trace(t)},parse:function(t){var e=this,n=[0],r=[],i=[null],a=[],o=this.table,s="",c=0,u=0,l=0,h=2,f=1,d=a.slice.call(arguments,1),p=Object.create(this.lexer),g={yy:{}};for(var y in this.yy)Object.prototype.hasOwnProperty.call(this.yy,y)&&(g.yy[y]=this.yy[y]);p.setInput(t,g.yy),g.yy.lexer=p,g.yy.parser=this,void 0===p.yylloc&&(p.yylloc={});var v=p.yylloc;a.push(v);var m=p.options&&p.options.ranges;function b(){var t;return"number"!=typeof(t=r.pop()||p.lex()||f)&&(t instanceof Array&&(t=(r=t).pop()),t=e.symbols_[t]||t),t}"function"==typeof g.yy.parseError?this.parseError=g.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;for(var x,_,k,w,E,T,C,A,S,M={};;){if(k=n[n.length-1],this.defaultActions[k]?w=this.defaultActions[k]:(null==x&&(x=b()),w=o[k]&&o[k][x]),void 0===w||!w.length||!w[0]){var O="";for(T in S=[],o[k])this.terminals_[T]&&T>h&&S.push("'"+this.terminals_[T]+"'");O=p.showPosition?"Parse error on line "+(c+1)+":\n"+p.showPosition()+"\nExpecting "+S.join(", ")+", got '"+(this.terminals_[x]||x)+"'":"Parse error on line "+(c+1)+": Unexpected "+(x==f?"end of 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3:return!0}}return!0}},c={EOF:1,parseError:function(t,e){if(!this.yy.parser)throw new Error(t);this.yy.parser.parseError(t,e)},setInput:function(t,e){return this.yy=e||this.yy||{},this._input=t,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var t=this._input[0];return this.yytext+=t,this.yyleng++,this.offset++,this.match+=t,this.matched+=t,t.match(/(?:\r\n?|\n).*/g)?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),t},unput:function(t){var e=t.length,n=t.split(/(?:\r\n?|\n)/g);this._input=t+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-e),this.offset-=e;var r=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),n.length-1&&(this.yylineno-=n.length-1);var i=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:n?(n.length===r.length?this.yylloc.first_column:0)+r[r.length-n.length].length-n[0].length:this.yylloc.first_column-e},this.options.ranges&&(this.yylloc.range=[i[0],i[0]+this.yyleng-e]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){return this.options.backtrack_lexer?(this._backtrack=!0,this):this.parseError("Lexical error on line "+(this.yylineno+1)+". You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},less:function(t){this.unput(this.match.slice(t))},pastInput:function(){var t=this.matched.substr(0,this.matched.length-this.match.length);return(t.length>20?"...":"")+t.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var t=this.match;return t.length<20&&(t+=this._input.substr(0,20-t.length)),(t.substr(0,20)+(t.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var t=this.pastInput(),e=new Array(t.length+1).join("-");return t+this.upcomingInput()+"\n"+e+"^"},test_match:function(t,e){var n,r,i;if(this.options.backtrack_lexer&&(i={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(i.yylloc.range=this.yylloc.range.slice(0))),(r=t[0].match(/(?:\r\n?|\n).*/g))&&(this.yylineno+=r.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:r?r[r.length-1].length-r[r.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+t[0].length},this.yytext+=t[0],this.match+=t[0],this.matches=t,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(t[0].length),this.matched+=t[0],n=this.performAction.call(this,this.yy,this,e,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),n)return n;if(this._backtrack){for(var a in i)this[a]=i[a];return!1}return!1},next:function(){if(this.done)return this.EOF;var t,e,n,r;this._input||(this.done=!0),this._more||(this.yytext="",this.match="");for(var i=this._currentRules(),a=0;a<i.length;a++)if((n=this._input.match(this.rules[i[a]]))&&(!e||n[0].length>e[0].length)){if(e=n,r=a,this.options.backtrack_lexer){if(!1!==(t=this.test_match(n,i[a])))return t;if(this._backtrack){e=!1;continue}return!1}if(!this.options.flex)break}return e?!1!==(t=this.test_match(e,i[r]))&&t:""===this._input?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+". Unrecognized text.\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var t=this.next();return t||this.lex()},begin:function(t){this.conditionStack.push(t)},popState:function(){return this.conditionStack.length-1>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(t){return(t=this.conditionStack.length-1-Math.abs(t||0))>=0?this.conditionStack[t]:"INITIAL"},pushState:function(t){this.begin(t)},stateStackSize:function(){return this.conditionStack.length},options:{"case-insensitive":!0},performAction:function(t,e,n,r){switch(n){case 0:return 12;case 1:case 2:case 3:break;case 4:return 4;case 5:return 15;case 6:return 17;case 7:return 20;case 8:return 21;case 9:return 19;case 10:case 11:return 8;case 12:return 5;case 13:return 26;case 14:this.begin("options");break;case 15:this.popState();break;case 16:return 11;case 17:this.begin("string");break;case 18:this.popState();break;case 19:return 23;case 20:return 18;case 21:return 7}},rules:[/^(?:(\r?\n)+)/i,/^(?:\s+)/i,/^(?:#[^\n]*)/i,/^(?:%[^\n]*)/i,/^(?:gitGraph\b)/i,/^(?:commit\b)/i,/^(?:branch\b)/i,/^(?:merge\b)/i,/^(?:reset\b)/i,/^(?:checkout\b)/i,/^(?:LR\b)/i,/^(?:BT\b)/i,/^(?::)/i,/^(?:\^)/i,/^(?:options\r?\n)/i,/^(?:end\r?\n)/i,/^(?:[^\n]+\r?\n)/i,/^(?:["])/i,/^(?:["])/i,/^(?:[^"]*)/i,/^(?:[a-zA-Z][-_\.a-zA-Z0-9]*[-_a-zA-Z0-9])/i,/^(?:$)/i],conditions:{options:{rules:[15,16],inclusive:!1},string:{rules:[18,19],inclusive:!1},INITIAL:{rules:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,17,20,21],inclusive:!0}}};function u(){this.yy={}}return s.lexer=c,u.prototype=s,s.Parser=u,new u}();e.parser=i,e.Parser=i.Parser,e.parse=function(){return i.parse.apply(i,arguments)},e.main=function(r){r[1]||(console.log("Usage: "+r[0]+" FILE"),t.exit(1));var i=n(19).readFileSync(n(20).normalize(r[1]),"utf8");return e.parser.parse(i)},n.c[n.s]===r&&e.main(t.argv.slice(1))}).call(this,n(14),n(7)(t))},function(t,e,n){(function(t,r){var i=function(){var t=function(t,e,n,r){for(n=n||{},r=t.length;r--;n[t[r]]=e);return n},e=[6,9,10],n={trace:function(){},yy:{},symbols_:{error:2,start:3,info:4,document:5,EOF:6,line:7,statement:8,NL:9,showInfo:10,$accept:0,$end:1},terminals_:{2:"error",4:"info",6:"EOF",9:"NL",10:"showInfo"},productions_:[0,[3,3],[5,0],[5,2],[7,1],[7,1],[8,1]],performAction:function(t,e,n,r,i,a,o){a.length;switch(i){case 1:return r;case 4:break;case 6:r.setInfo(!0)}},table:[{3:1,4:[1,2]},{1:[3]},t(e,[2,2],{5:3}),{6:[1,4],7:5,8:6,9:[1,7],10:[1,8]},{1:[2,1]},t(e,[2,3]),t(e,[2,4]),t(e,[2,5]),t(e,[2,6])],defaultActions:{4:[2,1]},parseError:function(t,e){if(!e.recoverable){var n=new Error(t);throw n.hash=e,n}this.trace(t)},parse:function(t){var e=this,n=[0],r=[],i=[null],a=[],o=this.table,s="",c=0,u=0,l=0,h=2,f=1,d=a.slice.call(arguments,1),p=Object.create(this.lexer),g={yy:{}};for(var y in this.yy)Object.prototype.hasOwnProperty.call(this.yy,y)&&(g.yy[y]=this.yy[y]);p.setInput(t,g.yy),g.yy.lexer=p,g.yy.parser=this,void 0===p.yylloc&&(p.yylloc={});var v=p.yylloc;a.push(v);var m=p.options&&p.options.ranges;function b(){var t;return"number"!=typeof(t=r.pop()||p.lex()||f)&&(t instanceof Array&&(t=(r=t).pop()),t=e.symbols_[t]||t),t}"function"==typeof g.yy.parseError?this.parseError=g.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;for(var x,_,k,w,E,T,C,A,S,M={};;){if(k=n[n.length-1],this.defaultActions[k]?w=this.defaultActions[k]:(null==x&&(x=b()),w=o[k]&&o[k][x]),void 0===w||!w.length||!w[0]){var O="";for(T in S=[],o[k])this.terminals_[T]&&T>h&&S.push("'"+this.terminals_[T]+"'");O=p.showPosition?"Parse error on line "+(c+1)+":\n"+p.showPosition()+"\nExpecting "+S.join(", ")+", got '"+(this.terminals_[x]||x)+"'":"Parse error on line "+(c+1)+": Unexpected "+(x==f?"end of input":"'"+(this.terminals_[x]||x)+"'"),this.parseError(O,{text:p.match,token:this.terminals_[x]||x,line:p.yylineno,loc:v,expected:S})}if(w[0]instanceof Array&&w.length>1)throw new Error("Parse Error: multiple actions possible at state: "+k+", token: "+x);switch(w[0]){case 1:n.push(x),i.push(p.yytext),a.push(p.yylloc),n.push(w[1]),x=null,_?(x=_,_=null):(u=p.yyleng,s=p.yytext,c=p.yylineno,v=p.yylloc,l>0&&l--);break;case 2:if(C=this.productions_[w[1]][1],M.$=i[i.length-C],M._$={first_line:a[a.length-(C||1)].first_line,last_line:a[a.length-1].last_line,first_column:a[a.length-(C||1)].first_column,last_column:a[a.length-1].last_column},m&&(M._$.range=[a[a.length-(C||1)].range[0],a[a.length-1].range[1]]),void 0!==(E=this.performAction.apply(M,[s,u,c,g.yy,w[1],i,a].concat(d))))return E;C&&(n=n.slice(0,-1*C*2),i=i.slice(0,-1*C),a=a.slice(0,-1*C)),n.push(this.productions_[w[1]][0]),i.push(M.$),a.push(M._$),A=o[n[n.length-2]][n[n.length-1]],n.push(A);break;case 3:return!0}}return!0}},r={EOF:1,parseError:function(t,e){if(!this.yy.parser)throw new Error(t);this.yy.parser.parseError(t,e)},setInput:function(t,e){return this.yy=e||this.yy||{},this._input=t,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var t=this._input[0];return this.yytext+=t,this.yyleng++,this.offset++,this.match+=t,this.matched+=t,t.match(/(?:\r\n?|\n).*/g)?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),t},unput:function(t){var e=t.length,n=t.split(/(?:\r\n?|\n)/g);this._input=t+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-e),this.offset-=e;var r=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),n.length-1&&(this.yylineno-=n.length-1);var i=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:n?(n.length===r.length?this.yylloc.first_column:0)+r[r.length-n.length].length-n[0].length:this.yylloc.first_column-e},this.options.ranges&&(this.yylloc.range=[i[0],i[0]+this.yyleng-e]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){return this.options.backtrack_lexer?(this._backtrack=!0,this):this.parseError("Lexical error on line "+(this.yylineno+1)+". You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},less:function(t){this.unput(this.match.slice(t))},pastInput:function(){var t=this.matched.substr(0,this.matched.length-this.match.length);return(t.length>20?"...":"")+t.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var t=this.match;return t.length<20&&(t+=this._input.substr(0,20-t.length)),(t.substr(0,20)+(t.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var t=this.pastInput(),e=new Array(t.length+1).join("-");return t+this.upcomingInput()+"\n"+e+"^"},test_match:function(t,e){var n,r,i;if(this.options.backtrack_lexer&&(i={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(i.yylloc.range=this.yylloc.range.slice(0))),(r=t[0].match(/(?:\r\n?|\n).*/g))&&(this.yylineno+=r.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:r?r[r.length-1].length-r[r.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+t[0].length},this.yytext+=t[0],this.match+=t[0],this.matches=t,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(t[0].length),this.matched+=t[0],n=this.performAction.call(this,this.yy,this,e,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),n)return n;if(this._backtrack){for(var a in i)this[a]=i[a];return!1}return!1},next:function(){if(this.done)return this.EOF;var t,e,n,r;this._input||(this.done=!0),this._more||(this.yytext="",this.match="");for(var i=this._currentRules(),a=0;a<i.length;a++)if((n=this._input.match(this.rules[i[a]]))&&(!e||n[0].length>e[0].length)){if(e=n,r=a,this.options.backtrack_lexer){if(!1!==(t=this.test_match(n,i[a])))return t;if(this._backtrack){e=!1;continue}return!1}if(!this.options.flex)break}return e?!1!==(t=this.test_match(e,i[r]))&&t:""===this._input?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+". Unrecognized text.\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var t=this.next();return t||this.lex()},begin:function(t){this.conditionStack.push(t)},popState:function(){return this.conditionStack.length-1>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(t){return(t=this.conditionStack.length-1-Math.abs(t||0))>=0?this.conditionStack[t]:"INITIAL"},pushState:function(t){this.begin(t)},stateStackSize:function(){return this.conditionStack.length},options:{"case-insensitive":!0},performAction:function(t,e,n,r){switch(n){case 0:return 4;case 1:return 9;case 2:return"space";case 3:return 10;case 4:return 6;case 5:return"TXT"}},rules:[/^(?:info\b)/i,/^(?:[\s\n\r]+)/i,/^(?:[\s]+)/i,/^(?:showInfo\b)/i,/^(?:$)/i,/^(?:.)/i],conditions:{INITIAL:{rules:[0,1,2,3,4,5],inclusive:!0}}};function i(){this.yy={}}return n.lexer=r,i.prototype=n,n.Parser=i,new i}();e.parser=i,e.Parser=i.Parser,e.parse=function(){return i.parse.apply(i,arguments)},e.main=function(r){r[1]||(console.log("Usage: "+r[0]+" FILE"),t.exit(1));var i=n(19).readFileSync(n(20).normalize(r[1]),"utf8");return e.parser.parse(i)},n.c[n.s]===r&&e.main(t.argv.slice(1))}).call(this,n(14),n(7)(t))},function(t,e,n){(function(t,r){var i=function(){var t=function(t,e,n,r){for(n=n||{},r=t.length;r--;n[t[r]]=e);return n},e=[1,4],n=[1,5],r=[1,6],i=[1,7],a=[1,9],o=[1,10,12,19,20,21,22],s=[1,6,10,12,19,20,21,22],c=[19,20,21],u=[1,22],l=[6,19,20,21,22],h={trace:function(){},yy:{},symbols_:{error:2,start:3,eol:4,directive:5,PIE:6,document:7,line:8,statement:9,txt:10,value:11,title:12,title_value:13,openDirective:14,typeDirective:15,closeDirective:16,":":17,argDirective:18,NEWLINE:19,";":20,EOF:21,open_directive:22,type_directive:23,arg_directive:24,close_directive:25,$accept:0,$end:1},terminals_:{2:"error",6:"PIE",10:"txt",11:"value",12:"title",13:"title_value",17:":",19:"NEWLINE",20:";",21:"EOF",22:"open_directive",23:"type_directive",24:"arg_directive",25:"close_directive"},productions_:[0,[3,2],[3,2],[3,2],[7,0],[7,2],[8,2],[9,0],[9,2],[9,2],[9,1],[5,3],[5,5],[4,1],[4,1],[4,1],[14,1],[15,1],[18,1],[16,1]],performAction:function(t,e,n,r,i,a,o){var s=a.length-1;switch(i){case 6:this.$=a[s-1];break;case 8:r.addSection(a[s-1],r.cleanupValue(a[s]));break;case 9:this.$=a[s].trim(),r.setTitle(this.$);break;case 16:r.parseDirective("%%{","open_directive");break;case 17:r.parseDirective(a[s],"type_directive");break;case 18:a[s]=a[s].trim().replace(/'/g,'"'),r.parseDirective(a[s],"arg_directive");break;case 19:r.parseDirective("}%%","close_directive","pie")}},table:[{3:1,4:2,5:3,6:e,14:8,19:n,20:r,21:i,22:a},{1:[3]},{3:10,4:2,5:3,6:e,14:8,19:n,20:r,21:i,22:a},{3:11,4:2,5:3,6:e,14:8,19:n,20:r,21:i,22:a},t(o,[2,4],{7:12}),t(s,[2,13]),t(s,[2,14]),t(s,[2,15]),{15:13,23:[1,14]},{23:[2,16]},{1:[2,1]},{1:[2,2]},t(c,[2,7],{14:8,8:15,9:16,5:19,1:[2,3],10:[1,17],12:[1,18],22:a}),{16:20,17:[1,21],25:u},t([17,25],[2,17]),t(o,[2,5]),{4:23,19:n,20:r,21:i},{11:[1,24]},{13:[1,25]},t(c,[2,10]),t(l,[2,11]),{18:26,24:[1,27]},t(l,[2,19]),t(o,[2,6]),t(c,[2,8]),t(c,[2,9]),{16:28,25:u},{25:[2,18]},t(l,[2,12])],defaultActions:{9:[2,16],10:[2,1],11:[2,2],27:[2,18]},parseError:function(t,e){if(!e.recoverable){var n=new Error(t);throw n.hash=e,n}this.trace(t)},parse:function(t){var e=this,n=[0],r=[],i=[null],a=[],o=this.table,s="",c=0,u=0,l=0,h=2,f=1,d=a.slice.call(arguments,1),p=Object.create(this.lexer),g={yy:{}};for(var y in this.yy)Object.prototype.hasOwnProperty.call(this.yy,y)&&(g.yy[y]=this.yy[y]);p.setInput(t,g.yy),g.yy.lexer=p,g.yy.parser=this,void 0===p.yylloc&&(p.yylloc={});var v=p.yylloc;a.push(v);var m=p.options&&p.options.ranges;function b(){var t;return"number"!=typeof(t=r.pop()||p.lex()||f)&&(t instanceof Array&&(t=(r=t).pop()),t=e.symbols_[t]||t),t}"function"==typeof g.yy.parseError?this.parseError=g.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;for(var x,_,k,w,E,T,C,A,S,M={};;){if(k=n[n.length-1],this.defaultActions[k]?w=this.defaultActions[k]:(null==x&&(x=b()),w=o[k]&&o[k][x]),void 0===w||!w.length||!w[0]){var O="";for(T in S=[],o[k])this.terminals_[T]&&T>h&&S.push("'"+this.terminals_[T]+"'");O=p.showPosition?"Parse error on line "+(c+1)+":\n"+p.showPosition()+"\nExpecting "+S.join(", ")+", got '"+(this.terminals_[x]||x)+"'":"Parse error on line "+(c+1)+": Unexpected "+(x==f?"end of input":"'"+(this.terminals_[x]||x)+"'"),this.parseError(O,{text:p.match,token:this.terminals_[x]||x,line:p.yylineno,loc:v,expected:S})}if(w[0]instanceof Array&&w.length>1)throw new Error("Parse Error: multiple actions possible at state: "+k+", token: "+x);switch(w[0]){case 1:n.push(x),i.push(p.yytext),a.push(p.yylloc),n.push(w[1]),x=null,_?(x=_,_=null):(u=p.yyleng,s=p.yytext,c=p.yylineno,v=p.yylloc,l>0&&l--);break;case 2:if(C=this.productions_[w[1]][1],M.$=i[i.length-C],M._$={first_line:a[a.length-(C||1)].first_line,last_line:a[a.length-1].last_line,first_column:a[a.length-(C||1)].first_column,last_column:a[a.length-1].last_column},m&&(M._$.range=[a[a.length-(C||1)].range[0],a[a.length-1].range[1]]),void 0!==(E=this.performAction.apply(M,[s,u,c,g.yy,w[1],i,a].concat(d))))return E;C&&(n=n.slice(0,-1*C*2),i=i.slice(0,-1*C),a=a.slice(0,-1*C)),n.push(this.productions_[w[1]][0]),i.push(M.$),a.push(M._$),A=o[n[n.length-2]][n[n.length-1]],n.push(A);break;case 3:return!0}}return!0}},f={EOF:1,parseError:function(t,e){if(!this.yy.parser)throw new Error(t);this.yy.parser.parseError(t,e)},setInput:function(t,e){return this.yy=e||this.yy||{},this._input=t,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var t=this._input[0];return this.yytext+=t,this.yyleng++,this.offset++,this.match+=t,this.matched+=t,t.match(/(?:\r\n?|\n).*/g)?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),t},unput:function(t){var e=t.length,n=t.split(/(?:\r\n?|\n)/g);this._input=t+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-e),this.offset-=e;var r=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),n.length-1&&(this.yylineno-=n.length-1);var i=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:n?(n.length===r.length?this.yylloc.first_column:0)+r[r.length-n.length].length-n[0].length:this.yylloc.first_column-e},this.options.ranges&&(this.yylloc.range=[i[0],i[0]+this.yyleng-e]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){return this.options.backtrack_lexer?(this._backtrack=!0,this):this.parseError("Lexical error on line "+(this.yylineno+1)+". You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},less:function(t){this.unput(this.match.slice(t))},pastInput:function(){var t=this.matched.substr(0,this.matched.length-this.match.length);return(t.length>20?"...":"")+t.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var t=this.match;return t.length<20&&(t+=this._input.substr(0,20-t.length)),(t.substr(0,20)+(t.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var t=this.pastInput(),e=new Array(t.length+1).join("-");return t+this.upcomingInput()+"\n"+e+"^"},test_match:function(t,e){var n,r,i;if(this.options.backtrack_lexer&&(i={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(i.yylloc.range=this.yylloc.range.slice(0))),(r=t[0].match(/(?:\r\n?|\n).*/g))&&(this.yylineno+=r.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:r?r[r.length-1].length-r[r.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+t[0].length},this.yytext+=t[0],this.match+=t[0],this.matches=t,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(t[0].length),this.matched+=t[0],n=this.performAction.call(this,this.yy,this,e,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),n)return n;if(this._backtrack){for(var a in i)this[a]=i[a];return!1}return!1},next:function(){if(this.done)return this.EOF;var t,e,n,r;this._input||(this.done=!0),this._more||(this.yytext="",this.match="");for(var i=this._currentRules(),a=0;a<i.length;a++)if((n=this._input.match(this.rules[i[a]]))&&(!e||n[0].length>e[0].length)){if(e=n,r=a,this.options.backtrack_lexer){if(!1!==(t=this.test_match(n,i[a])))return t;if(this._backtrack){e=!1;continue}return!1}if(!this.options.flex)break}return e?!1!==(t=this.test_match(e,i[r]))&&t:""===this._input?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+". Unrecognized text.\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var t=this.next();return t||this.lex()},begin:function(t){this.conditionStack.push(t)},popState:function(){return this.conditionStack.length-1>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(t){return(t=this.conditionStack.length-1-Math.abs(t||0))>=0?this.conditionStack[t]:"INITIAL"},pushState:function(t){this.begin(t)},stateStackSize:function(){return this.conditionStack.length},options:{"case-insensitive":!0},performAction:function(t,e,n,r){switch(n){case 0:return this.begin("open_directive"),22;case 1:return this.begin("type_directive"),23;case 2:return this.popState(),this.begin("arg_directive"),17;case 3:return this.popState(),this.popState(),25;case 4:return 24;case 5:case 6:break;case 7:return 19;case 8:case 9:break;case 10:return this.begin("title"),12;case 11:return this.popState(),"title_value";case 12:this.begin("string");break;case 13:this.popState();break;case 14:return"txt";case 15:return 6;case 16:return"value";case 17:return 21}},rules:[/^(?:%%\{)/i,/^(?:((?:(?!\}%%)[^:.])*))/i,/^(?::)/i,/^(?:\}%%)/i,/^(?:((?:(?!\}%%).|\n)*))/i,/^(?:%%(?!\{)[^\n]*)/i,/^(?:[^\}]%%[^\n]*)/i,/^(?:[\n\r]+)/i,/^(?:%%[^\n]*)/i,/^(?:[\s]+)/i,/^(?:title\b)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:["])/i,/^(?:["])/i,/^(?:[^"]*)/i,/^(?:pie\b)/i,/^(?::[\s]*[\d]+(?:\.[\d]+)?)/i,/^(?:$)/i],conditions:{close_directive:{rules:[],inclusive:!1},arg_directive:{rules:[3,4],inclusive:!1},type_directive:{rules:[2,3],inclusive:!1},open_directive:{rules:[1],inclusive:!1},title:{rules:[11],inclusive:!1},string:{rules:[13,14],inclusive:!1},INITIAL:{rules:[0,5,6,7,8,9,10,12,15,16,17],inclusive:!0}}};function d(){this.yy={}}return h.lexer=f,d.prototype=h,h.Parser=d,new d}();e.parser=i,e.Parser=i.Parser,e.parse=function(){return i.parse.apply(i,arguments)},e.main=function(r){r[1]||(console.log("Usage: "+r[0]+" FILE"),t.exit(1));var i=n(19).readFileSync(n(20).normalize(r[1]),"utf8");return e.parser.parse(i)},n.c[n.s]===r&&e.main(t.argv.slice(1))}).call(this,n(14),n(7)(t))},function(t,e,n){(function(t,r){var i=function(){var t=function(t,e,n,r){for(n=n||{},r=t.length;r--;n[t[r]]=e);return n},e=[1,2],n=[1,5],r=[6,9,11,23,37],i=[1,17],a=[1,20],o=[1,25],s=[1,26],c=[1,27],u=[1,28],l=[1,37],h=[23,34,35],f=[4,6,9,11,23,37],d=[30,31,32,33],p=[22,27],g={trace:function(){},yy:{},symbols_:{error:2,start:3,ER_DIAGRAM:4,document:5,EOF:6,directive:7,line:8,SPACE:9,statement:10,NEWLINE:11,openDirective:12,typeDirective:13,closeDirective:14,":":15,argDirective:16,entityName:17,relSpec:18,role:19,BLOCK_START:20,attributes:21,BLOCK_STOP:22,ALPHANUM:23,attribute:24,attributeType:25,attributeName:26,ATTRIBUTE_WORD:27,cardinality:28,relType:29,ZERO_OR_ONE:30,ZERO_OR_MORE:31,ONE_OR_MORE:32,ONLY_ONE:33,NON_IDENTIFYING:34,IDENTIFYING:35,WORD:36,open_directive:37,type_directive:38,arg_directive:39,close_directive:40,$accept:0,$end:1},terminals_:{2:"error",4:"ER_DIAGRAM",6:"EOF",9:"SPACE",11:"NEWLINE",15:":",20:"BLOCK_START",22:"BLOCK_STOP",23:"ALPHANUM",27:"ATTRIBUTE_WORD",30:"ZERO_OR_ONE",31:"ZERO_OR_MORE",32:"ONE_OR_MORE",33:"ONLY_ONE",34:"NON_IDENTIFYING",35:"IDENTIFYING",36:"WORD",37:"open_directive",38:"type_directive",39:"arg_directive",40:"close_directive"},productions_:[0,[3,3],[3,2],[5,0],[5,2],[8,2],[8,1],[8,1],[8,1],[7,4],[7,6],[10,1],[10,5],[10,4],[10,3],[10,1],[17,1],[21,1],[21,2],[24,2],[25,1],[26,1],[18,3],[28,1],[28,1],[28,1],[28,1],[29,1],[29,1],[19,1],[19,1],[12,1],[13,1],[16,1],[14,1]],performAction:function(t,e,n,r,i,a,o){var s=a.length-1;switch(i){case 1:break;case 3:this.$=[];break;case 4:a[s-1].push(a[s]),this.$=a[s-1];break;case 5:case 6:this.$=a[s];break;case 7:case 8:this.$=[];break;case 12:r.addEntity(a[s-4]),r.addEntity(a[s-2]),r.addRelationship(a[s-4],a[s],a[s-2],a[s-3]);break;case 13:r.addEntity(a[s-3]),r.addAttributes(a[s-3],a[s-1]);break;case 14:r.addEntity(a[s-2]);break;case 15:r.addEntity(a[s]);break;case 16:this.$=a[s];break;case 17:this.$=[a[s]];break;case 18:a[s].push(a[s-1]),this.$=a[s];break;case 19:this.$={attributeType:a[s-1],attributeName:a[s]};break;case 20:case 21:this.$=a[s];break;case 22:this.$={cardA:a[s],relType:a[s-1],cardB:a[s-2]};break;case 23:this.$=r.Cardinality.ZERO_OR_ONE;break;case 24:this.$=r.Cardinality.ZERO_OR_MORE;break;case 25:this.$=r.Cardinality.ONE_OR_MORE;break;case 26:this.$=r.Cardinality.ONLY_ONE;break;case 27:this.$=r.Identification.NON_IDENTIFYING;break;case 28:this.$=r.Identification.IDENTIFYING;break;case 29:this.$=a[s].replace(/"/g,"");break;case 30:this.$=a[s];break;case 31:r.parseDirective("%%{","open_directive");break;case 32:r.parseDirective(a[s],"type_directive");break;case 33:a[s]=a[s].trim().replace(/'/g,'"'),r.parseDirective(a[s],"arg_directive");break;case 34:r.parseDirective("}%%","close_directive","er")}},table:[{3:1,4:e,7:3,12:4,37:n},{1:[3]},t(r,[2,3],{5:6}),{3:7,4:e,7:3,12:4,37:n},{13:8,38:[1,9]},{38:[2,31]},{6:[1,10],7:15,8:11,9:[1,12],10:13,11:[1,14],12:4,17:16,23:i,37:n},{1:[2,2]},{14:18,15:[1,19],40:a},t([15,40],[2,32]),t(r,[2,8],{1:[2,1]}),t(r,[2,4]),{7:15,10:21,12:4,17:16,23:i,37:n},t(r,[2,6]),t(r,[2,7]),t(r,[2,11]),t(r,[2,15],{18:22,28:24,20:[1,23],30:o,31:s,32:c,33:u}),t([6,9,11,15,20,23,30,31,32,33,37],[2,16]),{11:[1,29]},{16:30,39:[1,31]},{11:[2,34]},t(r,[2,5]),{17:32,23:i},{21:33,22:[1,34],24:35,25:36,27:l},{29:38,34:[1,39],35:[1,40]},t(h,[2,23]),t(h,[2,24]),t(h,[2,25]),t(h,[2,26]),t(f,[2,9]),{14:41,40:a},{40:[2,33]},{15:[1,42]},{22:[1,43]},t(r,[2,14]),{21:44,22:[2,17],24:35,25:36,27:l},{26:45,27:[1,46]},{27:[2,20]},{28:47,30:o,31:s,32:c,33:u},t(d,[2,27]),t(d,[2,28]),{11:[1,48]},{19:49,23:[1,51],36:[1,50]},t(r,[2,13]),{22:[2,18]},t(p,[2,19]),t(p,[2,21]),{23:[2,22]},t(f,[2,10]),t(r,[2,12]),t(r,[2,29]),t(r,[2,30])],defaultActions:{5:[2,31],7:[2,2],20:[2,34],31:[2,33],37:[2,20],44:[2,18],47:[2,22]},parseError:function(t,e){if(!e.recoverable){var n=new Error(t);throw n.hash=e,n}this.trace(t)},parse:function(t){var e=this,n=[0],r=[],i=[null],a=[],o=this.table,s="",c=0,u=0,l=0,h=2,f=1,d=a.slice.call(arguments,1),p=Object.create(this.lexer),g={yy:{}};for(var y in this.yy)Object.prototype.hasOwnProperty.call(this.yy,y)&&(g.yy[y]=this.yy[y]);p.setInput(t,g.yy),g.yy.lexer=p,g.yy.parser=this,void 0===p.yylloc&&(p.yylloc={});var v=p.yylloc;a.push(v);var m=p.options&&p.options.ranges;function b(){var t;return"number"!=typeof(t=r.pop()||p.lex()||f)&&(t instanceof Array&&(t=(r=t).pop()),t=e.symbols_[t]||t),t}"function"==typeof g.yy.parseError?this.parseError=g.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;for(var x,_,k,w,E,T,C,A,S,M={};;){if(k=n[n.length-1],this.defaultActions[k]?w=this.defaultActions[k]:(null==x&&(x=b()),w=o[k]&&o[k][x]),void 0===w||!w.length||!w[0]){var O="";for(T in S=[],o[k])this.terminals_[T]&&T>h&&S.push("'"+this.terminals_[T]+"'");O=p.showPosition?"Parse error on line "+(c+1)+":\n"+p.showPosition()+"\nExpecting "+S.join(", ")+", got '"+(this.terminals_[x]||x)+"'":"Parse error on line "+(c+1)+": Unexpected "+(x==f?"end of input":"'"+(this.terminals_[x]||x)+"'"),this.parseError(O,{text:p.match,token:this.terminals_[x]||x,line:p.yylineno,loc:v,expected:S})}if(w[0]instanceof Array&&w.length>1)throw new Error("Parse Error: multiple actions possible at state: "+k+", token: "+x);switch(w[0]){case 1:n.push(x),i.push(p.yytext),a.push(p.yylloc),n.push(w[1]),x=null,_?(x=_,_=null):(u=p.yyleng,s=p.yytext,c=p.yylineno,v=p.yylloc,l>0&&l--);break;case 2:if(C=this.productions_[w[1]][1],M.$=i[i.length-C],M._$={first_line:a[a.length-(C||1)].first_line,last_line:a[a.length-1].last_line,first_column:a[a.length-(C||1)].first_column,last_column:a[a.length-1].last_column},m&&(M._$.range=[a[a.length-(C||1)].range[0],a[a.length-1].range[1]]),void 0!==(E=this.performAction.apply(M,[s,u,c,g.yy,w[1],i,a].concat(d))))return E;C&&(n=n.slice(0,-1*C*2),i=i.slice(0,-1*C),a=a.slice(0,-1*C)),n.push(this.productions_[w[1]][0]),i.push(M.$),a.push(M._$),A=o[n[n.length-2]][n[n.length-1]],n.push(A);break;case 3:return!0}}return!0}},y={EOF:1,parseError:function(t,e){if(!this.yy.parser)throw new Error(t);this.yy.parser.parseError(t,e)},setInput:function(t,e){return this.yy=e||this.yy||{},this._input=t,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var t=this._input[0];return this.yytext+=t,this.yyleng++,this.offset++,this.match+=t,this.matched+=t,t.match(/(?:\r\n?|\n).*/g)?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),t},unput:function(t){var e=t.length,n=t.split(/(?:\r\n?|\n)/g);this._input=t+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-e),this.offset-=e;var r=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),n.length-1&&(this.yylineno-=n.length-1);var i=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:n?(n.length===r.length?this.yylloc.first_column:0)+r[r.length-n.length].length-n[0].length:this.yylloc.first_column-e},this.options.ranges&&(this.yylloc.range=[i[0],i[0]+this.yyleng-e]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){return this.options.backtrack_lexer?(this._backtrack=!0,this):this.parseError("Lexical error on line "+(this.yylineno+1)+". You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},less:function(t){this.unput(this.match.slice(t))},pastInput:function(){var t=this.matched.substr(0,this.matched.length-this.match.length);return(t.length>20?"...":"")+t.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var t=this.match;return t.length<20&&(t+=this._input.substr(0,20-t.length)),(t.substr(0,20)+(t.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var t=this.pastInput(),e=new Array(t.length+1).join("-");return t+this.upcomingInput()+"\n"+e+"^"},test_match:function(t,e){var n,r,i;if(this.options.backtrack_lexer&&(i={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(i.yylloc.range=this.yylloc.range.slice(0))),(r=t[0].match(/(?:\r\n?|\n).*/g))&&(this.yylineno+=r.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:r?r[r.length-1].length-r[r.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+t[0].length},this.yytext+=t[0],this.match+=t[0],this.matches=t,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(t[0].length),this.matched+=t[0],n=this.performAction.call(this,this.yy,this,e,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),n)return n;if(this._backtrack){for(var a in i)this[a]=i[a];return!1}return!1},next:function(){if(this.done)return this.EOF;var t,e,n,r;this._input||(this.done=!0),this._more||(this.yytext="",this.match="");for(var i=this._currentRules(),a=0;a<i.length;a++)if((n=this._input.match(this.rules[i[a]]))&&(!e||n[0].length>e[0].length)){if(e=n,r=a,this.options.backtrack_lexer){if(!1!==(t=this.test_match(n,i[a])))return t;if(this._backtrack){e=!1;continue}return!1}if(!this.options.flex)break}return e?!1!==(t=this.test_match(e,i[r]))&&t:""===this._input?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+". Unrecognized text.\n"+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var t=this.next();return t||this.lex()},begin:function(t){this.conditionStack.push(t)},popState:function(){return this.conditionStack.length-1>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(t){return(t=this.conditionStack.length-1-Math.abs(t||0))>=0?this.conditionStack[t]:"INITIAL"},pushState:function(t){this.begin(t)},stateStackSize:function(){return this.conditionStack.length},options:{"case-insensitive":!0},performAction:function(t,e,n,r){switch(n){case 0:return this.begin("open_directive"),37;case 1:return this.begin("type_directive"),38;case 2:return this.popState(),this.begin("arg_directive"),15;case 3:return this.popState(),this.popState(),40;case 4:return 39;case 5:case 6:break;case 7:return 11;case 8:break;case 9:return 9;case 10:return 36;case 11:return 4;case 12:return this.begin("block"),20;case 13:break;case 14:return 27;case 15:break;case 16:return this.popState(),22;case 17:return e.yytext[0];case 18:return 30;case 19:return 31;case 20:return 32;case 21:return 33;case 22:return 30;case 23:return 31;case 24:return 32;case 25:return 34;case 26:return 35;case 27:case 28:return 34;case 29:return 23;case 30:return e.yytext[0];case 31:return 6}},rules:[/^(?:%%\{)/i,/^(?:((?:(?!\}%%)[^:.])*))/i,/^(?::)/i,/^(?:\}%%)/i,/^(?:((?:(?!\}%%).|\n)*))/i,/^(?:%(?!\{)[^\n]*)/i,/^(?:[^\}]%%[^\n]*)/i,/^(?:[\n]+)/i,/^(?:\s+)/i,/^(?:[\s]+)/i,/^(?:"[^"]*")/i,/^(?:erDiagram\b)/i,/^(?:\{)/i,/^(?:\s+)/i,/^(?:[A-Za-z][A-Za-z0-9\-_]*)/i,/^(?:[\n]+)/i,/^(?:\})/i,/^(?:.)/i,/^(?:\|o\b)/i,/^(?:\}o\b)/i,/^(?:\}\|)/i,/^(?:\|\|)/i,/^(?:o\|)/i,/^(?:o\{)/i,/^(?:\|\{)/i,/^(?:\.\.)/i,/^(?:--)/i,/^(?:\.-)/i,/^(?:-\.)/i,/^(?:[A-Za-z][A-Za-z0-9\-_]*)/i,/^(?:.)/i,/^(?:$)/i],conditions:{open_directive:{rules:[1],inclusive:!1},type_directive:{rules:[2,3],inclusive:!1},arg_directive:{rules:[3,4],inclusive:!1},block:{rules:[13,14,15,16,17],inclusive:!1},INITIAL:{rules:[0,5,6,7,8,9,10,11,12,18,19,20,21,22,23,24,25,26,27,28,29,30,31],inclusive:!0}}};function v(){this.yy={}}return g.lexer=y,v.prototype=g,g.Parser=v,new v}();e.parser=i,e.Parser=i.Parser,e.parse=function(){return i.parse.apply(i,arguments)},e.main=function(r){r[1]||(console.log("Usage: "+r[0]+" FILE"),t.exit(1));var i=n(19).readFileSync(n(20).normalize(r[1]),"utf8");return e.parser.parse(i)},n.c[n.s]===r&&e.main(t.argv.slice(1))}).call(this,n(14),n(7)(t))},function(t,e,n){"use strict";var r;Object.defineProperty(e,"__esModule",{value:!0}),function(t){t[t.ALL=0]="ALL",t[t.RGB=1]="RGB",t[t.HSL=2]="HSL"}(r||(r={})),e.TYPE=r},function(t,e,n){"use strict";var r=n(10);t.exports=i;function i(t){this._isDirected=!r.has(t,"directed")||t.directed,this._isMultigraph=!!r.has(t,"multigraph")&&t.multigraph,this._isCompound=!!r.has(t,"compound")&&t.compound,this._label=void 0,this._defaultNodeLabelFn=r.constant(void 0),this._defaultEdgeLabelFn=r.constant(void 0),this._nodes={},this._isCompound&&(this._parent={},this._children={},this._children["\0"]={}),this._in={},this._preds={},this._out={},this._sucs={},this._edgeObjs={},this._edgeLabels={}}function a(t,e){t[e]?t[e]++:t[e]=1}function o(t,e){--t[e]||delete t[e]}function s(t,e,n,i){var a=""+e,o=""+n;if(!t&&a>o){var s=a;a=o,o=s}return a+""+o+""+(r.isUndefined(i)?"\0":i)}function c(t,e,n,r){var i=""+e,a=""+n;if(!t&&i>a){var o=i;i=a,a=o}var s={v:i,w:a};return r&&(s.name=r),s}function u(t,e){return s(t,e.v,e.w,e.name)}i.prototype._nodeCount=0,i.prototype._edgeCount=0,i.prototype.isDirected=function(){return this._isDirected},i.prototype.isMultigraph=function(){return this._isMultigraph},i.prototype.isCompound=function(){return this._isCompound},i.prototype.setGraph=function(t){return this._label=t,this},i.prototype.graph=function(){return this._label},i.prototype.setDefaultNodeLabel=function(t){return r.isFunction(t)||(t=r.constant(t)),this._defaultNodeLabelFn=t,this},i.prototype.nodeCount=function(){return this._nodeCount},i.prototype.nodes=function(){return r.keys(this._nodes)},i.prototype.sources=function(){var t=this;return r.filter(this.nodes(),(function(e){return r.isEmpty(t._in[e])}))},i.prototype.sinks=function(){var t=this;return r.filter(this.nodes(),(function(e){return 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e=t,(n=[{key:"updateColors",value:function(){this.secondBkg=Object(q.lighten)(this.mainBkg,16),this.lineColor=this.mainContrastColor,this.arrowheadColor=this.mainContrastColor,this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.edgeLabelBackground=Object(q.lighten)(this.labelBackground,25),this.actorBorder=this.border1,this.actorBkg=this.mainBkg,this.actorTextColor=this.mainContrastColor,this.actorLineColor=this.mainContrastColor,this.signalColor=this.mainContrastColor,this.signalTextColor=this.mainContrastColor,this.labelBoxBkgColor=this.actorBkg,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.mainContrastColor,this.loopTextColor=this.mainContrastColor,this.noteBorderColor=this.border2,this.noteTextColor=this.mainBkg,this.activationBorderColor=this.border1,this.activationBkgColor=this.secondBkg,this.altSectionBkgColor=this.background,this.taskBkgColor=Object(q.lighten)(this.mainBkg,23),this.taskTextColor=this.darkTextColor,this.taskTextLightColor=this.mainContrastColor,this.taskTextOutsideColor=this.taskTextLightColor,this.gridColor=this.mainContrastColor,this.doneTaskBkgColor=this.mainContrastColor,this.taskTextDarkColor=this.darkTextColor,this.labelColor=this.textColor,this.altBackground=Object(q.lighten)(this.background,20),this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=Object(q.adjust)(this.primaryColor,{h:64}),this.fillType3=Object(q.adjust)(this.secondaryColor,{h:64}),this.fillType4=Object(q.adjust)(this.primaryColor,{h:-64}),this.fillType5=Object(q.adjust)(this.secondaryColor,{h:-64}),this.fillType6=Object(q.adjust)(this.primaryColor,{h:128}),this.fillType7=Object(q.adjust)(this.secondaryColor,{h:128}),this.classText=this.primaryTextColor}},{key:"calculate",value:function(t){var e=this;if("object"===Q(t)){var n=Object.keys(t);n.forEach((function(n){e[n]=t[n]})),this.updateColors(),n.forEach((function(n){e[n]=t[n]}))}else this.updateColors()}}])&&tt(e.prototype,n),r&&tt(e,r),t}();function nt(t){return(nt="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}function rt(t,e){for(var n=0;n<e.length;n++){var r=e[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(t,r.key,r)}}var it=function(){function t(){!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,t),this.background="#f4f4f4",this.primaryColor="#ECECFF",this.secondaryColor=Object(q.adjust)(this.primaryColor,{h:120}),this.secondaryColor="#ffffde",this.tertiaryColor=Object(q.adjust)(this.primaryColor,{h:-160}),this.primaryBorderColor=X(this.primaryColor,this.darkMode),this.secondaryBorderColor=X(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=X(this.tertiaryColor,this.darkMode),this.primaryTextColor=Object(q.invert)(this.primaryColor),this.secondaryTextColor=Object(q.invert)(this.secondaryColor),this.tertiaryTextColor=Object(q.invert)(this.tertiaryColor),this.lineColor=Object(q.invert)(this.background),this.textColor=Object(q.invert)(this.background),this.background="white",this.mainBkg="#ECECFF",this.secondBkg="#ffffde",this.lineColor="#333333",this.border1="#9370DB",this.border2="#aaaa33",this.arrowheadColor="#333333",this.fontFamily='"trebuchet ms", verdana, arial, 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e,n,r;return e=t,(n=[{key:"updateColors",value:function(){this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.titleColor=this.textColor,this.edgeLabelBackground=this.labelBackground,this.actorBorder=Object(q.lighten)(this.border1,23),this.actorBkg=this.mainBkg,this.labelBoxBkgColor=this.actorBkg,this.signalColor=this.textColor,this.signalTextColor=this.textColor,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.actorTextColor,this.loopTextColor=this.actorTextColor,this.noteBorderColor=this.border2,this.noteTextColor=this.actorTextColor,this.taskTextColor=this.taskTextLightColor,this.taskTextOutsideColor=this.taskTextDarkColor,this.classText=this.primaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=Object(q.adjust)(this.primaryColor,{h:64}),this.fillType3=Object(q.adjust)(this.secondaryColor,{h:64}),this.fillType4=Object(q.adjust)(this.primaryColor,{h:-64}),this.fillType5=Object(q.adjust)(this.secondaryColor,{h:-64}),this.fillType6=Object(q.adjust)(this.primaryColor,{h:128}),this.fillType7=Object(q.adjust)(this.secondaryColor,{h:128})}},{key:"calculate",value:function(t){var e=this;if("object"===nt(t)){var n=Object.keys(t);n.forEach((function(n){e[n]=t[n]})),this.updateColors(),n.forEach((function(n){e[n]=t[n]}))}else this.updateColors()}}])&&rt(e.prototype,n),r&&rt(e,r),t}();function at(t){return(at="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}function ot(t,e){for(var n=0;n<e.length;n++){var r=e[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(t,r.key,r)}}var st=function(){function t(){!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,t),this.background="#f4f4f4",this.primaryColor="#cde498",this.secondaryColor="#cdffb2",this.background="white",this.mainBkg="#cde498",this.secondBkg="#cdffb2",this.lineColor="green",this.border1="#13540c",this.border2="#6eaa49",this.arrowheadColor="green",this.fontFamily='"trebuchet ms", verdana, arial, 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e,n,r;return e=t,(n=[{key:"updateColors",value:function(){this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.actorBorder=Object(q.darken)(this.mainBkg,20),this.actorBkg=this.mainBkg,this.labelBoxBkgColor=this.actorBkg,this.labelTextColor=this.actorTextColor,this.loopTextColor=this.actorTextColor,this.noteBorderColor=this.border2,this.noteTextColor=this.actorTextColor,this.taskBorderColor=this.border1,this.taskTextColor=this.taskTextLightColor,this.taskTextOutsideColor=this.taskTextDarkColor,this.activeTaskBorderColor=this.taskBorderColor,this.activeTaskBkgColor=this.mainBkg,this.classText=this.primaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=Object(q.adjust)(this.primaryColor,{h:64}),this.fillType3=Object(q.adjust)(this.secondaryColor,{h:64}),this.fillType4=Object(q.adjust)(this.primaryColor,{h:-64}),this.fillType5=Object(q.adjust)(this.secondaryColor,{h:-64}),this.fillType6=Object(q.adjust)(this.primaryColor,{h:128}),this.fillType7=Object(q.adjust)(this.secondaryColor,{h:128})}},{key:"calculate",value:function(t){var e=this;if("object"===at(t)){var n=Object.keys(t);n.forEach((function(n){e[n]=t[n]})),this.updateColors(),n.forEach((function(n){e[n]=t[n]}))}else this.updateColors()}}])&&ot(e.prototype,n),r&&ot(e,r),t}();function ct(t){return(ct="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}function ut(t,e){for(var n=0;n<e.length;n++){var r=e[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(t,r.key,r)}}var lt=function(){function t(){!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,t),this.primaryColor="#eee",this.contrast="#26a",this.secondaryColor=Object(q.lighten)(this.contrast,55),this.background="#ffffff",this.tertiaryColor=Object(q.adjust)(this.primaryColor,{h:-160}),this.primaryBorderColor=X(this.primaryColor,this.darkMode),this.secondaryBorderColor=X(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=X(this.tertiaryColor,this.darkMode),this.primaryTextColor=Object(q.invert)(this.primaryColor),this.secondaryTextColor=Object(q.invert)(this.secondaryColor),this.tertiaryTextColor=Object(q.invert)(this.tertiaryColor),this.lineColor=Object(q.invert)(this.background),this.textColor=Object(q.invert)(this.background),this.altBackground=Object(q.lighten)(this.contrast,55),this.mainBkg="#eee",this.secondBkg="calculated",this.lineColor="#666",this.border1="#999",this.border2="calculated",this.note="#ffa",this.text="#333",this.critical="#d42",this.done="#bbb",this.arrowheadColor="#333333",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="calculated",this.edgeLabelBackground="white",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="calculated",this.actorLineColor="calculated",this.signalColor="calculated",this.signalTextColor="calculated",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="calculated",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="calculated",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="calculated",this.altSectionBkgColor="white",this.sectionBkgColor2="calculated",this.taskBorderColor="calculated",this.taskBkgColor="calculated",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="calculated",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="calculated",this.doneTaskBkgColor="calculated",this.doneTaskBorderColor="calculated",this.critBkgColor="calculated",this.critBorderColor="calculated",this.todayLineColor="calculated",this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222"}var e,n,r;return e=t,(n=[{key:"updateColors",value:function(){this.secondBkg=Object(q.lighten)(this.contrast,55),this.border2=this.contrast,this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.titleColor=this.text,this.actorBorder=Object(q.lighten)(this.border1,23),this.actorBkg=this.mainBkg,this.actorTextColor=this.text,this.actorLineColor=this.lineColor,this.signalColor=this.text,this.signalTextColor=this.text,this.labelBoxBkgColor=this.actorBkg,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.text,this.loopTextColor=this.text,this.noteBorderColor=Object(q.darken)(this.note,60),this.noteBkgColor=this.note,this.noteTextColor=this.actorTextColor,this.sectionBkgColor=Object(q.lighten)(this.contrast,30),this.sectionBkgColor2=Object(q.lighten)(this.contrast,30),this.taskBorderColor=Object(q.darken)(this.contrast,10),this.taskBkgColor=this.contrast,this.taskTextColor=this.taskTextLightColor,this.taskTextDarkColor=this.text,this.taskTextOutsideColor=this.taskTextDarkColor,this.activeTaskBorderColor=this.taskBorderColor,this.activeTaskBkgColor=this.mainBkg,this.gridColor=Object(q.lighten)(this.border1,30),this.doneTaskBkgColor=this.done,this.doneTaskBorderColor=this.lineColor,this.critBkgColor=this.critical,this.critBorderColor=Object(q.darken)(this.critBkgColor,10),this.todayLineColor=this.critBkgColor,this.classText=this.primaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=Object(q.adjust)(this.primaryColor,{h:64}),this.fillType3=Object(q.adjust)(this.secondaryColor,{h:64}),this.fillType4=Object(q.adjust)(this.primaryColor,{h:-64}),this.fillType5=Object(q.adjust)(this.secondaryColor,{h:-64}),this.fillType6=Object(q.adjust)(this.primaryColor,{h:128}),this.fillType7=Object(q.adjust)(this.secondaryColor,{h:128})}},{key:"calculate",value:function(t){var e=this;if("object"===ct(t)){var n=Object.keys(t);n.forEach((function(n){e[n]=t[n]})),this.updateColors(),n.forEach((function(n){e[n]=t[n]}))}else this.updateColors()}}])&&ut(e.prototype,n),r&&ut(e,r),t}(),ht={base:{getThemeVariables:function(t){var e=new K;return e.calculate(t),e}},dark:{getThemeVariables:function(t){var e=new et;return e.calculate(t),e}},default:{getThemeVariables:function(t){var e=new it;return e.calculate(t),e}},forest:{getThemeVariables:function(t){var e=new st;return e.calculate(t),e}},neutral:{getThemeVariables:function(t){var e=new lt;return e.calculate(t),e}}},ft={theme:"default",themeVariables:ht.default.getThemeVariables(),themeCSS:void 0,maxTextSize:5e4,fontFamily:'"trebuchet ms", verdana, arial, sans-serif;',logLevel:5,securityLevel:"strict",startOnLoad:!0,arrowMarkerAbsolute:!1,secure:["secure","securityLevel","startOnLoad","maxTextSize"],deterministicIds:!1,deterministicIDSeed:void 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0,useMaxWidth:!0},state:{dividerMargin:10,sizeUnit:5,padding:8,textHeight:10,titleShift:-15,noteMargin:10,forkWidth:70,forkHeight:7,miniPadding:2,fontSizeFactor:5.02,fontSize:24,labelHeight:16,edgeLengthFactor:"20",compositTitleSize:35,radius:5,useMaxWidth:!0},er:{diagramPadding:20,layoutDirection:"TB",minEntityWidth:100,minEntityHeight:75,entityPadding:15,stroke:"gray",fill:"honeydew",fontSize:12,useMaxWidth:!0},pie:{useWidth:void 0,useMaxWidth:!0}};ft.class.arrowMarkerAbsolute=ft.arrowMarkerAbsolute,ft.git.arrowMarkerAbsolute=ft.arrowMarkerAbsolute;var dt,pt=ft,gt=Object.freeze(pt),yt=F({},gt),vt=[],mt=F({},gt),bt=function(t,e){for(var n=F({},t),r={},i=0;i<e.length;i++){var a=e[i];kt(a),r=F(r,a)}if(n=F(n,r),r.theme){var o=F(dt.themeVariables||{},r.themeVariables);n.themeVariables=ht[n.theme].getThemeVariables(o)}return mt=n,n},xt=function(){return F({},yt)},_t=function(){return F({},mt)},kt=function(t){Object.keys(yt.secure).forEach((function(e){void 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i=this,a=0;function o(o){return function(s){a++;var c=i.sequenceItems.length-a+1;i.updateVal(s,"starty",e-c*cr.boxMargin,Math.min),i.updateVal(s,"stopy",r+c*cr.boxMargin,Math.max),i.updateVal(ur.data,"startx",t-c*cr.boxMargin,Math.min),i.updateVal(ur.data,"stopx",n+c*cr.boxMargin,Math.max),"activation"!==o&&(i.updateVal(s,"startx",t-c*cr.boxMargin,Math.min),i.updateVal(s,"stopx",n+c*cr.boxMargin,Math.max),i.updateVal(ur.data,"starty",e-c*cr.boxMargin,Math.min),i.updateVal(ur.data,"stopy",r+c*cr.boxMargin,Math.max))}}this.sequenceItems.forEach(o()),this.activations.forEach(o("activation"))},insert:function(t,e,n,r){var i=Math.min(t,n),a=Math.max(t,n),o=Math.min(e,r),s=Math.max(e,r);this.updateVal(ur.data,"startx",i,Math.min),this.updateVal(ur.data,"starty",o,Math.min),this.updateVal(ur.data,"stopx",a,Math.max),this.updateVal(ur.data,"stopy",s,Math.max),this.updateBounds(i,o,a,s)},newActivation:function(t,e,n){var r=n[t.from.actor],i=gr(t.from.actor).length||0,a=r.x+r.width/2+(i-1)*cr.activationWidth/2;this.activations.push({startx:a,starty:this.verticalPos+2,stopx:a+cr.activationWidth,stopy:void 0,actor:t.from.actor,anchored:$n.anchorElement(e)})},endActivation:function(t){var e=this.activations.map((function(t){return t.actor})).lastIndexOf(t.from.actor);return this.activations.splice(e,1)[0]},createLoop:function(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{message:void 0,wrap:!1,width:void 0},e=arguments.length>1?arguments[1]:void 0;return{startx:void 0,starty:this.verticalPos,stopx:void 0,stopy:void 0,title:t.message,wrap:t.wrap,width:t.width,height:0,fill:e}},newLoop:function(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{message:void 0,wrap:!1,width:void 0},e=arguments.length>1?arguments[1]:void 0;this.sequenceItems.push(this.createLoop(t,e))},endLoop:function(){return this.sequenceItems.pop()},addSectionToLoop:function(t){var e=this.sequenceItems.pop();e.sections=e.sections||[],e.sectionTitles=e.sectionTitles||[],e.sections.push({y:ur.getVerticalPos(),height:0}),e.sectionTitles.push(t),this.sequenceItems.push(e)},bumpVerticalPos:function(t){this.verticalPos=this.verticalPos+t,this.data.stopy=this.verticalPos},getVerticalPos:function(){return this.verticalPos},getBounds:function(){return{bounds:this.data,models:this.models}}},lr=function(t){return{fontFamily:t.messageFontFamily,fontSize:t.messageFontSize,fontWeight:t.messageFontWeight}},hr=function(t){return{fontFamily:t.noteFontFamily,fontSize:t.noteFontSize,fontWeight:t.noteFontWeight}},fr=function(t){return{fontFamily:t.actorFontFamily,fontSize:t.actorFontSize,fontWeight:t.actorFontWeight}},dr=function(t,e,n,r){for(var i=0,a=0,o=0;o<n.length;o++){var s=e[n[o]];s.width=s.width||cr.width,s.height=Math.max(s.height||cr.height,cr.height),s.margin=s.margin||cr.actorMargin,s.x=i+a,s.y=r,$n.drawActor(t,s,cr),ur.insert(s.x,r,s.x+s.width,s.height),i+=s.width,a+=s.margin,ur.models.addActor(s)}ur.bumpVerticalPos(cr.height)},pr=function(t){F(cr,t),t.fontFamily&&(cr.actorFontFamily=cr.noteFontFamily=cr.messageFontFamily=t.fontFamily),t.fontSize&&(cr.actorFontSize=cr.noteFontSize=cr.messageFontSize=t.fontSize),t.fontWeight&&(cr.actorFontWeight=cr.noteFontWeight=cr.messageFontWeight=t.fontWeight)},gr=function(t){return ur.activations.filter((function(e){return e.actor===t}))},yr=function(t,e){var n=e[t],r=gr(t);return[r.reduce((function(t,e){return Math.min(t,e.startx)}),n.x+n.width/2),r.reduce((function(t,e){return Math.max(t,e.stopx)}),n.x+n.width/2)]};function vr(t,e,n,r,i){ur.bumpVerticalPos(n);var a=r;if(e.id&&e.message&&t[e.id]){var o=t[e.id].width,s=lr(cr);e.message=H.wrapLabel("[".concat(e.message,"]"),o-2*cr.wrapPadding,s),e.width=o,e.wrap=!0;var u=H.calculateTextDimensions(e.message,s),l=Math.max(u.height,cr.labelBoxHeight);a=r+l,c.debug("".concat(l," - ").concat(e.message))}i(e),ur.bumpVerticalPos(a)}var mr=function(t,e){var n={};return e.forEach((function(e){if(t[e.to]&&t[e.from]){var r=t[e.to];if(e.placement===Wn.parser.yy.PLACEMENT.LEFTOF&&!r.prevActor)return;if(e.placement===Wn.parser.yy.PLACEMENT.RIGHTOF&&!r.nextActor)return;var i=void 0!==e.placement,a=!i,o=i?hr(cr):lr(cr),s=e.wrap?H.wrapLabel(e.message,cr.width-2*cr.wrapPadding,o):e.message,c=H.calculateTextDimensions(s,o).width+2*cr.wrapPadding;a&&e.from===r.nextActor?n[e.to]=Math.max(n[e.to]||0,c):a&&e.from===r.prevActor?n[e.from]=Math.max(n[e.from]||0,c):a&&e.from===e.to?(n[e.from]=Math.max(n[e.from]||0,c/2),n[e.to]=Math.max(n[e.to]||0,c/2)):e.placement===Wn.parser.yy.PLACEMENT.RIGHTOF?n[e.from]=Math.max(n[e.from]||0,c):e.placement===Wn.parser.yy.PLACEMENT.LEFTOF?n[r.prevActor]=Math.max(n[r.prevActor]||0,c):e.placement===Wn.parser.yy.PLACEMENT.OVER&&(r.prevActor&&(n[r.prevActor]=Math.max(n[r.prevActor]||0,c/2)),r.nextActor&&(n[e.from]=Math.max(n[e.from]||0,c/2)))}})),c.debug("maxMessageWidthPerActor:",n),n},br=function(t,e){var n=0;for(var r in Object.keys(t).forEach((function(e){var r=t[e];r.wrap&&(r.description=H.wrapLabel(r.description,cr.width-2*cr.wrapPadding,fr(cr)));var i=H.calculateTextDimensions(r.description,fr(cr));r.width=r.wrap?cr.width:Math.max(cr.width,i.width+2*cr.wrapPadding),r.height=r.wrap?Math.max(i.height,cr.height):cr.height,n=Math.max(n,r.height)})),e){var i=t[r];if(i){var a=t[i.nextActor];if(a){var o=e[r]+cr.actorMargin-i.width/2-a.width/2;i.margin=Math.max(o,cr.actorMargin)}}}return Math.max(n,cr.height)},xr=function(t,e){var n,r,i,a={},o=[];return t.forEach((function(t){switch(t.id=H.random({length:10}),t.type){case Wn.parser.yy.LINETYPE.LOOP_START:case Wn.parser.yy.LINETYPE.ALT_START:case Wn.parser.yy.LINETYPE.OPT_START:case Wn.parser.yy.LINETYPE.PAR_START:o.push({id:t.id,msg:t.message,from:Number.MAX_SAFE_INTEGER,to:Number.MIN_SAFE_INTEGER,width:0});break;case Wn.parser.yy.LINETYPE.ALT_ELSE:case Wn.parser.yy.LINETYPE.PAR_AND:t.message&&(n=o.pop(),a[n.id]=n,a[t.id]=n,o.push(n));break;case Wn.parser.yy.LINETYPE.LOOP_END:case Wn.parser.yy.LINETYPE.ALT_END:case Wn.parser.yy.LINETYPE.OPT_END:case Wn.parser.yy.LINETYPE.PAR_END:n=o.pop(),a[n.id]=n;break;case Wn.parser.yy.LINETYPE.ACTIVE_START:var s=e[t.from?t.from.actor:t.to.actor],u=gr(t.from?t.from.actor:t.to.actor).length,l=s.x+s.width/2+(u-1)*cr.activationWidth/2,h={startx:l,stopx:l+cr.activationWidth,actor:t.from.actor,enabled:!0};ur.activations.push(h);break;case Wn.parser.yy.LINETYPE.ACTIVE_END:var f=ur.activations.map((function(t){return t.actor})).lastIndexOf(t.from.actor);delete ur.activations.splice(f,1)[0]}void 0!==t.placement?(r=function(t,e){var n=e[t.from].x,r=e[t.to].x,i=t.wrap&&t.message,a=H.calculateTextDimensions(i?H.wrapLabel(t.message,cr.width,hr(cr)):t.message,hr(cr)),o={width:i?cr.width:Math.max(cr.width,a.width+2*cr.noteMargin),height:0,startx:e[t.from].x,stopx:0,starty:0,stopy:0,message:t.message};return t.placement===Wn.parser.yy.PLACEMENT.RIGHTOF?(o.width=i?Math.max(cr.width,a.width):Math.max(e[t.from].width/2+e[t.to].width/2,a.width+2*cr.noteMargin),o.startx=n+(e[t.from].width+cr.actorMargin)/2):t.placement===Wn.parser.yy.PLACEMENT.LEFTOF?(o.width=i?Math.max(cr.width,a.width+2*cr.noteMargin):Math.max(e[t.from].width/2+e[t.to].width/2,a.width+2*cr.noteMargin),o.startx=n-o.width+(e[t.from].width-cr.actorMargin)/2):t.to===t.from?(a=H.calculateTextDimensions(i?H.wrapLabel(t.message,Math.max(cr.width,e[t.from].width),hr(cr)):t.message,hr(cr)),o.width=i?Math.max(cr.width,e[t.from].width):Math.max(e[t.from].width,cr.width,a.width+2*cr.noteMargin),o.startx=n+(e[t.from].width-o.width)/2):(o.width=Math.abs(n+e[t.from].width/2-(r+e[t.to].width/2))+cr.actorMargin,o.startx=n<r?n+e[t.from].width/2-cr.actorMargin/2:r+e[t.to].width/2-cr.actorMargin/2),i&&(o.message=H.wrapLabel(t.message,o.width-2*cr.wrapPadding,hr(cr))),c.debug("NM:[".concat(o.startx,",").concat(o.stopx,",").concat(o.starty,",").concat(o.stopy,":").concat(o.width,",").concat(o.height,"=").concat(t.message,"]")),o}(t,e),t.noteModel=r,o.forEach((function(t){(n=t).from=Math.min(n.from,r.startx),n.to=Math.max(n.to,r.startx+r.width),n.width=Math.max(n.width,Math.abs(n.from-n.to))-cr.labelBoxWidth}))):(i=function(t,e){var n=!1;if([Wn.parser.yy.LINETYPE.SOLID_OPEN,Wn.parser.yy.LINETYPE.DOTTED_OPEN,Wn.parser.yy.LINETYPE.SOLID,Wn.parser.yy.LINETYPE.DOTTED,Wn.parser.yy.LINETYPE.SOLID_CROSS,Wn.parser.yy.LINETYPE.DOTTED_CROSS,Wn.parser.yy.LINETYPE.SOLID_POINT,Wn.parser.yy.LINETYPE.DOTTED_POINT].includes(t.type)&&(n=!0),!n)return{};var r=yr(t.from,e),i=yr(t.to,e),a=r[0]<=i[0]?1:0,o=r[0]<i[0]?0:1,s=r.concat(i),c=Math.abs(i[o]-r[a]);t.wrap&&t.message&&(t.message=H.wrapLabel(t.message,Math.max(c+2*cr.wrapPadding,cr.width),lr(cr)));var u=H.calculateTextDimensions(t.message,lr(cr));return{width:Math.max(t.wrap?0:u.width+2*cr.wrapPadding,c+2*cr.wrapPadding,cr.width),height:0,startx:r[a],stopx:i[o],starty:0,stopy:0,message:t.message,type:t.type,wrap:t.wrap,fromBounds:Math.min.apply(null,s),toBounds:Math.max.apply(null,s)}}(t,e),t.msgModel=i,i.startx&&i.stopx&&o.length>0&&o.forEach((function(r){if(n=r,i.startx===i.stopx){var a=e[t.from],o=e[t.to];n.from=Math.min(a.x-i.width/2,a.x-a.width/2,n.from),n.to=Math.max(o.x+i.width/2,o.x+a.width/2,n.to),n.width=Math.max(n.width,Math.abs(n.to-n.from))-cr.labelBoxWidth}else n.from=Math.min(i.startx,n.from),n.to=Math.max(i.stopx,n.to),n.width=Math.max(n.width,i.width)-cr.labelBoxWidth})))})),ur.activations=[],c.debug("Loop type widths:",a),a},_r={bounds:ur,drawActors:dr,setConf:pr,draw:function(t,e){cr=_t().sequence,Wn.parser.yy.clear(),Wn.parser.yy.setWrap(cr.wrap),Wn.parser.parse(t+"\n"),ur.init(),c.debug("C:".concat(JSON.stringify(cr,null,2)));var n=Object(d.select)('[id="'.concat(e,'"]')),r=Wn.parser.yy.getActors(),i=Wn.parser.yy.getActorKeys(),a=Wn.parser.yy.getMessages(),o=Wn.parser.yy.getTitle(),s=mr(r,a);cr.height=br(r,s),dr(n,r,i,0);var u=xr(a,r,s);$n.insertArrowHead(n),$n.insertArrowCrossHead(n),$n.insertArrowFilledHead(n),$n.insertSequenceNumber(n);var l=1;a.forEach((function(t){var e,i,a;switch(t.type){case Wn.parser.yy.LINETYPE.NOTE:i=t.noteModel,function(t,e){ur.bumpVerticalPos(cr.boxMargin),e.height=cr.boxMargin,e.starty=ur.getVerticalPos();var n=$n.getNoteRect();n.x=e.startx,n.y=e.starty,n.width=e.width||cr.width,n.class="note";var r=t.append("g"),i=$n.drawRect(r,n),a=$n.getTextObj();a.x=e.startx,a.y=e.starty,a.width=n.width,a.dy="1em",a.text=e.message,a.class="noteText",a.fontFamily=cr.noteFontFamily,a.fontSize=cr.noteFontSize,a.fontWeight=cr.noteFontWeight,a.anchor=cr.noteAlign,a.textMargin=cr.noteMargin,a.valign=cr.noteAlign;var o=Fn(r,a),s=Math.round(o.map((function(t){return(t._groups||t)[0][0].getBBox().height})).reduce((function(t,e){return t+e})));i.attr("height",s+2*cr.noteMargin),e.height+=s+2*cr.noteMargin,ur.bumpVerticalPos(s+2*cr.noteMargin),e.stopy=e.starty+s+2*cr.noteMargin,e.stopx=e.startx+n.width,ur.insert(e.startx,e.starty,e.stopx,e.stopy),ur.models.addNote(e)}(n,i);break;case Wn.parser.yy.LINETYPE.ACTIVE_START:ur.newActivation(t,n,r);break;case Wn.parser.yy.LINETYPE.ACTIVE_END:!function(t,e){var r=ur.endActivation(t);r.starty+18>e&&(r.starty=e-6,e+=12),$n.drawActivation(n,r,e,cr,gr(t.from.actor).length),ur.insert(r.startx,e-10,r.stopx,e)}(t,ur.getVerticalPos());break;case Wn.parser.yy.LINETYPE.LOOP_START:vr(u,t,cr.boxMargin,cr.boxMargin+cr.boxTextMargin,(function(t){return ur.newLoop(t)}));break;case Wn.parser.yy.LINETYPE.LOOP_END:e=ur.endLoop(),$n.drawLoop(n,e,"loop",cr),ur.bumpVerticalPos(e.stopy-ur.getVerticalPos()),ur.models.addLoop(e);break;case Wn.parser.yy.LINETYPE.RECT_START:vr(u,t,cr.boxMargin,cr.boxMargin,(function(t){return ur.newLoop(void 0,t.message)}));break;case Wn.parser.yy.LINETYPE.RECT_END:e=ur.endLoop(),$n.drawBackgroundRect(n,e),ur.models.addLoop(e),ur.bumpVerticalPos(e.stopy-ur.getVerticalPos());break;case Wn.parser.yy.LINETYPE.OPT_START:vr(u,t,cr.boxMargin,cr.boxMargin+cr.boxTextMargin,(function(t){return ur.newLoop(t)}));break;case Wn.parser.yy.LINETYPE.OPT_END:e=ur.endLoop(),$n.drawLoop(n,e,"opt",cr),ur.bumpVerticalPos(e.stopy-ur.getVerticalPos()),ur.models.addLoop(e);break;case Wn.parser.yy.LINETYPE.ALT_START:vr(u,t,cr.boxMargin,cr.boxMargin+cr.boxTextMargin,(function(t){return ur.newLoop(t)}));break;case Wn.parser.yy.LINETYPE.ALT_ELSE:vr(u,t,cr.boxMargin+cr.boxTextMargin,cr.boxMargin,(function(t){return ur.addSectionToLoop(t)}));break;case Wn.parser.yy.LINETYPE.ALT_END:e=ur.endLoop(),$n.drawLoop(n,e,"alt",cr),ur.bumpVerticalPos(e.stopy-ur.getVerticalPos()),ur.models.addLoop(e);break;case Wn.parser.yy.LINETYPE.PAR_START:vr(u,t,cr.boxMargin,cr.boxMargin+cr.boxTextMargin,(function(t){return ur.newLoop(t)}));break;case Wn.parser.yy.LINETYPE.PAR_AND:vr(u,t,cr.boxMargin+cr.boxTextMargin,cr.boxMargin,(function(t){return ur.addSectionToLoop(t)}));break;case Wn.parser.yy.LINETYPE.PAR_END:e=ur.endLoop(),$n.drawLoop(n,e,"par",cr),ur.bumpVerticalPos(e.stopy-ur.getVerticalPos()),ur.models.addLoop(e);break;default:try{(a=t.msgModel).starty=ur.getVerticalPos(),a.sequenceIndex=l,function(t,e){ur.bumpVerticalPos(10);var n=e.startx,r=e.stopx,i=e.starty,a=e.message,o=e.type,s=e.sequenceIndex,c=x.splitBreaks(a).length,u=H.calculateTextDimensions(a,lr(cr)),l=u.height/c;e.height+=l,ur.bumpVerticalPos(l);var h=$n.getTextObj();h.x=n,h.y=i+10,h.width=r-n,h.class="messageText",h.dy="1em",h.text=a,h.fontFamily=cr.messageFontFamily,h.fontSize=cr.messageFontSize,h.fontWeight=cr.messageFontWeight,h.anchor=cr.messageAlign,h.valign=cr.messageAlign,h.textMargin=cr.wrapPadding,h.tspan=!1,Fn(t,h);var f,d,p=u.height-10,g=u.width;if(n===r){d=ur.getVerticalPos()+p,cr.rightAngles?f=t.append("path").attr("d","M  ".concat(n,",").concat(d," H ").concat(n+Math.max(cr.width/2,g/2)," V ").concat(d+25," H ").concat(n)):(p+=cr.boxMargin,d=ur.getVerticalPos()+p,f=t.append("path").attr("d","M "+n+","+d+" C "+(n+60)+","+(d-10)+" "+(n+60)+","+(d+30)+" "+n+","+(d+20))),p+=30;var y=Math.max(g/2,cr.width/2);ur.insert(n-y,ur.getVerticalPos()-10+p,r+y,ur.getVerticalPos()+30+p)}else p+=cr.boxMargin,d=ur.getVerticalPos()+p,(f=t.append("line")).attr("x1",n),f.attr("y1",d),f.attr("x2",r),f.attr("y2",d),ur.insert(n,d-10,r,d);o===Wn.parser.yy.LINETYPE.DOTTED||o===Wn.parser.yy.LINETYPE.DOTTED_CROSS||o===Wn.parser.yy.LINETYPE.DOTTED_POINT||o===Wn.parser.yy.LINETYPE.DOTTED_OPEN?(f.style("stroke-dasharray","3, 3"),f.attr("class","messageLine1")):f.attr("class","messageLine0");var v="";cr.arrowMarkerAbsolute&&(v=(v=(v=window.location.protocol+"//"+window.location.host+window.location.pathname+window.location.search).replace(/\(/g,"\\(")).replace(/\)/g,"\\)")),f.attr("stroke-width",2),f.attr("stroke","none"),f.style("fill","none"),o!==Wn.parser.yy.LINETYPE.SOLID&&o!==Wn.parser.yy.LINETYPE.DOTTED||f.attr("marker-end","url("+v+"#arrowhead)"),o!==Wn.parser.yy.LINETYPE.SOLID_POINT&&o!==Wn.parser.yy.LINETYPE.DOTTED_POINT||f.attr("marker-end","url("+v+"#filled-head)"),o!==Wn.parser.yy.LINETYPE.SOLID_CROSS&&o!==Wn.parser.yy.LINETYPE.DOTTED_CROSS||f.attr("marker-end","url("+v+"#crosshead)"),(sr.showSequenceNumbers()||cr.showSequenceNumbers)&&(f.attr("marker-start","url("+v+"#sequencenumber)"),t.append("text").attr("x",n).attr("y",d+4).attr("font-family","sans-serif").attr("font-size","12px").attr("text-anchor","middle").attr("textLength","16px").attr("class","sequenceNumber").text(s)),ur.bumpVerticalPos(p),e.height+=p,e.stopy=e.starty+e.height,ur.insert(e.fromBounds,e.starty,e.toBounds,e.stopy)}(n,a),ur.models.addMessage(a)}catch(t){c.error("error while drawing message",t)}}[Wn.parser.yy.LINETYPE.SOLID_OPEN,Wn.parser.yy.LINETYPE.DOTTED_OPEN,Wn.parser.yy.LINETYPE.SOLID,Wn.parser.yy.LINETYPE.DOTTED,Wn.parser.yy.LINETYPE.SOLID_CROSS,Wn.parser.yy.LINETYPE.DOTTED_CROSS,Wn.parser.yy.LINETYPE.SOLID_POINT,Wn.parser.yy.LINETYPE.DOTTED_POINT].includes(t.type)&&l++})),cr.mirrorActors&&(ur.bumpVerticalPos(2*cr.boxMargin),dr(n,r,i,ur.getVerticalPos()));var h=ur.getBounds().bounds;c.debug("For line height fix Querying: #"+e+" .actor-line"),Object(d.selectAll)("#"+e+" .actor-line").attr("y2",h.stopy);var f=h.stopy-h.starty+2*cr.diagramMarginY;cr.mirrorActors&&(f=f-cr.boxMargin+cr.bottomMarginAdj);var p=h.stopx-h.startx+2*cr.diagramMarginX;o&&n.append("text").text(o).attr("x",(h.stopx-h.startx)/2-2*cr.diagramMarginX).attr("y",-25),W(n,f,p,cr.useMaxWidth);var g=o?40:0;n.attr("viewBox",h.startx-cr.diagramMarginX+" -"+(cr.diagramMarginY+g)+" "+p+" "+(f+g)),c.debug("models:",ur.models)}},kr=n(27),wr=n.n(kr);function Er(t){return 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p=o.height+2*r,g="attributeBoxOdd";s.forEach((function(e){var n=p+r+Math.max(e.tn.node().getBBox().height,e.nn.node().getBBox().height)/2;e.tn.attr("transform","translate("+i+","+n+")");var a=t.insert("rect","#"+e.tn.node().id).attr("class","er ".concat(g)).attr("fill",Qa.fill).attr("fill-opacity","100%").attr("stroke",Qa.stroke).attr("x",0).attr("y",p).attr("width",c+2*i+d/2).attr("height",e.tn.node().getBBox().height+2*r);e.nn.attr("transform","translate("+(parseFloat(a.attr("width"))+i)+","+n+")"),t.insert("rect","#"+e.nn.node().id).attr("class","er ".concat(g)).attr("fill",Qa.fill).attr("fill-opacity","100%").attr("stroke",Qa.stroke).attr("x","".concat(a.attr("x")+a.attr("width"))).attr("y",p).attr("width",u+2*i+d/2).attr("height",e.nn.node().getBBox().height+2*r),p+=Math.max(e.tn.node().getBBox().height,e.nn.node().getBBox().height)+2*r,g="attributeBoxOdd"==g?"attributeBoxEven":"attributeBoxOdd"}))}else 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a,o,s=to(i,Ga.getEntities(),r),u=function(t,e){return t.forEach((function(t){e.setEdge(t.entityA,t.entityB,{relationship:t},eo(t))})),t}(Ga.getRelationships(),r);ke.a.layout(r),a=i,(o=r).nodes().forEach((function(t){void 0!==t&&void 0!==o.node(t)&&a.select("#"+t).attr("transform","translate("+(o.node(t).x-o.node(t).width/2)+","+(o.node(t).y-o.node(t).height/2)+" )")})),u.forEach((function(t){!function(t,e,n,r){no++;var i=n.edge(e.entityA,e.entityB,eo(e)),a=Object(d.line)().x((function(t){return t.x})).y((function(t){return t.y})).curve(d.curveBasis),o=t.insert("path","#"+r).attr("class","er relationshipLine").attr("d",a(i.points)).attr("stroke",Qa.stroke).attr("fill","none");e.relSpec.relType===Ga.Identification.NON_IDENTIFYING&&o.attr("stroke-dasharray","8,8");var s="";switch(Qa.arrowMarkerAbsolute&&(s=(s=(s=window.location.protocol+"//"+window.location.host+window.location.pathname+window.location.search).replace(/\(/g,"\\(")).replace(/\)/g,"\\)")),e.relSpec.cardA){case 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e=Object(d.arc)().startAngle(3*Math.PI/2).endAngle(Math.PI/2*5).innerRadius(7.5).outerRadius(15/2.2);t.append("path").attr("class","mouth").attr("d",e).attr("transform","translate("+o.cx+","+(o.cy+7)+")")}(s):function(t){t.append("line").attr("class","mouth").attr("stroke",2).attr("x1",o.cx-5).attr("y1",o.cy+7).attr("x2",o.cx+5).attr("y2",o.cy+7).attr("class","mouth").attr("stroke-width","1px").attr("stroke","#666")}(s);var c=xo();c.x=e.x,c.y=e.y,c.fill=e.fill,c.width=n.width,c.height=n.height,c.class="task task-type-"+e.num,c.rx=3,c.ry=3,yo(i,c);var u=e.x+14;e.people.forEach((function(t){var n=e.actors[t],r={cx:u,cy:e.y,r:7,fill:n,stroke:"#000",title:t};vo(i,r),u+=10})),_o(n)(e.task,i,c.x,c.y,c.width,c.height,{class:"task"},n,e.colour)},Co=function(t){t.append("defs").append("marker").attr("id","arrowhead").attr("refX",5).attr("refY",2).attr("markerWidth",6).attr("markerHeight",4).attr("orient","auto").append("path").attr("d","M 0,0 V 4 L6,2 Z")};ao.parser.yy=go;var Ao={leftMargin:150,diagramMarginX:50,diagramMarginY:20,taskMargin:50,width:150,height:50,taskFontSize:14,taskFontFamily:'"Open-Sans", "sans-serif"',boxMargin:10,boxTextMargin:5,noteMargin:10,messageMargin:35,messageAlign:"center",bottomMarginAdj:1,activationWidth:10,textPlacement:"fo",actorColours:["#8FBC8F","#7CFC00","#00FFFF","#20B2AA","#B0E0E6","#FFFFE0"],sectionFills:["#191970","#8B008B","#4B0082","#2F4F4F","#800000","#8B4513","#00008B"],sectionColours:["#fff"]},So={};var Mo=Ao.leftMargin,Oo={data:{startx:void 0,stopx:void 0,starty:void 0,stopy:void 0},verticalPos:0,sequenceItems:[],init:function(){this.sequenceItems=[],this.data={startx:void 0,stopx:void 0,starty:void 0,stopy:void 0},this.verticalPos=0},updateVal:function(t,e,n,r){void 0===t[e]?t[e]=n:t[e]=r(n,t[e])},updateBounds:function(t,e,n,r){var i,a=this,o=0;this.sequenceItems.forEach((function(s){o++;var 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this.data}},Do=Ao.sectionFills,No=Ao.sectionColours,Bo=function(t,e,n){for(var r="",i=n+(2*Ao.height+Ao.diagramMarginY),a=0,o="#CCC",s="black",c=0,u=0;u<e.length;u++){var l=e[u];if(r!==l.section){o=Do[a%Do.length],c=a%Do.length,s=No[a%No.length];var h={x:u*Ao.taskMargin+u*Ao.width+Mo,y:50,text:l.section,fill:o,num:c,colour:s};wo(t,h,Ao),r=l.section,a++}var f=l.people.reduce((function(t,e){return So[e]&&(t[e]=So[e]),t}),{});l.x=u*Ao.taskMargin+u*Ao.width+Mo,l.y=i,l.width=Ao.diagramMarginX,l.height=Ao.diagramMarginY,l.colour=s,l.fill=o,l.num=c,l.actors=f,To(t,l,Ao),Oo.insert(l.x,l.y,l.x+l.width+Ao.taskMargin,450)}},Lo=function(t){Object.keys(t).forEach((function(e){Ao[e]=t[e]}))},Po=function(t,e){ao.parser.yy.clear(),ao.parser.parse(t+"\n"),Oo.init();var n=Object(d.select)("#"+e);n.attr("xmlns:xlink","http://www.w3.org/1999/xlink"),Co(n);var r=ao.parser.yy.getTasks(),i=ao.parser.yy.getTitle(),a=ao.parser.yy.getActors();for(var o in So)delete So[o];var s=0;a.forEach((function(t){So[t]=Ao.actorColours[s%Ao.actorColours.length],s++})),function(t){var e=60;Object.keys(So).forEach((function(n){var r=So[n];ko(t,{cx:20,cy:e,r:7,fill:r,stroke:"#000"});var i={x:40,y:e+7,fill:"#666",text:n,textMargin:5|Ao.boxTextMargin};Eo(t,i),e+=20}))}(n),Oo.insert(0,0,Mo,50*Object.keys(So).length),Bo(n,r,0);var c=Oo.getBounds();i&&n.append("text").text(i).attr("x",Mo).attr("font-size","4ex").attr("font-weight","bold").attr("y",25);var u=c.stopy-c.starty+2*Ao.diagramMarginY,l=Mo+c.stopx+2*Ao.diagramMarginX;W(n,u,l,Ao.useMaxWidth),n.append("line").attr("x1",Mo).attr("y1",4*Ao.height).attr("x2",l-Mo-4).attr("y2",4*Ao.height).attr("stroke-width",4).attr("stroke","black").attr("marker-end","url(#arrowhead)");var h=i?70:0;n.attr("viewBox","".concat(c.startx," -25 ").concat(l," ").concat(u+h)),n.attr("preserveAspectRatio","xMinYMin meet")},Io=function(t){return"g.classGroup text {\n  fill: ".concat(t.nodeBorder,";\n  fill: ").concat(t.classText,";\n  stroke: 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fill: ").concat(t.lineColor," !important;\n  stroke: ").concat(t.lineColor," !important;\n  stroke-width: 1;\n}\n\n#compositionEnd, .composition {\n  fill: ").concat(t.lineColor," !important;\n  stroke: ").concat(t.lineColor," !important;\n  stroke-width: 1;\n}\n\n#dependencyStart, .dependency {\n  fill: ").concat(t.lineColor," !important;\n  stroke: ").concat(t.lineColor," !important;\n  stroke-width: 1;\n}\n\n#dependencyStart, .dependency {\n  fill: ").concat(t.lineColor," !important;\n  stroke: ").concat(t.lineColor," !important;\n  stroke-width: 1;\n}\n\n#extensionStart, .extension {\n  fill: ").concat(t.lineColor," !important;\n  stroke: ").concat(t.lineColor," !important;\n  stroke-width: 1;\n}\n\n#extensionEnd, .extension {\n  fill: ").concat(t.lineColor," !important;\n  stroke: ").concat(t.lineColor," !important;\n  stroke-width: 1;\n}\n\n#aggregationStart, .aggregation {\n  fill: ").concat(t.mainBkg," !important;\n  stroke: ").concat(t.lineColor," !important;\n  stroke-width: 1;\n}\n\n#aggregationEnd, .aggregation {\n  fill: ").concat(t.mainBkg," !important;\n  stroke: ").concat(t.lineColor," !important;\n  stroke-width: 1;\n}\n\n.edgeTerminals {\n  font-size: 11px;\n}\n\n")},Fo=function(t){return".label {\n    font-family: ".concat(t.fontFamily,";\n    color: ").concat(t.nodeTextColor||t.textColor,";\n  }\n\n  .label text {\n    fill: ").concat(t.nodeTextColor||t.textColor,";\n  }\n\n  .node rect,\n  .node circle,\n  .node ellipse,\n  .node polygon,\n  .node path {\n    fill: ").concat(t.mainBkg,";\n    stroke: ").concat(t.nodeBorder,";\n    stroke-width: 1px;\n  }\n\n  .node .label {\n    text-align: center;\n  }\n  .node.clickable {\n    cursor: pointer;\n  }\n\n  .arrowheadPath {\n    fill: ").concat(t.arrowheadColor,";\n  }\n\n  .edgePath .path {\n    stroke: ").concat(t.lineColor,";\n    stroke-width: 1.5px;\n  }\n\n  .flowchart-link {\n    stroke: ").concat(t.lineColor,";\n    fill: none;\n  }\n\n  .edgeLabel {\n    background-color: ").concat(t.edgeLabelBackground,";\n    rect {\n      opacity: 0.5;\n      background-color: ").concat(t.edgeLabelBackground,";\n      fill: ").concat(t.edgeLabelBackground,";\n    }\n    text-align: center;\n  }\n\n  .cluster rect {\n    fill: ").concat(t.clusterBkg,";\n    stroke: ").concat(t.clusterBorder,";\n    stroke-width: 1px;\n  }\n\n  .cluster text {\n    fill: ").concat(t.titleColor,";\n  }\n\n  div.mermaidTooltip {\n    position: absolute;\n    text-align: center;\n    max-width: 200px;\n    padding: 2px;\n    font-family: ").concat(t.fontFamily,";\n    font-size: 12px;\n    background: ").concat(t.tertiaryColor,";\n    border: 1px solid ").concat(t.border2,";\n    border-radius: 2px;\n    pointer-events: none;\n    z-index: 100;\n  }\n")},jo=function(t){return"g.stateGroup text {\n  fill: ".concat(t.nodeBorder,";\n  stroke: none;\n  font-size: 10px;\n}\ng.stateGroup text {\n  fill: ").concat(t.textColor,";\n  stroke: none;\n  font-size: 10px;\n\n}\ng.stateGroup .state-title {\n  font-weight: bolder;\n  fill: ").concat(t.labelColor,";\n}\n\ng.stateGroup rect {\n  fill: ").concat(t.mainBkg,";\n  stroke: ").concat(t.nodeBorder,";\n}\n\ng.stateGroup line {\n  stroke: ").concat(t.lineColor,";\n  stroke-width: 1;\n}\n\n.transition {\n  stroke: ").concat(t.lineColor,";\n  stroke-width: 1;\n  fill: none;\n}\n\n.stateGroup .composit {\n  fill: ").concat(t.background,";\n  border-bottom: 1px\n}\n\n.stateGroup .alt-composit {\n  fill: #e0e0e0;\n  border-bottom: 1px\n}\n\n.state-note {\n  stroke: ").concat(t.noteBorderColor,";\n  fill: ").concat(t.noteBkgColor,";\n\n  text {\n    fill: black;\n    stroke: none;\n    font-size: 10px;\n  }\n}\n\n.stateLabel .box {\n  stroke: none;\n  stroke-width: 0;\n  fill: ").concat(t.mainBkg,";\n  opacity: 0.5;\n}\n\n.edgeLabel .label rect {\n  fill: ").concat(t.tertiaryColor,";\n  opacity: 0.5;\n}\n.edgeLabel .label text {\n  fill: ").concat(t.tertiaryTextColor,";\n}\n.label div .edgeLabel {\n  color: ").concat(t.tertiaryTextColor,";\n}\n\n.stateLabel text {\n  fill: ").concat(t.labelColor,";\n  font-size: 10px;\n  font-weight: bold;\n}\n\n.node circle.state-start {\n  fill: ").concat(t.lineColor,";\n  stroke: black;\n}\n.node circle.state-end {\n  fill: ").concat(t.primaryBorderColor,";\n  stroke: ").concat(t.background,";\n  stroke-width: 1.5\n}\n.end-state-inner {\n  fill: ").concat(t.background,";\n  // stroke: ").concat(t.background,";\n  stroke-width: 1.5\n}\n\n.node rect {\n  fill: ").concat(t.mainBkg,";\n  stroke: ").concat(t.nodeBorder,";\n  stroke-width: 1px;\n}\n#statediagram-barbEnd {\n  fill: ").concat(t.lineColor,";\n}\n\n.statediagram-cluster rect {\n  fill: ").concat(t.mainBkg,";\n  stroke: ").concat(t.nodeBorder,";\n  stroke-width: 1px;\n}\n\n.cluster-label, .nodeLabel {\n  color: ").concat(t.textColor,";\n}\n\n.statediagram-cluster rect.outer {\n  rx: 5px;\n  ry: 5px;\n}\n.statediagram-state .divider {\n  stroke: ").concat(t.nodeBorder,";\n}\n\n.statediagram-state .title-state {\n  rx: 5px;\n  ry: 5px;\n}\n.statediagram-cluster.statediagram-cluster .inner {\n  fill: ").concat(t.background,";\n}\n.statediagram-cluster.statediagram-cluster-alt .inner {\n  fill: #e0e0e0;\n}\n\n.statediagram-cluster .inner {\n  rx:0;\n  ry:0;\n}\n\n.statediagram-state rect.basic {\n  rx: 5px;\n  ry: 5px;\n}\n.statediagram-state rect.divider {\n  stroke-dasharray: 10,10;\n  fill: ").concat(t.altBackground?t.altBackground:"#efefef",";\n}\n\n.note-edge {\n  stroke-dasharray: 5;\n}\n\n.statediagram-note rect {\n  fill: ").concat(t.noteBkgColor,";\n  stroke: ").concat(t.noteBorderColor,";\n  stroke-width: 1px;\n  rx: 0;\n  ry: 0;\n}\n.statediagram-note rect {\n  fill: ").concat(t.noteBkgColor,";\n  stroke: ").concat(t.noteBorderColor,";\n  stroke-width: 1px;\n  rx: 0;\n  ry: 0;\n}\n\n.statediagram-note text {\n  fill: ").concat(t.noteTextColor,";\n}\n\n.statediagram-note .nodeLabel {\n  color: ").concat(t.noteTextColor,";\n}\n\n#dependencyStart, #dependencyEnd {\n  fill: ").concat(t.lineColor,";\n  stroke: ").concat(t.lineColor,";\n  stroke-width: 1;\n}\n")},Ro={flowchart:Fo,"flowchart-v2":Fo,sequence:function(t){return".actor {\n    stroke: ".concat(t.actorBorder,";\n    fill: ").concat(t.actorBkg,";\n  }\n\n  text.actor > tspan {\n    fill: ").concat(t.actorTextColor,";\n    stroke: none;\n  }\n\n  .actor-line {\n    stroke: ").concat(t.actorLineColor,";\n  }\n\n  .messageLine0 {\n    stroke-width: 1.5;\n    stroke-dasharray: none;\n    stroke: ").concat(t.signalColor,";\n  }\n\n  .messageLine1 {\n    stroke-width: 1.5;\n    stroke-dasharray: 2, 2;\n    stroke: ").concat(t.signalColor,";\n  }\n\n  #arrowhead path {\n    fill: ").concat(t.signalColor,";\n    stroke: ").concat(t.signalColor,";\n  }\n\n  .sequenceNumber {\n    fill: ").concat(t.sequenceNumberColor,";\n  }\n\n  #sequencenumber {\n    fill: ").concat(t.signalColor,";\n  }\n\n  #crosshead path {\n    fill: ").concat(t.signalColor,";\n    stroke: ").concat(t.signalColor,";\n  }\n\n  .messageText {\n    fill: ").concat(t.signalTextColor,";\n    stroke: ").concat(t.signalTextColor,";\n  }\n\n  .labelBox {\n    stroke: ").concat(t.labelBoxBorderColor,";\n    fill: ").concat(t.labelBoxBkgColor,";\n  }\n\n  .labelText, .labelText > tspan {\n    fill: ").concat(t.labelTextColor,";\n    stroke: none;\n  }\n\n  .loopText, .loopText > tspan {\n    fill: ").concat(t.loopTextColor,";\n    stroke: none;\n  }\n\n  .loopLine {\n    stroke-width: 2px;\n    stroke-dasharray: 2, 2;\n    stroke: ").concat(t.labelBoxBorderColor,";\n    fill: ").concat(t.labelBoxBorderColor,";\n  }\n\n  .note {\n    //stroke: #decc93;\n    stroke: ").concat(t.noteBorderColor,";\n    fill: ").concat(t.noteBkgColor,";\n  }\n\n  .noteText, .noteText > tspan {\n    fill: ").concat(t.noteTextColor,";\n    stroke: none;\n  }\n\n  .activation0 {\n    fill: ").concat(t.activationBkgColor,";\n    stroke: ").concat(t.activationBorderColor,";\n  }\n\n  .activation1 {\n    fill: ").concat(t.activationBkgColor,";\n    stroke: ").concat(t.activationBorderColor,";\n  }\n\n  .activation2 {\n    fill: ").concat(t.activationBkgColor,";\n    stroke: ").concat(t.activationBorderColor,";\n  }\n")},gantt:function(t){return'\n  .mermaid-main-font {\n    font-family: "trebuchet ms", verdana, arial, sans-serif;\n    font-family: var(--mermaid-font-family);\n  }\n\n  .section {\n    stroke: none;\n    opacity: 0.2;\n  }\n\n  .section0 {\n    fill: '.concat(t.sectionBkgColor,";\n  }\n\n  .section2 {\n    fill: ").concat(t.sectionBkgColor2,";\n  }\n\n  .section1,\n  .section3 {\n    fill: ").concat(t.altSectionBkgColor,";\n    opacity: 0.2;\n  }\n\n  .sectionTitle0 {\n    fill: ").concat(t.titleColor,";\n  }\n\n  .sectionTitle1 {\n    fill: ").concat(t.titleColor,";\n  }\n\n  .sectionTitle2 {\n    fill: ").concat(t.titleColor,";\n  }\n\n  .sectionTitle3 {\n    fill: ").concat(t.titleColor,";\n  }\n\n  .sectionTitle {\n    text-anchor: start;\n    font-size: 11px;\n    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+    <loc>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__poisson__sys_8cpp/</loc>
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+    <loc>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__sys_8cpp/</loc>
+  </url><url>
+    <loc>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds__uniform__vecs_8cpp/</loc>
+  </url><url>
+    <loc>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/files/lds_8cpp/</loc>
+  </url><url>
+    <loc>https://stanley-rozell.github.io/lds-ctrl-est/docs/api/namespaces/namespacestd/</loc>
+  </url><url>
+    <loc>https://stanley-rozell.github.io/lds-ctrl-est/tags/</loc>
+  </url>
+</urlset>
diff --git a/docs/svg/calendar.svg b/docs/svg/calendar.svg
new file mode 100644
index 00000000..f8481120
--- /dev/null
+++ b/docs/svg/calendar.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24"><path d="M20 3h-1V1h-2v2H7V1H5v2H4c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h16c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zm0 18H4V8h16v13z"/><path fill="none" d="M0 0h24v24H0z"/></svg>
\ No newline at end of file
diff --git a/docs/svg/edit.svg b/docs/svg/edit.svg
new file mode 100644
index 00000000..5b54e693
--- /dev/null
+++ b/docs/svg/edit.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24"><path d="M3 17.25V21h3.75L17.81 9.94l-3.75-3.75L3 17.25zM20.71 7.04c.39-.39.39-1.02 0-1.41l-2.34-2.34c-.39-.39-1.02-.39-1.41 0l-1.83 1.83 3.75 3.75 1.83-1.83z"/><path d="M0 0h24v24H0z" fill="none"/></svg>
\ No newline at end of file
diff --git a/docs/svg/menu.svg b/docs/svg/menu.svg
new file mode 100644
index 00000000..770b1923
--- /dev/null
+++ b/docs/svg/menu.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24"><path fill="none" d="M0 0h24v24H0V0z"/><path d="M3 18h18v-2H3v2zm0-5h18v-2H3v2zm0-7v2h18V6H3z"/></svg>
\ No newline at end of file
diff --git a/docs/svg/toc.svg b/docs/svg/toc.svg
new file mode 100644
index 00000000..1889904e
--- /dev/null
+++ b/docs/svg/toc.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24"><path d="M3 9h14V7H3v2zm0 4h14v-2H3v2zm0 4h14v-2H3v2zm16 0h2v-2h-2v2zm0-10v2h2V7h-2zm0 6h2v-2h-2v2z"/><path d="M0 0h24v24H0z" fill="none"/></svg>
\ No newline at end of file
diff --git a/docs/svg/translate.svg b/docs/svg/translate.svg
new file mode 100644
index 00000000..a1bbe166
--- /dev/null
+++ b/docs/svg/translate.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24"><path d="M0 0h24v24H0z" fill="none"/><path d="M12.87 15.07l-2.54-2.51.03-.03c1.74-1.94 2.98-4.17 3.71-6.53H17V4h-7V2H8v2H1v1.99h11.17C11.5 7.92 10.44 9.75 9 11.35 8.07 10.32 7.3 9.19 6.69 8h-2c.73 1.63 1.73 3.17 2.98 4.56l-5.09 5.02L4 19l5-5 3.11 3.11.76-2.04zM18.5 10h-2L12 22h2l1.12-3h4.75L21 22h2l-4.5-12zm-2.62 7l1.62-4.33L19.12 17h-3.24z"/></svg>
\ No newline at end of file
diff --git a/docs/tags/index.html b/docs/tags/index.html
new file mode 100644
index 00000000..5cd3e2d6
--- /dev/null
+++ b/docs/tags/index.html
@@ -0,0 +1,269 @@
+<!DOCTYPE html>
+<html lang="en" dir="ltr">
+<head>
+  <meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="description" content="">
+<meta name="theme-color" content="#FFFFFF"><meta property="og:url" content="https://stanley-rozell.github.io/lds-ctrl-est/tags/">
+  <meta property="og:site_name" content="LDS C&E">
+  <meta property="og:title" content="Tags">
+  <meta property="og:locale" content="en">
+  <meta property="og:type" content="website">
+<title>Tags | LDS C&amp;E</title>
+<link rel="manifest" href="/lds-ctrl-est/manifest.json">
+<link rel="icon" href="/lds-ctrl-est/favicon.png" type="image/x-icon">
+<link rel="stylesheet" href="/lds-ctrl-est/book.min.57f7f660871517a5bfcfb5e2de853d806f7e34d94ebd5f3f3bad62e9ddbae209.css" integrity="sha256-V/f2YIcVF6W/z7Xi3oU9gG9&#43;NNlOvV8/O61i6d264gk=" crossorigin="anonymous">
+  <script defer src="/lds-ctrl-est/flexsearch.min.js"></script>
+  <script defer src="/lds-ctrl-est/en.search.min.4f6b617641b714cbf70266302fdeb12a9567eb52ebfb017533f9c9af1b260392.js" integrity="sha256-T2thdkG3FMv3AmYwL96xKpVn61Lr&#43;wF1M/nJrxsmA5I=" crossorigin="anonymous"></script>
+<link rel="alternate" type="application/rss+xml" href="https://stanley-rozell.github.io/lds-ctrl-est/tags/index.xml" title="LDS C&E" />
+<!--
+Made with Book Theme
+https://github.com/alex-shpak/hugo-book
+-->
+  
+</head>
+<body dir="ltr">
+  <input type="checkbox" class="hidden toggle" id="menu-control" />
+  <input type="checkbox" class="hidden toggle" id="toc-control" />
+  <main class="container flex">
+    <aside class="book-menu">
+      <div class="book-menu-content">
+        
+  <nav>
+<h2 class="book-brand">
+  <a href="/lds-ctrl-est/"><img src="/lds-ctrl-est/ldsctrlest-logo.png" alt="Logo" /><span>LDS C&amp;E</span>
+  </a>
+</h2>
+
+
+<div class="book-search">
+  <input type="text" id="book-search-input" placeholder="Search" aria-label="Search" maxlength="64" data-hotkeys="s/" />
+  <div class="book-search-spinner hidden"></div>
+  <ul id="book-search-results"></ul>
+</div>
+
+
+
+
+
+
+
+
+
+
+
+  <ul>
+<li><strong>Library Terminology</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/terminology/model/"><strong>Model Definitions</strong></a></li>
+<li><a href="/lds-ctrl-est/docs/terminology/control-estimation/"><strong>Control &amp; Estimation</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/getting-started/"><strong>Getting Started</strong></a>
+<ul>
+<li><a href="/lds-ctrl-est/docs/getting-started/windows/"><strong>Windows</strong></a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>Tutorials</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_glds_control/">GLDS Control</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_plds_state_estimation/">PLDS Estimation</a></li>
+<li><a href="/lds-ctrl-est/docs/tutorials/eg_switched_plds_control/">Switched PLDS Control</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><strong>API Reference</strong>
+<ul>
+<li><a href="/lds-ctrl-est/docs/api/namespaces/">Namespaces</a></li>
+<li><a href="/lds-ctrl-est/docs/api/classes/">Classes</a></li>
+<li><a href="/lds-ctrl-est/docs/api/modules/">Modules</a></li>
+<li><a href="/lds-ctrl-est/docs/api/files/">Files</a></li>
+<li><a href="/lds-ctrl-est/docs/api/examples/">Examples</a></li>
+</ul>
+</li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/issues-contributing/"><strong>Reporting Issues &amp; Contributing</strong></a></li>
+</ul>
+<br />
+<ul>
+<li><a href="/lds-ctrl-est/acknowledgements/"><strong>Acknowledgements</strong></a></li>
+</ul>
+<br />
+
+
+
+
+
+
+  
+<ul>
+  
+  <li>
+    <a href="https://github.com/stanley-rozell/lds-ctrl-est" target="_blank" rel="noopener">
+        Github
+      </a>
+  </li>
+  
+</ul>
+
+
+
+
+
+
+</nav>
+
+
+
+
+  <script>(function(){var e=document.querySelector("aside .book-menu-content");addEventListener("beforeunload",function(){localStorage.setItem("menu.scrollTop",e.scrollTop)}),e.scrollTop=localStorage.getItem("menu.scrollTop")})()</script>
+
+
+ 
+      </div>
+    </aside>
+
+    <div class="book-page">
+      <header class="book-header">
+        
+  <div class="flex align-center justify-between">
+  <label for="menu-control">
+    <img src="/lds-ctrl-est/svg/menu.svg" class="book-icon" alt="Menu" />
+  </label>
+
+  <strong>Tags</strong>
+
+  <label for="toc-control">
+    
+    <img src="/lds-ctrl-est/svg/toc.svg" class="book-icon" alt="Table of Contents" />
+    
+  </label>
+</div>
+
+
+  
+  <aside class="hidden clearfix">
+    
+  <nav>
+  <ul>
+  
+    
+    <li class="book-section-flat">
+      <strong>Categories</strong>
+      <ul>
+      
+      </ul>
+    </li>
+    
+  
+    
+    <li class="book-section-flat">
+      <strong>Tags</strong>
+      <ul>
+      
+      </ul>
+    </li>
+    
+  
+  </ul>
+</nav>
+
+
+  </aside>
+  
+ 
+      </header>
+
+      
+      
+  
+
+  
+ 
+      
+
+      <footer class="book-footer">
+        
+  <div class="flex flex-wrap justify-between">
+
+
+
+
+
+</div>
+
+
+
+  <script>(function(){function e(e){const t=window.getSelection(),n=document.createRange();n.selectNodeContents(e),t.removeAllRanges(),t.addRange(n)}document.querySelectorAll("pre code").forEach(t=>{t.addEventListener("click",function(){e(t.parentElement),navigator.clipboard&&navigator.clipboard.writeText(t.parentElement.textContent)})})})()</script>
+
+
+ 
+        
+      </footer>
+
+      
+  
+ 
+
+      <label for="menu-control" class="hidden book-menu-overlay"></label>
+    </div>
+
+    
+    <aside class="book-toc">
+      <div class="book-toc-content">
+        
+  <nav>
+  <ul>
+  
+    
+    <li class="book-section-flat">
+      <strong>Categories</strong>
+      <ul>
+      
+      </ul>
+    </li>
+    
+  
+    
+    <li class="book-section-flat">
+      <strong>Tags</strong>
+      <ul>
+      
+      </ul>
+    </li>
+    
+  
+  </ul>
+</nav>
+
+ 
+      </div>
+    </aside>
+    
+  </main>
+
+  
+</body>
+</html>
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/tags/index.xml b/docs/tags/index.xml
new file mode 100644
index 00000000..195c9654
--- /dev/null
+++ b/docs/tags/index.xml
@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="utf-8" standalone="yes"?>
+<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
+  <channel>
+    <title>Tags on LDS C&amp;E</title>
+    <link>https://stanley-rozell.github.io/lds-ctrl-est/tags/</link>
+    <description>Recent content in Tags on LDS C&amp;E</description>
+    <generator>Hugo</generator>
+    <language>en</language>
+    <atom:link href="https://stanley-rozell.github.io/lds-ctrl-est/tags/index.xml" rel="self" type="application/rss+xml" />
+  </channel>
+</rss>
diff --git a/docs/tags/page/1/index.html b/docs/tags/page/1/index.html
new file mode 100644
index 00000000..0ed6b969
--- /dev/null
+++ b/docs/tags/page/1/index.html
@@ -0,0 +1,10 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <title>https://stanley-rozell.github.io/lds-ctrl-est/tags/</title>
+    <link rel="canonical" href="https://stanley-rozell.github.io/lds-ctrl-est/tags/">
+    <meta name="robots" content="noindex">
+    <meta charset="utf-8">
+    <meta http-equiv="refresh" content="0; url=https://stanley-rozell.github.io/lds-ctrl-est/tags/">
+  </head>
+</html>
diff --git a/misc/docs-hugo/content/docs/api/Classes/_index.md b/misc/docs-hugo/content/docs/api/Classes/_index.md
index e4e0dab9..563a2107 100644
--- a/misc/docs-hugo/content/docs/api/Classes/_index.md
+++ b/misc/docs-hugo/content/docs/api/Classes/_index.md
@@ -127,4 +127,4 @@ title: Classes
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_controller.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_controller.md
index 5b8c85f5..068274ea 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_controller.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_controller.md
@@ -771,4 +771,4 @@ size_t control_type_ {};
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_e_m.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_e_m.md
index 9e4b61d6..f6f16956 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_e_m.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_e_m.md
@@ -590,4 +590,4 @@ size_t n_t_tot_ {};
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_fit.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_fit.md
index 47139854..d5f764f8 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_fit.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_fit.md
@@ -558,4 +558,4 @@ size_t n_y_ {};
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_s_s_i_d.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_s_s_i_d.md
index e38934fa..bb3d76da 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_s_s_i_d.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_s_s_i_d.md
@@ -367,4 +367,4 @@ Matrix ext_obs_t_;
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_switched_controller.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_switched_controller.md
index f1cdc293..2b722820 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_switched_controller.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_switched_controller.md
@@ -357,4 +357,4 @@ UniformVectorList g_design_list_;
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_system.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_system.md
index 0b29291a..51ee6de9 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_system.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_system.md
@@ -883,4 +883,4 @@ Matrix Ke_m_;
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_uniform_matrix_list.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_uniform_matrix_list.md
index 437ac7f7..d6627c5c 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_uniform_matrix_list.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_uniform_matrix_list.md
@@ -258,4 +258,4 @@ void append(
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_uniform_system_list.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_uniform_system_list.md
index 936fae46..8b7fd408 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_uniform_system_list.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_uniform_system_list.md
@@ -232,4 +232,4 @@ inline UniformSystemList & operator=(
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_uniform_vector_list.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_uniform_vector_list.md
index 7f443dd4..9b577aae 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_uniform_vector_list.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1_uniform_vector_list.md
@@ -222,4 +222,4 @@ inline UniformVectorList & operator=(
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_controller.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_controller.md
index 8650210a..d534d012 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_controller.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_controller.md
@@ -113,4 +113,4 @@ inline virtual void set_y_ref(
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_fit.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_fit.md
index e7a6c3fb..759c2309 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_fit.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_fit.md
@@ -165,4 +165,4 @@ inline virtual View h(
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_fit_e_m.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_fit_e_m.md
index 73c6f4b0..a60ed44c 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_fit_e_m.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_fit_e_m.md
@@ -93,4 +93,4 @@ class lds::gaussian::FitEM;
 ---
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_fit_s_s_i_d.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_fit_s_s_i_d.md
index c14c9b7e..9c0dc318 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_fit_s_s_i_d.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_fit_s_s_i_d.md
@@ -60,4 +60,4 @@ Inherits from [lds::SSID< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s
 ---
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_switched_controller.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_switched_controller.md
index 74faeb8e..13d01a74 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_switched_controller.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_switched_controller.md
@@ -142,4 +142,4 @@ inline virtual void set_y_ref(
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_system.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_system.md
index 8cecdcfe..ca537555 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_system.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1gaussian_1_1_system.md
@@ -325,4 +325,4 @@ bool do_recurse_Ke_ {};
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_controller.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_controller.md
index ddf5c236..61c48e8e 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_controller.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_controller.md
@@ -113,4 +113,4 @@ inline virtual void set_y_ref(
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_fit.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_fit.md
index b875bbeb..cbd9e9d6 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_fit.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_fit.md
@@ -165,4 +165,4 @@ inline virtual const Matrix & R() const override
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_fit_e_m.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_fit_e_m.md
index bbf0f88b..88450fcc 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_fit_e_m.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_fit_e_m.md
@@ -93,4 +93,4 @@ class lds::poisson::FitEM;
 ---
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_fit_s_s_i_d.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_fit_s_s_i_d.md
index 3f33c77d..a5dbe5b4 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_fit_s_s_i_d.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_fit_s_s_i_d.md
@@ -60,4 +60,4 @@ Inherits from [lds::SSID< Fit >](/lds-ctrl-est/docs/api/classes/classlds_1_1_s_s
 ---
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_switched_controller.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_switched_controller.md
index f20892f1..4a4fa7af 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_switched_controller.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_switched_controller.md
@@ -142,4 +142,4 @@ inline virtual void set_y_ref(
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_system.md b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_system.md
index e4e2d313..e1509113 100644
--- a/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_system.md
+++ b/misc/docs-hugo/content/docs/api/Classes/classlds_1_1poisson_1_1_system.md
@@ -237,4 +237,4 @@ Eden UT, ..., Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Proc
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Examples/_index.md b/misc/docs-hugo/content/docs/api/Examples/_index.md
index 3bea99c0..4d2f6473 100644
--- a/misc/docs-hugo/content/docs/api/Examples/_index.md
+++ b/misc/docs-hugo/content/docs/api/Examples/_index.md
@@ -43,4 +43,4 @@ title: Examples
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Examples/eg_glds_ctrl_8cpp-example.md b/misc/docs-hugo/content/docs/api/Examples/eg_glds_ctrl_8cpp-example.md
index 55f80a7d..e7221dc9 100644
--- a/misc/docs-hugo/content/docs/api/Examples/eg_glds_ctrl_8cpp-example.md
+++ b/misc/docs-hugo/content/docs/api/Examples/eg_glds_ctrl_8cpp-example.md
@@ -272,4 +272,4 @@ _Filename: eg_glds_ctrl.cpp_
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Examples/eg_glds_du_plds_ctrl_8cpp-example.md b/misc/docs-hugo/content/docs/api/Examples/eg_glds_du_plds_ctrl_8cpp-example.md
index 33b998e0..d30c15fa 100644
--- a/misc/docs-hugo/content/docs/api/Examples/eg_glds_du_plds_ctrl_8cpp-example.md
+++ b/misc/docs-hugo/content/docs/api/Examples/eg_glds_du_plds_ctrl_8cpp-example.md
@@ -273,4 +273,4 @@ _Filename: eg_glds_du_plds_ctrl.cpp_
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Examples/eg_plds_ctrl_8cpp-example.md b/misc/docs-hugo/content/docs/api/Examples/eg_plds_ctrl_8cpp-example.md
index bee4b5d6..6325126b 100644
--- a/misc/docs-hugo/content/docs/api/Examples/eg_plds_ctrl_8cpp-example.md
+++ b/misc/docs-hugo/content/docs/api/Examples/eg_plds_ctrl_8cpp-example.md
@@ -242,4 +242,4 @@ _Filename: eg_plds_ctrl.cpp_
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Examples/eg_plds_est_8cpp-example.md b/misc/docs-hugo/content/docs/api/Examples/eg_plds_est_8cpp-example.md
index a8ad636f..6e16a62b 100644
--- a/misc/docs-hugo/content/docs/api/Examples/eg_plds_est_8cpp-example.md
+++ b/misc/docs-hugo/content/docs/api/Examples/eg_plds_est_8cpp-example.md
@@ -192,4 +192,4 @@ _Filename: eg_plds_est.cpp_
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Examples/eg_plds_switched_ctrl_8cpp-example.md b/misc/docs-hugo/content/docs/api/Examples/eg_plds_switched_ctrl_8cpp-example.md
index 43596029..345036f0 100644
--- a/misc/docs-hugo/content/docs/api/Examples/eg_plds_switched_ctrl_8cpp-example.md
+++ b/misc/docs-hugo/content/docs/api/Examples/eg_plds_switched_ctrl_8cpp-example.md
@@ -237,4 +237,4 @@ _Filename: eg_plds_switched_ctrl.cpp_
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/_index.md b/misc/docs-hugo/content/docs/api/Files/_index.md
index 9a1a41bf..ce7c693e 100644
--- a/misc/docs-hugo/content/docs/api/Files/_index.md
+++ b/misc/docs-hugo/content/docs/api/Files/_index.md
@@ -181,4 +181,4 @@ title: Files
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/dir_156a98879751e549d6939ca71a62d61f.md b/misc/docs-hugo/content/docs/api/Files/dir_156a98879751e549d6939ca71a62d61f.md
index b48621f7..97d6be39 100644
--- a/misc/docs-hugo/content/docs/api/Files/dir_156a98879751e549d6939ca71a62d61f.md
+++ b/misc/docs-hugo/content/docs/api/Files/dir_156a98879751e549d6939ca71a62d61f.md
@@ -46,4 +46,4 @@ title: ldsCtrlEst_h
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/dir_68267d1309a1af8e8297ef4c3efbcdba.md b/misc/docs-hugo/content/docs/api/Files/dir_68267d1309a1af8e8297ef4c3efbcdba.md
index 767ff3ed..3150193e 100644
--- a/misc/docs-hugo/content/docs/api/Files/dir_68267d1309a1af8e8297ef4c3efbcdba.md
+++ b/misc/docs-hugo/content/docs/api/Files/dir_68267d1309a1af8e8297ef4c3efbcdba.md
@@ -25,4 +25,4 @@ title: src
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/dir_d28a4824dc47e487b107a5db32ef43c4.md b/misc/docs-hugo/content/docs/api/Files/dir_d28a4824dc47e487b107a5db32ef43c4.md
index 3237c746..3c3a2d5c 100644
--- a/misc/docs-hugo/content/docs/api/Files/dir_d28a4824dc47e487b107a5db32ef43c4.md
+++ b/misc/docs-hugo/content/docs/api/Files/dir_d28a4824dc47e487b107a5db32ef43c4.md
@@ -25,4 +25,4 @@ title: examples
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/dir_d44c64559bbebec7f509842c48db8b23.md b/misc/docs-hugo/content/docs/api/Files/dir_d44c64559bbebec7f509842c48db8b23.md
index 7874871d..bcb00ace 100644
--- a/misc/docs-hugo/content/docs/api/Files/dir_d44c64559bbebec7f509842c48db8b23.md
+++ b/misc/docs-hugo/content/docs/api/Files/dir_d44c64559bbebec7f509842c48db8b23.md
@@ -21,4 +21,4 @@ title: include
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/eg__glds__ctrl_8cpp.md b/misc/docs-hugo/content/docs/api/Files/eg__glds__ctrl_8cpp.md
index 935a0731..65867012 100644
--- a/misc/docs-hugo/content/docs/api/Files/eg__glds__ctrl_8cpp.md
+++ b/misc/docs-hugo/content/docs/api/Files/eg__glds__ctrl_8cpp.md
@@ -331,4 +331,4 @@ auto main() -> int {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/eg__glds__du__plds__ctrl_8cpp.md b/misc/docs-hugo/content/docs/api/Files/eg__glds__du__plds__ctrl_8cpp.md
index c5958668..29328b07 100644
--- a/misc/docs-hugo/content/docs/api/Files/eg__glds__du__plds__ctrl_8cpp.md
+++ b/misc/docs-hugo/content/docs/api/Files/eg__glds__du__plds__ctrl_8cpp.md
@@ -332,4 +332,4 @@ auto main() -> int {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/eg__plds__ctrl_8cpp.md b/misc/docs-hugo/content/docs/api/Files/eg__plds__ctrl_8cpp.md
index 1fc2bc79..610d7c9c 100644
--- a/misc/docs-hugo/content/docs/api/Files/eg__plds__ctrl_8cpp.md
+++ b/misc/docs-hugo/content/docs/api/Files/eg__plds__ctrl_8cpp.md
@@ -301,4 +301,4 @@ auto main() -> int {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/eg__plds__est_8cpp.md b/misc/docs-hugo/content/docs/api/Files/eg__plds__est_8cpp.md
index 8114f4e8..cbe6f39c 100644
--- a/misc/docs-hugo/content/docs/api/Files/eg__plds__est_8cpp.md
+++ b/misc/docs-hugo/content/docs/api/Files/eg__plds__est_8cpp.md
@@ -261,4 +261,4 @@ Matrix random_walk(size_t n_t, const Matrix& Q, const Vector& x0) {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/eg__plds__switched__ctrl_8cpp.md b/misc/docs-hugo/content/docs/api/Files/eg__plds__switched__ctrl_8cpp.md
index 540db8ea..1607f006 100644
--- a/misc/docs-hugo/content/docs/api/Files/eg__plds__switched__ctrl_8cpp.md
+++ b/misc/docs-hugo/content/docs/api/Files/eg__plds__switched__ctrl_8cpp.md
@@ -293,4 +293,4 @@ auto main() -> int {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds_8cpp.md b/misc/docs-hugo/content/docs/api/Files/lds_8cpp.md
index 52875193..a0373fe2 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds_8cpp.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds_8cpp.md
@@ -159,4 +159,4 @@ Matrix calcCov(const Matrix& A, const Matrix& B) {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds_8h.md b/misc/docs-hugo/content/docs/api/Files/lds_8h.md
index 6d054aee..b1c31aa5 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds_8h.md
@@ -181,4 +181,4 @@ inline void Reassign(Matrix& some, const Matrix& other,
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__ctrl_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__ctrl_8h.md
index ac9d3a01..d59d941b 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__ctrl_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__ctrl_8h.md
@@ -548,4 +548,4 @@ void Controller<System>::InitVars(size_t control_type) {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__fit_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__fit_8h.md
index 8a0cbf0d..73af2d10 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__fit_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__fit_8h.md
@@ -148,4 +148,4 @@ class Fit {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__fit__em_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__fit__em_8h.md
index 4b678e48..94d7cd3c 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__fit__em_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__fit__em_8h.md
@@ -631,4 +631,4 @@ Vector EM<Fit>::UpdateTheta() {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__fit__ssid_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__fit__ssid_8h.md
index 05d7bdd1..9806fc24 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__fit__ssid_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__fit__ssid_8h.md
@@ -578,4 +578,4 @@ void SSID<Fit>::RecomputeExtObs() {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__gaussian_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__gaussian_8h.md
index 2f5b8380..071f63d5 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__gaussian_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__gaussian_8h.md
@@ -68,4 +68,4 @@ namespace gaussian {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__ctrl_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__ctrl_8h.md
index 201a48df..e8327e1c 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__ctrl_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__ctrl_8h.md
@@ -113,4 +113,4 @@ class Controller : public lds::Controller<System> {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit_8h.md
index 572148f5..f298bf9d 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit_8h.md
@@ -91,4 +91,4 @@ class Fit : public lds::Fit {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit__em_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit__em_8h.md
index 3537a36e..59dd183b 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit__em_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit__em_8h.md
@@ -89,4 +89,4 @@ class FitEM : public EM<Fit> {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit__ssid_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit__ssid_8h.md
index 7dc32c1a..007e8202 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit__ssid_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__fit__ssid_8h.md
@@ -88,4 +88,4 @@ class FitSSID : public SSID<Fit> {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sctrl_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sctrl_8h.md
index fcc3a305..65dc695a 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sctrl_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sctrl_8h.md
@@ -111,4 +111,4 @@ class SwitchedController : public lds::SwitchedController<System> {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sys_8cpp.md b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sys_8cpp.md
index 8843a354..9ce5f4c4 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sys_8cpp.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sys_8cpp.md
@@ -95,4 +95,4 @@ void lds::gaussian::System::Print() {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sys_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sys_8h.md
index bea98c6c..e1ef8254 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sys_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__gaussian__sys_8h.md
@@ -125,4 +125,4 @@ class System : public lds::System {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__poisson_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__poisson_8h.md
index 56413ec6..db699888 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__poisson_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__poisson_8h.md
@@ -72,4 +72,4 @@ static std::mt19937 rng = std::mt19937(
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__poisson__ctrl_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__poisson__ctrl_8h.md
index 47682eb1..e61dd9b7 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__poisson__ctrl_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__poisson__ctrl_8h.md
@@ -118,4 +118,4 @@ class Controller : public lds::Controller<System> {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit_8h.md
index e0da461b..4346006b 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit_8h.md
@@ -99,4 +99,4 @@ class Fit : public lds::Fit {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit__em_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit__em_8h.md
index 98906e9e..8387c31a 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit__em_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit__em_8h.md
@@ -95,4 +95,4 @@ class FitEM : public EM<Fit> {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit__ssid_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit__ssid_8h.md
index 922747b9..608be645 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit__ssid_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__poisson__fit__ssid_8h.md
@@ -87,4 +87,4 @@ class FitSSID : public SSID<Fit> {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__poisson__sctrl_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__poisson__sctrl_8h.md
index f9b45231..0a1be7ed 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__poisson__sctrl_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__poisson__sctrl_8h.md
@@ -114,4 +114,4 @@ class SwitchedController : public lds::SwitchedController<System> {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__poisson__sys_8cpp.md b/misc/docs-hugo/content/docs/api/Files/lds__poisson__sys_8cpp.md
index 4b1e16f4..92deabd5 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__poisson__sys_8cpp.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__poisson__sys_8cpp.md
@@ -91,4 +91,4 @@ const lds::Vector& lds::poisson::System::Simulate(const Vector& u_tm1) {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__poisson__sys_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__poisson__sys_8h.md
index d508364e..4fd50498 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__poisson__sys_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__poisson__sys_8h.md
@@ -105,4 +105,4 @@ class System : public lds::System {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__sctrl_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__sctrl_8h.md
index 54127497..7ada26be 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__sctrl_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__sctrl_8h.md
@@ -272,4 +272,4 @@ inline void SwitchedController<System>::Switch(size_t idx,
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__sys_8cpp.md b/misc/docs-hugo/content/docs/api/Files/lds__sys_8cpp.md
index 3c311f36..94530e39 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__sys_8cpp.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__sys_8cpp.md
@@ -181,4 +181,4 @@ void lds::System::Print() {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__sys_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__sys_8h.md
index 244b68e1..3a5c7583 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__sys_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__sys_8h.md
@@ -188,4 +188,4 @@ class System {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__uniform__mats_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__uniform__mats_8h.md
index 3f735c63..baf35789 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__uniform__mats_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__uniform__mats_8h.md
@@ -340,4 +340,4 @@ void UniformMatrixList<D>::CheckDimensions(std::array<size_t, 2> dim) {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__uniform__systems_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__uniform__systems_8h.md
index 7b49aa65..a16fae3d 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__uniform__systems_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__uniform__systems_8h.md
@@ -265,4 +265,4 @@ void UniformSystemList<System>::CheckDimensions(std::array<size_t, 3> dim) {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__uniform__vecs_8cpp.md b/misc/docs-hugo/content/docs/api/Files/lds__uniform__vecs_8cpp.md
index 2e6e9187..9ed8ab3c 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__uniform__vecs_8cpp.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__uniform__vecs_8cpp.md
@@ -104,4 +104,4 @@ void UniformVectorList::CheckDimensions(size_t dim) {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/lds__uniform__vecs_8h.md b/misc/docs-hugo/content/docs/api/Files/lds__uniform__vecs_8h.md
index 338d1489..9a0fd4a8 100644
--- a/misc/docs-hugo/content/docs/api/Files/lds__uniform__vecs_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/lds__uniform__vecs_8h.md
@@ -189,4 +189,4 @@ inline UniformVectorList& UniformVectorList::operator=(
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/mex__c__util_8h.md b/misc/docs-hugo/content/docs/api/Files/mex__c__util_8h.md
index e6da187b..b7aa4b0e 100644
--- a/misc/docs-hugo/content/docs/api/Files/mex__c__util_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/mex__c__util_8h.md
@@ -129,4 +129,4 @@ inline auto a2m_vec(arma::Col<T> const &arma_vec) -> mxArray * {
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Files/mex__cpp__util_8h.md b/misc/docs-hugo/content/docs/api/Files/mex__cpp__util_8h.md
index efc0b13c..365c97d4 100644
--- a/misc/docs-hugo/content/docs/api/Files/mex__cpp__util_8h.md
+++ b/misc/docs-hugo/content/docs/api/Files/mex__cpp__util_8h.md
@@ -150,4 +150,4 @@ matlab::data::TypedArray<T> s2m_vec(const std::vector<T>& std_vec,
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Modules/_index.md b/misc/docs-hugo/content/docs/api/Modules/_index.md
index 01cdf812..4c21142c 100644
--- a/misc/docs-hugo/content/docs/api/Modules/_index.md
+++ b/misc/docs-hugo/content/docs/api/Modules/_index.md
@@ -25,4 +25,4 @@ title: Modules
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Modules/group__control__masks.md b/misc/docs-hugo/content/docs/api/Modules/group__control__masks.md
index a846662f..604930d9 100644
--- a/misc/docs-hugo/content/docs/api/Modules/group__control__masks.md
+++ b/misc/docs-hugo/content/docs/api/Modules/group__control__masks.md
@@ -67,4 +67,4 @@ Adapt control setpoint adapted with re-estimated process disturbance `m`.
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Modules/group__defaults.md b/misc/docs-hugo/content/docs/api/Modules/group__defaults.md
index f72c3c16..37ee4609 100644
--- a/misc/docs-hugo/content/docs/api/Modules/group__defaults.md
+++ b/misc/docs-hugo/content/docs/api/Modules/group__defaults.md
@@ -57,4 +57,4 @@ static const data_t kDefaultR0 = 1e-2;
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Namespaces/_index.md b/misc/docs-hugo/content/docs/api/Namespaces/_index.md
index 41d6e411..85cd722a 100644
--- a/misc/docs-hugo/content/docs/api/Namespaces/_index.md
+++ b/misc/docs-hugo/content/docs/api/Namespaces/_index.md
@@ -39,4 +39,4 @@ title: Namespaces
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Namespaces/namespacearmamexc.md b/misc/docs-hugo/content/docs/api/Namespaces/namespacearmamexc.md
index 924e1250..6f6ddee1 100644
--- a/misc/docs-hugo/content/docs/api/Namespaces/namespacearmamexc.md
+++ b/misc/docs-hugo/content/docs/api/Namespaces/namespacearmamexc.md
@@ -121,4 +121,4 @@ inline mxArray * a2m_vec(
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:26 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Namespaces/namespacearmamexcpp.md b/misc/docs-hugo/content/docs/api/Namespaces/namespacearmamexcpp.md
index 777df93f..359f5d62 100644
--- a/misc/docs-hugo/content/docs/api/Namespaces/namespacearmamexcpp.md
+++ b/misc/docs-hugo/content/docs/api/Namespaces/namespacearmamexcpp.md
@@ -205,4 +205,4 @@ matlab::data::TypedArray< T > s2m_vec(
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:26 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Namespaces/namespacelds.md b/misc/docs-hugo/content/docs/api/Namespaces/namespacelds.md
index 221f358a..bf31f3a9 100644
--- a/misc/docs-hugo/content/docs/api/Namespaces/namespacelds.md
+++ b/misc/docs-hugo/content/docs/api/Namespaces/namespacelds.md
@@ -310,4 +310,4 @@ static const data_t kPi = arma::datum::pi;
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:26 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Namespaces/namespacelds_1_1gaussian.md b/misc/docs-hugo/content/docs/api/Namespaces/namespacelds_1_1gaussian.md
index e4dfbbe7..cb70e832 100644
--- a/misc/docs-hugo/content/docs/api/Namespaces/namespacelds_1_1gaussian.md
+++ b/misc/docs-hugo/content/docs/api/Namespaces/namespacelds_1_1gaussian.md
@@ -27,4 +27,4 @@ Linear Dynamical Systems with Gaussian observations. <br>
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Namespaces/namespacelds_1_1poisson.md b/misc/docs-hugo/content/docs/api/Namespaces/namespacelds_1_1poisson.md
index 7620c8d7..a3a3dc4b 100644
--- a/misc/docs-hugo/content/docs/api/Namespaces/namespacelds_1_1poisson.md
+++ b/misc/docs-hugo/content/docs/api/Namespaces/namespacelds_1_1poisson.md
@@ -53,4 +53,4 @@ static std::mt19937 rng = std::mt19937(
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Namespaces/namespacestd.md b/misc/docs-hugo/content/docs/api/Namespaces/namespacestd.md
index e7e779a1..595bfd45 100644
--- a/misc/docs-hugo/content/docs/api/Namespaces/namespacestd.md
+++ b/misc/docs-hugo/content/docs/api/Namespaces/namespacestd.md
@@ -15,4 +15,4 @@ title: std
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT
diff --git a/misc/docs-hugo/content/docs/api/Pages/_index.md b/misc/docs-hugo/content/docs/api/Pages/_index.md
index 0c91eafb..8fa6b31b 100644
--- a/misc/docs-hugo/content/docs/api/Pages/_index.md
+++ b/misc/docs-hugo/content/docs/api/Pages/_index.md
@@ -13,4 +13,4 @@ title: Pages
 
 -------------------------------
 
-Updated on  5 March 2025 at 21:41:27 EST
+Updated on 31 March 2025 at 16:04:30 EDT